diff --git a/.circleci/config.yml b/.circleci/config.yml
index ce6e67a54e..ecae22f872 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -699,6 +699,7 @@ jobs:
pip install "pytest-cov==5.0.0"
pip install "pytest-asyncio==0.21.1"
pip install "respx==0.21.1"
+ pip install "hypercorn==0.17.3"
# Run pytest and generate JUnit XML report
- run:
name: Run tests
@@ -1934,12 +1935,12 @@ jobs:
pip install prisma
pip install fastapi
pip install jsonschema
- pip install "httpx==0.24.1"
+ pip install "httpx==0.27.0"
pip install "anyio==3.7.1"
pip install "asyncio==3.4.3"
pip install "PyGithub==1.59.1"
pip install "google-cloud-aiplatform==1.59.0"
- pip install anthropic
+ pip install "anthropic==0.49.0"
# Run pytest and generate JUnit XML report
- run:
name: Build Docker image
@@ -1981,11 +1982,44 @@ jobs:
- run:
name: Wait for app to be ready
command: dockerize -wait http://localhost:4000 -timeout 5m
+ # Add Ruby installation and testing before the existing Node.js and Python tests
+ - run:
+ name: Install Ruby and Bundler
+ command: |
+ # Import GPG keys first
+ gpg --keyserver hkp://keyserver.ubuntu.com --recv-keys 409B6B1796C275462A1703113804BB82D39DC0E3 7D2BAF1CF37B13E2069D6956105BD0E739499BDB || {
+ curl -sSL https://rvm.io/mpapis.asc | gpg --import -
+ curl -sSL https://rvm.io/pkuczynski.asc | gpg --import -
+ }
+
+ # Install Ruby version manager (RVM)
+ curl -sSL https://get.rvm.io | bash -s stable
+
+ # Source RVM from the correct location
+ source $HOME/.rvm/scripts/rvm
+
+ # Install Ruby 3.2.2
+ rvm install 3.2.2
+ rvm use 3.2.2 --default
+
+ # Install latest Bundler
+ gem install bundler
+
+ - run:
+ name: Run Ruby tests
+ command: |
+ source $HOME/.rvm/scripts/rvm
+ cd tests/pass_through_tests/ruby_passthrough_tests
+ bundle install
+ bundle exec rspec
+ no_output_timeout: 30m
# New steps to run Node.js test
- run:
name: Install Node.js
command: |
+ export DEBIAN_FRONTEND=noninteractive
curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
+ sudo apt-get update
sudo apt-get install -y nodejs
node --version
npm --version
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index 3615d030bf..d50aefa8bb 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -6,6 +6,16 @@
+## Pre-Submission checklist
+
+**Please complete all items before asking a LiteLLM maintainer to review your PR**
+
+- [ ] I have Added testing in the `tests/litellm/` directory, **Adding at least 1 test is a hard requirement** - [see details](https://docs.litellm.ai/docs/extras/contributing_code)
+- [ ] I have added a screenshot of my new test passing locally
+- [ ] My PR passes all unit tests on (`make test-unit`)[https://docs.litellm.ai/docs/extras/contributing_code]
+- [ ] My PR's scope is as isolated as possible, it only solves 1 specific problem
+
+
## Type
@@ -20,10 +30,4 @@
## Changes
-
-
-## [REQUIRED] Testing - Attach a screenshot of any new tests passing locally
-If UI changes, send a screenshot/GIF of working UI fixes
-
-
diff --git a/.github/workflows/helm_unit_test.yml b/.github/workflows/helm_unit_test.yml
new file mode 100644
index 0000000000..c4b83af70a
--- /dev/null
+++ b/.github/workflows/helm_unit_test.yml
@@ -0,0 +1,27 @@
+name: Helm unit test
+
+on:
+ pull_request:
+ push:
+ branches:
+ - main
+
+jobs:
+ unit-test:
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v2
+
+ - name: Set up Helm 3.11.1
+ uses: azure/setup-helm@v1
+ with:
+ version: '3.11.1'
+
+ - name: Install Helm Unit Test Plugin
+ run: |
+ helm plugin install https://github.com/helm-unittest/helm-unittest --version v0.4.4
+
+ - name: Run unit tests
+ run:
+ helm unittest -f 'tests/*.yaml' deploy/charts/litellm-helm
\ No newline at end of file
diff --git a/.github/workflows/interpret_load_test.py b/.github/workflows/interpret_load_test.py
index 194d16b282..6b5e6535d7 100644
--- a/.github/workflows/interpret_load_test.py
+++ b/.github/workflows/interpret_load_test.py
@@ -54,27 +54,29 @@ def interpret_results(csv_file):
def _get_docker_run_command_stable_release(release_version):
return f"""
- \n\n
- ## Docker Run LiteLLM Proxy
+\n\n
+## Docker Run LiteLLM Proxy
- ```
- docker run \\
- -e STORE_MODEL_IN_DB=True \\
- -p 4000:4000 \\
- ghcr.io/berriai/litellm_stable_release_branch-{release_version}
+```
+docker run \\
+-e STORE_MODEL_IN_DB=True \\
+-p 4000:4000 \\
+ghcr.io/berriai/litellm:litellm_stable_release_branch-{release_version}
+```
"""
def _get_docker_run_command(release_version):
return f"""
- \n\n
- ## Docker Run LiteLLM Proxy
+\n\n
+## Docker Run LiteLLM Proxy
- ```
- docker run \\
- -e STORE_MODEL_IN_DB=True \\
- -p 4000:4000 \\
- ghcr.io/berriai/litellm:main-{release_version}
+```
+docker run \\
+-e STORE_MODEL_IN_DB=True \\
+-p 4000:4000 \\
+ghcr.io/berriai/litellm:main-{release_version}
+```
"""
diff --git a/.github/workflows/locustfile.py b/.github/workflows/locustfile.py
index 96dd8e1990..36dbeee9c4 100644
--- a/.github/workflows/locustfile.py
+++ b/.github/workflows/locustfile.py
@@ -8,7 +8,7 @@ class MyUser(HttpUser):
def chat_completion(self):
headers = {
"Content-Type": "application/json",
- "Authorization": "Bearer sk-ZoHqrLIs2-5PzJrqBaviAA",
+ "Authorization": "Bearer sk-8N1tLOOyH8TIxwOLahhIVg",
# Include any additional headers you may need for authentication, etc.
}
diff --git a/.gitignore b/.gitignore
index d760ba17f4..d35923f7c3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -77,3 +77,5 @@ litellm/proxy/_experimental/out/404.html
litellm/proxy/_experimental/out/model_hub.html
.mypy_cache/*
litellm/proxy/application.log
+tests/llm_translation/vertex_test_account.json
+tests/llm_translation/test_vertex_key.json
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000000..6bd3cb57d4
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,21 @@
+# LiteLLM Makefile
+# Simple Makefile for running tests and basic development tasks
+
+.PHONY: help test test-unit test-integration
+
+# Default target
+help:
+ @echo "Available commands:"
+ @echo " make test - Run all tests"
+ @echo " make test-unit - Run unit tests"
+ @echo " make test-integration - Run integration tests"
+
+# Testing
+test:
+ poetry run pytest tests/
+
+test-unit:
+ poetry run pytest tests/litellm/
+
+test-integration:
+ poetry run pytest tests/ -k "not litellm"
\ No newline at end of file
diff --git a/README.md b/README.md
index c52b12b66a..2d2f71e4d1 100644
--- a/README.md
+++ b/README.md
@@ -40,7 +40,7 @@ LiteLLM manages:
[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#openai-proxy---docs)
[**Jump to Supported LLM Providers**](https://github.com/BerriAI/litellm?tab=readme-ov-file#supported-providers-docs)
-🚨 **Stable Release:** Use docker images with the `-stable` tag. These have undergone 12 hour load tests, before being published.
+🚨 **Stable Release:** Use docker images with the `-stable` tag. These have undergone 12 hour load tests, before being published. [More information about the release cycle here](https://docs.litellm.ai/docs/proxy/release_cycle)
Support for more providers. Missing a provider or LLM Platform, raise a [feature request](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+).
@@ -340,71 +340,7 @@ curl 'http://0.0.0.0:4000/key/generate' \
## Contributing
-To contribute: Clone the repo locally -> Make a change -> Submit a PR with the change.
-
-Here's how to modify the repo locally:
-
-Step 1: Clone the repo
-
-```
-git clone https://github.com/BerriAI/litellm.git
-```
-
-Step 2: Install dependencies:
-
-```
-pip install -r requirements.txt
-```
-
-Step 3: Test your change:
-
-a. Add a pytest test within `tests/litellm/`
-
-This folder follows the same directory structure as `litellm/`.
-
-If a corresponding test file does not exist, create one.
-
-b. Run the test
-
-```
-cd tests/litellm # pwd: Documents/litellm/litellm/tests/litellm
-pytest /path/to/test_file.py
-```
-
-Step 4: Submit a PR with your changes! 🚀
-
-- push your fork to your GitHub repo
-- submit a PR from there
-
-### Building LiteLLM Docker Image
-
-Follow these instructions if you want to build / run the LiteLLM Docker Image yourself.
-
-Step 1: Clone the repo
-
-```
-git clone https://github.com/BerriAI/litellm.git
-```
-
-Step 2: Build the Docker Image
-
-Build using Dockerfile.non_root
-```
-docker build -f docker/Dockerfile.non_root -t litellm_test_image .
-```
-
-Step 3: Run the Docker Image
-
-Make sure config.yaml is present in the root directory. This is your litellm proxy config file.
-```
-docker run \
- -v $(pwd)/proxy_config.yaml:/app/config.yaml \
- -e DATABASE_URL="postgresql://xxxxxxxx" \
- -e LITELLM_MASTER_KEY="sk-1234" \
- -p 4000:4000 \
- litellm_test_image \
- --config /app/config.yaml --detailed_debug
-```
+Interested in contributing? Contributions to LiteLLM Python SDK, Proxy Server, and contributing LLM integrations are both accepted and highly encouraged! [See our Contribution Guide for more details](https://docs.litellm.ai/docs/extras/contributing_code)
# Enterprise
For companies that need better security, user management and professional support
diff --git a/deploy/charts/litellm-helm/Chart.yaml b/deploy/charts/litellm-helm/Chart.yaml
index 6232a2320d..f1f2fd8d64 100644
--- a/deploy/charts/litellm-helm/Chart.yaml
+++ b/deploy/charts/litellm-helm/Chart.yaml
@@ -18,7 +18,7 @@ type: application
# This is the chart version. This version number should be incremented each time you make changes
# to the chart and its templates, including the app version.
# Versions are expected to follow Semantic Versioning (https://semver.org/)
-version: 0.3.0
+version: 0.4.1
# This is the version number of the application being deployed. This version number should be
# incremented each time you make changes to the application. Versions are not expected to
diff --git a/deploy/charts/litellm-helm/templates/migrations-job.yaml b/deploy/charts/litellm-helm/templates/migrations-job.yaml
index 381e9e5433..e994c45548 100644
--- a/deploy/charts/litellm-helm/templates/migrations-job.yaml
+++ b/deploy/charts/litellm-helm/templates/migrations-job.yaml
@@ -48,6 +48,23 @@ spec:
{{- end }}
- name: DISABLE_SCHEMA_UPDATE
value: "false" # always run the migration from the Helm PreSync hook, override the value set
+ {{- with .Values.volumeMounts }}
+ volumeMounts:
+ {{- toYaml . | nindent 12 }}
+ {{- end }}
+ {{- with .Values.volumes }}
+ volumes:
+ {{- toYaml . | nindent 8 }}
+ {{- end }}
restartPolicy: OnFailure
+ {{- with .Values.affinity }}
+ affinity:
+ {{- toYaml . | nindent 8 }}
+ {{- end }}
+ {{- with .Values.tolerations }}
+ tolerations:
+ {{- toYaml . | nindent 8 }}
+ {{- end }}
+ ttlSecondsAfterFinished: {{ .Values.migrationJob.ttlSecondsAfterFinished }}
backoffLimit: {{ .Values.migrationJob.backoffLimit }}
{{- end }}
diff --git a/deploy/charts/litellm-helm/tests/deployment_tests.yaml b/deploy/charts/litellm-helm/tests/deployment_tests.yaml
new file mode 100644
index 0000000000..e7ce44b052
--- /dev/null
+++ b/deploy/charts/litellm-helm/tests/deployment_tests.yaml
@@ -0,0 +1,54 @@
+suite: test deployment
+templates:
+ - deployment.yaml
+ - configmap-litellm.yaml
+tests:
+ - it: should work
+ template: deployment.yaml
+ set:
+ image.tag: test
+ asserts:
+ - isKind:
+ of: Deployment
+ - matchRegex:
+ path: metadata.name
+ pattern: -litellm$
+ - equal:
+ path: spec.template.spec.containers[0].image
+ value: ghcr.io/berriai/litellm-database:test
+ - it: should work with tolerations
+ template: deployment.yaml
+ set:
+ tolerations:
+ - key: node-role.kubernetes.io/master
+ operator: Exists
+ effect: NoSchedule
+ asserts:
+ - equal:
+ path: spec.template.spec.tolerations[0].key
+ value: node-role.kubernetes.io/master
+ - equal:
+ path: spec.template.spec.tolerations[0].operator
+ value: Exists
+ - it: should work with affinity
+ template: deployment.yaml
+ set:
+ affinity:
+ nodeAffinity:
+ requiredDuringSchedulingIgnoredDuringExecution:
+ nodeSelectorTerms:
+ - matchExpressions:
+ - key: topology.kubernetes.io/zone
+ operator: In
+ values:
+ - antarctica-east1
+ asserts:
+ - equal:
+ path: spec.template.spec.affinity.nodeAffinity.requiredDuringSchedulingIgnoredDuringExecution.nodeSelectorTerms[0].matchExpressions[0].key
+ value: topology.kubernetes.io/zone
+ - equal:
+ path: spec.template.spec.affinity.nodeAffinity.requiredDuringSchedulingIgnoredDuringExecution.nodeSelectorTerms[0].matchExpressions[0].operator
+ value: In
+ - equal:
+ path: spec.template.spec.affinity.nodeAffinity.requiredDuringSchedulingIgnoredDuringExecution.nodeSelectorTerms[0].matchExpressions[0].values[0]
+ value: antarctica-east1
diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml
index 19cbf72321..9f21fc40ad 100644
--- a/deploy/charts/litellm-helm/values.yaml
+++ b/deploy/charts/litellm-helm/values.yaml
@@ -187,6 +187,7 @@ migrationJob:
backoffLimit: 4 # Backoff limit for Job restarts
disableSchemaUpdate: false # Skip schema migrations for specific environments. When True, the job will exit with code 0.
annotations: {}
+ ttlSecondsAfterFinished: 120
# Additional environment variables to be added to the deployment
envVars: {
diff --git a/docs/my-website/docs/anthropic_unified.md b/docs/my-website/docs/anthropic_unified.md
new file mode 100644
index 0000000000..71b9203399
--- /dev/null
+++ b/docs/my-website/docs/anthropic_unified.md
@@ -0,0 +1,92 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# [BETA] `/v1/messages`
+
+LiteLLM provides a BETA endpoint in the spec of Anthropic's `/v1/messages` endpoint.
+
+This currently just supports the Anthropic API.
+
+| Feature | Supported | Notes |
+|-------|-------|-------|
+| Cost Tracking | ✅ | |
+| Logging | ✅ | works across all integrations |
+| End-user Tracking | ✅ | |
+| Streaming | ✅ | |
+| Fallbacks | ✅ | between anthropic models |
+| Loadbalancing | ✅ | between anthropic models |
+
+Planned improvement:
+- Vertex AI Anthropic support
+- Bedrock Anthropic support
+
+## Usage
+
+
+
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+ - model_name: anthropic-claude
+ litellm_params:
+ model: claude-3-7-sonnet-latest
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/v1/messages' \
+-H 'content-type: application/json' \
+-H 'x-api-key: $LITELLM_API_KEY' \
+-H 'anthropic-version: 2023-06-01' \
+-d '{
+ "model": "anthropic-claude",
+ "messages": [
+ {
+ "role": "user",
+ "content": [
+ {
+ "type": "text",
+ "text": "List 5 important events in the XIX century"
+ }
+ ]
+ }
+ ],
+ "max_tokens": 4096
+}'
+```
+
+
+
+```python
+from litellm.llms.anthropic.experimental_pass_through.messages.handler import anthropic_messages
+import asyncio
+import os
+
+# set env
+os.environ["ANTHROPIC_API_KEY"] = "my-api-key"
+
+messages = [{"role": "user", "content": "Hello, can you tell me a short joke?"}]
+
+# Call the handler
+async def call():
+ response = await anthropic_messages(
+ messages=messages,
+ api_key=api_key,
+ model="claude-3-haiku-20240307",
+ max_tokens=100,
+ )
+
+asyncio.run(call())
+```
+
+
+
\ No newline at end of file
diff --git a/docs/my-website/docs/completion/reliable_completions.md b/docs/my-website/docs/completion/reliable_completions.md
index 94102e1944..f38917fe53 100644
--- a/docs/my-website/docs/completion/reliable_completions.md
+++ b/docs/my-website/docs/completion/reliable_completions.md
@@ -46,7 +46,7 @@ from litellm import completion
fallback_dict = {"gpt-3.5-turbo": "gpt-3.5-turbo-16k"}
messages = [{"content": "how does a court case get to the Supreme Court?" * 500, "role": "user"}]
-completion(model="gpt-3.5-turbo", messages=messages, context_window_fallback_dict=ctx_window_fallback_dict)
+completion(model="gpt-3.5-turbo", messages=messages, context_window_fallback_dict=fallback_dict)
```
### Fallbacks - Switch Models/API Keys/API Bases (SDK)
diff --git a/docs/my-website/docs/completion/vision.md b/docs/my-website/docs/completion/vision.md
index efb988b76f..1e18109b3b 100644
--- a/docs/my-website/docs/completion/vision.md
+++ b/docs/my-website/docs/completion/vision.md
@@ -189,4 +189,138 @@ Expected Response
```
-
\ No newline at end of file
+
+
+
+## Explicitly specify image type
+
+If you have images without a mime-type, or if litellm is incorrectly inferring the mime type of your image (e.g. calling `gs://` url's with vertex ai), you can set this explicity via the `format` param.
+
+```python
+"image_url": {
+ "url": "gs://my-gs-image",
+ "format": "image/jpeg"
+}
+```
+
+LiteLLM will use this for any API endpoint, which supports specifying mime-type (e.g. anthropic/bedrock/vertex ai).
+
+For others (e.g. openai), it will be ignored.
+
+
+
+
+```python
+import os
+from litellm import completion
+
+os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
+
+# openai call
+response = completion(
+ model = "claude-3-7-sonnet-latest",
+ messages=[
+ {
+ "role": "user",
+ "content": [
+ {
+ "type": "text",
+ "text": "What’s in this image?"
+ },
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
+ "format": "image/jpeg"
+ }
+ }
+ ]
+ }
+ ],
+)
+
+```
+
+
+
+
+1. Define vision models on config.yaml
+
+```yaml
+model_list:
+ - model_name: gpt-4-vision-preview # OpenAI gpt-4-vision-preview
+ litellm_params:
+ model: openai/gpt-4-vision-preview
+ api_key: os.environ/OPENAI_API_KEY
+ - model_name: llava-hf # Custom OpenAI compatible model
+ litellm_params:
+ model: openai/llava-hf/llava-v1.6-vicuna-7b-hf
+ api_base: http://localhost:8000
+ api_key: fake-key
+ model_info:
+ supports_vision: True # set supports_vision to True so /model/info returns this attribute as True
+
+```
+
+2. Run proxy server
+
+```bash
+litellm --config config.yaml
+```
+
+3. Test it using the OpenAI Python SDK
+
+
+```python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+ api_key="sk-1234", # your litellm proxy api key
+)
+
+response = client.chat.completions.create(
+ model = "gpt-4-vision-preview", # use model="llava-hf" to test your custom OpenAI endpoint
+ messages=[
+ {
+ "role": "user",
+ "content": [
+ {
+ "type": "text",
+ "text": "What’s in this image?"
+ },
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
+ "format": "image/jpeg"
+ }
+ }
+ ]
+ }
+ ],
+)
+
+```
+
+
+
+
+
+
+
+
+
+## Spec
+
+```
+"image_url": str
+
+OR
+
+"image_url": {
+ "url": "url OR base64 encoded str",
+ "detail": "openai-only param",
+ "format": "specify mime-type of image"
+}
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/data_security.md b/docs/my-website/docs/data_security.md
index 13cde26d5d..30128760f2 100644
--- a/docs/my-website/docs/data_security.md
+++ b/docs/my-website/docs/data_security.md
@@ -46,7 +46,7 @@ For security inquiries, please contact us at support@berri.ai
|-------------------|-------------------------------------------------------------------------------------------------|
| SOC 2 Type I | Certified. Report available upon request on Enterprise plan. |
| SOC 2 Type II | In progress. Certificate available by April 15th, 2025 |
-| ISO27001 | In progress. Certificate available by February 7th, 2025 |
+| ISO 27001 | Certified. Report available upon request on Enterprise |
## Supported Data Regions for LiteLLM Cloud
@@ -137,7 +137,7 @@ Point of contact email address for general security-related questions: krrish@be
Has the Vendor been audited / certified?
- SOC 2 Type I. Certified. Report available upon request on Enterprise plan.
- SOC 2 Type II. In progress. Certificate available by April 15th, 2025.
-- ISO27001. In progress. Certificate available by February 7th, 2025.
+- ISO 27001. Certified. Report available upon request on Enterprise plan.
Has an information security management system been implemented?
- Yes - [CodeQL](https://codeql.github.com/) and a comprehensive ISMS covering multiple security domains.
diff --git a/docs/my-website/docs/extras/contributing_code.md b/docs/my-website/docs/extras/contributing_code.md
new file mode 100644
index 0000000000..0fe7675ead
--- /dev/null
+++ b/docs/my-website/docs/extras/contributing_code.md
@@ -0,0 +1,96 @@
+# Contributing Code
+
+## **Checklist before submitting a PR**
+
+Here are the core requirements for any PR submitted to LiteLLM
+
+
+- [ ] Add testing, **Adding at least 1 test is a hard requirement** - [see details](#2-adding-testing-to-your-pr)
+- [ ] Ensure your PR passes the following tests:
+ - [ ] [Unit Tests](#3-running-unit-tests)
+ - [ ] Formatting / Linting Tests
+- [ ] Keep scope as isolated as possible. As a general rule, your changes should address 1 specific problem at a time
+
+
+
+## Quick start
+
+## 1. Setup your local dev environment
+
+
+Here's how to modify the repo locally:
+
+Step 1: Clone the repo
+
+```shell
+git clone https://github.com/BerriAI/litellm.git
+```
+
+Step 2: Install dev dependencies:
+
+```shell
+poetry install --with dev --extras proxy
+```
+
+That's it, your local dev environment is ready!
+
+## 2. Adding Testing to your PR
+
+- Add your test to the [`tests/litellm/` directory](https://github.com/BerriAI/litellm/tree/main/tests/litellm)
+
+- This directory 1:1 maps the the `litellm/` directory, and can only contain mocked tests.
+- Do not add real llm api calls to this directory.
+
+### 2.1 File Naming Convention for `tests/litellm/`
+
+The `tests/litellm/` directory follows the same directory structure as `litellm/`.
+
+- `litellm/proxy/test_caching_routes.py` maps to `litellm/proxy/caching_routes.py`
+- `test_{filename}.py` maps to `litellm/{filename}.py`
+
+## 3. Running Unit Tests
+
+run the following command on the root of the litellm directory
+
+```shell
+make test-unit
+```
+
+## 4. Submit a PR with your changes!
+
+- push your fork to your GitHub repo
+- submit a PR from there
+
+
+## Advanced
+### Building LiteLLM Docker Image
+
+Some people might want to build the LiteLLM docker image themselves. Follow these instructions if you want to build / run the LiteLLM Docker Image yourself.
+
+Step 1: Clone the repo
+
+```shell
+git clone https://github.com/BerriAI/litellm.git
+```
+
+Step 2: Build the Docker Image
+
+Build using Dockerfile.non_root
+
+```shell
+docker build -f docker/Dockerfile.non_root -t litellm_test_image .
+```
+
+Step 3: Run the Docker Image
+
+Make sure config.yaml is present in the root directory. This is your litellm proxy config file.
+
+```shell
+docker run \
+ -v $(pwd)/proxy_config.yaml:/app/config.yaml \
+ -e DATABASE_URL="postgresql://xxxxxxxx" \
+ -e LITELLM_MASTER_KEY="sk-1234" \
+ -p 4000:4000 \
+ litellm_test_image \
+ --config /app/config.yaml --detailed_debug
+```
diff --git a/docs/my-website/docs/observability/athina_integration.md b/docs/my-website/docs/observability/athina_integration.md
index 4994d553c6..ba93ea4c98 100644
--- a/docs/my-website/docs/observability/athina_integration.md
+++ b/docs/my-website/docs/observability/athina_integration.md
@@ -78,6 +78,9 @@ Following are the allowed fields in metadata, their types, and their description
* `context: Optional[Union[dict, str]]` - This is the context used as information for the prompt. For RAG applications, this is the "retrieved" data. You may log context as a string or as an object (dictionary).
* `expected_response: Optional[str]` - This is the reference response to compare against for evaluation purposes. This is useful for segmenting inference calls by expected response.
* `user_query: Optional[str]` - This is the user's query. For conversational applications, this is the user's last message.
+* `tags: Optional[list]` - This is a list of tags. This is useful for segmenting inference calls by tags.
+* `user_feedback: Optional[str]` - The end user’s feedback.
+* `model_options: Optional[dict]` - This is a dictionary of model options. This is useful for getting insights into how model behavior affects your end users.
* `custom_attributes: Optional[dict]` - This is a dictionary of custom attributes. This is useful for additional information about the inference.
## Using a self hosted deployment of Athina
diff --git a/docs/my-website/docs/pass_through/openai_passthrough.md b/docs/my-website/docs/pass_through/openai_passthrough.md
new file mode 100644
index 0000000000..2712369575
--- /dev/null
+++ b/docs/my-website/docs/pass_through/openai_passthrough.md
@@ -0,0 +1,95 @@
+# OpenAI Passthrough
+
+Pass-through endpoints for `/openai`
+
+## Overview
+
+| Feature | Supported | Notes |
+|-------|-------|-------|
+| Cost Tracking | ❌ | Not supported |
+| Logging | ✅ | Works across all integrations |
+| Streaming | ✅ | Fully supported |
+
+### When to use this?
+
+- For 90% of your use cases, you should use the [native LiteLLM OpenAI Integration](https://docs.litellm.ai/docs/providers/openai) (`/chat/completions`, `/embeddings`, `/completions`, `/images`, `/batches`, etc.)
+- Use this passthrough to call less popular or newer OpenAI endpoints that LiteLLM doesn't fully support yet, such as `/assistants`, `/threads`, `/vector_stores`
+
+Simply replace `https://api.openai.com` with `LITELLM_PROXY_BASE_URL/openai`
+
+## Usage Examples
+
+### Assistants API
+
+#### Create OpenAI Client
+
+Make sure you do the following:
+- Point `base_url` to your `LITELLM_PROXY_BASE_URL/openai`
+- Use your `LITELLM_API_KEY` as the `api_key`
+
+```python
+import openai
+
+client = openai.OpenAI(
+ base_url="http://0.0.0.0:4000/openai", # /openai
+ api_key="sk-anything" #
+)
+```
+
+#### Create an Assistant
+
+```python
+# Create an assistant
+assistant = client.beta.assistants.create(
+ name="Math Tutor",
+ instructions="You are a math tutor. Help solve equations.",
+ model="gpt-4o",
+)
+```
+
+#### Create a Thread
+```python
+# Create a thread
+thread = client.beta.threads.create()
+```
+
+#### Add a Message to the Thread
+```python
+# Add a message
+message = client.beta.threads.messages.create(
+ thread_id=thread.id,
+ role="user",
+ content="Solve 3x + 11 = 14",
+)
+```
+
+#### Run the Assistant
+```python
+# Create a run to get the assistant's response
+run = client.beta.threads.runs.create(
+ thread_id=thread.id,
+ assistant_id=assistant.id,
+)
+
+# Check run status
+run_status = client.beta.threads.runs.retrieve(
+ thread_id=thread.id,
+ run_id=run.id
+)
+```
+
+#### Retrieve Messages
+```python
+# List messages after the run completes
+messages = client.beta.threads.messages.list(
+ thread_id=thread.id
+)
+```
+
+#### Delete the Assistant
+
+```python
+# Delete the assistant when done
+client.beta.assistants.delete(assistant.id)
+```
+
diff --git a/docs/my-website/docs/projects/PDL.md b/docs/my-website/docs/projects/PDL.md
new file mode 100644
index 0000000000..5d6fd77555
--- /dev/null
+++ b/docs/my-website/docs/projects/PDL.md
@@ -0,0 +1,5 @@
+PDL - A YAML-based approach to prompt programming
+
+Github: https://github.com/IBM/prompt-declaration-language
+
+PDL is a declarative approach to prompt programming, helping users to accumulate messages implicitly, with support for model chaining and tool use.
\ No newline at end of file
diff --git a/docs/my-website/docs/projects/pgai.md b/docs/my-website/docs/projects/pgai.md
new file mode 100644
index 0000000000..bece5baf6a
--- /dev/null
+++ b/docs/my-website/docs/projects/pgai.md
@@ -0,0 +1,9 @@
+# pgai
+
+[pgai](https://github.com/timescale/pgai) is a suite of tools to develop RAG, semantic search, and other AI applications more easily with PostgreSQL.
+
+If you don't know what pgai is yet check out the [README](https://github.com/timescale/pgai)!
+
+If you're already familiar with pgai, you can find litellm specific docs here:
+- Litellm for [model calling](https://github.com/timescale/pgai/blob/main/docs/model_calling/litellm.md) in pgai
+- Use the [litellm provider](https://github.com/timescale/pgai/blob/main/docs/vectorizer/api-reference.md#aiembedding_litellm) to automatically create embeddings for your data via the pgai vectorizer.
diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md
index cda0bb97a6..55e9ba10d3 100644
--- a/docs/my-website/docs/providers/anthropic.md
+++ b/docs/my-website/docs/providers/anthropic.md
@@ -819,6 +819,114 @@ resp = litellm.completion(
print(f"\nResponse: {resp}")
```
+## Usage - Thinking / `reasoning_content`
+
+
+
+
+```python
+from litellm import completion
+
+resp = completion(
+ model="anthropic/claude-3-7-sonnet-20250219",
+ messages=[{"role": "user", "content": "What is the capital of France?"}],
+ thinking={"type": "enabled", "budget_tokens": 1024},
+)
+
+```
+
+
+
+
+
+1. Setup config.yaml
+
+```yaml
+- model_name: claude-3-7-sonnet-20250219
+ litellm_params:
+ model: anthropic/claude-3-7-sonnet-20250219
+ api_key: os.environ/ANTHROPIC_API_KEY
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer " \
+ -d '{
+ "model": "claude-3-7-sonnet-20250219",
+ "messages": [{"role": "user", "content": "What is the capital of France?"}],
+ "thinking": {"type": "enabled", "budget_tokens": 1024}
+ }'
+```
+
+
+
+
+
+**Expected Response**
+
+```python
+ModelResponse(
+ id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
+ created=1740470510,
+ model='claude-3-7-sonnet-20250219',
+ object='chat.completion',
+ system_fingerprint=None,
+ choices=[
+ Choices(
+ finish_reason='stop',
+ index=0,
+ message=Message(
+ content="The capital of France is Paris.",
+ role='assistant',
+ tool_calls=None,
+ function_call=None,
+ provider_specific_fields={
+ 'citations': None,
+ 'thinking_blocks': [
+ {
+ 'type': 'thinking',
+ 'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
+ 'signature': 'EuYBCkQYAiJAy6...'
+ }
+ ]
+ }
+ ),
+ thinking_blocks=[
+ {
+ 'type': 'thinking',
+ 'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
+ 'signature': 'EuYBCkQYAiJAy6AGB...'
+ }
+ ],
+ reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
+ )
+ ],
+ usage=Usage(
+ completion_tokens=68,
+ prompt_tokens=42,
+ total_tokens=110,
+ completion_tokens_details=None,
+ prompt_tokens_details=PromptTokensDetailsWrapper(
+ audio_tokens=None,
+ cached_tokens=0,
+ text_tokens=None,
+ image_tokens=None
+ ),
+ cache_creation_input_tokens=0,
+ cache_read_input_tokens=0
+ )
+)
+```
+
## **Passing Extra Headers to Anthropic API**
Pass `extra_headers: dict` to `litellm.completion`
@@ -1135,3 +1243,4 @@ curl http://0.0.0.0:4000/v1/chat/completions \
+
diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md
index 00fe45e99e..628132b448 100644
--- a/docs/my-website/docs/providers/bedrock.md
+++ b/docs/my-website/docs/providers/bedrock.md
@@ -63,9 +63,9 @@ model_list:
- model_name: bedrock-claude-v1
litellm_params:
model: bedrock/anthropic.claude-instant-v1
- aws_access_key_id: os.environ/CUSTOM_AWS_ACCESS_KEY_ID
- aws_secret_access_key: os.environ/CUSTOM_AWS_SECRET_ACCESS_KEY
- aws_region_name: os.environ/CUSTOM_AWS_REGION_NAME
+ aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
+ aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
+ aws_region_name: os.environ/AWS_REGION_NAME
```
All possible auth params:
@@ -286,9 +286,12 @@ print(response)
-## Usage - Function Calling
+## Usage - Function Calling / Tool calling
-LiteLLM uses Bedrock's Converse API for making tool calls
+LiteLLM supports tool calling via Bedrock's Converse and Invoke API's.
+
+
+
```python
from litellm import completion
@@ -333,6 +336,69 @@ assert isinstance(
response.choices[0].message.tool_calls[0].function.arguments, str
)
```
+
+
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+ - model_name: bedrock-claude-3-7
+ litellm_params:
+ model: bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0 # for bedrock invoke, specify `bedrock/invoke/`
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+-H "Content-Type: application/json" \
+-H "Authorization: Bearer $LITELLM_API_KEY" \
+-d '{
+ "model": "bedrock-claude-3-7",
+ "messages": [
+ {
+ "role": "user",
+ "content": "What'\''s the weather like in Boston today?"
+ }
+ ],
+ "tools": [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_current_weather",
+ "description": "Get the current weather in a given location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA"
+ },
+ "unit": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"]
+ }
+ },
+ "required": ["location"]
+ }
+ }
+ }
+ ],
+ "tool_choice": "auto"
+}'
+
+```
+
+
+
+
## Usage - Vision
@@ -377,6 +443,226 @@ print(f"\nResponse: {resp}")
```
+## Usage - 'thinking' / 'reasoning content'
+
+This is currently only supported for Anthropic's Claude 3.7 Sonnet + Deepseek R1.
+
+Works on v1.61.20+.
+
+Returns 2 new fields in `message` and `delta` object:
+- `reasoning_content` - string - The reasoning content of the response
+- `thinking_blocks` - list of objects (Anthropic only) - The thinking blocks of the response
+
+Each object has the following fields:
+- `type` - Literal["thinking"] - The type of thinking block
+- `thinking` - string - The thinking of the response. Also returned in `reasoning_content`
+- `signature` - string - A base64 encoded string, returned by Anthropic.
+
+The `signature` is required by Anthropic on subsequent calls, if 'thinking' content is passed in (only required to use `thinking` with tool calling). [Learn more](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking#understanding-thinking-blocks)
+
+
+
+
+```python
+from litellm import completion
+
+# set env
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+os.environ["AWS_REGION_NAME"] = ""
+
+
+resp = completion(
+ model="bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+ messages=[{"role": "user", "content": "What is the capital of France?"}],
+ thinking={"type": "enabled", "budget_tokens": 1024},
+)
+
+print(resp)
+```
+
+
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+ - model_name: bedrock-claude-3-7
+ litellm_params:
+ model: bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0
+ thinking: {"type": "enabled", "budget_tokens": 1024} # 👈 EITHER HERE OR ON REQUEST
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer " \
+ -d '{
+ "model": "bedrock-claude-3-7",
+ "messages": [{"role": "user", "content": "What is the capital of France?"}],
+ "thinking": {"type": "enabled", "budget_tokens": 1024} # 👈 EITHER HERE OR ON CONFIG.YAML
+ }'
+```
+
+
+
+
+
+**Expected Response**
+
+Same as [Anthropic API response](../providers/anthropic#usage---thinking--reasoning_content).
+
+```python
+{
+ "id": "chatcmpl-c661dfd7-7530-49c9-b0cc-d5018ba4727d",
+ "created": 1740640366,
+ "model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+ "object": "chat.completion",
+ "system_fingerprint": null,
+ "choices": [
+ {
+ "finish_reason": "stop",
+ "index": 0,
+ "message": {
+ "content": "The capital of France is Paris. It's not only the capital city but also the largest city in France, serving as the country's major cultural, economic, and political center.",
+ "role": "assistant",
+ "tool_calls": null,
+ "function_call": null,
+ "reasoning_content": "The capital of France is Paris. This is a straightforward factual question.",
+ "thinking_blocks": [
+ {
+ "type": "thinking",
+ "thinking": "The capital of France is Paris. This is a straightforward factual question.",
+ "signature": "EqoBCkgIARABGAIiQL2UoU0b1OHYi+yCHpBY7U6FQW8/FcoLewocJQPa2HnmLM+NECy50y44F/kD4SULFXi57buI9fAvyBwtyjlOiO0SDE3+r3spdg6PLOo9PBoMma2ku5OTAoR46j9VIjDRlvNmBvff7YW4WI9oU8XagaOBSxLPxElrhyuxppEn7m6bfT40dqBSTDrfiw4FYB4qEPETTI6TA6wtjGAAqmFqKTo="
+ }
+ ]
+ }
+ }
+ ],
+ "usage": {
+ "completion_tokens": 64,
+ "prompt_tokens": 42,
+ "total_tokens": 106,
+ "completion_tokens_details": null,
+ "prompt_tokens_details": null
+ }
+}
+```
+
+
+## Usage - Structured Output / JSON mode
+
+
+
+
+```python
+from litellm import completion
+import os
+from pydantic import BaseModel
+
+# set env
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+os.environ["AWS_REGION_NAME"] = ""
+
+class CalendarEvent(BaseModel):
+ name: str
+ date: str
+ participants: list[str]
+
+class EventsList(BaseModel):
+ events: list[CalendarEvent]
+
+response = completion(
+ model="bedrock/anthropic.claude-3-7-sonnet-20250219-v1:0", # specify invoke via `bedrock/invoke/anthropic.claude-3-7-sonnet-20250219-v1:0`
+ response_format=EventsList,
+ messages=[
+ {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
+ {"role": "user", "content": "Who won the world series in 2020?"}
+ ],
+)
+print(response.choices[0].message.content)
+```
+
+
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+ - model_name: bedrock-claude-3-7
+ litellm_params:
+ model: bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0 # specify invoke via `bedrock/invoke/`
+ aws_access_key_id: os.environ/CUSTOM_AWS_ACCESS_KEY_ID
+ aws_secret_access_key: os.environ/CUSTOM_AWS_SECRET_ACCESS_KEY
+ aws_region_name: os.environ/CUSTOM_AWS_REGION_NAME
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer $LITELLM_KEY" \
+ -d '{
+ "model": "bedrock-claude-3-7",
+ "messages": [
+ {
+ "role": "system",
+ "content": "You are a helpful assistant designed to output JSON."
+ },
+ {
+ "role": "user",
+ "content": "Who won the worlde series in 2020?"
+ }
+ ],
+ "response_format": {
+ "type": "json_schema",
+ "json_schema": {
+ "name": "math_reasoning",
+ "description": "reason about maths",
+ "schema": {
+ "type": "object",
+ "properties": {
+ "steps": {
+ "type": "array",
+ "items": {
+ "type": "object",
+ "properties": {
+ "explanation": { "type": "string" },
+ "output": { "type": "string" }
+ },
+ "required": ["explanation", "output"],
+ "additionalProperties": false
+ }
+ },
+ "final_answer": { "type": "string" }
+ },
+ "required": ["steps", "final_answer"],
+ "additionalProperties": false
+ },
+ "strict": true
+ }
+ }
+ }'
+```
+
+
+
## Usage - Bedrock Guardrails
Example of using [Bedrock Guardrails with LiteLLM](https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-use-converse-api.html)
@@ -1624,6 +1910,8 @@ curl http://0.0.0.0:4000/rerank \
"Capital punishment has existed in the United States since before it was a country."
],
"top_n": 3
+
+
}'
```
diff --git a/docs/my-website/docs/providers/cerebras.md b/docs/my-website/docs/providers/cerebras.md
index 4fabeb31cb..33bef5e107 100644
--- a/docs/my-website/docs/providers/cerebras.md
+++ b/docs/my-website/docs/providers/cerebras.md
@@ -23,14 +23,16 @@ import os
os.environ['CEREBRAS_API_KEY'] = ""
response = completion(
- model="cerebras/meta/llama3-70b-instruct",
+ model="cerebras/llama3-70b-instruct",
messages=[
{
"role": "user",
- "content": "What's the weather like in Boston today in Fahrenheit?",
+ "content": "What's the weather like in Boston today in Fahrenheit? (Write in JSON)",
}
],
max_tokens=10,
+
+ # The prompt should include JSON if 'json_object' is selected; otherwise, you will get error code 400.
response_format={ "type": "json_object" },
seed=123,
stop=["\n\n"],
@@ -50,16 +52,18 @@ import os
os.environ['CEREBRAS_API_KEY'] = ""
response = completion(
- model="cerebras/meta/llama3-70b-instruct",
+ model="cerebras/llama3-70b-instruct",
messages=[
{
"role": "user",
- "content": "What's the weather like in Boston today in Fahrenheit?",
+ "content": "What's the weather like in Boston today in Fahrenheit? (Write in JSON)",
}
],
stream=True,
max_tokens=10,
- response_format={ "type": "json_object" },
+
+ # The prompt should include JSON if 'json_object' is selected; otherwise, you will get error code 400.
+ response_format={ "type": "json_object" },
seed=123,
stop=["\n\n"],
temperature=0.2,
diff --git a/docs/my-website/docs/providers/cohere.md b/docs/my-website/docs/providers/cohere.md
index 1154dc3c4e..6b7a4743ec 100644
--- a/docs/my-website/docs/providers/cohere.md
+++ b/docs/my-website/docs/providers/cohere.md
@@ -108,7 +108,7 @@ response = embedding(
### Usage
-
+LiteLLM supports the v1 and v2 clients for Cohere rerank. By default, the `rerank` endpoint uses the v2 client, but you can specify the v1 client by explicitly calling `v1/rerank`
diff --git a/docs/my-website/docs/providers/infinity.md b/docs/my-website/docs/providers/infinity.md
index dd6986dfef..091503bf18 100644
--- a/docs/my-website/docs/providers/infinity.md
+++ b/docs/my-website/docs/providers/infinity.md
@@ -1,3 +1,6 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
# Infinity
| Property | Details |
@@ -12,6 +15,9 @@
```python
from litellm import rerank
+import os
+
+os.environ["INFINITY_API_BASE"] = "http://localhost:8080"
response = rerank(
model="infinity/rerank",
@@ -65,3 +71,114 @@ curl http://0.0.0.0:4000/rerank \
```
+## Supported Cohere Rerank API Params
+
+| Param | Type | Description |
+|-------|-------|-------|
+| `query` | `str` | The query to rerank the documents against |
+| `documents` | `list[str]` | The documents to rerank |
+| `top_n` | `int` | The number of documents to return |
+| `return_documents` | `bool` | Whether to return the documents in the response |
+
+### Usage - Return Documents
+
+
+
+
+```python
+response = rerank(
+ model="infinity/rerank",
+ query="What is the capital of France?",
+ documents=["Paris", "London", "Berlin", "Madrid"],
+ return_documents=True,
+)
+```
+
+
+
+
+
+```bash
+curl http://0.0.0.0:4000/rerank \
+ -H "Authorization: Bearer sk-1234" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "custom-infinity-rerank",
+ "query": "What is the capital of France?",
+ "documents": [
+ "Paris",
+ "London",
+ "Berlin",
+ "Madrid"
+ ],
+ "return_documents": True,
+ }'
+```
+
+
+
+
+## Pass Provider-specific Params
+
+Any unmapped params will be passed to the provider as-is.
+
+
+
+
+```python
+from litellm import rerank
+import os
+
+os.environ["INFINITY_API_BASE"] = "http://localhost:8080"
+
+response = rerank(
+ model="infinity/rerank",
+ query="What is the capital of France?",
+ documents=["Paris", "London", "Berlin", "Madrid"],
+ raw_scores=True, # 👈 PROVIDER-SPECIFIC PARAM
+)
+```
+
+
+
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+ - model_name: custom-infinity-rerank
+ litellm_params:
+ model: infinity/rerank
+ api_base: https://localhost:8080
+ raw_scores: True # 👈 EITHER SET PROVIDER-SPECIFIC PARAMS HERE OR IN REQUEST BODY
+```
+
+2. Start litellm
+
+```bash
+litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+3. Test it!
+
+```bash
+curl http://0.0.0.0:4000/rerank \
+ -H "Authorization: Bearer sk-1234" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "custom-infinity-rerank",
+ "query": "What is the capital of the United States?",
+ "documents": [
+ "Carson City is the capital city of the American state of Nevada.",
+ "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
+ "Washington, D.C. is the capital of the United States.",
+ "Capital punishment has existed in the United States since before it was a country."
+ ],
+ "raw_scores": True # 👈 PROVIDER-SPECIFIC PARAM
+ }'
+```
+
+
+
diff --git a/docs/my-website/docs/providers/litellm_proxy.md b/docs/my-website/docs/providers/litellm_proxy.md
index 69377b27f1..e204caba0a 100644
--- a/docs/my-website/docs/providers/litellm_proxy.md
+++ b/docs/my-website/docs/providers/litellm_proxy.md
@@ -3,13 +3,15 @@ import TabItem from '@theme/TabItem';
# LiteLLM Proxy (LLM Gateway)
-:::tip
-[LiteLLM Providers a **self hosted** proxy server (AI Gateway)](../simple_proxy) to call all the LLMs in the OpenAI format
+| Property | Details |
+|-------|-------|
+| Description | LiteLLM Proxy is an OpenAI-compatible gateway that allows you to interact with multiple LLM providers through a unified API. Simply use the `litellm_proxy/` prefix before the model name to route your requests through the proxy. |
+| Provider Route on LiteLLM | `litellm_proxy/` (add this prefix to the model name, to route any requests to litellm_proxy - e.g. `litellm_proxy/your-model-name`) |
+| Setup LiteLLM Gateway | [LiteLLM Gateway ↗](../simple_proxy) |
+| Supported Endpoints |`/chat/completions`, `/completions`, `/embeddings`, `/audio/speech`, `/audio/transcriptions`, `/images`, `/rerank` |
-:::
-**[LiteLLM Proxy](../simple_proxy) is OpenAI compatible**, you just need the `litellm_proxy/` prefix before the model
## Required Variables
@@ -83,7 +85,76 @@ for chunk in response:
print(chunk)
```
+## Embeddings
+```python
+import litellm
+
+response = litellm.embedding(
+ model="litellm_proxy/your-embedding-model",
+ input="Hello world",
+ api_base="your-litellm-proxy-url",
+ api_key="your-litellm-proxy-api-key"
+)
+```
+
+## Image Generation
+
+```python
+import litellm
+
+response = litellm.image_generation(
+ model="litellm_proxy/dall-e-3",
+ prompt="A beautiful sunset over mountains",
+ api_base="your-litellm-proxy-url",
+ api_key="your-litellm-proxy-api-key"
+)
+```
+
+## Audio Transcription
+
+```python
+import litellm
+
+response = litellm.transcription(
+ model="litellm_proxy/whisper-1",
+ file="your-audio-file",
+ api_base="your-litellm-proxy-url",
+ api_key="your-litellm-proxy-api-key"
+)
+```
+
+## Text to Speech
+
+```python
+import litellm
+
+response = litellm.speech(
+ model="litellm_proxy/tts-1",
+ input="Hello world",
+ api_base="your-litellm-proxy-url",
+ api_key="your-litellm-proxy-api-key"
+)
+```
+
+## Rerank
+
+```python
+import litellm
+
+import litellm
+
+response = litellm.rerank(
+ model="litellm_proxy/rerank-english-v2.0",
+ query="What is machine learning?",
+ documents=[
+ "Machine learning is a field of study in artificial intelligence",
+ "Biology is the study of living organisms"
+ ],
+ api_base="your-litellm-proxy-url",
+ api_key="your-litellm-proxy-api-key"
+)
+```
## **Usage with Langchain, LLamaindex, OpenAI Js, Anthropic SDK, Instructor**
#### [Follow this doc to see how to use litellm proxy with langchain, llamaindex, anthropic etc](../proxy/user_keys)
\ No newline at end of file
diff --git a/docs/my-website/docs/providers/sambanova.md b/docs/my-website/docs/providers/sambanova.md
index 9fa6ce8b60..7dd837e1b0 100644
--- a/docs/my-website/docs/providers/sambanova.md
+++ b/docs/my-website/docs/providers/sambanova.md
@@ -2,11 +2,11 @@ import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Sambanova
-https://community.sambanova.ai/t/create-chat-completion-api/
+https://cloud.sambanova.ai/
:::tip
-**We support ALL Sambanova models, just set `model=sambanova/` as a prefix when sending litellm requests. For the complete supported model list, visit https://sambanova.ai/technology/models **
+**We support ALL Sambanova models, just set `model=sambanova/` as a prefix when sending litellm requests. For the complete supported model list, visit https://docs.sambanova.ai/cloud/docs/get-started/supported-models **
:::
@@ -27,12 +27,11 @@ response = completion(
messages=[
{
"role": "user",
- "content": "What do you know about sambanova.ai",
+ "content": "What do you know about sambanova.ai. Give your response in json format",
}
],
max_tokens=10,
response_format={ "type": "json_object" },
- seed=123,
stop=["\n\n"],
temperature=0.2,
top_p=0.9,
@@ -54,13 +53,12 @@ response = completion(
messages=[
{
"role": "user",
- "content": "What do you know about sambanova.ai",
+ "content": "What do you know about sambanova.ai. Give your response in json format",
}
],
stream=True,
max_tokens=10,
response_format={ "type": "json_object" },
- seed=123,
stop=["\n\n"],
temperature=0.2,
top_p=0.9,
diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md
index cb8c031c06..10ac13ecaf 100644
--- a/docs/my-website/docs/providers/vertex.md
+++ b/docs/my-website/docs/providers/vertex.md
@@ -404,14 +404,16 @@ curl http://localhost:4000/v1/chat/completions \
If this was your initial VertexAI Grounding code,
```python
-import vertexai
+import vertexai
+from vertexai.generative_models import GenerativeModel, GenerationConfig, Tool, grounding
+
vertexai.init(project=project_id, location="us-central1")
model = GenerativeModel("gemini-1.5-flash-001")
# Use Google Search for grounding
-tool = Tool.from_google_search_retrieval(grounding.GoogleSearchRetrieval(disable_attributon=False))
+tool = Tool.from_google_search_retrieval(grounding.GoogleSearchRetrieval())
prompt = "When is the next total solar eclipse in US?"
response = model.generate_content(
@@ -428,7 +430,7 @@ print(response)
then, this is what it looks like now
```python
-from litellm import completion
+from litellm import completion
# !gcloud auth application-default login - run this to add vertex credentials to your env
@@ -852,6 +854,7 @@ litellm.vertex_location = "us-central1 # Your Location
| claude-3-5-sonnet@20240620 | `completion('vertex_ai/claude-3-5-sonnet@20240620', messages)` |
| claude-3-sonnet@20240229 | `completion('vertex_ai/claude-3-sonnet@20240229', messages)` |
| claude-3-haiku@20240307 | `completion('vertex_ai/claude-3-haiku@20240307', messages)` |
+| claude-3-7-sonnet@20250219 | `completion('vertex_ai/claude-3-7-sonnet@20250219', messages)` |
### Usage
@@ -926,6 +929,119 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
+
+### Usage - `thinking` / `reasoning_content`
+
+
+
+
+
+```python
+from litellm import completion
+
+resp = completion(
+ model="vertex_ai/claude-3-7-sonnet-20250219",
+ messages=[{"role": "user", "content": "What is the capital of France?"}],
+ thinking={"type": "enabled", "budget_tokens": 1024},
+)
+
+```
+
+
+
+
+
+1. Setup config.yaml
+
+```yaml
+- model_name: claude-3-7-sonnet-20250219
+ litellm_params:
+ model: vertex_ai/claude-3-7-sonnet-20250219
+ vertex_ai_project: "my-test-project"
+ vertex_ai_location: "us-west-1"
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer " \
+ -d '{
+ "model": "claude-3-7-sonnet-20250219",
+ "messages": [{"role": "user", "content": "What is the capital of France?"}],
+ "thinking": {"type": "enabled", "budget_tokens": 1024}
+ }'
+```
+
+
+
+
+
+**Expected Response**
+
+```python
+ModelResponse(
+ id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
+ created=1740470510,
+ model='claude-3-7-sonnet-20250219',
+ object='chat.completion',
+ system_fingerprint=None,
+ choices=[
+ Choices(
+ finish_reason='stop',
+ index=0,
+ message=Message(
+ content="The capital of France is Paris.",
+ role='assistant',
+ tool_calls=None,
+ function_call=None,
+ provider_specific_fields={
+ 'citations': None,
+ 'thinking_blocks': [
+ {
+ 'type': 'thinking',
+ 'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
+ 'signature': 'EuYBCkQYAiJAy6...'
+ }
+ ]
+ }
+ ),
+ thinking_blocks=[
+ {
+ 'type': 'thinking',
+ 'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
+ 'signature': 'EuYBCkQYAiJAy6AGB...'
+ }
+ ],
+ reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
+ )
+ ],
+ usage=Usage(
+ completion_tokens=68,
+ prompt_tokens=42,
+ total_tokens=110,
+ completion_tokens_details=None,
+ prompt_tokens_details=PromptTokensDetailsWrapper(
+ audio_tokens=None,
+ cached_tokens=0,
+ text_tokens=None,
+ image_tokens=None
+ ),
+ cache_creation_input_tokens=0,
+ cache_read_input_tokens=0
+ )
+)
+```
+
+
+
## Llama 3 API
| Model Name | Function Call |
@@ -1572,6 +1688,14 @@ assert isinstance(
Pass any file supported by Vertex AI, through LiteLLM.
+LiteLLM Supports the following image types passed in url
+
+```
+Images with Cloud Storage URIs - gs://cloud-samples-data/generative-ai/image/boats.jpeg
+Images with direct links - https://storage.googleapis.com/github-repo/img/gemini/intro/landmark3.jpg
+Videos with Cloud Storage URIs - https://storage.googleapis.com/github-repo/img/gemini/multimodality_usecases_overview/pixel8.mp4
+Base64 Encoded Local Images
+```
diff --git a/docs/my-website/docs/providers/vllm.md b/docs/my-website/docs/providers/vllm.md
index 9cc0ad487e..b5987167ec 100644
--- a/docs/my-website/docs/providers/vllm.md
+++ b/docs/my-website/docs/providers/vllm.md
@@ -157,6 +157,98 @@ curl -L -X POST 'http://0.0.0.0:4000/embeddings' \
+## Send Video URL to VLLM
+
+Example Implementation from VLLM [here](https://github.com/vllm-project/vllm/pull/10020)
+
+There are two ways to send a video url to VLLM:
+
+1. Pass the video url directly
+
+```
+{"type": "video_url", "video_url": {"url": video_url}},
+```
+
+2. Pass the video data as base64
+
+```
+{"type": "video_url", "video_url": {"url": f"data:video/mp4;base64,{video_data_base64}"}}
+```
+
+
+
+
+```python
+from litellm import completion
+
+response = completion(
+ model="hosted_vllm/qwen", # pass the vllm model name
+ messages=[
+ {
+ "role": "user",
+ "content": [
+ {
+ "type": "text",
+ "text": "Summarize the following video"
+ },
+ {
+ "type": "video_url",
+ "video_url": {
+ "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
+ }
+ }
+ ]
+ }
+ ],
+ api_base="https://hosted-vllm-api.co")
+
+print(response)
+```
+
+
+
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+ - model_name: my-model
+ litellm_params:
+ model: hosted_vllm/qwen # add hosted_vllm/ prefix to route as OpenAI provider
+ api_base: https://hosted-vllm-api.co # add api base for OpenAI compatible provider
+```
+
+2. Start the proxy
+
+```bash
+$ litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+3. Test it!
+
+```bash
+curl -X POST http://0.0.0.0:4000/chat/completions \
+-H "Authorization: Bearer sk-1234" \
+-H "Content-Type: application/json" \
+-d '{
+ "model": "my-model",
+ "messages": [
+ {"role": "user", "content":
+ [
+ {"type": "text", "text": "Summarize the following video"},
+ {"type": "video_url", "video_url": {"url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"}}
+ ]
+ }
+ ]
+}'
+```
+
+
+
+
+
## (Deprecated) for `vllm pip package`
### Using - `litellm.completion`
diff --git a/docs/my-website/docs/proxy/architecture.md b/docs/my-website/docs/proxy/architecture.md
index 832fd266b6..2b83583ed9 100644
--- a/docs/my-website/docs/proxy/architecture.md
+++ b/docs/my-website/docs/proxy/architecture.md
@@ -36,7 +36,7 @@ import TabItem from '@theme/TabItem';
- Virtual Key Rate Limit
- User Rate Limit
- Team Limit
- - The `_PROXY_track_cost_callback` updates spend / usage in the LiteLLM database. [Here is everything tracked in the DB per request](https://github.com/BerriAI/litellm/blob/ba41a72f92a9abf1d659a87ec880e8e319f87481/schema.prisma#L172)
+ - The `_ProxyDBLogger` updates spend / usage in the LiteLLM database. [Here is everything tracked in the DB per request](https://github.com/BerriAI/litellm/blob/ba41a72f92a9abf1d659a87ec880e8e319f87481/schema.prisma#L172)
## Frequently Asked Questions
diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md
index 83b6535f75..9e24437449 100644
--- a/docs/my-website/docs/proxy/config_settings.md
+++ b/docs/my-website/docs/proxy/config_settings.md
@@ -466,6 +466,9 @@ router_settings:
| OTEL_SERVICE_NAME | Service name identifier for OpenTelemetry
| OTEL_TRACER_NAME | Tracer name for OpenTelemetry tracing
| PAGERDUTY_API_KEY | API key for PagerDuty Alerting
+| PHOENIX_API_KEY | API key for Arize Phoenix
+| PHOENIX_COLLECTOR_ENDPOINT | API endpoint for Arize Phoenix
+| PHOENIX_COLLECTOR_HTTP_ENDPOINT | API http endpoint for Arize Phoenix
| POD_NAME | Pod name for the server, this will be [emitted to `datadog` logs](https://docs.litellm.ai/docs/proxy/logging#datadog) as `POD_NAME`
| PREDIBASE_API_BASE | Base URL for Predibase API
| PRESIDIO_ANALYZER_API_BASE | Base URL for Presidio Analyzer service
diff --git a/docs/my-website/docs/proxy/db_info.md b/docs/my-website/docs/proxy/db_info.md
index 1b87aa1e54..946089bf14 100644
--- a/docs/my-website/docs/proxy/db_info.md
+++ b/docs/my-website/docs/proxy/db_info.md
@@ -46,18 +46,17 @@ You can see the full DB Schema [here](https://github.com/BerriAI/litellm/blob/ma
| Table Name | Description | Row Insert Frequency |
|------------|-------------|---------------------|
-| LiteLLM_SpendLogs | Detailed logs of all API requests. Records token usage, spend, and timing information. Tracks which models and keys were used. | **High - every LLM API request** |
-| LiteLLM_ErrorLogs | Captures failed requests and errors. Stores exception details and request information. Helps with debugging and monitoring. | **Medium - on errors only** |
+| LiteLLM_SpendLogs | Detailed logs of all API requests. Records token usage, spend, and timing information. Tracks which models and keys were used. | **High - every LLM API request - Success or Failure** |
| LiteLLM_AuditLog | Tracks changes to system configuration. Records who made changes and what was modified. Maintains history of updates to teams, users, and models. | **Off by default**, **High - when enabled** |
-## Disable `LiteLLM_SpendLogs` & `LiteLLM_ErrorLogs`
+## Disable `LiteLLM_SpendLogs`
You can disable spend_logs and error_logs by setting `disable_spend_logs` and `disable_error_logs` to `True` on the `general_settings` section of your proxy_config.yaml file.
```yaml
general_settings:
disable_spend_logs: True # Disable writing spend logs to DB
- disable_error_logs: True # Disable writing error logs to DB
+ disable_error_logs: True # Only disable writing error logs to DB, regular spend logs will still be written unless `disable_spend_logs: True`
```
### What is the impact of disabling these logs?
diff --git a/docs/my-website/docs/proxy/logging_spec.md b/docs/my-website/docs/proxy/logging_spec.md
index 86ba907373..7da937e565 100644
--- a/docs/my-website/docs/proxy/logging_spec.md
+++ b/docs/my-website/docs/proxy/logging_spec.md
@@ -78,6 +78,7 @@ Inherits from `StandardLoggingUserAPIKeyMetadata` and adds:
| `api_base` | `Optional[str]` | Optional API base URL |
| `response_cost` | `Optional[str]` | Optional response cost |
| `additional_headers` | `Optional[StandardLoggingAdditionalHeaders]` | Additional headers |
+| `batch_models` | `Optional[List[str]]` | Only set for Batches API. Lists the models used for cost calculation |
## StandardLoggingModelInformation
diff --git a/docs/my-website/docs/proxy/master_key_rotations.md b/docs/my-website/docs/proxy/master_key_rotations.md
new file mode 100644
index 0000000000..1713679863
--- /dev/null
+++ b/docs/my-website/docs/proxy/master_key_rotations.md
@@ -0,0 +1,53 @@
+# Rotating Master Key
+
+Here are our recommended steps for rotating your master key.
+
+
+**1. Backup your DB**
+In case of any errors during the encryption/de-encryption process, this will allow you to revert back to current state without issues.
+
+**2. Call `/key/regenerate` with the new master key**
+
+```bash
+curl -L -X POST 'http://localhost:4000/key/regenerate' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "key": "sk-1234",
+ "new_master_key": "sk-PIp1h0RekR"
+}'
+```
+
+This will re-encrypt any models in your Proxy_ModelTable with the new master key.
+
+Expect to start seeing decryption errors in logs, as your old master key is no longer able to decrypt the new values.
+
+```bash
+ raise Exception("Unable to decrypt value={}".format(v))
+Exception: Unable to decrypt value=
+```
+
+**3. Update LITELLM_MASTER_KEY**
+
+In your environment variables update the value of LITELLM_MASTER_KEY to the new_master_key from Step 2.
+
+This ensures the key used for decryption from db is the new key.
+
+**4. Test it**
+
+Make a test request to a model stored on proxy with a litellm key (new master key or virtual key) and see if it works
+
+```bash
+ curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{
+ "model": "gpt-4o-mini", # 👈 REPLACE with 'public model name' for any db-model
+ "messages": [
+ {
+ "content": "Hey, how's it going",
+ "role": "user"
+ }
+ ],
+}'
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/proxy/prod.md b/docs/my-website/docs/proxy/prod.md
index d0b8c48174..d3ba2d6224 100644
--- a/docs/my-website/docs/proxy/prod.md
+++ b/docs/my-website/docs/proxy/prod.md
@@ -107,9 +107,9 @@ general_settings:
By default, LiteLLM writes several types of logs to the database:
- Every LLM API request to the `LiteLLM_SpendLogs` table
-- LLM Exceptions to the `LiteLLM_LogsErrors` table
+- LLM Exceptions to the `LiteLLM_SpendLogs` table
-If you're not viewing these logs on the LiteLLM UI (most users use Prometheus for monitoring), you can disable them by setting the following flags to `True`:
+If you're not viewing these logs on the LiteLLM UI, you can disable them by setting the following flags to `True`:
```yaml
general_settings:
diff --git a/docs/my-website/docs/proxy/release_cycle.md b/docs/my-website/docs/proxy/release_cycle.md
new file mode 100644
index 0000000000..947a4ae6b3
--- /dev/null
+++ b/docs/my-website/docs/proxy/release_cycle.md
@@ -0,0 +1,12 @@
+# Release Cycle
+
+Litellm Proxy has the following release cycle:
+
+- `v1.x.x-nightly`: These are releases which pass ci/cd.
+- `v1.x.x.rc`: These are releases which pass ci/cd + [manual review](https://github.com/BerriAI/litellm/discussions/8495#discussioncomment-12180711).
+- `v1.x.x` OR `v1.x.x-stable`: These are releases which pass ci/cd + manual review + 3 days of production testing.
+
+In production, we recommend using the latest `v1.x.x` release.
+
+
+Follow our release notes [here](https://github.com/BerriAI/litellm/releases).
\ No newline at end of file
diff --git a/docs/my-website/docs/reasoning_content.md b/docs/my-website/docs/reasoning_content.md
new file mode 100644
index 0000000000..5cf287e737
--- /dev/null
+++ b/docs/my-website/docs/reasoning_content.md
@@ -0,0 +1,357 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# 'Thinking' / 'Reasoning Content'
+
+Supported Providers:
+- Deepseek (`deepseek/`)
+- Anthropic API (`anthropic/`)
+- Bedrock (Anthropic + Deepseek) (`bedrock/`)
+- Vertex AI (Anthropic) (`vertexai/`)
+
+```python
+"message": {
+ ...
+ "reasoning_content": "The capital of France is Paris.",
+ "thinking_blocks": [
+ {
+ "type": "thinking",
+ "thinking": "The capital of France is Paris.",
+ "signature": "EqoBCkgIARABGAIiQL2UoU0b1OHYi+..."
+ }
+ ]
+}
+```
+
+## Quick Start
+
+
+
+
+```python
+from litellm import completion
+import os
+
+os.environ["ANTHROPIC_API_KEY"] = ""
+
+response = completion(
+ model="anthropic/claude-3-7-sonnet-20250219",
+ messages=[
+ {"role": "user", "content": "What is the capital of France?"},
+ ],
+ thinking={"type": "enabled", "budget_tokens": 1024} # 👈 REQUIRED FOR ANTHROPIC models (on `anthropic/`, `bedrock/`, `vertexai/`)
+)
+print(response.choices[0].message.content)
+```
+
+
+
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer $LITELLM_KEY" \
+ -d '{
+ "model": "anthropic/claude-3-7-sonnet-20250219",
+ "messages": [
+ {
+ "role": "user",
+ "content": "What is the capital of France?"
+ }
+ ],
+ "thinking": {"type": "enabled", "budget_tokens": 1024}
+}'
+```
+
+
+
+**Expected Response**
+
+```bash
+{
+ "id": "3b66124d79a708e10c603496b363574c",
+ "choices": [
+ {
+ "finish_reason": "stop",
+ "index": 0,
+ "message": {
+ "content": " won the FIFA World Cup in 2022.",
+ "role": "assistant",
+ "tool_calls": null,
+ "function_call": null
+ }
+ }
+ ],
+ "created": 1723323084,
+ "model": "deepseek/deepseek-chat",
+ "object": "chat.completion",
+ "system_fingerprint": "fp_7e0991cad4",
+ "usage": {
+ "completion_tokens": 12,
+ "prompt_tokens": 16,
+ "total_tokens": 28,
+ },
+ "service_tier": null
+}
+```
+
+## Tool Calling with `thinking`
+
+Here's how to use `thinking` blocks by Anthropic with tool calling.
+
+
+
+
+```python
+litellm._turn_on_debug()
+litellm.modify_params = True
+model = "anthropic/claude-3-7-sonnet-20250219" # works across Anthropic, Bedrock, Vertex AI
+# Step 1: send the conversation and available functions to the model
+messages = [
+ {
+ "role": "user",
+ "content": "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses",
+ }
+]
+tools = [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_current_weather",
+ "description": "Get the current weather in a given location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "The city and state",
+ },
+ "unit": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ },
+ },
+ "required": ["location"],
+ },
+ },
+ }
+]
+response = litellm.completion(
+ model=model,
+ messages=messages,
+ tools=tools,
+ tool_choice="auto", # auto is default, but we'll be explicit
+ thinking={"type": "enabled", "budget_tokens": 1024},
+)
+print("Response\n", response)
+response_message = response.choices[0].message
+tool_calls = response_message.tool_calls
+
+print("Expecting there to be 3 tool calls")
+assert (
+ len(tool_calls) > 0
+) # this has to call the function for SF, Tokyo and paris
+
+# Step 2: check if the model wanted to call a function
+print(f"tool_calls: {tool_calls}")
+if tool_calls:
+ # Step 3: call the function
+ # Note: the JSON response may not always be valid; be sure to handle errors
+ available_functions = {
+ "get_current_weather": get_current_weather,
+ } # only one function in this example, but you can have multiple
+ messages.append(
+ response_message
+ ) # extend conversation with assistant's reply
+ print("Response message\n", response_message)
+ # Step 4: send the info for each function call and function response to the model
+ for tool_call in tool_calls:
+ function_name = tool_call.function.name
+ if function_name not in available_functions:
+ # the model called a function that does not exist in available_functions - don't try calling anything
+ return
+ function_to_call = available_functions[function_name]
+ function_args = json.loads(tool_call.function.arguments)
+ function_response = function_to_call(
+ location=function_args.get("location"),
+ unit=function_args.get("unit"),
+ )
+ messages.append(
+ {
+ "tool_call_id": tool_call.id,
+ "role": "tool",
+ "name": function_name,
+ "content": function_response,
+ }
+ ) # extend conversation with function response
+ print(f"messages: {messages}")
+ second_response = litellm.completion(
+ model=model,
+ messages=messages,
+ seed=22,
+ # tools=tools,
+ drop_params=True,
+ thinking={"type": "enabled", "budget_tokens": 1024},
+ ) # get a new response from the model where it can see the function response
+ print("second response\n", second_response)
+```
+
+
+
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+ - model_name: claude-3-7-sonnet-thinking
+ litellm_params:
+ model: anthropic/claude-3-7-sonnet-20250219
+ api_key: os.environ/ANTHROPIC_API_KEY
+ thinking: {
+ "type": "enabled",
+ "budget_tokens": 1024
+ }
+```
+
+2. Run proxy
+
+```bash
+litellm --config config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+3. Make 1st call
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer $LITELLM_KEY" \
+ -d '{
+ "model": "claude-3-7-sonnet-thinking",
+ "messages": [
+ {"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"},
+ ],
+ "tools": [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_current_weather",
+ "description": "Get the current weather in a given location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "The city and state",
+ },
+ "unit": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ },
+ },
+ "required": ["location"],
+ },
+ },
+ }
+ ],
+ "tool_choice": "auto"
+ }'
+```
+
+4. Make 2nd call with tool call results
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer $LITELLM_KEY" \
+ -d '{
+ "model": "claude-3-7-sonnet-thinking",
+ "messages": [
+ {
+ "role": "user",
+ "content": "What\'s the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"
+ },
+ {
+ "role": "assistant",
+ "content": "I\'ll check the current weather for these three cities for you:",
+ "tool_calls": [
+ {
+ "index": 2,
+ "function": {
+ "arguments": "{\"location\": \"San Francisco\"}",
+ "name": "get_current_weather"
+ },
+ "id": "tooluse_mnqzmtWYRjCxUInuAdK7-w",
+ "type": "function"
+ }
+ ],
+ "function_call": null,
+ "reasoning_content": "The user is asking for the current weather in three different locations: San Francisco, Tokyo, and Paris. I have access to the `get_current_weather` function that can provide this information.\n\nThe function requires a `location` parameter, and has an optional `unit` parameter. The user hasn't specified which unit they prefer (celsius or fahrenheit), so I'll use the default provided by the function.\n\nI need to make three separate function calls, one for each location:\n1. San Francisco\n2. Tokyo\n3. Paris\n\nThen I'll compile the results into a response with three distinct weather reports as requested by the user.",
+ "thinking_blocks": [
+ {
+ "type": "thinking",
+ "thinking": "The user is asking for the current weather in three different locations: San Francisco, Tokyo, and Paris. I have access to the `get_current_weather` function that can provide this information.\n\nThe function requires a `location` parameter, and has an optional `unit` parameter. The user hasn't specified which unit they prefer (celsius or fahrenheit), so I'll use the default provided by the function.\n\nI need to make three separate function calls, one for each location:\n1. San Francisco\n2. Tokyo\n3. Paris\n\nThen I'll compile the results into a response with three distinct weather reports as requested by the user.",
+ "signature": "EqoBCkgIARABGAIiQCkBXENoyB+HstUOs/iGjG+bvDbIQRrxPsPpOSt5yDxX6iulZ/4K/w9Rt4J5Nb2+3XUYsyOH+CpZMfADYvItFR4SDPb7CmzoGKoolCMAJRoM62p1ZRASZhrD3swqIjAVY7vOAFWKZyPEJglfX/60+bJphN9W1wXR6rWrqn3MwUbQ5Mb/pnpeb10HMploRgUqEGKOd6fRKTkUoNDuAnPb55c="
+ }
+ ],
+ "provider_specific_fields": {
+ "reasoningContentBlocks": [
+ {
+ "reasoningText": {
+ "signature": "EqoBCkgIARABGAIiQCkBXENoyB+HstUOs/iGjG+bvDbIQRrxPsPpOSt5yDxX6iulZ/4K/w9Rt4J5Nb2+3XUYsyOH+CpZMfADYvItFR4SDPb7CmzoGKoolCMAJRoM62p1ZRASZhrD3swqIjAVY7vOAFWKZyPEJglfX/60+bJphN9W1wXR6rWrqn3MwUbQ5Mb/pnpeb10HMploRgUqEGKOd6fRKTkUoNDuAnPb55c=",
+ "text": "The user is asking for the current weather in three different locations: San Francisco, Tokyo, and Paris. I have access to the `get_current_weather` function that can provide this information.\n\nThe function requires a `location` parameter, and has an optional `unit` parameter. The user hasn't specified which unit they prefer (celsius or fahrenheit), so I'll use the default provided by the function.\n\nI need to make three separate function calls, one for each location:\n1. San Francisco\n2. Tokyo\n3. Paris\n\nThen I'll compile the results into a response with three distinct weather reports as requested by the user."
+ }
+ }
+ ]
+ }
+ },
+ {
+ "tool_call_id": "tooluse_mnqzmtWYRjCxUInuAdK7-w",
+ "role": "tool",
+ "name": "get_current_weather",
+ "content": "{\"location\": \"San Francisco\", \"temperature\": \"72\", \"unit\": \"fahrenheit\"}"
+ }
+ ]
+ }'
+```
+
+
+
+
+## Switching between Anthropic + Deepseek models
+
+Set `drop_params=True` to drop the 'thinking' blocks when swapping from Anthropic to Deepseek models. Suggest improvements to this approach [here](https://github.com/BerriAI/litellm/discussions/8927).
+
+```python
+litellm.drop_params = True # 👈 EITHER GLOBALLY or per request
+
+# or per request
+## Anthropic
+response = litellm.completion(
+ model="anthropic/claude-3-7-sonnet-20250219",
+ messages=[{"role": "user", "content": "What is the capital of France?"}],
+ thinking={"type": "enabled", "budget_tokens": 1024},
+ drop_params=True,
+)
+
+## Deepseek
+response = litellm.completion(
+ model="deepseek/deepseek-chat",
+ messages=[{"role": "user", "content": "What is the capital of France?"}],
+ thinking={"type": "enabled", "budget_tokens": 1024},
+ drop_params=True,
+)
+```
+
+## Spec
+
+
+These fields can be accessed via `response.choices[0].message.reasoning_content` and `response.choices[0].message.thinking_blocks`.
+
+- `reasoning_content` - str: The reasoning content from the model. Returned across all providers.
+- `thinking_blocks` - Optional[List[Dict[str, str]]]: A list of thinking blocks from the model. Only returned for Anthropic models.
+ - `type` - str: The type of thinking block.
+ - `thinking` - str: The thinking from the model.
+ - `signature` - str: The signature delta from the model.
+
diff --git a/docs/my-website/docs/rerank.md b/docs/my-website/docs/rerank.md
index 598c672942..cc58c374c7 100644
--- a/docs/my-website/docs/rerank.md
+++ b/docs/my-website/docs/rerank.md
@@ -111,7 +111,7 @@ curl http://0.0.0.0:4000/rerank \
| Provider | Link to Usage |
|-------------|--------------------|
-| Cohere | [Usage](#quick-start) |
+| Cohere (v1 + v2 clients) | [Usage](#quick-start) |
| Together AI| [Usage](../docs/providers/togetherai) |
| Azure AI| [Usage](../docs/providers/azure_ai) |
| Jina AI| [Usage](../docs/providers/jina_ai) |
diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md
index 79fefcf754..0ad28b24f4 100644
--- a/docs/my-website/docs/routing.md
+++ b/docs/my-website/docs/routing.md
@@ -952,8 +952,8 @@ router_settings:
```
Defaults:
-- allowed_fails: 0
-- cooldown_time: 60s
+- allowed_fails: 3
+- cooldown_time: 5s (`DEFAULT_COOLDOWN_TIME_SECONDS` in constants.py)
**Set Per Model**
diff --git a/docs/my-website/docs/secret.md b/docs/my-website/docs/secret.md
index a65c696f36..7676164259 100644
--- a/docs/my-website/docs/secret.md
+++ b/docs/my-website/docs/secret.md
@@ -96,6 +96,33 @@ litellm --config /path/to/config.yaml
```
+### Using K/V pairs in 1 AWS Secret
+
+You can read multiple keys from a single AWS Secret using the `primary_secret_name` parameter:
+
+```yaml
+general_settings:
+ key_management_system: "aws_secret_manager"
+ key_management_settings:
+ hosted_keys: [
+ "OPENAI_API_KEY_MODEL_1",
+ "OPENAI_API_KEY_MODEL_2",
+ ]
+ primary_secret_name: "litellm_secrets" # 👈 Read multiple keys from one JSON secret
+```
+
+The `primary_secret_name` allows you to read multiple keys from a single AWS Secret as a JSON object. For example, the "litellm_secrets" would contain:
+
+```json
+{
+ "OPENAI_API_KEY_MODEL_1": "sk-key1...",
+ "OPENAI_API_KEY_MODEL_2": "sk-key2..."
+}
+```
+
+This reduces the number of AWS Secrets you need to manage.
+
+
## Hashicorp Vault
@@ -353,4 +380,7 @@ general_settings:
# Hosted Keys Settings
hosted_keys: ["litellm_master_key"] # OPTIONAL. Specify which env keys you stored on AWS
+
+ # K/V pairs in 1 AWS Secret Settings
+ primary_secret_name: "litellm_secrets" # OPTIONAL. Read multiple keys from one JSON secret on AWS Secret Manager
```
\ No newline at end of file
diff --git a/docs/my-website/docs/tutorials/litellm_proxy_aporia.md b/docs/my-website/docs/tutorials/litellm_proxy_aporia.md
index 3b5bada2bc..143512f99c 100644
--- a/docs/my-website/docs/tutorials/litellm_proxy_aporia.md
+++ b/docs/my-website/docs/tutorials/litellm_proxy_aporia.md
@@ -2,9 +2,9 @@ import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Use LiteLLM AI Gateway with Aporia Guardrails
+# Aporia Guardrails with LiteLLM Gateway
-In this tutorial we will use LiteLLM Proxy with Aporia to detect PII in requests and profanity in responses
+In this tutorial we will use LiteLLM AI Gateway with Aporia to detect PII in requests and profanity in responses
## 1. Setup guardrails on Aporia
diff --git a/docs/my-website/docs/tutorials/openweb_ui.md b/docs/my-website/docs/tutorials/openweb_ui.md
new file mode 100644
index 0000000000..ab1e2e121e
--- /dev/null
+++ b/docs/my-website/docs/tutorials/openweb_ui.md
@@ -0,0 +1,103 @@
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# OpenWeb UI with LiteLLM
+
+This guide walks you through connecting OpenWeb UI to LiteLLM. Using LiteLLM with OpenWeb UI allows teams to
+- Access 100+ LLMs on OpenWeb UI
+- Track Spend / Usage, Set Budget Limits
+- Send Request/Response Logs to logging destinations like langfuse, s3, gcs buckets, etc.
+- Set access controls eg. Control what models OpenWebUI can access.
+
+## Quickstart
+
+- Make sure to setup LiteLLM with the [LiteLLM Getting Started Guide](https://docs.litellm.ai/docs/proxy/docker_quick_start)
+
+
+## 1. Start LiteLLM & OpenWebUI
+
+- OpenWebUI starts running on [http://localhost:3000](http://localhost:3000)
+- LiteLLM starts running on [http://localhost:4000](http://localhost:4000)
+
+
+## 2. Create a Virtual Key on LiteLLM
+
+Virtual Keys are API Keys that allow you to authenticate to LiteLLM Proxy. We will create a Virtual Key that will allow OpenWebUI to access LiteLLM.
+
+### 2.1 LiteLLM User Management Hierarchy
+
+On LiteLLM, you can create Organizations, Teams, Users and Virtual Keys. For this tutorial, we will create a Team and a Virtual Key.
+
+- `Organization` - An Organization is a group of Teams. (US Engineering, EU Developer Tools)
+- `Team` - A Team is a group of Users. (OpenWeb UI Team, Data Science Team, etc.)
+- `User` - A User is an individual user (employee, developer, eg. `krrish@litellm.ai`)
+- `Virtual Key` - A Virtual Key is an API Key that allows you to authenticate to LiteLLM Proxy. A Virtual Key is associated with a User or Team.
+
+Once the Team is created, you can invite Users to the Team. You can read more about LiteLLM's User Management [here](https://docs.litellm.ai/docs/proxy/user_management_heirarchy).
+
+### 2.2 Create a Team on LiteLLM
+
+Navigate to [http://localhost:4000/ui](http://localhost:4000/ui) and create a new team.
+
+
+
+### 2.2 Create a Virtual Key on LiteLLM
+
+Navigate to [http://localhost:4000/ui](http://localhost:4000/ui) and create a new virtual Key.
+
+LiteLLM allows you to specify what models are available on OpenWeb UI (by specifying the models the key will have access to).
+
+
+
+## 3. Connect OpenWeb UI to LiteLLM
+
+On OpenWeb UI, navigate to Settings -> Connections and create a new connection to LiteLLM
+
+Enter the following details:
+- URL: `http://localhost:4000` (your litellm proxy base url)
+- Key: `your-virtual-key` (the key you created in the previous step)
+
+
+
+### 3.1 Test Request
+
+On the top left corner, select models you should only see the models you gave the key access to in Step 2.
+
+Once you selected a model, enter your message content and click on `Submit`
+
+
+
+### 3.2 Tracking Spend / Usage
+
+After your request is made, navigate to `Logs` on the LiteLLM UI, you can see Team, Key, Model, Usage and Cost.
+
+
+
+
+
+## Render `thinking` content on OpenWeb UI
+
+OpenWebUI requires reasoning/thinking content to be rendered with `` tags. In order to render this for specific models, you can use the `merge_reasoning_content_in_choices` litellm parameter.
+
+Example litellm config.yaml:
+
+```yaml
+model_list:
+ - model_name: thinking-anthropic-claude-3-7-sonnet
+ litellm_params:
+ model: bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0
+ thinking: {"type": "enabled", "budget_tokens": 1024}
+ max_tokens: 1080
+ merge_reasoning_content_in_choices: true
+```
+
+### Test it on OpenWeb UI
+
+On the models dropdown select `thinking-anthropic-claude-3-7-sonnet`
+
+
+
+
+
+
diff --git a/docs/my-website/docusaurus.config.js b/docs/my-website/docusaurus.config.js
index cf20dfcd70..8d480131ff 100644
--- a/docs/my-website/docusaurus.config.js
+++ b/docs/my-website/docusaurus.config.js
@@ -44,7 +44,7 @@ const config = {
path: './release_notes',
routeBasePath: 'release_notes',
blogTitle: 'Release Notes',
- blogSidebarTitle: 'All Releases',
+ blogSidebarTitle: 'Releases',
blogSidebarCount: 'ALL',
postsPerPage: 'ALL',
showReadingTime: false,
diff --git a/docs/my-website/img/basic_litellm.gif b/docs/my-website/img/basic_litellm.gif
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new file mode 100644
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diff --git a/docs/my-website/img/litellm_setup_openweb.gif b/docs/my-website/img/litellm_setup_openweb.gif
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new file mode 100644
index 0000000000..385db583a4
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index 0000000000..1770460b2a
Binary files /dev/null and b/docs/my-website/img/release_notes/v1632_release.jpg differ
diff --git a/docs/my-website/release_notes/v1.57.8-stable/index.md b/docs/my-website/release_notes/v1.57.8-stable/index.md
index 9787444fde..d37a7b9ff8 100644
--- a/docs/my-website/release_notes/v1.57.8-stable/index.md
+++ b/docs/my-website/release_notes/v1.57.8-stable/index.md
@@ -18,13 +18,6 @@ hide_table_of_contents: false
`alerting`, `prometheus`, `secret management`, `management endpoints`, `ui`, `prompt management`, `finetuning`, `batch`
-:::note
-
-v1.57.8-stable, is currently being tested. It will be released on 2025-01-12.
-
-:::
-
-
## New / Updated Models
1. Mistral large pricing - https://github.com/BerriAI/litellm/pull/7452
diff --git a/docs/my-website/release_notes/v1.61.20-stable/index.md b/docs/my-website/release_notes/v1.61.20-stable/index.md
new file mode 100644
index 0000000000..132c1aa318
--- /dev/null
+++ b/docs/my-website/release_notes/v1.61.20-stable/index.md
@@ -0,0 +1,103 @@
+---
+title: v1.61.20-stable
+slug: v1.61.20-stable
+date: 2025-03-01T10:00:00
+authors:
+ - name: Krrish Dholakia
+ title: CEO, LiteLLM
+ url: https://www.linkedin.com/in/krish-d/
+ image_url: https://media.licdn.com/dms/image/v2/D4D03AQGrlsJ3aqpHmQ/profile-displayphoto-shrink_400_400/B4DZSAzgP7HYAg-/0/1737327772964?e=1743638400&v=beta&t=39KOXMUFedvukiWWVPHf3qI45fuQD7lNglICwN31DrI
+ - name: Ishaan Jaffer
+ title: CTO, LiteLLM
+ url: https://www.linkedin.com/in/reffajnaahsi/
+ image_url: https://media.licdn.com/dms/image/v2/D4D03AQGiM7ZrUwqu_Q/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1675971026692?e=1741824000&v=beta&t=eQnRdXPJo4eiINWTZARoYTfqh064pgZ-E21pQTSy8jc
+tags: [llm translation, rerank, ui, thinking, reasoning_content, claude-3-7-sonnet]
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+
+# v1.61.20-stable
+
+
+These are the changes since `v1.61.13-stable`.
+
+This release is primarily focused on:
+- LLM Translation improvements (claude-3-7-sonnet + 'thinking'/'reasoning_content' support)
+- UI improvements (add model flow, user management, etc)
+
+## Demo Instance
+
+Here's a Demo Instance to test changes:
+- Instance: https://demo.litellm.ai/
+- Login Credentials:
+ - Username: admin
+ - Password: sk-1234
+
+## New Models / Updated Models
+
+1. Anthropic 3-7 sonnet support + cost tracking (Anthropic API + Bedrock + Vertex AI + OpenRouter)
+ 1. Anthropic API [Start here](https://docs.litellm.ai/docs/providers/anthropic#usage---thinking--reasoning_content)
+ 2. Bedrock API [Start here](https://docs.litellm.ai/docs/providers/bedrock#usage---thinking--reasoning-content)
+ 3. Vertex AI API [See here](../../docs/providers/vertex#usage---thinking--reasoning_content)
+ 4. OpenRouter [See here](https://github.com/BerriAI/litellm/blob/ba5bdce50a0b9bc822de58c03940354f19a733ed/model_prices_and_context_window.json#L5626)
+2. Gpt-4.5-preview support + cost tracking [See here](https://github.com/BerriAI/litellm/blob/ba5bdce50a0b9bc822de58c03940354f19a733ed/model_prices_and_context_window.json#L79)
+3. Azure AI - Phi-4 cost tracking [See here](https://github.com/BerriAI/litellm/blob/ba5bdce50a0b9bc822de58c03940354f19a733ed/model_prices_and_context_window.json#L1773)
+4. Claude-3.5-sonnet - vision support updated on Anthropic API [See here](https://github.com/BerriAI/litellm/blob/ba5bdce50a0b9bc822de58c03940354f19a733ed/model_prices_and_context_window.json#L2888)
+5. Bedrock llama vision support [See here](https://github.com/BerriAI/litellm/blob/ba5bdce50a0b9bc822de58c03940354f19a733ed/model_prices_and_context_window.json#L7714)
+6. Cerebras llama3.3-70b pricing [See here](https://github.com/BerriAI/litellm/blob/ba5bdce50a0b9bc822de58c03940354f19a733ed/model_prices_and_context_window.json#L2697)
+
+## LLM Translation
+
+1. Infinity Rerank - support returning documents when return_documents=True [Start here](../../docs/providers/infinity#usage---returning-documents)
+2. Amazon Deepseek - `` param extraction into ‘reasoning_content’ [Start here](https://docs.litellm.ai/docs/providers/bedrock#bedrock-imported-models-deepseek-deepseek-r1)
+3. Amazon Titan Embeddings - filter out ‘aws_’ params from request body [Start here](https://docs.litellm.ai/docs/providers/bedrock#bedrock-embedding)
+4. Anthropic ‘thinking’ + ‘reasoning_content’ translation support (Anthropic API, Bedrock, Vertex AI) [Start here](https://docs.litellm.ai/docs/reasoning_content)
+5. VLLM - support ‘video_url’ [Start here](../../docs/providers/vllm#send-video-url-to-vllm)
+6. Call proxy via litellm SDK: Support `litellm_proxy/` for embedding, image_generation, transcription, speech, rerank [Start here](https://docs.litellm.ai/docs/providers/litellm_proxy)
+7. OpenAI Pass-through - allow using Assistants GET, DELETE on /openai pass through routes [Start here](https://docs.litellm.ai/docs/pass_through/openai_passthrough)
+8. Message Translation - fix openai message for assistant msg if role is missing - openai allows this
+9. O1/O3 - support ‘drop_params’ for o3-mini and o1 parallel_tool_calls param (not supported currently) [See here](https://docs.litellm.ai/docs/completion/drop_params)
+
+## Spend Tracking Improvements
+
+1. Cost tracking for rerank via Bedrock [See PR](https://github.com/BerriAI/litellm/commit/b682dc4ec8fd07acf2f4c981d2721e36ae2a49c5)
+2. Anthropic pass-through - fix race condition causing cost to not be tracked [See PR](https://github.com/BerriAI/litellm/pull/8874)
+3. Anthropic pass-through: Ensure accurate token counting [See PR](https://github.com/BerriAI/litellm/pull/8880)
+
+## Management Endpoints / UI
+
+1. Models Page - Allow sorting models by ‘created at’
+2. Models Page - Edit Model Flow Improvements
+3. Models Page - Fix Adding Azure, Azure AI Studio models on UI
+4. Internal Users Page - Allow Bulk Adding Internal Users on UI
+5. Internal Users Page - Allow sorting users by ‘created at’
+6. Virtual Keys Page - Allow searching for UserIDs on the dropdown when assigning a user to a team [See PR](https://github.com/BerriAI/litellm/pull/8844)
+7. Virtual Keys Page - allow creating a user when assigning keys to users [See PR](https://github.com/BerriAI/litellm/pull/8844)
+8. Model Hub Page - fix text overflow issue [See PR](https://github.com/BerriAI/litellm/pull/8749)
+9. Admin Settings Page - Allow adding MSFT SSO on UI
+10. Backend - don't allow creating duplicate internal users in DB
+
+## Helm
+
+1. support ttlSecondsAfterFinished on the migration job - [See PR](https://github.com/BerriAI/litellm/pull/8593)
+2. enhance migrations job with additional configurable properties - [See PR](https://github.com/BerriAI/litellm/pull/8636)
+
+## Logging / Guardrail Integrations
+
+1. Arize Phoenix support
+2. ‘No-log’ - fix ‘no-log’ param support on embedding calls
+
+## Performance / Loadbalancing / Reliability improvements
+
+1. Single Deployment Cooldown logic - Use allowed_fails or allowed_fail_policy if set [Start here](https://docs.litellm.ai/docs/routing#advanced-custom-retries-cooldowns-based-on-error-type)
+
+## General Proxy Improvements
+
+1. Hypercorn - fix reading / parsing request body
+2. Windows - fix running proxy in windows
+3. DD-Trace - fix dd-trace enablement on proxy
+
+## Complete Git Diff
+
+View the complete git diff [here](https://github.com/BerriAI/litellm/compare/v1.61.13-stable...v1.61.20-stable).
\ No newline at end of file
diff --git a/docs/my-website/release_notes/v1.63.0/index.md b/docs/my-website/release_notes/v1.63.0/index.md
new file mode 100644
index 0000000000..e74a2f9b86
--- /dev/null
+++ b/docs/my-website/release_notes/v1.63.0/index.md
@@ -0,0 +1,40 @@
+---
+title: v1.63.0 - Anthropic 'thinking' response update
+slug: v1.63.0
+date: 2025-03-05T10:00:00
+authors:
+ - name: Krrish Dholakia
+ title: CEO, LiteLLM
+ url: https://www.linkedin.com/in/krish-d/
+ image_url: https://media.licdn.com/dms/image/v2/D4D03AQGrlsJ3aqpHmQ/profile-displayphoto-shrink_400_400/B4DZSAzgP7HYAg-/0/1737327772964?e=1743638400&v=beta&t=39KOXMUFedvukiWWVPHf3qI45fuQD7lNglICwN31DrI
+ - name: Ishaan Jaffer
+ title: CTO, LiteLLM
+ url: https://www.linkedin.com/in/reffajnaahsi/
+ image_url: https://media.licdn.com/dms/image/v2/D4D03AQGiM7ZrUwqu_Q/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1675971026692?e=1741824000&v=beta&t=eQnRdXPJo4eiINWTZARoYTfqh064pgZ-E21pQTSy8jc
+tags: [llm translation, thinking, reasoning_content, claude-3-7-sonnet]
+hide_table_of_contents: false
+---
+
+v1.63.0 fixes Anthropic 'thinking' response on streaming to return the `signature` block. [Github Issue](https://github.com/BerriAI/litellm/issues/8964)
+
+
+
+It also moves the response structure from `signature_delta` to `signature` to be the same as Anthropic. [Anthropic Docs](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking#implementing-extended-thinking)
+
+
+## Diff
+
+```bash
+"message": {
+ ...
+ "reasoning_content": "The capital of France is Paris.",
+ "thinking_blocks": [
+ {
+ "type": "thinking",
+ "thinking": "The capital of France is Paris.",
+- "signature_delta": "EqoBCkgIARABGAIiQL2UoU0b1OHYi+..." # 👈 OLD FORMAT
++ "signature": "EqoBCkgIARABGAIiQL2UoU0b1OHYi+..." # 👈 KEY CHANGE
+ }
+ ]
+}
+```
diff --git a/docs/my-website/release_notes/v1.63.2-stable/index.md b/docs/my-website/release_notes/v1.63.2-stable/index.md
new file mode 100644
index 0000000000..0c359452dc
--- /dev/null
+++ b/docs/my-website/release_notes/v1.63.2-stable/index.md
@@ -0,0 +1,112 @@
+---
+title: v1.63.2-stable
+slug: v1.63.2-stable
+date: 2025-03-08T10:00:00
+authors:
+ - name: Krrish Dholakia
+ title: CEO, LiteLLM
+ url: https://www.linkedin.com/in/krish-d/
+ image_url: https://media.licdn.com/dms/image/v2/D4D03AQGrlsJ3aqpHmQ/profile-displayphoto-shrink_400_400/B4DZSAzgP7HYAg-/0/1737327772964?e=1743638400&v=beta&t=39KOXMUFedvukiWWVPHf3qI45fuQD7lNglICwN31DrI
+ - name: Ishaan Jaffer
+ title: CTO, LiteLLM
+ url: https://www.linkedin.com/in/reffajnaahsi/
+ image_url: https://media.licdn.com/dms/image/v2/D4D03AQGiM7ZrUwqu_Q/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1675971026692?e=1741824000&v=beta&t=eQnRdXPJo4eiINWTZARoYTfqh064pgZ-E21pQTSy8jc
+tags: [llm translation, thinking, reasoning_content, claude-3-7-sonnet]
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+
+
+These are the changes since `v1.61.20-stable`.
+
+This release is primarily focused on:
+- LLM Translation improvements (more `thinking` content improvements)
+- UI improvements (Error logs now shown on UI)
+
+
+:::info
+
+This release will be live on 03/09/2025
+
+:::
+
+
+
+
+## Demo Instance
+
+Here's a Demo Instance to test changes:
+- Instance: https://demo.litellm.ai/
+- Login Credentials:
+ - Username: admin
+ - Password: sk-1234
+
+
+## New Models / Updated Models
+
+1. Add `supports_pdf_input` for specific Bedrock Claude models [PR](https://github.com/BerriAI/litellm/commit/f63cf0030679fe1a43d03fb196e815a0f28dae92)
+2. Add pricing for amazon `eu` models [PR](https://github.com/BerriAI/litellm/commits/main/model_prices_and_context_window.json)
+3. Fix Azure O1 mini pricing [PR](https://github.com/BerriAI/litellm/commit/52de1949ef2f76b8572df751f9c868a016d4832c)
+
+## LLM Translation
+
+
+
+1. Support `/openai/` passthrough for Assistant endpoints. [Get Started](https://docs.litellm.ai/docs/pass_through/openai_passthrough)
+2. Bedrock Claude - fix tool calling transformation on invoke route. [Get Started](../../docs/providers/bedrock#usage---function-calling--tool-calling)
+3. Bedrock Claude - response_format support for claude on invoke route. [Get Started](../../docs/providers/bedrock#usage---structured-output--json-mode)
+4. Bedrock - pass `description` if set in response_format. [Get Started](../../docs/providers/bedrock#usage---structured-output--json-mode)
+5. Bedrock - Fix passing response_format: {"type": "text"}. [PR](https://github.com/BerriAI/litellm/commit/c84b489d5897755139aa7d4e9e54727ebe0fa540)
+6. OpenAI - Handle sending image_url as str to openai. [Get Started](https://docs.litellm.ai/docs/completion/vision)
+7. Deepseek - return 'reasoning_content' missing on streaming. [Get Started](https://docs.litellm.ai/docs/reasoning_content)
+8. Caching - Support caching on reasoning content. [Get Started](https://docs.litellm.ai/docs/proxy/caching)
+9. Bedrock - handle thinking blocks in assistant message. [Get Started](https://docs.litellm.ai/docs/providers/bedrock#usage---thinking--reasoning-content)
+10. Anthropic - Return `signature` on streaming. [Get Started](https://docs.litellm.ai/docs/providers/bedrock#usage---thinking--reasoning-content)
+- Note: We've also migrated from `signature_delta` to `signature`. [Read more](https://docs.litellm.ai/release_notes/v1.63.0)
+11. Support format param for specifying image type. [Get Started](../../docs/completion/vision.md#explicitly-specify-image-type)
+12. Anthropic - `/v1/messages` endpoint - `thinking` param support. [Get Started](../../docs/anthropic_unified.md)
+- Note: this refactors the [BETA] unified `/v1/messages` endpoint, to just work for the Anthropic API.
+13. Vertex AI - handle $id in response schema when calling vertex ai. [Get Started](https://docs.litellm.ai/docs/providers/vertex#json-schema)
+
+## Spend Tracking Improvements
+
+1. Batches API - Fix cost calculation to run on retrieve_batch. [Get Started](https://docs.litellm.ai/docs/batches)
+2. Batches API - Log batch models in spend logs / standard logging payload. [Get Started](../../docs/proxy/logging_spec.md#standardlogginghiddenparams)
+
+## Management Endpoints / UI
+
+
+
+1. Virtual Keys Page
+ - Allow team/org filters to be searchable on the Create Key Page
+ - Add created_by and updated_by fields to Keys table
+ - Show 'user_email' on key table
+ - Show 100 Keys Per Page, Use full height, increase width of key alias
+2. Logs Page
+ - Show Error Logs on LiteLLM UI
+ - Allow Internal Users to View their own logs
+3. Internal Users Page
+ - Allow admin to control default model access for internal users
+7. Fix session handling with cookies
+
+## Logging / Guardrail Integrations
+
+1. Fix prometheus metrics w/ custom metrics, when keys containing team_id make requests. [PR](https://github.com/BerriAI/litellm/pull/8935)
+
+## Performance / Loadbalancing / Reliability improvements
+
+1. Cooldowns - Support cooldowns on models called with client side credentials. [Get Started](https://docs.litellm.ai/docs/proxy/clientside_auth#pass-user-llm-api-keys--api-base)
+2. Tag-based Routing - ensures tag-based routing across all endpoints (`/embeddings`, `/image_generation`, etc.). [Get Started](https://docs.litellm.ai/docs/proxy/tag_routing)
+
+## General Proxy Improvements
+
+1. Raise BadRequestError when unknown model passed in request
+2. Enforce model access restrictions on Azure OpenAI proxy route
+3. Reliability fix - Handle emoji’s in text - fix orjson error
+4. Model Access Patch - don't overwrite litellm.anthropic_models when running auth checks
+5. Enable setting timezone information in docker image
+
+## Complete Git Diff
+
+[Here's the complete git diff](https://github.com/BerriAI/litellm/compare/v1.61.20-stable...v1.63.2-stable)
\ No newline at end of file
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index b62dd841e8..cf4f14b202 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -41,10 +41,12 @@ const sidebars = {
"proxy/deploy",
"proxy/prod",
"proxy/cli",
+ "proxy/release_cycle",
"proxy/model_management",
"proxy/health",
"proxy/debugging",
"proxy/spending_monitoring",
+ "proxy/master_key_rotations",
],
},
"proxy/demo",
@@ -242,6 +244,7 @@ const sidebars = {
"completion/document_understanding",
"completion/vision",
"completion/json_mode",
+ "reasoning_content",
"completion/prompt_caching",
"completion/predict_outputs",
"completion/prefix",
@@ -254,13 +257,19 @@ const sidebars = {
"completion/batching",
"completion/mock_requests",
"completion/reliable_completions",
- 'tutorials/litellm_proxy_aporia',
]
},
{
type: "category",
label: "Supported Endpoints",
+ link: {
+ type: "generated-index",
+ title: "Supported Endpoints",
+ description:
+ "Learn how to deploy + call models from different providers on LiteLLM",
+ slug: "/supported_endpoints",
+ },
items: [
{
type: "category",
@@ -279,6 +288,7 @@ const sidebars = {
},
"text_completion",
"embedding/supported_embedding",
+ "anthropic_unified",
{
type: "category",
label: "Image",
@@ -303,6 +313,7 @@ const sidebars = {
"pass_through/vertex_ai",
"pass_through/google_ai_studio",
"pass_through/cohere",
+ "pass_through/openai_passthrough",
"pass_through/anthropic_completion",
"pass_through/bedrock",
"pass_through/assembly_ai",
@@ -347,23 +358,6 @@ const sidebars = {
label: "LangChain, LlamaIndex, Instructor Integration",
items: ["langchain/langchain", "tutorials/instructor"],
},
- {
- type: "category",
- label: "Tutorials",
- items: [
-
- 'tutorials/azure_openai',
- 'tutorials/instructor',
- "tutorials/gradio_integration",
- "tutorials/huggingface_codellama",
- "tutorials/huggingface_tutorial",
- "tutorials/TogetherAI_liteLLM",
- "tutorials/finetuned_chat_gpt",
- "tutorials/text_completion",
- "tutorials/first_playground",
- "tutorials/model_fallbacks",
- ],
- },
],
},
{
@@ -380,13 +374,6 @@ const sidebars = {
"load_test_rpm",
]
},
- {
- type: "category",
- label: "Adding Providers",
- items: [
- "adding_provider/directory_structure",
- "adding_provider/new_rerank_provider"],
- },
{
type: "category",
label: "Logging & Observability",
@@ -421,12 +408,51 @@ const sidebars = {
"observability/opik_integration",
],
},
+ {
+ type: "category",
+ label: "Tutorials",
+ items: [
+ "tutorials/openweb_ui",
+ 'tutorials/litellm_proxy_aporia',
+ {
+ type: "category",
+ label: "LiteLLM Python SDK Tutorials",
+ items: [
+ 'tutorials/azure_openai',
+ 'tutorials/instructor',
+ "tutorials/gradio_integration",
+ "tutorials/huggingface_codellama",
+ "tutorials/huggingface_tutorial",
+ "tutorials/TogetherAI_liteLLM",
+ "tutorials/finetuned_chat_gpt",
+ "tutorials/text_completion",
+ "tutorials/first_playground",
+ "tutorials/model_fallbacks",
+ ],
+ },
+ ]
+ },
+ {
+ type: "category",
+ label: "Contributing",
+ items: [
+ "extras/contributing_code",
+ {
+ type: "category",
+ label: "Adding Providers",
+ items: [
+ "adding_provider/directory_structure",
+ "adding_provider/new_rerank_provider"],
+ },
+ "extras/contributing",
+ "contributing",
+ ]
+ },
{
type: "category",
label: "Extras",
items: [
- "extras/contributing",
"data_security",
"data_retention",
"migration_policy",
@@ -443,6 +469,7 @@ const sidebars = {
items: [
"projects/smolagents",
"projects/Docq.AI",
+ "projects/PDL",
"projects/OpenInterpreter",
"projects/Elroy",
"projects/dbally",
@@ -458,9 +485,9 @@ const sidebars = {
"projects/YiVal",
"projects/LiteLLM Proxy",
"projects/llm_cord",
+ "projects/pgai",
],
},
- "contributing",
"proxy/pii_masking",
"extras/code_quality",
"rules",
diff --git a/litellm/__init__.py b/litellm/__init__.py
index f4459db032..d66707f8b3 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -53,6 +53,7 @@ from litellm.constants import (
cohere_embedding_models,
bedrock_embedding_models,
known_tokenizer_config,
+ BEDROCK_INVOKE_PROVIDERS_LITERAL,
)
from litellm.types.guardrails import GuardrailItem
from litellm.proxy._types import (
@@ -95,6 +96,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
"galileo",
"braintrust",
"arize",
+ "arize_phoenix",
"langtrace",
"gcs_bucket",
"azure_storage",
@@ -275,8 +277,6 @@ disable_end_user_cost_tracking_prometheus_only: Optional[bool] = None
custom_prometheus_metadata_labels: List[str] = []
#### REQUEST PRIORITIZATION ####
priority_reservation: Optional[Dict[str, float]] = None
-
-
force_ipv4: bool = (
False # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
)
@@ -360,17 +360,7 @@ BEDROCK_CONVERSE_MODELS = [
"meta.llama3-2-11b-instruct-v1:0",
"meta.llama3-2-90b-instruct-v1:0",
]
-BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[
- "cohere",
- "anthropic",
- "mistral",
- "amazon",
- "meta",
- "llama",
- "ai21",
- "nova",
- "deepseek_r1",
-]
+
####### COMPLETION MODELS ###################
open_ai_chat_completion_models: List = []
open_ai_text_completion_models: List = []
@@ -808,9 +798,6 @@ from .llms.oobabooga.chat.transformation import OobaboogaConfig
from .llms.maritalk import MaritalkConfig
from .llms.openrouter.chat.transformation import OpenrouterConfig
from .llms.anthropic.chat.transformation import AnthropicConfig
-from .llms.anthropic.experimental_pass_through.transformation import (
- AnthropicExperimentalPassThroughConfig,
-)
from .llms.groq.stt.transformation import GroqSTTConfig
from .llms.anthropic.completion.transformation import AnthropicTextConfig
from .llms.triton.completion.transformation import TritonConfig
@@ -823,11 +810,15 @@ from .llms.predibase.chat.transformation import PredibaseConfig
from .llms.replicate.chat.transformation import ReplicateConfig
from .llms.cohere.completion.transformation import CohereTextConfig as CohereConfig
from .llms.cohere.rerank.transformation import CohereRerankConfig
+from .llms.cohere.rerank_v2.transformation import CohereRerankV2Config
from .llms.azure_ai.rerank.transformation import AzureAIRerankConfig
from .llms.infinity.rerank.transformation import InfinityRerankConfig
from .llms.jina_ai.rerank.transformation import JinaAIRerankConfig
from .llms.clarifai.chat.transformation import ClarifaiConfig
from .llms.ai21.chat.transformation import AI21ChatConfig, AI21ChatConfig as AI21Config
+from .llms.anthropic.experimental_pass_through.messages.transformation import (
+ AnthropicMessagesConfig,
+)
from .llms.together_ai.chat import TogetherAIConfig
from .llms.together_ai.completion.transformation import TogetherAITextCompletionConfig
from .llms.cloudflare.chat.transformation import CloudflareChatConfig
@@ -1018,6 +1009,7 @@ from .assistants.main import *
from .batches.main import *
from .batch_completion.main import * # type: ignore
from .rerank_api.main import *
+from .llms.anthropic.experimental_pass_through.messages.handler import *
from .realtime_api.main import _arealtime
from .fine_tuning.main import *
from .files.main import *
diff --git a/litellm/adapters/anthropic_adapter.py b/litellm/adapters/anthropic_adapter.py
deleted file mode 100644
index 961bc77527..0000000000
--- a/litellm/adapters/anthropic_adapter.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# What is this?
-## Translates OpenAI call to Anthropic `/v1/messages` format
-import traceback
-from typing import Any, Optional
-
-import litellm
-from litellm import ChatCompletionRequest, verbose_logger
-from litellm.integrations.custom_logger import CustomLogger
-from litellm.types.llms.anthropic import AnthropicMessagesRequest, AnthropicResponse
-from litellm.types.utils import AdapterCompletionStreamWrapper, ModelResponse
-
-
-class AnthropicAdapter(CustomLogger):
- def __init__(self) -> None:
- super().__init__()
-
- def translate_completion_input_params(
- self, kwargs
- ) -> Optional[ChatCompletionRequest]:
- """
- - translate params, where needed
- - pass rest, as is
- """
- request_body = AnthropicMessagesRequest(**kwargs) # type: ignore
-
- translated_body = litellm.AnthropicExperimentalPassThroughConfig().translate_anthropic_to_openai(
- anthropic_message_request=request_body
- )
-
- return translated_body
-
- def translate_completion_output_params(
- self, response: ModelResponse
- ) -> Optional[AnthropicResponse]:
-
- return litellm.AnthropicExperimentalPassThroughConfig().translate_openai_response_to_anthropic(
- response=response
- )
-
- def translate_completion_output_params_streaming(
- self, completion_stream: Any
- ) -> AdapterCompletionStreamWrapper | None:
- return AnthropicStreamWrapper(completion_stream=completion_stream)
-
-
-anthropic_adapter = AnthropicAdapter()
-
-
-class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
- """
- - first chunk return 'message_start'
- - content block must be started and stopped
- - finish_reason must map exactly to anthropic reason, else anthropic client won't be able to parse it.
- """
-
- sent_first_chunk: bool = False
- sent_content_block_start: bool = False
- sent_content_block_finish: bool = False
- sent_last_message: bool = False
- holding_chunk: Optional[Any] = None
-
- def __next__(self):
- try:
- if self.sent_first_chunk is False:
- self.sent_first_chunk = True
- return {
- "type": "message_start",
- "message": {
- "id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY",
- "type": "message",
- "role": "assistant",
- "content": [],
- "model": "claude-3-5-sonnet-20240620",
- "stop_reason": None,
- "stop_sequence": None,
- "usage": {"input_tokens": 25, "output_tokens": 1},
- },
- }
- if self.sent_content_block_start is False:
- self.sent_content_block_start = True
- return {
- "type": "content_block_start",
- "index": 0,
- "content_block": {"type": "text", "text": ""},
- }
-
- for chunk in self.completion_stream:
- if chunk == "None" or chunk is None:
- raise Exception
-
- processed_chunk = litellm.AnthropicExperimentalPassThroughConfig().translate_streaming_openai_response_to_anthropic(
- response=chunk
- )
- if (
- processed_chunk["type"] == "message_delta"
- and self.sent_content_block_finish is False
- ):
- self.holding_chunk = processed_chunk
- self.sent_content_block_finish = True
- return {
- "type": "content_block_stop",
- "index": 0,
- }
- elif self.holding_chunk is not None:
- return_chunk = self.holding_chunk
- self.holding_chunk = processed_chunk
- return return_chunk
- else:
- return processed_chunk
- if self.holding_chunk is not None:
- return_chunk = self.holding_chunk
- self.holding_chunk = None
- return return_chunk
- if self.sent_last_message is False:
- self.sent_last_message = True
- return {"type": "message_stop"}
- raise StopIteration
- except StopIteration:
- if self.sent_last_message is False:
- self.sent_last_message = True
- return {"type": "message_stop"}
- raise StopIteration
- except Exception as e:
- verbose_logger.error(
- "Anthropic Adapter - {}\n{}".format(e, traceback.format_exc())
- )
-
- async def __anext__(self):
- try:
- if self.sent_first_chunk is False:
- self.sent_first_chunk = True
- return {
- "type": "message_start",
- "message": {
- "id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY",
- "type": "message",
- "role": "assistant",
- "content": [],
- "model": "claude-3-5-sonnet-20240620",
- "stop_reason": None,
- "stop_sequence": None,
- "usage": {"input_tokens": 25, "output_tokens": 1},
- },
- }
- if self.sent_content_block_start is False:
- self.sent_content_block_start = True
- return {
- "type": "content_block_start",
- "index": 0,
- "content_block": {"type": "text", "text": ""},
- }
- async for chunk in self.completion_stream:
- if chunk == "None" or chunk is None:
- raise Exception
- processed_chunk = litellm.AnthropicExperimentalPassThroughConfig().translate_streaming_openai_response_to_anthropic(
- response=chunk
- )
- if (
- processed_chunk["type"] == "message_delta"
- and self.sent_content_block_finish is False
- ):
- self.holding_chunk = processed_chunk
- self.sent_content_block_finish = True
- return {
- "type": "content_block_stop",
- "index": 0,
- }
- elif self.holding_chunk is not None:
- return_chunk = self.holding_chunk
- self.holding_chunk = processed_chunk
- return return_chunk
- else:
- return processed_chunk
- if self.holding_chunk is not None:
- return_chunk = self.holding_chunk
- self.holding_chunk = None
- return return_chunk
- if self.sent_last_message is False:
- self.sent_last_message = True
- return {"type": "message_stop"}
- raise StopIteration
- except StopIteration:
- if self.sent_last_message is False:
- self.sent_last_message = True
- return {"type": "message_stop"}
- raise StopAsyncIteration
diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py
index f24eda0432..af53304e5a 100644
--- a/litellm/batches/batch_utils.py
+++ b/litellm/batches/batch_utils.py
@@ -1,76 +1,16 @@
-import asyncio
-import datetime
import json
-import threading
-from typing import Any, List, Literal, Optional
+from typing import Any, List, Literal, Tuple
import litellm
from litellm._logging import verbose_logger
-from litellm.constants import (
- BATCH_STATUS_POLL_INTERVAL_SECONDS,
- BATCH_STATUS_POLL_MAX_ATTEMPTS,
-)
-from litellm.files.main import afile_content
-from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.types.llms.openai import Batch
-from litellm.types.utils import StandardLoggingPayload, Usage
-
-
-async def batches_async_logging(
- batch_id: str,
- custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
- logging_obj: Optional[LiteLLMLoggingObj] = None,
- **kwargs,
-):
- """
- Async Job waits for the batch to complete and then logs the completed batch usage - cost, total tokens, prompt tokens, completion tokens
-
-
- Polls retrieve_batch until it returns a batch with status "completed" or "failed"
- """
- from .main import aretrieve_batch
-
- verbose_logger.debug(
- ".....in _batches_async_logging... polling retrieve to get batch status"
- )
- if logging_obj is None:
- raise ValueError(
- "logging_obj is None cannot calculate cost / log batch creation event"
- )
- for _ in range(BATCH_STATUS_POLL_MAX_ATTEMPTS):
- try:
- start_time = datetime.datetime.now()
- batch: Batch = await aretrieve_batch(batch_id, custom_llm_provider)
- verbose_logger.debug(
- "in _batches_async_logging... batch status= %s", batch.status
- )
-
- if batch.status == "completed":
- end_time = datetime.datetime.now()
- await _handle_completed_batch(
- batch=batch,
- custom_llm_provider=custom_llm_provider,
- logging_obj=logging_obj,
- start_time=start_time,
- end_time=end_time,
- **kwargs,
- )
- break
- elif batch.status == "failed":
- pass
- except Exception as e:
- verbose_logger.error("error in batches_async_logging", e)
- await asyncio.sleep(BATCH_STATUS_POLL_INTERVAL_SECONDS)
+from litellm.types.utils import CallTypes, Usage
async def _handle_completed_batch(
batch: Batch,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"],
- logging_obj: LiteLLMLoggingObj,
- start_time: datetime.datetime,
- end_time: datetime.datetime,
- **kwargs,
-) -> None:
+) -> Tuple[float, Usage, List[str]]:
"""Helper function to process a completed batch and handle logging"""
# Get batch results
file_content_dictionary = await _get_batch_output_file_content_as_dictionary(
@@ -87,52 +27,25 @@ async def _handle_completed_batch(
custom_llm_provider=custom_llm_provider,
)
- # Handle logging
- await _log_completed_batch(
- logging_obj=logging_obj,
- batch_usage=batch_usage,
- batch_cost=batch_cost,
- start_time=start_time,
- end_time=end_time,
- **kwargs,
- )
+ batch_models = _get_batch_models_from_file_content(file_content_dictionary)
+
+ return batch_cost, batch_usage, batch_models
-async def _log_completed_batch(
- logging_obj: LiteLLMLoggingObj,
- batch_usage: Usage,
- batch_cost: float,
- start_time: datetime.datetime,
- end_time: datetime.datetime,
- **kwargs,
-) -> None:
- """Helper function to handle all logging operations for a completed batch"""
- logging_obj.call_type = "batch_success"
-
- standard_logging_object = _create_standard_logging_object_for_completed_batch(
- kwargs=kwargs,
- start_time=start_time,
- end_time=end_time,
- logging_obj=logging_obj,
- batch_usage_object=batch_usage,
- response_cost=batch_cost,
- )
-
- logging_obj.model_call_details["standard_logging_object"] = standard_logging_object
-
- # Launch async and sync logging handlers
- asyncio.create_task(
- logging_obj.async_success_handler(
- result=None,
- start_time=start_time,
- end_time=end_time,
- cache_hit=None,
- )
- )
- threading.Thread(
- target=logging_obj.success_handler,
- args=(None, start_time, end_time),
- ).start()
+def _get_batch_models_from_file_content(
+ file_content_dictionary: List[dict],
+) -> List[str]:
+ """
+ Get the models from the file content
+ """
+ batch_models = []
+ for _item in file_content_dictionary:
+ if _batch_response_was_successful(_item):
+ _response_body = _get_response_from_batch_job_output_file(_item)
+ _model = _response_body.get("model")
+ if _model:
+ batch_models.append(_model)
+ return batch_models
async def _batch_cost_calculator(
@@ -159,6 +72,8 @@ async def _get_batch_output_file_content_as_dictionary(
"""
Get the batch output file content as a list of dictionaries
"""
+ from litellm.files.main import afile_content
+
if custom_llm_provider == "vertex_ai":
raise ValueError("Vertex AI does not support file content retrieval")
@@ -208,6 +123,7 @@ def _get_batch_job_cost_from_file_content(
total_cost += litellm.completion_cost(
completion_response=_response_body,
custom_llm_provider=custom_llm_provider,
+ call_type=CallTypes.aretrieve_batch.value,
)
verbose_logger.debug("total_cost=%s", total_cost)
return total_cost
@@ -264,30 +180,3 @@ def _batch_response_was_successful(batch_job_output_file: dict) -> bool:
"""
_response: dict = batch_job_output_file.get("response", None) or {}
return _response.get("status_code", None) == 200
-
-
-def _create_standard_logging_object_for_completed_batch(
- kwargs: dict,
- start_time: datetime.datetime,
- end_time: datetime.datetime,
- logging_obj: LiteLLMLoggingObj,
- batch_usage_object: Usage,
- response_cost: float,
-) -> StandardLoggingPayload:
- """
- Create a standard logging object for a completed batch
- """
- standard_logging_object = logging_obj.model_call_details.get(
- "standard_logging_object", None
- )
-
- if standard_logging_object is None:
- raise ValueError("unable to create standard logging object for completed batch")
-
- # Add Completed Batch Job Usage and Response Cost
- standard_logging_object["call_type"] = "batch_success"
- standard_logging_object["response_cost"] = response_cost
- standard_logging_object["total_tokens"] = batch_usage_object.total_tokens
- standard_logging_object["prompt_tokens"] = batch_usage_object.prompt_tokens
- standard_logging_object["completion_tokens"] = batch_usage_object.completion_tokens
- return standard_logging_object
diff --git a/litellm/batches/main.py b/litellm/batches/main.py
index 32428c9c18..2f4800043c 100644
--- a/litellm/batches/main.py
+++ b/litellm/batches/main.py
@@ -31,10 +31,9 @@ from litellm.types.llms.openai import (
RetrieveBatchRequest,
)
from litellm.types.router import GenericLiteLLMParams
+from litellm.types.utils import LiteLLMBatch
from litellm.utils import client, get_litellm_params, supports_httpx_timeout
-from .batch_utils import batches_async_logging
-
####### ENVIRONMENT VARIABLES ###################
openai_batches_instance = OpenAIBatchesAPI()
azure_batches_instance = AzureBatchesAPI()
@@ -85,17 +84,6 @@ async def acreate_batch(
else:
response = init_response
- # Start async logging job
- if response is not None:
- asyncio.create_task(
- batches_async_logging(
- logging_obj=kwargs.get("litellm_logging_obj", None),
- batch_id=response.id,
- custom_llm_provider=custom_llm_provider,
- **kwargs,
- )
- )
-
return response
except Exception as e:
raise e
@@ -111,7 +99,7 @@ def create_batch(
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
-) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
"""
Creates and executes a batch from an uploaded file of request
@@ -119,21 +107,26 @@ def create_batch(
"""
try:
optional_params = GenericLiteLLMParams(**kwargs)
+ litellm_call_id = kwargs.get("litellm_call_id", None)
+ proxy_server_request = kwargs.get("proxy_server_request", None)
+ model_info = kwargs.get("model_info", None)
_is_async = kwargs.pop("acreate_batch", False) is True
litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj", None)
### TIMEOUT LOGIC ###
timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
- litellm_params = get_litellm_params(
- custom_llm_provider=custom_llm_provider,
- litellm_call_id=kwargs.get("litellm_call_id", None),
- litellm_trace_id=kwargs.get("litellm_trace_id"),
- litellm_metadata=kwargs.get("litellm_metadata"),
- )
litellm_logging_obj.update_environment_variables(
model=None,
user=None,
optional_params=optional_params.model_dump(),
- litellm_params=litellm_params,
+ litellm_params={
+ "litellm_call_id": litellm_call_id,
+ "proxy_server_request": proxy_server_request,
+ "model_info": model_info,
+ "metadata": metadata,
+ "preset_cache_key": None,
+ "stream_response": {},
+ **optional_params.model_dump(exclude_unset=True),
+ },
custom_llm_provider=custom_llm_provider,
)
@@ -261,7 +254,7 @@ def create_batch(
response=httpx.Response(
status_code=400,
content="Unsupported provider",
- request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ request=httpx.Request(method="create_batch", url="https://github.com/BerriAI/litellm"), # type: ignore
),
)
return response
@@ -269,6 +262,7 @@ def create_batch(
raise e
+@client
async def aretrieve_batch(
batch_id: str,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
@@ -276,7 +270,7 @@ async def aretrieve_batch(
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
-) -> Batch:
+) -> LiteLLMBatch:
"""
Async: Retrieves a batch.
@@ -310,6 +304,7 @@ async def aretrieve_batch(
raise e
+@client
def retrieve_batch(
batch_id: str,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
@@ -317,7 +312,7 @@ def retrieve_batch(
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
-) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
"""
Retrieves a batch.
@@ -325,9 +320,23 @@ def retrieve_batch(
"""
try:
optional_params = GenericLiteLLMParams(**kwargs)
+
+ litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj", None)
### TIMEOUT LOGIC ###
timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
- # set timeout for 10 minutes by default
+ litellm_params = get_litellm_params(
+ custom_llm_provider=custom_llm_provider,
+ litellm_call_id=kwargs.get("litellm_call_id", None),
+ litellm_trace_id=kwargs.get("litellm_trace_id"),
+ litellm_metadata=kwargs.get("litellm_metadata"),
+ )
+ litellm_logging_obj.update_environment_variables(
+ model=None,
+ user=None,
+ optional_params=optional_params.model_dump(),
+ litellm_params=litellm_params,
+ custom_llm_provider=custom_llm_provider,
+ )
if (
timeout is not None
diff --git a/litellm/caching/caching.py b/litellm/caching/caching.py
index 26f94a94c2..415c49edff 100644
--- a/litellm/caching/caching.py
+++ b/litellm/caching/caching.py
@@ -13,26 +13,14 @@ import json
import time
import traceback
from enum import Enum
-from typing import Any, Dict, List, Optional, Set, Union
+from typing import Any, Dict, List, Optional, Union
-from openai.types.audio.transcription_create_params import TranscriptionCreateParams
-from openai.types.chat.completion_create_params import (
- CompletionCreateParamsNonStreaming,
- CompletionCreateParamsStreaming,
-)
-from openai.types.completion_create_params import (
- CompletionCreateParamsNonStreaming as TextCompletionCreateParamsNonStreaming,
-)
-from openai.types.completion_create_params import (
- CompletionCreateParamsStreaming as TextCompletionCreateParamsStreaming,
-)
-from openai.types.embedding_create_params import EmbeddingCreateParams
from pydantic import BaseModel
import litellm
from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.model_param_helper import ModelParamHelper
from litellm.types.caching import *
-from litellm.types.rerank import RerankRequest
from litellm.types.utils import all_litellm_params
from .base_cache import BaseCache
@@ -257,7 +245,7 @@ class Cache:
verbose_logger.debug("\nReturning preset cache key: %s", preset_cache_key)
return preset_cache_key
- combined_kwargs = self._get_relevant_args_to_use_for_cache_key()
+ combined_kwargs = ModelParamHelper._get_all_llm_api_params()
litellm_param_kwargs = all_litellm_params
for param in kwargs:
if param in combined_kwargs:
@@ -364,76 +352,6 @@ class Cache:
if "litellm_params" in kwargs:
kwargs["litellm_params"]["preset_cache_key"] = preset_cache_key
- def _get_relevant_args_to_use_for_cache_key(self) -> Set[str]:
- """
- Gets the supported kwargs for each call type and combines them
- """
- chat_completion_kwargs = self._get_litellm_supported_chat_completion_kwargs()
- text_completion_kwargs = self._get_litellm_supported_text_completion_kwargs()
- embedding_kwargs = self._get_litellm_supported_embedding_kwargs()
- transcription_kwargs = self._get_litellm_supported_transcription_kwargs()
- rerank_kwargs = self._get_litellm_supported_rerank_kwargs()
- exclude_kwargs = self._get_kwargs_to_exclude_from_cache_key()
-
- combined_kwargs = chat_completion_kwargs.union(
- text_completion_kwargs,
- embedding_kwargs,
- transcription_kwargs,
- rerank_kwargs,
- )
- combined_kwargs = combined_kwargs.difference(exclude_kwargs)
- return combined_kwargs
-
- def _get_litellm_supported_chat_completion_kwargs(self) -> Set[str]:
- """
- Get the litellm supported chat completion kwargs
-
- This follows the OpenAI API Spec
- """
- all_chat_completion_kwargs = set(
- CompletionCreateParamsNonStreaming.__annotations__.keys()
- ).union(set(CompletionCreateParamsStreaming.__annotations__.keys()))
- return all_chat_completion_kwargs
-
- def _get_litellm_supported_text_completion_kwargs(self) -> Set[str]:
- """
- Get the litellm supported text completion kwargs
-
- This follows the OpenAI API Spec
- """
- all_text_completion_kwargs = set(
- TextCompletionCreateParamsNonStreaming.__annotations__.keys()
- ).union(set(TextCompletionCreateParamsStreaming.__annotations__.keys()))
- return all_text_completion_kwargs
-
- def _get_litellm_supported_rerank_kwargs(self) -> Set[str]:
- """
- Get the litellm supported rerank kwargs
- """
- return set(RerankRequest.model_fields.keys())
-
- def _get_litellm_supported_embedding_kwargs(self) -> Set[str]:
- """
- Get the litellm supported embedding kwargs
-
- This follows the OpenAI API Spec
- """
- return set(EmbeddingCreateParams.__annotations__.keys())
-
- def _get_litellm_supported_transcription_kwargs(self) -> Set[str]:
- """
- Get the litellm supported transcription kwargs
-
- This follows the OpenAI API Spec
- """
- return set(TranscriptionCreateParams.__annotations__.keys())
-
- def _get_kwargs_to_exclude_from_cache_key(self) -> Set[str]:
- """
- Get the kwargs to exclude from the cache key
- """
- return set(["metadata"])
-
@staticmethod
def _get_hashed_cache_key(cache_key: str) -> str:
"""
diff --git a/litellm/caching/caching_handler.py b/litellm/caching/caching_handler.py
index 40c1001732..2a958c9eee 100644
--- a/litellm/caching/caching_handler.py
+++ b/litellm/caching/caching_handler.py
@@ -247,7 +247,6 @@ class LLMCachingHandler:
pass
else:
call_type = original_function.__name__
-
cached_result = self._convert_cached_result_to_model_response(
cached_result=cached_result,
call_type=call_type,
@@ -725,6 +724,7 @@ class LLMCachingHandler:
"""
Sync internal method to add the result to the cache
"""
+
new_kwargs = kwargs.copy()
new_kwargs.update(
convert_args_to_kwargs(
@@ -738,6 +738,7 @@ class LLMCachingHandler:
if self._should_store_result_in_cache(
original_function=self.original_function, kwargs=new_kwargs
):
+
litellm.cache.add_cache(result, **new_kwargs)
return
diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py
index 960d19c3f8..66245e7476 100644
--- a/litellm/caching/redis_cache.py
+++ b/litellm/caching/redis_cache.py
@@ -543,6 +543,7 @@ class RedisCache(BaseCache):
_redis_client: Redis = self.init_async_client() # type: ignore
start_time = time.time()
_used_ttl = self.get_ttl(ttl=ttl)
+ key = self.check_and_fix_namespace(key=key)
try:
result = await _redis_client.incrbyfloat(name=key, amount=value)
if _used_ttl is not None:
diff --git a/litellm/constants.py b/litellm/constants.py
index a1f7750d53..0288c45e40 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Literal
ROUTER_MAX_FALLBACKS = 5
DEFAULT_BATCH_SIZE = 512
@@ -120,6 +120,7 @@ OPENAI_CHAT_COMPLETION_PARAMS = [
"top_logprobs",
"reasoning_effort",
"extra_headers",
+ "thinking",
]
openai_compatible_endpoints: List = [
@@ -319,6 +320,17 @@ baseten_models: List = [
"31dxrj3",
] # FALCON 7B # WizardLM # Mosaic ML
+BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[
+ "cohere",
+ "anthropic",
+ "mistral",
+ "amazon",
+ "meta",
+ "llama",
+ "ai21",
+ "nova",
+ "deepseek_r1",
+]
open_ai_embedding_models: List = ["text-embedding-ada-002"]
cohere_embedding_models: List = [
diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index 488684f02b..1d10fa1f9e 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -239,6 +239,15 @@ def cost_per_token( # noqa: PLR0915
custom_llm_provider=custom_llm_provider,
billed_units=rerank_billed_units,
)
+ elif (
+ call_type == "aretrieve_batch"
+ or call_type == "retrieve_batch"
+ or call_type == CallTypes.aretrieve_batch
+ or call_type == CallTypes.retrieve_batch
+ ):
+ return batch_cost_calculator(
+ usage=usage_block, model=model, custom_llm_provider=custom_llm_provider
+ )
elif call_type == "atranscription" or call_type == "transcription":
return openai_cost_per_second(
model=model,
@@ -399,9 +408,12 @@ def _select_model_name_for_cost_calc(
if base_model is not None:
return_model = base_model
- completion_response_model: Optional[str] = getattr(
- completion_response, "model", None
- )
+ completion_response_model: Optional[str] = None
+ if completion_response is not None:
+ if isinstance(completion_response, BaseModel):
+ completion_response_model = getattr(completion_response, "model", None)
+ elif isinstance(completion_response, dict):
+ completion_response_model = completion_response.get("model", None)
hidden_params: Optional[dict] = getattr(completion_response, "_hidden_params", None)
if completion_response_model is None and hidden_params is not None:
if (
@@ -855,7 +867,10 @@ def rerank_cost(
try:
config = ProviderConfigManager.get_provider_rerank_config(
- model=model, provider=LlmProviders(custom_llm_provider)
+ model=model,
+ api_base=None,
+ present_version_params=[],
+ provider=LlmProviders(custom_llm_provider),
)
try:
@@ -954,3 +969,54 @@ def default_image_cost_calculator(
)
return cost_info["input_cost_per_pixel"] * height * width * n
+
+
+def batch_cost_calculator(
+ usage: Usage,
+ model: str,
+ custom_llm_provider: Optional[str] = None,
+) -> Tuple[float, float]:
+ """
+ Calculate the cost of a batch job
+ """
+
+ _, custom_llm_provider, _, _ = litellm.get_llm_provider(
+ model=model, custom_llm_provider=custom_llm_provider
+ )
+
+ verbose_logger.info(
+ "Calculating batch cost per token. model=%s, custom_llm_provider=%s",
+ model,
+ custom_llm_provider,
+ )
+
+ try:
+ model_info: Optional[ModelInfo] = litellm.get_model_info(
+ model=model, custom_llm_provider=custom_llm_provider
+ )
+ except Exception:
+ model_info = None
+
+ if not model_info:
+ return 0.0, 0.0
+
+ input_cost_per_token_batches = model_info.get("input_cost_per_token_batches")
+ input_cost_per_token = model_info.get("input_cost_per_token")
+ output_cost_per_token_batches = model_info.get("output_cost_per_token_batches")
+ output_cost_per_token = model_info.get("output_cost_per_token")
+ total_prompt_cost = 0.0
+ total_completion_cost = 0.0
+ if input_cost_per_token_batches:
+ total_prompt_cost = usage.prompt_tokens * input_cost_per_token_batches
+ elif input_cost_per_token:
+ total_prompt_cost = (
+ usage.prompt_tokens * (input_cost_per_token) / 2
+ ) # batch cost is usually half of the regular token cost
+ if output_cost_per_token_batches:
+ total_completion_cost = usage.completion_tokens * output_cost_per_token_batches
+ elif output_cost_per_token:
+ total_completion_cost = (
+ usage.completion_tokens * (output_cost_per_token) / 2
+ ) # batch cost is usually half of the regular token cost
+
+ return total_prompt_cost, total_completion_cost
diff --git a/litellm/exceptions.py b/litellm/exceptions.py
index f4166a5837..6a927f0712 100644
--- a/litellm/exceptions.py
+++ b/litellm/exceptions.py
@@ -118,6 +118,7 @@ class BadRequestError(openai.BadRequestError): # type: ignore
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
+ body: Optional[dict] = None,
):
self.status_code = 400
self.message = "litellm.BadRequestError: {}".format(message)
@@ -133,7 +134,7 @@ class BadRequestError(openai.BadRequestError): # type: ignore
self.max_retries = max_retries
self.num_retries = num_retries
super().__init__(
- self.message, response=response, body=None
+ self.message, response=response, body=body
) # Call the base class constructor with the parameters it needs
def __str__(self):
diff --git a/litellm/files/main.py b/litellm/files/main.py
index 9f81b2e385..e49066e84b 100644
--- a/litellm/files/main.py
+++ b/litellm/files/main.py
@@ -816,7 +816,7 @@ def file_content(
)
else:
raise litellm.exceptions.BadRequestError(
- message="LiteLLM doesn't support {} for 'file_content'. Only 'openai' and 'azure' are supported.".format(
+ message="LiteLLM doesn't support {} for 'custom_llm_provider'. Supported providers are 'openai', 'azure', 'vertex_ai'.".format(
custom_llm_provider
),
model="n/a",
diff --git a/litellm/integrations/_types/open_inference.py b/litellm/integrations/_types/open_inference.py
index bcfabe9b7b..b5076c0e42 100644
--- a/litellm/integrations/_types/open_inference.py
+++ b/litellm/integrations/_types/open_inference.py
@@ -283,4 +283,4 @@ class OpenInferenceSpanKindValues(Enum):
class OpenInferenceMimeTypeValues(Enum):
TEXT = "text/plain"
- JSON = "application/json"
+ JSON = "application/json"
\ No newline at end of file
diff --git a/litellm/integrations/arize/_utils.py b/litellm/integrations/arize/_utils.py
new file mode 100644
index 0000000000..9921d47aff
--- /dev/null
+++ b/litellm/integrations/arize/_utils.py
@@ -0,0 +1,121 @@
+import json
+from typing import TYPE_CHECKING, Any, Optional
+
+from litellm._logging import verbose_logger
+from litellm.types.utils import StandardLoggingPayload
+
+if TYPE_CHECKING:
+ from opentelemetry.trace import Span as _Span
+ Span = _Span
+else:
+ Span = Any
+
+
+def set_attributes(span: Span, kwargs, response_obj):
+ from openinference.semconv.trace import (
+ MessageAttributes,
+ OpenInferenceSpanKindValues,
+ SpanAttributes,
+ )
+
+ try:
+ litellm_params = kwargs.get("litellm_params", {}) or {}
+
+ #############################################
+ ############ LLM CALL METADATA ##############
+ #############################################
+ metadata = litellm_params.get("metadata", {}) or {}
+ span.set_attribute(SpanAttributes.METADATA, str(metadata))
+
+ #############################################
+ ########## LLM Request Attributes ###########
+ #############################################
+
+ # The name of the LLM a request is being made to
+ if kwargs.get("model"):
+ span.set_attribute(SpanAttributes.LLM_MODEL_NAME, kwargs.get("model"))
+
+ span.set_attribute(
+ SpanAttributes.OPENINFERENCE_SPAN_KIND,
+ OpenInferenceSpanKindValues.LLM.value,
+ )
+ messages = kwargs.get("messages")
+
+ # for /chat/completions
+ # https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
+ if messages:
+ span.set_attribute(
+ SpanAttributes.INPUT_VALUE,
+ messages[-1].get("content", ""), # get the last message for input
+ )
+
+ # LLM_INPUT_MESSAGES shows up under `input_messages` tab on the span page
+ for idx, msg in enumerate(messages):
+ # Set the role per message
+ span.set_attribute(
+ f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_ROLE}",
+ msg["role"],
+ )
+ # Set the content per message
+ span.set_attribute(
+ f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_CONTENT}",
+ msg.get("content", ""),
+ )
+
+ standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get(
+ "standard_logging_object"
+ )
+ if standard_logging_payload and (model_params := standard_logging_payload["model_parameters"]):
+ # The Generative AI Provider: Azure, OpenAI, etc.
+ span.set_attribute(
+ SpanAttributes.LLM_INVOCATION_PARAMETERS, json.dumps(model_params)
+ )
+
+ if model_params.get("user"):
+ user_id = model_params.get("user")
+ if user_id is not None:
+ span.set_attribute(SpanAttributes.USER_ID, user_id)
+
+ #############################################
+ ########## LLM Response Attributes ##########
+ # https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
+ #############################################
+ if hasattr(response_obj, 'get'):
+ for choice in response_obj.get("choices", []):
+ response_message = choice.get("message", {})
+ span.set_attribute(
+ SpanAttributes.OUTPUT_VALUE, response_message.get("content", "")
+ )
+
+ # This shows up under `output_messages` tab on the span page
+ # This code assumes a single response
+ span.set_attribute(
+ f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_ROLE}",
+ response_message.get("role"),
+ )
+ span.set_attribute(
+ f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}",
+ response_message.get("content", ""),
+ )
+
+ usage = response_obj.get("usage")
+ if usage:
+ span.set_attribute(
+ SpanAttributes.LLM_TOKEN_COUNT_TOTAL,
+ usage.get("total_tokens"),
+ )
+
+ # The number of tokens used in the LLM response (completion).
+ span.set_attribute(
+ SpanAttributes.LLM_TOKEN_COUNT_COMPLETION,
+ usage.get("completion_tokens"),
+ )
+
+ # The number of tokens used in the LLM prompt.
+ span.set_attribute(
+ SpanAttributes.LLM_TOKEN_COUNT_PROMPT,
+ usage.get("prompt_tokens"),
+ )
+ pass
+ except Exception as e:
+ verbose_logger.error(f"Error setting arize attributes: {e}")
diff --git a/litellm/integrations/arize/arize.py b/litellm/integrations/arize/arize.py
new file mode 100644
index 0000000000..652957e1ee
--- /dev/null
+++ b/litellm/integrations/arize/arize.py
@@ -0,0 +1,74 @@
+"""
+arize AI is OTEL compatible
+
+this file has Arize ai specific helper functions
+"""
+import os
+
+from typing import TYPE_CHECKING, Any
+from litellm.integrations.arize import _utils
+from litellm.types.integrations.arize import ArizeConfig
+
+if TYPE_CHECKING:
+ from litellm.types.integrations.arize import Protocol as _Protocol
+ from opentelemetry.trace import Span as _Span
+
+ Protocol = _Protocol
+ Span = _Span
+else:
+ Protocol = Any
+ Span = Any
+
+
+
+class ArizeLogger:
+
+ @staticmethod
+ def set_arize_attributes(span: Span, kwargs, response_obj):
+ _utils.set_attributes(span, kwargs, response_obj)
+ return
+
+
+ @staticmethod
+ def get_arize_config() -> ArizeConfig:
+ """
+ Helper function to get Arize configuration.
+
+ Returns:
+ ArizeConfig: A Pydantic model containing Arize configuration.
+
+ Raises:
+ ValueError: If required environment variables are not set.
+ """
+ space_key = os.environ.get("ARIZE_SPACE_KEY")
+ api_key = os.environ.get("ARIZE_API_KEY")
+
+ if not space_key:
+ raise ValueError("ARIZE_SPACE_KEY not found in environment variables")
+ if not api_key:
+ raise ValueError("ARIZE_API_KEY not found in environment variables")
+
+ grpc_endpoint = os.environ.get("ARIZE_ENDPOINT")
+ http_endpoint = os.environ.get("ARIZE_HTTP_ENDPOINT")
+
+ endpoint = None
+ protocol: Protocol = "otlp_grpc"
+
+ if grpc_endpoint:
+ protocol="otlp_grpc"
+ endpoint=grpc_endpoint
+ elif http_endpoint:
+ protocol="otlp_http"
+ endpoint=http_endpoint
+ else:
+ protocol="otlp_grpc"
+ endpoint = "https://otlp.arize.com/v1"
+
+ return ArizeConfig(
+ space_key=space_key,
+ api_key=api_key,
+ protocol=protocol,
+ endpoint=endpoint,
+ )
+
+
diff --git a/litellm/integrations/arize/arize_phoenix.py b/litellm/integrations/arize/arize_phoenix.py
new file mode 100644
index 0000000000..d7b7d5812b
--- /dev/null
+++ b/litellm/integrations/arize/arize_phoenix.py
@@ -0,0 +1,73 @@
+import os
+from typing import TYPE_CHECKING, Any
+from litellm.integrations.arize import _utils
+from litellm._logging import verbose_logger
+from litellm.types.integrations.arize_phoenix import ArizePhoenixConfig
+
+if TYPE_CHECKING:
+ from .opentelemetry import OpenTelemetryConfig as _OpenTelemetryConfig
+ from litellm.types.integrations.arize import Protocol as _Protocol
+ from opentelemetry.trace import Span as _Span
+
+ Protocol = _Protocol
+ OpenTelemetryConfig = _OpenTelemetryConfig
+ Span = _Span
+else:
+ Protocol = Any
+ OpenTelemetryConfig = Any
+ Span = Any
+
+
+ARIZE_HOSTED_PHOENIX_ENDPOINT = "https://app.phoenix.arize.com/v1/traces"
+
+class ArizePhoenixLogger:
+ @staticmethod
+ def set_arize_phoenix_attributes(span: Span, kwargs, response_obj):
+ _utils.set_attributes(span, kwargs, response_obj)
+ return
+
+ @staticmethod
+ def get_arize_phoenix_config() -> ArizePhoenixConfig:
+ """
+ Retrieves the Arize Phoenix configuration based on environment variables.
+
+ Returns:
+ ArizePhoenixConfig: A Pydantic model containing Arize Phoenix configuration.
+ """
+ api_key = os.environ.get("PHOENIX_API_KEY", None)
+ grpc_endpoint = os.environ.get("PHOENIX_COLLECTOR_ENDPOINT", None)
+ http_endpoint = os.environ.get("PHOENIX_COLLECTOR_HTTP_ENDPOINT", None)
+
+ endpoint = None
+ protocol: Protocol = "otlp_http"
+
+ if http_endpoint:
+ endpoint = http_endpoint
+ protocol = "otlp_http"
+ elif grpc_endpoint:
+ endpoint = grpc_endpoint
+ protocol = "otlp_grpc"
+ else:
+ endpoint = ARIZE_HOSTED_PHOENIX_ENDPOINT
+ protocol = "otlp_http"
+ verbose_logger.debug(
+ f"No PHOENIX_COLLECTOR_ENDPOINT or PHOENIX_COLLECTOR_HTTP_ENDPOINT found, using default endpoint with http: {ARIZE_HOSTED_PHOENIX_ENDPOINT}"
+ )
+
+ otlp_auth_headers = None
+ # If the endpoint is the Arize hosted Phoenix endpoint, use the api_key as the auth header as currently it is uses
+ # a slightly different auth header format than self hosted phoenix
+ if endpoint == ARIZE_HOSTED_PHOENIX_ENDPOINT:
+ if api_key is None:
+ raise ValueError("PHOENIX_API_KEY must be set when the Arize hosted Phoenix endpoint is used.")
+ otlp_auth_headers = f"api_key={api_key}"
+ elif api_key is not None:
+ # api_key/auth is optional for self hosted phoenix
+ otlp_auth_headers = f"Authorization=Bearer {api_key}"
+
+ return ArizePhoenixConfig(
+ otlp_auth_headers=otlp_auth_headers,
+ protocol=protocol,
+ endpoint=endpoint
+ )
+
diff --git a/litellm/integrations/arize_ai.py b/litellm/integrations/arize_ai.py
deleted file mode 100644
index 10c6af69b1..0000000000
--- a/litellm/integrations/arize_ai.py
+++ /dev/null
@@ -1,213 +0,0 @@
-"""
-arize AI is OTEL compatible
-
-this file has Arize ai specific helper functions
-"""
-
-import json
-from typing import TYPE_CHECKING, Any, Optional
-
-from litellm._logging import verbose_logger
-
-if TYPE_CHECKING:
- from opentelemetry.trace import Span as _Span
-
- from .opentelemetry import OpenTelemetryConfig as _OpenTelemetryConfig
-
- Span = _Span
- OpenTelemetryConfig = _OpenTelemetryConfig
-else:
- Span = Any
- OpenTelemetryConfig = Any
-
-import os
-
-from litellm.types.integrations.arize import *
-
-
-class ArizeLogger:
- @staticmethod
- def set_arize_ai_attributes(span: Span, kwargs, response_obj):
- from litellm.integrations._types.open_inference import (
- MessageAttributes,
- OpenInferenceSpanKindValues,
- SpanAttributes,
- )
-
- try:
-
- optional_params = kwargs.get("optional_params", {})
- # litellm_params = kwargs.get("litellm_params", {}) or {}
-
- #############################################
- ############ LLM CALL METADATA ##############
- #############################################
- # commented out for now - looks like Arize AI could not log this
- # metadata = litellm_params.get("metadata", {}) or {}
- # span.set_attribute(SpanAttributes.METADATA, str(metadata))
-
- #############################################
- ########## LLM Request Attributes ###########
- #############################################
-
- # The name of the LLM a request is being made to
- if kwargs.get("model"):
- span.set_attribute(SpanAttributes.LLM_MODEL_NAME, kwargs.get("model"))
-
- span.set_attribute(
- SpanAttributes.OPENINFERENCE_SPAN_KIND,
- OpenInferenceSpanKindValues.LLM.value,
- )
- messages = kwargs.get("messages")
-
- # for /chat/completions
- # https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
- if messages:
- span.set_attribute(
- SpanAttributes.INPUT_VALUE,
- messages[-1].get("content", ""), # get the last message for input
- )
-
- # LLM_INPUT_MESSAGES shows up under `input_messages` tab on the span page
- for idx, msg in enumerate(messages):
- # Set the role per message
- span.set_attribute(
- f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_ROLE}",
- msg["role"],
- )
- # Set the content per message
- span.set_attribute(
- f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_CONTENT}",
- msg.get("content", ""),
- )
-
- # The Generative AI Provider: Azure, OpenAI, etc.
- _optional_params = ArizeLogger.make_json_serializable(optional_params)
- _json_optional_params = json.dumps(_optional_params)
- span.set_attribute(
- SpanAttributes.LLM_INVOCATION_PARAMETERS, _json_optional_params
- )
-
- if optional_params.get("user"):
- span.set_attribute(SpanAttributes.USER_ID, optional_params.get("user"))
-
- #############################################
- ########## LLM Response Attributes ##########
- # https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
- #############################################
- for choice in response_obj.get("choices"):
- response_message = choice.get("message", {})
- span.set_attribute(
- SpanAttributes.OUTPUT_VALUE, response_message.get("content", "")
- )
-
- # This shows up under `output_messages` tab on the span page
- # This code assumes a single response
- span.set_attribute(
- f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_ROLE}",
- response_message["role"],
- )
- span.set_attribute(
- f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}",
- response_message.get("content", ""),
- )
-
- usage = response_obj.get("usage")
- if usage:
- span.set_attribute(
- SpanAttributes.LLM_TOKEN_COUNT_TOTAL,
- usage.get("total_tokens"),
- )
-
- # The number of tokens used in the LLM response (completion).
- span.set_attribute(
- SpanAttributes.LLM_TOKEN_COUNT_COMPLETION,
- usage.get("completion_tokens"),
- )
-
- # The number of tokens used in the LLM prompt.
- span.set_attribute(
- SpanAttributes.LLM_TOKEN_COUNT_PROMPT,
- usage.get("prompt_tokens"),
- )
- pass
- except Exception as e:
- verbose_logger.error(f"Error setting arize attributes: {e}")
-
- ###################### Helper functions ######################
-
- @staticmethod
- def _get_arize_config() -> ArizeConfig:
- """
- Helper function to get Arize configuration.
-
- Returns:
- ArizeConfig: A Pydantic model containing Arize configuration.
-
- Raises:
- ValueError: If required environment variables are not set.
- """
- space_key = os.environ.get("ARIZE_SPACE_KEY")
- api_key = os.environ.get("ARIZE_API_KEY")
-
- if not space_key:
- raise ValueError("ARIZE_SPACE_KEY not found in environment variables")
- if not api_key:
- raise ValueError("ARIZE_API_KEY not found in environment variables")
-
- grpc_endpoint = os.environ.get("ARIZE_ENDPOINT")
- http_endpoint = os.environ.get("ARIZE_HTTP_ENDPOINT")
- if grpc_endpoint is None and http_endpoint is None:
- # use default arize grpc endpoint
- verbose_logger.debug(
- "No ARIZE_ENDPOINT or ARIZE_HTTP_ENDPOINT found, using default endpoint: https://otlp.arize.com/v1"
- )
- grpc_endpoint = "https://otlp.arize.com/v1"
-
- return ArizeConfig(
- space_key=space_key,
- api_key=api_key,
- grpc_endpoint=grpc_endpoint,
- http_endpoint=http_endpoint,
- )
-
- @staticmethod
- def get_arize_opentelemetry_config() -> Optional[OpenTelemetryConfig]:
- """
- Helper function to get OpenTelemetry configuration for Arize.
-
- Args:
- arize_config (ArizeConfig): Arize configuration object.
-
- Returns:
- OpenTelemetryConfig: Configuration for OpenTelemetry.
- """
- from .opentelemetry import OpenTelemetryConfig
-
- arize_config = ArizeLogger._get_arize_config()
- if arize_config.http_endpoint:
- return OpenTelemetryConfig(
- exporter="otlp_http",
- endpoint=arize_config.http_endpoint,
- )
-
- # use default arize grpc endpoint
- return OpenTelemetryConfig(
- exporter="otlp_grpc",
- endpoint=arize_config.grpc_endpoint,
- )
-
- @staticmethod
- def make_json_serializable(payload: dict) -> dict:
- for key, value in payload.items():
- try:
- if isinstance(value, dict):
- # recursively sanitize dicts
- payload[key] = ArizeLogger.make_json_serializable(value.copy())
- elif not isinstance(value, (str, int, float, bool, type(None))):
- # everything else becomes a string
- payload[key] = str(value)
- except Exception:
- # non blocking if it can't cast to a str
- pass
- return payload
diff --git a/litellm/integrations/athina.py b/litellm/integrations/athina.py
index 754e980c2a..705dc11f1d 100644
--- a/litellm/integrations/athina.py
+++ b/litellm/integrations/athina.py
@@ -23,6 +23,9 @@ class AthinaLogger:
"context",
"expected_response",
"user_query",
+ "tags",
+ "user_feedback",
+ "model_options",
"custom_attributes",
]
@@ -81,7 +84,6 @@ class AthinaLogger:
for key in self.additional_keys:
if key in metadata:
data[key] = metadata[key]
-
response = litellm.module_level_client.post(
self.athina_logging_url,
headers=self.headers,
diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py
index 2558b0c2eb..4f4b05c84e 100644
--- a/litellm/integrations/datadog/datadog.py
+++ b/litellm/integrations/datadog/datadog.py
@@ -577,6 +577,4 @@ class DataDogLogger(
start_time_utc: Optional[datetimeObj],
end_time_utc: Optional[datetimeObj],
) -> Optional[dict]:
- raise NotImplementedError(
- "Datdog Integration for getting request/response payloads not implemented as yet"
- )
+ pass
diff --git a/litellm/integrations/langfuse/langfuse_prompt_management.py b/litellm/integrations/langfuse/langfuse_prompt_management.py
index cc2a6cf80d..1f4ca84db3 100644
--- a/litellm/integrations/langfuse/langfuse_prompt_management.py
+++ b/litellm/integrations/langfuse/langfuse_prompt_management.py
@@ -40,6 +40,7 @@ in_memory_dynamic_logger_cache = DynamicLoggingCache()
def langfuse_client_init(
langfuse_public_key=None,
langfuse_secret=None,
+ langfuse_secret_key=None,
langfuse_host=None,
flush_interval=1,
) -> LangfuseClass:
@@ -67,7 +68,10 @@ def langfuse_client_init(
)
# Instance variables
- secret_key = langfuse_secret or os.getenv("LANGFUSE_SECRET_KEY")
+
+ secret_key = (
+ langfuse_secret or langfuse_secret_key or os.getenv("LANGFUSE_SECRET_KEY")
+ )
public_key = langfuse_public_key or os.getenv("LANGFUSE_PUBLIC_KEY")
langfuse_host = langfuse_host or os.getenv(
"LANGFUSE_HOST", "https://cloud.langfuse.com"
@@ -190,6 +194,7 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge
langfuse_client = langfuse_client_init(
langfuse_public_key=dynamic_callback_params.get("langfuse_public_key"),
langfuse_secret=dynamic_callback_params.get("langfuse_secret"),
+ langfuse_secret_key=dynamic_callback_params.get("langfuse_secret_key"),
langfuse_host=dynamic_callback_params.get("langfuse_host"),
)
langfuse_prompt_client = self._get_prompt_from_id(
@@ -206,6 +211,7 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge
langfuse_client = langfuse_client_init(
langfuse_public_key=dynamic_callback_params.get("langfuse_public_key"),
langfuse_secret=dynamic_callback_params.get("langfuse_secret"),
+ langfuse_secret_key=dynamic_callback_params.get("langfuse_secret_key"),
langfuse_host=dynamic_callback_params.get("langfuse_host"),
)
langfuse_prompt_client = self._get_prompt_from_id(
diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py
index 8ca3ff7432..0ec7358037 100644
--- a/litellm/integrations/opentelemetry.py
+++ b/litellm/integrations/opentelemetry.py
@@ -444,9 +444,13 @@ class OpenTelemetry(CustomLogger):
):
try:
if self.callback_name == "arize":
- from litellm.integrations.arize_ai import ArizeLogger
+ from litellm.integrations.arize.arize import ArizeLogger
+ ArizeLogger.set_arize_attributes(span, kwargs, response_obj)
+ return
+ elif self.callback_name == "arize_phoenix":
+ from litellm.integrations.arize.arize_phoenix import ArizePhoenixLogger
- ArizeLogger.set_arize_ai_attributes(span, kwargs, response_obj)
+ ArizePhoenixLogger.set_arize_phoenix_attributes(span, kwargs, response_obj)
return
elif self.callback_name == "langtrace":
from litellm.integrations.langtrace import LangtraceAttributes
diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py
index 04050abf7b..d6e47b87ce 100644
--- a/litellm/integrations/prometheus.py
+++ b/litellm/integrations/prometheus.py
@@ -1560,10 +1560,18 @@ class PrometheusLogger(CustomLogger):
- Max Budget
- Budget Reset At
"""
- self.litellm_remaining_team_budget_metric.labels(
- team.team_id,
- team.team_alias or "",
- ).set(
+ enum_values = UserAPIKeyLabelValues(
+ team=team.team_id,
+ team_alias=team.team_alias or "",
+ )
+
+ _labels = prometheus_label_factory(
+ supported_enum_labels=PrometheusMetricLabels.get_labels(
+ label_name="litellm_remaining_team_budget_metric"
+ ),
+ enum_values=enum_values,
+ )
+ self.litellm_remaining_team_budget_metric.labels(**_labels).set(
self._safe_get_remaining_budget(
max_budget=team.max_budget,
spend=team.spend,
@@ -1571,16 +1579,22 @@ class PrometheusLogger(CustomLogger):
)
if team.max_budget is not None:
- self.litellm_team_max_budget_metric.labels(
- team.team_id,
- team.team_alias or "",
- ).set(team.max_budget)
+ _labels = prometheus_label_factory(
+ supported_enum_labels=PrometheusMetricLabels.get_labels(
+ label_name="litellm_team_max_budget_metric"
+ ),
+ enum_values=enum_values,
+ )
+ self.litellm_team_max_budget_metric.labels(**_labels).set(team.max_budget)
if team.budget_reset_at is not None:
- self.litellm_team_budget_remaining_hours_metric.labels(
- team.team_id,
- team.team_alias or "",
- ).set(
+ _labels = prometheus_label_factory(
+ supported_enum_labels=PrometheusMetricLabels.get_labels(
+ label_name="litellm_team_budget_remaining_hours_metric"
+ ),
+ enum_values=enum_values,
+ )
+ self.litellm_team_budget_remaining_hours_metric.labels(**_labels).set(
self._get_remaining_hours_for_budget_reset(
budget_reset_at=team.budget_reset_at
)
diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py
index ceb150946c..2036b93692 100644
--- a/litellm/litellm_core_utils/core_helpers.py
+++ b/litellm/litellm_core_utils/core_helpers.py
@@ -73,8 +73,19 @@ def remove_index_from_tool_calls(
def get_litellm_metadata_from_kwargs(kwargs: dict):
"""
Helper to get litellm metadata from all litellm request kwargs
+
+ Return `litellm_metadata` if it exists, otherwise return `metadata`
"""
- return kwargs.get("litellm_params", {}).get("metadata", {})
+ litellm_params = kwargs.get("litellm_params", {})
+ if litellm_params:
+ metadata = litellm_params.get("metadata", {})
+ litellm_metadata = litellm_params.get("litellm_metadata", {})
+ if litellm_metadata:
+ return litellm_metadata
+ elif metadata:
+ return metadata
+
+ return {}
# Helper functions used for OTEL logging
diff --git a/litellm/litellm_core_utils/dd_tracing.py b/litellm/litellm_core_utils/dd_tracing.py
index 4df3026144..1f866a998a 100644
--- a/litellm/litellm_core_utils/dd_tracing.py
+++ b/litellm/litellm_core_utils/dd_tracing.py
@@ -5,49 +5,69 @@ If the ddtrace package is not installed, the tracer will be a no-op.
"""
from contextlib import contextmanager
+from typing import TYPE_CHECKING, Any, Union
-try:
- from ddtrace import tracer as dd_tracer
+from litellm.secret_managers.main import get_secret_bool
- has_ddtrace = True
-except ImportError:
- has_ddtrace = False
+if TYPE_CHECKING:
+ from ddtrace.tracer import Tracer as DD_TRACER
+else:
+ DD_TRACER = Any
- @contextmanager
- def null_tracer(name, **kwargs):
- class NullSpan:
- def __enter__(self):
- return self
- def __exit__(self, *args):
- pass
+class NullSpan:
+ """A no-op span implementation."""
- def finish(self):
- pass
+ def __enter__(self):
+ return self
- yield NullSpan()
+ def __exit__(self, *args):
+ pass
- class NullTracer:
- def trace(self, name, **kwargs):
- class NullSpan:
- def __enter__(self):
- return self
+ def finish(self):
+ pass
- def __exit__(self, *args):
- pass
- def finish(self):
- pass
+@contextmanager
+def null_tracer(name, **kwargs):
+ """Context manager that yields a no-op span."""
+ yield NullSpan()
- return NullSpan()
- def wrap(self, name=None, **kwargs):
- def decorator(f):
- return f
+class NullTracer:
+ """A no-op tracer implementation."""
- return decorator
+ def trace(self, name, **kwargs):
+ return NullSpan()
- dd_tracer = NullTracer()
+ def wrap(self, name=None, **kwargs):
+ # If called with no arguments (as @tracer.wrap())
+ if callable(name):
+ return name
-# Export the tracer instance
-tracer = dd_tracer
+ # If called with arguments (as @tracer.wrap(name="something"))
+ def decorator(f):
+ return f
+
+ return decorator
+
+
+def _should_use_dd_tracer():
+ """Returns True if `USE_DDTRACE` is set to True in .env"""
+ return get_secret_bool("USE_DDTRACE", False) is True
+
+
+# Initialize tracer
+should_use_dd_tracer = _should_use_dd_tracer()
+tracer: Union[NullTracer, DD_TRACER] = NullTracer()
+# We need to ensure tracer is never None and always has the required methods
+if should_use_dd_tracer:
+ try:
+ from ddtrace import tracer as dd_tracer
+
+ # Define the type to match what's expected by the code using this module
+ tracer = dd_tracer
+ except ImportError:
+ tracer = NullTracer()
+else:
+ tracer = NullTracer()
diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py
index 9ac20de4c0..7a0cffab7b 100644
--- a/litellm/litellm_core_utils/exception_mapping_utils.py
+++ b/litellm/litellm_core_utils/exception_mapping_utils.py
@@ -278,6 +278,7 @@ def exception_type( # type: ignore # noqa: PLR0915
"This model's maximum context length is" in error_str
or "string too long. Expected a string with maximum length"
in error_str
+ or "model's maximum context limit" in error_str
):
exception_mapping_worked = True
raise ContextWindowExceededError(
@@ -330,6 +331,7 @@ def exception_type( # type: ignore # noqa: PLR0915
model=model,
response=getattr(original_exception, "response", None),
litellm_debug_info=extra_information,
+ body=getattr(original_exception, "body", None),
)
elif (
"Web server is returning an unknown error" in error_str
@@ -420,6 +422,7 @@ def exception_type( # type: ignore # noqa: PLR0915
llm_provider=custom_llm_provider,
response=getattr(original_exception, "response", None),
litellm_debug_info=extra_information,
+ body=getattr(original_exception, "body", None),
)
elif original_exception.status_code == 429:
exception_mapping_worked = True
@@ -692,6 +695,13 @@ def exception_type( # type: ignore # noqa: PLR0915
response=getattr(original_exception, "response", None),
litellm_debug_info=extra_information,
)
+ elif "model's maximum context limit" in error_str:
+ exception_mapping_worked = True
+ raise ContextWindowExceededError(
+ message=f"{custom_llm_provider}Exception: Context Window Error - {error_str}",
+ model=model,
+ llm_provider=custom_llm_provider,
+ )
elif "token_quota_reached" in error_str:
exception_mapping_worked = True
raise RateLimitError(
@@ -1952,6 +1962,7 @@ def exception_type( # type: ignore # noqa: PLR0915
model=model,
litellm_debug_info=extra_information,
response=getattr(original_exception, "response", None),
+ body=getattr(original_exception, "body", None),
)
elif (
"The api_key client option must be set either by passing api_key to the client or by setting"
@@ -1983,6 +1994,7 @@ def exception_type( # type: ignore # noqa: PLR0915
model=model,
litellm_debug_info=extra_information,
response=getattr(original_exception, "response", None),
+ body=getattr(original_exception, "body", None),
)
elif original_exception.status_code == 401:
exception_mapping_worked = True
diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py
index 3d8394f7af..cf62375f33 100644
--- a/litellm/litellm_core_utils/get_litellm_params.py
+++ b/litellm/litellm_core_utils/get_litellm_params.py
@@ -57,6 +57,7 @@ def get_litellm_params(
prompt_variables: Optional[dict] = None,
async_call: Optional[bool] = None,
ssl_verify: Optional[bool] = None,
+ merge_reasoning_content_in_choices: Optional[bool] = None,
**kwargs,
) -> dict:
litellm_params = {
@@ -75,7 +76,7 @@ def get_litellm_params(
"model_info": model_info,
"proxy_server_request": proxy_server_request,
"preset_cache_key": preset_cache_key,
- "no-log": no_log,
+ "no-log": no_log or kwargs.get("no-log"),
"stream_response": {}, # litellm_call_id: ModelResponse Dict
"input_cost_per_token": input_cost_per_token,
"input_cost_per_second": input_cost_per_second,
@@ -97,5 +98,6 @@ def get_litellm_params(
"prompt_variables": prompt_variables,
"async_call": async_call,
"ssl_verify": ssl_verify,
+ "merge_reasoning_content_in_choices": merge_reasoning_content_in_choices,
}
return litellm_params
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index 220306ee74..a3d9a57a49 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -25,6 +25,7 @@ from litellm import (
turn_off_message_logging,
)
from litellm._logging import _is_debugging_on, verbose_logger
+from litellm.batches.batch_utils import _handle_completed_batch
from litellm.caching.caching import DualCache, InMemoryCache
from litellm.caching.caching_handler import LLMCachingHandler
from litellm.cost_calculator import _select_model_name_for_cost_calc
@@ -33,6 +34,7 @@ from litellm.integrations.custom_logger import CustomLogger
from litellm.integrations.mlflow import MlflowLogger
from litellm.integrations.pagerduty.pagerduty import PagerDutyAlerting
from litellm.litellm_core_utils.get_litellm_params import get_litellm_params
+from litellm.litellm_core_utils.model_param_helper import ModelParamHelper
from litellm.litellm_core_utils.redact_messages import (
redact_message_input_output_from_custom_logger,
redact_message_input_output_from_logging,
@@ -49,9 +51,11 @@ from litellm.types.utils import (
CallTypes,
EmbeddingResponse,
ImageResponse,
+ LiteLLMBatch,
LiteLLMLoggingBaseClass,
ModelResponse,
ModelResponseStream,
+ RawRequestTypedDict,
StandardCallbackDynamicParams,
StandardLoggingAdditionalHeaders,
StandardLoggingHiddenParams,
@@ -69,7 +73,8 @@ from litellm.types.utils import (
from litellm.utils import _get_base_model_from_metadata, executor, print_verbose
from ..integrations.argilla import ArgillaLogger
-from ..integrations.arize_ai import ArizeLogger
+from ..integrations.arize.arize import ArizeLogger
+from ..integrations.arize.arize_phoenix import ArizePhoenixLogger
from ..integrations.athina import AthinaLogger
from ..integrations.azure_storage.azure_storage import AzureBlobStorageLogger
from ..integrations.braintrust_logging import BraintrustLogger
@@ -201,6 +206,7 @@ class Logging(LiteLLMLoggingBaseClass):
] = None,
applied_guardrails: Optional[List[str]] = None,
kwargs: Optional[Dict] = None,
+ log_raw_request_response: bool = False,
):
_input: Optional[str] = messages # save original value of messages
if messages is not None:
@@ -229,6 +235,7 @@ class Logging(LiteLLMLoggingBaseClass):
self.sync_streaming_chunks: List[Any] = (
[]
) # for generating complete stream response
+ self.log_raw_request_response = log_raw_request_response
# Initialize dynamic callbacks
self.dynamic_input_callbacks: Optional[
@@ -449,6 +456,18 @@ class Logging(LiteLLMLoggingBaseClass):
return model, messages, non_default_params
+ def _get_raw_request_body(self, data: Optional[Union[dict, str]]) -> dict:
+ if data is None:
+ return {"error": "Received empty dictionary for raw request body"}
+ if isinstance(data, str):
+ try:
+ return json.loads(data)
+ except Exception:
+ return {
+ "error": "Unable to parse raw request body. Got - {}".format(data)
+ }
+ return data
+
def _pre_call(self, input, api_key, model=None, additional_args={}):
"""
Common helper function across the sync + async pre-call function
@@ -464,6 +483,7 @@ class Logging(LiteLLMLoggingBaseClass):
self.model_call_details["model"] = model
def pre_call(self, input, api_key, model=None, additional_args={}): # noqa: PLR0915
+
# Log the exact input to the LLM API
litellm.error_logs["PRE_CALL"] = locals()
try:
@@ -481,28 +501,54 @@ class Logging(LiteLLMLoggingBaseClass):
additional_args=additional_args,
)
# log raw request to provider (like LangFuse) -- if opted in.
- if log_raw_request_response is True:
+ if (
+ self.log_raw_request_response is True
+ or log_raw_request_response is True
+ ):
+
_litellm_params = self.model_call_details.get("litellm_params", {})
_metadata = _litellm_params.get("metadata", {}) or {}
try:
# [Non-blocking Extra Debug Information in metadata]
- if (
- turn_off_message_logging is not None
- and turn_off_message_logging is True
- ):
+ if turn_off_message_logging is True:
+
_metadata["raw_request"] = (
"redacted by litellm. \
'litellm.turn_off_message_logging=True'"
)
else:
+
curl_command = self._get_request_curl_command(
api_base=additional_args.get("api_base", ""),
headers=additional_args.get("headers", {}),
additional_args=additional_args,
data=additional_args.get("complete_input_dict", {}),
)
+
_metadata["raw_request"] = str(curl_command)
+ # split up, so it's easier to parse in the UI
+ self.model_call_details["raw_request_typed_dict"] = (
+ RawRequestTypedDict(
+ raw_request_api_base=str(
+ additional_args.get("api_base") or ""
+ ),
+ raw_request_body=self._get_raw_request_body(
+ additional_args.get("complete_input_dict", {})
+ ),
+ raw_request_headers=self._get_masked_headers(
+ additional_args.get("headers", {}) or {},
+ ignore_sensitive_headers=True,
+ ),
+ error=None,
+ )
+ )
except Exception as e:
+ self.model_call_details["raw_request_typed_dict"] = (
+ RawRequestTypedDict(
+ error=str(e),
+ )
+ )
+ traceback.print_exc()
_metadata["raw_request"] = (
"Unable to Log \
raw request: {}".format(
@@ -635,9 +681,14 @@ class Logging(LiteLLMLoggingBaseClass):
)
verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n")
+ def _get_request_body(self, data: dict) -> str:
+ return str(data)
+
def _get_request_curl_command(
- self, api_base: str, headers: dict, additional_args: dict, data: dict
+ self, api_base: str, headers: Optional[dict], additional_args: dict, data: dict
) -> str:
+ if headers is None:
+ headers = {}
curl_command = "\n\nPOST Request Sent from LiteLLM:\n"
curl_command += "curl -X POST \\\n"
curl_command += f"{api_base} \\\n"
@@ -645,11 +696,10 @@ class Logging(LiteLLMLoggingBaseClass):
formatted_headers = " ".join(
[f"-H '{k}: {v}'" for k, v in masked_headers.items()]
)
-
curl_command += (
f"{formatted_headers} \\\n" if formatted_headers.strip() != "" else ""
)
- curl_command += f"-d '{str(data)}'\n"
+ curl_command += f"-d '{self._get_request_body(data)}'\n"
if additional_args.get("request_str", None) is not None:
# print the sagemaker / bedrock client request
curl_command = "\nRequest Sent from LiteLLM:\n"
@@ -658,12 +708,20 @@ class Logging(LiteLLMLoggingBaseClass):
curl_command = str(self.model_call_details)
return curl_command
- def _get_masked_headers(self, headers: dict):
+ def _get_masked_headers(
+ self, headers: dict, ignore_sensitive_headers: bool = False
+ ) -> dict:
"""
Internal debugging helper function
Masks the headers of the request sent from LiteLLM
"""
+ sensitive_keywords = [
+ "authorization",
+ "token",
+ "key",
+ "secret",
+ ]
return {
k: (
(v[:-44] + "*" * 44)
@@ -671,6 +729,11 @@ class Logging(LiteLLMLoggingBaseClass):
else "*****"
)
for k, v in headers.items()
+ if not ignore_sensitive_headers
+ or not any(
+ sensitive_keyword in k.lower()
+ for sensitive_keyword in sensitive_keywords
+ )
}
def post_call(
@@ -831,7 +894,7 @@ class Logging(LiteLLMLoggingBaseClass):
except Exception as e: # error creating kwargs for cost calculation
debug_info = StandardLoggingModelCostFailureDebugInformation(
error_str=str(e),
- traceback_str=traceback.format_exc(),
+ traceback_str=_get_traceback_str_for_error(str(e)),
)
verbose_logger.debug(
f"response_cost_failure_debug_information: {debug_info}"
@@ -869,6 +932,24 @@ class Logging(LiteLLMLoggingBaseClass):
return None
+ async def _response_cost_calculator_async(
+ self,
+ result: Union[
+ ModelResponse,
+ ModelResponseStream,
+ EmbeddingResponse,
+ ImageResponse,
+ TranscriptionResponse,
+ TextCompletionResponse,
+ HttpxBinaryResponseContent,
+ RerankResponse,
+ Batch,
+ FineTuningJob,
+ ],
+ cache_hit: Optional[bool] = None,
+ ) -> Optional[float]:
+ return self._response_cost_calculator(result=result, cache_hit=cache_hit)
+
def should_run_callback(
self, callback: litellm.CALLBACK_TYPES, litellm_params: dict, event_hook: str
) -> bool:
@@ -910,6 +991,9 @@ class Logging(LiteLLMLoggingBaseClass):
self.model_call_details["log_event_type"] = "successful_api_call"
self.model_call_details["end_time"] = end_time
self.model_call_details["cache_hit"] = cache_hit
+
+ if self.call_type == CallTypes.anthropic_messages.value:
+ result = self._handle_anthropic_messages_response_logging(result=result)
## if model in model cost map - log the response cost
## else set cost to None
if (
@@ -926,8 +1010,8 @@ class Logging(LiteLLMLoggingBaseClass):
or isinstance(result, TextCompletionResponse)
or isinstance(result, HttpxBinaryResponseContent) # tts
or isinstance(result, RerankResponse)
- or isinstance(result, Batch)
or isinstance(result, FineTuningJob)
+ or isinstance(result, LiteLLMBatch)
):
## HIDDEN PARAMS ##
hidden_params = getattr(result, "_hidden_params", {})
@@ -1523,6 +1607,20 @@ class Logging(LiteLLMLoggingBaseClass):
print_verbose(
"Logging Details LiteLLM-Async Success Call, cache_hit={}".format(cache_hit)
)
+
+ ## CALCULATE COST FOR BATCH JOBS
+ if self.call_type == CallTypes.aretrieve_batch.value and isinstance(
+ result, LiteLLMBatch
+ ):
+
+ response_cost, batch_usage, batch_models = await _handle_completed_batch(
+ batch=result, custom_llm_provider=self.custom_llm_provider
+ )
+
+ result._hidden_params["response_cost"] = response_cost
+ result._hidden_params["batch_models"] = batch_models
+ result.usage = batch_usage
+
start_time, end_time, result = self._success_handler_helper_fn(
start_time=start_time,
end_time=end_time,
@@ -1530,6 +1628,7 @@ class Logging(LiteLLMLoggingBaseClass):
cache_hit=cache_hit,
standard_logging_object=kwargs.get("standard_logging_object", None),
)
+
## BUILD COMPLETE STREAMED RESPONSE
if "async_complete_streaming_response" in self.model_call_details:
return # break out of this.
@@ -2268,6 +2367,37 @@ class Logging(LiteLLMLoggingBaseClass):
return complete_streaming_response
return None
+ def _handle_anthropic_messages_response_logging(self, result: Any) -> ModelResponse:
+ """
+ Handles logging for Anthropic messages responses.
+
+ Args:
+ result: The response object from the model call
+
+ Returns:
+ The the response object from the model call
+
+ - For Non-streaming responses, we need to transform the response to a ModelResponse object.
+ - For streaming responses, anthropic_messages handler calls success_handler with a assembled ModelResponse.
+ """
+ if self.stream and isinstance(result, ModelResponse):
+ return result
+
+ result = litellm.AnthropicConfig().transform_response(
+ raw_response=self.model_call_details["httpx_response"],
+ model_response=litellm.ModelResponse(),
+ model=self.model,
+ messages=[],
+ logging_obj=self,
+ optional_params={},
+ api_key="",
+ request_data={},
+ encoding=litellm.encoding,
+ json_mode=False,
+ litellm_params={},
+ )
+ return result
+
def set_callbacks(callback_list, function_id=None): # noqa: PLR0915
"""
@@ -2476,13 +2606,18 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
OpenTelemetryConfig,
)
- otel_config = ArizeLogger.get_arize_opentelemetry_config()
- if otel_config is None:
+ arize_config = ArizeLogger.get_arize_config()
+ if arize_config.endpoint is None:
raise ValueError(
"No valid endpoint found for Arize, please set 'ARIZE_ENDPOINT' to your GRPC endpoint or 'ARIZE_HTTP_ENDPOINT' to your HTTP endpoint"
)
+ otel_config = OpenTelemetryConfig(
+ exporter=arize_config.protocol,
+ endpoint=arize_config.endpoint,
+ )
+
os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
- f"space_key={os.getenv('ARIZE_SPACE_KEY')},api_key={os.getenv('ARIZE_API_KEY')}"
+ f"space_key={arize_config.space_key},api_key={arize_config.api_key}"
)
for callback in _in_memory_loggers:
if (
@@ -2493,6 +2628,35 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
_otel_logger = OpenTelemetry(config=otel_config, callback_name="arize")
_in_memory_loggers.append(_otel_logger)
return _otel_logger # type: ignore
+ elif logging_integration == "arize_phoenix":
+ from litellm.integrations.opentelemetry import (
+ OpenTelemetry,
+ OpenTelemetryConfig,
+ )
+
+ arize_phoenix_config = ArizePhoenixLogger.get_arize_phoenix_config()
+ otel_config = OpenTelemetryConfig(
+ exporter=arize_phoenix_config.protocol,
+ endpoint=arize_phoenix_config.endpoint,
+ )
+
+ # auth can be disabled on local deployments of arize phoenix
+ if arize_phoenix_config.otlp_auth_headers is not None:
+ os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
+ arize_phoenix_config.otlp_auth_headers
+ )
+
+ for callback in _in_memory_loggers:
+ if (
+ isinstance(callback, OpenTelemetry)
+ and callback.callback_name == "arize_phoenix"
+ ):
+ return callback # type: ignore
+ _otel_logger = OpenTelemetry(
+ config=otel_config, callback_name="arize_phoenix"
+ )
+ _in_memory_loggers.append(_otel_logger)
+ return _otel_logger # type: ignore
elif logging_integration == "otel":
from litellm.integrations.opentelemetry import OpenTelemetry
@@ -3050,6 +3214,7 @@ class StandardLoggingPayloadSetup:
response_cost=None,
additional_headers=None,
litellm_overhead_time_ms=None,
+ batch_models=None,
)
if hidden_params is not None:
for key in StandardLoggingHiddenParams.__annotations__.keys():
@@ -3079,10 +3244,26 @@ class StandardLoggingPayloadSetup:
str(original_exception.__class__.__name__) if original_exception else ""
)
_llm_provider_in_exception = getattr(original_exception, "llm_provider", "")
+
+ # Get traceback information (first 100 lines)
+ traceback_info = ""
+ if original_exception:
+ tb = getattr(original_exception, "__traceback__", None)
+ if tb:
+ import traceback
+
+ tb_lines = traceback.format_tb(tb)
+ traceback_info = "".join(tb_lines[:100]) # Limit to first 100 lines
+
+ # Get additional error details
+ error_message = str(original_exception)
+
return StandardLoggingPayloadErrorInformation(
error_code=error_status,
error_class=error_class,
llm_provider=_llm_provider_in_exception,
+ traceback=traceback_info,
+ error_message=error_message if original_exception else "",
)
@staticmethod
@@ -3147,6 +3328,7 @@ def get_standard_logging_object_payload(
api_base=None,
response_cost=None,
litellm_overhead_time_ms=None,
+ batch_models=None,
)
)
@@ -3279,7 +3461,9 @@ def get_standard_logging_object_payload(
requester_ip_address=clean_metadata.get("requester_ip_address", None),
messages=kwargs.get("messages"),
response=final_response_obj,
- model_parameters=kwargs.get("optional_params", None),
+ model_parameters=ModelParamHelper.get_standard_logging_model_parameters(
+ kwargs.get("optional_params", None) or {}
+ ),
hidden_params=clean_hidden_params,
model_map_information=model_cost_information,
error_str=error_str,
@@ -3429,6 +3613,7 @@ def create_dummy_standard_logging_payload() -> StandardLoggingPayload:
response_cost=None,
additional_headers=None,
litellm_overhead_time_ms=None,
+ batch_models=None,
)
# Convert numeric values to appropriate types
diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
index 46d40be9c5..ebb1032a19 100644
--- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
+++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
@@ -9,6 +9,7 @@ from typing import Dict, Iterable, List, Literal, Optional, Tuple, Union
import litellm
from litellm._logging import verbose_logger
from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
+from litellm.types.llms.openai import ChatCompletionThinkingBlock
from litellm.types.utils import (
ChatCompletionDeltaToolCall,
ChatCompletionMessageToolCall,
@@ -128,12 +129,7 @@ def convert_to_streaming_response(response_object: Optional[dict] = None):
model_response_object = ModelResponse(stream=True)
choice_list = []
for idx, choice in enumerate(response_object["choices"]):
- delta = Delta(
- content=choice["message"].get("content", None),
- role=choice["message"]["role"],
- function_call=choice["message"].get("function_call", None),
- tool_calls=choice["message"].get("tool_calls", None),
- )
+ delta = Delta(**choice["message"])
finish_reason = choice.get("finish_reason", None)
if finish_reason is None:
# gpt-4 vision can return 'finish_reason' or 'finish_details'
@@ -243,6 +239,24 @@ def _parse_content_for_reasoning(
return None, message_text
+def _extract_reasoning_content(message: dict) -> Tuple[Optional[str], Optional[str]]:
+ """
+ Extract reasoning content and main content from a message.
+
+ Args:
+ message (dict): The message dictionary that may contain reasoning_content
+
+ Returns:
+ tuple[Optional[str], Optional[str]]: A tuple of (reasoning_content, content)
+ """
+ if "reasoning_content" in message:
+ return message["reasoning_content"], message["content"]
+ elif "reasoning" in message:
+ return message["reasoning"], message["content"]
+ else:
+ return _parse_content_for_reasoning(message.get("content"))
+
+
class LiteLLMResponseObjectHandler:
@staticmethod
@@ -456,11 +470,16 @@ def convert_to_model_response_object( # noqa: PLR0915
provider_specific_fields[field] = choice["message"][field]
# Handle reasoning models that display `reasoning_content` within `content`
-
- reasoning_content, content = _parse_content_for_reasoning(
- choice["message"].get("content")
+ reasoning_content, content = _extract_reasoning_content(
+ choice["message"]
)
+ # Handle thinking models that display `thinking_blocks` within `content`
+ thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
+ if "thinking_blocks" in choice["message"]:
+ thinking_blocks = choice["message"]["thinking_blocks"]
+ provider_specific_fields["thinking_blocks"] = thinking_blocks
+
if reasoning_content:
provider_specific_fields["reasoning_content"] = (
reasoning_content
@@ -473,6 +492,8 @@ def convert_to_model_response_object( # noqa: PLR0915
tool_calls=tool_calls,
audio=choice["message"].get("audio", None),
provider_specific_fields=provider_specific_fields,
+ reasoning_content=reasoning_content,
+ thinking_blocks=thinking_blocks,
)
finish_reason = choice.get("finish_reason", None)
if finish_reason is None:
diff --git a/litellm/litellm_core_utils/model_param_helper.py b/litellm/litellm_core_utils/model_param_helper.py
new file mode 100644
index 0000000000..09a2c15a77
--- /dev/null
+++ b/litellm/litellm_core_utils/model_param_helper.py
@@ -0,0 +1,133 @@
+from typing import Set
+
+from openai.types.audio.transcription_create_params import TranscriptionCreateParams
+from openai.types.chat.completion_create_params import (
+ CompletionCreateParamsNonStreaming,
+ CompletionCreateParamsStreaming,
+)
+from openai.types.completion_create_params import (
+ CompletionCreateParamsNonStreaming as TextCompletionCreateParamsNonStreaming,
+)
+from openai.types.completion_create_params import (
+ CompletionCreateParamsStreaming as TextCompletionCreateParamsStreaming,
+)
+from openai.types.embedding_create_params import EmbeddingCreateParams
+
+from litellm.types.rerank import RerankRequest
+
+
+class ModelParamHelper:
+
+ @staticmethod
+ def get_standard_logging_model_parameters(
+ model_parameters: dict,
+ ) -> dict:
+ """ """
+ standard_logging_model_parameters: dict = {}
+ supported_model_parameters = (
+ ModelParamHelper._get_relevant_args_to_use_for_logging()
+ )
+
+ for key, value in model_parameters.items():
+ if key in supported_model_parameters:
+ standard_logging_model_parameters[key] = value
+ return standard_logging_model_parameters
+
+ @staticmethod
+ def get_exclude_params_for_model_parameters() -> Set[str]:
+ return set(["messages", "prompt", "input"])
+
+ @staticmethod
+ def _get_relevant_args_to_use_for_logging() -> Set[str]:
+ """
+ Gets all relevant llm api params besides the ones with prompt content
+ """
+ all_openai_llm_api_params = ModelParamHelper._get_all_llm_api_params()
+ # Exclude parameters that contain prompt content
+ combined_kwargs = all_openai_llm_api_params.difference(
+ set(ModelParamHelper.get_exclude_params_for_model_parameters())
+ )
+ return combined_kwargs
+
+ @staticmethod
+ def _get_all_llm_api_params() -> Set[str]:
+ """
+ Gets the supported kwargs for each call type and combines them
+ """
+ chat_completion_kwargs = (
+ ModelParamHelper._get_litellm_supported_chat_completion_kwargs()
+ )
+ text_completion_kwargs = (
+ ModelParamHelper._get_litellm_supported_text_completion_kwargs()
+ )
+ embedding_kwargs = ModelParamHelper._get_litellm_supported_embedding_kwargs()
+ transcription_kwargs = (
+ ModelParamHelper._get_litellm_supported_transcription_kwargs()
+ )
+ rerank_kwargs = ModelParamHelper._get_litellm_supported_rerank_kwargs()
+ exclude_kwargs = ModelParamHelper._get_exclude_kwargs()
+
+ combined_kwargs = chat_completion_kwargs.union(
+ text_completion_kwargs,
+ embedding_kwargs,
+ transcription_kwargs,
+ rerank_kwargs,
+ )
+ combined_kwargs = combined_kwargs.difference(exclude_kwargs)
+ return combined_kwargs
+
+ @staticmethod
+ def _get_litellm_supported_chat_completion_kwargs() -> Set[str]:
+ """
+ Get the litellm supported chat completion kwargs
+
+ This follows the OpenAI API Spec
+ """
+ all_chat_completion_kwargs = set(
+ CompletionCreateParamsNonStreaming.__annotations__.keys()
+ ).union(set(CompletionCreateParamsStreaming.__annotations__.keys()))
+ return all_chat_completion_kwargs
+
+ @staticmethod
+ def _get_litellm_supported_text_completion_kwargs() -> Set[str]:
+ """
+ Get the litellm supported text completion kwargs
+
+ This follows the OpenAI API Spec
+ """
+ all_text_completion_kwargs = set(
+ TextCompletionCreateParamsNonStreaming.__annotations__.keys()
+ ).union(set(TextCompletionCreateParamsStreaming.__annotations__.keys()))
+ return all_text_completion_kwargs
+
+ @staticmethod
+ def _get_litellm_supported_rerank_kwargs() -> Set[str]:
+ """
+ Get the litellm supported rerank kwargs
+ """
+ return set(RerankRequest.model_fields.keys())
+
+ @staticmethod
+ def _get_litellm_supported_embedding_kwargs() -> Set[str]:
+ """
+ Get the litellm supported embedding kwargs
+
+ This follows the OpenAI API Spec
+ """
+ return set(EmbeddingCreateParams.__annotations__.keys())
+
+ @staticmethod
+ def _get_litellm_supported_transcription_kwargs() -> Set[str]:
+ """
+ Get the litellm supported transcription kwargs
+
+ This follows the OpenAI API Spec
+ """
+ return set(TranscriptionCreateParams.__annotations__.keys())
+
+ @staticmethod
+ def _get_exclude_kwargs() -> Set[str]:
+ """
+ Get the kwargs to exclude from the cache key
+ """
+ return set(["metadata"])
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index bf2153a878..df7aa2cbd0 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -187,53 +187,125 @@ def ollama_pt(
final_prompt_value="### Response:",
messages=messages,
)
- elif "llava" in model:
- prompt = ""
- images = []
- for message in messages:
- if isinstance(message["content"], str):
- prompt += message["content"]
- elif isinstance(message["content"], list):
- # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models
- for element in message["content"]:
- if isinstance(element, dict):
- if element["type"] == "text":
- prompt += element["text"]
- elif element["type"] == "image_url":
- base64_image = convert_to_ollama_image(
- element["image_url"]["url"]
- )
- images.append(base64_image)
- return {"prompt": prompt, "images": images}
else:
+ user_message_types = {"user", "tool", "function"}
+ msg_i = 0
+ images = []
prompt = ""
- for message in messages:
- role = message["role"]
- content = message.get("content", "")
+ while msg_i < len(messages):
+ init_msg_i = msg_i
+ user_content_str = ""
+ ## MERGE CONSECUTIVE USER CONTENT ##
+ while (
+ msg_i < len(messages) and messages[msg_i]["role"] in user_message_types
+ ):
+ msg_content = messages[msg_i].get("content")
+ if msg_content:
+ if isinstance(msg_content, list):
+ for m in msg_content:
+ if m.get("type", "") == "image_url":
+ if isinstance(m["image_url"], str):
+ images.append(m["image_url"])
+ elif isinstance(m["image_url"], dict):
+ images.append(m["image_url"]["url"])
+ elif m.get("type", "") == "text":
+ user_content_str += m["text"]
+ else:
+ # Tool message content will always be a string
+ user_content_str += msg_content
- if "tool_calls" in message:
- tool_calls = []
+ msg_i += 1
- for call in message["tool_calls"]:
- call_id: str = call["id"]
- function_name: str = call["function"]["name"]
- arguments = json.loads(call["function"]["arguments"])
+ if user_content_str:
+ prompt += f"### User:\n{user_content_str}\n\n"
- tool_calls.append(
- {
- "id": call_id,
- "type": "function",
- "function": {"name": function_name, "arguments": arguments},
- }
+ assistant_content_str = ""
+ ## MERGE CONSECUTIVE ASSISTANT CONTENT ##
+ while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
+ msg_content = messages[msg_i].get("content")
+ if msg_content:
+ if isinstance(msg_content, list):
+ for m in msg_content:
+ if m.get("type", "") == "text":
+ assistant_content_str += m["text"]
+ elif isinstance(msg_content, str):
+ # Tool message content will always be a string
+ assistant_content_str += msg_content
+
+ tool_calls = messages[msg_i].get("tool_calls")
+ ollama_tool_calls = []
+ if tool_calls:
+ for call in tool_calls:
+ call_id: str = call["id"]
+ function_name: str = call["function"]["name"]
+ arguments = json.loads(call["function"]["arguments"])
+
+ ollama_tool_calls.append(
+ {
+ "id": call_id,
+ "type": "function",
+ "function": {
+ "name": function_name,
+ "arguments": arguments,
+ },
+ }
+ )
+
+ if ollama_tool_calls:
+ assistant_content_str += (
+ f"Tool Calls: {json.dumps(ollama_tool_calls, indent=2)}"
)
- prompt += f"### Assistant:\nTool Calls: {json.dumps(tool_calls, indent=2)}\n\n"
+ msg_i += 1
- elif "tool_call_id" in message:
- prompt += f"### User:\n{message['content']}\n\n"
+ if assistant_content_str:
+ prompt += f"### Assistant:\n{assistant_content_str}\n\n"
- elif content:
- prompt += f"### {role.capitalize()}:\n{content}\n\n"
+ if msg_i == init_msg_i: # prevent infinite loops
+ raise litellm.BadRequestError(
+ message=BAD_MESSAGE_ERROR_STR + f"passed in {messages[msg_i]}",
+ model=model,
+ llm_provider="ollama",
+ )
+ # prompt = ""
+ # images = []
+ # for message in messages:
+ # if isinstance(message["content"], str):
+ # prompt += message["content"]
+ # elif isinstance(message["content"], list):
+ # # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models
+ # for element in message["content"]:
+ # if isinstance(element, dict):
+ # if element["type"] == "text":
+ # prompt += element["text"]
+ # elif element["type"] == "image_url":
+ # base64_image = convert_to_ollama_image(
+ # element["image_url"]["url"]
+ # )
+ # images.append(base64_image)
+
+ # if "tool_calls" in message:
+ # tool_calls = []
+
+ # for call in message["tool_calls"]:
+ # call_id: str = call["id"]
+ # function_name: str = call["function"]["name"]
+ # arguments = json.loads(call["function"]["arguments"])
+
+ # tool_calls.append(
+ # {
+ # "id": call_id,
+ # "type": "function",
+ # "function": {"name": function_name, "arguments": arguments},
+ # }
+ # )
+
+ # prompt += f"### Assistant:\nTool Calls: {json.dumps(tool_calls, indent=2)}\n\n"
+
+ # elif "tool_call_id" in message:
+ # prompt += f"### User:\n{message['content']}\n\n"
+
+ return {"prompt": prompt, "images": images}
return prompt
@@ -680,12 +752,13 @@ def convert_generic_image_chunk_to_openai_image_obj(
Return:
"data:image/jpeg;base64,{base64_image}"
"""
- return "data:{};{},{}".format(
- image_chunk["media_type"], image_chunk["type"], image_chunk["data"]
- )
+ media_type = image_chunk["media_type"]
+ return "data:{};{},{}".format(media_type, image_chunk["type"], image_chunk["data"])
-def convert_to_anthropic_image_obj(openai_image_url: str) -> GenericImageParsingChunk:
+def convert_to_anthropic_image_obj(
+ openai_image_url: str, format: Optional[str]
+) -> GenericImageParsingChunk:
"""
Input:
"image_url": "data:image/jpeg;base64,{base64_image}",
@@ -702,7 +775,11 @@ def convert_to_anthropic_image_obj(openai_image_url: str) -> GenericImageParsing
openai_image_url = convert_url_to_base64(url=openai_image_url)
# Extract the media type and base64 data
media_type, base64_data = openai_image_url.split("data:")[1].split(";base64,")
- media_type = media_type.replace("\\/", "/")
+
+ if format:
+ media_type = format
+ else:
+ media_type = media_type.replace("\\/", "/")
return GenericImageParsingChunk(
type="base64",
@@ -820,11 +897,12 @@ def anthropic_messages_pt_xml(messages: list):
if isinstance(messages[msg_i]["content"], list):
for m in messages[msg_i]["content"]:
if m.get("type", "") == "image_url":
+ format = m["image_url"].get("format")
user_content.append(
{
"type": "image",
"source": convert_to_anthropic_image_obj(
- m["image_url"]["url"]
+ m["image_url"]["url"], format=format
),
}
)
@@ -1156,10 +1234,13 @@ def convert_to_anthropic_tool_result(
)
elif content["type"] == "image_url":
if isinstance(content["image_url"], str):
- image_chunk = convert_to_anthropic_image_obj(content["image_url"])
- else:
image_chunk = convert_to_anthropic_image_obj(
- content["image_url"]["url"]
+ content["image_url"], format=None
+ )
+ else:
+ format = content["image_url"].get("format")
+ image_chunk = convert_to_anthropic_image_obj(
+ content["image_url"]["url"], format=format
)
anthropic_content_list.append(
AnthropicMessagesImageParam(
@@ -1282,6 +1363,7 @@ def add_cache_control_to_content(
AnthropicMessagesImageParam,
AnthropicMessagesTextParam,
AnthropicMessagesDocumentParam,
+ ChatCompletionThinkingBlock,
],
orignal_content_element: Union[dict, AllMessageValues],
):
@@ -1317,6 +1399,7 @@ def _anthropic_content_element_factory(
data=image_chunk["data"],
),
)
+
return _anthropic_content_element
@@ -1368,13 +1451,16 @@ def anthropic_messages_pt( # noqa: PLR0915
for m in user_message_types_block["content"]:
if m.get("type", "") == "image_url":
m = cast(ChatCompletionImageObject, m)
+ format: Optional[str] = None
if isinstance(m["image_url"], str):
image_chunk = convert_to_anthropic_image_obj(
- openai_image_url=m["image_url"]
+ openai_image_url=m["image_url"], format=None
)
else:
+ format = m["image_url"].get("format")
image_chunk = convert_to_anthropic_image_obj(
- openai_image_url=m["image_url"]["url"]
+ openai_image_url=m["image_url"]["url"],
+ format=format,
)
_anthropic_content_element = (
@@ -1444,16 +1530,33 @@ def anthropic_messages_pt( # noqa: PLR0915
## MERGE CONSECUTIVE ASSISTANT CONTENT ##
while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
assistant_content_block: ChatCompletionAssistantMessage = messages[msg_i] # type: ignore
+
+ thinking_blocks = assistant_content_block.get("thinking_blocks", None)
+ if (
+ thinking_blocks is not None
+ ): # IMPORTANT: ADD THIS FIRST, ELSE ANTHROPIC WILL RAISE AN ERROR
+ assistant_content.extend(thinking_blocks)
if "content" in assistant_content_block and isinstance(
assistant_content_block["content"], list
):
for m in assistant_content_block["content"]:
- # handle text
+ # handle thinking blocks
+ thinking_block = cast(str, m.get("thinking", ""))
+ text_block = cast(str, m.get("text", ""))
if (
- m.get("type", "") == "text" and len(m.get("text", "")) > 0
+ m.get("type", "") == "thinking" and len(thinking_block) > 0
+ ): # don't pass empty text blocks. anthropic api raises errors.
+ anthropic_message: Union[
+ ChatCompletionThinkingBlock,
+ AnthropicMessagesTextParam,
+ ] = cast(ChatCompletionThinkingBlock, m)
+ assistant_content.append(anthropic_message)
+ # handle text
+ elif (
+ m.get("type", "") == "text" and len(text_block) > 0
): # don't pass empty text blocks. anthropic api raises errors.
anthropic_message = AnthropicMessagesTextParam(
- type="text", text=m.get("text")
+ type="text", text=text_block
)
_cached_message = add_cache_control_to_content(
anthropic_content_element=anthropic_message,
@@ -1506,6 +1609,7 @@ def anthropic_messages_pt( # noqa: PLR0915
msg_i += 1
if assistant_content:
+
new_messages.append({"role": "assistant", "content": assistant_content})
if msg_i == init_msg_i: # prevent infinite loops
@@ -1514,17 +1618,6 @@ def anthropic_messages_pt( # noqa: PLR0915
model=model,
llm_provider=llm_provider,
)
- if not new_messages or new_messages[0]["role"] != "user":
- if litellm.modify_params:
- new_messages.insert(
- 0, {"role": "user", "content": [{"type": "text", "text": "."}]}
- )
- else:
- raise Exception(
- "Invalid first message={}. Should always start with 'role'='user' for Anthropic. System prompt is sent separately for Anthropic. set 'litellm.modify_params = True' or 'litellm_settings:modify_params = True' on proxy, to insert a placeholder user message - '.' as the first message, ".format(
- new_messages
- )
- )
if new_messages[-1]["role"] == "assistant":
if isinstance(new_messages[-1]["content"], str):
@@ -2145,6 +2238,10 @@ from email.message import Message
import httpx
+from litellm.types.llms.bedrock import (
+ BedrockConverseReasoningContentBlock,
+ BedrockConverseReasoningTextBlock,
+)
from litellm.types.llms.bedrock import ContentBlock as BedrockContentBlock
from litellm.types.llms.bedrock import DocumentBlock as BedrockDocumentBlock
from litellm.types.llms.bedrock import ImageBlock as BedrockImageBlock
@@ -2291,8 +2388,11 @@ class BedrockImageProcessor:
)
@classmethod
- def process_image_sync(cls, image_url: str) -> BedrockContentBlock:
+ def process_image_sync(
+ cls, image_url: str, format: Optional[str] = None
+ ) -> BedrockContentBlock:
"""Synchronous image processing."""
+
if "base64" in image_url:
img_bytes, mime_type, image_format = cls._parse_base64_image(image_url)
elif "http://" in image_url or "https://" in image_url:
@@ -2303,11 +2403,17 @@ class BedrockImageProcessor:
"Unsupported image type. Expected either image url or base64 encoded string"
)
+ if format:
+ mime_type = format
+ image_format = mime_type.split("/")[1]
+
image_format = cls._validate_format(mime_type, image_format)
return cls._create_bedrock_block(img_bytes, mime_type, image_format)
@classmethod
- async def process_image_async(cls, image_url: str) -> BedrockContentBlock:
+ async def process_image_async(
+ cls, image_url: str, format: Optional[str]
+ ) -> BedrockContentBlock:
"""Asynchronous image processing."""
if "base64" in image_url:
@@ -2322,6 +2428,10 @@ class BedrockImageProcessor:
"Unsupported image type. Expected either image url or base64 encoded string"
)
+ if format: # override with user-defined params
+ mime_type = format
+ image_format = mime_type.split("/")[1]
+
image_format = cls._validate_format(mime_type, image_format)
return cls._create_bedrock_block(img_bytes, mime_type, image_format)
@@ -2809,12 +2919,14 @@ class BedrockConverseMessagesProcessor:
_part = BedrockContentBlock(text=element["text"])
_parts.append(_part)
elif element["type"] == "image_url":
+ format: Optional[str] = None
if isinstance(element["image_url"], dict):
image_url = element["image_url"]["url"]
+ format = element["image_url"].get("format")
else:
image_url = element["image_url"]
_part = await BedrockImageProcessor.process_image_async( # type: ignore
- image_url=image_url
+ image_url=image_url, format=format
)
_parts.append(_part) # type: ignore
_cache_point_block = (
@@ -2914,7 +3026,14 @@ class BedrockConverseMessagesProcessor:
assistants_parts: List[BedrockContentBlock] = []
for element in _assistant_content:
if isinstance(element, dict):
- if element["type"] == "text":
+ if element["type"] == "thinking":
+ thinking_block = BedrockConverseMessagesProcessor.translate_thinking_blocks_to_reasoning_content_blocks(
+ thinking_blocks=[
+ cast(ChatCompletionThinkingBlock, element)
+ ]
+ )
+ assistants_parts.extend(thinking_block)
+ elif element["type"] == "text":
assistants_part = BedrockContentBlock(
text=element["text"]
)
@@ -2957,6 +3076,28 @@ class BedrockConverseMessagesProcessor:
return contents
+ @staticmethod
+ def translate_thinking_blocks_to_reasoning_content_blocks(
+ thinking_blocks: List[ChatCompletionThinkingBlock],
+ ) -> List[BedrockContentBlock]:
+ reasoning_content_blocks: List[BedrockContentBlock] = []
+ for thinking_block in thinking_blocks:
+ reasoning_text = thinking_block.get("thinking")
+ reasoning_signature = thinking_block.get("signature")
+ text_block = BedrockConverseReasoningTextBlock(
+ text=reasoning_text or "",
+ )
+ if reasoning_signature is not None:
+ text_block["signature"] = reasoning_signature
+ reasoning_content_block = BedrockConverseReasoningContentBlock(
+ reasoningText=text_block,
+ )
+ bedrock_content_block = BedrockContentBlock(
+ reasoningContent=reasoning_content_block
+ )
+ reasoning_content_blocks.append(bedrock_content_block)
+ return reasoning_content_blocks
+
def _bedrock_converse_messages_pt( # noqa: PLR0915
messages: List,
@@ -3018,12 +3159,15 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915
_part = BedrockContentBlock(text=element["text"])
_parts.append(_part)
elif element["type"] == "image_url":
+ format: Optional[str] = None
if isinstance(element["image_url"], dict):
image_url = element["image_url"]["url"]
+ format = element["image_url"].get("format")
else:
image_url = element["image_url"]
_part = BedrockImageProcessor.process_image_sync( # type: ignore
- image_url=image_url
+ image_url=image_url,
+ format=format,
)
_parts.append(_part) # type: ignore
_cache_point_block = (
@@ -3103,17 +3247,36 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915
assistant_content: List[BedrockContentBlock] = []
## MERGE CONSECUTIVE ASSISTANT CONTENT ##
while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
+
assistant_message_block = get_assistant_message_block_or_continue_message(
message=messages[msg_i],
assistant_continue_message=assistant_continue_message,
)
_assistant_content = assistant_message_block.get("content", None)
+ thinking_blocks = cast(
+ Optional[List[ChatCompletionThinkingBlock]],
+ assistant_message_block.get("thinking_blocks"),
+ )
+
+ if thinking_blocks is not None:
+ assistant_content.extend(
+ BedrockConverseMessagesProcessor.translate_thinking_blocks_to_reasoning_content_blocks(
+ thinking_blocks
+ )
+ )
if _assistant_content is not None and isinstance(_assistant_content, list):
assistants_parts: List[BedrockContentBlock] = []
for element in _assistant_content:
if isinstance(element, dict):
- if element["type"] == "text":
+ if element["type"] == "thinking":
+ thinking_block = BedrockConverseMessagesProcessor.translate_thinking_blocks_to_reasoning_content_blocks(
+ thinking_blocks=[
+ cast(ChatCompletionThinkingBlock, element)
+ ]
+ )
+ assistants_parts.extend(thinking_block)
+ elif element["type"] == "text":
assistants_part = BedrockContentBlock(text=element["text"])
assistants_parts.append(assistants_part)
elif element["type"] == "image_url":
diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index 5e9fb7aa76..5d5a8bf256 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -5,7 +5,7 @@ import threading
import time
import traceback
import uuid
-from typing import Any, Callable, Dict, List, Optional, cast
+from typing import Any, Callable, Dict, List, Optional, Union, cast
import httpx
from pydantic import BaseModel
@@ -14,6 +14,8 @@ import litellm
from litellm import verbose_logger
from litellm.litellm_core_utils.redact_messages import LiteLLMLoggingObject
from litellm.litellm_core_utils.thread_pool_executor import executor
+from litellm.types.llms.openai import ChatCompletionChunk
+from litellm.types.router import GenericLiteLLMParams
from litellm.types.utils import Delta
from litellm.types.utils import GenericStreamingChunk as GChunk
from litellm.types.utils import (
@@ -69,6 +71,17 @@ class CustomStreamWrapper:
self.completion_stream = completion_stream
self.sent_first_chunk = False
self.sent_last_chunk = False
+
+ litellm_params: GenericLiteLLMParams = GenericLiteLLMParams(
+ **self.logging_obj.model_call_details.get("litellm_params", {})
+ )
+ self.merge_reasoning_content_in_choices: bool = (
+ litellm_params.merge_reasoning_content_in_choices or False
+ )
+ self.sent_first_thinking_block = False
+ self.sent_last_thinking_block = False
+ self.thinking_content = ""
+
self.system_fingerprint: Optional[str] = None
self.received_finish_reason: Optional[str] = None
self.intermittent_finish_reason: Optional[str] = (
@@ -86,12 +99,7 @@ class CustomStreamWrapper:
self.holding_chunk = ""
self.complete_response = ""
self.response_uptil_now = ""
- _model_info = (
- self.logging_obj.model_call_details.get("litellm_params", {}).get(
- "model_info", {}
- )
- or {}
- )
+ _model_info: Dict = litellm_params.model_info or {}
_api_base = get_api_base(
model=model or "",
@@ -110,7 +118,7 @@ class CustomStreamWrapper:
) # GUARANTEE OPENAI HEADERS IN RESPONSE
self._response_headers = _response_headers
- self.response_id = None
+ self.response_id: Optional[str] = None
self.logging_loop = None
self.rules = Rules()
self.stream_options = stream_options or getattr(
@@ -629,7 +637,10 @@ class CustomStreamWrapper:
if isinstance(chunk, bytes):
chunk = chunk.decode("utf-8")
if "text_output" in chunk:
- response = chunk.replace("data: ", "").strip()
+ response = (
+ CustomStreamWrapper._strip_sse_data_from_chunk(chunk) or ""
+ )
+ response = response.strip()
parsed_response = json.loads(response)
else:
return {
@@ -713,7 +724,7 @@ class CustomStreamWrapper:
def is_delta_empty(self, delta: Delta) -> bool:
is_empty = True
- if delta.content is not None:
+ if delta.content:
is_empty = False
elif delta.tool_calls is not None:
is_empty = False
@@ -721,16 +732,45 @@ class CustomStreamWrapper:
is_empty = False
return is_empty
- def return_processed_chunk_logic( # noqa
+ def set_model_id(
+ self, id: str, model_response: ModelResponseStream
+ ) -> ModelResponseStream:
+ """
+ Set the model id and response id to the given id.
+
+ Ensure model id is always the same across all chunks.
+
+ If first chunk sent + id set, use that id for all chunks.
+ """
+ if self.response_id is None:
+ self.response_id = id
+ if self.response_id is not None and isinstance(self.response_id, str):
+ model_response.id = self.response_id
+ return model_response
+
+ def copy_model_response_level_provider_specific_fields(
+ self,
+ original_chunk: Union[ModelResponseStream, ChatCompletionChunk],
+ model_response: ModelResponseStream,
+ ) -> ModelResponseStream:
+ """
+ Copy provider_specific_fields from original_chunk to model_response.
+ """
+ provider_specific_fields = getattr(
+ original_chunk, "provider_specific_fields", None
+ )
+ if provider_specific_fields is not None:
+ model_response.provider_specific_fields = provider_specific_fields
+ for k, v in provider_specific_fields.items():
+ setattr(model_response, k, v)
+ return model_response
+
+ def is_chunk_non_empty(
self,
completion_obj: Dict[str, Any],
model_response: ModelResponseStream,
response_obj: Dict[str, Any],
- ):
-
- print_verbose(
- f"completion_obj: {completion_obj}, model_response.choices[0]: {model_response.choices[0]}, response_obj: {response_obj}"
- )
+ ) -> bool:
if (
"content" in completion_obj
and (
@@ -746,13 +786,40 @@ class CustomStreamWrapper:
"function_call" in completion_obj
and completion_obj["function_call"] is not None
)
+ or (
+ "reasoning_content" in model_response.choices[0].delta
+ and model_response.choices[0].delta.reasoning_content is not None
+ )
or (model_response.choices[0].delta.provider_specific_fields is not None)
+ or (
+ "provider_specific_fields" in model_response
+ and model_response.choices[0].delta.provider_specific_fields is not None
+ )
or (
"provider_specific_fields" in response_obj
and response_obj["provider_specific_fields"] is not None
)
- ): # cannot set content of an OpenAI Object to be an empty string
+ ):
+ return True
+ else:
+ return False
+ def return_processed_chunk_logic( # noqa
+ self,
+ completion_obj: Dict[str, Any],
+ model_response: ModelResponseStream,
+ response_obj: Dict[str, Any],
+ ):
+
+ print_verbose(
+ f"completion_obj: {completion_obj}, model_response.choices[0]: {model_response.choices[0]}, response_obj: {response_obj}"
+ )
+ is_chunk_non_empty = self.is_chunk_non_empty(
+ completion_obj, model_response, response_obj
+ )
+ if (
+ is_chunk_non_empty
+ ): # cannot set content of an OpenAI Object to be an empty string
self.safety_checker()
hold, model_response_str = self.check_special_tokens(
chunk=completion_obj["content"],
@@ -763,14 +830,12 @@ class CustomStreamWrapper:
## check if openai/azure chunk
original_chunk = response_obj.get("original_chunk", None)
if original_chunk:
- model_response.id = original_chunk.id
- self.response_id = original_chunk.id
if len(original_chunk.choices) > 0:
choices = []
for choice in original_chunk.choices:
try:
if isinstance(choice, BaseModel):
- choice_json = choice.model_dump()
+ choice_json = choice.model_dump() # type: ignore
choice_json.pop(
"finish_reason", None
) # for mistral etc. which return a value in their last chunk (not-openai compatible).
@@ -798,9 +863,10 @@ class CustomStreamWrapper:
model_response.choices[0].delta, "role"
):
_initial_delta = model_response.choices[0].delta.model_dump()
+
_initial_delta.pop("role", None)
model_response.choices[0].delta = Delta(**_initial_delta)
- print_verbose(
+ verbose_logger.debug(
f"model_response.choices[0].delta: {model_response.choices[0].delta}"
)
else:
@@ -817,6 +883,10 @@ class CustomStreamWrapper:
_index: Optional[int] = completion_obj.get("index")
if _index is not None:
model_response.choices[0].index = _index
+
+ self._optional_combine_thinking_block_in_choices(
+ model_response=model_response
+ )
print_verbose(f"returning model_response: {model_response}")
return model_response
else:
@@ -842,6 +912,9 @@ class CustomStreamWrapper:
_is_delta_empty = self.is_delta_empty(delta=model_response.choices[0].delta)
if _is_delta_empty:
+ model_response.choices[0].delta = Delta(
+ content=None
+ ) # ensure empty delta chunk returned
# get any function call arguments
model_response.choices[0].finish_reason = map_finish_reason(
finish_reason=self.received_finish_reason
@@ -870,7 +943,49 @@ class CustomStreamWrapper:
self.chunks.append(model_response)
return
- def chunk_creator(self, chunk): # type: ignore # noqa: PLR0915
+ def _optional_combine_thinking_block_in_choices(
+ self, model_response: ModelResponseStream
+ ) -> None:
+ """
+ UI's Like OpenWebUI expect to get 1 chunk with ... tags in the chunk content
+
+ In place updates the model_response object with reasoning_content in content with ... tags
+
+ Enabled when `merge_reasoning_content_in_choices=True` passed in request params
+
+
+ """
+ if self.merge_reasoning_content_in_choices is True:
+ reasoning_content = getattr(
+ model_response.choices[0].delta, "reasoning_content", None
+ )
+ if reasoning_content:
+ if self.sent_first_thinking_block is False:
+ model_response.choices[0].delta.content += (
+ "" + reasoning_content
+ )
+ self.sent_first_thinking_block = True
+ elif (
+ self.sent_first_thinking_block is True
+ and hasattr(model_response.choices[0].delta, "reasoning_content")
+ and model_response.choices[0].delta.reasoning_content
+ ):
+ model_response.choices[0].delta.content = reasoning_content
+ elif (
+ self.sent_first_thinking_block is True
+ and not self.sent_last_thinking_block
+ and model_response.choices[0].delta.content
+ ):
+ model_response.choices[0].delta.content = (
+ "" + model_response.choices[0].delta.content
+ )
+ self.sent_last_thinking_block = True
+
+ if hasattr(model_response.choices[0].delta, "reasoning_content"):
+ del model_response.choices[0].delta.reasoning_content
+ return
+
+ def chunk_creator(self, chunk: Any): # type: ignore # noqa: PLR0915
model_response = self.model_response_creator()
response_obj: Dict[str, Any] = {}
@@ -886,16 +1001,13 @@ class CustomStreamWrapper:
) # check if chunk is a generic streaming chunk
) or (
self.custom_llm_provider
- and (
- self.custom_llm_provider == "anthropic"
- or self.custom_llm_provider in litellm._custom_providers
- )
+ and self.custom_llm_provider in litellm._custom_providers
):
if self.received_finish_reason is not None:
if "provider_specific_fields" not in chunk:
raise StopIteration
- anthropic_response_obj: GChunk = chunk
+ anthropic_response_obj: GChunk = cast(GChunk, chunk)
completion_obj["content"] = anthropic_response_obj["text"]
if anthropic_response_obj["is_finished"]:
self.received_finish_reason = anthropic_response_obj[
@@ -927,7 +1039,7 @@ class CustomStreamWrapper:
].items():
setattr(model_response, key, value)
- response_obj = anthropic_response_obj
+ response_obj = cast(Dict[str, Any], anthropic_response_obj)
elif self.model == "replicate" or self.custom_llm_provider == "replicate":
response_obj = self.handle_replicate_chunk(chunk)
completion_obj["content"] = response_obj["text"]
@@ -989,6 +1101,7 @@ class CustomStreamWrapper:
try:
completion_obj["content"] = chunk.text
except Exception as e:
+ original_exception = e
if "Part has no text." in str(e):
## check for function calling
function_call = (
@@ -1030,7 +1143,7 @@ class CustomStreamWrapper:
_model_response.choices = [_streaming_response]
response_obj = {"original_chunk": _model_response}
else:
- raise e
+ raise original_exception
if (
hasattr(chunk.candidates[0], "finish_reason")
and chunk.candidates[0].finish_reason.name
@@ -1093,8 +1206,9 @@ class CustomStreamWrapper:
total_tokens=response_obj["usage"].total_tokens,
)
elif self.custom_llm_provider == "text-completion-codestral":
- response_obj = litellm.CodestralTextCompletionConfig()._chunk_parser(
- chunk
+ response_obj = cast(
+ Dict[str, Any],
+ litellm.CodestralTextCompletionConfig()._chunk_parser(chunk),
)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
@@ -1156,8 +1270,9 @@ class CustomStreamWrapper:
self.received_finish_reason = response_obj["finish_reason"]
if response_obj.get("original_chunk", None) is not None:
if hasattr(response_obj["original_chunk"], "id"):
- model_response.id = response_obj["original_chunk"].id
- self.response_id = model_response.id
+ model_response = self.set_model_id(
+ response_obj["original_chunk"].id, model_response
+ )
if hasattr(response_obj["original_chunk"], "system_fingerprint"):
model_response.system_fingerprint = response_obj[
"original_chunk"
@@ -1206,8 +1321,16 @@ class CustomStreamWrapper:
): # function / tool calling branch - only set for openai/azure compatible endpoints
# enter this branch when no content has been passed in response
original_chunk = response_obj.get("original_chunk", None)
- model_response.id = original_chunk.id
- self.response_id = original_chunk.id
+ if hasattr(original_chunk, "id"):
+ model_response = self.set_model_id(
+ original_chunk.id, model_response
+ )
+ if hasattr(original_chunk, "provider_specific_fields"):
+ model_response = (
+ self.copy_model_response_level_provider_specific_fields(
+ original_chunk, model_response
+ )
+ )
if original_chunk.choices and len(original_chunk.choices) > 0:
delta = original_chunk.choices[0].delta
if delta is not None and (
@@ -1708,6 +1831,42 @@ class CustomStreamWrapper:
extra_kwargs={},
)
+ @staticmethod
+ def _strip_sse_data_from_chunk(chunk: Optional[str]) -> Optional[str]:
+ """
+ Strips the 'data: ' prefix from Server-Sent Events (SSE) chunks.
+
+ Some providers like sagemaker send it as `data:`, need to handle both
+
+ SSE messages are prefixed with 'data: ' which is part of the protocol,
+ not the actual content from the LLM. This method removes that prefix
+ and returns the actual content.
+
+ Args:
+ chunk: The SSE chunk that may contain the 'data: ' prefix (string or bytes)
+
+ Returns:
+ The chunk with the 'data: ' prefix removed, or the original chunk
+ if no prefix was found. Returns None if input is None.
+
+ See OpenAI Python Ref for this: https://github.com/openai/openai-python/blob/041bf5a8ec54da19aad0169671793c2078bd6173/openai/api_requestor.py#L100
+ """
+ if chunk is None:
+ return None
+
+ if isinstance(chunk, str):
+ # OpenAI sends `data: `
+ if chunk.startswith("data: "):
+ # Strip the prefix and any leading whitespace that might follow it
+ _length_of_sse_data_prefix = len("data: ")
+ return chunk[_length_of_sse_data_prefix:]
+ elif chunk.startswith("data:"):
+ # Sagemaker sends `data:`, no trailing whitespace
+ _length_of_sse_data_prefix = len("data:")
+ return chunk[_length_of_sse_data_prefix:]
+
+ return chunk
+
def calculate_total_usage(chunks: List[ModelResponse]) -> Usage:
"""Assume most recent usage chunk has total usage uptil then."""
diff --git a/litellm/llms/aiohttp_openai/chat/transformation.py b/litellm/llms/aiohttp_openai/chat/transformation.py
index 53157ad113..625704dbea 100644
--- a/litellm/llms/aiohttp_openai/chat/transformation.py
+++ b/litellm/llms/aiohttp_openai/chat/transformation.py
@@ -26,7 +26,7 @@ else:
class AiohttpOpenAIChatConfig(OpenAILikeChatConfig):
def get_complete_url(
self,
- api_base: str,
+ api_base: Optional[str],
model: str,
optional_params: dict,
stream: Optional[bool] = None,
@@ -35,6 +35,8 @@ class AiohttpOpenAIChatConfig(OpenAILikeChatConfig):
Ensure - /v1/chat/completions is at the end of the url
"""
+ if api_base is None:
+ api_base = "https://api.openai.com"
if not api_base.endswith("/chat/completions"):
api_base += "/chat/completions"
diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py
index c58aa00a10..f2c5f390d7 100644
--- a/litellm/llms/anthropic/chat/handler.py
+++ b/litellm/llms/anthropic/chat/handler.py
@@ -30,10 +30,16 @@ from litellm.types.llms.anthropic import (
UsageDelta,
)
from litellm.types.llms.openai import (
+ ChatCompletionThinkingBlock,
ChatCompletionToolCallChunk,
ChatCompletionUsageBlock,
)
-from litellm.types.utils import GenericStreamingChunk
+from litellm.types.utils import (
+ Delta,
+ GenericStreamingChunk,
+ ModelResponseStream,
+ StreamingChoices,
+)
from litellm.utils import CustomStreamWrapper, ModelResponse, ProviderConfigManager
from ...base import BaseLLM
@@ -468,7 +474,10 @@ class ModelResponseIterator:
if len(self.content_blocks) == 0:
return False
- if self.content_blocks[0]["delta"]["type"] == "text_delta":
+ if (
+ self.content_blocks[0]["delta"]["type"] == "text_delta"
+ or self.content_blocks[0]["delta"]["type"] == "thinking_delta"
+ ):
return False
for block in self.content_blocks:
@@ -506,11 +515,22 @@ class ModelResponseIterator:
return usage_block
- def _content_block_delta_helper(self, chunk: dict):
+ def _content_block_delta_helper(self, chunk: dict) -> Tuple[
+ str,
+ Optional[ChatCompletionToolCallChunk],
+ List[ChatCompletionThinkingBlock],
+ Dict[str, Any],
+ ]:
+ """
+ Helper function to handle the content block delta
+ """
+
text = ""
tool_use: Optional[ChatCompletionToolCallChunk] = None
provider_specific_fields = {}
content_block = ContentBlockDelta(**chunk) # type: ignore
+ thinking_blocks: List[ChatCompletionThinkingBlock] = []
+
self.content_blocks.append(content_block)
if "text" in content_block["delta"]:
text = content_block["delta"]["text"]
@@ -526,19 +546,45 @@ class ModelResponseIterator:
}
elif "citation" in content_block["delta"]:
provider_specific_fields["citation"] = content_block["delta"]["citation"]
+ elif (
+ "thinking" in content_block["delta"]
+ or "signature" in content_block["delta"]
+ ):
+ thinking_blocks = [
+ ChatCompletionThinkingBlock(
+ type="thinking",
+ thinking=content_block["delta"].get("thinking") or "",
+ signature=content_block["delta"].get("signature"),
+ )
+ ]
+ provider_specific_fields["thinking_blocks"] = thinking_blocks
+ return text, tool_use, thinking_blocks, provider_specific_fields
- return text, tool_use, provider_specific_fields
+ def _handle_reasoning_content(
+ self, thinking_blocks: List[ChatCompletionThinkingBlock]
+ ) -> Optional[str]:
+ """
+ Handle the reasoning content
+ """
+ reasoning_content = None
+ for block in thinking_blocks:
+ if reasoning_content is None:
+ reasoning_content = ""
+ if "thinking" in block:
+ reasoning_content += block["thinking"]
+ return reasoning_content
- def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
+ def chunk_parser(self, chunk: dict) -> ModelResponseStream:
try:
type_chunk = chunk.get("type", "") or ""
text = ""
tool_use: Optional[ChatCompletionToolCallChunk] = None
- is_finished = False
finish_reason = ""
usage: Optional[ChatCompletionUsageBlock] = None
provider_specific_fields: Dict[str, Any] = {}
+ reasoning_content: Optional[str] = None
+ thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
index = int(chunk.get("index", 0))
if type_chunk == "content_block_delta":
@@ -546,9 +592,13 @@ class ModelResponseIterator:
Anthropic content chunk
chunk = {'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': 'Hello'}}
"""
- text, tool_use, provider_specific_fields = (
+ text, tool_use, thinking_blocks, provider_specific_fields = (
self._content_block_delta_helper(chunk=chunk)
)
+ if thinking_blocks:
+ reasoning_content = self._handle_reasoning_content(
+ thinking_blocks=thinking_blocks
+ )
elif type_chunk == "content_block_start":
"""
event: content_block_start
@@ -570,9 +620,11 @@ class ModelResponseIterator:
"index": self.tool_index,
}
elif type_chunk == "content_block_stop":
+
ContentBlockStop(**chunk) # type: ignore
# check if tool call content block
is_empty = self.check_empty_tool_call_args()
+
if is_empty:
tool_use = {
"id": None,
@@ -595,7 +647,6 @@ class ModelResponseIterator:
or "stop"
)
usage = self._handle_usage(anthropic_usage_chunk=message_delta["usage"])
- is_finished = True
elif type_chunk == "message_start":
"""
Anthropic
@@ -634,16 +685,27 @@ class ModelResponseIterator:
text, tool_use = self._handle_json_mode_chunk(text=text, tool_use=tool_use)
- returned_chunk = GenericStreamingChunk(
- text=text,
- tool_use=tool_use,
- is_finished=is_finished,
- finish_reason=finish_reason,
+ returned_chunk = ModelResponseStream(
+ choices=[
+ StreamingChoices(
+ index=index,
+ delta=Delta(
+ content=text,
+ tool_calls=[tool_use] if tool_use is not None else None,
+ provider_specific_fields=(
+ provider_specific_fields
+ if provider_specific_fields
+ else None
+ ),
+ thinking_blocks=(
+ thinking_blocks if thinking_blocks else None
+ ),
+ reasoning_content=reasoning_content,
+ ),
+ finish_reason=finish_reason,
+ )
+ ],
usage=usage,
- index=index,
- provider_specific_fields=(
- provider_specific_fields if provider_specific_fields else None
- ),
)
return returned_chunk
@@ -754,7 +816,7 @@ class ModelResponseIterator:
except ValueError as e:
raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")
- def convert_str_chunk_to_generic_chunk(self, chunk: str) -> GenericStreamingChunk:
+ def convert_str_chunk_to_generic_chunk(self, chunk: str) -> ModelResponseStream:
"""
Convert a string chunk to a GenericStreamingChunk
@@ -774,11 +836,4 @@ class ModelResponseIterator:
data_json = json.loads(str_line[5:])
return self.chunk_parser(chunk=data_json)
else:
- return GenericStreamingChunk(
- text="",
- is_finished=False,
- finish_reason="",
- usage=None,
- index=0,
- tool_use=None,
- )
+ return ModelResponseStream()
diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index fb2f4dd2c6..383c1cd3e5 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -1,6 +1,6 @@
import json
import time
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
import httpx
@@ -23,6 +23,7 @@ from litellm.types.llms.openai import (
AllMessageValues,
ChatCompletionCachedContent,
ChatCompletionSystemMessage,
+ ChatCompletionThinkingBlock,
ChatCompletionToolCallChunk,
ChatCompletionToolCallFunctionChunk,
ChatCompletionToolParam,
@@ -80,7 +81,7 @@ class AnthropicConfig(BaseConfig):
return super().get_config()
def get_supported_openai_params(self, model: str):
- return [
+ params = [
"stream",
"stop",
"temperature",
@@ -95,6 +96,11 @@ class AnthropicConfig(BaseConfig):
"user",
]
+ if "claude-3-7-sonnet" in model:
+ params.append("thinking")
+
+ return params
+
def get_json_schema_from_pydantic_object(
self, response_format: Union[Any, Dict, None]
) -> Optional[dict]:
@@ -117,15 +123,16 @@ class AnthropicConfig(BaseConfig):
prompt_caching_set: bool = False,
pdf_used: bool = False,
is_vertex_request: bool = False,
+ user_anthropic_beta_headers: Optional[List[str]] = None,
) -> dict:
- betas = []
+ betas = set()
if prompt_caching_set:
- betas.append("prompt-caching-2024-07-31")
+ betas.add("prompt-caching-2024-07-31")
if computer_tool_used:
- betas.append("computer-use-2024-10-22")
+ betas.add("computer-use-2024-10-22")
if pdf_used:
- betas.append("pdfs-2024-09-25")
+ betas.add("pdfs-2024-09-25")
headers = {
"anthropic-version": anthropic_version or "2023-06-01",
"x-api-key": api_key,
@@ -133,6 +140,9 @@ class AnthropicConfig(BaseConfig):
"content-type": "application/json",
}
+ if user_anthropic_beta_headers is not None:
+ betas.update(user_anthropic_beta_headers)
+
# Don't send any beta headers to Vertex, Vertex has failed requests when they are sent
if is_vertex_request is True:
pass
@@ -283,18 +293,6 @@ class AnthropicConfig(BaseConfig):
new_stop = new_v
return new_stop
- def _add_tools_to_optional_params(
- self, optional_params: dict, tools: List[AllAnthropicToolsValues]
- ) -> dict:
- if "tools" not in optional_params:
- optional_params["tools"] = tools
- else:
- optional_params["tools"] = [
- *optional_params["tools"],
- *tools,
- ]
- return optional_params
-
def map_openai_params(
self,
non_default_params: dict,
@@ -335,6 +333,10 @@ class AnthropicConfig(BaseConfig):
optional_params["top_p"] = value
if param == "response_format" and isinstance(value, dict):
+ ignore_response_format_types = ["text"]
+ if value["type"] in ignore_response_format_types: # value is a no-op
+ continue
+
json_schema: Optional[dict] = None
if "response_schema" in value:
json_schema = value["response_schema"]
@@ -358,7 +360,8 @@ class AnthropicConfig(BaseConfig):
optional_params["json_mode"] = True
if param == "user":
optional_params["metadata"] = {"user_id": value}
-
+ if param == "thinking":
+ optional_params["thinking"] = value
return optional_params
def _create_json_tool_call_for_response_format(
@@ -581,6 +584,50 @@ class AnthropicConfig(BaseConfig):
)
return _message
+ def extract_response_content(self, completion_response: dict) -> Tuple[
+ str,
+ Optional[List[Any]],
+ Optional[List[ChatCompletionThinkingBlock]],
+ Optional[str],
+ List[ChatCompletionToolCallChunk],
+ ]:
+ text_content = ""
+ citations: Optional[List[Any]] = None
+ thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
+ reasoning_content: Optional[str] = None
+ tool_calls: List[ChatCompletionToolCallChunk] = []
+ for idx, content in enumerate(completion_response["content"]):
+ if content["type"] == "text":
+ text_content += content["text"]
+ ## TOOL CALLING
+ elif content["type"] == "tool_use":
+ tool_calls.append(
+ ChatCompletionToolCallChunk(
+ id=content["id"],
+ type="function",
+ function=ChatCompletionToolCallFunctionChunk(
+ name=content["name"],
+ arguments=json.dumps(content["input"]),
+ ),
+ index=idx,
+ )
+ )
+ ## CITATIONS
+ if content.get("citations", None) is not None:
+ if citations is None:
+ citations = []
+ citations.append(content["citations"])
+ if content.get("thinking", None) is not None:
+ if thinking_blocks is None:
+ thinking_blocks = []
+ thinking_blocks.append(cast(ChatCompletionThinkingBlock, content))
+ if thinking_blocks is not None:
+ reasoning_content = ""
+ for block in thinking_blocks:
+ if "thinking" in block:
+ reasoning_content += block["thinking"]
+ return text_content, citations, thinking_blocks, reasoning_content, tool_calls
+
def transform_response(
self,
model: str,
@@ -628,32 +675,24 @@ class AnthropicConfig(BaseConfig):
)
else:
text_content = ""
- citations: List[Any] = []
+ citations: Optional[List[Any]] = None
+ thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
+ reasoning_content: Optional[str] = None
tool_calls: List[ChatCompletionToolCallChunk] = []
- for idx, content in enumerate(completion_response["content"]):
- if content["type"] == "text":
- text_content += content["text"]
- ## TOOL CALLING
- elif content["type"] == "tool_use":
- tool_calls.append(
- ChatCompletionToolCallChunk(
- id=content["id"],
- type="function",
- function=ChatCompletionToolCallFunctionChunk(
- name=content["name"],
- arguments=json.dumps(content["input"]),
- ),
- index=idx,
- )
- )
- ## CITATIONS
- if content.get("citations", None) is not None:
- citations.append(content["citations"])
+
+ text_content, citations, thinking_blocks, reasoning_content, tool_calls = (
+ self.extract_response_content(completion_response=completion_response)
+ )
_message = litellm.Message(
tool_calls=tool_calls,
content=text_content or None,
- provider_specific_fields={"citations": citations},
+ provider_specific_fields={
+ "citations": citations,
+ "thinking_blocks": thinking_blocks,
+ },
+ thinking_blocks=thinking_blocks,
+ reasoning_content=reasoning_content,
)
## HANDLE JSON MODE - anthropic returns single function call
@@ -748,6 +787,13 @@ class AnthropicConfig(BaseConfig):
headers=cast(httpx.Headers, headers),
)
+ def _get_user_anthropic_beta_headers(
+ self, anthropic_beta_header: Optional[str]
+ ) -> Optional[List[str]]:
+ if anthropic_beta_header is None:
+ return None
+ return anthropic_beta_header.split(",")
+
def validate_environment(
self,
headers: dict,
@@ -768,13 +814,18 @@ class AnthropicConfig(BaseConfig):
prompt_caching_set = self.is_cache_control_set(messages=messages)
computer_tool_used = self.is_computer_tool_used(tools=tools)
pdf_used = self.is_pdf_used(messages=messages)
+ user_anthropic_beta_headers = self._get_user_anthropic_beta_headers(
+ anthropic_beta_header=headers.get("anthropic-beta")
+ )
anthropic_headers = self.get_anthropic_headers(
computer_tool_used=computer_tool_used,
prompt_caching_set=prompt_caching_set,
pdf_used=pdf_used,
api_key=api_key,
is_vertex_request=optional_params.get("is_vertex_request", False),
+ user_anthropic_beta_headers=user_anthropic_beta_headers,
)
headers = {**headers, **anthropic_headers}
+
return headers
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
new file mode 100644
index 0000000000..a7dfff74d9
--- /dev/null
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
@@ -0,0 +1,179 @@
+"""
+- call /messages on Anthropic API
+- Make streaming + non-streaming request - just pass it through direct to Anthropic. No need to do anything special here
+- Ensure requests are logged in the DB - stream + non-stream
+
+"""
+
+import json
+from typing import Any, AsyncIterator, Dict, Optional, Union, cast
+
+import httpx
+
+import litellm
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.anthropic_messages.transformation import (
+ BaseAnthropicMessagesConfig,
+)
+from litellm.llms.custom_httpx.http_handler import (
+ AsyncHTTPHandler,
+ get_async_httpx_client,
+)
+from litellm.types.router import GenericLiteLLMParams
+from litellm.types.utils import ProviderSpecificHeader
+from litellm.utils import ProviderConfigManager, client
+
+
+class AnthropicMessagesHandler:
+
+ @staticmethod
+ async def _handle_anthropic_streaming(
+ response: httpx.Response,
+ request_body: dict,
+ litellm_logging_obj: LiteLLMLoggingObj,
+ ) -> AsyncIterator:
+ """Helper function to handle Anthropic streaming responses using the existing logging handlers"""
+ from datetime import datetime
+
+ from litellm.proxy.pass_through_endpoints.streaming_handler import (
+ PassThroughStreamingHandler,
+ )
+ from litellm.proxy.pass_through_endpoints.success_handler import (
+ PassThroughEndpointLogging,
+ )
+ from litellm.proxy.pass_through_endpoints.types import EndpointType
+
+ # Create success handler object
+ passthrough_success_handler_obj = PassThroughEndpointLogging()
+
+ # Use the existing streaming handler for Anthropic
+ start_time = datetime.now()
+ return PassThroughStreamingHandler.chunk_processor(
+ response=response,
+ request_body=request_body,
+ litellm_logging_obj=litellm_logging_obj,
+ endpoint_type=EndpointType.ANTHROPIC,
+ start_time=start_time,
+ passthrough_success_handler_obj=passthrough_success_handler_obj,
+ url_route="/v1/messages",
+ )
+
+
+@client
+async def anthropic_messages(
+ api_key: str,
+ model: str,
+ stream: bool = False,
+ api_base: Optional[str] = None,
+ client: Optional[AsyncHTTPHandler] = None,
+ custom_llm_provider: Optional[str] = None,
+ **kwargs,
+) -> Union[Dict[str, Any], AsyncIterator]:
+ """
+ Makes Anthropic `/v1/messages` API calls In the Anthropic API Spec
+ """
+ # Use provided client or create a new one
+ optional_params = GenericLiteLLMParams(**kwargs)
+ model, _custom_llm_provider, dynamic_api_key, dynamic_api_base = (
+ litellm.get_llm_provider(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ api_base=optional_params.api_base,
+ api_key=optional_params.api_key,
+ )
+ )
+ anthropic_messages_provider_config: Optional[BaseAnthropicMessagesConfig] = (
+ ProviderConfigManager.get_provider_anthropic_messages_config(
+ model=model,
+ provider=litellm.LlmProviders(_custom_llm_provider),
+ )
+ )
+ if anthropic_messages_provider_config is None:
+ raise ValueError(
+ f"Anthropic messages provider config not found for model: {model}"
+ )
+ if client is None or not isinstance(client, AsyncHTTPHandler):
+ async_httpx_client = get_async_httpx_client(
+ llm_provider=litellm.LlmProviders.ANTHROPIC
+ )
+ else:
+ async_httpx_client = client
+
+ litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj", None)
+
+ # Prepare headers
+ provider_specific_header = cast(
+ Optional[ProviderSpecificHeader], kwargs.get("provider_specific_header", None)
+ )
+ extra_headers = (
+ provider_specific_header.get("extra_headers", {})
+ if provider_specific_header
+ else {}
+ )
+ headers = anthropic_messages_provider_config.validate_environment(
+ headers=extra_headers or {},
+ model=model,
+ api_key=api_key,
+ )
+
+ litellm_logging_obj.update_environment_variables(
+ model=model,
+ optional_params=dict(optional_params),
+ litellm_params={
+ "metadata": kwargs.get("metadata", {}),
+ "preset_cache_key": None,
+ "stream_response": {},
+ **optional_params.model_dump(exclude_unset=True),
+ },
+ custom_llm_provider=_custom_llm_provider,
+ )
+ litellm_logging_obj.model_call_details.update(kwargs)
+
+ # Prepare request body
+ request_body = kwargs.copy()
+ request_body = {
+ k: v
+ for k, v in request_body.items()
+ if k
+ in anthropic_messages_provider_config.get_supported_anthropic_messages_params(
+ model=model
+ )
+ }
+ request_body["stream"] = stream
+ request_body["model"] = model
+ litellm_logging_obj.stream = stream
+
+ # Make the request
+ request_url = anthropic_messages_provider_config.get_complete_url(
+ api_base=api_base, model=model
+ )
+
+ litellm_logging_obj.pre_call(
+ input=[{"role": "user", "content": json.dumps(request_body)}],
+ api_key="",
+ additional_args={
+ "complete_input_dict": request_body,
+ "api_base": str(request_url),
+ "headers": headers,
+ },
+ )
+
+ response = await async_httpx_client.post(
+ url=request_url,
+ headers=headers,
+ data=json.dumps(request_body),
+ stream=stream,
+ )
+ response.raise_for_status()
+
+ # used for logging + cost tracking
+ litellm_logging_obj.model_call_details["httpx_response"] = response
+
+ if stream:
+ return await AnthropicMessagesHandler._handle_anthropic_streaming(
+ response=response,
+ request_body=request_body,
+ litellm_logging_obj=litellm_logging_obj,
+ )
+ else:
+ return response.json()
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
new file mode 100644
index 0000000000..e9b598f18d
--- /dev/null
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
@@ -0,0 +1,47 @@
+from typing import Optional
+
+from litellm.llms.base_llm.anthropic_messages.transformation import (
+ BaseAnthropicMessagesConfig,
+)
+
+DEFAULT_ANTHROPIC_API_BASE = "https://api.anthropic.com"
+DEFAULT_ANTHROPIC_API_VERSION = "2023-06-01"
+
+
+class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
+ def get_supported_anthropic_messages_params(self, model: str) -> list:
+ return [
+ "messages",
+ "model",
+ "system",
+ "max_tokens",
+ "stop_sequences",
+ "temperature",
+ "top_p",
+ "top_k",
+ "tools",
+ "tool_choice",
+ "thinking",
+ # TODO: Add Anthropic `metadata` support
+ # "metadata",
+ ]
+
+ def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+ api_base = api_base or DEFAULT_ANTHROPIC_API_BASE
+ if not api_base.endswith("/v1/messages"):
+ api_base = f"{api_base}/v1/messages"
+ return api_base
+
+ def validate_environment(
+ self,
+ headers: dict,
+ model: str,
+ api_key: Optional[str] = None,
+ ) -> dict:
+ if "x-api-key" not in headers:
+ headers["x-api-key"] = api_key
+ if "anthropic-version" not in headers:
+ headers["anthropic-version"] = DEFAULT_ANTHROPIC_API_VERSION
+ if "content-type" not in headers:
+ headers["content-type"] = "application/json"
+ return headers
diff --git a/litellm/llms/anthropic/experimental_pass_through/transformation.py b/litellm/llms/anthropic/experimental_pass_through/transformation.py
deleted file mode 100644
index b24cf47ad4..0000000000
--- a/litellm/llms/anthropic/experimental_pass_through/transformation.py
+++ /dev/null
@@ -1,412 +0,0 @@
-import json
-from typing import List, Literal, Optional, Tuple, Union
-
-from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingChoice
-
-from litellm.types.llms.anthropic import (
- AllAnthropicToolsValues,
- AnthopicMessagesAssistantMessageParam,
- AnthropicFinishReason,
- AnthropicMessagesRequest,
- AnthropicMessagesToolChoice,
- AnthropicMessagesUserMessageParam,
- AnthropicResponse,
- AnthropicResponseContentBlockText,
- AnthropicResponseContentBlockToolUse,
- AnthropicResponseUsageBlock,
- ContentBlockDelta,
- ContentJsonBlockDelta,
- ContentTextBlockDelta,
- MessageBlockDelta,
- MessageDelta,
- UsageDelta,
-)
-from litellm.types.llms.openai import (
- AllMessageValues,
- ChatCompletionAssistantMessage,
- ChatCompletionAssistantToolCall,
- ChatCompletionImageObject,
- ChatCompletionImageUrlObject,
- ChatCompletionRequest,
- ChatCompletionSystemMessage,
- ChatCompletionTextObject,
- ChatCompletionToolCallFunctionChunk,
- ChatCompletionToolChoiceFunctionParam,
- ChatCompletionToolChoiceObjectParam,
- ChatCompletionToolChoiceValues,
- ChatCompletionToolMessage,
- ChatCompletionToolParam,
- ChatCompletionToolParamFunctionChunk,
- ChatCompletionUserMessage,
-)
-from litellm.types.utils import Choices, ModelResponse, Usage
-
-
-class AnthropicExperimentalPassThroughConfig:
- def __init__(self):
- pass
-
- ### FOR [BETA] `/v1/messages` endpoint support
-
- def translatable_anthropic_params(self) -> List:
- """
- Which anthropic params, we need to translate to the openai format.
- """
- return ["messages", "metadata", "system", "tool_choice", "tools"]
-
- def translate_anthropic_messages_to_openai( # noqa: PLR0915
- self,
- messages: List[
- Union[
- AnthropicMessagesUserMessageParam,
- AnthopicMessagesAssistantMessageParam,
- ]
- ],
- ) -> List:
- new_messages: List[AllMessageValues] = []
- for m in messages:
- user_message: Optional[ChatCompletionUserMessage] = None
- tool_message_list: List[ChatCompletionToolMessage] = []
- new_user_content_list: List[
- Union[ChatCompletionTextObject, ChatCompletionImageObject]
- ] = []
- ## USER MESSAGE ##
- if m["role"] == "user":
- ## translate user message
- message_content = m.get("content")
- if message_content and isinstance(message_content, str):
- user_message = ChatCompletionUserMessage(
- role="user", content=message_content
- )
- elif message_content and isinstance(message_content, list):
- for content in message_content:
- if content["type"] == "text":
- text_obj = ChatCompletionTextObject(
- type="text", text=content["text"]
- )
- new_user_content_list.append(text_obj)
- elif content["type"] == "image":
- image_url = ChatCompletionImageUrlObject(
- url=f"data:{content['type']};base64,{content['source']}"
- )
- image_obj = ChatCompletionImageObject(
- type="image_url", image_url=image_url
- )
-
- new_user_content_list.append(image_obj)
- elif content["type"] == "tool_result":
- if "content" not in content:
- tool_result = ChatCompletionToolMessage(
- role="tool",
- tool_call_id=content["tool_use_id"],
- content="",
- )
- tool_message_list.append(tool_result)
- elif isinstance(content["content"], str):
- tool_result = ChatCompletionToolMessage(
- role="tool",
- tool_call_id=content["tool_use_id"],
- content=content["content"],
- )
- tool_message_list.append(tool_result)
- elif isinstance(content["content"], list):
- for c in content["content"]:
- if c["type"] == "text":
- tool_result = ChatCompletionToolMessage(
- role="tool",
- tool_call_id=content["tool_use_id"],
- content=c["text"],
- )
- tool_message_list.append(tool_result)
- elif c["type"] == "image":
- image_str = (
- f"data:{c['type']};base64,{c['source']}"
- )
- tool_result = ChatCompletionToolMessage(
- role="tool",
- tool_call_id=content["tool_use_id"],
- content=image_str,
- )
- tool_message_list.append(tool_result)
-
- if user_message is not None:
- new_messages.append(user_message)
-
- if len(new_user_content_list) > 0:
- new_messages.append({"role": "user", "content": new_user_content_list}) # type: ignore
-
- if len(tool_message_list) > 0:
- new_messages.extend(tool_message_list)
-
- ## ASSISTANT MESSAGE ##
- assistant_message_str: Optional[str] = None
- tool_calls: List[ChatCompletionAssistantToolCall] = []
- if m["role"] == "assistant":
- if isinstance(m["content"], str):
- assistant_message_str = m["content"]
- elif isinstance(m["content"], list):
- for content in m["content"]:
- if content["type"] == "text":
- if assistant_message_str is None:
- assistant_message_str = content["text"]
- else:
- assistant_message_str += content["text"]
- elif content["type"] == "tool_use":
- function_chunk = ChatCompletionToolCallFunctionChunk(
- name=content["name"],
- arguments=json.dumps(content["input"]),
- )
-
- tool_calls.append(
- ChatCompletionAssistantToolCall(
- id=content["id"],
- type="function",
- function=function_chunk,
- )
- )
-
- if assistant_message_str is not None or len(tool_calls) > 0:
- assistant_message = ChatCompletionAssistantMessage(
- role="assistant",
- content=assistant_message_str,
- )
- if len(tool_calls) > 0:
- assistant_message["tool_calls"] = tool_calls
- new_messages.append(assistant_message)
-
- return new_messages
-
- def translate_anthropic_tool_choice_to_openai(
- self, tool_choice: AnthropicMessagesToolChoice
- ) -> ChatCompletionToolChoiceValues:
- if tool_choice["type"] == "any":
- return "required"
- elif tool_choice["type"] == "auto":
- return "auto"
- elif tool_choice["type"] == "tool":
- tc_function_param = ChatCompletionToolChoiceFunctionParam(
- name=tool_choice.get("name", "")
- )
- return ChatCompletionToolChoiceObjectParam(
- type="function", function=tc_function_param
- )
- else:
- raise ValueError(
- "Incompatible tool choice param submitted - {}".format(tool_choice)
- )
-
- def translate_anthropic_tools_to_openai(
- self, tools: List[AllAnthropicToolsValues]
- ) -> List[ChatCompletionToolParam]:
- new_tools: List[ChatCompletionToolParam] = []
- mapped_tool_params = ["name", "input_schema", "description"]
- for tool in tools:
- function_chunk = ChatCompletionToolParamFunctionChunk(
- name=tool["name"],
- )
- if "input_schema" in tool:
- function_chunk["parameters"] = tool["input_schema"] # type: ignore
- if "description" in tool:
- function_chunk["description"] = tool["description"] # type: ignore
-
- for k, v in tool.items():
- if k not in mapped_tool_params: # pass additional computer kwargs
- function_chunk.setdefault("parameters", {}).update({k: v})
- new_tools.append(
- ChatCompletionToolParam(type="function", function=function_chunk)
- )
-
- return new_tools
-
- def translate_anthropic_to_openai(
- self, anthropic_message_request: AnthropicMessagesRequest
- ) -> ChatCompletionRequest:
- """
- This is used by the beta Anthropic Adapter, for translating anthropic `/v1/messages` requests to the openai format.
- """
- new_messages: List[AllMessageValues] = []
-
- ## CONVERT ANTHROPIC MESSAGES TO OPENAI
- new_messages = self.translate_anthropic_messages_to_openai(
- messages=anthropic_message_request["messages"]
- )
- ## ADD SYSTEM MESSAGE TO MESSAGES
- if "system" in anthropic_message_request:
- new_messages.insert(
- 0,
- ChatCompletionSystemMessage(
- role="system", content=anthropic_message_request["system"]
- ),
- )
-
- new_kwargs: ChatCompletionRequest = {
- "model": anthropic_message_request["model"],
- "messages": new_messages,
- }
- ## CONVERT METADATA (user_id)
- if "metadata" in anthropic_message_request:
- if "user_id" in anthropic_message_request["metadata"]:
- new_kwargs["user"] = anthropic_message_request["metadata"]["user_id"]
-
- # Pass litellm proxy specific metadata
- if "litellm_metadata" in anthropic_message_request:
- # metadata will be passed to litellm.acompletion(), it's a litellm_param
- new_kwargs["metadata"] = anthropic_message_request.pop("litellm_metadata")
-
- ## CONVERT TOOL CHOICE
- if "tool_choice" in anthropic_message_request:
- new_kwargs["tool_choice"] = self.translate_anthropic_tool_choice_to_openai(
- tool_choice=anthropic_message_request["tool_choice"]
- )
- ## CONVERT TOOLS
- if "tools" in anthropic_message_request:
- new_kwargs["tools"] = self.translate_anthropic_tools_to_openai(
- tools=anthropic_message_request["tools"]
- )
-
- translatable_params = self.translatable_anthropic_params()
- for k, v in anthropic_message_request.items():
- if k not in translatable_params: # pass remaining params as is
- new_kwargs[k] = v # type: ignore
-
- return new_kwargs
-
- def _translate_openai_content_to_anthropic(
- self, choices: List[Choices]
- ) -> List[
- Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse]
- ]:
- new_content: List[
- Union[
- AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse
- ]
- ] = []
- for choice in choices:
- if (
- choice.message.tool_calls is not None
- and len(choice.message.tool_calls) > 0
- ):
- for tool_call in choice.message.tool_calls:
- new_content.append(
- AnthropicResponseContentBlockToolUse(
- type="tool_use",
- id=tool_call.id,
- name=tool_call.function.name or "",
- input=json.loads(tool_call.function.arguments),
- )
- )
- elif choice.message.content is not None:
- new_content.append(
- AnthropicResponseContentBlockText(
- type="text", text=choice.message.content
- )
- )
-
- return new_content
-
- def _translate_openai_finish_reason_to_anthropic(
- self, openai_finish_reason: str
- ) -> AnthropicFinishReason:
- if openai_finish_reason == "stop":
- return "end_turn"
- elif openai_finish_reason == "length":
- return "max_tokens"
- elif openai_finish_reason == "tool_calls":
- return "tool_use"
- return "end_turn"
-
- def translate_openai_response_to_anthropic(
- self, response: ModelResponse
- ) -> AnthropicResponse:
- ## translate content block
- anthropic_content = self._translate_openai_content_to_anthropic(choices=response.choices) # type: ignore
- ## extract finish reason
- anthropic_finish_reason = self._translate_openai_finish_reason_to_anthropic(
- openai_finish_reason=response.choices[0].finish_reason # type: ignore
- )
- # extract usage
- usage: Usage = getattr(response, "usage")
- anthropic_usage = AnthropicResponseUsageBlock(
- input_tokens=usage.prompt_tokens or 0,
- output_tokens=usage.completion_tokens or 0,
- )
- translated_obj = AnthropicResponse(
- id=response.id,
- type="message",
- role="assistant",
- model=response.model or "unknown-model",
- stop_sequence=None,
- usage=anthropic_usage,
- content=anthropic_content,
- stop_reason=anthropic_finish_reason,
- )
-
- return translated_obj
-
- def _translate_streaming_openai_chunk_to_anthropic(
- self, choices: List[OpenAIStreamingChoice]
- ) -> Tuple[
- Literal["text_delta", "input_json_delta"],
- Union[ContentTextBlockDelta, ContentJsonBlockDelta],
- ]:
- text: str = ""
- partial_json: Optional[str] = None
- for choice in choices:
- if choice.delta.content is not None:
- text += choice.delta.content
- elif choice.delta.tool_calls is not None:
- partial_json = ""
- for tool in choice.delta.tool_calls:
- if (
- tool.function is not None
- and tool.function.arguments is not None
- ):
- partial_json += tool.function.arguments
-
- if partial_json is not None:
- return "input_json_delta", ContentJsonBlockDelta(
- type="input_json_delta", partial_json=partial_json
- )
- else:
- return "text_delta", ContentTextBlockDelta(type="text_delta", text=text)
-
- def translate_streaming_openai_response_to_anthropic(
- self, response: ModelResponse
- ) -> Union[ContentBlockDelta, MessageBlockDelta]:
- ## base case - final chunk w/ finish reason
- if response.choices[0].finish_reason is not None:
- delta = MessageDelta(
- stop_reason=self._translate_openai_finish_reason_to_anthropic(
- response.choices[0].finish_reason
- ),
- )
- if getattr(response, "usage", None) is not None:
- litellm_usage_chunk: Optional[Usage] = response.usage # type: ignore
- elif (
- hasattr(response, "_hidden_params")
- and "usage" in response._hidden_params
- ):
- litellm_usage_chunk = response._hidden_params["usage"]
- else:
- litellm_usage_chunk = None
- if litellm_usage_chunk is not None:
- usage_delta = UsageDelta(
- input_tokens=litellm_usage_chunk.prompt_tokens or 0,
- output_tokens=litellm_usage_chunk.completion_tokens or 0,
- )
- else:
- usage_delta = UsageDelta(input_tokens=0, output_tokens=0)
- return MessageBlockDelta(
- type="message_delta", delta=delta, usage=usage_delta
- )
- (
- type_of_content,
- content_block_delta,
- ) = self._translate_streaming_openai_chunk_to_anthropic(
- choices=response.choices # type: ignore
- )
- return ContentBlockDelta(
- type="content_block_delta",
- index=response.choices[0].index,
- delta=content_block_delta,
- )
diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py
index 5294bd7141..dcd5af7b96 100644
--- a/litellm/llms/azure/azure.py
+++ b/litellm/llms/azure/azure.py
@@ -540,10 +540,14 @@ class AzureChatCompletion(BaseLLM):
status_code = getattr(e, "status_code", 500)
error_headers = getattr(e, "headers", None)
error_response = getattr(e, "response", None)
+ error_body = getattr(e, "body", None)
if error_headers is None and error_response:
error_headers = getattr(error_response, "headers", None)
raise AzureOpenAIError(
- status_code=status_code, message=str(e), headers=error_headers
+ status_code=status_code,
+ message=str(e),
+ headers=error_headers,
+ body=error_body,
)
async def acompletion(
@@ -649,6 +653,7 @@ class AzureChatCompletion(BaseLLM):
raise AzureOpenAIError(status_code=500, message=str(e))
except Exception as e:
message = getattr(e, "message", str(e))
+ body = getattr(e, "body", None)
## LOGGING
logging_obj.post_call(
input=data["messages"],
@@ -659,7 +664,7 @@ class AzureChatCompletion(BaseLLM):
if hasattr(e, "status_code"):
raise e
else:
- raise AzureOpenAIError(status_code=500, message=message)
+ raise AzureOpenAIError(status_code=500, message=message, body=body)
def streaming(
self,
@@ -805,10 +810,14 @@ class AzureChatCompletion(BaseLLM):
error_headers = getattr(e, "headers", None)
error_response = getattr(e, "response", None)
message = getattr(e, "message", str(e))
+ error_body = getattr(e, "body", None)
if error_headers is None and error_response:
error_headers = getattr(error_response, "headers", None)
raise AzureOpenAIError(
- status_code=status_code, message=message, headers=error_headers
+ status_code=status_code,
+ message=message,
+ headers=error_headers,
+ body=error_body,
)
async def aembedding(
diff --git a/litellm/llms/azure/batches/handler.py b/litellm/llms/azure/batches/handler.py
index 5fae527670..d36ae648ab 100644
--- a/litellm/llms/azure/batches/handler.py
+++ b/litellm/llms/azure/batches/handler.py
@@ -2,7 +2,7 @@
Azure Batches API Handler
"""
-from typing import Any, Coroutine, Optional, Union
+from typing import Any, Coroutine, Optional, Union, cast
import httpx
@@ -14,6 +14,7 @@ from litellm.types.llms.openai import (
CreateBatchRequest,
RetrieveBatchRequest,
)
+from litellm.types.utils import LiteLLMBatch
class AzureBatchesAPI:
@@ -64,9 +65,9 @@ class AzureBatchesAPI:
self,
create_batch_data: CreateBatchRequest,
azure_client: AsyncAzureOpenAI,
- ) -> Batch:
+ ) -> LiteLLMBatch:
response = await azure_client.batches.create(**create_batch_data)
- return response
+ return LiteLLMBatch(**response.model_dump())
def create_batch(
self,
@@ -78,7 +79,7 @@ class AzureBatchesAPI:
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None,
- ) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+ ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
azure_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = (
self.get_azure_openai_client(
api_key=api_key,
@@ -103,16 +104,16 @@ class AzureBatchesAPI:
return self.acreate_batch( # type: ignore
create_batch_data=create_batch_data, azure_client=azure_client
)
- response = azure_client.batches.create(**create_batch_data)
- return response
+ response = cast(AzureOpenAI, azure_client).batches.create(**create_batch_data)
+ return LiteLLMBatch(**response.model_dump())
async def aretrieve_batch(
self,
retrieve_batch_data: RetrieveBatchRequest,
client: AsyncAzureOpenAI,
- ) -> Batch:
+ ) -> LiteLLMBatch:
response = await client.batches.retrieve(**retrieve_batch_data)
- return response
+ return LiteLLMBatch(**response.model_dump())
def retrieve_batch(
self,
@@ -149,8 +150,10 @@ class AzureBatchesAPI:
return self.aretrieve_batch( # type: ignore
retrieve_batch_data=retrieve_batch_data, client=azure_client
)
- response = azure_client.batches.retrieve(**retrieve_batch_data)
- return response
+ response = cast(AzureOpenAI, azure_client).batches.retrieve(
+ **retrieve_batch_data
+ )
+ return LiteLLMBatch(**response.model_dump())
async def acancel_batch(
self,
diff --git a/litellm/llms/azure/common_utils.py b/litellm/llms/azure/common_utils.py
index 2a96f5c39c..43f3480ed6 100644
--- a/litellm/llms/azure/common_utils.py
+++ b/litellm/llms/azure/common_utils.py
@@ -17,6 +17,7 @@ class AzureOpenAIError(BaseLLMException):
request: Optional[httpx.Request] = None,
response: Optional[httpx.Response] = None,
headers: Optional[Union[httpx.Headers, dict]] = None,
+ body: Optional[dict] = None,
):
super().__init__(
status_code=status_code,
@@ -24,6 +25,7 @@ class AzureOpenAIError(BaseLLMException):
request=request,
response=response,
headers=headers,
+ body=body,
)
diff --git a/litellm/llms/azure_ai/chat/transformation.py b/litellm/llms/azure_ai/chat/transformation.py
index afedc95001..46a1a6bf9c 100644
--- a/litellm/llms/azure_ai/chat/transformation.py
+++ b/litellm/llms/azure_ai/chat/transformation.py
@@ -1,4 +1,5 @@
from typing import Any, List, Optional, Tuple, cast
+from urllib.parse import urlparse
import httpx
from httpx import Response
@@ -28,16 +29,29 @@ class AzureAIStudioConfig(OpenAIConfig):
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
- if api_base and "services.ai.azure.com" in api_base:
+ if api_base and self._should_use_api_key_header(api_base):
headers["api-key"] = api_key
else:
headers["Authorization"] = f"Bearer {api_key}"
return headers
+ def _should_use_api_key_header(self, api_base: str) -> bool:
+ """
+ Returns True if the request should use `api-key` header for authentication.
+ """
+ parsed_url = urlparse(api_base)
+ host = parsed_url.hostname
+ if host and (
+ host.endswith(".services.ai.azure.com")
+ or host.endswith(".openai.azure.com")
+ ):
+ return True
+ return False
+
def get_complete_url(
self,
- api_base: str,
+ api_base: Optional[str],
model: str,
optional_params: dict,
stream: Optional[bool] = None,
@@ -58,6 +72,10 @@ class AzureAIStudioConfig(OpenAIConfig):
- A complete URL string, e.g.,
"https://litellm8397336933.services.ai.azure.com/models/chat/completions?api-version=2024-05-01-preview"
"""
+ if api_base is None:
+ raise ValueError(
+ f"api_base is required for Azure AI Studio. Please set the api_base parameter. Passed `api_base={api_base}`"
+ )
original_url = httpx.URL(api_base)
# Extract api_version or use default
diff --git a/litellm/llms/azure_ai/rerank/transformation.py b/litellm/llms/azure_ai/rerank/transformation.py
index 4465e0d70a..842511f30d 100644
--- a/litellm/llms/azure_ai/rerank/transformation.py
+++ b/litellm/llms/azure_ai/rerank/transformation.py
@@ -17,7 +17,6 @@ class AzureAIRerankConfig(CohereRerankConfig):
"""
Azure AI Rerank - Follows the same Spec as Cohere Rerank
"""
-
def get_complete_url(self, api_base: Optional[str], model: str) -> str:
if api_base is None:
raise ValueError(
diff --git a/litellm/llms/base_llm/anthropic_messages/transformation.py b/litellm/llms/base_llm/anthropic_messages/transformation.py
new file mode 100644
index 0000000000..7619ffbbf6
--- /dev/null
+++ b/litellm/llms/base_llm/anthropic_messages/transformation.py
@@ -0,0 +1,35 @@
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any, Optional
+
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+ LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+ LiteLLMLoggingObj = Any
+
+
+class BaseAnthropicMessagesConfig(ABC):
+ @abstractmethod
+ def validate_environment(
+ self,
+ headers: dict,
+ model: str,
+ api_key: Optional[str] = None,
+ ) -> dict:
+ pass
+
+ @abstractmethod
+ def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+ """
+ OPTIONAL
+
+ Get the complete url for the request
+
+ Some providers need `model` in `api_base`
+ """
+ return api_base or ""
+
+ @abstractmethod
+ def get_supported_anthropic_messages_params(self, model: str) -> list:
+ pass
diff --git a/litellm/llms/base_llm/base_utils.py b/litellm/llms/base_llm/base_utils.py
index a7e65cdfbf..919cdbfd02 100644
--- a/litellm/llms/base_llm/base_utils.py
+++ b/litellm/llms/base_llm/base_utils.py
@@ -9,6 +9,7 @@ from typing import List, Optional, Type, Union
from openai.lib import _parsing, _pydantic
from pydantic import BaseModel
+from litellm._logging import verbose_logger
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import ProviderSpecificModelInfo
@@ -132,6 +133,9 @@ def map_developer_role_to_system_role(
new_messages: List[AllMessageValues] = []
for m in messages:
if m["role"] == "developer":
+ verbose_logger.debug(
+ "Translating developer role to system role for non-OpenAI providers."
+ ) # ensure user knows what's happening with their input.
new_messages.append({"role": "system", "content": m["content"]})
else:
new_messages.append(m)
diff --git a/litellm/llms/base_llm/chat/transformation.py b/litellm/llms/base_llm/chat/transformation.py
index d98931d23b..8327a10464 100644
--- a/litellm/llms/base_llm/chat/transformation.py
+++ b/litellm/llms/base_llm/chat/transformation.py
@@ -18,7 +18,6 @@ from typing import (
import httpx
from pydantic import BaseModel
-from litellm._logging import verbose_logger
from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.types.llms.openai import (
@@ -52,6 +51,7 @@ class BaseLLMException(Exception):
headers: Optional[Union[dict, httpx.Headers]] = None,
request: Optional[httpx.Request] = None,
response: Optional[httpx.Response] = None,
+ body: Optional[dict] = None,
):
self.status_code = status_code
self.message: str = message
@@ -68,6 +68,7 @@ class BaseLLMException(Exception):
self.response = httpx.Response(
status_code=status_code, request=self.request
)
+ self.body = body
super().__init__(
self.message
) # Call the base class constructor with the parameters it needs
@@ -112,6 +113,19 @@ class BaseConfig(ABC):
"""
return False
+ def _add_tools_to_optional_params(self, optional_params: dict, tools: List) -> dict:
+ """
+ Helper util to add tools to optional_params.
+ """
+ if "tools" not in optional_params:
+ optional_params["tools"] = tools
+ else:
+ optional_params["tools"] = [
+ *optional_params["tools"],
+ *tools,
+ ]
+ return optional_params
+
def translate_developer_role_to_system_role(
self,
messages: List[AllMessageValues],
@@ -121,9 +135,6 @@ class BaseConfig(ABC):
Overriden by OpenAI/Azure
"""
- verbose_logger.debug(
- "Translating developer role to system role for non-OpenAI providers."
- ) # ensure user knows what's happening with their input.
return map_developer_role_to_system_role(messages=messages)
def should_retry_llm_api_inside_llm_translation_on_http_error(
@@ -162,6 +173,7 @@ class BaseConfig(ABC):
optional_params: dict,
value: dict,
is_response_format_supported: bool,
+ enforce_tool_choice: bool = True,
) -> dict:
"""
Follow similar approach to anthropic - translate to a single tool call.
@@ -199,9 +211,11 @@ class BaseConfig(ABC):
optional_params.setdefault("tools", [])
optional_params["tools"].append(_tool)
- optional_params["tool_choice"] = _tool_choice
+ if enforce_tool_choice:
+ optional_params["tool_choice"] = _tool_choice
+
optional_params["json_mode"] = True
- else:
+ elif is_response_format_supported:
optional_params["response_format"] = value
return optional_params
@@ -253,7 +267,7 @@ class BaseConfig(ABC):
def get_complete_url(
self,
- api_base: str,
+ api_base: Optional[str],
model: str,
optional_params: dict,
stream: Optional[bool] = None,
@@ -265,6 +279,8 @@ class BaseConfig(ABC):
Some providers need `model` in `api_base`
"""
+ if api_base is None:
+ raise ValueError("api_base is required")
return api_base
@abstractmethod
@@ -319,6 +335,7 @@ class BaseConfig(ABC):
data: dict,
messages: list,
client: Optional[AsyncHTTPHandler] = None,
+ json_mode: Optional[bool] = None,
) -> CustomStreamWrapper:
raise NotImplementedError
@@ -332,6 +349,7 @@ class BaseConfig(ABC):
data: dict,
messages: list,
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+ json_mode: Optional[bool] = None,
) -> CustomStreamWrapper:
raise NotImplementedError
diff --git a/litellm/llms/base_llm/rerank/transformation.py b/litellm/llms/base_llm/rerank/transformation.py
index 524ed0f8d9..8701fe57bf 100644
--- a/litellm/llms/base_llm/rerank/transformation.py
+++ b/litellm/llms/base_llm/rerank/transformation.py
@@ -77,6 +77,7 @@ class BaseRerankConfig(ABC):
rank_fields: Optional[List[str]] = None,
return_documents: Optional[bool] = True,
max_chunks_per_doc: Optional[int] = None,
+ max_tokens_per_doc: Optional[int] = None,
) -> OptionalRerankParams:
pass
diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py
index 8158ceab8f..86b47675d4 100644
--- a/litellm/llms/bedrock/base_aws_llm.py
+++ b/litellm/llms/bedrock/base_aws_llm.py
@@ -2,13 +2,14 @@ import hashlib
import json
import os
from datetime import datetime
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast, get_args
import httpx
from pydantic import BaseModel
from litellm._logging import verbose_logger
from litellm.caching.caching import DualCache
+from litellm.constants import BEDROCK_INVOKE_PROVIDERS_LITERAL
from litellm.litellm_core_utils.dd_tracing import tracer
from litellm.secret_managers.main import get_secret
@@ -223,6 +224,60 @@ class BaseAWSLLM:
# Catch any unexpected errors and return None
return None
+ @staticmethod
+ def _get_provider_from_model_path(
+ model_path: str,
+ ) -> Optional[BEDROCK_INVOKE_PROVIDERS_LITERAL]:
+ """
+ Helper function to get the provider from a model path with format: provider/model-name
+
+ Args:
+ model_path (str): The model path (e.g., 'llama/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n' or 'anthropic/model-name')
+
+ Returns:
+ Optional[str]: The provider name, or None if no valid provider found
+ """
+ parts = model_path.split("/")
+ if len(parts) >= 1:
+ provider = parts[0]
+ if provider in get_args(BEDROCK_INVOKE_PROVIDERS_LITERAL):
+ return cast(BEDROCK_INVOKE_PROVIDERS_LITERAL, provider)
+ return None
+
+ @staticmethod
+ def get_bedrock_invoke_provider(
+ model: str,
+ ) -> Optional[BEDROCK_INVOKE_PROVIDERS_LITERAL]:
+ """
+ Helper function to get the bedrock provider from the model
+
+ handles 3 scenarions:
+ 1. model=invoke/anthropic.claude-3-5-sonnet-20240620-v1:0 -> Returns `anthropic`
+ 2. model=anthropic.claude-3-5-sonnet-20240620-v1:0 -> Returns `anthropic`
+ 3. model=llama/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n -> Returns `llama`
+ 4. model=us.amazon.nova-pro-v1:0 -> Returns `nova`
+ """
+ if model.startswith("invoke/"):
+ model = model.replace("invoke/", "", 1)
+
+ _split_model = model.split(".")[0]
+ if _split_model in get_args(BEDROCK_INVOKE_PROVIDERS_LITERAL):
+ return cast(BEDROCK_INVOKE_PROVIDERS_LITERAL, _split_model)
+
+ # If not a known provider, check for pattern with two slashes
+ provider = BaseAWSLLM._get_provider_from_model_path(model)
+ if provider is not None:
+ return provider
+
+ # check if provider == "nova"
+ if "nova" in model:
+ return "nova"
+ else:
+ for provider in get_args(BEDROCK_INVOKE_PROVIDERS_LITERAL):
+ if provider in model:
+ return provider
+ return None
+
def _get_aws_region_name(
self, optional_params: dict, model: Optional[str] = None
) -> str:
@@ -499,6 +554,7 @@ class BaseAWSLLM:
aws_access_key_id = optional_params.pop("aws_access_key_id", None)
aws_session_token = optional_params.pop("aws_session_token", None)
aws_region_name = self._get_aws_region_name(optional_params, model)
+ optional_params.pop("aws_region_name", None)
aws_role_name = optional_params.pop("aws_role_name", None)
aws_session_name = optional_params.pop("aws_session_name", None)
aws_profile_name = optional_params.pop("aws_profile_name", None)
diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py
index 68ae3af478..0b0d55f23d 100644
--- a/litellm/llms/bedrock/chat/converse_transformation.py
+++ b/litellm/llms/bedrock/chat/converse_transformation.py
@@ -23,6 +23,7 @@ from litellm.types.llms.openai import (
AllMessageValues,
ChatCompletionResponseMessage,
ChatCompletionSystemMessage,
+ ChatCompletionThinkingBlock,
ChatCompletionToolCallChunk,
ChatCompletionToolCallFunctionChunk,
ChatCompletionToolParam,
@@ -116,6 +117,10 @@ class AmazonConverseConfig(BaseConfig):
# only anthropic and mistral support tool choice config. otherwise (E.g. cohere) will fail the call - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
supported_params.append("tool_choice")
+ if (
+ "claude-3-7" in model
+ ): # [TODO]: move to a 'supports_reasoning_content' param from model cost map
+ supported_params.append("thinking")
return supported_params
def map_tool_choice_values(
@@ -162,6 +167,7 @@ class AmazonConverseConfig(BaseConfig):
self,
json_schema: Optional[dict] = None,
schema_name: str = "json_tool_call",
+ description: Optional[str] = None,
) -> ChatCompletionToolParam:
"""
Handles creating a tool call for getting responses in JSON format.
@@ -184,11 +190,15 @@ class AmazonConverseConfig(BaseConfig):
else:
_input_schema = json_schema
+ tool_param_function_chunk = ChatCompletionToolParamFunctionChunk(
+ name=schema_name, parameters=_input_schema
+ )
+ if description:
+ tool_param_function_chunk["description"] = description
+
_tool = ChatCompletionToolParam(
type="function",
- function=ChatCompletionToolParamFunctionChunk(
- name=schema_name, parameters=_input_schema
- ),
+ function=tool_param_function_chunk,
)
return _tool
@@ -201,15 +211,26 @@ class AmazonConverseConfig(BaseConfig):
messages: Optional[List[AllMessageValues]] = None,
) -> dict:
for param, value in non_default_params.items():
- if param == "response_format":
+ if param == "response_format" and isinstance(value, dict):
+
+ ignore_response_format_types = ["text"]
+ if value["type"] in ignore_response_format_types: # value is a no-op
+ continue
+
json_schema: Optional[dict] = None
schema_name: str = ""
+ description: Optional[str] = None
if "response_schema" in value:
json_schema = value["response_schema"]
schema_name = "json_tool_call"
elif "json_schema" in value:
json_schema = value["json_schema"]["schema"]
schema_name = value["json_schema"]["name"]
+ description = value["json_schema"].get("description")
+
+ if "type" in value and value["type"] == "text":
+ continue
+
"""
Follow similar approach to anthropic - translate to a single tool call.
@@ -218,12 +239,14 @@ class AmazonConverseConfig(BaseConfig):
- You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool
- Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective.
"""
- _tool_choice = {"name": schema_name, "type": "tool"}
_tool = self._create_json_tool_call_for_response_format(
json_schema=json_schema,
schema_name=schema_name if schema_name != "" else "json_tool_call",
+ description=description,
+ )
+ optional_params = self._add_tools_to_optional_params(
+ optional_params=optional_params, tools=[_tool]
)
- optional_params["tools"] = [_tool]
if litellm.utils.supports_tool_choice(
model=model, custom_llm_provider=self.custom_llm_provider
):
@@ -249,15 +272,18 @@ class AmazonConverseConfig(BaseConfig):
optional_params["temperature"] = value
if param == "top_p":
optional_params["topP"] = value
- if param == "tools":
- optional_params["tools"] = value
+ if param == "tools" and isinstance(value, list):
+ optional_params = self._add_tools_to_optional_params(
+ optional_params=optional_params, tools=value
+ )
if param == "tool_choice":
_tool_choice_value = self.map_tool_choice_values(
model=model, tool_choice=value, drop_params=drop_params # type: ignore
)
if _tool_choice_value is not None:
optional_params["tool_choice"] = _tool_choice_value
-
+ if param == "thinking":
+ optional_params["thinking"] = value
return optional_params
@overload
@@ -545,6 +571,37 @@ class AmazonConverseConfig(BaseConfig):
encoding=encoding,
)
+ def _transform_reasoning_content(
+ self, reasoning_content_blocks: List[BedrockConverseReasoningContentBlock]
+ ) -> str:
+ """
+ Extract the reasoning text from the reasoning content blocks
+
+ Ensures deepseek reasoning content compatible output.
+ """
+ reasoning_content_str = ""
+ for block in reasoning_content_blocks:
+ if "reasoningText" in block:
+ reasoning_content_str += block["reasoningText"]["text"]
+ return reasoning_content_str
+
+ def _transform_thinking_blocks(
+ self, thinking_blocks: List[BedrockConverseReasoningContentBlock]
+ ) -> List[ChatCompletionThinkingBlock]:
+ """Return a consistent format for thinking blocks between Anthropic and Bedrock."""
+ thinking_blocks_list: List[ChatCompletionThinkingBlock] = []
+ for block in thinking_blocks:
+ if "reasoningText" in block:
+ _thinking_block = ChatCompletionThinkingBlock(type="thinking")
+ _text = block["reasoningText"].get("text")
+ _signature = block["reasoningText"].get("signature")
+ if _text is not None:
+ _thinking_block["thinking"] = _text
+ if _signature is not None:
+ _thinking_block["signature"] = _signature
+ thinking_blocks_list.append(_thinking_block)
+ return thinking_blocks_list
+
def _transform_response(
self,
model: str,
@@ -618,6 +675,10 @@ class AmazonConverseConfig(BaseConfig):
chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"}
content_str = ""
tools: List[ChatCompletionToolCallChunk] = []
+ reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = (
+ None
+ )
+
if message is not None:
for idx, content in enumerate(message["content"]):
"""
@@ -644,8 +705,22 @@ class AmazonConverseConfig(BaseConfig):
index=idx,
)
tools.append(_tool_response_chunk)
- chat_completion_message["content"] = content_str
+ if "reasoningContent" in content:
+ if reasoningContentBlocks is None:
+ reasoningContentBlocks = []
+ reasoningContentBlocks.append(content["reasoningContent"])
+ if reasoningContentBlocks is not None:
+ chat_completion_message["provider_specific_fields"] = {
+ "reasoningContentBlocks": reasoningContentBlocks,
+ }
+ chat_completion_message["reasoning_content"] = (
+ self._transform_reasoning_content(reasoningContentBlocks)
+ )
+ chat_completion_message["thinking_blocks"] = (
+ self._transform_thinking_blocks(reasoningContentBlocks)
+ )
+ chat_completion_message["content"] = content_str
if json_mode is True and tools is not None and len(tools) == 1:
# to support 'json_schema' logic on bedrock models
json_mode_content_str: Optional[str] = tools[0]["function"].get("arguments")
diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py
index 35173f3c56..27289164f7 100644
--- a/litellm/llms/bedrock/chat/invoke_handler.py
+++ b/litellm/llms/bedrock/chat/invoke_handler.py
@@ -26,7 +26,6 @@ import httpx # type: ignore
import litellm
from litellm import verbose_logger
-from litellm._logging import print_verbose
from litellm.caching.caching import InMemoryCache
from litellm.litellm_core_utils.core_helpers import map_finish_reason
from litellm.litellm_core_utils.litellm_logging import Logging
@@ -51,13 +50,19 @@ from litellm.llms.custom_httpx.http_handler import (
)
from litellm.types.llms.bedrock import *
from litellm.types.llms.openai import (
+ ChatCompletionThinkingBlock,
ChatCompletionToolCallChunk,
ChatCompletionToolCallFunctionChunk,
ChatCompletionUsageBlock,
)
-from litellm.types.utils import ChatCompletionMessageToolCall, Choices
+from litellm.types.utils import ChatCompletionMessageToolCall, Choices, Delta
from litellm.types.utils import GenericStreamingChunk as GChunk
-from litellm.types.utils import ModelResponse, ModelResponseStream, Usage
+from litellm.types.utils import (
+ ModelResponse,
+ ModelResponseStream,
+ StreamingChoices,
+ Usage,
+)
from litellm.utils import CustomStreamWrapper, get_secret
from ..base_aws_llm import BaseAWSLLM
@@ -212,7 +217,6 @@ async def make_call(
api_key="",
data=data,
messages=messages,
- print_verbose=print_verbose,
encoding=litellm.encoding,
) # type: ignore
completion_stream: Any = MockResponseIterator(
@@ -222,6 +226,7 @@ async def make_call(
decoder: AWSEventStreamDecoder = AmazonAnthropicClaudeStreamDecoder(
model=model,
sync_stream=False,
+ json_mode=json_mode,
)
completion_stream = decoder.aiter_bytes(
response.aiter_bytes(chunk_size=1024)
@@ -298,7 +303,6 @@ def make_sync_call(
api_key="",
data=data,
messages=messages,
- print_verbose=print_verbose,
encoding=litellm.encoding,
) # type: ignore
completion_stream: Any = MockResponseIterator(
@@ -308,6 +312,7 @@ def make_sync_call(
decoder: AWSEventStreamDecoder = AmazonAnthropicClaudeStreamDecoder(
model=model,
sync_stream=True,
+ json_mode=json_mode,
)
completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024))
elif bedrock_invoke_provider == "deepseek_r1":
@@ -525,7 +530,7 @@ class BedrockLLM(BaseAWSLLM):
].message.tool_calls:
_tool_call = {**tool_call.dict(), "index": 0}
_tool_calls.append(_tool_call)
- delta_obj = litellm.utils.Delta(
+ delta_obj = Delta(
content=getattr(
model_response.choices[0].message, "content", None
),
@@ -1146,27 +1151,6 @@ class BedrockLLM(BaseAWSLLM):
)
return streaming_response
- @staticmethod
- def get_bedrock_invoke_provider(
- model: str,
- ) -> Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL]:
- """
- Helper function to get the bedrock provider from the model
-
- handles 2 scenarions:
- 1. model=anthropic.claude-3-5-sonnet-20240620-v1:0 -> Returns `anthropic`
- 2. model=llama/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n -> Returns `llama`
- """
- _split_model = model.split(".")[0]
- if _split_model in get_args(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL):
- return cast(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL, _split_model)
-
- # If not a known provider, check for pattern with two slashes
- provider = BedrockLLM._get_provider_from_model_path(model)
- if provider is not None:
- return provider
- return None
-
@staticmethod
def _get_provider_from_model_path(
model_path: str,
@@ -1258,14 +1242,40 @@ class AWSEventStreamDecoder:
return True
return False
- def converse_chunk_parser(self, chunk_data: dict) -> GChunk:
+ def extract_reasoning_content_str(
+ self, reasoning_content_block: BedrockConverseReasoningContentBlockDelta
+ ) -> Optional[str]:
+ if "text" in reasoning_content_block:
+ return reasoning_content_block["text"]
+ return None
+
+ def translate_thinking_blocks(
+ self, thinking_block: BedrockConverseReasoningContentBlockDelta
+ ) -> Optional[List[ChatCompletionThinkingBlock]]:
+ """
+ Translate the thinking blocks to a string
+ """
+
+ thinking_blocks_list: List[ChatCompletionThinkingBlock] = []
+ _thinking_block = ChatCompletionThinkingBlock(type="thinking")
+ if "text" in thinking_block:
+ _thinking_block["thinking"] = thinking_block["text"]
+ elif "signature" in thinking_block:
+ _thinking_block["signature"] = thinking_block["signature"]
+ _thinking_block["thinking"] = "" # consistent with anthropic response
+ thinking_blocks_list.append(_thinking_block)
+ return thinking_blocks_list
+
+ def converse_chunk_parser(self, chunk_data: dict) -> ModelResponseStream:
try:
verbose_logger.debug("\n\nRaw Chunk: {}\n\n".format(chunk_data))
text = ""
tool_use: Optional[ChatCompletionToolCallChunk] = None
- is_finished = False
finish_reason = ""
usage: Optional[ChatCompletionUsageBlock] = None
+ provider_specific_fields: dict = {}
+ reasoning_content: Optional[str] = None
+ thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
index = int(chunk_data.get("contentBlockIndex", 0))
if "start" in chunk_data:
@@ -1305,6 +1315,22 @@ class AWSEventStreamDecoder:
},
"index": index,
}
+ elif "reasoningContent" in delta_obj:
+ provider_specific_fields = {
+ "reasoningContent": delta_obj["reasoningContent"],
+ }
+ reasoning_content = self.extract_reasoning_content_str(
+ delta_obj["reasoningContent"]
+ )
+ thinking_blocks = self.translate_thinking_blocks(
+ delta_obj["reasoningContent"]
+ )
+ if (
+ thinking_blocks
+ and len(thinking_blocks) > 0
+ and reasoning_content is None
+ ):
+ reasoning_content = "" # set to non-empty string to ensure consistency with Anthropic
elif (
"contentBlockIndex" in chunk_data
): # stop block, no 'start' or 'delta' object
@@ -1321,7 +1347,6 @@ class AWSEventStreamDecoder:
}
elif "stopReason" in chunk_data:
finish_reason = map_finish_reason(chunk_data.get("stopReason", "stop"))
- is_finished = True
elif "usage" in chunk_data:
usage = ChatCompletionUsageBlock(
prompt_tokens=chunk_data.get("inputTokens", 0),
@@ -1329,18 +1354,33 @@ class AWSEventStreamDecoder:
total_tokens=chunk_data.get("totalTokens", 0),
)
- response = GChunk(
- text=text,
- tool_use=tool_use,
- is_finished=is_finished,
- finish_reason=finish_reason,
- usage=usage,
- index=index,
- )
-
+ model_response_provider_specific_fields = {}
if "trace" in chunk_data:
trace = chunk_data.get("trace")
- response["provider_specific_fields"] = {"trace": trace}
+ model_response_provider_specific_fields["trace"] = trace
+ response = ModelResponseStream(
+ choices=[
+ StreamingChoices(
+ finish_reason=finish_reason,
+ index=index,
+ delta=Delta(
+ content=text,
+ role="assistant",
+ tool_calls=[tool_use] if tool_use else None,
+ provider_specific_fields=(
+ provider_specific_fields
+ if provider_specific_fields
+ else None
+ ),
+ thinking_blocks=thinking_blocks,
+ reasoning_content=reasoning_content,
+ ),
+ )
+ ],
+ usage=usage,
+ provider_specific_fields=model_response_provider_specific_fields,
+ )
+
return response
except Exception as e:
raise Exception("Received streaming error - {}".format(str(e)))
@@ -1474,6 +1514,7 @@ class AmazonAnthropicClaudeStreamDecoder(AWSEventStreamDecoder):
self,
model: str,
sync_stream: bool,
+ json_mode: Optional[bool] = None,
) -> None:
"""
Child class of AWSEventStreamDecoder that handles the streaming response from the Anthropic family of models
@@ -1484,9 +1525,10 @@ class AmazonAnthropicClaudeStreamDecoder(AWSEventStreamDecoder):
self.anthropic_model_response_iterator = AnthropicModelResponseIterator(
streaming_response=None,
sync_stream=sync_stream,
+ json_mode=json_mode,
)
- def _chunk_parser(self, chunk_data: dict) -> GChunk:
+ def _chunk_parser(self, chunk_data: dict) -> ModelResponseStream:
return self.anthropic_model_response_iterator.chunk_parser(chunk=chunk_data)
diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py
index 8fbb3e6703..d7ceec1f1c 100644
--- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py
+++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py
@@ -2,6 +2,7 @@ from typing import Any, List, Optional, cast
from httpx import Response
+from litellm import verbose_logger
from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
_parse_content_for_reasoning,
)
@@ -93,7 +94,12 @@ class AmazonDeepseekR1ResponseIterator(BaseModelResponseIterator):
"""
try:
typed_chunk = AmazonDeepSeekR1StreamingResponse(**chunk) # type: ignore
- if "" in typed_chunk["generation"]:
+ generated_content = typed_chunk["generation"]
+ if generated_content == "" and not self.has_finished_thinking:
+ verbose_logger.debug(
+ "Deepseek r1: received, setting has_finished_thinking to True"
+ )
+ generated_content = ""
self.has_finished_thinking = True
prompt_token_count = typed_chunk.get("prompt_token_count") or 0
@@ -110,12 +116,12 @@ class AmazonDeepseekR1ResponseIterator(BaseModelResponseIterator):
finish_reason=typed_chunk["stop_reason"],
delta=Delta(
content=(
- typed_chunk["generation"]
+ generated_content
if self.has_finished_thinking
else None
),
reasoning_content=(
- typed_chunk["generation"]
+ generated_content
if not self.has_finished_thinking
else None
),
@@ -124,5 +130,6 @@ class AmazonDeepseekR1ResponseIterator(BaseModelResponseIterator):
],
usage=usage,
)
+
except Exception as e:
raise e
diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude2_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude2_transformation.py
index 085cf0b9ca..d0d06ef2b2 100644
--- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude2_transformation.py
+++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude2_transformation.py
@@ -3,8 +3,10 @@ from typing import Optional
import litellm
+from .base_invoke_transformation import AmazonInvokeConfig
-class AmazonAnthropicConfig:
+
+class AmazonAnthropicConfig(AmazonInvokeConfig):
"""
Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=claude
@@ -57,9 +59,7 @@ class AmazonAnthropicConfig:
and v is not None
}
- def get_supported_openai_params(
- self,
- ):
+ def get_supported_openai_params(self, model: str):
return [
"max_tokens",
"max_completion_tokens",
@@ -69,7 +69,13 @@ class AmazonAnthropicConfig:
"stream",
]
- def map_openai_params(self, non_default_params: dict, optional_params: dict):
+ def map_openai_params(
+ self,
+ non_default_params: dict,
+ optional_params: dict,
+ model: str,
+ drop_params: bool,
+ ):
for param, value in non_default_params.items():
if param == "max_tokens" or param == "max_completion_tokens":
optional_params["max_tokens_to_sample"] = value
diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
index 09842aef01..0cac339a3c 100644
--- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
+++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
@@ -2,7 +2,7 @@ from typing import TYPE_CHECKING, Any, List, Optional
import httpx
-import litellm
+from litellm.llms.anthropic.chat.transformation import AnthropicConfig
from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
AmazonInvokeConfig,
)
@@ -17,7 +17,7 @@ else:
LiteLLMLoggingObj = Any
-class AmazonAnthropicClaude3Config(AmazonInvokeConfig):
+class AmazonAnthropicClaude3Config(AmazonInvokeConfig, AnthropicConfig):
"""
Reference:
https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=claude
@@ -28,18 +28,8 @@ class AmazonAnthropicClaude3Config(AmazonInvokeConfig):
anthropic_version: str = "bedrock-2023-05-31"
- def get_supported_openai_params(self, model: str):
- return [
- "max_tokens",
- "max_completion_tokens",
- "tools",
- "tool_choice",
- "stream",
- "stop",
- "temperature",
- "top_p",
- "extra_headers",
- ]
+ def get_supported_openai_params(self, model: str) -> List[str]:
+ return AnthropicConfig.get_supported_openai_params(self, model)
def map_openai_params(
self,
@@ -47,21 +37,14 @@ class AmazonAnthropicClaude3Config(AmazonInvokeConfig):
optional_params: dict,
model: str,
drop_params: bool,
- ):
- for param, value in non_default_params.items():
- if param == "max_tokens" or param == "max_completion_tokens":
- optional_params["max_tokens"] = value
- if param == "tools":
- optional_params["tools"] = value
- if param == "stream":
- optional_params["stream"] = value
- if param == "stop":
- optional_params["stop_sequences"] = value
- if param == "temperature":
- optional_params["temperature"] = value
- if param == "top_p":
- optional_params["top_p"] = value
- return optional_params
+ ) -> dict:
+ return AnthropicConfig.map_openai_params(
+ self,
+ non_default_params,
+ optional_params,
+ model,
+ drop_params,
+ )
def transform_request(
self,
@@ -71,7 +54,8 @@ class AmazonAnthropicClaude3Config(AmazonInvokeConfig):
litellm_params: dict,
headers: dict,
) -> dict:
- _anthropic_request = litellm.AnthropicConfig().transform_request(
+ _anthropic_request = AnthropicConfig.transform_request(
+ self,
model=model,
messages=messages,
optional_params=optional_params,
@@ -80,6 +64,7 @@ class AmazonAnthropicClaude3Config(AmazonInvokeConfig):
)
_anthropic_request.pop("model", None)
+ _anthropic_request.pop("stream", None)
if "anthropic_version" not in _anthropic_request:
_anthropic_request["anthropic_version"] = self.anthropic_version
@@ -99,7 +84,8 @@ class AmazonAnthropicClaude3Config(AmazonInvokeConfig):
api_key: Optional[str] = None,
json_mode: Optional[bool] = None,
) -> ModelResponse:
- return litellm.AnthropicConfig().transform_response(
+ return AnthropicConfig.transform_response(
+ self,
model=model,
raw_response=raw_response,
model_response=model_response,
diff --git a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py
index b7d4f0ae6d..5414429d4c 100644
--- a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py
+++ b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py
@@ -73,7 +73,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
def get_complete_url(
self,
- api_base: str,
+ api_base: Optional[str],
model: str,
optional_params: dict,
stream: Optional[bool] = None,
@@ -129,7 +129,6 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
## CREDENTIALS ##
# pop aws_secret_access_key, aws_access_key_id, aws_session_token, aws_region_name from kwargs, since completion calls fail with them
- extra_headers = optional_params.get("extra_headers", None)
aws_secret_access_key = optional_params.get("aws_secret_access_key", None)
aws_access_key_id = optional_params.get("aws_access_key_id", None)
aws_session_token = optional_params.get("aws_session_token", None)
@@ -155,9 +154,10 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
)
sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
- headers = {"Content-Type": "application/json"}
- if extra_headers is not None:
- headers = {"Content-Type": "application/json", **extra_headers}
+ if headers is not None:
+ headers = {"Content-Type": "application/json", **headers}
+ else:
+ headers = {"Content-Type": "application/json"}
request = AWSRequest(
method="POST",
@@ -166,12 +166,13 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
headers=headers,
)
sigv4.add_auth(request)
- if (
- extra_headers is not None and "Authorization" in extra_headers
- ): # prevent sigv4 from overwriting the auth header
- request.headers["Authorization"] = extra_headers["Authorization"]
- return dict(request.headers)
+ request_headers_dict = dict(request.headers)
+ if (
+ headers is not None and "Authorization" in headers
+ ): # prevent sigv4 from overwriting the auth header
+ request_headers_dict["Authorization"] = headers["Authorization"]
+ return request_headers_dict
def transform_request(
self,
@@ -443,7 +444,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
- return {}
+ return headers
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
@@ -461,6 +462,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
data: dict,
messages: list,
client: Optional[AsyncHTTPHandler] = None,
+ json_mode: Optional[bool] = None,
) -> CustomStreamWrapper:
streaming_response = CustomStreamWrapper(
completion_stream=None,
@@ -475,6 +477,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
logging_obj=logging_obj,
fake_stream=True if "ai21" in api_base else False,
bedrock_invoke_provider=self.get_bedrock_invoke_provider(model),
+ json_mode=json_mode,
),
model=model,
custom_llm_provider="bedrock",
@@ -493,6 +496,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
data: dict,
messages: list,
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+ json_mode: Optional[bool] = None,
) -> CustomStreamWrapper:
if client is None or isinstance(client, AsyncHTTPHandler):
client = _get_httpx_client(params={})
@@ -509,6 +513,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
logging_obj=logging_obj,
fake_stream=True if "ai21" in api_base else False,
bedrock_invoke_provider=self.get_bedrock_invoke_provider(model),
+ json_mode=json_mode,
),
model=model,
custom_llm_provider="bedrock",
@@ -534,7 +539,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
"""
Helper function to get the bedrock provider from the model
- handles 3 scenarions:
+ handles 4 scenarios:
1. model=invoke/anthropic.claude-3-5-sonnet-20240620-v1:0 -> Returns `anthropic`
2. model=anthropic.claude-3-5-sonnet-20240620-v1:0 -> Returns `anthropic`
3. model=llama/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n -> Returns `llama`
@@ -555,6 +560,10 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
# check if provider == "nova"
if "nova" in model:
return "nova"
+
+ for provider in get_args(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL):
+ if provider in model:
+ return provider
return None
@staticmethod
diff --git a/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py b/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py
index 7aa42b0bf2..6c1147f24a 100644
--- a/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py
+++ b/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py
@@ -1,5 +1,5 @@
"""
-Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Titan multimodal /invoke format.
+Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Titan multimodal /invoke format.
Why separate file? Make it easy to see how transformation works
diff --git a/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py b/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py
index a68bc6962c..8056e9e9b2 100644
--- a/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py
+++ b/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py
@@ -1,5 +1,5 @@
"""
-Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Titan V2 /invoke format.
+Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Titan V2 /invoke format.
Why separate file? Make it easy to see how transformation works
diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py
index 659dbc6715..9e4e4e22d0 100644
--- a/litellm/llms/bedrock/embed/embedding.py
+++ b/litellm/llms/bedrock/embed/embedding.py
@@ -1,5 +1,5 @@
"""
-Handles embedding calls to Bedrock's `/invoke` endpoint
+Handles embedding calls to Bedrock's `/invoke` endpoint
"""
import copy
@@ -350,6 +350,11 @@ class BedrockEmbedding(BaseAWSLLM):
### TRANSFORMATION ###
provider = model.split(".")[0]
inference_params = copy.deepcopy(optional_params)
+ inference_params = {
+ k: v
+ for k, v in inference_params.items()
+ if k.lower() not in self.aws_authentication_params
+ }
inference_params.pop(
"user", None
) # make sure user is not passed in for bedrock call
diff --git a/litellm/llms/bedrock/image/image_handler.py b/litellm/llms/bedrock/image/image_handler.py
index 4bd63fd21b..59a80b2222 100644
--- a/litellm/llms/bedrock/image/image_handler.py
+++ b/litellm/llms/bedrock/image/image_handler.py
@@ -10,6 +10,8 @@ import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.litellm_logging import Logging as LitellmLogging
from litellm.llms.custom_httpx.http_handler import (
+ AsyncHTTPHandler,
+ HTTPHandler,
_get_httpx_client,
get_async_httpx_client,
)
@@ -51,6 +53,7 @@ class BedrockImageGeneration(BaseAWSLLM):
aimg_generation: bool = False,
api_base: Optional[str] = None,
extra_headers: Optional[dict] = None,
+ client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
):
prepared_request = self._prepare_request(
model=model,
@@ -69,9 +72,15 @@ class BedrockImageGeneration(BaseAWSLLM):
logging_obj=logging_obj,
prompt=prompt,
model_response=model_response,
+ client=(
+ client
+ if client is not None and isinstance(client, AsyncHTTPHandler)
+ else None
+ ),
)
- client = _get_httpx_client()
+ if client is None or not isinstance(client, HTTPHandler):
+ client = _get_httpx_client()
try:
response = client.post(url=prepared_request.endpoint_url, headers=prepared_request.prepped.headers, data=prepared_request.body) # type: ignore
response.raise_for_status()
@@ -99,13 +108,14 @@ class BedrockImageGeneration(BaseAWSLLM):
logging_obj: LitellmLogging,
prompt: str,
model_response: ImageResponse,
+ client: Optional[AsyncHTTPHandler] = None,
) -> ImageResponse:
"""
Asynchronous handler for bedrock image generation
Awaits the response from the bedrock image generation endpoint
"""
- async_client = get_async_httpx_client(
+ async_client = client or get_async_httpx_client(
llm_provider=litellm.LlmProviders.BEDROCK,
params={"timeout": timeout},
)
diff --git a/litellm/llms/cloudflare/chat/transformation.py b/litellm/llms/cloudflare/chat/transformation.py
index 1ef6da5a4b..555e3c21f4 100644
--- a/litellm/llms/cloudflare/chat/transformation.py
+++ b/litellm/llms/cloudflare/chat/transformation.py
@@ -11,6 +11,7 @@ from litellm.llms.base_llm.chat.transformation import (
BaseLLMException,
LiteLLMLoggingObj,
)
+from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import (
ChatCompletionToolCallChunk,
@@ -75,11 +76,16 @@ class CloudflareChatConfig(BaseConfig):
def get_complete_url(
self,
- api_base: str,
+ api_base: Optional[str],
model: str,
optional_params: dict,
stream: Optional[bool] = None,
) -> str:
+ if api_base is None:
+ account_id = get_secret_str("CLOUDFLARE_ACCOUNT_ID")
+ api_base = (
+ f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/"
+ )
return api_base + model
def get_supported_openai_params(self, model: str) -> List[str]:
diff --git a/litellm/llms/codestral/completion/transformation.py b/litellm/llms/codestral/completion/transformation.py
index 84551cd553..5955e91deb 100644
--- a/litellm/llms/codestral/completion/transformation.py
+++ b/litellm/llms/codestral/completion/transformation.py
@@ -84,7 +84,9 @@ class CodestralTextCompletionConfig(OpenAITextCompletionConfig):
finish_reason = None
logprobs = None
- chunk_data = chunk_data.replace("data:", "")
+ chunk_data = (
+ litellm.CustomStreamWrapper._strip_sse_data_from_chunk(chunk_data) or ""
+ )
chunk_data = chunk_data.strip()
if len(chunk_data) == 0 or chunk_data == "[DONE]":
return {
diff --git a/litellm/llms/cohere/rerank/transformation.py b/litellm/llms/cohere/rerank/transformation.py
index e0836a71f7..f3624d9216 100644
--- a/litellm/llms/cohere/rerank/transformation.py
+++ b/litellm/llms/cohere/rerank/transformation.py
@@ -52,6 +52,7 @@ class CohereRerankConfig(BaseRerankConfig):
rank_fields: Optional[List[str]] = None,
return_documents: Optional[bool] = True,
max_chunks_per_doc: Optional[int] = None,
+ max_tokens_per_doc: Optional[int] = None,
) -> OptionalRerankParams:
"""
Map Cohere rerank params
@@ -147,4 +148,4 @@ class CohereRerankConfig(BaseRerankConfig):
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
) -> BaseLLMException:
- return CohereError(message=error_message, status_code=status_code)
+ return CohereError(message=error_message, status_code=status_code)
\ No newline at end of file
diff --git a/litellm/llms/cohere/rerank_v2/transformation.py b/litellm/llms/cohere/rerank_v2/transformation.py
new file mode 100644
index 0000000000..a93cb982a7
--- /dev/null
+++ b/litellm/llms/cohere/rerank_v2/transformation.py
@@ -0,0 +1,80 @@
+from typing import Any, Dict, List, Optional, Union
+
+from litellm.llms.cohere.rerank.transformation import CohereRerankConfig
+from litellm.types.rerank import OptionalRerankParams, RerankRequest
+
+class CohereRerankV2Config(CohereRerankConfig):
+ """
+ Reference: https://docs.cohere.com/v2/reference/rerank
+ """
+
+ def __init__(self) -> None:
+ pass
+
+ def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+ if api_base:
+ # Remove trailing slashes and ensure clean base URL
+ api_base = api_base.rstrip("/")
+ if not api_base.endswith("/v2/rerank"):
+ api_base = f"{api_base}/v2/rerank"
+ return api_base
+ return "https://api.cohere.ai/v2/rerank"
+
+ def get_supported_cohere_rerank_params(self, model: str) -> list:
+ return [
+ "query",
+ "documents",
+ "top_n",
+ "max_tokens_per_doc",
+ "rank_fields",
+ "return_documents",
+ ]
+
+ def map_cohere_rerank_params(
+ self,
+ non_default_params: Optional[dict],
+ model: str,
+ drop_params: bool,
+ query: str,
+ documents: List[Union[str, Dict[str, Any]]],
+ custom_llm_provider: Optional[str] = None,
+ top_n: Optional[int] = None,
+ rank_fields: Optional[List[str]] = None,
+ return_documents: Optional[bool] = True,
+ max_chunks_per_doc: Optional[int] = None,
+ max_tokens_per_doc: Optional[int] = None,
+ ) -> OptionalRerankParams:
+ """
+ Map Cohere rerank params
+
+ No mapping required - returns all supported params
+ """
+ return OptionalRerankParams(
+ query=query,
+ documents=documents,
+ top_n=top_n,
+ rank_fields=rank_fields,
+ return_documents=return_documents,
+ max_tokens_per_doc=max_tokens_per_doc,
+ )
+
+ def transform_rerank_request(
+ self,
+ model: str,
+ optional_rerank_params: OptionalRerankParams,
+ headers: dict,
+ ) -> dict:
+ if "query" not in optional_rerank_params:
+ raise ValueError("query is required for Cohere rerank")
+ if "documents" not in optional_rerank_params:
+ raise ValueError("documents is required for Cohere rerank")
+ rerank_request = RerankRequest(
+ model=model,
+ query=optional_rerank_params["query"],
+ documents=optional_rerank_params["documents"],
+ top_n=optional_rerank_params.get("top_n", None),
+ rank_fields=optional_rerank_params.get("rank_fields", None),
+ return_documents=optional_rerank_params.get("return_documents", None),
+ max_tokens_per_doc=optional_rerank_params.get("max_tokens_per_doc", None),
+ )
+ return rerank_request.model_dump(exclude_none=True)
\ No newline at end of file
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index 142a3e4701..9d67fd1a85 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -159,6 +159,7 @@ class BaseLLMHTTPHandler:
encoding: Any,
api_key: Optional[str] = None,
client: Optional[AsyncHTTPHandler] = None,
+ json_mode: bool = False,
):
if client is None:
async_httpx_client = get_async_httpx_client(
@@ -190,6 +191,7 @@ class BaseLLMHTTPHandler:
optional_params=optional_params,
litellm_params=litellm_params,
encoding=encoding,
+ json_mode=json_mode,
)
def completion(
@@ -211,6 +213,7 @@ class BaseLLMHTTPHandler:
headers: Optional[dict] = {},
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
):
+ json_mode: bool = optional_params.pop("json_mode", False)
provider_config = ProviderConfigManager.get_provider_chat_config(
model=model, provider=litellm.LlmProviders(custom_llm_provider)
@@ -286,6 +289,7 @@ class BaseLLMHTTPHandler:
else None
),
litellm_params=litellm_params,
+ json_mode=json_mode,
)
else:
@@ -309,6 +313,7 @@ class BaseLLMHTTPHandler:
if client is not None and isinstance(client, AsyncHTTPHandler)
else None
),
+ json_mode=json_mode,
)
if stream is True:
@@ -327,6 +332,7 @@ class BaseLLMHTTPHandler:
data=data,
messages=messages,
client=client,
+ json_mode=json_mode,
)
completion_stream, headers = self.make_sync_call(
provider_config=provider_config,
@@ -380,6 +386,7 @@ class BaseLLMHTTPHandler:
optional_params=optional_params,
litellm_params=litellm_params,
encoding=encoding,
+ json_mode=json_mode,
)
def make_sync_call(
@@ -453,6 +460,7 @@ class BaseLLMHTTPHandler:
litellm_params: dict,
fake_stream: bool = False,
client: Optional[AsyncHTTPHandler] = None,
+ json_mode: Optional[bool] = None,
):
if provider_config.has_custom_stream_wrapper is True:
return provider_config.get_async_custom_stream_wrapper(
@@ -464,6 +472,7 @@ class BaseLLMHTTPHandler:
data=data,
messages=messages,
client=client,
+ json_mode=json_mode,
)
completion_stream, _response_headers = await self.make_async_call_stream_helper(
@@ -710,6 +719,7 @@ class BaseLLMHTTPHandler:
model: str,
custom_llm_provider: str,
logging_obj: LiteLLMLoggingObj,
+ provider_config: BaseRerankConfig,
optional_rerank_params: OptionalRerankParams,
timeout: Optional[Union[float, httpx.Timeout]],
model_response: RerankResponse,
@@ -720,9 +730,6 @@ class BaseLLMHTTPHandler:
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
) -> RerankResponse:
- provider_config = ProviderConfigManager.get_provider_rerank_config(
- model=model, provider=litellm.LlmProviders(custom_llm_provider)
- )
# get config from model, custom llm provider
headers = provider_config.validate_environment(
api_key=api_key,
@@ -866,7 +873,9 @@ class BaseLLMHTTPHandler:
elif isinstance(audio_file, bytes):
# Assume it's already binary data
binary_data = audio_file
- elif isinstance(audio_file, io.BufferedReader):
+ elif isinstance(audio_file, io.BufferedReader) or isinstance(
+ audio_file, io.BytesIO
+ ):
# Handle file-like objects
binary_data = audio_file.read()
diff --git a/litellm/llms/databricks/streaming_utils.py b/litellm/llms/databricks/streaming_utils.py
index 0deaa06988..2db53df908 100644
--- a/litellm/llms/databricks/streaming_utils.py
+++ b/litellm/llms/databricks/streaming_utils.py
@@ -89,7 +89,7 @@ class ModelResponseIterator:
raise RuntimeError(f"Error receiving chunk from stream: {e}")
try:
- chunk = chunk.replace("data:", "")
+ chunk = litellm.CustomStreamWrapper._strip_sse_data_from_chunk(chunk) or ""
chunk = chunk.strip()
if len(chunk) > 0:
json_chunk = json.loads(chunk)
@@ -134,7 +134,7 @@ class ModelResponseIterator:
raise RuntimeError(f"Error receiving chunk from stream: {e}")
try:
- chunk = chunk.replace("data:", "")
+ chunk = litellm.CustomStreamWrapper._strip_sse_data_from_chunk(chunk) or ""
chunk = chunk.strip()
if chunk == "[DONE]":
raise StopAsyncIteration
diff --git a/litellm/llms/deepseek/chat/transformation.py b/litellm/llms/deepseek/chat/transformation.py
index e6704de1a1..747129ddd8 100644
--- a/litellm/llms/deepseek/chat/transformation.py
+++ b/litellm/llms/deepseek/chat/transformation.py
@@ -34,3 +34,21 @@ class DeepSeekChatConfig(OpenAIGPTConfig):
) # type: ignore
dynamic_api_key = api_key or get_secret_str("DEEPSEEK_API_KEY")
return api_base, dynamic_api_key
+
+ def get_complete_url(
+ self,
+ api_base: Optional[str],
+ model: str,
+ optional_params: dict,
+ stream: Optional[bool] = None,
+ ) -> str:
+ """
+ If api_base is not provided, use the default DeepSeek /chat/completions endpoint.
+ """
+ if not api_base:
+ api_base = "https://api.deepseek.com/beta"
+
+ if not api_base.endswith("/chat/completions"):
+ api_base = f"{api_base}/chat/completions"
+
+ return api_base
diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py
index d64d7b6d29..1c82f24ac0 100644
--- a/litellm/llms/fireworks_ai/chat/transformation.py
+++ b/litellm/llms/fireworks_ai/chat/transformation.py
@@ -90,6 +90,11 @@ class FireworksAIConfig(OpenAIGPTConfig):
) -> dict:
supported_openai_params = self.get_supported_openai_params(model=model)
+ is_tools_set = any(
+ param == "tools" and value is not None
+ for param, value in non_default_params.items()
+ )
+
for param, value in non_default_params.items():
if param == "tool_choice":
if value == "required":
@@ -98,18 +103,30 @@ class FireworksAIConfig(OpenAIGPTConfig):
else:
# pass through the value of tool choice
optional_params["tool_choice"] = value
- elif (
- param == "response_format" and value.get("type", None) == "json_schema"
- ):
- optional_params["response_format"] = {
- "type": "json_object",
- "schema": value["json_schema"]["schema"],
- }
+ elif param == "response_format":
+
+ if (
+ is_tools_set
+ ): # fireworks ai doesn't support tools and response_format together
+ optional_params = self._add_response_format_to_tools(
+ optional_params=optional_params,
+ value=value,
+ is_response_format_supported=False,
+ enforce_tool_choice=False, # tools and response_format are both set, don't enforce tool_choice
+ )
+ elif "json_schema" in value:
+ optional_params["response_format"] = {
+ "type": "json_object",
+ "schema": value["json_schema"]["schema"],
+ }
+ else:
+ optional_params["response_format"] = value
elif param == "max_completion_tokens":
optional_params["max_tokens"] = value
elif param in supported_openai_params:
if value is not None:
optional_params[param] = value
+
return optional_params
def _add_transform_inline_image_block(
diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py
index 6aa4cf5b52..fbc1916dcc 100644
--- a/litellm/llms/gemini/chat/transformation.py
+++ b/litellm/llms/gemini/chat/transformation.py
@@ -114,12 +114,16 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig):
if element.get("type") == "image_url":
img_element = element
_image_url: Optional[str] = None
+ format: Optional[str] = None
if isinstance(img_element.get("image_url"), dict):
_image_url = img_element["image_url"].get("url") # type: ignore
+ format = img_element["image_url"].get("format") # type: ignore
else:
_image_url = img_element.get("image_url") # type: ignore
if _image_url and "https://" in _image_url:
- image_obj = convert_to_anthropic_image_obj(_image_url)
+ image_obj = convert_to_anthropic_image_obj(
+ _image_url, format=format
+ )
img_element["image_url"] = ( # type: ignore
convert_generic_image_chunk_to_openai_image_obj(
image_obj
diff --git a/litellm/llms/jina_ai/rerank/transformation.py b/litellm/llms/jina_ai/rerank/transformation.py
index 4adb9cb0ec..8d0a9b1431 100644
--- a/litellm/llms/jina_ai/rerank/transformation.py
+++ b/litellm/llms/jina_ai/rerank/transformation.py
@@ -44,6 +44,7 @@ class JinaAIRerankConfig(BaseRerankConfig):
rank_fields: Optional[List[str]] = None,
return_documents: Optional[bool] = True,
max_chunks_per_doc: Optional[int] = None,
+ max_tokens_per_doc: Optional[int] = None,
) -> OptionalRerankParams:
optional_params = {}
supported_params = self.get_supported_cohere_rerank_params(model)
diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py
index da981b6afb..283b2a2437 100644
--- a/litellm/llms/ollama/completion/transformation.py
+++ b/litellm/llms/ollama/completion/transformation.py
@@ -353,7 +353,7 @@ class OllamaConfig(BaseConfig):
def get_complete_url(
self,
- api_base: str,
+ api_base: Optional[str],
model: str,
optional_params: dict,
stream: Optional[bool] = None,
@@ -365,6 +365,8 @@ class OllamaConfig(BaseConfig):
Some providers need `model` in `api_base`
"""
+ if api_base is None:
+ api_base = "http://localhost:11434"
if api_base.endswith("/api/generate"):
url = api_base
else:
diff --git a/litellm/llms/ollama_chat.py b/litellm/llms/ollama_chat.py
index 1047012c2e..6f421680b4 100644
--- a/litellm/llms/ollama_chat.py
+++ b/litellm/llms/ollama_chat.py
@@ -1,7 +1,7 @@
import json
import time
import uuid
-from typing import Any, List, Optional
+from typing import Any, List, Optional, Union
import aiohttp
import httpx
@@ -9,7 +9,11 @@ from pydantic import BaseModel
import litellm
from litellm import verbose_logger
-from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
+from litellm.llms.custom_httpx.http_handler import (
+ AsyncHTTPHandler,
+ HTTPHandler,
+ get_async_httpx_client,
+)
from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
from litellm.types.llms.ollama import OllamaToolCall, OllamaToolCallFunction
from litellm.types.llms.openai import ChatCompletionAssistantToolCall
@@ -205,6 +209,7 @@ def get_ollama_response( # noqa: PLR0915
api_key: Optional[str] = None,
acompletion: bool = False,
encoding=None,
+ client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
):
if api_base.endswith("/api/chat"):
url = api_base
@@ -301,7 +306,11 @@ def get_ollama_response( # noqa: PLR0915
headers: Optional[dict] = None
if api_key is not None:
headers = {"Authorization": "Bearer {}".format(api_key)}
- response = litellm.module_level_client.post(
+
+ sync_client = litellm.module_level_client
+ if client is not None and isinstance(client, HTTPHandler):
+ sync_client = client
+ response = sync_client.post(
url=url,
json=data,
headers=headers,
@@ -508,6 +517,7 @@ async def ollama_async_streaming(
verbose_logger.exception(
"LiteLLM.ollama(): Exception occured - {}".format(str(e))
)
+ raise e
async def ollama_acompletion(
diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py
index 84a57bbaa6..1f34d63681 100644
--- a/litellm/llms/openai/chat/gpt_transformation.py
+++ b/litellm/llms/openai/chat/gpt_transformation.py
@@ -20,7 +20,11 @@ from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
from litellm.llms.base_llm.base_utils import BaseLLMModelInfo
from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
from litellm.secret_managers.main import get_secret_str
-from litellm.types.llms.openai import AllMessageValues
+from litellm.types.llms.openai import (
+ AllMessageValues,
+ ChatCompletionImageObject,
+ ChatCompletionImageUrlObject,
+)
from litellm.types.utils import ModelResponse, ModelResponseStream
from litellm.utils import convert_to_model_response_object
@@ -178,6 +182,27 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
def _transform_messages(
self, messages: List[AllMessageValues], model: str
) -> List[AllMessageValues]:
+ """OpenAI no longer supports image_url as a string, so we need to convert it to a dict"""
+ for message in messages:
+ message_content = message.get("content")
+ if message_content and isinstance(message_content, list):
+ for content_item in message_content:
+ if content_item.get("type") == "image_url":
+ content_item = cast(ChatCompletionImageObject, content_item)
+ if isinstance(content_item["image_url"], str):
+ content_item["image_url"] = {
+ "url": content_item["image_url"],
+ }
+ elif isinstance(content_item["image_url"], dict):
+ litellm_specific_params = {"format"}
+ new_image_url_obj = ChatCompletionImageUrlObject(
+ **{ # type: ignore
+ k: v
+ for k, v in content_item["image_url"].items()
+ if k not in litellm_specific_params
+ }
+ )
+ content_item["image_url"] = new_image_url_obj
return messages
def transform_request(
@@ -263,7 +288,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
def get_complete_url(
self,
- api_base: str,
+ api_base: Optional[str],
model: str,
optional_params: dict,
stream: Optional[bool] = None,
@@ -274,6 +299,8 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
Returns:
str: The complete URL for the API call.
"""
+ if api_base is None:
+ api_base = "https://api.openai.com"
endpoint = "chat/completions"
# Remove trailing slash from api_base if present
diff --git a/litellm/llms/openai/chat/o_series_transformation.py b/litellm/llms/openai/chat/o_series_transformation.py
index 9e68fca46f..b2ffda6e7d 100644
--- a/litellm/llms/openai/chat/o_series_transformation.py
+++ b/litellm/llms/openai/chat/o_series_transformation.py
@@ -19,6 +19,7 @@ from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage
from litellm.utils import (
supports_function_calling,
+ supports_parallel_function_calling,
supports_response_schema,
supports_system_messages,
)
@@ -76,14 +77,19 @@ class OpenAIOSeriesConfig(OpenAIGPTConfig):
model, custom_llm_provider
)
_supports_response_schema = supports_response_schema(model, custom_llm_provider)
+ _supports_parallel_tool_calls = supports_parallel_function_calling(
+ model, custom_llm_provider
+ )
if not _supports_function_calling:
non_supported_params.append("tools")
non_supported_params.append("tool_choice")
- non_supported_params.append("parallel_tool_calls")
non_supported_params.append("function_call")
non_supported_params.append("functions")
+ if not _supports_parallel_tool_calls:
+ non_supported_params.append("parallel_tool_calls")
+
if not _supports_response_schema:
non_supported_params.append("response_format")
@@ -146,4 +152,5 @@ class OpenAIOSeriesConfig(OpenAIGPTConfig):
)
messages[i] = new_message # Replace the old message with the new one
+ messages = super()._transform_messages(messages, model)
return messages
diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py
index 98a55b4bd3..a8412f867b 100644
--- a/litellm/llms/openai/common_utils.py
+++ b/litellm/llms/openai/common_utils.py
@@ -19,6 +19,7 @@ class OpenAIError(BaseLLMException):
request: Optional[httpx.Request] = None,
response: Optional[httpx.Response] = None,
headers: Optional[Union[dict, httpx.Headers]] = None,
+ body: Optional[dict] = None,
):
self.status_code = status_code
self.message = message
@@ -39,6 +40,7 @@ class OpenAIError(BaseLLMException):
headers=self.headers,
request=self.request,
response=self.response,
+ body=body,
)
diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py
index 5465a24945..7935c46293 100644
--- a/litellm/llms/openai/openai.py
+++ b/litellm/llms/openai/openai.py
@@ -37,6 +37,7 @@ from litellm.llms.custom_httpx.http_handler import _DEFAULT_TTL_FOR_HTTPX_CLIENT
from litellm.types.utils import (
EmbeddingResponse,
ImageResponse,
+ LiteLLMBatch,
ModelResponse,
ModelResponseStream,
)
@@ -731,10 +732,14 @@ class OpenAIChatCompletion(BaseLLM):
error_headers = getattr(e, "headers", None)
error_text = getattr(e, "text", str(e))
error_response = getattr(e, "response", None)
+ error_body = getattr(e, "body", None)
if error_headers is None and error_response:
error_headers = getattr(error_response, "headers", None)
raise OpenAIError(
- status_code=status_code, message=error_text, headers=error_headers
+ status_code=status_code,
+ message=error_text,
+ headers=error_headers,
+ body=error_body,
)
async def acompletion(
@@ -827,13 +832,17 @@ class OpenAIChatCompletion(BaseLLM):
except Exception as e:
exception_response = getattr(e, "response", None)
status_code = getattr(e, "status_code", 500)
+ exception_body = getattr(e, "body", None)
error_headers = getattr(e, "headers", None)
if error_headers is None and exception_response:
error_headers = getattr(exception_response, "headers", None)
message = getattr(e, "message", str(e))
raise OpenAIError(
- status_code=status_code, message=message, headers=error_headers
+ status_code=status_code,
+ message=message,
+ headers=error_headers,
+ body=exception_body,
)
def streaming(
@@ -972,6 +981,7 @@ class OpenAIChatCompletion(BaseLLM):
error_headers = getattr(e, "headers", None)
status_code = getattr(e, "status_code", 500)
error_response = getattr(e, "response", None)
+ exception_body = getattr(e, "body", None)
if error_headers is None and error_response:
error_headers = getattr(error_response, "headers", None)
if response is not None and hasattr(response, "text"):
@@ -979,6 +989,7 @@ class OpenAIChatCompletion(BaseLLM):
status_code=status_code,
message=f"{str(e)}\n\nOriginal Response: {response.text}", # type: ignore
headers=error_headers,
+ body=exception_body,
)
else:
if type(e).__name__ == "ReadTimeout":
@@ -986,16 +997,21 @@ class OpenAIChatCompletion(BaseLLM):
status_code=408,
message=f"{type(e).__name__}",
headers=error_headers,
+ body=exception_body,
)
elif hasattr(e, "status_code"):
raise OpenAIError(
status_code=getattr(e, "status_code", 500),
message=str(e),
headers=error_headers,
+ body=exception_body,
)
else:
raise OpenAIError(
- status_code=500, message=f"{str(e)}", headers=error_headers
+ status_code=500,
+ message=f"{str(e)}",
+ headers=error_headers,
+ body=exception_body,
)
def get_stream_options(
@@ -1755,9 +1771,9 @@ class OpenAIBatchesAPI(BaseLLM):
self,
create_batch_data: CreateBatchRequest,
openai_client: AsyncOpenAI,
- ) -> Batch:
+ ) -> LiteLLMBatch:
response = await openai_client.batches.create(**create_batch_data)
- return response
+ return LiteLLMBatch(**response.model_dump())
def create_batch(
self,
@@ -1769,7 +1785,7 @@ class OpenAIBatchesAPI(BaseLLM):
max_retries: Optional[int],
organization: Optional[str],
client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
- ) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+ ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
api_key=api_key,
api_base=api_base,
@@ -1792,17 +1808,18 @@ class OpenAIBatchesAPI(BaseLLM):
return self.acreate_batch( # type: ignore
create_batch_data=create_batch_data, openai_client=openai_client
)
- response = openai_client.batches.create(**create_batch_data)
- return response
+ response = cast(OpenAI, openai_client).batches.create(**create_batch_data)
+
+ return LiteLLMBatch(**response.model_dump())
async def aretrieve_batch(
self,
retrieve_batch_data: RetrieveBatchRequest,
openai_client: AsyncOpenAI,
- ) -> Batch:
+ ) -> LiteLLMBatch:
verbose_logger.debug("retrieving batch, args= %s", retrieve_batch_data)
response = await openai_client.batches.retrieve(**retrieve_batch_data)
- return response
+ return LiteLLMBatch(**response.model_dump())
def retrieve_batch(
self,
@@ -1837,8 +1854,8 @@ class OpenAIBatchesAPI(BaseLLM):
return self.aretrieve_batch( # type: ignore
retrieve_batch_data=retrieve_batch_data, openai_client=openai_client
)
- response = openai_client.batches.retrieve(**retrieve_batch_data)
- return response
+ response = cast(OpenAI, openai_client).batches.retrieve(**retrieve_batch_data)
+ return LiteLLMBatch(**response.model_dump())
async def acancel_batch(
self,
diff --git a/litellm/llms/openai/transcriptions/handler.py b/litellm/llms/openai/transcriptions/handler.py
index 5e1746319e..d9dd3c123b 100644
--- a/litellm/llms/openai/transcriptions/handler.py
+++ b/litellm/llms/openai/transcriptions/handler.py
@@ -112,6 +112,7 @@ class OpenAIAudioTranscription(OpenAIChatCompletion):
api_base=api_base,
timeout=timeout,
max_retries=max_retries,
+ client=client,
)
## LOGGING
diff --git a/litellm/llms/openrouter/chat/transformation.py b/litellm/llms/openrouter/chat/transformation.py
index 5a4c2ff209..4b95ec87cf 100644
--- a/litellm/llms/openrouter/chat/transformation.py
+++ b/litellm/llms/openrouter/chat/transformation.py
@@ -6,7 +6,16 @@ Calls done in OpenAI/openai.py as OpenRouter is openai-compatible.
Docs: https://openrouter.ai/docs/parameters
"""
+from typing import Any, AsyncIterator, Iterator, Optional, Union
+
+import httpx
+
+from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.types.utils import ModelResponse, ModelResponseStream
+
from ...openai.chat.gpt_transformation import OpenAIGPTConfig
+from ..common_utils import OpenRouterException
class OpenrouterConfig(OpenAIGPTConfig):
@@ -37,3 +46,43 @@ class OpenrouterConfig(OpenAIGPTConfig):
extra_body # openai client supports `extra_body` param
)
return mapped_openai_params
+
+ def get_error_class(
+ self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+ ) -> BaseLLMException:
+ return OpenRouterException(
+ message=error_message,
+ status_code=status_code,
+ headers=headers,
+ )
+
+ def get_model_response_iterator(
+ self,
+ streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
+ sync_stream: bool,
+ json_mode: Optional[bool] = False,
+ ) -> Any:
+ return OpenRouterChatCompletionStreamingHandler(
+ streaming_response=streaming_response,
+ sync_stream=sync_stream,
+ json_mode=json_mode,
+ )
+
+
+class OpenRouterChatCompletionStreamingHandler(BaseModelResponseIterator):
+
+ def chunk_parser(self, chunk: dict) -> ModelResponseStream:
+ try:
+ new_choices = []
+ for choice in chunk["choices"]:
+ choice["delta"]["reasoning_content"] = choice["delta"].get("reasoning")
+ new_choices.append(choice)
+ return ModelResponseStream(
+ id=chunk["id"],
+ object="chat.completion.chunk",
+ created=chunk["created"],
+ model=chunk["model"],
+ choices=new_choices,
+ )
+ except Exception as e:
+ raise e
diff --git a/litellm/llms/openrouter/common_utils.py b/litellm/llms/openrouter/common_utils.py
new file mode 100644
index 0000000000..96e53a5aae
--- /dev/null
+++ b/litellm/llms/openrouter/common_utils.py
@@ -0,0 +1,5 @@
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+
+
+class OpenRouterException(BaseLLMException):
+ pass
diff --git a/litellm/llms/perplexity/chat/transformation.py b/litellm/llms/perplexity/chat/transformation.py
index 8f71cc153f..dab64283ec 100644
--- a/litellm/llms/perplexity/chat/transformation.py
+++ b/litellm/llms/perplexity/chat/transformation.py
@@ -37,6 +37,7 @@ class PerplexityChatConfig(OpenAIGPTConfig):
"response_format",
"stream",
"temperature",
- "top_p" "max_retries",
+ "top_p",
+ "max_retries",
"extra_headers",
]
diff --git a/litellm/llms/replicate/chat/transformation.py b/litellm/llms/replicate/chat/transformation.py
index e9934dada8..39aaad6808 100644
--- a/litellm/llms/replicate/chat/transformation.py
+++ b/litellm/llms/replicate/chat/transformation.py
@@ -138,7 +138,7 @@ class ReplicateConfig(BaseConfig):
def get_complete_url(
self,
- api_base: str,
+ api_base: Optional[str],
model: str,
optional_params: dict,
stream: Optional[bool] = None,
diff --git a/litellm/llms/sagemaker/common_utils.py b/litellm/llms/sagemaker/common_utils.py
index 49e4989ff1..9884f420c3 100644
--- a/litellm/llms/sagemaker/common_utils.py
+++ b/litellm/llms/sagemaker/common_utils.py
@@ -3,6 +3,7 @@ from typing import AsyncIterator, Iterator, List, Optional, Union
import httpx
+import litellm
from litellm import verbose_logger
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.types.utils import GenericStreamingChunk as GChunk
@@ -78,7 +79,11 @@ class AWSEventStreamDecoder:
message = self._parse_message_from_event(event)
if message:
# remove data: prefix and "\n\n" at the end
- message = message.replace("data:", "").replace("\n\n", "")
+ message = (
+ litellm.CustomStreamWrapper._strip_sse_data_from_chunk(message)
+ or ""
+ )
+ message = message.replace("\n\n", "")
# Accumulate JSON data
accumulated_json += message
@@ -127,7 +132,11 @@ class AWSEventStreamDecoder:
if message:
verbose_logger.debug("sagemaker parsed chunk bytes %s", message)
# remove data: prefix and "\n\n" at the end
- message = message.replace("data:", "").replace("\n\n", "")
+ message = (
+ litellm.CustomStreamWrapper._strip_sse_data_from_chunk(message)
+ or ""
+ )
+ message = message.replace("\n\n", "")
# Accumulate JSON data
accumulated_json += message
diff --git a/litellm/llms/sagemaker/completion/handler.py b/litellm/llms/sagemaker/completion/handler.py
index 0a403dc484..4aff5f5d71 100644
--- a/litellm/llms/sagemaker/completion/handler.py
+++ b/litellm/llms/sagemaker/completion/handler.py
@@ -433,6 +433,10 @@ class SagemakerLLM(BaseAWSLLM):
"messages": messages,
}
prepared_request = await asyncified_prepare_request(**prepared_request_args)
+ if model_id is not None: # Fixes https://github.com/BerriAI/litellm/issues/8889
+ prepared_request.headers.update(
+ {"X-Amzn-SageMaker-Inference-Component": model_id}
+ )
completion_stream = await self.make_async_call(
api_base=prepared_request.url,
headers=prepared_request.headers, # type: ignore
@@ -511,7 +515,7 @@ class SagemakerLLM(BaseAWSLLM):
# Add model_id as InferenceComponentName header
# boto3 doc: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
prepared_request.headers.update(
- {"X-Amzn-SageMaker-Inference-Componen": model_id}
+ {"X-Amzn-SageMaker-Inference-Component": model_id}
)
# make async httpx post request here
try:
diff --git a/litellm/llms/sambanova/chat.py b/litellm/llms/sambanova/chat.py
index 4eea1914ce..abf55d44fb 100644
--- a/litellm/llms/sambanova/chat.py
+++ b/litellm/llms/sambanova/chat.py
@@ -11,7 +11,7 @@ from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
class SambanovaConfig(OpenAIGPTConfig):
"""
- Reference: https://community.sambanova.ai/t/create-chat-completion-api/
+ Reference: https://docs.sambanova.ai/cloud/api-reference/
Below are the parameters:
"""
diff --git a/litellm/llms/triton/completion/transformation.py b/litellm/llms/triton/completion/transformation.py
index 0cd6940063..4037c32365 100644
--- a/litellm/llms/triton/completion/transformation.py
+++ b/litellm/llms/triton/completion/transformation.py
@@ -3,7 +3,7 @@ Translates from OpenAI's `/v1/chat/completions` endpoint to Triton's `/generate`
"""
import json
-from typing import Any, Dict, List, Literal, Optional, Union
+from typing import Any, AsyncIterator, Dict, Iterator, List, Literal, Optional, Union
from httpx import Headers, Response
@@ -67,6 +67,20 @@ class TritonConfig(BaseConfig):
optional_params[param] = value
return optional_params
+ def get_complete_url(
+ self,
+ api_base: Optional[str],
+ model: str,
+ optional_params: dict,
+ stream: Optional[bool] = None,
+ ) -> str:
+ if api_base is None:
+ raise ValueError("api_base is required")
+ llm_type = self._get_triton_llm_type(api_base)
+ if llm_type == "generate" and stream:
+ return api_base + "_stream"
+ return api_base
+
def transform_response(
self,
model: str,
@@ -149,6 +163,18 @@ class TritonConfig(BaseConfig):
else:
raise ValueError(f"Invalid Triton API base: {api_base}")
+ def get_model_response_iterator(
+ self,
+ streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
+ sync_stream: bool,
+ json_mode: Optional[bool] = False,
+ ) -> Any:
+ return TritonResponseIterator(
+ streaming_response=streaming_response,
+ sync_stream=sync_stream,
+ json_mode=json_mode,
+ )
+
class TritonGenerateConfig(TritonConfig):
"""
@@ -204,7 +230,7 @@ class TritonGenerateConfig(TritonConfig):
return model_response
-class TritonInferConfig(TritonGenerateConfig):
+class TritonInferConfig(TritonConfig):
"""
Transformations for triton /infer endpoint (his is an infer model with a custom model on triton)
"""
diff --git a/litellm/llms/vertex_ai/batches/handler.py b/litellm/llms/vertex_ai/batches/handler.py
index 0274cd5b05..b82268bef6 100644
--- a/litellm/llms/vertex_ai/batches/handler.py
+++ b/litellm/llms/vertex_ai/batches/handler.py
@@ -9,8 +9,12 @@ from litellm.llms.custom_httpx.http_handler import (
get_async_httpx_client,
)
from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
-from litellm.types.llms.openai import Batch, CreateBatchRequest
-from litellm.types.llms.vertex_ai import VertexAIBatchPredictionJob
+from litellm.types.llms.openai import CreateBatchRequest
+from litellm.types.llms.vertex_ai import (
+ VERTEX_CREDENTIALS_TYPES,
+ VertexAIBatchPredictionJob,
+)
+from litellm.types.utils import LiteLLMBatch
from .transformation import VertexAIBatchTransformation
@@ -25,12 +29,12 @@ class VertexAIBatchPrediction(VertexLLM):
_is_async: bool,
create_batch_data: CreateBatchRequest,
api_base: Optional[str],
- vertex_credentials: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
vertex_project: Optional[str],
vertex_location: Optional[str],
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
- ) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+ ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
sync_handler = _get_httpx_client()
@@ -98,7 +102,7 @@ class VertexAIBatchPrediction(VertexLLM):
vertex_batch_request: VertexAIBatchPredictionJob,
api_base: str,
headers: Dict[str, str],
- ) -> Batch:
+ ) -> LiteLLMBatch:
client = get_async_httpx_client(
llm_provider=litellm.LlmProviders.VERTEX_AI,
)
@@ -130,12 +134,12 @@ class VertexAIBatchPrediction(VertexLLM):
_is_async: bool,
batch_id: str,
api_base: Optional[str],
- vertex_credentials: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
vertex_project: Optional[str],
vertex_location: Optional[str],
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
- ) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+ ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
sync_handler = _get_httpx_client()
access_token, project_id = self._ensure_access_token(
@@ -196,7 +200,7 @@ class VertexAIBatchPrediction(VertexLLM):
self,
api_base: str,
headers: Dict[str, str],
- ) -> Batch:
+ ) -> LiteLLMBatch:
client = get_async_httpx_client(
llm_provider=litellm.LlmProviders.VERTEX_AI,
)
diff --git a/litellm/llms/vertex_ai/batches/transformation.py b/litellm/llms/vertex_ai/batches/transformation.py
index 32cabdcf56..a97f312d48 100644
--- a/litellm/llms/vertex_ai/batches/transformation.py
+++ b/litellm/llms/vertex_ai/batches/transformation.py
@@ -4,8 +4,9 @@ from typing import Dict
from litellm.llms.vertex_ai.common_utils import (
_convert_vertex_datetime_to_openai_datetime,
)
-from litellm.types.llms.openai import Batch, BatchJobStatus, CreateBatchRequest
+from litellm.types.llms.openai import BatchJobStatus, CreateBatchRequest
from litellm.types.llms.vertex_ai import *
+from litellm.types.utils import LiteLLMBatch
class VertexAIBatchTransformation:
@@ -47,8 +48,8 @@ class VertexAIBatchTransformation:
@classmethod
def transform_vertex_ai_batch_response_to_openai_batch_response(
cls, response: VertexBatchPredictionResponse
- ) -> Batch:
- return Batch(
+ ) -> LiteLLMBatch:
+ return LiteLLMBatch(
id=cls._get_batch_id_from_vertex_ai_batch_response(response),
completion_window="24hrs",
created_at=_convert_vertex_datetime_to_openai_datetime(
diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py
index a412a1f0db..f7149c349a 100644
--- a/litellm/llms/vertex_ai/common_utils.py
+++ b/litellm/llms/vertex_ai/common_utils.py
@@ -170,6 +170,9 @@ def _build_vertex_schema(parameters: dict):
strip_field(
parameters, field_name="$schema"
) # 5. Remove $schema - json schema value, not supported by OpenAPI - causes vertex errors.
+ strip_field(
+ parameters, field_name="$id"
+ ) # 6. Remove id - json schema value, not supported by OpenAPI - causes vertex errors.
return parameters
diff --git a/litellm/llms/vertex_ai/files/handler.py b/litellm/llms/vertex_ai/files/handler.py
index 4bae106045..266169cdfb 100644
--- a/litellm/llms/vertex_ai/files/handler.py
+++ b/litellm/llms/vertex_ai/files/handler.py
@@ -9,6 +9,7 @@ from litellm.integrations.gcs_bucket.gcs_bucket_base import (
)
from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
from litellm.types.llms.openai import CreateFileRequest, FileObject
+from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES
from .transformation import VertexAIFilesTransformation
@@ -34,7 +35,7 @@ class VertexAIFilesHandler(GCSBucketBase):
self,
create_file_data: CreateFileRequest,
api_base: Optional[str],
- vertex_credentials: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
vertex_project: Optional[str],
vertex_location: Optional[str],
timeout: Union[float, httpx.Timeout],
@@ -70,7 +71,7 @@ class VertexAIFilesHandler(GCSBucketBase):
_is_async: bool,
create_file_data: CreateFileRequest,
api_base: Optional[str],
- vertex_credentials: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
vertex_project: Optional[str],
vertex_location: Optional[str],
timeout: Union[float, httpx.Timeout],
diff --git a/litellm/llms/vertex_ai/fine_tuning/handler.py b/litellm/llms/vertex_ai/fine_tuning/handler.py
index 8564b8cb69..3cf409c78e 100644
--- a/litellm/llms/vertex_ai/fine_tuning/handler.py
+++ b/litellm/llms/vertex_ai/fine_tuning/handler.py
@@ -13,6 +13,7 @@ from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import Ver
from litellm.types.fine_tuning import OpenAIFineTuningHyperparameters
from litellm.types.llms.openai import FineTuningJobCreate
from litellm.types.llms.vertex_ai import (
+ VERTEX_CREDENTIALS_TYPES,
FineTuneHyperparameters,
FineTuneJobCreate,
FineTunesupervisedTuningSpec,
@@ -222,7 +223,7 @@ class VertexFineTuningAPI(VertexLLM):
create_fine_tuning_job_data: FineTuningJobCreate,
vertex_project: Optional[str],
vertex_location: Optional[str],
- vertex_credentials: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
api_base: Optional[str],
timeout: Union[float, httpx.Timeout],
kwargs: Optional[dict] = None,
diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py
index 8109c8bf61..d6bafc7c60 100644
--- a/litellm/llms/vertex_ai/gemini/transformation.py
+++ b/litellm/llms/vertex_ai/gemini/transformation.py
@@ -55,10 +55,11 @@ else:
LiteLLMLoggingObj = Any
-def _process_gemini_image(image_url: str) -> PartType:
+def _process_gemini_image(image_url: str, format: Optional[str] = None) -> PartType:
"""
Given an image URL, return the appropriate PartType for Gemini
"""
+
try:
# GCS URIs
if "gs://" in image_url:
@@ -66,25 +67,30 @@ def _process_gemini_image(image_url: str) -> PartType:
extension_with_dot = os.path.splitext(image_url)[-1] # Ex: ".png"
extension = extension_with_dot[1:] # Ex: "png"
- file_type = get_file_type_from_extension(extension)
+ if not format:
+ file_type = get_file_type_from_extension(extension)
- # Validate the file type is supported by Gemini
- if not is_gemini_1_5_accepted_file_type(file_type):
- raise Exception(f"File type not supported by gemini - {file_type}")
+ # Validate the file type is supported by Gemini
+ if not is_gemini_1_5_accepted_file_type(file_type):
+ raise Exception(f"File type not supported by gemini - {file_type}")
- mime_type = get_file_mime_type_for_file_type(file_type)
+ mime_type = get_file_mime_type_for_file_type(file_type)
+ else:
+ mime_type = format
file_data = FileDataType(mime_type=mime_type, file_uri=image_url)
return PartType(file_data=file_data)
elif (
"https://" in image_url
- and (image_type := _get_image_mime_type_from_url(image_url)) is not None
+ and (image_type := format or _get_image_mime_type_from_url(image_url))
+ is not None
):
+
file_data = FileDataType(file_uri=image_url, mime_type=image_type)
return PartType(file_data=file_data)
elif "http://" in image_url or "https://" in image_url or "base64" in image_url:
# https links for unsupported mime types and base64 images
- image = convert_to_anthropic_image_obj(image_url)
+ image = convert_to_anthropic_image_obj(image_url, format=format)
_blob = BlobType(data=image["data"], mime_type=image["media_type"])
return PartType(inline_data=_blob)
raise Exception("Invalid image received - {}".format(image_url))
@@ -159,11 +165,15 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915
elif element["type"] == "image_url":
element = cast(ChatCompletionImageObject, element)
img_element = element
+ format: Optional[str] = None
if isinstance(img_element["image_url"], dict):
image_url = img_element["image_url"]["url"]
+ format = img_element["image_url"].get("format")
else:
image_url = img_element["image_url"]
- _part = _process_gemini_image(image_url=image_url)
+ _part = _process_gemini_image(
+ image_url=image_url, format=format
+ )
_parts.append(_part)
user_content.extend(_parts)
elif (
diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
index dff63ce148..294939a3c5 100644
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -40,6 +40,7 @@ from litellm.types.llms.openai import (
ChatCompletionUsageBlock,
)
from litellm.types.llms.vertex_ai import (
+ VERTEX_CREDENTIALS_TYPES,
Candidates,
ContentType,
FunctionCallingConfig,
@@ -930,7 +931,7 @@ class VertexLLM(VertexBase):
client: Optional[AsyncHTTPHandler] = None,
vertex_project: Optional[str] = None,
vertex_location: Optional[str] = None,
- vertex_credentials: Optional[str] = None,
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES] = None,
gemini_api_key: Optional[str] = None,
extra_headers: Optional[dict] = None,
) -> CustomStreamWrapper:
@@ -1018,11 +1019,10 @@ class VertexLLM(VertexBase):
client: Optional[AsyncHTTPHandler] = None,
vertex_project: Optional[str] = None,
vertex_location: Optional[str] = None,
- vertex_credentials: Optional[str] = None,
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES] = None,
gemini_api_key: Optional[str] = None,
extra_headers: Optional[dict] = None,
) -> Union[ModelResponse, CustomStreamWrapper]:
-
should_use_v1beta1_features = self.is_using_v1beta1_features(
optional_params=optional_params
)
@@ -1123,7 +1123,7 @@ class VertexLLM(VertexBase):
timeout: Optional[Union[float, httpx.Timeout]],
vertex_project: Optional[str],
vertex_location: Optional[str],
- vertex_credentials: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
gemini_api_key: Optional[str],
litellm_params: dict,
logger_fn=None,
@@ -1408,7 +1408,8 @@ class ModelResponseIterator:
return self.chunk_parser(chunk=json_chunk)
def handle_accumulated_json_chunk(self, chunk: str) -> GenericStreamingChunk:
- message = chunk.replace("data:", "").replace("\n\n", "")
+ chunk = litellm.CustomStreamWrapper._strip_sse_data_from_chunk(chunk) or ""
+ message = chunk.replace("\n\n", "")
# Accumulate JSON data
self.accumulated_json += message
@@ -1431,7 +1432,7 @@ class ModelResponseIterator:
def _common_chunk_parsing_logic(self, chunk: str) -> GenericStreamingChunk:
try:
- chunk = chunk.replace("data:", "")
+ chunk = litellm.CustomStreamWrapper._strip_sse_data_from_chunk(chunk) or ""
if len(chunk) > 0:
"""
Check if initial chunk valid json
diff --git a/litellm/llms/vertex_ai/image_generation/image_generation_handler.py b/litellm/llms/vertex_ai/image_generation/image_generation_handler.py
index bb39fcb1ad..1d5322c08d 100644
--- a/litellm/llms/vertex_ai/image_generation/image_generation_handler.py
+++ b/litellm/llms/vertex_ai/image_generation/image_generation_handler.py
@@ -11,6 +11,7 @@ from litellm.llms.custom_httpx.http_handler import (
get_async_httpx_client,
)
from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
+from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES
from litellm.types.utils import ImageResponse
@@ -44,7 +45,7 @@ class VertexImageGeneration(VertexLLM):
prompt: str,
vertex_project: Optional[str],
vertex_location: Optional[str],
- vertex_credentials: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
model_response: ImageResponse,
logging_obj: Any,
model: Optional[
@@ -139,7 +140,7 @@ class VertexImageGeneration(VertexLLM):
prompt: str,
vertex_project: Optional[str],
vertex_location: Optional[str],
- vertex_credentials: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
model_response: litellm.ImageResponse,
logging_obj: Any,
model: Optional[
diff --git a/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py b/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py
index 10c73e815c..18bc72db46 100644
--- a/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py
+++ b/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py
@@ -9,6 +9,7 @@ from litellm.llms.custom_httpx.http_handler import (
)
from litellm.llms.openai.openai import HttpxBinaryResponseContent
from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
+from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES
class VertexInput(TypedDict, total=False):
@@ -45,7 +46,7 @@ class VertexTextToSpeechAPI(VertexLLM):
logging_obj,
vertex_project: Optional[str],
vertex_location: Optional[str],
- vertex_credentials: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
api_base: Optional[str],
timeout: Union[float, httpx.Timeout],
model: str,
diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
index ad52472130..fb2393631b 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
@@ -160,7 +160,8 @@ class VertexAIPartnerModels(VertexBase):
url=default_api_base,
)
- model = model.split("@")[0]
+ if "codestral" in model or "mistral" in model:
+ model = model.split("@")[0]
if "codestral" in model and litellm_params.get("text_completion") is True:
optional_params["model"] = model
diff --git a/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py b/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py
index 0f73db30a0..3ef40703e8 100644
--- a/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py
+++ b/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py
@@ -41,7 +41,7 @@ class VertexEmbedding(VertexBase):
client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
vertex_project: Optional[str] = None,
vertex_location: Optional[str] = None,
- vertex_credentials: Optional[str] = None,
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES] = None,
gemini_api_key: Optional[str] = None,
extra_headers: Optional[dict] = None,
) -> EmbeddingResponse:
@@ -148,7 +148,7 @@ class VertexEmbedding(VertexBase):
client: Optional[AsyncHTTPHandler] = None,
vertex_project: Optional[str] = None,
vertex_location: Optional[str] = None,
- vertex_credentials: Optional[str] = None,
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES] = None,
gemini_api_key: Optional[str] = None,
extra_headers: Optional[dict] = None,
encoding=None,
diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py
index 71346a2e01..8286cb515f 100644
--- a/litellm/llms/vertex_ai/vertex_llm_base.py
+++ b/litellm/llms/vertex_ai/vertex_llm_base.py
@@ -12,6 +12,7 @@ from litellm._logging import verbose_logger
from litellm.litellm_core_utils.asyncify import asyncify
from litellm.llms.base import BaseLLM
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES
from .common_utils import _get_gemini_url, _get_vertex_url, all_gemini_url_modes
@@ -34,7 +35,7 @@ class VertexBase(BaseLLM):
return vertex_region or "us-central1"
def load_auth(
- self, credentials: Optional[str], project_id: Optional[str]
+ self, credentials: Optional[VERTEX_CREDENTIALS_TYPES], project_id: Optional[str]
) -> Tuple[Any, str]:
import google.auth as google_auth
from google.auth import identity_pool
@@ -42,29 +43,36 @@ class VertexBase(BaseLLM):
Request, # type: ignore[import-untyped]
)
- if credentials is not None and isinstance(credentials, str):
+ if credentials is not None:
import google.oauth2.service_account
- verbose_logger.debug(
- "Vertex: Loading vertex credentials from %s", credentials
- )
- verbose_logger.debug(
- "Vertex: checking if credentials is a valid path, os.path.exists(%s)=%s, current dir %s",
- credentials,
- os.path.exists(credentials),
- os.getcwd(),
- )
+ if isinstance(credentials, str):
+ verbose_logger.debug(
+ "Vertex: Loading vertex credentials from %s", credentials
+ )
+ verbose_logger.debug(
+ "Vertex: checking if credentials is a valid path, os.path.exists(%s)=%s, current dir %s",
+ credentials,
+ os.path.exists(credentials),
+ os.getcwd(),
+ )
- try:
- if os.path.exists(credentials):
- json_obj = json.load(open(credentials))
- else:
- json_obj = json.loads(credentials)
- except Exception:
- raise Exception(
- "Unable to load vertex credentials from environment. Got={}".format(
- credentials
+ try:
+ if os.path.exists(credentials):
+ json_obj = json.load(open(credentials))
+ else:
+ json_obj = json.loads(credentials)
+ except Exception:
+ raise Exception(
+ "Unable to load vertex credentials from environment. Got={}".format(
+ credentials
+ )
)
+ elif isinstance(credentials, dict):
+ json_obj = credentials
+ else:
+ raise ValueError(
+ "Invalid credentials type: {}".format(type(credentials))
)
# Check if the JSON object contains Workload Identity Federation configuration
@@ -109,7 +117,7 @@ class VertexBase(BaseLLM):
def _ensure_access_token(
self,
- credentials: Optional[str],
+ credentials: Optional[VERTEX_CREDENTIALS_TYPES],
project_id: Optional[str],
custom_llm_provider: Literal[
"vertex_ai", "vertex_ai_beta", "gemini"
@@ -202,7 +210,7 @@ class VertexBase(BaseLLM):
gemini_api_key: Optional[str],
vertex_project: Optional[str],
vertex_location: Optional[str],
- vertex_credentials: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
stream: Optional[bool],
custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"],
api_base: Optional[str],
@@ -253,7 +261,7 @@ class VertexBase(BaseLLM):
async def _ensure_access_token_async(
self,
- credentials: Optional[str],
+ credentials: Optional[VERTEX_CREDENTIALS_TYPES],
project_id: Optional[str],
custom_llm_provider: Literal[
"vertex_ai", "vertex_ai_beta", "gemini"
diff --git a/litellm/llms/watsonx/chat/transformation.py b/litellm/llms/watsonx/chat/transformation.py
index 208da82ef5..d5e0ed6544 100644
--- a/litellm/llms/watsonx/chat/transformation.py
+++ b/litellm/llms/watsonx/chat/transformation.py
@@ -80,7 +80,7 @@ class IBMWatsonXChatConfig(IBMWatsonXMixin, OpenAIGPTConfig):
def get_complete_url(
self,
- api_base: str,
+ api_base: Optional[str],
model: str,
optional_params: dict,
stream: Optional[bool] = None,
diff --git a/litellm/llms/watsonx/completion/transformation.py b/litellm/llms/watsonx/completion/transformation.py
index ebebbde021..7a4df23944 100644
--- a/litellm/llms/watsonx/completion/transformation.py
+++ b/litellm/llms/watsonx/completion/transformation.py
@@ -315,7 +315,7 @@ class IBMWatsonXAIConfig(IBMWatsonXMixin, BaseConfig):
def get_complete_url(
self,
- api_base: str,
+ api_base: Optional[str],
model: str,
optional_params: dict,
stream: Optional[bool] = None,
diff --git a/litellm/main.py b/litellm/main.py
index cc74080245..b90030a6bb 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -94,7 +94,7 @@ from litellm.utils import (
read_config_args,
supports_httpx_timeout,
token_counter,
- validate_chat_completion_messages,
+ validate_and_fix_openai_messages,
validate_chat_completion_tool_choice,
)
@@ -166,6 +166,7 @@ from .llms.vertex_ai.vertex_model_garden.main import VertexAIModelGardenModels
from .llms.vllm.completion import handler as vllm_handler
from .llms.watsonx.chat.handler import WatsonXChatHandler
from .llms.watsonx.common_utils import IBMWatsonXMixin
+from .types.llms.anthropic import AnthropicThinkingParam
from .types.llms.openai import (
ChatCompletionAssistantMessage,
ChatCompletionAudioParam,
@@ -341,6 +342,7 @@ async def acompletion(
model_list: Optional[list] = None, # pass in a list of api_base,keys, etc.
extra_headers: Optional[dict] = None,
# Optional liteLLM function params
+ thinking: Optional[AnthropicThinkingParam] = None,
**kwargs,
) -> Union[ModelResponse, CustomStreamWrapper]:
"""
@@ -431,6 +433,7 @@ async def acompletion(
"reasoning_effort": reasoning_effort,
"extra_headers": extra_headers,
"acompletion": True, # assuming this is a required parameter
+ "thinking": thinking,
}
if custom_llm_provider is None:
_, custom_llm_provider, _, _ = get_llm_provider(
@@ -800,6 +803,7 @@ def completion( # type: ignore # noqa: PLR0915
api_key: Optional[str] = None,
model_list: Optional[list] = None, # pass in a list of api_base,keys, etc.
# Optional liteLLM function params
+ thinking: Optional[AnthropicThinkingParam] = None,
**kwargs,
) -> Union[ModelResponse, CustomStreamWrapper]:
"""
@@ -851,7 +855,7 @@ def completion( # type: ignore # noqa: PLR0915
if model is None:
raise ValueError("model param not passed in.")
# validate messages
- messages = validate_chat_completion_messages(messages=messages)
+ messages = validate_and_fix_openai_messages(messages=messages)
# validate tool_choice
tool_choice = validate_chat_completion_tool_choice(tool_choice=tool_choice)
######### unpacking kwargs #####################
@@ -1106,6 +1110,7 @@ def completion( # type: ignore # noqa: PLR0915
parallel_tool_calls=parallel_tool_calls,
messages=messages,
reasoning_effort=reasoning_effort,
+ thinking=thinking,
**non_default_params,
)
@@ -1154,6 +1159,9 @@ def completion( # type: ignore # noqa: PLR0915
prompt_id=prompt_id,
prompt_variables=prompt_variables,
ssl_verify=ssl_verify,
+ merge_reasoning_content_in_choices=kwargs.get(
+ "merge_reasoning_content_in_choices", None
+ ),
)
logging.update_environment_variables(
model=model,
@@ -2266,23 +2274,22 @@ def completion( # type: ignore # noqa: PLR0915
data = {"model": model, "messages": messages, **optional_params}
## COMPLETION CALL
- response = openai_like_chat_completion.completion(
+ response = base_llm_http_handler.completion(
model=model,
+ stream=stream,
messages=messages,
- headers=headers,
- api_key=api_key,
+ acompletion=acompletion,
api_base=api_base,
model_response=model_response,
- print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
- logger_fn=logger_fn,
- logging_obj=logging,
- acompletion=acompletion,
- timeout=timeout, # type: ignore
custom_llm_provider="openrouter",
- custom_prompt_dict=custom_prompt_dict,
+ timeout=timeout,
+ headers=headers,
encoding=encoding,
+ api_key=api_key,
+ logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
+ client=client,
)
## LOGGING
logging.post_call(
@@ -2848,6 +2855,7 @@ def completion( # type: ignore # noqa: PLR0915
acompletion=acompletion,
model_response=model_response,
encoding=encoding,
+ client=client,
)
if acompletion is True or optional_params.get("stream", False) is True:
return generator
@@ -3409,6 +3417,7 @@ def embedding( # noqa: PLR0915
or custom_llm_provider == "openai"
or custom_llm_provider == "together_ai"
or custom_llm_provider == "nvidia_nim"
+ or custom_llm_provider == "litellm_proxy"
):
api_base = (
api_base
@@ -3485,7 +3494,8 @@ def embedding( # noqa: PLR0915
# set API KEY
if api_key is None:
api_key = (
- litellm.api_key
+ api_key
+ or litellm.api_key
or litellm.openai_like_key
or get_secret_str("OPENAI_LIKE_API_KEY")
)
@@ -3890,42 +3900,19 @@ async def atext_completion(
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
- _, custom_llm_provider, _, _ = get_llm_provider(
- model=model, api_base=kwargs.get("api_base", None)
- )
-
- if (
- custom_llm_provider == "openai"
- or custom_llm_provider == "azure"
- or custom_llm_provider == "azure_text"
- or custom_llm_provider == "custom_openai"
- or custom_llm_provider == "anyscale"
- or custom_llm_provider == "mistral"
- or custom_llm_provider == "openrouter"
- or custom_llm_provider == "deepinfra"
- or custom_llm_provider == "perplexity"
- or custom_llm_provider == "groq"
- or custom_llm_provider == "nvidia_nim"
- or custom_llm_provider == "cerebras"
- or custom_llm_provider == "sambanova"
- or custom_llm_provider == "ai21_chat"
- or custom_llm_provider == "ai21"
- or custom_llm_provider == "volcengine"
- or custom_llm_provider == "text-completion-codestral"
- or custom_llm_provider == "deepseek"
- or custom_llm_provider == "text-completion-openai"
- or custom_llm_provider == "huggingface"
- or custom_llm_provider == "ollama"
- or custom_llm_provider == "vertex_ai"
- or custom_llm_provider in litellm.openai_compatible_providers
- ): # currently implemented aiohttp calls for just azure and openai, soon all.
- # Await normally
- response = await loop.run_in_executor(None, func_with_context)
- if asyncio.iscoroutine(response):
- response = await response
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if isinstance(init_response, dict) or isinstance(
+ init_response, TextCompletionResponse
+ ): ## CACHING SCENARIO
+ if isinstance(init_response, dict):
+ response = TextCompletionResponse(**init_response)
+ else:
+ response = init_response
+ elif asyncio.iscoroutine(init_response):
+ response = await init_response
else:
- # Call the synchronous function using run_in_executor
- response = await loop.run_in_executor(None, func_with_context)
+ response = init_response # type: ignore
+
if (
kwargs.get("stream", False) is True
or isinstance(response, TextCompletionStreamWrapper)
@@ -4514,6 +4501,7 @@ def image_generation( # noqa: PLR0915
non_default_params = {
k: v for k, v in kwargs.items() if k not in default_params
} # model-specific params - pass them straight to the model/provider
+
optional_params = get_optional_params_image_gen(
model=model,
n=n,
@@ -4525,6 +4513,7 @@ def image_generation( # noqa: PLR0915
custom_llm_provider=custom_llm_provider,
**non_default_params,
)
+
logging: Logging = litellm_logging_obj
logging.update_environment_variables(
model=model,
@@ -4596,7 +4585,10 @@ def image_generation( # noqa: PLR0915
client=client,
headers=headers,
)
- elif custom_llm_provider == "openai":
+ elif (
+ custom_llm_provider == "openai"
+ or custom_llm_provider in litellm.openai_compatible_providers
+ ):
model_response = openai_chat_completions.image_generation(
model=model,
prompt=prompt,
@@ -4620,6 +4612,7 @@ def image_generation( # noqa: PLR0915
optional_params=optional_params,
model_response=model_response,
aimg_generation=aimg_generation,
+ client=client,
)
elif custom_llm_provider == "vertex_ai":
vertex_ai_project = (
@@ -5042,8 +5035,7 @@ def transcription(
)
elif (
custom_llm_provider == "openai"
- or custom_llm_provider == "groq"
- or custom_llm_provider == "fireworks_ai"
+ or custom_llm_provider in litellm.openai_compatible_providers
):
api_base = (
api_base
@@ -5201,7 +5193,10 @@ def speech(
custom_llm_provider=custom_llm_provider,
)
response: Optional[HttpxBinaryResponseContent] = None
- if custom_llm_provider == "openai":
+ if (
+ custom_llm_provider == "openai"
+ or custom_llm_provider in litellm.openai_compatible_providers
+ ):
if voice is None or not (isinstance(voice, str)):
raise litellm.BadRequestError(
message="'voice' is required to be passed as a string for OpenAI TTS",
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 5e8d9353ad..04b2f7f6fc 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -6,7 +6,7 @@
"input_cost_per_token": 0.0000,
"output_cost_per_token": 0.000,
"litellm_provider": "one of https://docs.litellm.ai/docs/providers",
- "mode": "one of chat, embedding, completion, image_generation, audio_transcription, audio_speech",
+ "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_vision": true,
@@ -76,6 +76,44 @@
"supports_system_messages": true,
"supports_tool_choice": true
},
+ "gpt-4.5-preview": {
+ "max_tokens": 16384,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 16384,
+ "input_cost_per_token": 0.000075,
+ "output_cost_per_token": 0.00015,
+ "input_cost_per_token_batches": 0.0000375,
+ "output_cost_per_token_batches": 0.000075,
+ "cache_read_input_token_cost": 0.0000375,
+ "litellm_provider": "openai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_response_schema": true,
+ "supports_vision": true,
+ "supports_prompt_caching": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true
+ },
+ "gpt-4.5-preview-2025-02-27": {
+ "max_tokens": 16384,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 16384,
+ "input_cost_per_token": 0.000075,
+ "output_cost_per_token": 0.00015,
+ "input_cost_per_token_batches": 0.0000375,
+ "output_cost_per_token_batches": 0.000075,
+ "cache_read_input_token_cost": 0.0000375,
+ "litellm_provider": "openai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_response_schema": true,
+ "supports_vision": true,
+ "supports_prompt_caching": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true
+ },
"gpt-4o-audio-preview": {
"max_tokens": 16384,
"max_input_tokens": 128000,
@@ -893,7 +931,7 @@
"input_cost_per_token": 0.000000,
"output_cost_per_token": 0.000000,
"litellm_provider": "openai",
- "mode": "moderations"
+ "mode": "moderation"
},
"text-moderation-007": {
"max_tokens": 32768,
@@ -902,7 +940,7 @@
"input_cost_per_token": 0.000000,
"output_cost_per_token": 0.000000,
"litellm_provider": "openai",
- "mode": "moderations"
+ "mode": "moderation"
},
"text-moderation-latest": {
"max_tokens": 32768,
@@ -911,7 +949,7 @@
"input_cost_per_token": 0.000000,
"output_cost_per_token": 0.000000,
"litellm_provider": "openai",
- "mode": "moderations"
+ "mode": "moderation"
},
"256-x-256/dall-e-2": {
"mode": "image_generation",
@@ -1030,9 +1068,9 @@
"max_tokens": 65536,
"max_input_tokens": 128000,
"max_output_tokens": 65536,
- "input_cost_per_token": 0.000003,
- "output_cost_per_token": 0.000012,
- "cache_read_input_token_cost": 0.0000015,
+ "input_cost_per_token": 0.00000121,
+ "output_cost_per_token": 0.00000484,
+ "cache_read_input_token_cost": 0.000000605,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true,
@@ -1044,9 +1082,9 @@
"max_tokens": 65536,
"max_input_tokens": 128000,
"max_output_tokens": 65536,
- "input_cost_per_token": 0.000003,
- "output_cost_per_token": 0.000012,
- "cache_read_input_token_cost": 0.0000015,
+ "input_cost_per_token": 0.00000121,
+ "output_cost_per_token": 0.00000484,
+ "cache_read_input_token_cost": 0.000000605,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true,
@@ -1409,7 +1447,7 @@
"mode": "chat",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
- "deprecation_date": "2025-03-31",
+ "deprecation_date": "2025-05-31",
"supports_tool_choice": true
},
"azure/gpt-3.5-turbo-0125": {
@@ -1587,13 +1625,23 @@
"max_tokens": 8192,
"max_input_tokens": 128000,
"max_output_tokens": 8192,
- "input_cost_per_token": 0.0,
- "input_cost_per_token_cache_hit": 0.0,
- "output_cost_per_token": 0.0,
+ "input_cost_per_token": 0.00000135,
+ "output_cost_per_token": 0.0000054,
"litellm_provider": "azure_ai",
"mode": "chat",
- "supports_prompt_caching": true,
- "supports_tool_choice": true
+ "supports_tool_choice": true,
+ "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/deepseek-r1-improved-performance-higher-limits-and-transparent-pricing/4386367"
+ },
+ "azure_ai/deepseek-v3": {
+ "max_tokens": 8192,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.00000114,
+ "output_cost_per_token": 0.00000456,
+ "litellm_provider": "azure_ai",
+ "mode": "chat",
+ "supports_tool_choice": true,
+ "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/announcing-deepseek-v3-on-azure-ai-foundry-and-github/4390438"
},
"azure_ai/jamba-instruct": {
"max_tokens": 4096,
@@ -1605,6 +1653,17 @@
"mode": "chat",
"supports_tool_choice": true
},
+ "azure_ai/mistral-nemo": {
+ "max_tokens": 4096,
+ "max_input_tokens": 131072,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.00000015,
+ "output_cost_per_token": 0.00000015,
+ "litellm_provider": "azure_ai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice"
+ },
"azure_ai/mistral-large": {
"max_tokens": 8191,
"max_input_tokens": 32000,
@@ -1732,6 +1791,43 @@
"source":"https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice",
"supports_tool_choice": true
},
+ "azure_ai/Phi-4-mini-instruct": {
+ "max_tokens": 4096,
+ "max_input_tokens": 131072,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0,
+ "output_cost_per_token": 0,
+ "litellm_provider": "azure_ai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/models-featured#microsoft"
+ },
+ "azure_ai/Phi-4-multimodal-instruct": {
+ "max_tokens": 4096,
+ "max_input_tokens": 131072,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0,
+ "output_cost_per_token": 0,
+ "litellm_provider": "azure_ai",
+ "mode": "chat",
+ "supports_audio_input": true,
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/models-featured#microsoft"
+ },
+ "azure_ai/Phi-4": {
+ "max_tokens": 16384,
+ "max_input_tokens": 16384,
+ "max_output_tokens": 16384,
+ "input_cost_per_token": 0.000000125,
+ "output_cost_per_token": 0.0000005,
+ "litellm_provider": "azure_ai",
+ "mode": "chat",
+ "supports_vision": false,
+ "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/affordable-innovation-unveiling-the-pricing-of-phi-3-slms-on-models-as-a-service/4156495",
+ "supports_function_calling": true,
+ "supports_tool_choice": true
+ },
"azure_ai/Phi-3.5-mini-instruct": {
"max_tokens": 4096,
"max_input_tokens": 128000,
@@ -1943,8 +2039,8 @@
"max_tokens": 8191,
"max_input_tokens": 32000,
"max_output_tokens": 8191,
- "input_cost_per_token": 0.000001,
- "output_cost_per_token": 0.000003,
+ "input_cost_per_token": 0.0000001,
+ "output_cost_per_token": 0.0000003,
"litellm_provider": "mistral",
"supports_function_calling": true,
"mode": "chat",
@@ -1955,8 +2051,8 @@
"max_tokens": 8191,
"max_input_tokens": 32000,
"max_output_tokens": 8191,
- "input_cost_per_token": 0.000001,
- "output_cost_per_token": 0.000003,
+ "input_cost_per_token": 0.0000001,
+ "output_cost_per_token": 0.0000003,
"litellm_provider": "mistral",
"supports_function_calling": true,
"mode": "chat",
@@ -2731,6 +2827,26 @@
"supports_tool_choice": true
},
"claude-3-5-haiku-20241022": {
+ "max_tokens": 8192,
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0000008,
+ "output_cost_per_token": 0.000004,
+ "cache_creation_input_token_cost": 0.000001,
+ "cache_read_input_token_cost": 0.0000008,
+ "litellm_provider": "anthropic",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 264,
+ "supports_assistant_prefill": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "deprecation_date": "2025-10-01",
+ "supports_tool_choice": true
+ },
+ "claude-3-5-haiku-latest": {
"max_tokens": 8192,
"max_input_tokens": 200000,
"max_output_tokens": 8192,
@@ -2741,13 +2857,34 @@
"litellm_provider": "anthropic",
"mode": "chat",
"supports_function_calling": true,
+ "supports_vision": true,
"tool_use_system_prompt_tokens": 264,
"supports_assistant_prefill": true,
+ "supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_response_schema": true,
"deprecation_date": "2025-10-01",
"supports_tool_choice": true
},
+ "claude-3-opus-latest": {
+ "max_tokens": 4096,
+ "max_input_tokens": 200000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.000015,
+ "output_cost_per_token": 0.000075,
+ "cache_creation_input_token_cost": 0.00001875,
+ "cache_read_input_token_cost": 0.0000015,
+ "litellm_provider": "anthropic",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 395,
+ "supports_assistant_prefill": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "deprecation_date": "2025-03-01",
+ "supports_tool_choice": true
+ },
"claude-3-opus-20240229": {
"max_tokens": 4096,
"max_input_tokens": 200000,
@@ -2784,6 +2921,26 @@
"deprecation_date": "2025-07-21",
"supports_tool_choice": true
},
+ "claude-3-5-sonnet-latest": {
+ "max_tokens": 8192,
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.000003,
+ "output_cost_per_token": 0.000015,
+ "cache_creation_input_token_cost": 0.00000375,
+ "cache_read_input_token_cost": 0.0000003,
+ "litellm_provider": "anthropic",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 159,
+ "supports_assistant_prefill": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "deprecation_date": "2025-06-01",
+ "supports_tool_choice": true
+ },
"claude-3-5-sonnet-20240620": {
"max_tokens": 8192,
"max_input_tokens": 200000,
@@ -2798,11 +2955,52 @@
"supports_vision": true,
"tool_use_system_prompt_tokens": 159,
"supports_assistant_prefill": true,
+ "supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_response_schema": true,
"deprecation_date": "2025-06-01",
"supports_tool_choice": true
},
+ "claude-3-7-sonnet-latest": {
+ "max_tokens": 128000,
+ "max_input_tokens": 200000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.000003,
+ "output_cost_per_token": 0.000015,
+ "cache_creation_input_token_cost": 0.00000375,
+ "cache_read_input_token_cost": 0.0000003,
+ "litellm_provider": "anthropic",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 159,
+ "supports_assistant_prefill": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "deprecation_date": "2025-06-01",
+ "supports_tool_choice": true
+ },
+ "claude-3-7-sonnet-20250219": {
+ "max_tokens": 128000,
+ "max_input_tokens": 200000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.000003,
+ "output_cost_per_token": 0.000015,
+ "cache_creation_input_token_cost": 0.00000375,
+ "cache_read_input_token_cost": 0.0000003,
+ "litellm_provider": "anthropic",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 159,
+ "supports_assistant_prefill": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "deprecation_date": "2026-02-01",
+ "supports_tool_choice": true
+ },
"claude-3-5-sonnet-20241022": {
"max_tokens": 8192,
"max_input_tokens": 200000,
@@ -4012,6 +4210,7 @@
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
+ "supports_pdf_input": true,
"supports_vision": true,
"supports_assistant_prefill": true,
"supports_tool_choice": true
@@ -4025,6 +4224,7 @@
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
+ "supports_pdf_input": true,
"supports_vision": true,
"supports_assistant_prefill": true,
"supports_tool_choice": true
@@ -4038,6 +4238,7 @@
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
+ "supports_pdf_input": true,
"supports_vision": true,
"supports_assistant_prefill": true,
"supports_tool_choice": true
@@ -4051,10 +4252,31 @@
"litellm_provider": "vertex_ai-anthropic_models",
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+ "supports_pdf_input": true,
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},
+ "vertex_ai/claude-3-7-sonnet@20250219": {
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+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.000003,
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+ "cache_creation_input_token_cost": 0.00000375,
+ "cache_read_input_token_cost": 0.0000003,
+ "litellm_provider": "vertex_ai-anthropic_models",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 159,
+ "supports_assistant_prefill": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "deprecation_date": "2025-06-01",
+ "supports_tool_choice": true
+ },
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"max_tokens": 4096,
"max_input_tokens": 200000,
@@ -4090,6 +4312,7 @@
"litellm_provider": "vertex_ai-anthropic_models",
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+ "supports_pdf_input": true,
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},
@@ -4102,6 +4325,7 @@
"litellm_provider": "vertex_ai-anthropic_models",
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+ "supports_pdf_input": true,
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},
@@ -4332,6 +4556,12 @@
"mode": "image_generation",
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
},
+ "vertex_ai/imagen-3.0-generate-002": {
+ "output_cost_per_image": 0.04,
+ "litellm_provider": "vertex_ai-image-models",
+ "mode": "image_generation",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
+ },
"vertex_ai/imagen-3.0-generate-001": {
"output_cost_per_image": 0.04,
"litellm_provider": "vertex_ai-image-models",
@@ -5457,6 +5687,35 @@
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},
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+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.000003,
+ "output_cost_per_token": 0.000015,
+ "input_cost_per_image": 0.0048,
+ "litellm_provider": "openrouter",
+ "mode": "chat",
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+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 159,
+ "supports_assistant_prefill": true,
+ "supports_tool_choice": true
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+ "openrouter/anthropic/claude-3.7-sonnet:beta": {
+ "max_tokens": 8192,
+ "max_input_tokens": 200000,
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+ "input_cost_per_token": 0.000003,
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+ "input_cost_per_image": 0.0048,
+ "litellm_provider": "openrouter",
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+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 159,
+ "supports_tool_choice": true
+ },
"openrouter/anthropic/claude-3-sonnet": {
"max_tokens": 200000,
"input_cost_per_token": 0.000003,
@@ -5869,6 +6128,26 @@
"mode": "chat",
"supports_tool_choice": true
},
+ "jamba-large-1.6": {
+ "max_tokens": 256000,
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "input_cost_per_token": 0.000002,
+ "output_cost_per_token": 0.000008,
+ "litellm_provider": "ai21",
+ "mode": "chat",
+ "supports_tool_choice": true
+ },
+ "jamba-mini-1.6": {
+ "max_tokens": 256000,
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "input_cost_per_token": 0.0000002,
+ "output_cost_per_token": 0.0000004,
+ "litellm_provider": "ai21",
+ "mode": "chat",
+ "supports_tool_choice": true
+ },
"j2-mid": {
"max_tokens": 8192,
"max_input_tokens": 8192,
@@ -6237,6 +6516,18 @@
"supports_prompt_caching": true,
"supports_response_schema": true
},
+ "eu.amazon.nova-micro-v1:0": {
+ "max_tokens": 4096,
+ "max_input_tokens": 300000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.000000046,
+ "output_cost_per_token": 0.000000184,
+ "litellm_provider": "bedrock_converse",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true
+ },
"amazon.nova-lite-v1:0": {
"max_tokens": 4096,
"max_input_tokens": 128000,
@@ -6265,6 +6556,20 @@
"supports_prompt_caching": true,
"supports_response_schema": true
},
+ "eu.amazon.nova-lite-v1:0": {
+ "max_tokens": 4096,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.000000078,
+ "output_cost_per_token": 0.000000312,
+ "litellm_provider": "bedrock_converse",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true
+ },
"amazon.nova-pro-v1:0": {
"max_tokens": 4096,
"max_input_tokens": 300000,
@@ -6293,6 +6598,21 @@
"supports_prompt_caching": true,
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},
+ "eu.amazon.nova-pro-v1:0": {
+ "max_tokens": 4096,
+ "max_input_tokens": 300000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.00000105,
+ "output_cost_per_token": 0.0000042,
+ "litellm_provider": "bedrock_converse",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "source": "https://aws.amazon.com/bedrock/pricing/"
+ },
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"max_tokens": 4096,
"max_input_tokens": 200000,
@@ -6304,8 +6624,25 @@
"supports_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
+ "supports_pdf_input": true,
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},
+ "bedrock/invoke/anthropic.claude-3-5-sonnet-20240620-v1:0": {
+ "max_tokens": 4096,
+ "max_input_tokens": 200000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.000003,
+ "output_cost_per_token": 0.000015,
+ "litellm_provider": "bedrock",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_response_schema": true,
+ "supports_vision": true,
+ "supports_tool_choice": true,
+ "metadata": {
+ "notes": "Anthropic via Invoke route does not currently support pdf input."
+ }
+ },
"anthropic.claude-3-5-sonnet-20240620-v1:0": {
"max_tokens": 4096,
"max_input_tokens": 200000,
@@ -6317,6 +6654,22 @@
"supports_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
+ "supports_pdf_input": true,
+ "supports_tool_choice": true
+ },
+ "anthropic.claude-3-7-sonnet-20250219-v1:0": {
+ "max_tokens": 8192,
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.000003,
+ "output_cost_per_token": 0.000015,
+ "litellm_provider": "bedrock_converse",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "supports_assistant_prefill": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
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},
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@@ -6329,6 +6682,7 @@
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
+ "supports_pdf_input": true,
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"supports_prompt_caching": true,
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@@ -6345,6 +6699,7 @@
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+ "supports_pdf_input": true,
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},
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@@ -6356,6 +6711,7 @@
"litellm_provider": "bedrock",
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+ "supports_pdf_input": true,
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"supports_prompt_caching": true,
@@ -6385,6 +6741,7 @@
"supports_function_calling": true,
"supports_response_schema": true,
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+ "supports_pdf_input": true,
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},
"us.anthropic.claude-3-5-sonnet-20240620-v1:0": {
@@ -6398,6 +6755,7 @@
"supports_function_calling": true,
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+ "supports_pdf_input": true,
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},
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@@ -6410,11 +6768,27 @@
"mode": "chat",
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+ "supports_pdf_input": true,
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"supports_prompt_caching": true,
"supports_response_schema": true,
"supports_tool_choice": true
},
+ "us.anthropic.claude-3-7-sonnet-20250219-v1:0": {
+ "max_tokens": 8192,
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.000003,
+ "output_cost_per_token": 0.000015,
+ "litellm_provider": "bedrock_converse",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "supports_assistant_prefill": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true
+ },
"us.anthropic.claude-3-haiku-20240307-v1:0": {
"max_tokens": 4096,
"max_input_tokens": 200000,
@@ -6426,6 +6800,7 @@
"supports_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
+ "supports_pdf_input": true,
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},
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@@ -6437,6 +6812,7 @@
"litellm_provider": "bedrock",
"mode": "chat",
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+ "supports_pdf_input": true,
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"supports_response_schema": true,
@@ -6466,6 +6842,7 @@
"supports_function_calling": true,
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"supports_vision": true,
+ "supports_pdf_input": true,
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},
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@@ -6479,6 +6856,7 @@
"supports_function_calling": true,
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+ "supports_pdf_input": true,
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},
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@@ -6491,6 +6869,7 @@
"mode": "chat",
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"supports_vision": true,
+ "supports_pdf_input": true,
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"supports_prompt_caching": true,
"supports_response_schema": true,
@@ -6507,6 +6886,7 @@
"supports_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
+ "supports_pdf_input": true,
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},
"eu.anthropic.claude-3-5-haiku-20241022-v1:0": {
@@ -6519,6 +6899,7 @@
"mode": "chat",
"supports_function_calling": true,
"supports_assistant_prefill": true,
+ "supports_pdf_input": true,
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rename from litellm/proxy/_experimental/out/_next/static/U8RyuuryVJGbBYlXSdaKT/_buildManifest.js
rename to litellm/proxy/_experimental/out/_next/static/Z1erUy-o9upLJI4iG8OBo/_buildManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/U8RyuuryVJGbBYlXSdaKT/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/Z1erUy-o9upLJI4iG8OBo/_ssgManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/U8RyuuryVJGbBYlXSdaKT/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/Z1erUy-o9upLJI4iG8OBo/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/157-cf7bc8b3ae1b80ba.js b/litellm/proxy/_experimental/out/_next/static/chunks/157-cf7bc8b3ae1b80ba.js
new file mode 100644
index 0000000000..6a596c25d8
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