Merge branch 'main' into litellm_redis_cache_usage

.circleci/config.yml

@@ -202,6 +202,7 @@ jobs:
               -e REDIS_PORT=$REDIS_PORT \
               -e AZURE_FRANCE_API_KEY=$AZURE_FRANCE_API_KEY \
               -e AZURE_EUROPE_API_KEY=$AZURE_EUROPE_API_KEY \
+              -e MISTRAL_API_KEY=$MISTRAL_API_KEY \
               -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
               -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
               -e AWS_REGION_NAME=$AWS_REGION_NAME \

docs/my-website/docs/observability/raw_request_response.md

@@ -4,6 +4,7 @@ import Image from '@theme/IdealImage';
 
 See the raw request/response sent by LiteLLM in your logging provider (OTEL/Langfuse/etc.).
 
+**on SDK**
 ```python
 # pip install langfuse 
 import litellm
@@ -33,6 +34,13 @@ response = litellm.completion(
 )
 ```
 
+**on Proxy**
+
+```yaml
+litellm_settings:
+  log_raw_request_response: True
+```
+
 **Expected Log**
 
 <Image img={require('../../img/raw_request_log.png')}/>

docs/my-website/docs/providers/azure.md

@@ -68,6 +68,7 @@ response = litellm.completion(
 
 | Model Name       | Function Call                          |
 |------------------|----------------------------------------|
+| gpt-4o            | `completion('azure/<your deployment name>', messages)`         |
 | gpt-4            | `completion('azure/<your deployment name>', messages)`         |
 | gpt-4-0314            | `completion('azure/<your deployment name>', messages)`         | 
 | gpt-4-0613            | `completion('azure/<your deployment name>', messages)`         |
@@ -85,7 +86,8 @@ response = litellm.completion(
 ## Azure OpenAI Vision Models 
 | Model Name            | Function Call                                                   |
 |-----------------------|-----------------------------------------------------------------|
-| gpt-4-vision   | `response = completion(model="azure/<your deployment name>", messages=messages)` |
+| gpt-4-vision   | `completion(model="azure/<your deployment name>", messages=messages)` |
+| gpt-4o            | `completion('azure/<your deployment name>', messages)`         |
 
 #### Usage
 ```python

docs/my-website/docs/providers/bedrock.md

@@ -144,16 +144,135 @@ print(response)
 </TabItem>
 </Tabs>
 
+## Set temperature, top p, etc.
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+import os
+from litellm import completion
+
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+os.environ["AWS_REGION_NAME"] = ""
+
+response = completion(
+  model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+  messages=[{ "content": "Hello, how are you?","role": "user"}],
+  temperature=0.7,
+  top_p=1
+)
+```
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+**Set on yaml**
+
+```yaml
+model_list:
+  - model_name: bedrock-claude-v1
+    litellm_params:
+      model: bedrock/anthropic.claude-instant-v1
+      temperature: <your-temp>
+      top_p: <your-top-p>
+```
+
+**Set on request**
+
+```python
+
+import openai
+client = openai.OpenAI(
+    api_key="anything",
+    base_url="http://0.0.0.0:4000"
+)
+
+# request sent to model set on litellm proxy, `litellm --model`
+response = client.chat.completions.create(model="bedrock-claude-v1", messages = [
+    {
+        "role": "user",
+        "content": "this is a test request, write a short poem"
+    }
+],
+temperature=0.7,
+top_p=1
+)
+
+print(response)
+
+```
+
+</TabItem>
+</Tabs>
+
+## Pass provider-specific params 
+
+If you pass a non-openai param to litellm, we'll assume it's provider-specific and send it as a kwarg in the request body. [See more](../completion/input.md#provider-specific-params)
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+import os
+from litellm import completion
+
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+os.environ["AWS_REGION_NAME"] = ""
+
+response = completion(
+  model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+  messages=[{ "content": "Hello, how are you?","role": "user"}],
+  top_k=1 # 👈 PROVIDER-SPECIFIC PARAM
+)
+```
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+**Set on yaml**
+
+```yaml
+model_list:
+  - model_name: bedrock-claude-v1
+    litellm_params:
+      model: bedrock/anthropic.claude-instant-v1
+      top_k: 1 # 👈 PROVIDER-SPECIFIC PARAM
+```
+
+**Set on request**
+
+```python
+
+import openai
+client = openai.OpenAI(
+    api_key="anything",
+    base_url="http://0.0.0.0:4000"
+)
+
+# request sent to model set on litellm proxy, `litellm --model`
+response = client.chat.completions.create(model="bedrock-claude-v1", messages = [
+    {
+        "role": "user",
+        "content": "this is a test request, write a short poem"
+    }
+],
+temperature=0.7,
+extra_body={
+    top_k=1 # 👈 PROVIDER-SPECIFIC PARAM
+}
+)
+
+print(response)
+
+```
+
+</TabItem>
+</Tabs>
+
 ## Usage - Function Calling 
 
-:::info 
+LiteLLM uses Bedrock's Converse API for making tool calls
-
-Claude returns it's output as an XML Tree. [Here is how we translate it](https://github.com/BerriAI/litellm/blob/49642a5b00a53b1babc1a753426a8afcac85dbbe/litellm/llms/prompt_templates/factory.py#L734).
-
-You can see the raw response via `response._hidden_params["original_response"]`.
-
-Claude hallucinates, e.g. returning the list param `value` as `<value>\n<item>apple</item>\n<item>banana</item>\n</value>` or `<value>\n<list>\n<item>apple</item>\n<item>banana</item>\n</list>\n</value>`.
-:::
 
 ```python
 from litellm import completion

docs/my-website/docs/providers/clarifai.md

@@ -1,10 +1,13 @@
 # Clarifai
 Anthropic, OpenAI, Mistral, Llama and Gemini LLMs are Supported on Clarifai. 
 
+:::warning 
+
+Streaming is not yet supported on using clarifai and litellm. Tracking support here: https://github.com/BerriAI/litellm/issues/4162
+
+:::
+
 ## Pre-Requisites
-
-`pip install clarifai`
-
 `pip install litellm`
 
 ## Required Environment Variables
@@ -12,6 +15,7 @@ To obtain your Clarifai Personal access token follow this [link](https://docs.cl
 
 ```python
 os.environ["CLARIFAI_API_KEY"] = "YOUR_CLARIFAI_PAT"  # CLARIFAI_PAT
+
 ```
 
 ## Usage
@@ -68,7 +72,7 @@ Example  Usage - Note: liteLLM supports all models deployed on Clarifai
 | clarifai/meta.Llama-2.codeLlama-70b-Python   | `completion('clarifai/meta.Llama-2.codeLlama-70b-Python', messages)`| 
 | clarifai/meta.Llama-2.codeLlama-70b-Instruct | `completion('clarifai/meta.Llama-2.codeLlama-70b-Instruct', messages)` |   
 
-## Mistal LLMs
+## Mistral LLMs
 | Model Name                                  | Function Call                                                         |
 |---------------------------------------------|------------------------------------------------------------------------|
 | clarifai/mistralai.completion.mixtral-8x22B            | `completion('clarifai/mistralai.completion.mixtral-8x22B', messages)`               |

docs/my-website/docs/providers/vertex.md

@@ -8,6 +8,152 @@ import TabItem from '@theme/TabItem';
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
 
+## 🆕 `vertex_ai_beta/` route 
+
+New `vertex_ai_beta/` route. Adds support for system messages, tool_choice params, etc. by moving to httpx client (instead of vertex sdk).
+
+```python
+from litellm import completion
+import json 
+
+## GET CREDENTIALS 
+file_path = 'path/to/vertex_ai_service_account.json'
+
+# Load the JSON file
+with open(file_path, 'r') as file:
+    vertex_credentials = json.load(file)
+
+# Convert to JSON string
+vertex_credentials_json = json.dumps(vertex_credentials)
+
+## COMPLETION CALL 
+response = completion(
+  model="vertex_ai_beta/gemini-pro",
+  messages=[{ "content": "Hello, how are you?","role": "user"}],
+  vertex_credentials=vertex_credentials_json
+)
+```
+
+### **System Message**
+
+```python
+from litellm import completion
+import json 
+
+## GET CREDENTIALS 
+file_path = 'path/to/vertex_ai_service_account.json'
+
+# Load the JSON file
+with open(file_path, 'r') as file:
+    vertex_credentials = json.load(file)
+
+# Convert to JSON string
+vertex_credentials_json = json.dumps(vertex_credentials)
+
+
+response = completion(
+  model="vertex_ai_beta/gemini-pro",
+  messages=[{"content": "You are a good bot.","role": "system"}, {"content": "Hello, how are you?","role": "user"}], 
+  vertex_credentials=vertex_credentials_json
+)
+```
+
+### **Function Calling**
+
+Force Gemini to make tool calls with `tool_choice="required"`.
+
+```python
+from litellm import completion
+import json 
+
+## GET CREDENTIALS 
+file_path = 'path/to/vertex_ai_service_account.json'
+
+# Load the JSON file
+with open(file_path, 'r') as file:
+    vertex_credentials = json.load(file)
+
+# Convert to JSON string
+vertex_credentials_json = json.dumps(vertex_credentials)
+
+
+messages = [
+    {
+        "role": "system",
+        "content": "Your name is Litellm Bot, you are a helpful assistant",
+    },
+    # User asks for their name and weather in San Francisco
+    {
+        "role": "user",
+        "content": "Hello, what is your name and can you tell me the weather?",
+    },
+]
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_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",
+                    }
+                },
+                "required": ["location"],
+            },
+        },
+    }
+]
+
+data = {
+    "model": "vertex_ai_beta/gemini-1.5-pro-preview-0514"),
+    "messages": messages,
+    "tools": tools,
+    "tool_choice": "required",
+    "vertex_credentials": vertex_credentials_json
+}
+
+## COMPLETION CALL 
+print(completion(**data))
+```
+
+### **JSON Schema**
+
+```python 
+from litellm import completion 
+
+## GET CREDENTIALS 
+file_path = 'path/to/vertex_ai_service_account.json'
+
+# Load the JSON file
+with open(file_path, 'r') as file:
+    vertex_credentials = json.load(file)
+
+# Convert to JSON string
+vertex_credentials_json = json.dumps(vertex_credentials)
+
+messages = [
+    {
+        "role": "user",
+        "content": """
+List 5 popular cookie recipes.
+
+Using this JSON schema:
+
+    Recipe = {"recipe_name": str}
+
+Return a `list[Recipe]`
+        """
+    }
+]
+
+completion(model="vertex_ai_beta/gemini-1.5-flash-preview-0514", messages=messages, response_format={ "type": "json_object" })
+```
+
 ## Pre-requisites
 * `pip install google-cloud-aiplatform` (pre-installed on proxy docker image)
 * Authentication: 
@@ -140,7 +286,7 @@ In certain use-cases you may need to make calls to the models and pass [safety s
 
 ```python
 response = completion(
-    model="gemini/gemini-pro", 
+    model="vertex_ai/gemini-pro", 
     messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]
     safety_settings=[
         {
@@ -363,8 +509,8 @@ response = completion(
 ## Gemini 1.5 Pro (and Vision)
 | Model Name       | Function Call                        |
 |------------------|--------------------------------------|
-| gemini-1.5-pro   | `completion('gemini-1.5-pro', messages)`, `completion('vertex_ai/gemini-pro', messages)` |
+| gemini-1.5-pro   | `completion('gemini-1.5-pro', messages)`, `completion('vertex_ai/gemini-1.5-pro', messages)` |
-| gemini-1.5-flash-preview-0514   | `completion('gemini-1.5-flash-preview-0514', messages)`, `completion('vertex_ai/gemini-pro', messages)` |
+| gemini-1.5-flash-preview-0514   | `completion('gemini-1.5-flash-preview-0514', messages)`, `completion('vertex_ai/gemini-1.5-flash-preview-0514', messages)` |
 | gemini-1.5-pro-preview-0514   | `completion('gemini-1.5-pro-preview-0514', messages)`, `completion('vertex_ai/gemini-1.5-pro-preview-0514', messages)` |
 
 
@@ -558,6 +704,29 @@ All models listed [here](https://github.com/BerriAI/litellm/blob/57f37f743886a02
 | text-embedding-preview-0409 | `embedding(model="vertex_ai/text-embedding-preview-0409", input)` |
 | text-multilingual-embedding-preview-0409 | `embedding(model="vertex_ai/text-multilingual-embedding-preview-0409", input)` | 
 
+### Advanced Use `task_type` and `title` (Vertex Specific Params)
+
+👉 `task_type` and `title` are vertex specific params
+
+LiteLLM Supported Vertex Specific Params
+
+```python
+auto_truncate: Optional[bool] = None
+task_type: Optional[Literal["RETRIEVAL_QUERY","RETRIEVAL_DOCUMENT", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING", "QUESTION_ANSWERING", "FACT_VERIFICATION"]] = None
+title: Optional[str] = None # The title of the document to be embedded. (only valid with task_type=RETRIEVAL_DOCUMENT).
+```
+
+**Example Usage with LiteLLM**
+```python
+response = litellm.embedding(
+    model="vertex_ai/text-embedding-004",
+    input=["good morning from litellm", "gm"]
+    task_type = "RETRIEVAL_DOCUMENT",
+    dimensions=1,
+    auto_truncate=True,
+)
+```
+
 ## Image Generation Models
 
 Usage 
@@ -657,6 +826,3 @@ s/o @[Darien Kindlund](https://www.linkedin.com/in/kindlund/) for this tutorial
 
 
 
-
-
-

docs/my-website/docs/proxy/alerting.md

@@ -1,3 +1,5 @@
+import Image from '@theme/IdealImage';
+
 # 🚨 Alerting / Webhooks
 
 Get alerts for:
@@ -15,6 +17,11 @@ Get alerts for:
 - **Spend** Weekly & Monthly spend per Team, Tag
 
 
+Works across: 
+- [Slack](#quick-start)
+- [Discord](#advanced---using-discord-webhooks)
+- [Microsoft Teams](#advanced---using-ms-teams-webhooks)
+
 ## Quick Start
 
 Set up a slack alert channel to receive alerts from proxy.
@@ -108,6 +115,48 @@ AlertType = Literal[
 ```
 
 
+## Advanced - Using MS Teams Webhooks
+
+MS Teams provides a slack compatible webhook url that you can use for alerting
+
+##### Quick Start
+
+1. [Get a webhook url](https://learn.microsoft.com/en-us/microsoftteams/platform/webhooks-and-connectors/how-to/add-incoming-webhook?tabs=newteams%2Cdotnet#create-an-incoming-webhook) for your Microsoft Teams channel 
+
+2. Add it to your .env
+
+```bash
+SLACK_WEBHOOK_URL="https://berriai.webhook.office.com/webhookb2/...6901/IncomingWebhook/b55fa0c2a48647be8e6effedcd540266/e04b1092-4a3e-44a2-ab6b-29a0a4854d1d"
+```
+
+3. Add it to your litellm config 
+
+```yaml
+model_list: 
+    model_name: "azure-model"
+    litellm_params:
+        model: "azure/gpt-35-turbo"
+        api_key: "my-bad-key" # 👈 bad key
+
+general_settings: 
+    alerting: ["slack"]
+    alerting_threshold: 300 # sends alerts if requests hang for 5min+ and responses take 5min+ 
+```
+
+4. Run health check!
+
+Call the proxy `/health/services` endpoint to test if your alerting connection is correctly setup.
+
+```bash
+curl --location 'http://0.0.0.0:4000/health/services?service=slack' \
+--header 'Authorization: Bearer sk-1234'
+```
+
+
+**Expected Response**
+
+<Image img={require('../../img/ms_teams_alerting.png')}/>
+
 ## Advanced - Using Discord Webhooks
 
 Discord provides a slack compatible webhook url that you can use for alerting
@@ -139,7 +188,6 @@ environment_variables:
     SLACK_WEBHOOK_URL: "https://discord.com/api/webhooks/1240030362193760286/cTLWt5ATn1gKmcy_982rl5xmYHsrM1IWJdmCL1AyOmU9JdQXazrp8L1_PYgUtgxj8x4f/slack"
 ```
 
-That's it ! You're ready to go !
 
 ## Advanced - [BETA] Webhooks for Budget Alerts
 

docs/my-website/docs/proxy/deploy.md

@@ -1,5 +1,6 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
+import Image from '@theme/IdealImage';
 
 # 🐳 Docker, Deploying LiteLLM Proxy
 
@@ -537,7 +538,9 @@ ghcr.io/berriai/litellm-database:main-latest --config your_config.yaml
 
 ## Advanced Deployment Settings
 
-### Customization of the server root path
+### 1. Customization of the server root path (custom Proxy base url)
+
+💥 Use this when you want to serve LiteLLM on a custom base url path like `https://localhost:4000/api/v1` 
 
 :::info
 
@@ -548,9 +551,29 @@ In a Kubernetes deployment, it's possible to utilize a shared DNS to host multip
 Customize the root path to eliminate the need for employing multiple DNS configurations during deployment.
 
 👉 Set `SERVER_ROOT_PATH` in your .env and this will be set as your server root path
+```
+export SERVER_ROOT_PATH="/api/v1"
+```
 
+**Step 1. Run Proxy with `SERVER_ROOT_PATH` set in your env **
 
-### Setting SSL Certification 
+```shell
+docker run --name litellm-proxy \
+-e DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname> \
+-e SERVER_ROOT_PATH="/api/v1" \
+-p 4000:4000 \
+ghcr.io/berriai/litellm-database:main-latest --config your_config.yaml
+```
+
+After running the proxy you can access it on `http://0.0.0.0:4000/api/v1/` (since we set `SERVER_ROOT_PATH="/api/v1"`)
+
+**Step 2. Verify Running on correct path**
+
+<Image img={require('../../img/custom_root_path.png')} />
+
+**That's it**, that's all you need to run the proxy on a custom root path
+
+### 2. Setting SSL Certification 
 
 Use this, If you need to set ssl certificates for your on prem litellm proxy
 

docs/my-website/docs/proxy/prometheus.md

@@ -1,4 +1,4 @@
-# Grafana, Prometheus metrics [BETA]
+# 📈 Prometheus metrics [BETA]
 
 LiteLLM Exposes a `/metrics` endpoint for Prometheus to Poll
 
@@ -54,6 +54,13 @@ http://localhost:4000/metrics
 | `litellm_total_tokens`         | input + output tokens per `"user", "key", "model", "team", "end-user"`     |
 | `litellm_llm_api_failed_requests_metric`   | Number of failed LLM API requests per `"user", "key", "model", "team", "end-user"`    |
 
+### Budget Metrics
+| Metric Name          | Description                          |
+|----------------------|--------------------------------------|
+| `litellm_remaining_team_budget_metric`             | Remaining Budget for Team (A team created on LiteLLM) |
+| `litellm_remaining_api_key_budget_metric`                | Remaining Budget for API Key (A key Created on LiteLLM)|
+
+
 ## Monitor System Health
 
 To monitor the health of litellm adjacent services (redis / postgres), do:

docs/my-website/docs/proxy/self_serve.md

@@ -124,3 +124,17 @@ LiteLLM Enterprise: Enable [SSO login](./ui.md#setup-ssoauth-for-ui)
 
 
 <Image img={require('../../img/ui_self_serve_create_key.png')}  style={{ width: '800px', height: 'auto' }} />
+
+
+## Advanced
+### Setting custom logout URLs
+
+Set `PROXY_LOGOUT_URL` in your .env if you want users to get redirected to a specific URL when they click logout
+
+```
+export PROXY_LOGOUT_URL="https://www.google.com"
+```
+
+<Image img={require('../../img/ui_logout.png')}  style={{ width: '400px', height: 'auto' }} />
+
+

docs/my-website/docs/proxy/team_budgets.md

@@ -0,0 +1,123 @@
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# 💰 Setting Team Budgets
+
+Track spend, set budgets for your Internal Team
+
+## Setting Monthly Team Budgets
+
+### 1. Create a team 
+- Set `max_budget=000000001` ($ value the team is allowed to spend)
+- Set `budget_duration="1d"` (How frequently the budget should update)
+
+
+Create a new team and set `max_budget` and `budget_duration`
+```shell
+curl -X POST 'http://0.0.0.0:4000/team/new' \
+     -H 'Authorization: Bearer sk-1234' \
+     -H 'Content-Type: application/json' \
+     -d '{
+            "team_alias": "QA Prod Bot", 
+            "max_budget": 0.000000001, 
+            "budget_duration": "1d"
+        }' 
+```
+
+Response
+```shell
+{
+ "team_alias": "QA Prod Bot",
+ "team_id": "de35b29e-6ca8-4f47-b804-2b79d07aa99a",
+ "max_budget": 0.0001,
+ "budget_duration": "1d",
+ "budget_reset_at": "2024-06-14T22:48:36.594000Z"
+}  
+```
+
+
+
+Possible values for `budget_duration`
+
+| `budget_duration` | When Budget will reset |
+| --- | --- |
+| `budget_duration="1s"` | every 1 second |
+| `budget_duration="1m"` | every 1 min |
+| `budget_duration="1h"` | every 1 hour |
+| `budget_duration="1d"` | every 1 day |
+| `budget_duration="1mo"` | start of every month |
+
+
+### 2. Create a key for the `team`
+
+Create a key for `team_id="de35b29e-6ca8-4f47-b804-2b79d07aa99a"` from Step 1 
+
+💡 **The Budget for Team="QA Prod Bot" budget will apply to this team**
+
+```shell
+curl -X POST 'http://0.0.0.0:4000/key/generate' \
+     -H 'Authorization: Bearer sk-1234' \
+     -H 'Content-Type: application/json' \
+     -d '{"team_id": "de35b29e-6ca8-4f47-b804-2b79d07aa99a"}'
+```
+
+Response
+
+```shell
+{"team_id":"de35b29e-6ca8-4f47-b804-2b79d07aa99a", "key":"sk-5qtncoYjzRcxMM4bDRktNQ"}
+```
+
+
+### 3. Test It
+
+Use the key from step 2 and run this Request twice
+```shell
+curl -X POST 'http://0.0.0.0:4000/chat/completions' \
+     -H 'Authorization: Bearer sk-mso-JSykEGri86KyOvgxBw' \
+     -H 'Content-Type: application/json' \
+     -d ' {
+           "model": "llama3",
+           "messages": [
+             {
+               "role": "user",
+               "content": "hi"
+             }
+           ]
+         }'
+```
+
+On the 2nd response - expect to see the following exception
+
+```shell
+{
+ "error": {
+   "message": "Budget has been exceeded! Current cost: 3.5e-06, Max budget: 1e-09",
+   "type": "auth_error",
+   "param": null,
+   "code": 400
+ }
+}
+```
+
+## Advanced
+
+### Prometheus metrics for `remaining_budget`
+
+[More info about Prometheus metrics here](https://docs.litellm.ai/docs/proxy/prometheus)
+
+You'll need the following in your proxy config.yaml
+
+```yaml
+litellm_settings:
+  success_callback: ["prometheus"]
+  failure_callback: ["prometheus"]
+```
+
+Expect to see this metric on prometheus to track the Remaining Budget for the team
+
+```shell
+litellm_remaining_team_budget_metric{team_alias="QA Prod Bot",team_id="de35b29e-6ca8-4f47-b804-2b79d07aa99a"} 9.699999999999992e-06
+```
+
+

docs/my-website/sidebars.js

@@ -44,6 +44,7 @@ const sidebars = {
         "proxy/self_serve",
         "proxy/users",
         "proxy/customers",
+        "proxy/team_budgets",
         "proxy/billing",
         "proxy/user_keys",
         "proxy/virtual_keys",
@@ -54,6 +55,7 @@ const sidebars = {
           items: ["proxy/logging", "proxy/streaming_logging"],
         },
         "proxy/ui",
+        "proxy/prometheus",
         "proxy/email",
         "proxy/multiple_admins",
         "proxy/team_based_routing",
@@ -70,7 +72,6 @@ const sidebars = {
         "proxy/pii_masking",
         "proxy/prompt_injection",
         "proxy/caching",
-        "proxy/prometheus",
         "proxy/call_hooks",
         "proxy/rules",
         "proxy/cli", 

enterprise/enterprise_hooks/banned_keywords.py

@@ -93,7 +93,7 @@ class _ENTERPRISE_BannedKeywords(CustomLogger):
             response.choices[0], litellm.utils.Choices
         ):
             for word in self.banned_keywords_list:
-                self.test_violation(test_str=response.choices[0].message.content)
+                self.test_violation(test_str=response.choices[0].message.content or "")
 
     async def async_post_call_streaming_hook(
         self,

litellm/__init__.py

@@ -73,7 +73,7 @@ token: Optional[str] = (
 )
 telemetry = True
 max_tokens = 256  # OpenAI Defaults
-drop_params = False
+drop_params = bool(os.getenv("LITELLM_DROP_PARAMS", False))
 modify_params = False
 retry = True
 ### AUTH ###
@@ -605,6 +605,7 @@ provider_list: List = [
     "together_ai",
     "openrouter",
     "vertex_ai",
+    "vertex_ai_beta",
     "palm",
     "gemini",
     "ai21",
@@ -765,7 +766,8 @@ from .llms.gemini import GeminiConfig
 from .llms.nlp_cloud import NLPCloudConfig
 from .llms.aleph_alpha import AlephAlphaConfig
 from .llms.petals import PetalsConfig
-from .llms.vertex_ai import VertexAIConfig
+from .llms.vertex_httpx import VertexGeminiConfig
+from .llms.vertex_ai import VertexAIConfig, VertexAITextEmbeddingConfig
 from .llms.vertex_ai_anthropic import VertexAIAnthropicConfig
 from .llms.sagemaker import SagemakerConfig
 from .llms.ollama import OllamaConfig
@@ -787,7 +789,9 @@ from .llms.openai import (
     OpenAIConfig,
     OpenAITextCompletionConfig,
     MistralConfig,
+    MistralEmbeddingConfig,
     DeepInfraConfig,
+    AzureAIStudioConfig,
 )
 from .llms.azure import (
     AzureOpenAIConfig,

litellm/caching.py

@@ -1192,7 +1192,7 @@ class S3Cache(BaseCache):
             return cached_response
         except botocore.exceptions.ClientError as e:
             if e.response["Error"]["Code"] == "NoSuchKey":
-                verbose_logger.error(
+                verbose_logger.debug(
                     f"S3 Cache: The specified key '{key}' does not exist in the S3 bucket."
                 )
                 return None

litellm/exceptions.py

@@ -26,7 +26,7 @@ class AuthenticationError(openai.AuthenticationError):  # type: ignore
         num_retries: Optional[int] = None,
     ):
         self.status_code = 401
-        self.message = message
+        self.message = "litellm.AuthenticationError: {}".format(message)
         self.llm_provider = llm_provider
         self.model = model
         self.litellm_debug_info = litellm_debug_info
@@ -72,7 +72,7 @@ class NotFoundError(openai.NotFoundError):  # type: ignore
         num_retries: Optional[int] = None,
     ):
         self.status_code = 404
-        self.message = message
+        self.message = "litellm.NotFoundError: {}".format(message)
         self.model = model
         self.llm_provider = llm_provider
         self.litellm_debug_info = litellm_debug_info
@@ -117,7 +117,7 @@ class BadRequestError(openai.BadRequestError):  # type: ignore
         num_retries: Optional[int] = None,
     ):
         self.status_code = 400
-        self.message = message
+        self.message = "litellm.BadRequestError: {}".format(message)
         self.model = model
         self.llm_provider = llm_provider
         self.litellm_debug_info = litellm_debug_info
@@ -162,7 +162,7 @@ class UnprocessableEntityError(openai.UnprocessableEntityError):  # type: ignore
         num_retries: Optional[int] = None,
     ):
         self.status_code = 422
-        self.message = message
+        self.message = "litellm.UnprocessableEntityError: {}".format(message)
         self.model = model
         self.llm_provider = llm_provider
         self.litellm_debug_info = litellm_debug_info
@@ -204,7 +204,7 @@ class Timeout(openai.APITimeoutError):  # type: ignore
             request=request
         )  # Call the base class constructor with the parameters it needs
         self.status_code = 408
-        self.message = message
+        self.message = "litellm.Timeout: {}".format(message)
         self.model = model
         self.llm_provider = llm_provider
         self.litellm_debug_info = litellm_debug_info
@@ -241,7 +241,7 @@ class PermissionDeniedError(openai.PermissionDeniedError):  # type:ignore
         num_retries: Optional[int] = None,
     ):
         self.status_code = 403
-        self.message = message
+        self.message = "litellm.PermissionDeniedError: {}".format(message)
         self.llm_provider = llm_provider
         self.model = model
         self.litellm_debug_info = litellm_debug_info
@@ -280,7 +280,7 @@ class RateLimitError(openai.RateLimitError):  # type: ignore
         num_retries: Optional[int] = None,
     ):
         self.status_code = 429
-        self.message = message
+        self.message = "litellm.RateLimitError: {}".format(message)
         self.llm_provider = llm_provider
         self.model = model
         self.litellm_debug_info = litellm_debug_info
@@ -328,7 +328,7 @@ class ContextWindowExceededError(BadRequestError):  # type: ignore
         litellm_debug_info: Optional[str] = None,
     ):
         self.status_code = 400
-        self.message = message
+        self.message = "litellm.ContextWindowExceededError: {}".format(message)
         self.model = model
         self.llm_provider = llm_provider
         self.litellm_debug_info = litellm_debug_info
@@ -368,7 +368,7 @@ class RejectedRequestError(BadRequestError):  # type: ignore
         litellm_debug_info: Optional[str] = None,
     ):
         self.status_code = 400
-        self.message = message
+        self.message = "litellm.RejectedRequestError: {}".format(message)
         self.model = model
         self.llm_provider = llm_provider
         self.litellm_debug_info = litellm_debug_info
@@ -411,7 +411,7 @@ class ContentPolicyViolationError(BadRequestError):  # type: ignore
         litellm_debug_info: Optional[str] = None,
     ):
         self.status_code = 400
-        self.message = message
+        self.message = "litellm.ContentPolicyViolationError: {}".format(message)
         self.model = model
         self.llm_provider = llm_provider
         self.litellm_debug_info = litellm_debug_info
@@ -452,7 +452,7 @@ class ServiceUnavailableError(openai.APIStatusError):  # type: ignore
         num_retries: Optional[int] = None,
     ):
         self.status_code = 503
-        self.message = message
+        self.message = "litellm.ServiceUnavailableError: {}".format(message)
         self.llm_provider = llm_provider
         self.model = model
         self.litellm_debug_info = litellm_debug_info
@@ -501,7 +501,7 @@ class InternalServerError(openai.InternalServerError):  # type: ignore
         num_retries: Optional[int] = None,
     ):
         self.status_code = 500
-        self.message = message
+        self.message = "litellm.InternalServerError: {}".format(message)
         self.llm_provider = llm_provider
         self.model = model
         self.litellm_debug_info = litellm_debug_info
@@ -552,7 +552,7 @@ class APIError(openai.APIError):  # type: ignore
         num_retries: Optional[int] = None,
     ):
         self.status_code = status_code
-        self.message = message
+        self.message = "litellm.APIError: {}".format(message)
         self.llm_provider = llm_provider
         self.model = model
         self.litellm_debug_info = litellm_debug_info
@@ -589,7 +589,7 @@ class APIConnectionError(openai.APIConnectionError):  # type: ignore
         max_retries: Optional[int] = None,
         num_retries: Optional[int] = None,
     ):
-        self.message = message
+        self.message = "litellm.APIConnectionError: {}".format(message)
         self.llm_provider = llm_provider
         self.model = model
         self.status_code = 500
@@ -626,7 +626,7 @@ class APIResponseValidationError(openai.APIResponseValidationError):  # type: ig
         max_retries: Optional[int] = None,
         num_retries: Optional[int] = None,
     ):
-        self.message = message
+        self.message = "litellm.APIResponseValidationError: {}".format(message)
         self.llm_provider = llm_provider
         self.model = model
         request = httpx.Request(method="POST", url="https://api.openai.com/v1")

litellm/integrations/prometheus.py

@@ -8,6 +8,7 @@ import traceback
 import datetime, subprocess, sys
 import litellm, uuid
 from litellm._logging import print_verbose, verbose_logger
+from typing import Optional, Union
 
 
 class PrometheusLogger:
@@ -17,33 +18,76 @@ class PrometheusLogger:
         **kwargs,
     ):
         try:
-            from prometheus_client import Counter
+            from prometheus_client import Counter, Gauge
 
             self.litellm_llm_api_failed_requests_metric = Counter(
                 name="litellm_llm_api_failed_requests_metric",
                 documentation="Total number of failed LLM API calls via litellm",
-                labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
+                labelnames=[
+                    "end_user",
+                    "hashed_api_key",
+                    "model",
+                    "team",
+                    "team_alias",
+                    "user",
+                ],
             )
 
             self.litellm_requests_metric = Counter(
                 name="litellm_requests_metric",
                 documentation="Total number of LLM calls to litellm",
-                labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
+                labelnames=[
+                    "end_user",
+                    "hashed_api_key",
+                    "model",
+                    "team",
+                    "team_alias",
+                    "user",
+                ],
             )
 
             # Counter for spend
             self.litellm_spend_metric = Counter(
                 "litellm_spend_metric",
                 "Total spend on LLM requests",
-                labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
+                labelnames=[
+                    "end_user",
+                    "hashed_api_key",
+                    "model",
+                    "team",
+                    "team_alias",
+                    "user",
+                ],
             )
 
             # Counter for total_output_tokens
             self.litellm_tokens_metric = Counter(
                 "litellm_total_tokens",
                 "Total number of input + output tokens from LLM requests",
-                labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
+                labelnames=[
+                    "end_user",
+                    "hashed_api_key",
+                    "model",
+                    "team",
+                    "team_alias",
+                    "user",
+                ],
             )
+
+            # Remaining Budget for Team
+            self.litellm_remaining_team_budget_metric = Gauge(
+                "litellm_remaining_team_budget_metric",
+                "Remaining budget for team",
+                labelnames=["team_id", "team_alias"],
+            )
+
+            # Remaining Budget for API Key
+            self.litellm_remaining_api_key_budget_metric = Gauge(
+                "litellm_remaining_api_key_budget_metric",
+                "Remaining budget for api key",
+                labelnames=["hashed_api_key", "api_key_alias"],
+            )
+
         except Exception as e:
             print_verbose(f"Got exception on init prometheus client {str(e)}")
             raise e
@@ -51,7 +95,9 @@ class PrometheusLogger:
     async def _async_log_event(
         self, kwargs, response_obj, start_time, end_time, print_verbose, user_id
     ):
-        self.log_event(kwargs, response_obj, start_time, end_time, print_verbose)
+        self.log_event(
+            kwargs, response_obj, start_time, end_time, user_id, print_verbose
+        )
 
     def log_event(
         self, kwargs, response_obj, start_time, end_time, user_id, print_verbose
@@ -72,9 +118,36 @@ class PrometheusLogger:
                 "user_api_key_user_id", None
             )
             user_api_key = litellm_params.get("metadata", {}).get("user_api_key", None)
+            user_api_key_alias = litellm_params.get("metadata", {}).get(
+                "user_api_key_alias", None
+            )
             user_api_team = litellm_params.get("metadata", {}).get(
                 "user_api_key_team_id", None
             )
+            user_api_team_alias = litellm_params.get("metadata", {}).get(
+                "user_api_key_team_alias", None
+            )
+
+            _team_spend = litellm_params.get("metadata", {}).get(
+                "user_api_key_team_spend", None
+            )
+            _team_max_budget = litellm_params.get("metadata", {}).get(
+                "user_api_key_team_max_budget", None
+            )
+            _remaining_team_budget = safe_get_remaining_budget(
+                max_budget=_team_max_budget, spend=_team_spend
+            )
+
+            _api_key_spend = litellm_params.get("metadata", {}).get(
+                "user_api_key_spend", None
+            )
+            _api_key_max_budget = litellm_params.get("metadata", {}).get(
+                "user_api_key_max_budget", None
+            )
+            _remaining_api_key_budget = safe_get_remaining_budget(
+                max_budget=_api_key_max_budget, spend=_api_key_spend
+            )
+
             if response_obj is not None:
                 tokens_used = response_obj.get("usage", {}).get("total_tokens", 0)
             else:
@@ -94,19 +167,47 @@ class PrometheusLogger:
                 user_api_key = hash_token(user_api_key)
 
             self.litellm_requests_metric.labels(
-                end_user_id, user_api_key, model, user_api_team, user_id
+                end_user_id,
+                user_api_key,
+                model,
+                user_api_team,
+                user_api_team_alias,
+                user_id,
             ).inc()
             self.litellm_spend_metric.labels(
-                end_user_id, user_api_key, model, user_api_team, user_id
+                end_user_id,
+                user_api_key,
+                model,
+                user_api_team,
+                user_api_team_alias,
+                user_id,
             ).inc(response_cost)
             self.litellm_tokens_metric.labels(
-                end_user_id, user_api_key, model, user_api_team, user_id
+                end_user_id,
+                user_api_key,
+                model,
+                user_api_team,
+                user_api_team_alias,
+                user_id,
             ).inc(tokens_used)
 
+            self.litellm_remaining_team_budget_metric.labels(
+                user_api_team, user_api_team_alias
+            ).set(_remaining_team_budget)
+
+            self.litellm_remaining_api_key_budget_metric.labels(
+                user_api_key, user_api_key_alias
+            ).set(_remaining_api_key_budget)
+
             ### FAILURE INCREMENT ###
             if "exception" in kwargs:
                 self.litellm_llm_api_failed_requests_metric.labels(
-                    end_user_id, user_api_key, model, user_api_team, user_id
+                    end_user_id,
+                    user_api_key,
+                    model,
+                    user_api_team,
+                    user_api_team_alias,
+                    user_id,
                 ).inc()
         except Exception as e:
             verbose_logger.error(
@@ -114,3 +215,15 @@ class PrometheusLogger:
             )
             verbose_logger.debug(traceback.format_exc())
             pass
+
+
+def safe_get_remaining_budget(
+    max_budget: Optional[float], spend: Optional[float]
+) -> float:
+    if max_budget is None:
+        return float("inf")
+
+    if spend is None:
+        return max_budget
+
+    return max_budget - spend

litellm/litellm_core_utils/redact_messages.py

@@ -0,0 +1,65 @@
+# +-----------------------------------------------+
+# |                                               |
+# |           Give Feedback / Get Help            |
+# | https://github.com/BerriAI/litellm/issues/new |
+# |                                               |
+# +-----------------------------------------------+
+#
+#  Thank you users! We ❤️ you! - Krrish & Ishaan
+
+import copy
+from typing import TYPE_CHECKING, Any
+import litellm
+
+if TYPE_CHECKING:
+    from litellm.utils import Logging as _LiteLLMLoggingObject
+
+    LiteLLMLoggingObject = _LiteLLMLoggingObject
+else:
+    LiteLLMLoggingObject = Any
+
+
+def redact_message_input_output_from_logging(
+    litellm_logging_obj: LiteLLMLoggingObject, result
+):
+    """
+    Removes messages, prompts, input, response from logging. This modifies the data in-place
+    only redacts when litellm.turn_off_message_logging == True
+    """
+    # check if user opted out of logging message/response to callbacks
+    if litellm.turn_off_message_logging is not True:
+        return result
+
+    _result = copy.deepcopy(result)
+    # remove messages, prompts, input, response from logging
+    litellm_logging_obj.model_call_details["messages"] = [
+        {"role": "user", "content": "redacted-by-litellm"}
+    ]
+    litellm_logging_obj.model_call_details["prompt"] = ""
+    litellm_logging_obj.model_call_details["input"] = ""
+
+    # response cleaning
+    # ChatCompletion Responses
+    if (
+        litellm_logging_obj.stream is True
+        and "complete_streaming_response" in litellm_logging_obj.model_call_details
+    ):
+        _streaming_response = litellm_logging_obj.model_call_details[
+            "complete_streaming_response"
+        ]
+        for choice in _streaming_response.choices:
+            if isinstance(choice, litellm.Choices):
+                choice.message.content = "redacted-by-litellm"
+            elif isinstance(choice, litellm.utils.StreamingChoices):
+                choice.delta.content = "redacted-by-litellm"
+    else:
+        if _result is not None:
+            if isinstance(_result, litellm.ModelResponse):
+                if hasattr(_result, "choices") and _result.choices is not None:
+                    for choice in _result.choices:
+                        if isinstance(choice, litellm.Choices):
+                            choice.message.content = "redacted-by-litellm"
+                        elif isinstance(choice, litellm.utils.StreamingChoices):
+                            choice.delta.content = "redacted-by-litellm"
+
+    return _result

litellm/llms/azure.py

@@ -36,6 +36,9 @@ from ..types.llms.openai import (
     AsyncAssistantStreamManager,
     AssistantStreamManager,
 )
+from litellm.caching import DualCache
+
+azure_ad_cache = DualCache()
 
 
 class AzureOpenAIError(Exception):
@@ -309,9 +312,10 @@ def select_azure_base_url_or_endpoint(azure_client_params: dict):
 
 def get_azure_ad_token_from_oidc(azure_ad_token: str):
     azure_client_id = os.getenv("AZURE_CLIENT_ID", None)
-    azure_tenant = os.getenv("AZURE_TENANT_ID", None)
+    azure_tenant_id = os.getenv("AZURE_TENANT_ID", None)
+    azure_authority_host = os.getenv("AZURE_AUTHORITY_HOST", "https://login.microsoftonline.com")
 
-    if azure_client_id is None or azure_tenant is None:
+    if azure_client_id is None or azure_tenant_id is None:
         raise AzureOpenAIError(
             status_code=422,
             message="AZURE_CLIENT_ID and AZURE_TENANT_ID must be set",
@@ -325,8 +329,19 @@ def get_azure_ad_token_from_oidc(azure_ad_token: str):
             message="OIDC token could not be retrieved from secret manager.",
         )
 
+    azure_ad_token_cache_key = json.dumps({
+        "azure_client_id": azure_client_id,
+        "azure_tenant_id": azure_tenant_id,
+        "azure_authority_host": azure_authority_host,
+        "oidc_token": oidc_token,
+    })
+
+    azure_ad_token_access_token = azure_ad_cache.get_cache(azure_ad_token_cache_key)
+    if azure_ad_token_access_token is not None:
+        return azure_ad_token_access_token
+
     req_token = httpx.post(
-        f"https://login.microsoftonline.com/{azure_tenant}/oauth2/v2.0/token",
+        f"{azure_authority_host}/{azure_tenant_id}/oauth2/v2.0/token",
         data={
             "client_id": azure_client_id,
             "grant_type": "client_credentials",
@@ -342,12 +357,23 @@ def get_azure_ad_token_from_oidc(azure_ad_token: str):
             message=req_token.text,
         )
 
-    possible_azure_ad_token = req_token.json().get("access_token", None)
+    azure_ad_token_json = req_token.json()
+    azure_ad_token_access_token = azure_ad_token_json.get("access_token", None)
+    azure_ad_token_expires_in = azure_ad_token_json.get("expires_in", None)
 
-    if possible_azure_ad_token is None:
+    if azure_ad_token_access_token is None:
-        raise AzureOpenAIError(status_code=422, message="Azure AD Token not returned")
+        raise AzureOpenAIError(
+            status_code=422, message="Azure AD Token access_token not returned"
+        )
 
-    return possible_azure_ad_token
+    if azure_ad_token_expires_in is None:
+        raise AzureOpenAIError(
+            status_code=422, message="Azure AD Token expires_in not returned"
+        )
+
+    azure_ad_cache.set_cache(key=azure_ad_token_cache_key, value=azure_ad_token_access_token, ttl=azure_ad_token_expires_in)
+
+    return azure_ad_token_access_token
 
 
 class AzureChatCompletion(BaseLLM):

litellm/llms/bedrock_httpx.py

@@ -53,7 +53,9 @@ from litellm.types.llms.openai import (
     ChatCompletionToolCallFunctionChunk,
     ChatCompletionDeltaChunk,
 )
+from litellm.caching import DualCache
 
+iam_cache = DualCache()
 
 class AmazonCohereChatConfig:
     """
@@ -325,11 +327,16 @@ class BedrockLLM(BaseLLM):
         ) = params_to_check
 
         ### CHECK STS ###
-        if (
+        if aws_web_identity_token is not None and aws_role_name is not None and aws_session_name is not None:
-            aws_web_identity_token is not None
+            iam_creds_cache_key = json.dumps({
-            and aws_role_name is not None
+                "aws_web_identity_token": aws_web_identity_token,
-            and aws_session_name is not None
+                "aws_role_name": aws_role_name,
-        ):
+                "aws_session_name": aws_session_name,
+                "aws_region_name": aws_region_name,
+            })
+
+            iam_creds_dict = iam_cache.get_cache(iam_creds_cache_key)
+            if iam_creds_dict is None:
                 oidc_token = get_secret(aws_web_identity_token)
 
                 if oidc_token is None:
@@ -338,7 +345,11 @@ class BedrockLLM(BaseLLM):
                         status_code=401,
                     )
 
-            sts_client = boto3.client("sts")
+                sts_client = boto3.client(
+                    "sts",
+                    region_name=aws_region_name,
+                    endpoint_url=f"https://sts.{aws_region_name}.amazonaws.com"
+                )
 
                 # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
                 # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
@@ -349,14 +360,20 @@ class BedrockLLM(BaseLLM):
                     DurationSeconds=3600,
                 )
 
-            session = boto3.Session(
+                iam_creds_dict = {
-                aws_access_key_id=sts_response["Credentials"]["AccessKeyId"],
+                    "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"],
-                aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"],
+                    "aws_secret_access_key": sts_response["Credentials"]["SecretAccessKey"],
-                aws_session_token=sts_response["Credentials"]["SessionToken"],
+                    "aws_session_token": sts_response["Credentials"]["SessionToken"],
-                region_name=aws_region_name,
+                    "region_name": aws_region_name,
-            )
+                }
 
-            return session.get_credentials()
+                iam_cache.set_cache(key=iam_creds_cache_key, value=json.dumps(iam_creds_dict), ttl=3600 - 60)
+
+            session = boto3.Session(**iam_creds_dict)
+
+            iam_creds = session.get_credentials()
+
+            return iam_creds
         elif aws_role_name is not None and aws_session_name is not None:
             sts_client = boto3.client(
                 "sts",
@@ -1416,11 +1433,16 @@ class BedrockConverseLLM(BaseLLM):
         ) = params_to_check
 
         ### CHECK STS ###
-        if (
+        if aws_web_identity_token is not None and aws_role_name is not None and aws_session_name is not None:
-            aws_web_identity_token is not None
+            iam_creds_cache_key = json.dumps({
-            and aws_role_name is not None
+                "aws_web_identity_token": aws_web_identity_token,
-            and aws_session_name is not None
+                "aws_role_name": aws_role_name,
-        ):
+                "aws_session_name": aws_session_name,
+                "aws_region_name": aws_region_name,
+            })
+
+            iam_creds_dict = iam_cache.get_cache(iam_creds_cache_key)
+            if iam_creds_dict is None:
                 oidc_token = get_secret(aws_web_identity_token)
 
                 if oidc_token is None:
@@ -1429,7 +1451,11 @@ class BedrockConverseLLM(BaseLLM):
                         status_code=401,
                     )
 
-            sts_client = boto3.client("sts")
+                sts_client = boto3.client(
+                    "sts",
+                    region_name=aws_region_name,
+                    endpoint_url=f"https://sts.{aws_region_name}.amazonaws.com"
+                )
 
                 # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
                 # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
@@ -1440,14 +1466,20 @@ class BedrockConverseLLM(BaseLLM):
                     DurationSeconds=3600,
                 )
 
-            session = boto3.Session(
+                iam_creds_dict = {
-                aws_access_key_id=sts_response["Credentials"]["AccessKeyId"],
+                    "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"],
-                aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"],
+                    "aws_secret_access_key": sts_response["Credentials"]["SecretAccessKey"],
-                aws_session_token=sts_response["Credentials"]["SessionToken"],
+                    "aws_session_token": sts_response["Credentials"]["SessionToken"],
-                region_name=aws_region_name,
+                    "region_name": aws_region_name,
-            )
+                }
 
-            return session.get_credentials()
+                iam_cache.set_cache(key=iam_creds_cache_key, value=json.dumps(iam_creds_dict), ttl=3600 - 60)
+
+            session = boto3.Session(**iam_creds_dict)
+
+            iam_creds = session.get_credentials()
+
+            return iam_creds
         elif aws_role_name is not None and aws_session_name is not None:
             sts_client = boto3.client(
                 "sts",

litellm/llms/clarifai.py

@@ -139,6 +139,7 @@ def process_response(
 
 def convert_model_to_url(model: str, api_base: str):
     user_id, app_id, model_id = model.split(".")
+    model_id = model_id.lower()
     return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs"
 
 
@@ -171,19 +172,55 @@ async def async_completion(
 
     async_handler = AsyncHTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0))
     response = await async_handler.post(
-        api_base, headers=headers, data=json.dumps(data)
+        url=model, headers=headers, data=json.dumps(data)
     )
 
-    return process_response(
+    logging_obj.post_call(
-        model=model,
+        input=prompt,
-        prompt=prompt,
-        response=response,
-        model_response=model_response,
         api_key=api_key,
-        data=data,
+        original_response=response.text,
-        encoding=encoding,
+        additional_args={"complete_input_dict": data},
-        logging_obj=logging_obj,
     )
+    ## RESPONSE OBJECT
+    try:
+        completion_response = response.json()
+    except Exception:
+        raise ClarifaiError(
+            message=response.text, status_code=response.status_code, url=model
+        )
+    # print(completion_response)
+    try:
+        choices_list = []
+        for idx, item in enumerate(completion_response["outputs"]):
+            if len(item["data"]["text"]["raw"]) > 0:
+                message_obj = Message(content=item["data"]["text"]["raw"])
+            else:
+                message_obj = Message(content=None)
+            choice_obj = Choices(
+                finish_reason="stop",
+                index=idx + 1,  # check
+                message=message_obj,
+            )
+            choices_list.append(choice_obj)
+        model_response["choices"] = choices_list
+
+    except Exception as e:
+        raise ClarifaiError(
+            message=traceback.format_exc(), status_code=response.status_code, url=model
+        )
+
+    # Calculate Usage
+    prompt_tokens = len(encoding.encode(prompt))
+    completion_tokens = len(
+        encoding.encode(model_response["choices"][0]["message"].get("content"))
+    )
+    model_response["model"] = model
+    model_response["usage"] = Usage(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        total_tokens=prompt_tokens + completion_tokens,
+    )
+    return model_response
 
 
 def completion(
@@ -241,7 +278,7 @@ def completion(
         additional_args={
             "complete_input_dict": data,
             "headers": headers,
-            "api_base": api_base,
+            "api_base": model,
         },
     )
     if acompletion == True:

litellm/llms/custom_httpx/http_handler.py

@@ -12,6 +12,15 @@ class AsyncHTTPHandler:
         timeout: Optional[Union[float, httpx.Timeout]] = None,
         concurrent_limit=1000,
     ):
+        self.timeout = timeout
+        self.client = self.create_client(
+            timeout=timeout, concurrent_limit=concurrent_limit
+        )
+
+    def create_client(
+        self, timeout: Optional[Union[float, httpx.Timeout]], concurrent_limit: int
+    ) -> httpx.AsyncClient:
+
         async_proxy_mounts = None
         # Check if the HTTP_PROXY and HTTPS_PROXY environment variables are set and use them accordingly.
         http_proxy = os.getenv("HTTP_PROXY", None)
@@ -39,7 +48,8 @@ class AsyncHTTPHandler:
         if timeout is None:
             timeout = _DEFAULT_TIMEOUT
         # Create a client with a connection pool
-        self.client = httpx.AsyncClient(
+
+        return httpx.AsyncClient(
             timeout=timeout,
             limits=httpx.Limits(
                 max_connections=concurrent_limit,
@@ -83,11 +93,48 @@ class AsyncHTTPHandler:
             response = await self.client.send(req, stream=stream)
             response.raise_for_status()
             return response
+        except httpx.RemoteProtocolError:
+            # Retry the request with a new session if there is a connection error
+            new_client = self.create_client(timeout=self.timeout, concurrent_limit=1)
+            try:
+                return await self.single_connection_post_request(
+                    url=url,
+                    client=new_client,
+                    data=data,
+                    json=json,
+                    params=params,
+                    headers=headers,
+                    stream=stream,
+                )
+            finally:
+                await new_client.aclose()
         except httpx.HTTPStatusError as e:
             raise e
         except Exception as e:
             raise e
 
+    async def single_connection_post_request(
+        self,
+        url: str,
+        client: httpx.AsyncClient,
+        data: Optional[Union[dict, str]] = None,  # type: ignore
+        json: Optional[dict] = None,
+        params: Optional[dict] = None,
+        headers: Optional[dict] = None,
+        stream: bool = False,
+    ):
+        """
+        Making POST request for a single connection client.
+
+        Used for retrying connection client errors.
+        """
+        req = client.build_request(
+            "POST", url, data=data, json=json, params=params, headers=headers  # type: ignore
+        )
+        response = await client.send(req, stream=stream)
+        response.raise_for_status()
+        return response
+
     def __del__(self) -> None:
         try:
             asyncio.get_running_loop().create_task(self.close())

litellm/llms/openai.py

@@ -28,6 +28,7 @@ from .prompt_templates.factory import prompt_factory, custom_prompt
 from openai import OpenAI, AsyncOpenAI
 from ..types.llms.openai import *
 import openai
+from litellm.types.utils import ProviderField
 
 
 class OpenAIError(Exception):
@@ -164,6 +165,68 @@ class MistralConfig:
         return optional_params
 
 
+class MistralEmbeddingConfig:
+    """
+    Reference: https://docs.mistral.ai/api/#operation/createEmbedding
+    """
+
+    def __init__(
+        self,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self):
+        return [
+            "encoding_format",
+        ]
+
+    def map_openai_params(self, non_default_params: dict, optional_params: dict):
+        for param, value in non_default_params.items():
+            if param == "encoding_format":
+                optional_params["encoding_format"] = value
+        return optional_params
+
+
+class AzureAIStudioConfig:
+    def get_required_params(self) -> List[ProviderField]:
+        """For a given provider, return it's required fields with a description"""
+        return [
+            ProviderField(
+                field_name="api_key",
+                field_type="string",
+                field_description="Your Azure AI Studio API Key.",
+                field_value="zEJ...",
+            ),
+            ProviderField(
+                field_name="api_base",
+                field_type="string",
+                field_description="Your Azure AI Studio API Base.",
+                field_value="https://Mistral-serverless.",
+            ),
+        ]
+
+
 class DeepInfraConfig:
     """
     Reference: https://deepinfra.com/docs/advanced/openai_api

litellm/llms/vertex_ai.py

@@ -4,6 +4,7 @@ from enum import Enum
 import requests  # type: ignore
 import time
 from typing import Callable, Optional, Union, List, Literal, Any
+from pydantic import BaseModel
 from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
 import litellm, uuid
 import httpx, inspect  # type: ignore
@@ -12,7 +13,12 @@ from litellm.llms.prompt_templates.factory import (
     convert_to_gemini_tool_call_result,
     convert_to_gemini_tool_call_invoke,
 )
-from litellm.types.files import get_file_mime_type_for_file_type, get_file_type_from_extension, is_gemini_1_5_accepted_file_type, is_video_file_type
+from litellm.types.files import (
+    get_file_mime_type_for_file_type,
+    get_file_type_from_extension,
+    is_gemini_1_5_accepted_file_type,
+    is_video_file_type,
+)
 
 
 class VertexAIError(Exception):
@@ -611,7 +617,7 @@ def completion(
             llm_model = None
 
         # NOTE: async prediction and streaming under "private" mode isn't supported by aiplatform right now
-        if acompletion == True:
+        if acompletion is True:
             data = {
                 "llm_model": llm_model,
                 "mode": mode,
@@ -643,7 +649,7 @@ def completion(
             tools = optional_params.pop("tools", None)
             content = _gemini_convert_messages_with_history(messages=messages)
             stream = optional_params.pop("stream", False)
-            if stream == True:
+            if stream is True:
                 request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), safety_settings={safety_settings}, stream={stream})\n"
                 logging_obj.pre_call(
                     input=prompt,
@@ -1293,6 +1299,95 @@ async def async_streaming(
     return streamwrapper
 
 
+class VertexAITextEmbeddingConfig(BaseModel):
+    """
+    Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#TextEmbeddingInput
+
+    Args:
+        auto_truncate: Optional(bool) If True, will truncate input text to fit within the model's max input length.
+        task_type: Optional(str) The type of task to be performed. The default is "RETRIEVAL_QUERY".
+        title: Optional(str) The title of the document to be embedded. (only valid with task_type=RETRIEVAL_DOCUMENT).
+    """
+
+    auto_truncate: Optional[bool] = None
+    task_type: Optional[
+        Literal[
+            "RETRIEVAL_QUERY",
+            "RETRIEVAL_DOCUMENT",
+            "SEMANTIC_SIMILARITY",
+            "CLASSIFICATION",
+            "CLUSTERING",
+            "QUESTION_ANSWERING",
+            "FACT_VERIFICATION",
+        ]
+    ] = None
+    title: Optional[str] = None
+
+    def __init__(
+        self,
+        auto_truncate: Optional[bool] = None,
+        task_type: Optional[
+            Literal[
+                "RETRIEVAL_QUERY",
+                "RETRIEVAL_DOCUMENT",
+                "SEMANTIC_SIMILARITY",
+                "CLASSIFICATION",
+                "CLUSTERING",
+                "QUESTION_ANSWERING",
+                "FACT_VERIFICATION",
+            ]
+        ] = None,
+        title: Optional[str] = None,
+    ) -> None:
+        locals_ = locals()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self):
+        return [
+            "dimensions",
+        ]
+
+    def map_openai_params(self, non_default_params: dict, optional_params: dict):
+        for param, value in non_default_params.items():
+            if param == "dimensions":
+                optional_params["output_dimensionality"] = value
+        return optional_params
+
+    def get_mapped_special_auth_params(self) -> dict:
+        """
+        Common auth params across bedrock/vertex_ai/azure/watsonx
+        """
+        return {"project": "vertex_project", "region_name": "vertex_location"}
+
+    def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
+        mapped_params = self.get_mapped_special_auth_params()
+
+        for param, value in non_default_params.items():
+            if param in mapped_params:
+                optional_params[mapped_params[param]] = value
+        return optional_params
+
+
 def embedding(
     model: str,
     input: Union[list, str],
@@ -1316,7 +1411,7 @@ def embedding(
             message="vertexai import failed please run `pip install google-cloud-aiplatform`",
         )
 
-    from vertexai.language_models import TextEmbeddingModel
+    from vertexai.language_models import TextEmbeddingModel, TextEmbeddingInput
     import google.auth  # type: ignore
 
     ## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744
@@ -1347,6 +1442,16 @@ def embedding(
     if isinstance(input, str):
         input = [input]
 
+    if optional_params is not None and isinstance(optional_params, dict):
+        if optional_params.get("task_type") or optional_params.get("title"):
+            # if user passed task_type or title, cast to TextEmbeddingInput
+            _task_type = optional_params.pop("task_type", None)
+            _title = optional_params.pop("title", None)
+            input = [
+                TextEmbeddingInput(text=x, task_type=_task_type, title=_title)
+                for x in input
+            ]
+
     try:
         llm_model = TextEmbeddingModel.from_pretrained(model)
     except Exception as e:
@@ -1363,7 +1468,8 @@ def embedding(
             encoding=encoding,
         )
 
-    request_str = f"""embeddings = llm_model.get_embeddings({input})"""
+    _input_dict = {"texts": input, **optional_params}
+    request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
     ## LOGGING PRE-CALL
     logging_obj.pre_call(
         input=input,
@@ -1375,7 +1481,7 @@ def embedding(
     )
 
     try:
-        embeddings = llm_model.get_embeddings(input)
+        embeddings = llm_model.get_embeddings(**_input_dict)
     except Exception as e:
         raise VertexAIError(status_code=500, message=str(e))
 
@@ -1383,6 +1489,7 @@ def embedding(
     logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
     ## Populate OpenAI compliant dictionary
     embedding_response = []
+    input_tokens: int = 0
     for idx, embedding in enumerate(embeddings):
         embedding_response.append(
             {
@@ -1391,14 +1498,10 @@ def embedding(
                 "embedding": embedding.values,
             }
         )
+        input_tokens += embedding.statistics.token_count
     model_response["object"] = "list"
     model_response["data"] = embedding_response
     model_response["model"] = model
-    input_tokens = 0
-
-    input_str = "".join(input)
-
-    input_tokens += len(encoding.encode(input_str))
 
     usage = Usage(
         prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
@@ -1420,7 +1523,8 @@ async def async_embedding(
     """
     Async embedding implementation
     """
-    request_str = f"""embeddings = llm_model.get_embeddings({input})"""
+    _input_dict = {"texts": input, **optional_params}
+    request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
     ## LOGGING PRE-CALL
     logging_obj.pre_call(
         input=input,
@@ -1432,7 +1536,7 @@ async def async_embedding(
     )
 
     try:
-        embeddings = await client.get_embeddings_async(input)
+        embeddings = await client.get_embeddings_async(**_input_dict)
     except Exception as e:
         raise VertexAIError(status_code=500, message=str(e))
 
@@ -1440,6 +1544,7 @@ async def async_embedding(
     logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
     ## Populate OpenAI compliant dictionary
     embedding_response = []
+    input_tokens: int = 0
     for idx, embedding in enumerate(embeddings):
         embedding_response.append(
             {
@@ -1448,18 +1553,13 @@ async def async_embedding(
                 "embedding": embedding.values,
             }
         )
+        input_tokens += embedding.statistics.token_count
+
     model_response["object"] = "list"
     model_response["data"] = embedding_response
     model_response["model"] = model
-    input_tokens = 0
-
-    input_str = "".join(input)
-
-    input_tokens += len(encoding.encode(input_str))
-
     usage = Usage(
         prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
     )
     model_response.usage = usage
-
     return model_response

litellm/llms/vertex_httpx.py

@@ -1,3 +1,7 @@
+# What is this?
+## httpx client for vertex ai calls
+## Initial implementation - covers gemini + image gen calls
+from functools import partial
 import os, types
 import json
 from enum import Enum
@@ -9,6 +13,284 @@ import litellm, uuid
 import httpx, inspect  # type: ignore
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
 from .base import BaseLLM
+from litellm.types.llms.vertex_ai import (
+    ContentType,
+    SystemInstructions,
+    PartType,
+    RequestBody,
+    GenerateContentResponseBody,
+    FunctionCallingConfig,
+    FunctionDeclaration,
+    Tools,
+    ToolConfig,
+    GenerationConfig,
+)
+from litellm.llms.vertex_ai import _gemini_convert_messages_with_history
+from litellm.types.utils import GenericStreamingChunk
+from litellm.types.llms.openai import (
+    ChatCompletionUsageBlock,
+    ChatCompletionToolCallChunk,
+    ChatCompletionToolCallFunctionChunk,
+    ChatCompletionResponseMessage,
+)
+
+
+class VertexGeminiConfig:
+    """
+    Reference: https://cloud.google.com/vertex-ai/docs/generative-ai/chat/test-chat-prompts
+    Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+
+    The class `VertexAIConfig` provides configuration for the VertexAI's API interface. Below are the parameters:
+
+    - `temperature` (float): This controls the degree of randomness in token selection.
+
+    - `max_output_tokens` (integer): This sets the limitation for the maximum amount of token in the text output. In this case, the default value is 256.
+
+    - `top_p` (float): The tokens are selected from the most probable to the least probable until the sum of their probabilities equals the `top_p` value. Default is 0.95.
+
+    - `top_k` (integer): The value of `top_k` determines how many of the most probable tokens are considered in the selection. For example, a `top_k` of 1 means the selected token is the most probable among all tokens. The default value is 40.
+
+    - `response_mime_type` (str): The MIME type of the response. The default value is 'text/plain'.
+
+    - `candidate_count` (int): Number of generated responses to return.
+
+    - `stop_sequences` (List[str]): The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
+
+    - `frequency_penalty` (float): This parameter is used to penalize the model from repeating the same output. The default value is 0.0.
+
+    - `presence_penalty` (float): This parameter is used to penalize the model from generating the same output as the input. The default value is 0.0.
+
+    Note: Please make sure to modify the default parameters as required for your use case.
+    """
+
+    temperature: Optional[float] = None
+    max_output_tokens: Optional[int] = None
+    top_p: Optional[float] = None
+    top_k: Optional[int] = None
+    response_mime_type: Optional[str] = None
+    candidate_count: Optional[int] = None
+    stop_sequences: Optional[list] = None
+    frequency_penalty: Optional[float] = None
+    presence_penalty: Optional[float] = None
+
+    def __init__(
+        self,
+        temperature: Optional[float] = None,
+        max_output_tokens: Optional[int] = None,
+        top_p: Optional[float] = None,
+        top_k: Optional[int] = None,
+        response_mime_type: Optional[str] = None,
+        candidate_count: Optional[int] = None,
+        stop_sequences: Optional[list] = None,
+        frequency_penalty: Optional[float] = None,
+        presence_penalty: Optional[float] = None,
+    ) -> None:
+        locals_ = locals()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self):
+        return [
+            "temperature",
+            "top_p",
+            "max_tokens",
+            "stream",
+            "tools",
+            "tool_choice",
+            "response_format",
+            "n",
+            "stop",
+        ]
+
+    def map_tool_choice_values(
+        self, model: str, tool_choice: Union[str, dict]
+    ) -> Optional[ToolConfig]:
+        if tool_choice == "none":
+            return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="NONE"))
+        elif tool_choice == "required":
+            return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="ANY"))
+        elif tool_choice == "auto":
+            return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="AUTO"))
+        elif isinstance(tool_choice, dict):
+            # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
+            name = tool_choice.get("function", {}).get("name", "")
+            return ToolConfig(
+                functionCallingConfig=FunctionCallingConfig(
+                    mode="ANY", allowed_function_names=[name]
+                )
+            )
+        else:
+            raise litellm.utils.UnsupportedParamsError(
+                message="VertexAI doesn't support tool_choice={}. Supported tool_choice values=['auto', 'required', json object]. To drop it from the call, set `litellm.drop_params = True.".format(
+                    tool_choice
+                ),
+                status_code=400,
+            )
+
+    def map_openai_params(
+        self,
+        model: str,
+        non_default_params: dict,
+        optional_params: dict,
+    ):
+        for param, value in non_default_params.items():
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["top_p"] = value
+            if (
+                param == "stream" and value is True
+            ):  # sending stream = False, can cause it to get passed unchecked and raise issues
+                optional_params["stream"] = value
+            if param == "n":
+                optional_params["candidate_count"] = value
+            if param == "stop":
+                if isinstance(value, str):
+                    optional_params["stop_sequences"] = [value]
+                elif isinstance(value, list):
+                    optional_params["stop_sequences"] = value
+            if param == "max_tokens":
+                optional_params["max_output_tokens"] = value
+            if param == "response_format" and value["type"] == "json_object":  # type: ignore
+                optional_params["response_mime_type"] = "application/json"
+            if param == "frequency_penalty":
+                optional_params["frequency_penalty"] = value
+            if param == "presence_penalty":
+                optional_params["presence_penalty"] = value
+            if param == "tools" and isinstance(value, list):
+                gtool_func_declarations = []
+                for tool in value:
+                    gtool_func_declaration = FunctionDeclaration(
+                        name=tool["function"]["name"],
+                        description=tool["function"].get("description", ""),
+                        parameters=tool["function"].get("parameters", {}),
+                    )
+                    gtool_func_declarations.append(gtool_func_declaration)
+                optional_params["tools"] = [
+                    Tools(function_declarations=gtool_func_declarations)
+                ]
+            if param == "tool_choice" and (
+                isinstance(value, str) or isinstance(value, dict)
+            ):
+                _tool_choice_value = self.map_tool_choice_values(
+                    model=model, tool_choice=value  # type: ignore
+                )
+                if _tool_choice_value is not None:
+                    optional_params["tool_choice"] = _tool_choice_value
+        return optional_params
+
+    def get_mapped_special_auth_params(self) -> dict:
+        """
+        Common auth params across bedrock/vertex_ai/azure/watsonx
+        """
+        return {"project": "vertex_project", "region_name": "vertex_location"}
+
+    def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
+        mapped_params = self.get_mapped_special_auth_params()
+
+        for param, value in non_default_params.items():
+            if param in mapped_params:
+                optional_params[mapped_params[param]] = value
+        return optional_params
+
+    def get_eu_regions(self) -> List[str]:
+        """
+        Source: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations#available-regions
+        """
+        return [
+            "europe-central2",
+            "europe-north1",
+            "europe-southwest1",
+            "europe-west1",
+            "europe-west2",
+            "europe-west3",
+            "europe-west4",
+            "europe-west6",
+            "europe-west8",
+            "europe-west9",
+        ]
+
+
+async def make_call(
+    client: Optional[AsyncHTTPHandler],
+    api_base: str,
+    headers: dict,
+    data: str,
+    model: str,
+    messages: list,
+    logging_obj,
+):
+    if client is None:
+        client = AsyncHTTPHandler()  # Create a new client if none provided
+
+    response = await client.post(api_base, headers=headers, data=data, stream=True)
+
+    if response.status_code != 200:
+        raise VertexAIError(status_code=response.status_code, message=response.text)
+
+    completion_stream = ModelResponseIterator(
+        streaming_response=response.aiter_bytes(chunk_size=2056)
+    )
+    # LOGGING
+    logging_obj.post_call(
+        input=messages,
+        api_key="",
+        original_response="first stream response received",
+        additional_args={"complete_input_dict": data},
+    )
+
+    return completion_stream
+
+
+def make_sync_call(
+    client: Optional[HTTPHandler],
+    api_base: str,
+    headers: dict,
+    data: str,
+    model: str,
+    messages: list,
+    logging_obj,
+):
+    if client is None:
+        client = HTTPHandler()  # Create a new client if none provided
+
+    response = client.post(api_base, headers=headers, data=data, stream=True)
+
+    if response.status_code != 200:
+        raise VertexAIError(status_code=response.status_code, message=response.read())
+
+    completion_stream = ModelResponseIterator(
+        streaming_response=response.iter_bytes(chunk_size=2056)
+    )
+
+    # LOGGING
+    logging_obj.post_call(
+        input=messages,
+        api_key="",
+        original_response="first stream response received",
+        additional_args={"complete_input_dict": data},
+    )
+
+    return completion_stream
 
 
 class VertexAIError(Exception):
@@ -33,16 +315,125 @@ class VertexLLM(BaseLLM):
         self.project_id: Optional[str] = None
         self.async_handler: Optional[AsyncHTTPHandler] = None
 
-    def load_auth(self) -> Tuple[Any, str]:
+    def _process_response(
+        self,
+        model: str,
+        response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: litellm.utils.Logging,
+        optional_params: dict,
+        api_key: str,
+        data: Union[dict, str],
+        messages: List,
+        print_verbose,
+        encoding,
+    ) -> ModelResponse:
+
+        ## LOGGING
+        logging_obj.post_call(
+            input=messages,
+            api_key="",
+            original_response=response.text,
+            additional_args={"complete_input_dict": data},
+        )
+
+        print_verbose(f"raw model_response: {response.text}")
+
+        ## RESPONSE OBJECT
+        try:
+            completion_response = GenerateContentResponseBody(**response.json())  # type: ignore
+        except Exception as e:
+            raise VertexAIError(
+                message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format(
+                    response.text, str(e)
+                ),
+                status_code=422,
+            )
+
+        model_response.choices = []  # type: ignore
+
+        ## GET MODEL ##
+        model_response.model = model
+        ## GET TEXT ##
+        chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"}
+        content_str = ""
+        tools: List[ChatCompletionToolCallChunk] = []
+        for idx, candidate in enumerate(completion_response["candidates"]):
+            if "content" not in candidate:
+                continue
+
+            if "text" in candidate["content"]["parts"][0]:
+                content_str = candidate["content"]["parts"][0]["text"]
+
+            if "functionCall" in candidate["content"]["parts"][0]:
+                _function_chunk = ChatCompletionToolCallFunctionChunk(
+                    name=candidate["content"]["parts"][0]["functionCall"]["name"],
+                    arguments=json.dumps(
+                        candidate["content"]["parts"][0]["functionCall"]["args"]
+                    ),
+                )
+                _tool_response_chunk = ChatCompletionToolCallChunk(
+                    id=f"call_{str(uuid.uuid4())}",
+                    type="function",
+                    function=_function_chunk,
+                )
+                tools.append(_tool_response_chunk)
+
+            chat_completion_message["content"] = content_str
+            chat_completion_message["tool_calls"] = tools
+
+            choice = litellm.Choices(
+                finish_reason=candidate.get("finishReason", "stop"),
+                index=candidate.get("index", idx),
+                message=chat_completion_message,  # type: ignore
+                logprobs=None,
+                enhancements=None,
+            )
+
+            model_response.choices.append(choice)
+
+        ## GET USAGE ##
+        usage = litellm.Usage(
+            prompt_tokens=completion_response["usageMetadata"]["promptTokenCount"],
+            completion_tokens=completion_response["usageMetadata"][
+                "candidatesTokenCount"
+            ],
+            total_tokens=completion_response["usageMetadata"]["totalTokenCount"],
+        )
+
+        setattr(model_response, "usage", usage)
+
+        return model_response
+
+    def get_vertex_region(self, vertex_region: Optional[str]) -> str:
+        return vertex_region or "us-central1"
+
+    def load_auth(
+        self, credentials: Optional[str], project_id: Optional[str]
+    ) -> Tuple[Any, str]:
         from google.auth.transport.requests import Request  # type: ignore[import-untyped]
         from google.auth.credentials import Credentials  # type: ignore[import-untyped]
         import google.auth as google_auth
 
-        credentials, project_id = google_auth.default(
+        if credentials is not None and isinstance(credentials, str):
+            import google.oauth2.service_account
+
+            json_obj = json.loads(credentials)
+
+            creds = google.oauth2.service_account.Credentials.from_service_account_info(
+                json_obj,
                 scopes=["https://www.googleapis.com/auth/cloud-platform"],
             )
 
-        credentials.refresh(Request())
+            if project_id is None:
+                project_id = creds.project_id
+        else:
+            creds, project_id = google_auth.default(
+                quota_project_id=project_id,
+                scopes=["https://www.googleapis.com/auth/cloud-platform"],
+            )
+
+        creds.refresh(Request())
 
         if not project_id:
             raise ValueError("Could not resolve project_id")
@@ -52,38 +443,272 @@ class VertexLLM(BaseLLM):
                 f"Expected project_id to be a str but got {type(project_id)}"
             )
 
-        return credentials, project_id
+        return creds, project_id
 
     def refresh_auth(self, credentials: Any) -> None:
         from google.auth.transport.requests import Request  # type: ignore[import-untyped]
 
         credentials.refresh(Request())
 
-    def _prepare_request(self, request: httpx.Request) -> None:
+    def _ensure_access_token(
-        access_token = self._ensure_access_token()
+        self, credentials: Optional[str], project_id: Optional[str]
-
+    ) -> Tuple[str, str]:
-        if request.headers.get("Authorization"):
+        """
-            # already authenticated, nothing for us to do
+        Returns auth token and project id
-            return
+        """
-
+        if self.access_token is not None and self.project_id is not None:
-        request.headers["Authorization"] = f"Bearer {access_token}"
+            return self.access_token, self.project_id
-
-    def _ensure_access_token(self) -> str:
-        if self.access_token is not None:
-            return self.access_token
 
         if not self._credentials:
-            self._credentials, project_id = self.load_auth()
+            self._credentials, project_id = self.load_auth(
+                credentials=credentials, project_id=project_id
+            )
             if not self.project_id:
                 self.project_id = project_id
         else:
             self.refresh_auth(self._credentials)
 
-        if not self._credentials.token:
+            if not self.project_id:
+                self.project_id = self._credentials.project_id
+
+        if not self.project_id:
+            raise ValueError("Could not resolve project_id")
+
+        if not self._credentials or not self._credentials.token:
             raise RuntimeError("Could not resolve API token from the environment")
 
-        assert isinstance(self._credentials.token, str)
+        return self._credentials.token, self.project_id
-        return self._credentials.token
+
+    async def async_streaming(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        data: str,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        encoding,
+        logging_obj,
+        stream,
+        optional_params: dict,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> CustomStreamWrapper:
+        streaming_response = CustomStreamWrapper(
+            completion_stream=None,
+            make_call=partial(
+                make_call,
+                client=client,
+                api_base=api_base,
+                headers=headers,
+                data=data,
+                model=model,
+                messages=messages,
+                logging_obj=logging_obj,
+            ),
+            model=model,
+            custom_llm_provider="vertex_ai_beta",
+            logging_obj=logging_obj,
+        )
+        return streaming_response
+
+    async def async_completion(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        data: str,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        encoding,
+        logging_obj,
+        stream,
+        optional_params: dict,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> Union[ModelResponse, CustomStreamWrapper]:
+        if client is None:
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    timeout = httpx.Timeout(timeout)
+                _params["timeout"] = timeout
+            client = AsyncHTTPHandler(**_params)  # type: ignore
+        else:
+            client = client  # type: ignore
+
+        try:
+            response = await client.post(api_base, headers=headers, json=data)  # type: ignore
+            response.raise_for_status()
+        except httpx.HTTPStatusError as err:
+            error_code = err.response.status_code
+            raise VertexAIError(status_code=error_code, message=err.response.text)
+        except httpx.TimeoutException:
+            raise VertexAIError(status_code=408, message="Timeout error occurred.")
+
+        return self._process_response(
+            model=model,
+            response=response,
+            model_response=model_response,
+            logging_obj=logging_obj,
+            api_key="",
+            data=data,
+            messages=messages,
+            print_verbose=print_verbose,
+            optional_params=optional_params,
+            encoding=encoding,
+        )
+
+    def completion(
+        self,
+        model: str,
+        messages: list,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        encoding,
+        logging_obj,
+        optional_params: dict,
+        acompletion: bool,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        vertex_project: Optional[str],
+        vertex_location: Optional[str],
+        vertex_credentials: Optional[str],
+        litellm_params=None,
+        logger_fn=None,
+        extra_headers: Optional[dict] = None,
+        client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
+    ) -> Union[ModelResponse, CustomStreamWrapper]:
+
+        auth_header, vertex_project = self._ensure_access_token(
+            credentials=vertex_credentials, project_id=vertex_project
+        )
+        vertex_location = self.get_vertex_region(vertex_region=vertex_location)
+        stream: Optional[bool] = optional_params.pop("stream", None)  # type: ignore
+
+        ### SET RUNTIME ENDPOINT ###
+        url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:generateContent"
+
+        ## TRANSFORMATION ##
+        # Separate system prompt from rest of message
+        system_prompt_indices = []
+        system_content_blocks: List[PartType] = []
+        for idx, message in enumerate(messages):
+            if message["role"] == "system":
+                _system_content_block = PartType(text=message["content"])
+                system_content_blocks.append(_system_content_block)
+                system_prompt_indices.append(idx)
+        if len(system_prompt_indices) > 0:
+            for idx in reversed(system_prompt_indices):
+                messages.pop(idx)
+        system_instructions = SystemInstructions(parts=system_content_blocks)
+        content = _gemini_convert_messages_with_history(messages=messages)
+        tools: Optional[Tools] = optional_params.pop("tools", None)
+        tool_choice: Optional[ToolConfig] = optional_params.pop("tool_choice", None)
+        generation_config: Optional[GenerationConfig] = GenerationConfig(
+            **optional_params
+        )
+        data = RequestBody(system_instruction=system_instructions, contents=content)
+        if tools is not None:
+            data["tools"] = tools
+        if tool_choice is not None:
+            data["toolConfig"] = tool_choice
+        if generation_config is not None:
+            data["generationConfig"] = generation_config
+
+        headers = {
+            "Content-Type": "application/json; charset=utf-8",
+            "Authorization": f"Bearer {auth_header}",
+        }
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=messages,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": url,
+                "headers": headers,
+            },
+        )
+
+        ### ROUTING (ASYNC, STREAMING, SYNC)
+        if acompletion:
+            ### ASYNC COMPLETION
+            return self.async_completion(
+                model=model,
+                messages=messages,
+                data=data,  # type: ignore
+                api_base=url,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                encoding=encoding,
+                logging_obj=logging_obj,
+                optional_params=optional_params,
+                stream=stream,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                headers=headers,
+                timeout=timeout,
+                client=client,  # type: ignore
+            )
+
+        ## SYNC STREAMING CALL ##
+        if stream is not None and stream is True:
+            streaming_response = CustomStreamWrapper(
+                completion_stream=None,
+                make_call=partial(
+                    make_sync_call,
+                    client=None,
+                    api_base=url,
+                    headers=headers,  # type: ignore
+                    data=json.dumps(data),
+                    model=model,
+                    messages=messages,
+                    logging_obj=logging_obj,
+                ),
+                model=model,
+                custom_llm_provider="vertex_ai_beta",
+                logging_obj=logging_obj,
+            )
+
+            return streaming_response
+        ## COMPLETION CALL ##
+        if client is None or isinstance(client, AsyncHTTPHandler):
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    timeout = httpx.Timeout(timeout)
+                _params["timeout"] = timeout
+            client = HTTPHandler(**_params)  # type: ignore
+        else:
+            client = client
+        try:
+            response = client.post(url=url, headers=headers, json=data)  # type: ignore
+            response.raise_for_status()
+        except httpx.HTTPStatusError as err:
+            error_code = err.response.status_code
+            raise VertexAIError(status_code=error_code, message=response.text)
+        except httpx.TimeoutException:
+            raise VertexAIError(status_code=408, message="Timeout error occurred.")
+
+        return self._process_response(
+            model=model,
+            response=response,
+            model_response=model_response,
+            logging_obj=logging_obj,
+            optional_params=optional_params,
+            api_key="",
+            data=data,  # type: ignore
+            messages=messages,
+            print_verbose=print_verbose,
+            encoding=encoding,
+        )
 
     def image_generation(
         self,
@@ -163,7 +788,7 @@ class VertexLLM(BaseLLM):
         } \
         "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration:predict"
         """
-        auth_header = self._ensure_access_token()
+        auth_header, _ = self._ensure_access_token(credentials=None, project_id=None)
         optional_params = optional_params or {
             "sampleCount": 1
         }  # default optional params
@@ -222,3 +847,84 @@ class VertexLLM(BaseLLM):
         model_response.data = _response_data
 
         return model_response
+
+
+class ModelResponseIterator:
+    def __init__(self, streaming_response):
+        self.streaming_response = streaming_response
+        self.response_iterator = iter(self.streaming_response)
+
+    def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
+        try:
+            processed_chunk = GenerateContentResponseBody(**chunk)  # type: ignore
+            text = ""
+            tool_use: Optional[ChatCompletionToolCallChunk] = None
+            is_finished = False
+            finish_reason = ""
+            usage: Optional[ChatCompletionUsageBlock] = None
+
+            gemini_chunk = processed_chunk["candidates"][0]
+
+            if (
+                "content" in gemini_chunk
+                and "text" in gemini_chunk["content"]["parts"][0]
+            ):
+                text = gemini_chunk["content"]["parts"][0]["text"]
+
+            if "finishReason" in gemini_chunk:
+                finish_reason = map_finish_reason(
+                    finish_reason=gemini_chunk["finishReason"]
+                )
+                is_finished = True
+
+            if "usageMetadata" in processed_chunk:
+                usage = ChatCompletionUsageBlock(
+                    prompt_tokens=processed_chunk["usageMetadata"]["promptTokenCount"],
+                    completion_tokens=processed_chunk["usageMetadata"][
+                        "candidatesTokenCount"
+                    ],
+                    total_tokens=processed_chunk["usageMetadata"]["totalTokenCount"],
+                )
+
+            returned_chunk = GenericStreamingChunk(
+                text=text,
+                tool_use=tool_use,
+                is_finished=is_finished,
+                finish_reason=finish_reason,
+                usage=usage,
+                index=0,
+            )
+            return returned_chunk
+        except json.JSONDecodeError:
+            raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
+
+    # Sync iterator
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        try:
+            chunk = next(self.response_iterator)
+            chunk = chunk.decode()
+            json_chunk = json.loads(chunk)
+            return self.chunk_parser(chunk=json_chunk)
+        except StopIteration:
+            raise StopIteration
+        except ValueError as e:
+            raise RuntimeError(f"Error parsing chunk: {e}")
+
+    # Async iterator
+    def __aiter__(self):
+        self.async_response_iterator = self.streaming_response.__aiter__()
+        return self
+
+    async def __anext__(self):
+        try:
+            chunk = await self.async_response_iterator.__anext__()
+            chunk = chunk.decode()
+            json_chunk = json.loads(chunk)
+            return self.chunk_parser(chunk=json_chunk)
+        except StopAsyncIteration:
+            raise StopAsyncIteration
+        except ValueError as e:
+            raise RuntimeError(f"Error parsing chunk: {e}")

litellm/main.py

@@ -329,12 +329,14 @@ async def acompletion(
             or custom_llm_provider == "ollama_chat"
             or custom_llm_provider == "replicate"
             or custom_llm_provider == "vertex_ai"
+            or custom_llm_provider == "vertex_ai_beta"
             or custom_llm_provider == "gemini"
             or custom_llm_provider == "sagemaker"
             or custom_llm_provider == "anthropic"
             or custom_llm_provider == "predibase"
             or custom_llm_provider == "bedrock"
             or custom_llm_provider == "databricks"
+            or custom_llm_provider == "clarifai"
             or custom_llm_provider in litellm.openai_compatible_providers
         ):  # currently implemented aiohttp calls for just azure, openai, hf, ollama, vertex ai soon all.
             init_response = await loop.run_in_executor(None, func_with_context)
@@ -1875,6 +1877,42 @@ def completion(
                 )
                 return response
             response = model_response
+        elif custom_llm_provider == "vertex_ai_beta":
+            vertex_ai_project = (
+                optional_params.pop("vertex_project", None)
+                or optional_params.pop("vertex_ai_project", None)
+                or litellm.vertex_project
+                or get_secret("VERTEXAI_PROJECT")
+            )
+            vertex_ai_location = (
+                optional_params.pop("vertex_location", None)
+                or optional_params.pop("vertex_ai_location", None)
+                or litellm.vertex_location
+                or get_secret("VERTEXAI_LOCATION")
+            )
+            vertex_credentials = (
+                optional_params.pop("vertex_credentials", None)
+                or optional_params.pop("vertex_ai_credentials", None)
+                or get_secret("VERTEXAI_CREDENTIALS")
+            )
+            new_params = deepcopy(optional_params)
+            response = vertex_chat_completion.completion(  # type: ignore
+                model=model,
+                messages=messages,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                optional_params=new_params,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                encoding=encoding,
+                vertex_location=vertex_ai_location,
+                vertex_project=vertex_ai_project,
+                vertex_credentials=vertex_credentials,
+                logging_obj=logging,
+                acompletion=acompletion,
+                timeout=timeout,
+            )
+
         elif custom_llm_provider == "vertex_ai":
             vertex_ai_project = (
                 optional_params.pop("vertex_project", None)
@@ -1893,6 +1931,7 @@ def completion(
                 or optional_params.pop("vertex_ai_credentials", None)
                 or get_secret("VERTEXAI_CREDENTIALS")
             )
+
             new_params = deepcopy(optional_params)
             if "claude-3" in model:
                 model_response = vertex_ai_anthropic.completion(

litellm/model_prices_and_context_window_backup.json

@@ -3347,6 +3347,24 @@
         "litellm_provider": "deepinfra",
         "mode": "chat"
     },
+    "deepinfra/meta-llama/Meta-Llama-3-8B-Instruct": {
+        "max_tokens": 8191,
+        "max_input_tokens": 8191,
+        "max_output_tokens": 4096,
+        "input_cost_per_token": 0.00000008,
+        "output_cost_per_token": 0.00000008,
+        "litellm_provider": "deepinfra",
+        "mode": "chat"
+    },
+    "deepinfra/meta-llama/Meta-Llama-3-70B-Instruct": {
+        "max_tokens": 8191,
+        "max_input_tokens": 8191,
+        "max_output_tokens": 4096,
+        "input_cost_per_token": 0.00000059,
+        "output_cost_per_token": 0.00000079,
+        "litellm_provider": "deepinfra",
+        "mode": "chat"
+    },
     "deepinfra/01-ai/Yi-34B-200K": {
         "max_tokens": 4096,
         "max_input_tokens": 200000,

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litellm/proxy/_experimental/out/model_hub.txt

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litellm/proxy/_experimental/out/onboarding.txt

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litellm/proxy/_super_secret_config.yaml

@@ -11,7 +11,7 @@ model_list:
 - model_name: fake-openai-endpoint
   litellm_params:
     model: predibase/llama-3-8b-instruct
-    api_base: "http://0.0.0.0:8081"
+    api_base: "http://0.0.0.0:8000"
     api_key: os.environ/PREDIBASE_API_KEY
     tenant_id: os.environ/PREDIBASE_TENANT_ID
     max_retries: 0
@@ -55,7 +55,16 @@ model_list:
     model: textembedding-gecko-multilingual@001
     vertex_project: my-project-9d5c
     vertex_location: us-central1
-
+- model_name: lbl/command-r-plus
+  litellm_params:
+    model: openai/lbl/command-r-plus
+    api_key: "os.environ/VLLM_API_KEY"
+    api_base: http://vllm-command:8000/v1
+    rpm: 1000
+    input_cost_per_token: 0
+    output_cost_per_token: 0
+  model_info:
+    max_input_tokens: 80920
 assistant_settings:
   custom_llm_provider: openai
   litellm_params: 

litellm/proxy/_types.py

@@ -224,6 +224,7 @@ class LiteLLMRoutes(enum.Enum):
         "/key/delete",
         "/global/spend/logs",
         "/global/predict/spend/logs",
+        "/sso/get/logout_url",
     ]
 
     management_routes: List = [  # key

litellm/proxy/common_utils/management_endpoint_utils.py

@@ -32,7 +32,7 @@ def management_endpoint_wrapper(func):
 
                 if open_telemetry_logger is not None:
                     _http_request: Request = kwargs.get("http_request")
-
+                    if _http_request:
                         _route = _http_request.url.path
                         _request_body: dict = await _read_request_body(
                             request=_http_request
@@ -67,6 +67,7 @@ def management_endpoint_wrapper(func):
 
                 if open_telemetry_logger is not None:
                     _http_request: Request = kwargs.get("http_request")
+                    if _http_request:
                         _route = _http_request.url.path
                         _request_body: dict = await _read_request_body(
                             request=_http_request

litellm/proxy/hooks/azure_content_safety.py

@@ -140,7 +140,7 @@ class _PROXY_AzureContentSafety(
             response.choices[0], litellm.utils.Choices
         ):
             await self.test_violation(
-                content=response.choices[0].message.content, source="output"
+                content=response.choices[0].message.content or "", source="output"
             )
 
     # async def async_post_call_streaming_hook(

litellm/proxy/litellm_pre_call_utils.py

@@ -79,10 +79,6 @@ async def add_litellm_data_to_request(
                     data["cache"][k] = v
 
     verbose_proxy_logger.debug("receiving data: %s", data)
-    # users can pass in 'user' param to /chat/completions. Don't override it
-    if data.get("user", None) is None and user_api_key_dict.user_id is not None:
-        # if users are using user_api_key_auth, set `user` in `data`
-        data["user"] = user_api_key_dict.user_id
 
     if "metadata" not in data:
         data["metadata"] = {}
@@ -108,6 +104,15 @@ async def add_litellm_data_to_request(
     data["metadata"]["user_api_key_team_alias"] = getattr(
         user_api_key_dict, "team_alias", None
     )
+
+    # Team spend, budget - used by prometheus.py
+    data["metadata"]["user_api_key_team_max_budget"] = user_api_key_dict.team_max_budget
+    data["metadata"]["user_api_key_team_spend"] = user_api_key_dict.team_spend
+
+    # API Key spend, budget - used by prometheus.py
+    data["metadata"]["user_api_key_spend"] = user_api_key_dict.spend
+    data["metadata"]["user_api_key_max_budget"] = user_api_key_dict.max_budget
+
     data["metadata"]["user_api_key_metadata"] = user_api_key_dict.metadata
     _headers = dict(request.headers)
     _headers.pop(

litellm/proxy/proxy_config.yaml

@@ -14,17 +14,18 @@ model_list:
     litellm_params:
       model: openai/*
       api_key: os.environ/OPENAI_API_KEY
-  - model_name: my-triton-model
+  - model_name: mistral-embed
     litellm_params:
-      model: triton/any"
+      model: mistral/mistral-embed
-      api_base: https://exampleopenaiendpoint-production.up.railway.app/triton/embeddings
 
 general_settings:
   master_key: sk-1234
 
 litellm_settings:
-  callbacks: ["otel"]
+  success_callback: ["prometheus"]
+  failure_callback: ["prometheus"]
   store_audit_logs: true
+  turn_off_message_logging: true
   redact_messages_in_exceptions: True
   enforced_params:  
     - user

litellm/proxy/proxy_server.py

@@ -879,6 +879,7 @@ async def user_api_key_auth(
 
         ## check for cache hit (In-Memory Cache)
         original_api_key = api_key  # (Patch: For DynamoDB Backwards Compatibility)
+        _user_role = None
         if api_key.startswith("sk-"):
             api_key = hash_token(token=api_key)
         valid_token: Optional[UserAPIKeyAuth] = user_api_key_cache.get_cache(  # type: ignore
@@ -1512,7 +1513,7 @@ async def user_api_key_auth(
                 ):
                     return UserAPIKeyAuth(
                         api_key=api_key,
-                        user_role="app_owner",
+                        user_role=_user_role,
                         parent_otel_span=parent_otel_span,
                         **valid_token_dict,
                     )
@@ -6649,7 +6650,7 @@ async def generate_key_fn(
 
         # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
         if litellm.store_audit_logs is True:
-            _updated_values = json.dumps(response)
+            _updated_values = json.dumps(response, default=str)
             asyncio.create_task(
                 create_audit_log_for_update(
                     request_data=LiteLLM_AuditLogs(
@@ -6754,10 +6755,10 @@ async def update_key_fn(
 
         # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
         if litellm.store_audit_logs is True:
-            _updated_values = json.dumps(data_json)
+            _updated_values = json.dumps(data_json, default=str)
 
             _before_value = existing_key_row.json(exclude_none=True)
-            _before_value = json.dumps(_before_value)
+            _before_value = json.dumps(_before_value, default=str)
 
             asyncio.create_task(
                 create_audit_log_for_update(
@@ -6853,7 +6854,7 @@ async def delete_key_fn(
                 )
 
                 key_row = key_row.json(exclude_none=True)
-                _key_row = json.dumps(key_row)
+                _key_row = json.dumps(key_row, default=str)
 
                 asyncio.create_task(
                     create_audit_log_for_update(
@@ -7057,6 +7058,7 @@ async def info_key_fn(
     "/spend/keys",
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def spend_key_fn():
     """
@@ -7089,6 +7091,7 @@ async def spend_key_fn():
     "/spend/users",
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def spend_user_fn(
     user_id: Optional[str] = fastapi.Query(
@@ -7219,6 +7222,7 @@ async def view_spend_tags(
     responses={
         200: {"model": List[LiteLLM_SpendLogs]},
     },
+    include_in_schema=False,
 )
 async def get_global_activity(
     start_date: Optional[str] = fastapi.Query(
@@ -7322,6 +7326,7 @@ async def get_global_activity(
     responses={
         200: {"model": List[LiteLLM_SpendLogs]},
     },
+    include_in_schema=False,
 )
 async def get_global_activity_model(
     start_date: Optional[str] = fastapi.Query(
@@ -7468,6 +7473,7 @@ async def get_global_activity_model(
     responses={
         200: {"model": List[LiteLLM_SpendLogs]},
     },
+    include_in_schema=False,
 )
 async def get_global_activity_exceptions_per_deployment(
     model_group: str = fastapi.Query(
@@ -7620,6 +7626,7 @@ async def get_global_activity_exceptions_per_deployment(
     responses={
         200: {"model": List[LiteLLM_SpendLogs]},
     },
+    include_in_schema=False,
 )
 async def get_global_activity_exceptions(
     model_group: str = fastapi.Query(
@@ -7830,7 +7837,6 @@ async def get_global_spend_provider(
     "/global/spend/report",
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
-    include_in_schema=False,
     responses={
         200: {"model": List[LiteLLM_SpendLogs]},
     },
@@ -8530,6 +8536,7 @@ async def global_spend_reset():
     "/global/spend/logs",
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def global_spend_logs(
     api_key: str = fastapi.Query(
@@ -8575,6 +8582,7 @@ async def global_spend_logs(
     "/global/spend",
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def global_spend():
     """
@@ -8601,6 +8609,7 @@ async def global_spend():
     "/global/spend/keys",
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def global_spend_keys(
     limit: int = fastapi.Query(
@@ -8628,6 +8637,7 @@ async def global_spend_keys(
     "/global/spend/teams",
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def global_spend_per_team():
     """
@@ -8752,6 +8762,7 @@ async def global_view_all_end_users():
     "/global/spend/end_users",
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def global_spend_end_users(data: Optional[GlobalEndUsersSpend] = None):
     """
@@ -8804,6 +8815,7 @@ LIMIT 100
     "/global/spend/models",
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def global_spend_models(
     limit: int = fastapi.Query(
@@ -8832,6 +8844,7 @@ async def global_spend_models(
     "/global/predict/spend/logs",
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def global_predict_spend_logs(request: Request):
     from enterprise.utils import _forecast_daily_cost
@@ -8863,7 +8876,7 @@ async def new_user(data: NewUserRequest):
     - organization_id: Optional[str] - specify the org a user belongs to.
     - user_email: Optional[str] - Specify a user email.
     - send_invite_email: Optional[bool] - Specify if an invite email should be sent.
-    - user_role: Optional[str] - Specify a user role - "admin", "app_owner", "app_user"
+    - user_role: Optional[str] - Specify a user role - "proxy_admin", "proxy_admin_viewer", "internal_user", "internal_user_viewer", "team", "customer". Info about each role here: `https://github.com/BerriAI/litellm/litellm/proxy/_types.py#L20`
     - max_budget: Optional[float] - Specify max budget for a given user.
     - models: Optional[list] - Model_name's a user is allowed to call. (if empty, key is allowed to call all models)
     - tpm_limit: Optional[int] - Specify tpm limit for a given user (Tokens per minute)
@@ -9947,16 +9960,18 @@ async def new_team(
     """
     Allow users to create a new team. Apply user permissions to their team.
 
-    [ASK FOR HELP](https://github.com/BerriAI/litellm/issues)
+    👉 [Detailed Doc on setting team budgets](https://docs.litellm.ai/docs/proxy/team_budgets)
+
 
     Parameters:
     - team_alias: Optional[str] - User defined team alias
     - team_id: Optional[str] - The team id of the user. If none passed, we'll generate it.
     - members_with_roles: List[{"role": "admin" or "user", "user_id": "<user-id>"}] - A list of users and their roles in the team. Get user_id when making a new user via `/user/new`.
-    - metadata: Optional[dict] - Metadata for team, store information for team. Example metadata = {"team": "core-infra", "app": "app2", "email": "ishaan@berri.ai" }
+    - metadata: Optional[dict] - Metadata for team, store information for team. Example metadata = {"extra_info": "some info"}
     - tpm_limit: Optional[int] - The TPM (Tokens Per Minute) limit for this team - all keys with this team_id will have at max this TPM limit
     - rpm_limit: Optional[int] - The RPM (Requests Per Minute) limit for this team - all keys associated with this team_id will have at max this RPM limit
     - max_budget: Optional[float] - The maximum budget allocated to the team - all keys for this team_id will have at max this max_budget
+    - budget_duration: Optional[str] - The duration of the budget for the team. Doc [here](https://docs.litellm.ai/docs/proxy/team_budgets)
     - models: Optional[list] - A list of models associated with the team - all keys for this team_id will have at most, these models. If empty, assumes all models are allowed.
     - blocked: bool - Flag indicating if the team is blocked or not - will stop all calls from keys with this team_id.
 
@@ -9981,6 +9996,21 @@ async def new_team(
         {"role": "user", "user_id": "user-2434"}]
     }'
 
+    ```
+
+     ```
+    curl --location 'http://0.0.0.0:4000/team/new' \
+
+    --header 'Authorization: Bearer sk-1234' \
+
+    --header 'Content-Type: application/json' \
+
+    --data '{
+                "team_alias": "QA Prod Bot", 
+                "max_budget": 0.000000001, 
+                "budget_duration": "1d"
+            }'
+
     ```
     """
     global prisma_client
@@ -10110,7 +10140,8 @@ async def new_team(
     # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
     if litellm.store_audit_logs is True:
         _updated_values = complete_team_data.json(exclude_none=True)
-        _updated_values = json.dumps(_updated_values)
+
+        _updated_values = json.dumps(_updated_values, default=str)
 
         asyncio.create_task(
             create_audit_log_for_update(
@@ -10174,6 +10205,7 @@ async def create_audit_log_for_update(request_data: LiteLLM_AuditLogs):
 @management_endpoint_wrapper
 async def update_team(
     data: UpdateTeamRequest,
+    http_request: Request,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
     litellm_changed_by: Optional[str] = Header(
         None,
@@ -10192,6 +10224,7 @@ async def update_team(
     - tpm_limit: Optional[int] - The TPM (Tokens Per Minute) limit for this team - all keys with this team_id will have at max this TPM limit
     - rpm_limit: Optional[int] - The RPM (Requests Per Minute) limit for this team - all keys associated with this team_id will have at max this RPM limit
     - max_budget: Optional[float] - The maximum budget allocated to the team - all keys for this team_id will have at max this max_budget
+    - budget_duration: Optional[str] - The duration of the budget for the team. Doc [here](https://docs.litellm.ai/docs/proxy/team_budgets)
     - models: Optional[list] - A list of models associated with the team - all keys for this team_id will have at most, these models. If empty, assumes all models are allowed.
     - blocked: bool - Flag indicating if the team is blocked or not - will stop all calls from keys with this team_id.
 
@@ -10209,6 +10242,20 @@ async def update_team(
         "tpm_limit": 100
     }'
     ```
+
+    Example - Update Team `max_budget` budget
+    ```
+    curl --location 'http://0.0.0.0:8000/team/update' \
+
+    --header 'Authorization: Bearer sk-1234' \
+
+    --header 'Content-Type: application/json' \
+
+    --data-raw '{
+        "team_id": "litellm-test-client-id-new",
+        "max_budget": 10
+    }'
+    ```
     """
     global prisma_client
 
@@ -10248,8 +10295,8 @@ async def update_team(
     # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
     if litellm.store_audit_logs is True:
         _before_value = existing_team_row.json(exclude_none=True)
-        _before_value = json.dumps(_before_value)
+        _before_value = json.dumps(_before_value, default=str)
-        _after_value: str = json.dumps(updated_kv)
+        _after_value: str = json.dumps(updated_kv, default=str)
 
         asyncio.create_task(
             create_audit_log_for_update(
@@ -11408,7 +11455,7 @@ async def model_info_v2(
     for _model in all_models:
         # provided model_info in config.yaml
         model_info = _model.get("model_info", {})
-        if debug == True:
+        if debug is True:
             _openai_client = "None"
             if llm_router is not None:
                 _openai_client = (
@@ -11433,7 +11480,7 @@ async def model_info_v2(
             litellm_model = litellm_params.get("model", None)
             try:
                 litellm_model_info = litellm.get_model_info(model=litellm_model)
-            except:
+            except Exception:
                 litellm_model_info = {}
         # 3rd pass on the model, try seeing if we can find model but without the "/" in model cost map
         if litellm_model_info == {}:
@@ -11444,8 +11491,10 @@ async def model_info_v2(
             if len(split_model) > 0:
                 litellm_model = split_model[-1]
             try:
-                litellm_model_info = litellm.get_model_info(model=litellm_model)
+                litellm_model_info = litellm.get_model_info(
-            except:
+                    model=litellm_model, custom_llm_provider=split_model[0]
+                )
+            except Exception:
                 litellm_model_info = {}
         for k, v in litellm_model_info.items():
             if k not in model_info:
@@ -11956,7 +12005,9 @@ async def model_info_v1(
             if len(split_model) > 0:
                 litellm_model = split_model[-1]
             try:
-                litellm_model_info = litellm.get_model_info(model=litellm_model)
+                litellm_model_info = litellm.get_model_info(
+                    model=litellm_model, custom_llm_provider=split_model[0]
+                )
             except:
                 litellm_model_info = {}
         for k, v in litellm_model_info.items():
@@ -12223,6 +12274,7 @@ async def alerting_settings(
     "/queue/chat/completions",
     tags=["experimental"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def async_queue_request(
     request: Request,
@@ -12334,18 +12386,10 @@ async def async_queue_request(
         )
 
 
-@router.get(
-    "/ollama_logs", dependencies=[Depends(user_api_key_auth)], tags=["experimental"]
-)
-async def retrieve_server_log(request: Request):
-    filepath = os.path.expanduser("~/.ollama/logs/server.log")
-    return FileResponse(filepath)
-
-
 #### LOGIN ENDPOINTS ####
 
 
-@app.get("/sso/key/generate", tags=["experimental"])
+@app.get("/sso/key/generate", tags=["experimental"], include_in_schema=False)
 async def google_login(request: Request):
     """
     Create Proxy API Keys using Google Workspace SSO. Requires setting PROXY_BASE_URL in .env
@@ -12939,7 +12983,7 @@ def get_image():
         return FileResponse(logo_path, media_type="image/jpeg")
 
 
-@app.get("/sso/callback", tags=["experimental"])
+@app.get("/sso/callback", tags=["experimental"], include_in_schema=False)
 async def auth_callback(request: Request):
     """Verify login"""
     global general_settings, ui_access_mode, premium_user
@@ -13244,6 +13288,7 @@ async def auth_callback(request: Request):
     tags=["Invite Links"],
     dependencies=[Depends(user_api_key_auth)],
     response_model=InvitationModel,
+    include_in_schema=False,
 )
 async def new_invitation(
     data: InvitationNew, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth)
@@ -13308,6 +13353,7 @@ async def new_invitation(
     tags=["Invite Links"],
     dependencies=[Depends(user_api_key_auth)],
     response_model=InvitationModel,
+    include_in_schema=False,
 )
 async def invitation_info(
     invitation_id: str, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth)
@@ -13359,6 +13405,7 @@ async def invitation_info(
     tags=["Invite Links"],
     dependencies=[Depends(user_api_key_auth)],
     response_model=InvitationModel,
+    include_in_schema=False,
 )
 async def invitation_update(
     data: InvitationUpdate,
@@ -13419,6 +13466,7 @@ async def invitation_update(
     tags=["Invite Links"],
     dependencies=[Depends(user_api_key_auth)],
     response_model=InvitationModel,
+    include_in_schema=False,
 )
 async def invitation_delete(
     data: InvitationDelete,
@@ -13471,6 +13519,7 @@ async def invitation_delete(
     "/config/update",
     tags=["config.yaml"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def update_config(config_info: ConfigYAML):
     """
@@ -13628,6 +13677,7 @@ Keep it more precise, to prevent overwrite other values unintentially
     "/config/field/update",
     tags=["config.yaml"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def update_config_general_settings(
     data: ConfigFieldUpdate,
@@ -13706,6 +13756,7 @@ async def update_config_general_settings(
     tags=["config.yaml"],
     dependencies=[Depends(user_api_key_auth)],
     response_model=ConfigFieldInfo,
+    include_in_schema=False,
 )
 async def get_config_general_settings(
     field_name: str,
@@ -13766,6 +13817,7 @@ async def get_config_general_settings(
     "/config/list",
     tags=["config.yaml"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def get_config_list(
     config_type: Literal["general_settings"],
@@ -13842,6 +13894,7 @@ async def get_config_list(
     "/config/field/delete",
     tags=["config.yaml"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def delete_config_general_settings(
     data: ConfigFieldDelete,
@@ -14097,6 +14150,7 @@ async def get_config():
     "/config/yaml",
     tags=["config.yaml"],
     dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
 )
 async def config_yaml_endpoint(config_info: ConfigYAML):
     """
@@ -14743,6 +14797,22 @@ async def cache_flushall():
         )
 
 
+@router.get(
+    "/get/litellm_model_cost_map",
+    include_in_schema=False,
+    dependencies=[Depends(user_api_key_auth)],
+)
+async def get_litellm_model_cost_map():
+    try:
+        _model_cost_map = litellm.model_cost
+        return _model_cost_map
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Internal Server Error ({str(e)})",
+        )
+
+
 @router.get("/", dependencies=[Depends(user_api_key_auth)])
 async def home(request: Request):
     return "LiteLLM: RUNNING"

litellm/proxy/utils.py

@@ -1,4 +1,4 @@
-from typing import Optional, List, Any, Literal, Union, TYPE_CHECKING
+from typing import Optional, List, Any, Literal, Union, TYPE_CHECKING, Tuple
 import os
 import subprocess
 import hashlib
@@ -2103,14 +2103,32 @@ def get_logging_payload(
         raise e
 
 
-def _duration_in_seconds(duration: str):
+def _extract_from_regex(duration: str) -> Tuple[int, str]:
-    match = re.match(r"(\d+)([smhd]?)", duration)
+    match = re.match(r"(\d+)(mo|[smhd]?)", duration)
+
     if not match:
         raise ValueError("Invalid duration format")
 
     value, unit = match.groups()
     value = int(value)
 
+    return value, unit
+
+
+def _duration_in_seconds(duration: str) -> int:
+    """
+    Parameters:
+    - duration:
+        - "<number>s" - seconds
+        - "<number>m" - minutes
+        - "<number>h" - hours
+        - "<number>d" - days
+        - "<number>mo" - months
+
+    Returns time in seconds till when budget needs to be reset
+    """
+    value, unit = _extract_from_regex(duration=duration)
+
     if unit == "s":
         return value
     elif unit == "m":
@@ -2119,6 +2137,22 @@ def _duration_in_seconds(duration: str):
         return value * 3600
     elif unit == "d":
         return value * 86400
+    elif unit == "mo":
+        now = time.time()
+        current_time = datetime.fromtimestamp(now)
+
+        # Calculate the first day of the next month
+        if current_time.month == 12:
+            next_month = datetime(year=current_time.year + 1, month=1, day=1)
+        else:
+            next_month = datetime(
+                year=current_time.year, month=current_time.month + value, day=1
+            )
+
+        # Calculate the duration until the first day of the next month
+        duration_until_next_month = next_month - current_time
+        return int(duration_until_next_month.total_seconds())
+
     else:
         raise ValueError("Unsupported duration unit")
 

litellm/router.py

@@ -3618,6 +3618,7 @@ class Router:
                 except Exception:
                     model_info = None
                 # get llm provider
+                model, llm_provider = "", ""
                 try:
                     model, llm_provider, _, _ = litellm.get_llm_provider(
                         model=litellm_params.model,

litellm/tests/test_amazing_vertex_completion.py

@@ -503,13 +503,35 @@ async def test_async_vertexai_streaming_response():
 # asyncio.run(test_async_vertexai_streaming_response())
 
 
-def test_gemini_pro_vision():
+@pytest.mark.parametrize("provider", ["vertex_ai"])  # "vertex_ai_beta"
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_gemini_pro_vision(provider, sync_mode):
     try:
         load_vertex_ai_credentials()
         litellm.set_verbose = True
         litellm.num_retries = 3
+        if sync_mode:
             resp = litellm.completion(
-            model="vertex_ai/gemini-1.5-flash-preview-0514",
+                model="{}/gemini-1.5-flash-preview-0514".format(provider),
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": "Whats in this image?"},
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": "gs://cloud-samples-data/generative-ai/image/boats.jpeg"
+                                },
+                            },
+                        ],
+                    }
+                ],
+            )
+        else:
+            resp = await litellm.acompletion(
+                model="{}/gemini-1.5-flash-preview-0514".format(provider),
                 messages=[
                     {
                         "role": "user",
@@ -532,6 +554,8 @@ def test_gemini_pro_vision():
         # DO Not DELETE this ASSERT
         # Google counts the prompt tokens for us, we should ensure we use the tokens from the orignal response
         assert prompt_tokens == 263  # the gemini api returns 263 to us
+
+        # assert False
     except litellm.RateLimitError as e:
         pass
     except Exception as e:
@@ -591,9 +615,111 @@ def test_gemini_pro_vision_base64():
             pytest.fail(f"An exception occurred - {str(e)}")
 
 
-@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call")
+@pytest.mark.parametrize("provider", ["vertex_ai_beta"])  # "vertex_ai",
+@pytest.mark.parametrize("sync_mode", [True])  # "vertex_ai",
 @pytest.mark.asyncio
-async def test_gemini_pro_function_calling(sync_mode):
+async def test_gemini_pro_function_calling_httpx(provider, sync_mode):
+    try:
+        load_vertex_ai_credentials()
+        litellm.set_verbose = True
+
+        messages = [
+            {
+                "role": "system",
+                "content": "Your name is Litellm Bot, you are a helpful assistant",
+            },
+            # User asks for their name and weather in San Francisco
+            {
+                "role": "user",
+                "content": "Hello, what is your name and can you tell me the weather?",
+            },
+        ]
+
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_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",
+                            }
+                        },
+                        "required": ["location"],
+                    },
+                },
+            }
+        ]
+
+        data = {
+            "model": "{}/gemini-1.5-pro".format(provider),
+            "messages": messages,
+            "tools": tools,
+            "tool_choice": "required",
+        }
+        if sync_mode:
+            response = litellm.completion(**data)
+        else:
+            response = await litellm.acompletion(**data)
+
+        print(f"response: {response}")
+
+        assert response.choices[0].message.tool_calls[0].function.arguments is not None
+        assert isinstance(
+            response.choices[0].message.tool_calls[0].function.arguments, str
+        )
+    except litellm.RateLimitError as e:
+        pass
+    except Exception as e:
+        if "429 Quota exceeded" in str(e):
+            pass
+        else:
+            pytest.fail("An unexpected exception occurred - {}".format(str(e)))
+
+
+@pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call")
+@pytest.mark.parametrize("provider", ["vertex_ai_beta"])  # "vertex_ai",
+@pytest.mark.asyncio
+async def test_gemini_pro_json_schema_httpx(provider):
+    load_vertex_ai_credentials()
+    litellm.set_verbose = True
+    messages = [
+        {
+            "role": "user",
+            "content": """
+    List 5 popular cookie recipes.
+
+    Using this JSON schema:
+
+        Recipe = {"recipe_name": str}
+
+    Return a `list[Recipe]`
+            """,
+        }
+    ]
+
+    response = completion(
+        model="vertex_ai_beta/gemini-1.5-flash-preview-0514",
+        messages=messages,
+        response_format={"type": "json_object"},
+    )
+
+    assert response.choices[0].message.content is not None
+    response_json = json.loads(response.choices[0].message.content)
+
+    assert isinstance(response_json, dict) or isinstance(response_json, list)
+
+
+@pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call")
+@pytest.mark.parametrize("sync_mode", [True])
+@pytest.mark.parametrize("provider", ["vertex_ai"])
+@pytest.mark.asyncio
+async def test_gemini_pro_function_calling(provider, sync_mode):
     try:
         load_vertex_ai_credentials()
         litellm.set_verbose = True
@@ -655,7 +781,7 @@ async def test_gemini_pro_function_calling(sync_mode):
         ]
 
         data = {
-            "model": "vertex_ai/gemini-1.5-pro-preview-0514",
+            "model": "{}/gemini-1.5-pro-preview-0514".format(provider),
             "messages": messages,
             "tools": tools,
         }
@@ -810,14 +936,24 @@ def test_vertexai_embedding():
         pytest.fail(f"Error occurred: {e}")
 
 
+@pytest.mark.skip(
+    reason="new test - works locally running into vertex version issues on ci/cd"
+)
 def test_vertexai_embedding_embedding_latest():
     try:
         load_vertex_ai_credentials()
         litellm.set_verbose = True
+
         response = embedding(
             model="vertex_ai/text-embedding-004",
-            input=["good morning from litellm", "this is another item"],
+            input=["hi"],
+            dimensions=1,
+            auto_truncate=True,
+            task_type="RETRIEVAL_QUERY",
         )
+
+        assert len(response.data[0]["embedding"]) == 1
+        assert response.usage.prompt_tokens > 0
         print(f"response:", response)
     except litellm.RateLimitError as e:
         pass

litellm/tests/test_bedrock_completion.py

@@ -220,13 +220,13 @@ def test_completion_bedrock_claude_sts_oidc_auth():
     aws_web_identity_token = "oidc/circleci_v2/"
     aws_region_name = os.environ["AWS_REGION_NAME"]
     # aws_role_name = os.environ["AWS_TEMP_ROLE_NAME"]
-    # TODO: This is using David's IAM role, we should use Litellm's IAM role eventually
+    # TODO: This is using ai.moda's IAM role, we should use LiteLLM's IAM role eventually
     aws_role_name = "arn:aws:iam::335785316107:role/litellm-github-unit-tests-circleci"
 
     try:
         litellm.set_verbose = True
 
-        response = completion(
+        response_1 = completion(
             model="bedrock/anthropic.claude-3-haiku-20240307-v1:0",
             messages=messages,
             max_tokens=10,
@@ -236,8 +236,40 @@ def test_completion_bedrock_claude_sts_oidc_auth():
             aws_role_name=aws_role_name,
             aws_session_name="my-test-session",
         )
-        # Add any assertions here to check the response
+        print(response_1)
-        print(response)
+        assert len(response_1.choices) > 0
+        assert len(response_1.choices[0].message.content) > 0
+
+        # This second call is to verify that the cache isn't breaking anything
+        response_2 = completion(
+            model="bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+            messages=messages,
+            max_tokens=5,
+            temperature=0.2,
+            aws_region_name=aws_region_name,
+            aws_web_identity_token=aws_web_identity_token,
+            aws_role_name=aws_role_name,
+            aws_session_name="my-test-session",
+        )
+        print(response_2)
+        assert len(response_2.choices) > 0
+        assert len(response_2.choices[0].message.content) > 0
+
+        # This third call is to verify that the cache isn't used for a different region
+        response_3 = completion(
+            model="bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+            messages=messages,
+            max_tokens=6,
+            temperature=0.3,
+            aws_region_name="us-east-1",
+            aws_web_identity_token=aws_web_identity_token,
+            aws_role_name=aws_role_name,
+            aws_session_name="my-test-session",
+        )
+        print(response_3)
+        assert len(response_3.choices) > 0
+        assert len(response_3.choices[0].message.content) > 0
+
     except RateLimitError:
         pass
     except Exception as e:
@@ -255,7 +287,7 @@ def test_completion_bedrock_httpx_command_r_sts_oidc_auth():
     aws_web_identity_token = "oidc/circleci_v2/"
     aws_region_name = os.environ["AWS_REGION_NAME"]
     # aws_role_name = os.environ["AWS_TEMP_ROLE_NAME"]
-    # TODO: This is using David's IAM role, we should use Litellm's IAM role eventually
+    # TODO: This is using ai.moda's IAM role, we should use LiteLLM's IAM role eventually
     aws_role_name = "arn:aws:iam::335785316107:role/litellm-github-unit-tests-circleci"
 
     try:

litellm/tests/test_get_model_info.py

@@ -7,6 +7,7 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 import litellm
 from litellm import get_model_info
+import pytest
 
 
 def test_get_model_info_simple_model_name():
@@ -23,3 +24,16 @@ def test_get_model_info_custom_llm_with_model_name():
     """
     model = "anthropic/claude-3-opus-20240229"
     litellm.get_model_info(model)
+
+
+def test_get_model_info_custom_llm_with_same_name_vllm():
+    """
+    Tests if {custom_llm_provider}/{model_name} name given, and model exists in model info, the object is returned
+    """
+    model = "command-r-plus"
+    provider = "openai"  # vllm is openai-compatible
+    try:
+        litellm.get_model_info(model, custom_llm_provider=provider)
+        pytest.fail("Expected get model info to fail for an unmapped model/provider")
+    except Exception:
+        pass

litellm/tests/test_key_generate_prisma.py

@@ -2217,6 +2217,7 @@ async def test_create_update_team(prisma_client):
             tpm_limit=30,
             rpm_limit=30,
         ),
+        http_request=Request(scope={"type": "http"}),
         user_api_key_dict=UserAPIKeyAuth(
             user_role=LitellmUserRoles.PROXY_ADMIN,
             api_key="sk-1234",

litellm/tests/test_router_debug_logs.py

@@ -81,7 +81,7 @@ def test_async_fallbacks(caplog):
     # Define the expected log messages
     # - error request, falling back notice, success notice
     expected_logs = [
-        "litellm.acompletion(model=gpt-3.5-turbo)\x1b[31m Exception AuthenticationError: OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: bad-key. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}\x1b[0m",
+        "litellm.acompletion(model=gpt-3.5-turbo)\x1b[31m Exception litellm.AuthenticationError: AuthenticationError: OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: bad-key. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}\x1b[0m",
         "Falling back to model_group = azure/gpt-3.5-turbo",
         "litellm.acompletion(model=azure/chatgpt-v-2)\x1b[32m 200 OK\x1b[0m",
         "Successful fallback b/w models.",

litellm/tests/test_streaming.py

@@ -557,7 +557,13 @@ async def test_completion_predibase_streaming(sync_mode):
         print(f"complete_response: {complete_response}")
     except litellm.Timeout as e:
         pass
+    except litellm.InternalServerError as e:
+        pass
     except Exception as e:
+        print("ERROR class", e.__class__)
+        print("ERROR message", e)
+        print("ERROR traceback", traceback.format_exc())
+
         pytest.fail(f"Error occurred: {e}")
 
 
@@ -1029,7 +1035,8 @@ def test_completion_claude_stream_bad_key():
 # test_completion_replicate_stream()
 
 
-def test_vertex_ai_stream():
+@pytest.mark.parametrize("provider", ["vertex_ai"])  # "vertex_ai_beta"
+def test_vertex_ai_stream(provider):
     from litellm.tests.test_amazing_vertex_completion import load_vertex_ai_credentials
 
     load_vertex_ai_credentials()
@@ -1042,7 +1049,7 @@ def test_vertex_ai_stream():
         try:
             print("making request", model)
             response = completion(
-                model=model,
+                model="{}/{}".format(provider, model),
                 messages=[
                     {"role": "user", "content": "write 10 line code code for saying hi"}
                 ],

litellm/tests/test_utils.py

@@ -3,6 +3,7 @@ from unittest import mock
 
 from dotenv import load_dotenv
 import copy
+from datetime import datetime
 
 load_dotenv()
 import os
@@ -25,6 +26,7 @@ from litellm.utils import (
     get_max_tokens,
     get_supported_openai_params,
 )
+from litellm.proxy.utils import _duration_in_seconds, _extract_from_regex
 
 # Assuming your trim_messages, shorten_message_to_fit_limit, and get_token_count functions are all in a module named 'message_utils'
 
@@ -395,3 +397,89 @@ def test_get_supported_openai_params() -> None:
 
     # Unmapped provider
     assert get_supported_openai_params("nonexistent") is None
+
+
+def test_redact_msgs_from_logs():
+    """
+    Tests that turn_off_message_logging does not modify the response_obj
+
+    On the proxy some users were seeing the redaction impact client side responses
+    """
+    from litellm.litellm_core_utils.redact_messages import (
+        redact_message_input_output_from_logging,
+    )
+    from litellm.utils import Logging
+
+    litellm.turn_off_message_logging = True
+
+    response_obj = litellm.ModelResponse(
+        choices=[
+            {
+                "finish_reason": "stop",
+                "index": 0,
+                "message": {
+                    "content": "I'm LLaMA, an AI assistant developed by Meta AI that can understand and respond to human input in a conversational manner.",
+                    "role": "assistant",
+                },
+            }
+        ]
+    )
+
+    _redacted_response_obj = redact_message_input_output_from_logging(
+        result=response_obj,
+        litellm_logging_obj=Logging(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "hi"}],
+            stream=False,
+            call_type="acompletion",
+            litellm_call_id="1234",
+            start_time=datetime.now(),
+            function_id="1234",
+        ),
+    )
+
+    # Assert the response_obj content is NOT modified
+    assert (
+        response_obj.choices[0].message.content
+        == "I'm LLaMA, an AI assistant developed by Meta AI that can understand and respond to human input in a conversational manner."
+    )
+
+    litellm.turn_off_message_logging = False
+    print("Test passed")
+
+
+@pytest.mark.parametrize(
+    "duration, unit",
+    [("7s", "s"), ("7m", "m"), ("7h", "h"), ("7d", "d"), ("7mo", "mo")],
+)
+def test_extract_from_regex(duration, unit):
+    value, _unit = _extract_from_regex(duration=duration)
+
+    assert value == 7
+    assert _unit == unit
+
+
+def test_duration_in_seconds():
+    """
+    Test if duration int is correctly calculated for different str
+    """
+    import time
+
+    now = time.time()
+    current_time = datetime.fromtimestamp(now)
+    print("current_time={}".format(current_time))
+    # Calculate the first day of the next month
+    if current_time.month == 12:
+        next_month = datetime(year=current_time.year + 1, month=1, day=1)
+    else:
+        next_month = datetime(
+            year=current_time.year, month=current_time.month + 1, day=1
+        )
+    print("next_month={}".format(next_month))
+    # Calculate the duration until the first day of the next month
+    duration_until_next_month = next_month - current_time
+    expected_duration = int(duration_until_next_month.total_seconds())
+
+    value = _duration_in_seconds(duration="1mo")
+
+    assert value - expected_duration < 2

litellm/types/llms/openai.py

@@ -323,3 +323,9 @@ class ChatCompletionResponseMessage(TypedDict, total=False):
     content: Optional[str]
     tool_calls: List[ChatCompletionToolCallChunk]
     role: Literal["assistant"]
+
+
+class ChatCompletionUsageBlock(TypedDict):
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int

litellm/types/llms/vertex_ai.py

@@ -9,6 +9,7 @@ from typing_extensions import (
     runtime_checkable,
     Required,
 )
+from enum import Enum
 
 
 class Field(TypedDict):
@@ -48,6 +49,190 @@ class PartType(TypedDict, total=False):
     function_response: FunctionResponse
 
 
+class HttpxFunctionCall(TypedDict):
+    name: str
+    args: dict
+
+
+class HttpxPartType(TypedDict, total=False):
+    text: str
+    inline_data: BlobType
+    file_data: FileDataType
+    functionCall: HttpxFunctionCall
+    function_response: FunctionResponse
+
+
+class HttpxContentType(TypedDict, total=False):
+    role: Literal["user", "model"]
+    parts: Required[List[HttpxPartType]]
+
+
 class ContentType(TypedDict, total=False):
     role: Literal["user", "model"]
     parts: Required[List[PartType]]
+
+
+class SystemInstructions(TypedDict):
+    parts: Required[List[PartType]]
+
+
+class Schema(TypedDict, total=False):
+    type: Literal["STRING", "INTEGER", "BOOLEAN", "NUMBER", "ARRAY", "OBJECT"]
+    description: str
+    enum: List[str]
+    items: List["Schema"]
+    properties: "Schema"
+    required: List[str]
+    nullable: bool
+
+
+class FunctionDeclaration(TypedDict, total=False):
+    name: Required[str]
+    description: str
+    parameters: Schema
+    response: Schema
+
+
+class FunctionCallingConfig(TypedDict, total=False):
+    mode: Literal["ANY", "AUTO", "NONE"]
+    allowed_function_names: List[str]
+
+
+HarmCategory = Literal[
+    "HARM_CATEGORY_UNSPECIFIED",
+    "HARM_CATEGORY_HATE_SPEECH",
+    "HARM_CATEGORY_DANGEROUS_CONTENT",
+    "HARM_CATEGORY_HARASSMENT",
+    "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+]
+HarmBlockThreshold = Literal[
+    "HARM_BLOCK_THRESHOLD_UNSPECIFIED",
+    "BLOCK_LOW_AND_ABOVE",
+    "BLOCK_MEDIUM_AND_ABOVE",
+    "BLOCK_ONLY_HIGH",
+    "BLOCK_NONE",
+]
+HarmBlockMethod = Literal["HARM_BLOCK_METHOD_UNSPECIFIED", "SEVERITY", "PROBABILITY"]
+
+HarmProbability = Literal[
+    "HARM_PROBABILITY_UNSPECIFIED", "NEGLIGIBLE", "LOW", "MEDIUM", "HIGH"
+]
+
+HarmSeverity = Literal[
+    "HARM_SEVERITY_UNSPECIFIED",
+    "HARM_SEVERITY_NEGLIGIBLE",
+    "HARM_SEVERITY_LOW",
+    "HARM_SEVERITY_MEDIUM",
+    "HARM_SEVERITY_HIGH",
+]
+
+
+class SafetSettingsConfig(TypedDict, total=False):
+    category: HarmCategory
+    threshold: HarmBlockThreshold
+    max_influential_terms: int
+    method: HarmBlockMethod
+
+
+class GenerationConfig(TypedDict, total=False):
+    temperature: float
+    top_p: float
+    top_k: float
+    candidate_count: int
+    max_output_tokens: int
+    stop_sequences: List[str]
+    presence_penalty: float
+    frequency_penalty: float
+    response_mime_type: Literal["text/plain", "application/json"]
+
+
+class Tools(TypedDict):
+    function_declarations: List[FunctionDeclaration]
+
+
+class ToolConfig(TypedDict):
+    functionCallingConfig: FunctionCallingConfig
+
+
+class RequestBody(TypedDict, total=False):
+    contents: Required[List[ContentType]]
+    system_instruction: SystemInstructions
+    tools: Tools
+    toolConfig: ToolConfig
+    safetySettings: SafetSettingsConfig
+    generationConfig: GenerationConfig
+
+
+class SafetyRatings(TypedDict):
+    category: HarmCategory
+    probability: HarmProbability
+    probabilityScore: int
+    severity: HarmSeverity
+    blocked: bool
+
+
+class Date(TypedDict):
+    year: int
+    month: int
+    date: int
+
+
+class Citation(TypedDict):
+    startIndex: int
+    endIndex: int
+    uri: str
+    title: str
+    license: str
+    publicationDate: Date
+
+
+class CitationMetadata(TypedDict):
+    citations: List[Citation]
+
+
+class SearchEntryPoint(TypedDict, total=False):
+    renderedContent: str
+    sdkBlob: str
+
+
+class GroundingMetadata(TypedDict, total=False):
+    webSearchQueries: List[str]
+    searchEntryPoint: SearchEntryPoint
+
+
+class Candidates(TypedDict, total=False):
+    index: int
+    content: HttpxContentType
+    finishReason: Literal[
+        "FINISH_REASON_UNSPECIFIED",
+        "STOP",
+        "MAX_TOKENS",
+        "SAFETY",
+        "RECITATION",
+        "OTHER",
+        "BLOCKLIST",
+        "PROHIBITED_CONTENT",
+        "SPII",
+    ]
+    safetyRatings: SafetyRatings
+    citationMetadata: CitationMetadata
+    groundingMetadata: GroundingMetadata
+    finishMessage: str
+
+
+class PromptFeedback(TypedDict):
+    blockReason: str
+    safetyRatings: List[SafetyRatings]
+    blockReasonMessage: str
+
+
+class UsageMetadata(TypedDict):
+    promptTokenCount: int
+    totalTokenCount: int
+    candidatesTokenCount: int
+
+
+class GenerateContentResponseBody(TypedDict, total=False):
+    candidates: Required[List[Candidates]]
+    promptFeedback: PromptFeedback
+    usageMetadata: Required[UsageMetadata]

litellm/types/utils.py

@@ -1,6 +1,8 @@
 from typing import List, Optional, Union, Dict, Tuple, Literal
 from typing_extensions import TypedDict
 from enum import Enum
+from typing_extensions import override, Required, Dict
+from .llms.openai import ChatCompletionUsageBlock, ChatCompletionToolCallChunk
 
 
 class LiteLLMCommonStrings(Enum):
@@ -37,3 +39,12 @@ class ModelInfo(TypedDict):
         "completion", "embedding", "image_generation", "chat", "audio_transcription"
     ]
     supported_openai_params: Optional[List[str]]
+
+
+class GenericStreamingChunk(TypedDict):
+    text: Required[str]
+    tool_use: Optional[ChatCompletionToolCallChunk]
+    is_finished: Required[bool]
+    finish_reason: Required[str]
+    usage: Optional[ChatCompletionUsageBlock]
+    index: int

litellm/utils.py

@@ -35,6 +35,9 @@ import litellm._service_logger  # for storing API inputs, outputs, and metadata
 from litellm.llms.custom_httpx.http_handler import HTTPHandler, AsyncHTTPHandler
 from litellm.caching import DualCache
 from litellm.types.utils import CostPerToken, ProviderField, ModelInfo
+from litellm.litellm_core_utils.redact_messages import (
+    redact_message_input_output_from_logging,
+)
 
 oidc_cache = DualCache()
 
@@ -518,15 +521,18 @@ class Choices(OpenAIObject):
         self,
         finish_reason=None,
         index=0,
-        message=None,
+        message: Optional[Union[Message, dict]] = None,
         logprobs=None,
         enhancements=None,
         **params,
     ):
         super(Choices, self).__init__(**params)
-        self.finish_reason = (
+        if finish_reason is not None:
-            map_finish_reason(finish_reason) or "stop"
+            self.finish_reason = map_finish_reason(
+                finish_reason
             )  # set finish_reason for all responses
+        else:
+            self.finish_reason = "stop"
         self.index = index
         if message is None:
             self.message = Message()
@@ -1134,13 +1140,15 @@ class TranscriptionResponse(OpenAIObject):
 def print_verbose(
     print_statement,
     logger_only: bool = False,
-    log_level: Literal["DEBUG", "INFO"] = "DEBUG",
+    log_level: Literal["DEBUG", "INFO", "ERROR"] = "DEBUG",
 ):
     try:
         if log_level == "DEBUG":
             verbose_logger.debug(print_statement)
         elif log_level == "INFO":
             verbose_logger.info(print_statement)
+        elif log_level == "ERROR":
+            verbose_logger.error(print_statement)
         if litellm.set_verbose == True and logger_only == False:
             print(print_statement)  # noqa
     except:
@@ -1473,7 +1481,9 @@ class Logging:
                     print_verbose(
                         f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
                     )
-            self.redact_message_input_output_from_logging(result=original_response)
+            original_response = redact_message_input_output_from_logging(
+                litellm_logging_obj=self, result=original_response
+            )
             # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made
 
             callbacks = litellm.input_callback + self.dynamic_input_callbacks
@@ -1624,6 +1634,12 @@ class Logging:
                             end_time=end_time,
                         )
                     except Exception as e:
+                        print_verbose(
+                            "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while building complete streaming response in success logging {}\n{}".format(
+                                str(e), traceback.format_exc()
+                            ),
+                            log_level="ERROR",
+                        )
                         complete_streaming_response = None
                 else:
                     self.sync_streaming_chunks.append(result)
@@ -1664,7 +1680,9 @@ class Logging:
             else:
                 callbacks = litellm.success_callback
 
-            self.redact_message_input_output_from_logging(result=result)
+            result = redact_message_input_output_from_logging(
+                result=result, litellm_logging_obj=self
+            )
 
             for callback in callbacks:
                 try:
@@ -2214,7 +2232,10 @@ class Logging:
                         capture_exception(e)
         except:
             print_verbose(
-                f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}"
+                "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {}\n{}".format(
+                    str(e), traceback.format_exc()
+                ),
+                log_level="ERROR",
             )
             pass
 
@@ -2224,7 +2245,7 @@ class Logging:
         """
         Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions.
         """
-        print_verbose(f"Logging Details LiteLLM-Async Success Call")
+        print_verbose("Logging Details LiteLLM-Async Success Call")
         start_time, end_time, result = self._success_handler_helper_fn(
             start_time=start_time, end_time=end_time, result=result, cache_hit=cache_hit
         )
@@ -2243,7 +2264,10 @@ class Logging:
                     )
                 except Exception as e:
                     print_verbose(
-                        f"Error occurred building stream chunk: {traceback.format_exc()}"
+                        "Error occurred building stream chunk in success logging: {}\n{}".format(
+                            str(e), traceback.format_exc()
+                        ),
+                        log_level="ERROR",
                     )
                     complete_streaming_response = None
             else:
@@ -2254,7 +2278,7 @@ class Logging:
                 complete_streaming_response
             )
             try:
-                if self.model_call_details.get("cache_hit", False) == True:
+                if self.model_call_details.get("cache_hit", False) is True:
                     self.model_call_details["response_cost"] = 0.0
                 else:
                     # check if base_model set on azure
@@ -2270,8 +2294,8 @@ class Logging:
                     f"Model={self.model}; cost={self.model_call_details['response_cost']}"
                 )
             except litellm.NotFoundError as e:
-                verbose_logger.debug(
+                verbose_logger.error(
-                    f"Model={self.model} not found in completion cost map."
+                    f"Model={self.model} not found in completion cost map. Setting 'response_cost' to None"
                 )
                 self.model_call_details["response_cost"] = None
 
@@ -2291,7 +2315,9 @@ class Logging:
         else:
             callbacks = litellm._async_success_callback
 
-        self.redact_message_input_output_from_logging(result=result)
+        result = redact_message_input_output_from_logging(
+            result=result, litellm_logging_obj=self
+        )
 
         for callback in callbacks:
             # check if callback can run for this request
@@ -2501,7 +2527,9 @@ class Logging:
 
             result = None  # result sent to all loggers, init this to None incase it's not created
 
-            self.redact_message_input_output_from_logging(result=result)
+            result = redact_message_input_output_from_logging(
+                result=result, litellm_logging_obj=self
+            )
             for callback in callbacks:
                 try:
                     if callback == "lite_debugger":
@@ -2725,41 +2753,6 @@ class Logging:
                     f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}"
                 )
 
-    def redact_message_input_output_from_logging(self, result):
-        """
-        Removes messages, prompts, input, response from logging. This modifies the data in-place
-        only redacts when litellm.turn_off_message_logging == True
-        """
-        # check if user opted out of logging message/response to callbacks
-        if litellm.turn_off_message_logging is True:
-            # remove messages, prompts, input, response from logging
-            self.model_call_details["messages"] = [
-                {"role": "user", "content": "redacted-by-litellm"}
-            ]
-            self.model_call_details["prompt"] = ""
-            self.model_call_details["input"] = ""
-
-            # response cleaning
-            # ChatCompletion Responses
-            if self.stream and "complete_streaming_response" in self.model_call_details:
-                _streaming_response = self.model_call_details[
-                    "complete_streaming_response"
-                ]
-                for choice in _streaming_response.choices:
-                    if isinstance(choice, litellm.Choices):
-                        choice.message.content = "redacted-by-litellm"
-                    elif isinstance(choice, litellm.utils.StreamingChoices):
-                        choice.delta.content = "redacted-by-litellm"
-            else:
-                if result is not None:
-                    if isinstance(result, litellm.ModelResponse):
-                        if hasattr(result, "choices") and result.choices is not None:
-                            for choice in result.choices:
-                                if isinstance(choice, litellm.Choices):
-                                    choice.message.content = "redacted-by-litellm"
-                                elif isinstance(choice, litellm.utils.StreamingChoices):
-                                    choice.delta.content = "redacted-by-litellm"
-
 
 def exception_logging(
     additional_args={},
@@ -2822,7 +2815,9 @@ class Rules:
                     raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider="", model=model)  # type: ignore
         return True
 
-    def post_call_rules(self, input: str, model: str):
+    def post_call_rules(self, input: Optional[str], model: str) -> bool:
+        if input is None:
+            return True
         for rule in litellm.post_call_rules:
             if callable(rule):
                 decision = rule(input)
@@ -3101,9 +3096,9 @@ def client(original_function):
                         pass
                     else:
                         if isinstance(original_response, ModelResponse):
-                            model_response = original_response["choices"][0]["message"][
+                            model_response = original_response.choices[
-                                "content"
+                                0
-                            ]
+                            ].message.content
                             ### POST-CALL RULES ###
                             rules_obj.post_call_rules(input=model_response, model=model)
         except Exception as e:
@@ -3563,7 +3558,7 @@ def client(original_function):
                     if cached_result is not None and not isinstance(
                         cached_result, list
                     ):
-                        print_verbose(f"Cache Hit!")
+                        print_verbose("Cache Hit!", log_level="INFO")
                         cache_hit = True
                         end_time = datetime.datetime.now()
                         (
@@ -4898,6 +4893,18 @@ def get_optional_params_embeddings(
         )
         final_params = {**optional_params, **kwargs}
         return final_params
+    if custom_llm_provider == "vertex_ai":
+        supported_params = get_supported_openai_params(
+            model=model,
+            custom_llm_provider="vertex_ai",
+            request_type="embeddings",
+        )
+        _check_valid_arg(supported_params=supported_params)
+        optional_params = litellm.VertexAITextEmbeddingConfig().map_openai_params(
+            non_default_params=non_default_params, optional_params={}
+        )
+        final_params = {**optional_params, **kwargs}
+        return final_params
     if custom_llm_provider == "vertex_ai":
         if len(non_default_params.keys()) > 0:
             if litellm.drop_params is True:  # drop the unsupported non-default values
@@ -4931,7 +4938,18 @@ def get_optional_params_embeddings(
                 message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.",
             )
         return {**non_default_params, **kwargs}
-
+    if custom_llm_provider == "mistral":
+        supported_params = get_supported_openai_params(
+            model=model,
+            custom_llm_provider="mistral",
+            request_type="embeddings",
+        )
+        _check_valid_arg(supported_params=supported_params)
+        optional_params = litellm.MistralEmbeddingConfig().map_openai_params(
+            non_default_params=non_default_params, optional_params={}
+        )
+        final_params = {**optional_params, **kwargs}
+        return final_params
     if (
         custom_llm_provider != "openai"
         and custom_llm_provider != "azure"
@@ -5381,6 +5399,16 @@ def get_optional_params(
         print_verbose(
             f"(end) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK - optional_params: {optional_params}"
         )
+    elif custom_llm_provider == "vertex_ai_beta":
+        supported_params = get_supported_openai_params(
+            model=model, custom_llm_provider=custom_llm_provider
+        )
+        _check_valid_arg(supported_params=supported_params)
+        optional_params = litellm.VertexGeminiConfig().map_openai_params(
+            non_default_params=non_default_params,
+            optional_params=optional_params,
+            model=model,
+        )
     elif (
         custom_llm_provider == "vertex_ai" and model in litellm.vertex_anthropic_models
     ):
@@ -6340,7 +6368,10 @@ def get_supported_openai_params(
             "max_retries",
         ]
     elif custom_llm_provider == "mistral":
+        if request_type == "chat_completion":
             return litellm.MistralConfig().get_supported_openai_params()
+        elif request_type == "embeddings":
+            return litellm.MistralEmbeddingConfig().get_supported_openai_params()
     elif custom_llm_provider == "replicate":
         return [
             "stream",
@@ -6382,7 +6413,10 @@ def get_supported_openai_params(
     elif custom_llm_provider == "palm" or custom_llm_provider == "gemini":
         return ["temperature", "top_p", "stream", "n", "stop", "max_tokens"]
     elif custom_llm_provider == "vertex_ai":
+        if request_type == "chat_completion":
             return litellm.VertexAIConfig().get_supported_openai_params()
+        elif request_type == "embeddings":
+            return litellm.VertexAITextEmbeddingConfig().get_supported_openai_params()
     elif custom_llm_provider == "sagemaker":
         return ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
     elif custom_llm_provider == "aleph_alpha":
@@ -6919,13 +6953,14 @@ def get_max_tokens(model: str):
         )
 
 
-def get_model_info(model: str) -> ModelInfo:
+def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> ModelInfo:
     """
     Get a dict for the maximum tokens (context window),
     input_cost_per_token, output_cost_per_token  for a given model.
 
     Parameters:
-    model (str): The name of the model.
+    - model (str): The name of the model.
+    - custom_llm_provider (str | null): the provider used for the model. If provided, used to check if the litellm model info is for that provider.
 
     Returns:
         dict: A dictionary containing the following information:
@@ -6979,12 +7014,14 @@ def get_model_info(model: str) -> ModelInfo:
         if model in azure_llms:
             model = azure_llms[model]
         ##########################
+        if custom_llm_provider is None:
             # Get custom_llm_provider
-        split_model, custom_llm_provider = model, ""
             try:
                 split_model, custom_llm_provider, _, _ = get_llm_provider(model=model)
             except:
                 pass
+        else:
+            split_model = model
         #########################
 
         supported_openai_params = litellm.get_supported_openai_params(
@@ -7009,10 +7046,20 @@ def get_model_info(model: str) -> ModelInfo:
             if model in litellm.model_cost:
                 _model_info = litellm.model_cost[model]
                 _model_info["supported_openai_params"] = supported_openai_params
+                if (
+                    "litellm_provider" in _model_info
+                    and _model_info["litellm_provider"] != custom_llm_provider
+                ):
+                    raise Exception
                 return _model_info
             if split_model in litellm.model_cost:
                 _model_info = litellm.model_cost[split_model]
                 _model_info["supported_openai_params"] = supported_openai_params
+                if (
+                    "litellm_provider" in _model_info
+                    and _model_info["litellm_provider"] != custom_llm_provider
+                ):
+                    raise Exception
                 return _model_info
             else:
                 raise ValueError(
@@ -7192,6 +7239,9 @@ def get_provider_fields(custom_llm_provider: str) -> List[ProviderField]:
     elif custom_llm_provider == "ollama":
         return litellm.OllamaConfig().get_required_params()
 
+    elif custom_llm_provider == "azure_ai":
+        return litellm.AzureAIStudioConfig().get_required_params()
+
     else:
         return []
 
@@ -10066,6 +10116,14 @@ def get_secret(
                 return oidc_token
             else:
                 raise ValueError("Github OIDC provider failed")
+        elif oidc_provider == "azure":
+            # https://azure.github.io/azure-workload-identity/docs/quick-start.html
+            azure_federated_token_file = os.getenv("AZURE_FEDERATED_TOKEN_FILE")
+            if azure_federated_token_file is None:
+                raise ValueError("AZURE_FEDERATED_TOKEN_FILE not found in environment")
+            with open(azure_federated_token_file, "r") as f:
+                oidc_token = f.read()
+                return oidc_token
         else:
             raise ValueError("Unsupported OIDC provider")
 
@@ -11218,6 +11276,34 @@ class CustomStreamWrapper:
                             )
                 else:
                     completion_obj["content"] = str(chunk)
+            elif self.custom_llm_provider and (
+                self.custom_llm_provider == "vertex_ai_beta"
+            ):
+                from litellm.types.utils import (
+                    GenericStreamingChunk as UtilsStreamingChunk,
+                )
+
+                if self.received_finish_reason is not None:
+                    raise StopIteration
+                response_obj: UtilsStreamingChunk = chunk
+                completion_obj["content"] = response_obj["text"]
+                if response_obj["is_finished"]:
+                    self.received_finish_reason = response_obj["finish_reason"]
+
+                if (
+                    self.stream_options
+                    and self.stream_options.get("include_usage", False) is True
+                    and response_obj["usage"] is not None
+                ):
+                    self.sent_stream_usage = True
+                    model_response.usage = litellm.Usage(
+                        prompt_tokens=response_obj["usage"]["prompt_tokens"],
+                        completion_tokens=response_obj["usage"]["completion_tokens"],
+                        total_tokens=response_obj["usage"]["total_tokens"],
+                    )
+
+                if "tool_use" in response_obj and response_obj["tool_use"] is not None:
+                    completion_obj["tool_calls"] = [response_obj["tool_use"]]
             elif self.custom_llm_provider and (self.custom_llm_provider == "vertex_ai"):
                 import proto  # type: ignore
 
@@ -11895,6 +11981,7 @@ class CustomStreamWrapper:
                 or self.custom_llm_provider == "ollama"
                 or self.custom_llm_provider == "ollama_chat"
                 or self.custom_llm_provider == "vertex_ai"
+                or self.custom_llm_provider == "vertex_ai_beta"
                 or self.custom_llm_provider == "sagemaker"
                 or self.custom_llm_provider == "gemini"
                 or self.custom_llm_provider == "replicate"

log.txt

@@ -0,0 +1,10 @@
+============================= test session starts ==============================
+platform darwin -- Python 3.11.4, pytest-8.2.0, pluggy-1.5.0 -- /Users/krrishdholakia/Documents/litellm/litellm/proxy/myenv/bin/python3.11
+cachedir: .pytest_cache
+rootdir: /Users/krrishdholakia/Documents/litellm
+configfile: pyproject.toml
+plugins: logfire-0.35.0, asyncio-0.23.6, mock-3.14.0, anyio-4.2.0
+asyncio: mode=Mode.STRICT
+collecting ... collected 0 items
+
+============================ no tests ran in 0.00s =============================

model_prices_and_context_window.json

@@ -3347,6 +3347,24 @@
         "litellm_provider": "deepinfra",
         "mode": "chat"
     },
+    "deepinfra/meta-llama/Meta-Llama-3-8B-Instruct": {
+        "max_tokens": 8191,
+        "max_input_tokens": 8191,
+        "max_output_tokens": 4096,
+        "input_cost_per_token": 0.00000008,
+        "output_cost_per_token": 0.00000008,
+        "litellm_provider": "deepinfra",
+        "mode": "chat"
+    },
+    "deepinfra/meta-llama/Meta-Llama-3-70B-Instruct": {
+        "max_tokens": 8191,
+        "max_input_tokens": 8191,
+        "max_output_tokens": 4096,
+        "input_cost_per_token": 0.00000059,
+        "output_cost_per_token": 0.00000079,
+        "litellm_provider": "deepinfra",
+        "mode": "chat"
+    },
     "deepinfra/01-ai/Yi-34B-200K": {
         "max_tokens": 4096,
         "max_input_tokens": 200000,

proxy_server_config.yaml

@@ -85,6 +85,9 @@ model_list:
     litellm_params:
       model: openai/*
       api_key: os.environ/OPENAI_API_KEY
+  - model_name: mistral-embed
+    litellm_params:
+      model: mistral/mistral-embed
   - model_name: gpt-instruct # [PROD TEST] - tests if `/health` automatically infers this to be a text completion model
     litellm_params:
       model: text-completion-openai/gpt-3.5-turbo-instruct

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.40.9"
+version = "1.40.12"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -85,7 +85,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.40.9"
+version = "1.40.12"
 version_files = [
     "pyproject.toml:^version"
 ]

tests/test_openai_endpoints.py

@@ -22,6 +22,7 @@ async def generate_key(
         "text-embedding-ada-002",
         "dall-e-2",
         "fake-openai-endpoint-2",
+        "mistral-embed",
     ],
 ):
     url = "http://0.0.0.0:4000/key/generate"
@@ -197,14 +198,14 @@ async def completion(session, key):
         return response
 
 
-async def embeddings(session, key):
+async def embeddings(session, key, model="text-embedding-ada-002"):
     url = "http://0.0.0.0:4000/embeddings"
     headers = {
         "Authorization": f"Bearer {key}",
         "Content-Type": "application/json",
     }
     data = {
-        "model": "text-embedding-ada-002",
+        "model": model,
         "input": ["hello world"],
     }
 
@@ -408,6 +409,9 @@ async def test_embeddings():
         key_2 = key_gen["key"]
         await embeddings(session=session, key=key_2)
 
+        # embedding request with non OpenAI model
+        await embeddings(session=session, key=key, model="mistral-embed")
+
 
 @pytest.mark.asyncio
 async def test_image_generation():

ui/litellm-dashboard/out/_next/static/48nWsJi-LJrUlOLzcK-Yz/_buildManifest.js

@@ -1 +0,0 @@
-self.__BUILD_MANIFEST={__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/_error":["static/chunks/pages/_error-d6107f1aac0c574c.js"],sortedPages:["/_app","/_error"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB();

ui/litellm-dashboard/out/_next/static/48nWsJi-LJrUlOLzcK-Yz/_ssgManifest.js

@@ -1 +0,0 @@
-self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()