Merge branch 'main' into litellm_dev_11_13_2024

README.md

@@ -305,6 +305,36 @@ Step 4: Submit a PR with your changes! 🚀
 - push your fork to your GitHub repo
 - submit a PR from there
 
+### Building LiteLLM Docker Image 
+
+Follow these instructions if you want to build / run the LiteLLM Docker Image yourself.
+
+Step 1: Clone the repo
+
+```
+git clone https://github.com/BerriAI/litellm.git
+```
+
+Step 2: Build the Docker Image
+
+Build using Dockerfile.non_root
+```
+docker build -f docker/Dockerfile.non_root -t litellm_test_image .
+```
+
+Step 3: Run the Docker Image
+
+Make sure config.yaml is present in the root directory. This is your litellm proxy config file.
+```
+docker run \
+    -v $(pwd)/proxy_config.yaml:/app/config.yaml \
+    -e DATABASE_URL="postgresql://xxxxxxxx" \
+    -e LITELLM_MASTER_KEY="sk-1234" \
+    -p 4000:4000 \
+    litellm_test_image \
+    --config /app/config.yaml --detailed_debug
+```
+
 # Enterprise
 For companies that need better security, user management and professional support
 

deploy/charts/litellm-helm/templates/migrations-job.yaml

@@ -13,18 +13,18 @@ spec:
     spec:
       containers:
         - name: prisma-migrations
-          image: "ghcr.io/berriai/litellm:main-stable"
+          image: ghcr.io/berriai/litellm-database:main-latest
           command: ["python", "litellm/proxy/prisma_migration.py"]
           workingDir: "/app"
           env:
-            {{- if .Values.db.deployStandalone }}
+            {{- if .Values.db.useExisting }}
-            - name: DATABASE_URL
-              value: postgresql://{{ .Values.postgresql.auth.username }}:{{ .Values.postgresql.auth.password }}@{{ .Release.Name }}-postgresql/{{ .Values.postgresql.auth.database }}
-            {{- else if .Values.db.useExisting }}
             - name: DATABASE_URL
               value: {{ .Values.db.url | quote }}
+            {{- else }}
+            - name: DATABASE_URL
+              value: postgresql://{{ .Values.postgresql.auth.username }}:{{ .Values.postgresql.auth.password }}@{{ .Release.Name }}-postgresql/{{ .Values.postgresql.auth.database }}
             {{- end }}
             - name: DISABLE_SCHEMA_UPDATE
-              value: "{{ .Values.migrationJob.disableSchemaUpdate }}"
+              value: "false" # always run the migration from the Helm PreSync hook, override the value set
       restartPolicy: OnFailure
   backoffLimit: {{ .Values.migrationJob.backoffLimit }}

docs/my-website/docs/completion/json_mode.md

@@ -75,6 +75,7 @@ Works for:
 - Google AI Studio - Gemini models
 - Vertex AI models (Gemini + Anthropic)
 - Bedrock Models
+- Anthropic API Models
 
 <Tabs>
 <TabItem value="sdk" label="SDK">

docs/my-website/docs/completion/prefix.md

@@ -93,7 +93,7 @@ curl http://0.0.0.0:4000/v1/chat/completions \
 
 ## Check Model Support 
 
-Call `litellm.get_model_info` to check if a model/provider supports `response_format`. 
+Call `litellm.get_model_info` to check if a model/provider supports `prefix`. 
 
 <Tabs>
 <TabItem value="sdk" label="SDK">

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

@@ -957,3 +957,69 @@ curl http://0.0.0.0:4000/v1/chat/completions \
 ```
 </TabItem>
 </Tabs>
+
+## Usage - passing 'user_id' to Anthropic
+
+LiteLLM translates the OpenAI `user` param to Anthropic's `metadata[user_id]` param.
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+response = completion(
+    model="claude-3-5-sonnet-20240620",
+    messages=messages,
+    user="user_123",
+)
+```
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+    - model_name: claude-3-5-sonnet-20240620
+      litellm_params:
+        model: anthropic/claude-3-5-sonnet-20240620
+        api_key: os.environ/ANTHROPIC_API_KEY
+```
+
+2. Start Proxy
+
+```
+litellm --config /path/to/config.yaml
+```
+
+3. Test it! 
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
+  -d '{
+    "model": "claude-3-5-sonnet-20240620",
+    "messages": [{"role": "user", "content": "What is Anthropic?"}],
+    "user": "user_123"
+  }'
+```
+
+</TabItem>
+</Tabs>
+
+## All Supported OpenAI Params
+
+```
+"stream",
+"stop",
+"temperature",
+"top_p",
+"max_tokens",
+"max_completion_tokens",
+"tools",
+"tool_choice",
+"extra_headers",
+"parallel_tool_calls",
+"response_format",
+"user"
+```

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

@@ -37,7 +37,7 @@ os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
 messages = [{ "content": "There's a llama in my garden 😱 What should I do?","role": "user"}]
 
 # e.g. Call 'https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct' from Serverless Inference API
-response = litellm.completion(
+response = completion(
     model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct",
     messages=[{ "content": "Hello, how are you?","role": "user"}],
     stream=True
@@ -165,14 +165,14 @@ Steps to use
 
 ```python
 import os
-import litellm
+from litellm import completion
 
 os.environ["HUGGINGFACE_API_KEY"] = ""
 
 # TGI model: Call https://huggingface.co/glaiveai/glaive-coder-7b
 # add the 'huggingface/' prefix to the model to set huggingface as the provider
 # set api base to your deployed api endpoint from hugging face
-response = litellm.completion(
+response = completion(
     model="huggingface/glaiveai/glaive-coder-7b",
     messages=[{ "content": "Hello, how are you?","role": "user"}],
     api_base="https://wjiegasee9bmqke2.us-east-1.aws.endpoints.huggingface.cloud"
@@ -383,6 +383,8 @@ def default_pt(messages):
 #### Custom prompt templates
 
 ```python
+import litellm
+
 # Create your own custom prompt template works
 litellm.register_prompt_template(
 	    model="togethercomputer/LLaMA-2-7B-32K",

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

@@ -1,6 +1,13 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
 # Jina AI
 https://jina.ai/embeddings/
 
+Supported endpoints: 
+- /embeddings
+- /rerank
+
 ## API Key
 ```python
 # env variable
@@ -8,6 +15,10 @@ os.environ['JINA_AI_API_KEY']
 ```
 
 ## Sample Usage - Embedding
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
 ```python
 from litellm import embedding
 import os
@@ -19,6 +30,142 @@ response = embedding(
 )
 print(response)
 ```
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+1. Add to config.yaml
+```yaml
+model_list:
+  - model_name: embedding-model
+    litellm_params:
+      model: jina_ai/jina-embeddings-v3
+      api_key: os.environ/JINA_AI_API_KEY
+```
+
+2. Start proxy 
+
+```bash
+litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000/
+```
+
+3. Test it! 
+
+```bash 
+curl -L -X POST 'http://0.0.0.0:4000/embeddings' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{"input": ["hello world"], "model": "embedding-model"}'
+```
+
+</TabItem>
+</Tabs>
+
+## Sample Usage - Rerank
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+from litellm import rerank
+import os
+
+os.environ["JINA_AI_API_KEY"] = "sk-..."
+
+query = "What is the capital of the United States?"
+documents = [
+    "Carson City is the capital city of the American state of Nevada.",
+    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
+    "Washington, D.C. is the capital of the United States.",
+    "Capital punishment has existed in the United States since before it was a country.",
+]
+
+response = rerank(
+    model="jina_ai/jina-reranker-v2-base-multilingual",
+    query=query,
+    documents=documents,
+    top_n=3,
+)
+print(response)
+```
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+1. Add to config.yaml
+```yaml
+model_list:
+  - model_name: rerank-model
+    litellm_params:
+      model: jina_ai/jina-reranker-v2-base-multilingual
+      api_key: os.environ/JINA_AI_API_KEY
+```
+
+2. Start proxy 
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it! 
+
+```bash 
+curl -L -X POST 'http://0.0.0.0:4000/rerank' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+    "model": "rerank-model",
+    "query": "What is the capital of the United States?",
+    "documents": [
+        "Carson City is the capital city of the American state of Nevada.",
+        "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
+        "Washington, D.C. is the capital of the United States.",
+        "Capital punishment has existed in the United States since before it was a country."
+    ],
+    "top_n": 3
+}'
+```
+
+</TabItem>
+</Tabs>
 
 ## Supported Models
 All models listed here https://jina.ai/embeddings/ are supported
+
+## Supported Optional Rerank Parameters
+
+All cohere rerank parameters are supported. 
+
+## Supported Optional Embeddings Parameters
+
+```
+dimensions
+```
+
+## Provider-specific parameters
+
+Pass any jina ai specific parameters as a keyword argument to the `embedding` or `rerank` function, e.g. 
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+response = embedding(
+    model="jina_ai/jina-embeddings-v3",
+    input=["good morning from litellm"],
+    dimensions=1536,
+    my_custom_param="my_custom_value", # any other jina ai specific parameters
+)
+```
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/embeddings' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{"input": ["good morning from litellm"], "model": "jina_ai/jina-embeddings-v3", "dimensions": 1536, "my_custom_param": "my_custom_value"}'
+```
+
+</TabItem>
+</Tabs>

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

@@ -1562,6 +1562,10 @@ curl http://0.0.0.0:4000/v1/chat/completions \
 ## **Embedding Models**
 
 #### Usage - Embedding
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
 ```python
 import litellm
 from litellm import embedding
@@ -1574,6 +1578,49 @@ response = embedding(
 )
 print(response)
 ```
+</TabItem>
+
+<TabItem value="proxy" label="LiteLLM PROXY">
+
+
+1. Add model to config.yaml
+```yaml
+model_list:
+  - model_name: snowflake-arctic-embed-m-long-1731622468876
+    litellm_params:
+      model: vertex_ai/<your-model-id>
+      vertex_project: "adroit-crow-413218"
+      vertex_location: "us-central1"
+      vertex_credentials: adroit-crow-413218-a956eef1a2a8.json 
+
+litellm_settings:
+  drop_params: True
+```
+
+2. Start Proxy 
+
+```
+$ litellm --config /path/to/config.yaml
+```
+
+3. Make Request using OpenAI Python SDK, Langchain Python SDK
+
+```python
+import openai
+
+client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
+
+response = client.embeddings.create(
+    model="snowflake-arctic-embed-m-long-1731622468876", 
+    input = ["good morning from litellm", "this is another item"],
+)
+
+print(response)
+```
+
+
+</TabItem>
+</Tabs>
 
 #### Supported Embedding Models
 All models listed [here](https://github.com/BerriAI/litellm/blob/57f37f743886a0249f630a6792d49dffc2c5d9b7/model_prices_and_context_window.json#L835) are supported
@@ -1589,6 +1636,7 @@ All models listed [here](https://github.com/BerriAI/litellm/blob/57f37f743886a02
 | textembedding-gecko@003 | `embedding(model="vertex_ai/textembedding-gecko@003", input)` | 
 | 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)` | 
+| Fine-tuned OR Custom Embedding models | `embedding(model="vertex_ai/<your-model-id>", input)` | 
 
 ### Supported OpenAI (Unified) Params
 

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

@@ -791,9 +791,9 @@ general_settings:
 | store_model_in_db | boolean | If true, allows `/model/new` endpoint to store model information in db. Endpoint disabled by default. [Doc on `/model/new` endpoint](./model_management.md#create-a-new-model) |
 | max_request_size_mb | int | The maximum size for requests in MB. Requests above this size will be rejected. |
 | max_response_size_mb | int | The maximum size for responses in MB. LLM Responses above this size will not be sent. |
-| proxy_budget_rescheduler_min_time | int | The minimum time (in seconds) to wait before checking db for budget resets. |
+| proxy_budget_rescheduler_min_time | int | The minimum time (in seconds) to wait before checking db for budget resets. **Default is 597 seconds** |
-| proxy_budget_rescheduler_max_time | int | The maximum time (in seconds) to wait before checking db for budget resets. |
+| proxy_budget_rescheduler_max_time | int | The maximum time (in seconds) to wait before checking db for budget resets. **Default is 605 seconds** |
-| proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. |
+| proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 10 seconds** |
 | alerting_args | dict | Args for Slack Alerting [Doc on Slack Alerting](./alerting.md) |
 | custom_key_generate | str | Custom function for key generation [Doc on custom key generation](./virtual_keys.md#custom--key-generate) |
 | allowed_ips | List[str] | List of IPs allowed to access the proxy. If not set, all IPs are allowed. |

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

@@ -66,10 +66,16 @@ Removes any field with `user_api_key_*` from metadata.
 Found under `kwargs["standard_logging_object"]`. This is a standard payload, logged for every response.
 
 ```python
+
 class StandardLoggingPayload(TypedDict):
     id: str
+    trace_id: str  # Trace multiple LLM calls belonging to same overall request (e.g. fallbacks/retries)
     call_type: str
     response_cost: float
+    response_cost_failure_debug_info: Optional[
+        StandardLoggingModelCostFailureDebugInformation
+    ]
+    status: StandardLoggingPayloadStatus
     total_tokens: int
     prompt_tokens: int
     completion_tokens: int
@@ -84,13 +90,13 @@ class StandardLoggingPayload(TypedDict):
     metadata: StandardLoggingMetadata
     cache_hit: Optional[bool]
     cache_key: Optional[str]
-    saved_cache_cost: Optional[float]
+    saved_cache_cost: float
     request_tags: list
     end_user: Optional[str]
-    requester_ip_address: Optional[str]         # IP address of requester
+    requester_ip_address: Optional[str]
-    requester_metadata: Optional[dict]          # metadata passed in request in the "metadata" field
     messages: Optional[Union[str, list, dict]]
     response: Optional[Union[str, list, dict]]
+    error_str: Optional[str]
     model_parameters: dict
     hidden_params: StandardLoggingHiddenParams
 
@@ -99,12 +105,47 @@ class StandardLoggingHiddenParams(TypedDict):
     cache_key: Optional[str]
     api_base: Optional[str]
     response_cost: Optional[str]
-    additional_headers: Optional[dict]
+    additional_headers: Optional[StandardLoggingAdditionalHeaders]
 
+class StandardLoggingAdditionalHeaders(TypedDict, total=False):
+    x_ratelimit_limit_requests: int
+    x_ratelimit_limit_tokens: int
+    x_ratelimit_remaining_requests: int
+    x_ratelimit_remaining_tokens: int
+
+class StandardLoggingMetadata(StandardLoggingUserAPIKeyMetadata):
+    """
+    Specific metadata k,v pairs logged to integration for easier cost tracking
+    """
+
+    spend_logs_metadata: Optional[
+        dict
+    ]  # special param to log k,v pairs to spendlogs for a call
+    requester_ip_address: Optional[str]
+    requester_metadata: Optional[dict]
 
 class StandardLoggingModelInformation(TypedDict):
     model_map_key: str
     model_map_value: Optional[ModelInfo]
+  
+
+StandardLoggingPayloadStatus = Literal["success", "failure"]
+
+class StandardLoggingModelCostFailureDebugInformation(TypedDict, total=False):
+    """
+    Debug information, if cost tracking fails.
+
+    Avoid logging sensitive information like response or optional params
+    """
+
+    error_str: Required[str]
+    traceback_str: Required[str]
+    model: str
+    cache_hit: Optional[bool]
+    custom_llm_provider: Optional[str]
+    base_model: Optional[str]
+    call_type: str
+    custom_pricing: Optional[bool]
 ```
 
 ## Langfuse

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

@@ -1,5 +1,6 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
+import Image from '@theme/IdealImage';
 
 # ⚡ Best Practices for Production
 
@@ -112,7 +113,35 @@ general_settings:
   disable_spend_logs: True
 ```
 
-## 7. Set LiteLLM Salt Key 
+## 7. Use Helm PreSync Hook for Database Migrations [BETA]
+
+To ensure only one service manages database migrations, use our [Helm PreSync hook for Database Migrations](https://github.com/BerriAI/litellm/blob/main/deploy/charts/litellm-helm/templates/migrations-job.yaml). This ensures migrations are handled during `helm upgrade` or `helm install`, while LiteLLM pods explicitly disable migrations.
+
+
+1. **Helm PreSync Hook**:
+   - The Helm PreSync hook is configured in the chart to run database migrations during deployments.
+   - The hook always sets `DISABLE_SCHEMA_UPDATE=false`, ensuring migrations are executed reliably.
+  
+  Reference Settings to set on ArgoCD for `values.yaml`
+
+  ```yaml
+  db:
+    useExisting: true # use existing Postgres DB
+    url: postgresql://ishaanjaffer0324:3rnwpOBau6hT@ep-withered-mud-a5dkdpke.us-east-2.aws.neon.tech/test-argo-cd?sslmode=require # url of existing Postgres DB
+  ```
+
+2. **LiteLLM Pods**:
+   - Set `DISABLE_SCHEMA_UPDATE=true` in LiteLLM pod configurations to prevent them from running migrations.
+   
+   Example configuration for LiteLLM pod:
+   ```yaml
+   env:
+     - name: DISABLE_SCHEMA_UPDATE
+       value: "true"
+   ```
+
+
+## 8. Set LiteLLM Salt Key 
 
 If you plan on using the DB, set a salt key for encrypting/decrypting variables in the DB. 
 

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

@@ -749,3 +749,18 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
     "mock_testing_fallbacks": true
 }'
 ```
+
+### Disable Fallbacks per key
+
+You can disable fallbacks per key by setting `disable_fallbacks: true` in your key metadata.
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+    "metadata": {
+        "disable_fallbacks": true
+    }
+}'
+```

docs/my-website/docs/rerank.md

@@ -114,3 +114,4 @@ curl http://0.0.0.0:4000/rerank \
 | Cohere      |   [Usage](#quick-start)                 |
 | Together AI|   [Usage](../docs/providers/togetherai)                 |  
 | Azure AI|   [Usage](../docs/providers/azure_ai)                 |  
+| Jina AI|   [Usage](../docs/providers/jina_ai)                 |  

docs/my-website/docs/secret.md

@@ -1,3 +1,6 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
 # Secret Manager
 LiteLLM supports reading secrets from Azure Key Vault, Google Secret Manager
 
@@ -59,14 +62,35 @@ os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2
 ```
 
 2. Enable AWS Secret Manager in config. 
+
+<Tabs>
+<TabItem value="read_only" label="Read Keys from AWS Secret Manager">
+
 ```yaml
 general_settings:
   master_key: os.environ/litellm_master_key 
   key_management_system: "aws_secret_manager" # 👈 KEY CHANGE
   key_management_settings: 
     hosted_keys: ["litellm_master_key"] # 👈 Specify which env keys you stored on AWS 
+
 ```
 
+</TabItem>
+
+<TabItem value="write_only" label="Write Virtual Keys to AWS Secret Manager">
+
+This will only store virtual keys in AWS Secret Manager. No keys will be read from AWS Secret Manager.
+
+```yaml
+general_settings:
+  key_management_system: "aws_secret_manager" # 👈 KEY CHANGE
+  key_management_settings: 
+    store_virtual_keys: true
+    access_mode: "write_only" # Literal["read_only", "write_only", "read_and_write"]
+```
+</TabItem>
+</Tabs>
+
 3. Run proxy
 
 ```bash
@@ -181,16 +205,14 @@ litellm --config /path/to/config.yaml
 
 Use encrypted keys from Google KMS on the proxy
 
-### Usage with LiteLLM Proxy Server
+Step 1. Add keys to env 
-
-## Step 1. Add keys to env 
 ```
 export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials.json"
 export GOOGLE_KMS_RESOURCE_NAME="projects/*/locations/*/keyRings/*/cryptoKeys/*"
 export PROXY_DATABASE_URL_ENCRYPTED=b'\n$\x00D\xac\xb4/\x8e\xc...'
 ```
 
-## Step 2: Update Config
+Step 2: Update Config
 
 ```yaml
 general_settings:
@@ -199,7 +221,7 @@ general_settings:
   master_key: sk-1234
 ```
 
-## Step 3: Start + test proxy
+Step 3: Start + test proxy
 
 ```
 $ litellm --config /path/to/config.yaml
@@ -215,3 +237,17 @@ $ litellm --test
 <!-- 
 ## .env Files
 If no secret manager client is specified, Litellm automatically uses the `.env` file to manage sensitive data. -->
+
+
+## All Secret Manager Settings
+
+All settings related to secret management
+
+```yaml
+general_settings:
+  key_management_system: "aws_secret_manager" # REQUIRED
+  key_management_settings:  
+    store_virtual_keys: true # OPTIONAL. Defaults to False, when True will store virtual keys in secret manager
+    access_mode: "write_only" # OPTIONAL. Literal["read_only", "write_only", "read_and_write"]. Defaults to "read_only"
+    hosted_keys: ["litellm_master_key"] # OPTIONAL. Specify which env keys you stored on AWS
+```

litellm/__init__.py

@@ -305,7 +305,7 @@ secret_manager_client: Optional[Any] = (
 )
 _google_kms_resource_name: Optional[str] = None
 _key_management_system: Optional[KeyManagementSystem] = None
-_key_management_settings: Optional[KeyManagementSettings] = None
+_key_management_settings: KeyManagementSettings = KeyManagementSettings()
 #### PII MASKING ####
 output_parse_pii: bool = False
 #############################################
@@ -962,6 +962,8 @@ from .utils import (
     supports_response_schema,
     supports_parallel_function_calling,
     supports_vision,
+    supports_audio_input,
+    supports_audio_output,
     supports_system_messages,
     get_litellm_params,
     acreate,

litellm/cost_calculator.py

@@ -46,6 +46,9 @@ from litellm.llms.OpenAI.cost_calculation import (
 from litellm.llms.OpenAI.cost_calculation import cost_per_token as openai_cost_per_token
 from litellm.llms.OpenAI.cost_calculation import cost_router as openai_cost_router
 from litellm.llms.together_ai.cost_calculator import get_model_params_and_category
+from litellm.llms.vertex_ai_and_google_ai_studio.image_generation.cost_calculator import (
+    cost_calculator as vertex_ai_image_cost_calculator,
+)
 from litellm.types.llms.openai import HttpxBinaryResponseContent
 from litellm.types.rerank import RerankResponse
 from litellm.types.router import SPECIAL_MODEL_INFO_PARAMS
@@ -667,9 +670,11 @@ def completion_cost(  # noqa: PLR0915
         ):
             ### IMAGE GENERATION COST CALCULATION ###
             if custom_llm_provider == "vertex_ai":
-                # https://cloud.google.com/vertex-ai/generative-ai/pricing
+                if isinstance(completion_response, ImageResponse):
-                # Vertex Charges Flat $0.20 per image
+                    return vertex_ai_image_cost_calculator(
-                return 0.020
+                        model=model,
+                        image_response=completion_response,
+                    )
             elif custom_llm_provider == "bedrock":
                 if isinstance(completion_response, ImageResponse):
                     return bedrock_image_cost_calculator(

litellm/litellm_core_utils/exception_mapping_utils.py

@@ -239,7 +239,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"ContextWindowExceededError: {exception_provider} - {message}",
                         llm_provider=custom_llm_provider,
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                         litellm_debug_info=extra_information,
                     )
                 elif (
@@ -251,7 +251,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"{exception_provider} - {message}",
                         llm_provider=custom_llm_provider,
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                         litellm_debug_info=extra_information,
                     )
                 elif "A timeout occurred" in error_str:
@@ -271,7 +271,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"ContentPolicyViolationError: {exception_provider} - {message}",
                         llm_provider=custom_llm_provider,
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                         litellm_debug_info=extra_information,
                     )
                 elif (
@@ -283,7 +283,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"{exception_provider} - {message}",
                         llm_provider=custom_llm_provider,
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                         litellm_debug_info=extra_information,
                     )
                 elif "Web server is returning an unknown error" in error_str:
@@ -299,7 +299,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"RateLimitError: {exception_provider} - {message}",
                         model=model,
                         llm_provider=custom_llm_provider,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                         litellm_debug_info=extra_information,
                     )
                 elif (
@@ -311,7 +311,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"AuthenticationError: {exception_provider} - {message}",
                         llm_provider=custom_llm_provider,
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                         litellm_debug_info=extra_information,
                     )
                 elif "Mistral API raised a streaming error" in error_str:
@@ -335,7 +335,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"{exception_provider} - {message}",
                             llm_provider=custom_llm_provider,
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 401:
@@ -344,7 +344,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AuthenticationError: {exception_provider} - {message}",
                             llm_provider=custom_llm_provider,
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 404:
@@ -353,7 +353,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"NotFoundError: {exception_provider} - {message}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 408:
@@ -516,7 +516,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"ReplicateException - {error_str}",
                         llm_provider="replicate",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "input is too long" in error_str:
                     exception_mapping_worked = True
@@ -524,7 +524,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"ReplicateException - {error_str}",
                         model=model,
                         llm_provider="replicate",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif exception_type == "ModelError":
                     exception_mapping_worked = True
@@ -532,7 +532,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"ReplicateException - {error_str}",
                         model=model,
                         llm_provider="replicate",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "Request was throttled" in error_str:
                     exception_mapping_worked = True
@@ -540,7 +540,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"ReplicateException - {error_str}",
                         llm_provider="replicate",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif hasattr(original_exception, "status_code"):
                     if original_exception.status_code == 401:
@@ -549,7 +549,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"ReplicateException - {original_exception.message}",
                             llm_provider="replicate",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif (
                         original_exception.status_code == 400
@@ -560,7 +560,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"ReplicateException - {original_exception.message}",
                             model=model,
                             llm_provider="replicate",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 422:
                         exception_mapping_worked = True
@@ -568,7 +568,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"ReplicateException - {original_exception.message}",
                             model=model,
                             llm_provider="replicate",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 408:
                         exception_mapping_worked = True
@@ -583,7 +583,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"ReplicateException - {original_exception.message}",
                             llm_provider="replicate",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 429:
                         exception_mapping_worked = True
@@ -591,7 +591,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"ReplicateException - {original_exception.message}",
                             llm_provider="replicate",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 500:
                         exception_mapping_worked = True
@@ -599,7 +599,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"ReplicateException - {original_exception.message}",
                             llm_provider="replicate",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                 exception_mapping_worked = True
                 raise APIError(
@@ -631,7 +631,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"{custom_llm_provider}Exception: Authentication Error - {error_str}",
                         llm_provider=custom_llm_provider,
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                         litellm_debug_info=extra_information,
                     )
                 elif "token_quota_reached" in error_str:
@@ -640,7 +640,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"{custom_llm_provider}Exception: Rate Limit Errror - {error_str}",
                         llm_provider=custom_llm_provider,
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     "The server received an invalid response from an upstream server."
@@ -750,7 +750,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"BedrockException - {error_str}\n. Enable 'litellm.modify_params=True' (for PROXY do: `litellm_settings::modify_params: True`) to insert a dummy assistant message and fix this error.",
                         model=model,
                         llm_provider="bedrock",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "Malformed input request" in error_str:
                     exception_mapping_worked = True
@@ -758,7 +758,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"BedrockException - {error_str}",
                         model=model,
                         llm_provider="bedrock",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "A conversation must start with a user message." in error_str:
                     exception_mapping_worked = True
@@ -766,7 +766,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"BedrockException - {error_str}\n. Pass in default user message via `completion(..,user_continue_message=)` or enable `litellm.modify_params=True`.\nFor Proxy: do via `litellm_settings::modify_params: True` or user_continue_message under `litellm_params`",
                         model=model,
                         llm_provider="bedrock",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     "Unable to locate credentials" in error_str
@@ -778,7 +778,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"BedrockException Invalid Authentication - {error_str}",
                         model=model,
                         llm_provider="bedrock",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "AccessDeniedException" in error_str:
                     exception_mapping_worked = True
@@ -786,7 +786,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"BedrockException PermissionDeniedError - {error_str}",
                         model=model,
                         llm_provider="bedrock",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     "throttlingException" in error_str
@@ -797,7 +797,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"BedrockException: Rate Limit Error - {error_str}",
                         model=model,
                         llm_provider="bedrock",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     "Connect timeout on endpoint URL" in error_str
@@ -836,7 +836,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"BedrockException - {original_exception.message}",
                             llm_provider="bedrock",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 400:
                         exception_mapping_worked = True
@@ -844,7 +844,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"BedrockException - {original_exception.message}",
                             llm_provider="bedrock",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 404:
                         exception_mapping_worked = True
@@ -852,7 +852,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"BedrockException - {original_exception.message}",
                             llm_provider="bedrock",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 408:
                         exception_mapping_worked = True
@@ -868,7 +868,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"BedrockException - {original_exception.message}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 429:
@@ -877,7 +877,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"BedrockException - {original_exception.message}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 503:
@@ -886,7 +886,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"BedrockException - {original_exception.message}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 504:  # gateway timeout error
@@ -907,7 +907,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"litellm.BadRequestError: SagemakerException - {error_str}",
                         model=model,
                         llm_provider="sagemaker",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     "Input validation error: `best_of` must be > 0 and <= 2"
@@ -918,7 +918,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message="SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints",
                         model=model,
                         llm_provider="sagemaker",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     "`inputs` tokens + `max_new_tokens` must be <=" in error_str
@@ -929,7 +929,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"SagemakerException - {error_str}",
                         model=model,
                         llm_provider="sagemaker",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif hasattr(original_exception, "status_code"):
                     if original_exception.status_code == 500:
@@ -951,7 +951,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"SagemakerException - {original_exception.message}",
                             llm_provider=custom_llm_provider,
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 400:
                         exception_mapping_worked = True
@@ -959,7 +959,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"SagemakerException - {original_exception.message}",
                             llm_provider=custom_llm_provider,
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 404:
                         exception_mapping_worked = True
@@ -967,7 +967,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"SagemakerException - {original_exception.message}",
                             llm_provider=custom_llm_provider,
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 408:
                         exception_mapping_worked = True
@@ -986,7 +986,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"SagemakerException - {original_exception.message}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 429:
@@ -995,7 +995,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"SagemakerException - {original_exception.message}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 503:
@@ -1004,7 +1004,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"SagemakerException - {original_exception.message}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 504:  # gateway timeout error
@@ -1217,7 +1217,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message="GeminiException - Invalid api key",
                         model=model,
                         llm_provider="palm",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 if (
                     "504 Deadline expired before operation could complete." in error_str
@@ -1235,7 +1235,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"GeminiException - {error_str}",
                         model=model,
                         llm_provider="palm",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 if (
                     "500 An internal error has occurred." in error_str
@@ -1262,7 +1262,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"GeminiException - {error_str}",
                             model=model,
                             llm_provider="palm",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                 # Dailed: Error occurred: 400 Request payload size exceeds the limit: 20000 bytes
             elif custom_llm_provider == "cloudflare":
@@ -1272,7 +1272,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"Cloudflare Exception - {original_exception.message}",
                         llm_provider="cloudflare",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 if "must have required property" in error_str:
                     exception_mapping_worked = True
@@ -1280,7 +1280,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"Cloudflare Exception - {original_exception.message}",
                         llm_provider="cloudflare",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
             elif (
                 custom_llm_provider == "cohere" or custom_llm_provider == "cohere_chat"
@@ -1294,7 +1294,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"CohereException - {original_exception.message}",
                         llm_provider="cohere",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "too many tokens" in error_str:
                     exception_mapping_worked = True
@@ -1302,7 +1302,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"CohereException - {original_exception.message}",
                         model=model,
                         llm_provider="cohere",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif hasattr(original_exception, "status_code"):
                     if (
@@ -1314,7 +1314,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"CohereException - {original_exception.message}",
                             llm_provider="cohere",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 408:
                         exception_mapping_worked = True
@@ -1329,7 +1329,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"CohereException - {original_exception.message}",
                             llm_provider="cohere",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                 elif (
                     "CohereConnectionError" in exception_type
@@ -1339,7 +1339,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"CohereException - {original_exception.message}",
                         llm_provider="cohere",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "invalid type:" in error_str:
                     exception_mapping_worked = True
@@ -1347,7 +1347,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"CohereException - {original_exception.message}",
                         llm_provider="cohere",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "Unexpected server error" in error_str:
                     exception_mapping_worked = True
@@ -1355,7 +1355,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"CohereException - {original_exception.message}",
                         llm_provider="cohere",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 else:
                     if hasattr(original_exception, "status_code"):
@@ -1375,7 +1375,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=error_str,
                         model=model,
                         llm_provider="huggingface",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "A valid user token is required" in error_str:
                     exception_mapping_worked = True
@@ -1383,7 +1383,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=error_str,
                         llm_provider="huggingface",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "Rate limit reached" in error_str:
                     exception_mapping_worked = True
@@ -1391,7 +1391,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=error_str,
                         llm_provider="huggingface",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 if hasattr(original_exception, "status_code"):
                     if original_exception.status_code == 401:
@@ -1400,7 +1400,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"HuggingfaceException - {original_exception.message}",
                             llm_provider="huggingface",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 400:
                         exception_mapping_worked = True
@@ -1408,7 +1408,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"HuggingfaceException - {original_exception.message}",
                             model=model,
                             llm_provider="huggingface",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 408:
                         exception_mapping_worked = True
@@ -1423,7 +1423,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"HuggingfaceException - {original_exception.message}",
                             llm_provider="huggingface",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 503:
                         exception_mapping_worked = True
@@ -1431,7 +1431,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"HuggingfaceException - {original_exception.message}",
                             llm_provider="huggingface",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     else:
                         exception_mapping_worked = True
@@ -1450,7 +1450,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AI21Exception - {original_exception.message}",
                             model=model,
                             llm_provider="ai21",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     if "Bad or missing API token." in original_exception.message:
                         exception_mapping_worked = True
@@ -1458,7 +1458,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AI21Exception - {original_exception.message}",
                             model=model,
                             llm_provider="ai21",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                 if hasattr(original_exception, "status_code"):
                     if original_exception.status_code == 401:
@@ -1467,7 +1467,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AI21Exception - {original_exception.message}",
                             llm_provider="ai21",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 408:
                         exception_mapping_worked = True
@@ -1482,7 +1482,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AI21Exception - {original_exception.message}",
                             model=model,
                             llm_provider="ai21",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 429:
                         exception_mapping_worked = True
@@ -1490,7 +1490,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AI21Exception - {original_exception.message}",
                             llm_provider="ai21",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     else:
                         exception_mapping_worked = True
@@ -1509,7 +1509,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"NLPCloudException - {error_str}",
                             model=model,
                             llm_provider="nlp_cloud",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif "value is not a valid" in error_str:
                         exception_mapping_worked = True
@@ -1517,7 +1517,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"NLPCloudException - {error_str}",
                             model=model,
                             llm_provider="nlp_cloud",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     else:
                         exception_mapping_worked = True
@@ -1542,7 +1542,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"NLPCloudException - {original_exception.message}",
                             llm_provider="nlp_cloud",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif (
                         original_exception.status_code == 401
@@ -1553,7 +1553,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"NLPCloudException - {original_exception.message}",
                             llm_provider="nlp_cloud",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif (
                         original_exception.status_code == 522
@@ -1574,7 +1574,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"NLPCloudException - {original_exception.message}",
                             llm_provider="nlp_cloud",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif (
                         original_exception.status_code == 500
@@ -1597,7 +1597,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"NLPCloudException - {original_exception.message}",
                             model=model,
                             llm_provider="nlp_cloud",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     else:
                         exception_mapping_worked = True
@@ -1623,7 +1623,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"TogetherAIException - {error_response['error']}",
                         model=model,
                         llm_provider="together_ai",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     "error" in error_response
@@ -1634,7 +1634,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"TogetherAIException - {error_response['error']}",
                         llm_provider="together_ai",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     "error" in error_response
@@ -1645,7 +1645,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"TogetherAIException - {error_response['error']}",
                         model=model,
                         llm_provider="together_ai",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "A timeout occurred" in error_str:
                     exception_mapping_worked = True
@@ -1664,7 +1664,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"TogetherAIException - {error_response['error']}",
                         model=model,
                         llm_provider="together_ai",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     "error_type" in error_response
@@ -1675,7 +1675,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"TogetherAIException - {error_response['error']}",
                         model=model,
                         llm_provider="together_ai",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 if hasattr(original_exception, "status_code"):
                     if original_exception.status_code == 408:
@@ -1691,7 +1691,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"TogetherAIException - {error_response['error']}",
                             model=model,
                             llm_provider="together_ai",
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 429:
                         exception_mapping_worked = True
@@ -1699,7 +1699,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"TogetherAIException - {original_exception.message}",
                             llm_provider="together_ai",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 524:
                         exception_mapping_worked = True
@@ -1727,7 +1727,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"AlephAlphaException - {original_exception.message}",
                         llm_provider="aleph_alpha",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "InvalidToken" in error_str or "No token provided" in error_str:
                     exception_mapping_worked = True
@@ -1735,7 +1735,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"AlephAlphaException - {original_exception.message}",
                         llm_provider="aleph_alpha",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif hasattr(original_exception, "status_code"):
                     verbose_logger.debug(
@@ -1754,7 +1754,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AlephAlphaException - {original_exception.message}",
                             llm_provider="aleph_alpha",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 429:
                         exception_mapping_worked = True
@@ -1762,7 +1762,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AlephAlphaException - {original_exception.message}",
                             llm_provider="aleph_alpha",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 500:
                         exception_mapping_worked = True
@@ -1770,7 +1770,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AlephAlphaException - {original_exception.message}",
                             llm_provider="aleph_alpha",
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                         )
                     raise original_exception
                 raise original_exception
@@ -1787,7 +1787,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"OllamaException: Invalid Model/Model not loaded - {original_exception}",
                         model=model,
                         llm_provider="ollama",
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "Failed to establish a new connection" in error_str:
                     exception_mapping_worked = True
@@ -1795,7 +1795,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"OllamaException: {original_exception}",
                         llm_provider="ollama",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "Invalid response object from API" in error_str:
                     exception_mapping_worked = True
@@ -1803,7 +1803,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         message=f"OllamaException: {original_exception}",
                         llm_provider="ollama",
                         model=model,
-                        response=original_exception.response,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "Read timed out" in error_str:
                     exception_mapping_worked = True
@@ -1837,6 +1837,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         llm_provider="azure",
                         model=model,
                         litellm_debug_info=extra_information,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "This model's maximum context length is" in error_str:
                     exception_mapping_worked = True
@@ -1845,6 +1846,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         llm_provider="azure",
                         model=model,
                         litellm_debug_info=extra_information,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "DeploymentNotFound" in error_str:
                     exception_mapping_worked = True
@@ -1853,6 +1855,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         llm_provider="azure",
                         model=model,
                         litellm_debug_info=extra_information,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     (
@@ -1873,6 +1876,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         llm_provider="azure",
                         model=model,
                         litellm_debug_info=extra_information,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "invalid_request_error" in error_str:
                     exception_mapping_worked = True
@@ -1881,6 +1885,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         llm_provider="azure",
                         model=model,
                         litellm_debug_info=extra_information,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif (
                     "The api_key client option must be set either by passing api_key to the client or by setting"
@@ -1892,6 +1897,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         llm_provider=custom_llm_provider,
                         model=model,
                         litellm_debug_info=extra_information,
+                        response=getattr(original_exception, "response", None),
                     )
                 elif "Connection error" in error_str:
                     exception_mapping_worked = True
@@ -1910,6 +1916,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             llm_provider="azure",
                             model=model,
                             litellm_debug_info=extra_information,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 401:
                         exception_mapping_worked = True
@@ -1918,6 +1925,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             llm_provider="azure",
                             model=model,
                             litellm_debug_info=extra_information,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 408:
                         exception_mapping_worked = True
@@ -1934,6 +1942,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             model=model,
                             llm_provider="azure",
                             litellm_debug_info=extra_information,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 429:
                         exception_mapping_worked = True
@@ -1942,6 +1951,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             model=model,
                             llm_provider="azure",
                             litellm_debug_info=extra_information,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 503:
                         exception_mapping_worked = True
@@ -1950,6 +1960,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             model=model,
                             llm_provider="azure",
                             litellm_debug_info=extra_information,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 504:  # gateway timeout error
                         exception_mapping_worked = True
@@ -1989,7 +2000,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"{exception_provider} - {error_str}",
                             llm_provider=custom_llm_provider,
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 401:
@@ -1998,7 +2009,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AuthenticationError: {exception_provider} - {error_str}",
                             llm_provider=custom_llm_provider,
                             model=model,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 404:
@@ -2007,7 +2018,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"NotFoundError: {exception_provider} - {error_str}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 408:
@@ -2024,7 +2035,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"BadRequestError: {exception_provider} - {error_str}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 429:
@@ -2033,7 +2044,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"RateLimitError: {exception_provider} - {error_str}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 503:
@@ -2042,7 +2053,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"ServiceUnavailableError: {exception_provider} - {error_str}",
                             model=model,
                             llm_provider=custom_llm_provider,
-                            response=original_exception.response,
+                            response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
                     elif original_exception.status_code == 504:  # gateway timeout error

litellm/litellm_core_utils/litellm_logging.py

@@ -202,6 +202,7 @@ class Logging:
         start_time,
         litellm_call_id: str,
         function_id: str,
+        litellm_trace_id: Optional[str] = None,
         dynamic_input_callbacks: Optional[
             List[Union[str, Callable, CustomLogger]]
         ] = None,
@@ -239,6 +240,7 @@ class Logging:
         self.start_time = start_time  # log the call start time
         self.call_type = call_type
         self.litellm_call_id = litellm_call_id
+        self.litellm_trace_id = litellm_trace_id
         self.function_id = function_id
         self.streaming_chunks: List[Any] = []  # for generating complete stream response
         self.sync_streaming_chunks: List[Any] = (
@@ -275,6 +277,11 @@ class Logging:
         self.completion_start_time: Optional[datetime.datetime] = None
         self._llm_caching_handler: Optional[LLMCachingHandler] = None
 
+        self.model_call_details = {
+            "litellm_trace_id": litellm_trace_id,
+            "litellm_call_id": litellm_call_id,
+        }
+
     def process_dynamic_callbacks(self):
         """
         Initializes CustomLogger compatible callbacks in self.dynamic_* callbacks
@@ -382,7 +389,8 @@ class Logging:
         self.logger_fn = litellm_params.get("logger_fn", None)
         verbose_logger.debug(f"self.optional_params: {self.optional_params}")
 
-        self.model_call_details = {
+        self.model_call_details.update(
+            {
                 "model": self.model,
                 "messages": self.messages,
                 "optional_params": self.optional_params,
@@ -397,6 +405,7 @@ class Logging:
                 **self.optional_params,
                 **additional_params,
             }
+        )
 
         ## check if stream options is set ##  - used by CustomStreamWrapper for easy instrumentation
         if "stream_options" in additional_params:
@@ -2823,6 +2832,7 @@ def get_standard_logging_object_payload(
 
         payload: StandardLoggingPayload = StandardLoggingPayload(
             id=str(id),
+            trace_id=kwargs.get("litellm_trace_id"),  # type: ignore
             call_type=call_type or "",
             cache_hit=cache_hit,
             status=status,

litellm/llms/anthropic/chat/handler.py

@@ -44,7 +44,9 @@ from litellm.types.llms.openai import (
     ChatCompletionToolCallFunctionChunk,
     ChatCompletionUsageBlock,
 )
-from litellm.types.utils import GenericStreamingChunk, PromptTokensDetailsWrapper
+from litellm.types.utils import GenericStreamingChunk
+from litellm.types.utils import Message as LitellmMessage
+from litellm.types.utils import PromptTokensDetailsWrapper
 from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
 
 from ...base import BaseLLM
@@ -94,6 +96,7 @@ async def make_call(
     messages: list,
     logging_obj,
     timeout: Optional[Union[float, httpx.Timeout]],
+    json_mode: bool,
 ) -> Tuple[Any, httpx.Headers]:
     if client is None:
         client = litellm.module_level_aclient
@@ -119,7 +122,9 @@ async def make_call(
         raise AnthropicError(status_code=500, message=str(e))
 
     completion_stream = ModelResponseIterator(
-        streaming_response=response.aiter_lines(), sync_stream=False
+        streaming_response=response.aiter_lines(),
+        sync_stream=False,
+        json_mode=json_mode,
     )
 
     # LOGGING
@@ -142,6 +147,7 @@ def make_sync_call(
     messages: list,
     logging_obj,
     timeout: Optional[Union[float, httpx.Timeout]],
+    json_mode: bool,
 ) -> Tuple[Any, httpx.Headers]:
     if client is None:
         client = litellm.module_level_client  # re-use a module level client
@@ -175,7 +181,7 @@ def make_sync_call(
         )
 
     completion_stream = ModelResponseIterator(
-        streaming_response=response.iter_lines(), sync_stream=True
+        streaming_response=response.iter_lines(), sync_stream=True, json_mode=json_mode
     )
 
     # LOGGING
@@ -270,11 +276,12 @@ class AnthropicChatCompletion(BaseLLM):
                     "arguments"
                 )
                 if json_mode_content_str is not None:
-                    args = json.loads(json_mode_content_str)
+                    _converted_message = self._convert_tool_response_to_message(
-                    values: Optional[dict] = args.get("values")
+                        tool_calls=tool_calls,
-                    if values is not None:
+                    )
-                        _message = litellm.Message(content=json.dumps(values))
+                    if _converted_message is not None:
                         completion_response["stop_reason"] = "stop"
+                        _message = _converted_message
             model_response.choices[0].message = _message  # type: ignore
             model_response._hidden_params["original_response"] = completion_response[
                 "content"
@@ -318,6 +325,37 @@ class AnthropicChatCompletion(BaseLLM):
         model_response._hidden_params = _hidden_params
         return model_response
 
+    @staticmethod
+    def _convert_tool_response_to_message(
+        tool_calls: List[ChatCompletionToolCallChunk],
+    ) -> Optional[LitellmMessage]:
+        """
+        In JSON mode, Anthropic API returns JSON schema as a tool call, we need to convert it to a message to follow the OpenAI format
+
+        """
+        ## HANDLE JSON MODE - anthropic returns single function call
+        json_mode_content_str: Optional[str] = tool_calls[0]["function"].get(
+            "arguments"
+        )
+        try:
+            if json_mode_content_str is not None:
+                args = json.loads(json_mode_content_str)
+                if (
+                    isinstance(args, dict)
+                    and (values := args.get("values")) is not None
+                ):
+                    _message = litellm.Message(content=json.dumps(values))
+                    return _message
+                else:
+                    # a lot of the times the `values` key is not present in the tool response
+                    # relevant issue: https://github.com/BerriAI/litellm/issues/6741
+                    _message = litellm.Message(content=json.dumps(args))
+                    return _message
+        except json.JSONDecodeError:
+            # json decode error does occur, return the original tool response str
+            return litellm.Message(content=json_mode_content_str)
+        return None
+
     async def acompletion_stream_function(
         self,
         model: str,
@@ -334,6 +372,7 @@ class AnthropicChatCompletion(BaseLLM):
         stream,
         _is_function_call,
         data: dict,
+        json_mode: bool,
         optional_params=None,
         litellm_params=None,
         logger_fn=None,
@@ -350,6 +389,7 @@ class AnthropicChatCompletion(BaseLLM):
             messages=messages,
             logging_obj=logging_obj,
             timeout=timeout,
+            json_mode=json_mode,
         )
         streamwrapper = CustomStreamWrapper(
             completion_stream=completion_stream,
@@ -440,8 +480,8 @@ class AnthropicChatCompletion(BaseLLM):
         logging_obj,
         optional_params: dict,
         timeout: Union[float, httpx.Timeout],
+        litellm_params: dict,
         acompletion=None,
-        litellm_params=None,
         logger_fn=None,
         headers={},
         client=None,
@@ -464,6 +504,7 @@ class AnthropicChatCompletion(BaseLLM):
             model=model,
             messages=messages,
             optional_params=optional_params,
+            litellm_params=litellm_params,
             headers=headers,
             _is_function_call=_is_function_call,
             is_vertex_request=is_vertex_request,
@@ -500,6 +541,7 @@ class AnthropicChatCompletion(BaseLLM):
                     optional_params=optional_params,
                     stream=stream,
                     _is_function_call=_is_function_call,
+                    json_mode=json_mode,
                     litellm_params=litellm_params,
                     logger_fn=logger_fn,
                     headers=headers,
@@ -547,6 +589,7 @@ class AnthropicChatCompletion(BaseLLM):
                     messages=messages,
                     logging_obj=logging_obj,
                     timeout=timeout,
+                    json_mode=json_mode,
                 )
                 return CustomStreamWrapper(
                     completion_stream=completion_stream,
@@ -605,11 +648,14 @@ class AnthropicChatCompletion(BaseLLM):
 
 
 class ModelResponseIterator:
-    def __init__(self, streaming_response, sync_stream: bool):
+    def __init__(
+        self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False
+    ):
         self.streaming_response = streaming_response
         self.response_iterator = self.streaming_response
         self.content_blocks: List[ContentBlockDelta] = []
         self.tool_index = -1
+        self.json_mode = json_mode
 
     def check_empty_tool_call_args(self) -> bool:
         """
@@ -771,6 +817,8 @@ class ModelResponseIterator:
                     status_code=500,  # it looks like Anthropic API does not return a status code in the chunk error - default to 500
                 )
 
+            text, tool_use = self._handle_json_mode_chunk(text=text, tool_use=tool_use)
+
             returned_chunk = GenericStreamingChunk(
                 text=text,
                 tool_use=tool_use,
@@ -785,6 +833,34 @@ class ModelResponseIterator:
         except json.JSONDecodeError:
             raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
 
+    def _handle_json_mode_chunk(
+        self, text: str, tool_use: Optional[ChatCompletionToolCallChunk]
+    ) -> Tuple[str, Optional[ChatCompletionToolCallChunk]]:
+        """
+        If JSON mode is enabled, convert the tool call to a message.
+
+        Anthropic returns the JSON schema as part of the tool call
+        OpenAI returns the JSON schema as part of the content, this handles placing it in the content
+
+        Args:
+            text: str
+            tool_use: Optional[ChatCompletionToolCallChunk]
+        Returns:
+            Tuple[str, Optional[ChatCompletionToolCallChunk]]
+
+            text: The text to use in the content
+            tool_use: The ChatCompletionToolCallChunk to use in the chunk response
+        """
+        if self.json_mode is True and tool_use is not None:
+            message = AnthropicChatCompletion._convert_tool_response_to_message(
+                tool_calls=[tool_use]
+            )
+            if message is not None:
+                text = message.content or ""
+                tool_use = None
+
+        return text, tool_use
+
     # Sync iterator
     def __iter__(self):
         return self

litellm/llms/anthropic/chat/transformation.py

@@ -91,6 +91,7 @@ class AnthropicConfig:
             "extra_headers",
             "parallel_tool_calls",
             "response_format",
+            "user",
         ]
 
     def get_cache_control_headers(self) -> dict:
@@ -246,6 +247,28 @@ class AnthropicConfig:
                 anthropic_tools.append(new_tool)
         return anthropic_tools
 
+    def _map_stop_sequences(
+        self, stop: Optional[Union[str, List[str]]]
+    ) -> Optional[List[str]]:
+        new_stop: Optional[List[str]] = None
+        if isinstance(stop, str):
+            if (
+                stop == "\n"
+            ) and litellm.drop_params is True:  # anthropic doesn't allow whitespace characters as stop-sequences
+                return new_stop
+            new_stop = [stop]
+        elif isinstance(stop, list):
+            new_v = []
+            for v in stop:
+                if (
+                    v == "\n"
+                ) and litellm.drop_params is True:  # anthropic doesn't allow whitespace characters as stop-sequences
+                    continue
+                new_v.append(v)
+            if len(new_v) > 0:
+                new_stop = new_v
+        return new_stop
+
     def map_openai_params(
         self,
         non_default_params: dict,
@@ -271,26 +294,10 @@ class AnthropicConfig:
                     optional_params["tool_choice"] = _tool_choice
             if param == "stream" and value is True:
                 optional_params["stream"] = value
-            if param == "stop":
+            if param == "stop" and (isinstance(value, str) or isinstance(value, list)):
-                if isinstance(value, str):
+                _value = self._map_stop_sequences(value)
-                    if (
+                if _value is not None:
-                        value == "\n"
+                    optional_params["stop_sequences"] = _value
-                    ) and litellm.drop_params is True:  # anthropic doesn't allow whitespace characters as stop-sequences
-                        continue
-                    value = [value]
-                elif isinstance(value, list):
-                    new_v = []
-                    for v in value:
-                        if (
-                            v == "\n"
-                        ) and litellm.drop_params is True:  # anthropic doesn't allow whitespace characters as stop-sequences
-                            continue
-                        new_v.append(v)
-                    if len(new_v) > 0:
-                        value = new_v
-                    else:
-                        continue
-                optional_params["stop_sequences"] = value
             if param == "temperature":
                 optional_params["temperature"] = value
             if param == "top_p":
@@ -314,7 +321,8 @@ class AnthropicConfig:
                 optional_params["tools"] = [_tool]
                 optional_params["tool_choice"] = _tool_choice
                 optional_params["json_mode"] = True
-
+            if param == "user":
+                optional_params["metadata"] = {"user_id": value}
         ## VALIDATE REQUEST
         """
         Anthropic doesn't support tool calling without `tools=` param specified.
@@ -465,6 +473,7 @@ class AnthropicConfig:
         model: str,
         messages: List[AllMessageValues],
         optional_params: dict,
+        litellm_params: dict,
         headers: dict,
         _is_function_call: bool,
         is_vertex_request: bool,
@@ -502,6 +511,15 @@ class AnthropicConfig:
         if "tools" in optional_params:
             _is_function_call = True
 
+        ## Handle user_id in metadata
+        _litellm_metadata = litellm_params.get("metadata", None)
+        if (
+            _litellm_metadata
+            and isinstance(_litellm_metadata, dict)
+            and "user_id" in _litellm_metadata
+        ):
+            optional_params["metadata"] = {"user_id": _litellm_metadata["user_id"]}
+
         data = {
             "messages": anthropic_messages,
             **optional_params,

litellm/llms/bedrock/image/amazon_stability3_transformation.py

@@ -53,8 +53,14 @@ class AmazonStability3Config:
             sd3-medium
             sd3.5-large
             sd3.5-large-turbo
+
+        Stability ultra models
+            stable-image-ultra-v1
         """
-        if model and ("sd3" in model or "sd3.5" in model):
+        if model:
+            if "sd3" in model or "sd3.5" in model:
+                return True
+            if "stable-image-ultra-v1" in model:
                 return True
         return False
 

litellm/llms/custom_httpx/types.py

@@ -8,3 +8,4 @@ class httpxSpecialProvider(str, Enum):
     GuardrailCallback = "guardrail_callback"
     Caching = "caching"
     Oauth2Check = "oauth2_check"
+    SecretManager = "secret_manager"

litellm/llms/jina_ai/embedding/transformation.py

@@ -76,4 +76,4 @@ class JinaAIEmbeddingConfig:
             or get_secret_str("JINA_AI_API_KEY")
             or get_secret_str("JINA_AI_TOKEN")
         )
-        return LlmProviders.OPENAI_LIKE.value, api_base, dynamic_api_key
+        return LlmProviders.JINA_AI.value, api_base, dynamic_api_key

litellm/llms/jina_ai/rerank/handler.py

@@ -0,0 +1,96 @@
+"""
+Re rank api
+
+LiteLLM supports the re rank API format, no paramter transformation occurs
+"""
+
+import uuid
+from typing import Any, Dict, List, Optional, Union
+
+import httpx
+from pydantic import BaseModel
+
+import litellm
+from litellm.llms.base import BaseLLM
+from litellm.llms.custom_httpx.http_handler import (
+    _get_httpx_client,
+    get_async_httpx_client,
+)
+from litellm.llms.jina_ai.rerank.transformation import JinaAIRerankConfig
+from litellm.types.rerank import RerankRequest, RerankResponse
+
+
+class JinaAIRerank(BaseLLM):
+    def rerank(
+        self,
+        model: str,
+        api_key: str,
+        query: str,
+        documents: List[Union[str, Dict[str, Any]]],
+        top_n: Optional[int] = None,
+        rank_fields: Optional[List[str]] = None,
+        return_documents: Optional[bool] = True,
+        max_chunks_per_doc: Optional[int] = None,
+        _is_async: Optional[bool] = False,
+    ) -> RerankResponse:
+        client = _get_httpx_client()
+
+        request_data = RerankRequest(
+            model=model,
+            query=query,
+            top_n=top_n,
+            documents=documents,
+            rank_fields=rank_fields,
+            return_documents=return_documents,
+        )
+
+        # exclude None values from request_data
+        request_data_dict = request_data.dict(exclude_none=True)
+
+        if _is_async:
+            return self.async_rerank(request_data_dict, api_key)  # type: ignore # Call async method
+
+        response = client.post(
+            "https://api.jina.ai/v1/rerank",
+            headers={
+                "accept": "application/json",
+                "content-type": "application/json",
+                "authorization": f"Bearer {api_key}",
+            },
+            json=request_data_dict,
+        )
+
+        if response.status_code != 200:
+            raise Exception(response.text)
+
+        _json_response = response.json()
+
+        return JinaAIRerankConfig()._transform_response(_json_response)
+
+    async def async_rerank(  # New async method
+        self,
+        request_data_dict: Dict[str, Any],
+        api_key: str,
+    ) -> RerankResponse:
+        client = get_async_httpx_client(
+            llm_provider=litellm.LlmProviders.JINA_AI
+        )  # Use async client
+
+        response = await client.post(
+            "https://api.jina.ai/v1/rerank",
+            headers={
+                "accept": "application/json",
+                "content-type": "application/json",
+                "authorization": f"Bearer {api_key}",
+            },
+            json=request_data_dict,
+        )
+
+        if response.status_code != 200:
+            raise Exception(response.text)
+
+        _json_response = response.json()
+
+        return JinaAIRerankConfig()._transform_response(_json_response)
+
+    pass

litellm/llms/jina_ai/rerank/transformation.py

@@ -0,0 +1,36 @@
+"""
+Transformation logic from Cohere's /v1/rerank format to Jina AI's  `/v1/rerank` format. 
+
+Why separate file? Make it easy to see how transformation works
+
+Docs - https://jina.ai/reranker
+"""
+
+import uuid
+from typing import List, Optional
+
+from litellm.types.rerank import (
+    RerankBilledUnits,
+    RerankResponse,
+    RerankResponseMeta,
+    RerankTokens,
+)
+
+
+class JinaAIRerankConfig:
+    def _transform_response(self, response: dict) -> RerankResponse:
+
+        _billed_units = RerankBilledUnits(**response.get("usage", {}))
+        _tokens = RerankTokens(**response.get("usage", {}))
+        rerank_meta = RerankResponseMeta(billed_units=_billed_units, tokens=_tokens)
+
+        _results: Optional[List[dict]] = response.get("results")
+
+        if _results is None:
+            raise ValueError(f"No results found in the response={response}")
+
+        return RerankResponse(
+            id=response.get("id") or str(uuid.uuid4()),
+            results=_results,
+            meta=rerank_meta,
+        )  # Return response

litellm/llms/ollama.py

@@ -185,6 +185,8 @@ class OllamaConfig:
           "name": "mistral"
         }'
         """
+        if model.startswith("ollama/") or model.startswith("ollama_chat/"):
+            model = model.split("/", 1)[1]
         api_base = get_secret_str("OLLAMA_API_BASE") or "http://localhost:11434"
 
         try:

litellm/llms/together_ai/rerank/handler.py

@@ -15,7 +15,14 @@ from litellm.llms.custom_httpx.http_handler import (
     _get_httpx_client,
     get_async_httpx_client,
 )
-from litellm.types.rerank import RerankRequest, RerankResponse
+from litellm.llms.together_ai.rerank.transformation import TogetherAIRerankConfig
+from litellm.types.rerank import (
+    RerankBilledUnits,
+    RerankRequest,
+    RerankResponse,
+    RerankResponseMeta,
+    RerankTokens,
+)
 
 
 class TogetherAIRerank(BaseLLM):
@@ -65,13 +72,7 @@ class TogetherAIRerank(BaseLLM):
 
         _json_response = response.json()
 
-        response = RerankResponse(
+        return TogetherAIRerankConfig()._transform_response(_json_response)
-            id=_json_response.get("id"),
-            results=_json_response.get("results"),
-            meta=_json_response.get("meta") or {},
-        )
-
-        return response
 
     async def async_rerank(  # New async method
         self,
@@ -97,10 +98,4 @@ class TogetherAIRerank(BaseLLM):
 
         _json_response = response.json()
 
-        return RerankResponse(
+        return TogetherAIRerankConfig()._transform_response(_json_response)
-            id=_json_response.get("id"),
-            results=_json_response.get("results"),
-            meta=_json_response.get("meta") or {},
-        )  # Return response
-
-    pass

litellm/llms/together_ai/rerank/transformation.py

@@ -0,0 +1,34 @@
+"""
+Transformation logic from Cohere's /v1/rerank format to Together AI's  `/v1/rerank` format. 
+
+Why separate file? Make it easy to see how transformation works
+"""
+
+import uuid
+from typing import List, Optional
+
+from litellm.types.rerank import (
+    RerankBilledUnits,
+    RerankResponse,
+    RerankResponseMeta,
+    RerankTokens,
+)
+
+
+class TogetherAIRerankConfig:
+    def _transform_response(self, response: dict) -> RerankResponse:
+
+        _billed_units = RerankBilledUnits(**response.get("usage", {}))
+        _tokens = RerankTokens(**response.get("usage", {}))
+        rerank_meta = RerankResponseMeta(billed_units=_billed_units, tokens=_tokens)
+
+        _results: Optional[List[dict]] = response.get("results")
+
+        if _results is None:
+            raise ValueError(f"No results found in the response={response}")
+
+        return RerankResponse(
+            id=response.get("id") or str(uuid.uuid4()),
+            results=_results,
+            meta=rerank_meta,
+        )  # Return response

litellm/llms/vertex_ai_and_google_ai_studio/common_utils.py

@@ -89,6 +89,9 @@ def _get_vertex_url(
     elif mode == "embedding":
         endpoint = "predict"
         url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}"
+        if model.isdigit():
+            # https://us-central1-aiplatform.googleapis.com/v1/projects/$PROJECT_ID/locations/us-central1/endpoints/$ENDPOINT_ID:predict
+            url = f"https://{vertex_location}-aiplatform.googleapis.com/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}"
 
     if not url or not endpoint:
         raise ValueError(f"Unable to get vertex url/endpoint for mode: {mode}")

litellm/llms/vertex_ai_and_google_ai_studio/image_generation/cost_calculator.py

@@ -0,0 +1,25 @@
+"""
+Vertex AI Image Generation Cost Calculator
+"""
+
+from typing import Optional
+
+import litellm
+from litellm.types.utils import ImageResponse
+
+
+def cost_calculator(
+    model: str,
+    image_response: ImageResponse,
+) -> float:
+    """
+    Vertex AI Image Generation Cost Calculator
+    """
+    _model_info = litellm.get_model_info(
+        model=model,
+        custom_llm_provider="vertex_ai",
+    )
+
+    output_cost_per_image: float = _model_info.get("output_cost_per_image") or 0.0
+    num_images: int = len(image_response.data)
+    return output_cost_per_image * num_images

litellm/llms/vertex_ai_and_google_ai_studio/vertex_embeddings/embedding_handler.py

@@ -96,7 +96,7 @@ class VertexEmbedding(VertexBase):
         headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers)
         vertex_request: VertexEmbeddingRequest = (
             litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
-                input=input, optional_params=optional_params
+                input=input, optional_params=optional_params, model=model
             )
         )
 
@@ -188,7 +188,7 @@ class VertexEmbedding(VertexBase):
         headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers)
         vertex_request: VertexEmbeddingRequest = (
             litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
-                input=input, optional_params=optional_params
+                input=input, optional_params=optional_params, model=model
             )
         )
 

litellm/llms/vertex_ai_and_google_ai_studio/vertex_embeddings/transformation.py

@@ -101,11 +101,16 @@ class VertexAITextEmbeddingConfig(BaseModel):
         return optional_params
 
     def transform_openai_request_to_vertex_embedding_request(
-        self, input: Union[list, str], optional_params: dict
+        self, input: Union[list, str], optional_params: dict, model: str
     ) -> VertexEmbeddingRequest:
         """
         Transforms an openai request to a vertex embedding request.
         """
+        if model.isdigit():
+            return self._transform_openai_request_to_fine_tuned_embedding_request(
+                input, optional_params, model
+            )
+
         vertex_request: VertexEmbeddingRequest = VertexEmbeddingRequest()
         vertex_text_embedding_input_list: List[TextEmbeddingInput] = []
         task_type: Optional[TaskType] = optional_params.get("task_type")
@@ -125,6 +130,47 @@ class VertexAITextEmbeddingConfig(BaseModel):
 
         return vertex_request
 
+    def _transform_openai_request_to_fine_tuned_embedding_request(
+        self, input: Union[list, str], optional_params: dict, model: str
+    ) -> VertexEmbeddingRequest:
+        """
+        Transforms an openai request to a vertex fine-tuned embedding request.
+
+        Vertex Doc: https://console.cloud.google.com/vertex-ai/model-garden?hl=en&project=adroit-crow-413218&pageState=(%22galleryStateKey%22:(%22f%22:(%22g%22:%5B%5D,%22o%22:%5B%5D),%22s%22:%22%22))
+        Sample Request:
+
+        ```json
+        {
+            "instances" : [
+                {
+                "inputs": "How would the Future of AI in 10 Years look?",
+                "parameters": {
+                    "max_new_tokens": 128,
+                    "temperature": 1.0,
+                    "top_p": 0.9,
+                    "top_k": 10
+                }
+                }
+            ]
+        }
+        ```
+        """
+        vertex_request: VertexEmbeddingRequest = VertexEmbeddingRequest()
+        vertex_text_embedding_input_list: List[TextEmbeddingFineTunedInput] = []
+        if isinstance(input, str):
+            input = [input]  # Convert single string to list for uniform processing
+
+        for text in input:
+            embedding_input = TextEmbeddingFineTunedInput(inputs=text)
+            vertex_text_embedding_input_list.append(embedding_input)
+
+        vertex_request["instances"] = vertex_text_embedding_input_list
+        vertex_request["parameters"] = TextEmbeddingFineTunedParameters(
+            **optional_params
+        )
+
+        return vertex_request
+
     def create_embedding_input(
         self,
         content: str,
@@ -157,6 +203,11 @@ class VertexAITextEmbeddingConfig(BaseModel):
         """
         Transforms a vertex embedding response to an openai response.
         """
+        if model.isdigit():
+            return self._transform_vertex_response_to_openai_for_fine_tuned_models(
+                response, model, model_response
+            )
+
         _predictions = response["predictions"]
 
         embedding_response = []
@@ -181,3 +232,35 @@ class VertexAITextEmbeddingConfig(BaseModel):
         )
         setattr(model_response, "usage", usage)
         return model_response
+
+    def _transform_vertex_response_to_openai_for_fine_tuned_models(
+        self, response: dict, model: str, model_response: litellm.EmbeddingResponse
+    ) -> litellm.EmbeddingResponse:
+        """
+        Transforms a vertex fine-tuned model embedding response to an openai response format.
+        """
+        _predictions = response["predictions"]
+
+        embedding_response = []
+        # For fine-tuned models, we don't get token counts in the response
+        input_tokens = 0
+
+        for idx, embedding_values in enumerate(_predictions):
+            embedding_response.append(
+                {
+                    "object": "embedding",
+                    "index": idx,
+                    "embedding": embedding_values[
+                        0
+                    ],  # The embedding values are nested one level deeper
+                }
+            )
+
+        model_response.object = "list"
+        model_response.data = embedding_response
+        model_response.model = model
+        usage = Usage(
+            prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
+        )
+        setattr(model_response, "usage", usage)
+        return model_response

litellm/llms/vertex_ai_and_google_ai_studio/vertex_embeddings/types.py

@@ -23,14 +23,27 @@ class TextEmbeddingInput(TypedDict, total=False):
     title: Optional[str]
 
 
+# Fine-tuned models require a different input format
+# Ref: https://console.cloud.google.com/vertex-ai/model-garden?hl=en&project=adroit-crow-413218&pageState=(%22galleryStateKey%22:(%22f%22:(%22g%22:%5B%5D,%22o%22:%5B%5D),%22s%22:%22%22))
+class TextEmbeddingFineTunedInput(TypedDict, total=False):
+    inputs: str
+
+
+class TextEmbeddingFineTunedParameters(TypedDict, total=False):
+    max_new_tokens: Optional[int]
+    temperature: Optional[float]
+    top_p: Optional[float]
+    top_k: Optional[int]
+
+
 class EmbeddingParameters(TypedDict, total=False):
     auto_truncate: Optional[bool]
     output_dimensionality: Optional[int]
 
 
 class VertexEmbeddingRequest(TypedDict, total=False):
-    instances: List[TextEmbeddingInput]
+    instances: Union[List[TextEmbeddingInput], List[TextEmbeddingFineTunedInput]]
-    parameters: Optional[EmbeddingParameters]
+    parameters: Optional[Union[EmbeddingParameters, TextEmbeddingFineTunedParameters]]
 
 
 # Example usage:

litellm/main.py

@@ -1066,6 +1066,7 @@ def completion(  # type: ignore # noqa: PLR0915
             azure_ad_token_provider=kwargs.get("azure_ad_token_provider"),
             user_continue_message=kwargs.get("user_continue_message"),
             base_model=base_model,
+            litellm_trace_id=kwargs.get("litellm_trace_id"),
         )
         logging.update_environment_variables(
             model=model,
@@ -3455,7 +3456,7 @@ def embedding(  # noqa: PLR0915
                 client=client,
                 aembedding=aembedding,
             )
-        elif custom_llm_provider == "openai_like":
+        elif custom_llm_provider == "openai_like" or custom_llm_provider == "jina_ai":
             api_base = (
                 api_base or litellm.api_base or get_secret_str("OPENAI_LIKE_API_BASE")
             )

litellm/model_prices_and_context_window_backup.json

@@ -2986,19 +2986,19 @@
         "supports_function_calling": true
     },
     "vertex_ai/imagegeneration@006": {
-        "cost_per_image": 0.020,
+        "output_cost_per_image": 0.020,
         "litellm_provider": "vertex_ai-image-models",
         "mode": "image_generation",
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
     },
     "vertex_ai/imagen-3.0-generate-001": {
-        "cost_per_image": 0.04,
+        "output_cost_per_image": 0.04,
         "litellm_provider": "vertex_ai-image-models",
         "mode": "image_generation",
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
     },
     "vertex_ai/imagen-3.0-fast-generate-001": {
-        "cost_per_image": 0.02,
+        "output_cost_per_image": 0.02,
         "litellm_provider": "vertex_ai-image-models",
         "mode": "image_generation",
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
@@ -5620,6 +5620,13 @@
         "litellm_provider": "bedrock",
         "mode": "image_generation"
     },
+    "stability.stable-image-ultra-v1:0": {
+        "max_tokens": 77, 
+        "max_input_tokens": 77, 
+        "output_cost_per_image": 0.14,
+        "litellm_provider": "bedrock",
+        "mode": "image_generation"
+    },
     "sagemaker/meta-textgeneration-llama-2-7b": {
         "max_tokens": 4096, 
         "max_input_tokens": 4096, 

litellm/proxy/_new_secret_config.yaml

@@ -1,122 +1,15 @@
 model_list:
-  - model_name: "*"
+  # GPT-4 Turbo Models
-    litellm_params:
-      model: claude-3-5-sonnet-20240620
-      api_key: os.environ/ANTHROPIC_API_KEY
-  - model_name: claude-3-5-sonnet-aihubmix
-    litellm_params:
-      model: openai/claude-3-5-sonnet-20240620
-      input_cost_per_token: 0.000003 # 3$/M
-      output_cost_per_token: 0.000015 # 15$/M
-      api_base: "https://exampleopenaiendpoint-production.up.railway.app"
-      api_key: my-fake-key
-  - model_name: fake-openai-endpoint-2
-    litellm_params:
-      model: openai/my-fake-model
-      api_key: my-fake-key
-      api_base: https://exampleopenaiendpoint-production.up.railway.app/
-      stream_timeout: 0.001
-      timeout: 1
-      rpm: 1
-  - model_name: fake-openai-endpoint
-    litellm_params:
-      model: openai/my-fake-model
-      api_key: my-fake-key
-      api_base: https://exampleopenaiendpoint-production.up.railway.app/
-  ## bedrock chat completions
-  - model_name: "*anthropic.claude*"
-    litellm_params:
-      model: bedrock/*anthropic.claude*
-      aws_access_key_id: os.environ/BEDROCK_AWS_ACCESS_KEY_ID
-      aws_secret_access_key: os.environ/BEDROCK_AWS_SECRET_ACCESS_KEY
-      aws_region_name: os.environ/AWS_REGION_NAME
-      guardrailConfig:
-        "guardrailIdentifier": "h4dsqwhp6j66"
-        "guardrailVersion": "2"
-        "trace": "enabled"
-        
-## bedrock embeddings
-  - model_name: "*amazon.titan-embed-*"
-    litellm_params:
-      model: bedrock/amazon.titan-embed-*
-      aws_access_key_id: os.environ/BEDROCK_AWS_ACCESS_KEY_ID
-      aws_secret_access_key: os.environ/BEDROCK_AWS_SECRET_ACCESS_KEY
-      aws_region_name: os.environ/AWS_REGION_NAME
-  - model_name: "*cohere.embed-*"
-    litellm_params:
-      model: bedrock/cohere.embed-*
-      aws_access_key_id: os.environ/BEDROCK_AWS_ACCESS_KEY_ID
-      aws_secret_access_key: os.environ/BEDROCK_AWS_SECRET_ACCESS_KEY
-      aws_region_name: os.environ/AWS_REGION_NAME
-  
-  - model_name: "bedrock/*"
-    litellm_params:
-      model: bedrock/*
-      aws_access_key_id: os.environ/BEDROCK_AWS_ACCESS_KEY_ID
-      aws_secret_access_key: os.environ/BEDROCK_AWS_SECRET_ACCESS_KEY
-      aws_region_name: os.environ/AWS_REGION_NAME
-
   - model_name: gpt-4
     litellm_params:
-      model: azure/chatgpt-v-2
+      model: gpt-4
-      api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
+  - model_name: rerank-model
-      api_version: "2023-05-15"
+    litellm_params:
-      api_key: os.environ/AZURE_API_KEY # The `os.environ/` prefix tells litellm to read this from the env. See https://docs.litellm.ai/docs/simple_proxy#load-api-keys-from-vault
+      model: jina_ai/jina-reranker-v2-base-multilingual
-      rpm: 480
-      timeout: 300
-      stream_timeout: 60
-
-litellm_settings:
-  fallbacks: [{ "claude-3-5-sonnet-20240620": ["claude-3-5-sonnet-aihubmix"] }]
-  # callbacks: ["otel", "prometheus"]
-  default_redis_batch_cache_expiry: 10
-  # default_team_settings: 
-  #   - team_id: "dbe2f686-a686-4896-864a-4c3924458709"
-  #     success_callback: ["langfuse"]
-  #     langfuse_public_key: os.environ/LANGFUSE_PUB_KEY_1 # Project 1
-  #     langfuse_secret: os.environ/LANGFUSE_PRIVATE_KEY_1 # Project 1
-
-# litellm_settings:
-#   cache: True
-#   cache_params:
-#     type: redis
-
-#     # disable caching on the actual API call
-#     supported_call_types: []
-
-#     # see https://docs.litellm.ai/docs/proxy/prod#3-use-redis-porthost-password-not-redis_url
-#     host: os.environ/REDIS_HOST
-#     port: os.environ/REDIS_PORT
-#     password: os.environ/REDIS_PASSWORD
-
-#   # see https://docs.litellm.ai/docs/proxy/caching#turn-on-batch_redis_requests
-#   # see https://docs.litellm.ai/docs/proxy/prometheus
-#   callbacks: ['otel']
 
 
-# # router_settings:
+router_settings:
-# #   routing_strategy: latency-based-routing
+  model_group_alias:
-# #   routing_strategy_args:
+    "gpt-4-turbo": # Aliased model name
-# #     # only assign 40% of traffic to the fastest deployment to avoid overloading it
+      model: "gpt-4"  # Actual model name in 'model_list'
-# #     lowest_latency_buffer: 0.4
+      hidden: true
-
-# #     # consider last five minutes of calls for latency calculation
-# #     ttl: 300
-# #   redis_host: os.environ/REDIS_HOST
-# #   redis_port: os.environ/REDIS_PORT
-# #   redis_password: os.environ/REDIS_PASSWORD
-  
-# # # see https://docs.litellm.ai/docs/proxy/prod#1-use-this-configyaml
-# # general_settings:
-# #   master_key: os.environ/LITELLM_MASTER_KEY
-# #   database_url: os.environ/DATABASE_URL
-# #   disable_master_key_return: true
-# #   # alerting: ['slack', 'email']
-# #   alerting: ['email']
-
-# #   # Batch write spend updates every 60s
-# #   proxy_batch_write_at: 60
-
-# #   # see https://docs.litellm.ai/docs/proxy/caching#advanced---user-api-key-cache-ttl
-# #   # our api keys rarely change
-# #   user_api_key_cache_ttl: 3600

litellm/proxy/_types.py

@@ -1128,7 +1128,16 @@ class KeyManagementSystem(enum.Enum):
 
 
 class KeyManagementSettings(LiteLLMBase):
-    hosted_keys: List
+    hosted_keys: Optional[List] = None
+    store_virtual_keys: Optional[bool] = False
+    """
+    If True, virtual keys created by litellm will be stored in the secret manager
+    """
+
+    access_mode: Literal["read_only", "write_only", "read_and_write"] = "read_only"
+    """
+    Access mode for the secret manager, when write_only will only use for writing secrets
+    """
 
 
 class TeamDefaultSettings(LiteLLMBase):

litellm/proxy/auth/auth_checks.py

@@ -8,6 +8,7 @@ Run checks for:
 2. If user is in budget 
 3. If end_user ('user' passed to /chat/completions, /embeddings endpoint) is in budget 
 """
+
 import time
 import traceback
 from datetime import datetime

litellm/proxy/hooks/key_management_event_hooks.py

@@ -0,0 +1,267 @@
+import asyncio
+import json
+import uuid
+from datetime import datetime, timezone
+from re import A
+from typing import Any, List, Optional
+
+from fastapi import status
+
+import litellm
+from litellm._logging import verbose_proxy_logger
+from litellm.proxy._types import (
+    GenerateKeyRequest,
+    KeyManagementSystem,
+    KeyRequest,
+    LiteLLM_AuditLogs,
+    LiteLLM_VerificationToken,
+    LitellmTableNames,
+    ProxyErrorTypes,
+    ProxyException,
+    UpdateKeyRequest,
+    UserAPIKeyAuth,
+    WebhookEvent,
+)
+
+
+class KeyManagementEventHooks:
+
+    @staticmethod
+    async def async_key_generated_hook(
+        data: GenerateKeyRequest,
+        response: dict,
+        user_api_key_dict: UserAPIKeyAuth,
+        litellm_changed_by: Optional[str] = None,
+    ):
+        """
+        Hook that runs after a successful /key/generate request
+
+        Handles the following:
+        - Sending Email with Key Details
+        - Storing Audit Logs for key generation
+        - Storing Generated Key in DB
+        """
+        from litellm.proxy.management_helpers.audit_logs import (
+            create_audit_log_for_update,
+        )
+        from litellm.proxy.proxy_server import (
+            general_settings,
+            litellm_proxy_admin_name,
+            proxy_logging_obj,
+        )
+
+        if data.send_invite_email is True:
+            await KeyManagementEventHooks._send_key_created_email(response)
+
+        # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
+        if litellm.store_audit_logs is True:
+            _updated_values = json.dumps(response, default=str)
+            asyncio.create_task(
+                create_audit_log_for_update(
+                    request_data=LiteLLM_AuditLogs(
+                        id=str(uuid.uuid4()),
+                        updated_at=datetime.now(timezone.utc),
+                        changed_by=litellm_changed_by
+                        or user_api_key_dict.user_id
+                        or litellm_proxy_admin_name,
+                        changed_by_api_key=user_api_key_dict.api_key,
+                        table_name=LitellmTableNames.KEY_TABLE_NAME,
+                        object_id=response.get("token_id", ""),
+                        action="created",
+                        updated_values=_updated_values,
+                        before_value=None,
+                    )
+                )
+            )
+        # store the generated key in the secret manager
+        await KeyManagementEventHooks._store_virtual_key_in_secret_manager(
+            secret_name=data.key_alias or f"virtual-key-{uuid.uuid4()}",
+            secret_token=response.get("token", ""),
+        )
+
+    @staticmethod
+    async def async_key_updated_hook(
+        data: UpdateKeyRequest,
+        existing_key_row: Any,
+        response: Any,
+        user_api_key_dict: UserAPIKeyAuth,
+        litellm_changed_by: Optional[str] = None,
+    ):
+        """
+        Post /key/update processing hook
+
+        Handles the following:
+        - Storing Audit Logs for key update
+        """
+        from litellm.proxy.management_helpers.audit_logs import (
+            create_audit_log_for_update,
+        )
+        from litellm.proxy.proxy_server import litellm_proxy_admin_name
+
+        # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
+        if litellm.store_audit_logs is True:
+            _updated_values = json.dumps(data.json(exclude_none=True), default=str)
+
+            _before_value = existing_key_row.json(exclude_none=True)
+            _before_value = json.dumps(_before_value, default=str)
+
+            asyncio.create_task(
+                create_audit_log_for_update(
+                    request_data=LiteLLM_AuditLogs(
+                        id=str(uuid.uuid4()),
+                        updated_at=datetime.now(timezone.utc),
+                        changed_by=litellm_changed_by
+                        or user_api_key_dict.user_id
+                        or litellm_proxy_admin_name,
+                        changed_by_api_key=user_api_key_dict.api_key,
+                        table_name=LitellmTableNames.KEY_TABLE_NAME,
+                        object_id=data.key,
+                        action="updated",
+                        updated_values=_updated_values,
+                        before_value=_before_value,
+                    )
+                )
+            )
+        pass
+
+    @staticmethod
+    async def async_key_deleted_hook(
+        data: KeyRequest,
+        keys_being_deleted: List[LiteLLM_VerificationToken],
+        response: dict,
+        user_api_key_dict: UserAPIKeyAuth,
+        litellm_changed_by: Optional[str] = None,
+    ):
+        """
+        Post /key/delete processing hook
+
+        Handles the following:
+        - Storing Audit Logs for key deletion
+        """
+        from litellm.proxy.management_helpers.audit_logs import (
+            create_audit_log_for_update,
+        )
+        from litellm.proxy.proxy_server import litellm_proxy_admin_name, prisma_client
+
+        # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
+        # we do this after the first for loop, since first for loop is for validation. we only want this inserted after validation passes
+        if litellm.store_audit_logs is True:
+            # make an audit log for each team deleted
+            for key in data.keys:
+                key_row = await prisma_client.get_data(  # type: ignore
+                    token=key, table_name="key", query_type="find_unique"
+                )
+
+                if key_row is None:
+                    raise ProxyException(
+                        message=f"Key {key} not found",
+                        type=ProxyErrorTypes.bad_request_error,
+                        param="key",
+                        code=status.HTTP_404_NOT_FOUND,
+                    )
+
+                key_row = key_row.json(exclude_none=True)
+                _key_row = json.dumps(key_row, default=str)
+
+                asyncio.create_task(
+                    create_audit_log_for_update(
+                        request_data=LiteLLM_AuditLogs(
+                            id=str(uuid.uuid4()),
+                            updated_at=datetime.now(timezone.utc),
+                            changed_by=litellm_changed_by
+                            or user_api_key_dict.user_id
+                            or litellm_proxy_admin_name,
+                            changed_by_api_key=user_api_key_dict.api_key,
+                            table_name=LitellmTableNames.KEY_TABLE_NAME,
+                            object_id=key,
+                            action="deleted",
+                            updated_values="{}",
+                            before_value=_key_row,
+                        )
+                    )
+                )
+        # delete the keys from the secret manager
+        await KeyManagementEventHooks._delete_virtual_keys_from_secret_manager(
+            keys_being_deleted=keys_being_deleted
+        )
+        pass
+
+    @staticmethod
+    async def _store_virtual_key_in_secret_manager(secret_name: str, secret_token: str):
+        """
+        Store a virtual key in the secret manager
+
+        Args:
+            secret_name: Name of the virtual key
+            secret_token: Value of the virtual key (example: sk-1234)
+        """
+        if litellm._key_management_settings is not None:
+            if litellm._key_management_settings.store_virtual_keys is True:
+                from litellm.secret_managers.aws_secret_manager_v2 import (
+                    AWSSecretsManagerV2,
+                )
+
+                # store the key in the secret manager
+                if (
+                    litellm._key_management_system
+                    == KeyManagementSystem.AWS_SECRET_MANAGER
+                    and isinstance(litellm.secret_manager_client, AWSSecretsManagerV2)
+                ):
+                    await litellm.secret_manager_client.async_write_secret(
+                        secret_name=secret_name,
+                        secret_value=secret_token,
+                    )
+
+    @staticmethod
+    async def _delete_virtual_keys_from_secret_manager(
+        keys_being_deleted: List[LiteLLM_VerificationToken],
+    ):
+        """
+        Deletes virtual keys from the secret manager
+
+        Args:
+            keys_being_deleted: List of keys being deleted, this is passed down from the /key/delete operation
+        """
+        if litellm._key_management_settings is not None:
+            if litellm._key_management_settings.store_virtual_keys is True:
+                from litellm.secret_managers.aws_secret_manager_v2 import (
+                    AWSSecretsManagerV2,
+                )
+
+                if isinstance(litellm.secret_manager_client, AWSSecretsManagerV2):
+                    for key in keys_being_deleted:
+                        if key.key_alias is not None:
+                            await litellm.secret_manager_client.async_delete_secret(
+                                secret_name=key.key_alias
+                            )
+                        else:
+                            verbose_proxy_logger.warning(
+                                f"KeyManagementEventHooks._delete_virtual_key_from_secret_manager: Key alias not found for key {key.token}. Skipping deletion from secret manager."
+                            )
+
+    @staticmethod
+    async def _send_key_created_email(response: dict):
+        from litellm.proxy.proxy_server import general_settings, proxy_logging_obj
+
+        if "email" not in general_settings.get("alerting", []):
+            raise ValueError(
+                "Email alerting not setup on config.yaml. Please set `alerting=['email']. \nDocs: https://docs.litellm.ai/docs/proxy/email`"
+            )
+        event = WebhookEvent(
+            event="key_created",
+            event_group="key",
+            event_message="API Key Created",
+            token=response.get("token", ""),
+            spend=response.get("spend", 0.0),
+            max_budget=response.get("max_budget", 0.0),
+            user_id=response.get("user_id", None),
+            team_id=response.get("team_id", "Default Team"),
+            key_alias=response.get("key_alias", None),
+        )
+
+        # If user configured email alerting - send an Email letting their end-user know the key was created
+        asyncio.create_task(
+            proxy_logging_obj.slack_alerting_instance.send_key_created_or_user_invited_email(
+                webhook_event=event,
+            )
+        )

litellm/proxy/litellm_pre_call_utils.py

@@ -274,6 +274,51 @@ class LiteLLMProxyRequestSetup:
         )
         return user_api_key_logged_metadata
 
+    @staticmethod
+    def add_key_level_controls(
+        key_metadata: dict, data: dict, _metadata_variable_name: str
+    ):
+        data = data.copy()
+        if "cache" in key_metadata:
+            data["cache"] = {}
+            if isinstance(key_metadata["cache"], dict):
+                for k, v in key_metadata["cache"].items():
+                    if k in SupportedCacheControls:
+                        data["cache"][k] = v
+
+        ## KEY-LEVEL SPEND LOGS / TAGS
+        if "tags" in key_metadata and key_metadata["tags"] is not None:
+            if "tags" in data[_metadata_variable_name] and isinstance(
+                data[_metadata_variable_name]["tags"], list
+            ):
+                data[_metadata_variable_name]["tags"].extend(key_metadata["tags"])
+            else:
+                data[_metadata_variable_name]["tags"] = key_metadata["tags"]
+        if "spend_logs_metadata" in key_metadata and isinstance(
+            key_metadata["spend_logs_metadata"], dict
+        ):
+            if "spend_logs_metadata" in data[_metadata_variable_name] and isinstance(
+                data[_metadata_variable_name]["spend_logs_metadata"], dict
+            ):
+                for key, value in key_metadata["spend_logs_metadata"].items():
+                    if (
+                        key not in data[_metadata_variable_name]["spend_logs_metadata"]
+                    ):  # don't override k-v pair sent by request (user request)
+                        data[_metadata_variable_name]["spend_logs_metadata"][
+                            key
+                        ] = value
+            else:
+                data[_metadata_variable_name]["spend_logs_metadata"] = key_metadata[
+                    "spend_logs_metadata"
+                ]
+
+        ## KEY-LEVEL DISABLE FALLBACKS
+        if "disable_fallbacks" in key_metadata and isinstance(
+            key_metadata["disable_fallbacks"], bool
+        ):
+            data["disable_fallbacks"] = key_metadata["disable_fallbacks"]
+        return data
+
 
 async def add_litellm_data_to_request(  # noqa: PLR0915
     data: dict,
@@ -389,37 +434,11 @@ async def add_litellm_data_to_request(  # noqa: PLR0915
 
     ### KEY-LEVEL Controls
     key_metadata = user_api_key_dict.metadata
-    if "cache" in key_metadata:
+    data = LiteLLMProxyRequestSetup.add_key_level_controls(
-        data["cache"] = {}
+        key_metadata=key_metadata,
-        if isinstance(key_metadata["cache"], dict):
+        data=data,
-            for k, v in key_metadata["cache"].items():
+        _metadata_variable_name=_metadata_variable_name,
-                if k in SupportedCacheControls:
+    )
-                    data["cache"][k] = v
-
-    ## KEY-LEVEL SPEND LOGS / TAGS
-    if "tags" in key_metadata and key_metadata["tags"] is not None:
-        if "tags" in data[_metadata_variable_name] and isinstance(
-            data[_metadata_variable_name]["tags"], list
-        ):
-            data[_metadata_variable_name]["tags"].extend(key_metadata["tags"])
-        else:
-            data[_metadata_variable_name]["tags"] = key_metadata["tags"]
-    if "spend_logs_metadata" in key_metadata and isinstance(
-        key_metadata["spend_logs_metadata"], dict
-    ):
-        if "spend_logs_metadata" in data[_metadata_variable_name] and isinstance(
-            data[_metadata_variable_name]["spend_logs_metadata"], dict
-        ):
-            for key, value in key_metadata["spend_logs_metadata"].items():
-                if (
-                    key not in data[_metadata_variable_name]["spend_logs_metadata"]
-                ):  # don't override k-v pair sent by request (user request)
-                    data[_metadata_variable_name]["spend_logs_metadata"][key] = value
-        else:
-            data[_metadata_variable_name]["spend_logs_metadata"] = key_metadata[
-                "spend_logs_metadata"
-            ]
-
     ## TEAM-LEVEL SPEND LOGS/TAGS
     team_metadata = user_api_key_dict.team_metadata or {}
     if "tags" in team_metadata and team_metadata["tags"] is not None:

litellm/proxy/management_endpoints/key_management_endpoints.py

@@ -17,7 +17,7 @@ import secrets
 import traceback
 import uuid
 from datetime import datetime, timedelta, timezone
-from typing import List, Optional
+from typing import List, Optional, Tuple
 
 import fastapi
 from fastapi import APIRouter, Depends, Header, HTTPException, Query, Request, status
@@ -31,6 +31,7 @@ from litellm.proxy.auth.auth_checks import (
     get_key_object,
 )
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+from litellm.proxy.hooks.key_management_event_hooks import KeyManagementEventHooks
 from litellm.proxy.management_helpers.utils import management_endpoint_wrapper
 from litellm.proxy.utils import _duration_in_seconds, _hash_token_if_needed
 from litellm.secret_managers.main import get_secret
@@ -234,48 +235,12 @@ async def generate_key_fn(  # noqa: PLR0915
             data.soft_budget
         )  # include the user-input soft budget in the response
 
-        if data.send_invite_email is True:
-            if "email" not in general_settings.get("alerting", []):
-                raise ValueError(
-                    "Email alerting not setup on config.yaml. Please set `alerting=['email']. \nDocs: https://docs.litellm.ai/docs/proxy/email`"
-                )
-            event = WebhookEvent(
-                event="key_created",
-                event_group="key",
-                event_message="API Key Created",
-                token=response.get("token", ""),
-                spend=response.get("spend", 0.0),
-                max_budget=response.get("max_budget", 0.0),
-                user_id=response.get("user_id", None),
-                team_id=response.get("team_id", "Default Team"),
-                key_alias=response.get("key_alias", None),
-            )
-
-            # If user configured email alerting - send an Email letting their end-user know the key was created
         asyncio.create_task(
-                proxy_logging_obj.slack_alerting_instance.send_key_created_or_user_invited_email(
+            KeyManagementEventHooks.async_key_generated_hook(
-                    webhook_event=event,
+                data=data,
-                )
+                response=response,
-            )
+                user_api_key_dict=user_api_key_dict,
-
+                litellm_changed_by=litellm_changed_by,
-        # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
-        if litellm.store_audit_logs is True:
-            _updated_values = json.dumps(response, default=str)
-            asyncio.create_task(
-                create_audit_log_for_update(
-                    request_data=LiteLLM_AuditLogs(
-                        id=str(uuid.uuid4()),
-                        updated_at=datetime.now(timezone.utc),
-                        changed_by=litellm_changed_by
-                        or user_api_key_dict.user_id
-                        or litellm_proxy_admin_name,
-                        changed_by_api_key=user_api_key_dict.api_key,
-                        table_name=LitellmTableNames.KEY_TABLE_NAME,
-                        object_id=response.get("token_id", ""),
-                        action="created",
-                        updated_values=_updated_values,
-                        before_value=None,
-                    )
             )
         )
 
@@ -407,28 +372,13 @@ async def update_key_fn(
             proxy_logging_obj=proxy_logging_obj,
         )
 
-        # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
-        if litellm.store_audit_logs is True:
-            _updated_values = json.dumps(data_json, default=str)
-
-            _before_value = existing_key_row.json(exclude_none=True)
-            _before_value = json.dumps(_before_value, default=str)
-
         asyncio.create_task(
-                create_audit_log_for_update(
+            KeyManagementEventHooks.async_key_updated_hook(
-                    request_data=LiteLLM_AuditLogs(
+                data=data,
-                        id=str(uuid.uuid4()),
+                existing_key_row=existing_key_row,
-                        updated_at=datetime.now(timezone.utc),
+                response=response,
-                        changed_by=litellm_changed_by
+                user_api_key_dict=user_api_key_dict,
-                        or user_api_key_dict.user_id
+                litellm_changed_by=litellm_changed_by,
-                        or litellm_proxy_admin_name,
-                        changed_by_api_key=user_api_key_dict.api_key,
-                        table_name=LitellmTableNames.KEY_TABLE_NAME,
-                        object_id=data.key,
-                        action="updated",
-                        updated_values=_updated_values,
-                        before_value=_before_value,
-                    )
             )
         )
 
@@ -496,6 +446,9 @@ async def delete_key_fn(
             user_custom_key_generate,
         )
 
+        if prisma_client is None:
+            raise Exception("Not connected to DB!")
+
         keys = data.keys
         if len(keys) == 0:
             raise ProxyException(
@@ -516,45 +469,7 @@ async def delete_key_fn(
         ):
             user_id = None  # unless they're admin
 
-        # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
+        number_deleted_keys, _keys_being_deleted = await delete_verification_token(
-        # we do this after the first for loop, since first for loop is for validation. we only want this inserted after validation passes
-        if litellm.store_audit_logs is True:
-            # make an audit log for each team deleted
-            for key in data.keys:
-                key_row = await prisma_client.get_data(  # type: ignore
-                    token=key, table_name="key", query_type="find_unique"
-                )
-
-                if key_row is None:
-                    raise ProxyException(
-                        message=f"Key {key} not found",
-                        type=ProxyErrorTypes.bad_request_error,
-                        param="key",
-                        code=status.HTTP_404_NOT_FOUND,
-                    )
-
-                key_row = key_row.json(exclude_none=True)
-                _key_row = json.dumps(key_row, default=str)
-
-                asyncio.create_task(
-                    create_audit_log_for_update(
-                        request_data=LiteLLM_AuditLogs(
-                            id=str(uuid.uuid4()),
-                            updated_at=datetime.now(timezone.utc),
-                            changed_by=litellm_changed_by
-                            or user_api_key_dict.user_id
-                            or litellm_proxy_admin_name,
-                            changed_by_api_key=user_api_key_dict.api_key,
-                            table_name=LitellmTableNames.KEY_TABLE_NAME,
-                            object_id=key,
-                            action="deleted",
-                            updated_values="{}",
-                            before_value=_key_row,
-                        )
-                    )
-                )
-
-        number_deleted_keys = await delete_verification_token(
             tokens=keys, user_id=user_id
         )
         if number_deleted_keys is None:
@@ -588,6 +503,16 @@ async def delete_key_fn(
             f"/keys/delete - cache after delete: {user_api_key_cache.in_memory_cache.cache_dict}"
         )
 
+        asyncio.create_task(
+            KeyManagementEventHooks.async_key_deleted_hook(
+                data=data,
+                keys_being_deleted=_keys_being_deleted,
+                user_api_key_dict=user_api_key_dict,
+                litellm_changed_by=litellm_changed_by,
+                response=number_deleted_keys,
+            )
+        )
+
         return {"deleted_keys": keys}
     except Exception as e:
         if isinstance(e, HTTPException):
@@ -1026,11 +951,35 @@ async def generate_key_helper_fn(  # noqa: PLR0915
     return key_data
 
 
-async def delete_verification_token(tokens: List, user_id: Optional[str] = None):
+async def delete_verification_token(
+    tokens: List, user_id: Optional[str] = None
+) -> Tuple[Optional[Dict], List[LiteLLM_VerificationToken]]:
+    """
+    Helper that deletes the list of tokens from the database
+
+    Args:
+        tokens: List of tokens to delete
+        user_id: Optional user_id to filter by
+
+    Returns:
+        Tuple[Optional[Dict], List[LiteLLM_VerificationToken]]:
+            Optional[Dict]:
+                - Number of deleted tokens
+            List[LiteLLM_VerificationToken]:
+                - List of keys being deleted, this contains information about the key_alias, token, and user_id being deleted,
+                this is passed down to the KeyManagementEventHooks to delete the keys from the secret manager and handle audit logs
+    """
     from litellm.proxy.proxy_server import litellm_proxy_admin_name, prisma_client
 
     try:
         if prisma_client:
+            tokens = [_hash_token_if_needed(token=key) for key in tokens]
+            _keys_being_deleted = (
+                await prisma_client.db.litellm_verificationtoken.find_many(
+                    where={"token": {"in": tokens}}
+                )
+            )
+
             # Assuming 'db' is your Prisma Client instance
             # check if admin making request - don't filter by user-id
             if user_id == litellm_proxy_admin_name:
@@ -1060,7 +1009,7 @@ async def delete_verification_token(tokens: List, user_id: Optional[str] = None)
         )
         verbose_proxy_logger.debug(traceback.format_exc())
         raise e
-    return deleted_tokens
+    return deleted_tokens, _keys_being_deleted
 
 
 @router.post(

litellm/proxy/proxy_cli.py

@@ -265,7 +265,6 @@ def run_server(  # noqa: PLR0915
             ProxyConfig,
             app,
             load_aws_kms,
-            load_aws_secret_manager,
             load_from_azure_key_vault,
             load_google_kms,
             save_worker_config,
@@ -278,7 +277,6 @@ def run_server(  # noqa: PLR0915
                 ProxyConfig,
                 app,
                 load_aws_kms,
-                load_aws_secret_manager,
                 load_from_azure_key_vault,
                 load_google_kms,
                 save_worker_config,
@@ -295,7 +293,6 @@ def run_server(  # noqa: PLR0915
                     ProxyConfig,
                     app,
                     load_aws_kms,
-                    load_aws_secret_manager,
                     load_from_azure_key_vault,
                     load_google_kms,
                     save_worker_config,
@@ -559,8 +556,14 @@ def run_server(  # noqa: PLR0915
                         key_management_system
                         == KeyManagementSystem.AWS_SECRET_MANAGER.value  # noqa: F405
                     ):
+                        from litellm.secret_managers.aws_secret_manager_v2 import (
+                            AWSSecretsManagerV2,
+                        )
+
                         ### LOAD FROM AWS SECRET MANAGER ###
-                        load_aws_secret_manager(use_aws_secret_manager=True)
+                        AWSSecretsManagerV2.load_aws_secret_manager(
+                            use_aws_secret_manager=True
+                        )
                     elif key_management_system == KeyManagementSystem.AWS_KMS.value:
                         load_aws_kms(use_aws_kms=True)
                     elif (

litellm/proxy/proxy_config.yaml

@@ -7,6 +7,8 @@ model_list:
 
 
 
-litellm_settings:
+general_settings:
-  callbacks: ["gcs_bucket"]
+  key_management_system: "aws_secret_manager" 
-
+  key_management_settings: 
+    store_virtual_keys: true
+    access_mode: "write_only"

litellm/proxy/proxy_server.py

@@ -245,10 +245,7 @@ from litellm.router import (
 from litellm.router import ModelInfo as RouterModelInfo
 from litellm.router import updateDeployment
 from litellm.scheduler import DefaultPriorities, FlowItem, Scheduler
-from litellm.secret_managers.aws_secret_manager import (
+from litellm.secret_managers.aws_secret_manager import load_aws_kms
-    load_aws_kms,
-    load_aws_secret_manager,
-)
 from litellm.secret_managers.google_kms import load_google_kms
 from litellm.secret_managers.main import (
     get_secret,
@@ -1825,8 +1822,13 @@ class ProxyConfig:
                     key_management_system
                     == KeyManagementSystem.AWS_SECRET_MANAGER.value  # noqa: F405
                 ):
-                    ### LOAD FROM AWS SECRET MANAGER ###
+                    from litellm.secret_managers.aws_secret_manager_v2 import (
-                    load_aws_secret_manager(use_aws_secret_manager=True)
+                        AWSSecretsManagerV2,
+                    )
+
+                    AWSSecretsManagerV2.load_aws_secret_manager(
+                        use_aws_secret_manager=True
+                    )
                 elif key_management_system == KeyManagementSystem.AWS_KMS.value:
                     load_aws_kms(use_aws_kms=True)
                 elif (

litellm/rerank_api/main.py

@@ -8,7 +8,8 @@ from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.llms.azure_ai.rerank import AzureAIRerank
 from litellm.llms.cohere.rerank import CohereRerank
-from litellm.llms.together_ai.rerank import TogetherAIRerank
+from litellm.llms.jina_ai.rerank.handler import JinaAIRerank
+from litellm.llms.together_ai.rerank.handler import TogetherAIRerank
 from litellm.secret_managers.main import get_secret
 from litellm.types.rerank import RerankRequest, RerankResponse
 from litellm.types.router import *
@@ -19,6 +20,7 @@ from litellm.utils import client, exception_type, supports_httpx_timeout
 cohere_rerank = CohereRerank()
 together_rerank = TogetherAIRerank()
 azure_ai_rerank = AzureAIRerank()
+jina_ai_rerank = JinaAIRerank()
 #################################################
 
 
@@ -247,7 +249,23 @@ def rerank(
                 api_key=api_key,
                 _is_async=_is_async,
             )
+        elif _custom_llm_provider == "jina_ai":
 
+            if dynamic_api_key is None:
+                raise ValueError(
+                    "Jina AI API key is required, please set 'JINA_AI_API_KEY' in your environment"
+                )
+            response = jina_ai_rerank.rerank(
+                model=model,
+                api_key=dynamic_api_key,
+                query=query,
+                documents=documents,
+                top_n=top_n,
+                rank_fields=rank_fields,
+                return_documents=return_documents,
+                max_chunks_per_doc=max_chunks_per_doc,
+                _is_async=_is_async,
+            )
         else:
             raise ValueError(f"Unsupported provider: {_custom_llm_provider}")
 

litellm/router.py

@@ -679,9 +679,8 @@ class Router:
             kwargs["model"] = model
             kwargs["messages"] = messages
             kwargs["original_function"] = self._completion
-            kwargs.get("request_timeout", self.timeout)
+            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
-            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
+
-            kwargs.setdefault("metadata", {}).update({"model_group": model})
             response = self.function_with_fallbacks(**kwargs)
             return response
         except Exception as e:
@@ -783,8 +782,7 @@ class Router:
             kwargs["stream"] = stream
             kwargs["original_function"] = self._acompletion
             kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
-
+            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
-            kwargs.setdefault("metadata", {}).update({"model_group": model})
 
             request_priority = kwargs.get("priority") or self.default_priority
 
@@ -948,6 +946,17 @@ class Router:
                 self.fail_calls[model_name] += 1
             raise e
 
+    def _update_kwargs_before_fallbacks(self, model: str, kwargs: dict) -> None:
+        """
+        Adds/updates to kwargs:
+        - num_retries
+        - litellm_trace_id
+        - metadata
+        """
+        kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
+        kwargs.setdefault("litellm_trace_id", str(uuid.uuid4()))
+        kwargs.setdefault("metadata", {}).update({"model_group": model})
+
     def _update_kwargs_with_default_litellm_params(self, kwargs: dict) -> None:
         """
         Adds default litellm params to kwargs, if set.
@@ -1511,9 +1520,7 @@ class Router:
             kwargs["model"] = model
             kwargs["file"] = file
             kwargs["original_function"] = self._atranscription
-            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
+            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
-            kwargs.get("request_timeout", self.timeout)
-            kwargs.setdefault("metadata", {}).update({"model_group": model})
             response = await self.async_function_with_fallbacks(**kwargs)
 
             return response
@@ -1688,9 +1695,7 @@ class Router:
             kwargs["model"] = model
             kwargs["input"] = input
             kwargs["original_function"] = self._arerank
-            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
+            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
-            kwargs.get("request_timeout", self.timeout)
-            kwargs.setdefault("metadata", {}).update({"model_group": model})
 
             response = await self.async_function_with_fallbacks(**kwargs)
 
@@ -1839,9 +1844,7 @@ class Router:
             kwargs["model"] = model
             kwargs["prompt"] = prompt
             kwargs["original_function"] = self._atext_completion
-            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
+            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
-            kwargs.get("request_timeout", self.timeout)
-            kwargs.setdefault("metadata", {}).update({"model_group": model})
             response = await self.async_function_with_fallbacks(**kwargs)
 
             return response
@@ -2112,9 +2115,7 @@ class Router:
             kwargs["model"] = model
             kwargs["input"] = input
             kwargs["original_function"] = self._aembedding
-            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
+            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
-            kwargs.get("request_timeout", self.timeout)
-            kwargs.setdefault("metadata", {}).update({"model_group": model})
             response = await self.async_function_with_fallbacks(**kwargs)
             return response
         except Exception as e:
@@ -2609,6 +2610,7 @@ class Router:
         If it fails after num_retries, fall back to another model group
         """
         model_group: Optional[str] = kwargs.get("model")
+        disable_fallbacks: Optional[bool] = kwargs.pop("disable_fallbacks", False)
         fallbacks: Optional[List] = kwargs.get("fallbacks", self.fallbacks)
         context_window_fallbacks: Optional[List] = kwargs.get(
             "context_window_fallbacks", self.context_window_fallbacks
@@ -2616,6 +2618,7 @@ class Router:
         content_policy_fallbacks: Optional[List] = kwargs.get(
             "content_policy_fallbacks", self.content_policy_fallbacks
         )
+
         try:
             self._handle_mock_testing_fallbacks(
                 kwargs=kwargs,
@@ -2635,7 +2638,7 @@ class Router:
             original_model_group: Optional[str] = kwargs.get("model")  # type: ignore
             fallback_failure_exception_str = ""
 
-            if original_model_group is None:
+            if disable_fallbacks is True or original_model_group is None:
                 raise e
 
             input_kwargs = {

litellm/secret_managers/aws_secret_manager.py

@@ -23,28 +23,6 @@ def validate_environment():
         raise ValueError("Missing required environment variable - AWS_REGION_NAME")
 
 
-def load_aws_secret_manager(use_aws_secret_manager: Optional[bool]):
-    if use_aws_secret_manager is None or use_aws_secret_manager is False:
-        return
-    try:
-        import boto3
-        from botocore.exceptions import ClientError
-
-        validate_environment()
-
-        # Create a Secrets Manager client
-        session = boto3.session.Session()  # type: ignore
-        client = session.client(
-            service_name="secretsmanager", region_name=os.getenv("AWS_REGION_NAME")
-        )
-
-        litellm.secret_manager_client = client
-        litellm._key_management_system = KeyManagementSystem.AWS_SECRET_MANAGER
-
-    except Exception as e:
-        raise e
-
-
 def load_aws_kms(use_aws_kms: Optional[bool]):
     if use_aws_kms is None or use_aws_kms is False:
         return

litellm/secret_managers/aws_secret_manager_v2.py

@@ -0,0 +1,310 @@
+"""
+This is a file for the AWS Secret Manager Integration
+
+Handles Async Operations for:
+- Read Secret
+- Write Secret
+- Delete Secret
+
+Relevant issue: https://github.com/BerriAI/litellm/issues/1883
+
+Requires:
+* `os.environ["AWS_REGION_NAME"], 
+* `pip install boto3>=1.28.57`
+"""
+
+import ast
+import asyncio
+import base64
+import json
+import os
+import re
+import sys
+from typing import Any, Dict, Optional, Union
+
+import httpx
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.llms.base_aws_llm import BaseAWSLLM
+from litellm.llms.custom_httpx.http_handler import (
+    _get_httpx_client,
+    get_async_httpx_client,
+)
+from litellm.llms.custom_httpx.types import httpxSpecialProvider
+from litellm.proxy._types import KeyManagementSystem
+
+
+class AWSSecretsManagerV2(BaseAWSLLM):
+    @classmethod
+    def validate_environment(cls):
+        if "AWS_REGION_NAME" not in os.environ:
+            raise ValueError("Missing required environment variable - AWS_REGION_NAME")
+
+    @classmethod
+    def load_aws_secret_manager(cls, use_aws_secret_manager: Optional[bool]):
+        """
+        Initialize AWSSecretsManagerV2 and sets litellm.secret_manager_client = AWSSecretsManagerV2() and litellm._key_management_system = KeyManagementSystem.AWS_SECRET_MANAGER
+        """
+        if use_aws_secret_manager is None or use_aws_secret_manager is False:
+            return
+        try:
+            import boto3
+
+            cls.validate_environment()
+            litellm.secret_manager_client = cls()
+            litellm._key_management_system = KeyManagementSystem.AWS_SECRET_MANAGER
+
+        except Exception as e:
+            raise e
+
+    async def async_read_secret(
+        self,
+        secret_name: str,
+        optional_params: Optional[dict] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> Optional[str]:
+        """
+        Async function to read a secret from AWS Secrets Manager
+
+        Returns:
+            str: Secret value
+        Raises:
+            ValueError: If the secret is not found or an HTTP error occurs
+        """
+        endpoint_url, headers, body = self._prepare_request(
+            action="GetSecretValue",
+            secret_name=secret_name,
+            optional_params=optional_params,
+        )
+
+        async_client = get_async_httpx_client(
+            llm_provider=httpxSpecialProvider.SecretManager,
+            params={"timeout": timeout},
+        )
+
+        try:
+            response = await async_client.post(
+                url=endpoint_url, headers=headers, data=body.decode("utf-8")
+            )
+            response.raise_for_status()
+            return response.json()["SecretString"]
+        except httpx.TimeoutException:
+            raise ValueError("Timeout error occurred")
+        except Exception as e:
+            verbose_logger.exception(
+                "Error reading secret from AWS Secrets Manager: %s", str(e)
+            )
+        return None
+
+    def sync_read_secret(
+        self,
+        secret_name: str,
+        optional_params: Optional[dict] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> Optional[str]:
+        """
+        Sync function to read a secret from AWS Secrets Manager
+
+        Done for backwards compatibility with existing codebase, since get_secret is a sync function
+        """
+
+        # self._prepare_request uses these env vars, we cannot read them from AWS Secrets Manager. If we do we'd get stuck in an infinite loop
+        if secret_name in [
+            "AWS_ACCESS_KEY_ID",
+            "AWS_SECRET_ACCESS_KEY",
+            "AWS_REGION_NAME",
+            "AWS_REGION",
+            "AWS_BEDROCK_RUNTIME_ENDPOINT",
+        ]:
+            return os.getenv(secret_name)
+
+        endpoint_url, headers, body = self._prepare_request(
+            action="GetSecretValue",
+            secret_name=secret_name,
+            optional_params=optional_params,
+        )
+
+        sync_client = _get_httpx_client(
+            params={"timeout": timeout},
+        )
+
+        try:
+            response = sync_client.post(
+                url=endpoint_url, headers=headers, data=body.decode("utf-8")
+            )
+            response.raise_for_status()
+            return response.json()["SecretString"]
+        except httpx.TimeoutException:
+            raise ValueError("Timeout error occurred")
+        except Exception as e:
+            verbose_logger.exception(
+                "Error reading secret from AWS Secrets Manager: %s", str(e)
+            )
+        return None
+
+    async def async_write_secret(
+        self,
+        secret_name: str,
+        secret_value: str,
+        description: Optional[str] = None,
+        client_request_token: Optional[str] = None,
+        optional_params: Optional[dict] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> dict:
+        """
+        Async function to write a secret to AWS Secrets Manager
+
+        Args:
+            secret_name: Name of the secret
+            secret_value: Value to store (can be a JSON string)
+            description: Optional description for the secret
+            client_request_token: Optional unique identifier to ensure idempotency
+            optional_params: Additional AWS parameters
+            timeout: Request timeout
+        """
+        import uuid
+
+        # Prepare the request data
+        data = {"Name": secret_name, "SecretString": secret_value}
+        if description:
+            data["Description"] = description
+
+        data["ClientRequestToken"] = str(uuid.uuid4())
+
+        endpoint_url, headers, body = self._prepare_request(
+            action="CreateSecret",
+            secret_name=secret_name,
+            secret_value=secret_value,
+            optional_params=optional_params,
+            request_data=data,  # Pass the complete request data
+        )
+
+        async_client = get_async_httpx_client(
+            llm_provider=httpxSpecialProvider.SecretManager,
+            params={"timeout": timeout},
+        )
+
+        try:
+            response = await async_client.post(
+                url=endpoint_url, headers=headers, data=body.decode("utf-8")
+            )
+            response.raise_for_status()
+            return response.json()
+        except httpx.HTTPStatusError as err:
+            raise ValueError(f"HTTP error occurred: {err.response.text}")
+        except httpx.TimeoutException:
+            raise ValueError("Timeout error occurred")
+
+    async def async_delete_secret(
+        self,
+        secret_name: str,
+        recovery_window_in_days: Optional[int] = 7,
+        optional_params: Optional[dict] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> dict:
+        """
+        Async function to delete a secret from AWS Secrets Manager
+
+        Args:
+            secret_name: Name of the secret to delete
+            recovery_window_in_days: Number of days before permanent deletion (default: 7)
+            optional_params: Additional AWS parameters
+            timeout: Request timeout
+
+        Returns:
+            dict: Response from AWS Secrets Manager containing deletion details
+        """
+        # Prepare the request data
+        data = {
+            "SecretId": secret_name,
+            "RecoveryWindowInDays": recovery_window_in_days,
+        }
+
+        endpoint_url, headers, body = self._prepare_request(
+            action="DeleteSecret",
+            secret_name=secret_name,
+            optional_params=optional_params,
+            request_data=data,
+        )
+
+        async_client = get_async_httpx_client(
+            llm_provider=httpxSpecialProvider.SecretManager,
+            params={"timeout": timeout},
+        )
+
+        try:
+            response = await async_client.post(
+                url=endpoint_url, headers=headers, data=body.decode("utf-8")
+            )
+            response.raise_for_status()
+            return response.json()
+        except httpx.HTTPStatusError as err:
+            raise ValueError(f"HTTP error occurred: {err.response.text}")
+        except httpx.TimeoutException:
+            raise ValueError("Timeout error occurred")
+
+    def _prepare_request(
+        self,
+        action: str,  # "GetSecretValue" or "PutSecretValue"
+        secret_name: str,
+        secret_value: Optional[str] = None,
+        optional_params: Optional[dict] = None,
+        request_data: Optional[dict] = None,
+    ) -> tuple[str, Any, bytes]:
+        """Prepare the AWS Secrets Manager request"""
+        try:
+            import boto3
+            from botocore.auth import SigV4Auth
+            from botocore.awsrequest import AWSRequest
+            from botocore.credentials import Credentials
+        except ImportError:
+            raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
+        optional_params = optional_params or {}
+        boto3_credentials_info = self._get_boto_credentials_from_optional_params(
+            optional_params
+        )
+
+        # Get endpoint
+        _, endpoint_url = self.get_runtime_endpoint(
+            api_base=None,
+            aws_bedrock_runtime_endpoint=boto3_credentials_info.aws_bedrock_runtime_endpoint,
+            aws_region_name=boto3_credentials_info.aws_region_name,
+        )
+        endpoint_url = endpoint_url.replace("bedrock-runtime", "secretsmanager")
+
+        # Use provided request_data if available, otherwise build default data
+        if request_data:
+            data = request_data
+        else:
+            data = {"SecretId": secret_name}
+            if secret_value and action == "PutSecretValue":
+                data["SecretString"] = secret_value
+
+        body = json.dumps(data).encode("utf-8")
+        headers = {
+            "Content-Type": "application/x-amz-json-1.1",
+            "X-Amz-Target": f"secretsmanager.{action}",
+        }
+
+        # Sign request
+        request = AWSRequest(
+            method="POST", url=endpoint_url, data=body, headers=headers
+        )
+        SigV4Auth(
+            boto3_credentials_info.credentials,
+            "secretsmanager",
+            boto3_credentials_info.aws_region_name,
+        ).add_auth(request)
+        prepped = request.prepare()
+
+        return endpoint_url, prepped.headers, body
+
+
+# if __name__ == "__main__":
+#     print("loading aws secret manager v2")
+#     aws_secret_manager_v2 = AWSSecretsManagerV2()
+
+#     print("writing secret to aws secret manager v2")
+#     asyncio.run(aws_secret_manager_v2.async_write_secret(secret_name="test_secret_3", secret_value="test_value_2"))
+#     print("reading secret from aws secret manager v2")

litellm/secret_managers/main.py

@@ -5,7 +5,7 @@ import json
 import os
 import sys
 import traceback
-from typing import Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional, Union
 
 import httpx
 from dotenv import load_dotenv
@@ -198,7 +198,10 @@ def get_secret(  # noqa: PLR0915
             raise ValueError("Unsupported OIDC provider")
 
     try:
-        if litellm.secret_manager_client is not None:
+        if (
+            _should_read_secret_from_secret_manager()
+            and litellm.secret_manager_client is not None
+        ):
             try:
                 client = litellm.secret_manager_client
                 key_manager = "local"
@@ -207,7 +210,8 @@ def get_secret(  # noqa: PLR0915
 
                 if key_management_settings is not None:
                     if (
-                        secret_name not in key_management_settings.hosted_keys
+                        key_management_settings.hosted_keys is not None
+                        and secret_name not in key_management_settings.hosted_keys
                     ):  # allow user to specify which keys to check in hosted key manager
                         key_manager = "local"
 
@@ -268,25 +272,13 @@ def get_secret(  # noqa: PLR0915
                     if isinstance(secret, str):
                         secret = secret.strip()
                 elif key_manager == KeyManagementSystem.AWS_SECRET_MANAGER.value:
-                    try:
+                    from litellm.secret_managers.aws_secret_manager_v2 import (
-                        get_secret_value_response = client.get_secret_value(
+                        AWSSecretsManagerV2,
-                            SecretId=secret_name
                     )
-                        print_verbose(
-                            f"get_secret_value_response: {get_secret_value_response}"
-                        )
-                    except Exception as e:
-                        print_verbose(f"An error occurred - {str(e)}")
-                        # For a list of exceptions thrown, see
-                        # https://docs.aws.amazon.com/secretsmanager/latest/apireference/API_GetSecretValue.html
-                        raise e
 
-                    # assume there is 1 secret per secret_name
+                    if isinstance(client, AWSSecretsManagerV2):
-                    secret_dict = json.loads(get_secret_value_response["SecretString"])
+                        secret = client.sync_read_secret(secret_name=secret_name)
-                    print_verbose(f"secret_dict: {secret_dict}")
+                        print_verbose(f"get_secret_value_response: {secret}")
-                    for k, v in secret_dict.items():
-                        secret = v
-                        print_verbose(f"secret: {secret}")
                 elif key_manager == KeyManagementSystem.GOOGLE_SECRET_MANAGER.value:
                     try:
                         secret = client.get_secret_from_google_secret_manager(
@@ -332,3 +324,21 @@ def get_secret(  # noqa: PLR0915
             return default_value
         else:
             raise e
+
+
+def _should_read_secret_from_secret_manager() -> bool:
+    """
+    Returns True if the secret manager should be used to read the secret, False otherwise
+
+    - If the secret manager client is not set, return False
+    - If the `_key_management_settings` access mode is "read_only" or "read_and_write", return True
+    - Otherwise, return False
+    """
+    if litellm.secret_manager_client is not None:
+        if litellm._key_management_settings is not None:
+            if (
+                litellm._key_management_settings.access_mode == "read_only"
+                or litellm._key_management_settings.access_mode == "read_and_write"
+            ):
+                return True
+    return False

litellm/types/rerank.py

@@ -7,6 +7,7 @@ https://docs.cohere.com/reference/rerank
 from typing import List, Optional, Union
 
 from pydantic import BaseModel, PrivateAttr
+from typing_extensions import TypedDict
 
 
 class RerankRequest(BaseModel):
@@ -19,10 +20,26 @@ class RerankRequest(BaseModel):
     max_chunks_per_doc: Optional[int] = None
 
 
+class RerankBilledUnits(TypedDict, total=False):
+    search_units: int
+    total_tokens: int
+
+
+class RerankTokens(TypedDict, total=False):
+    input_tokens: int
+    output_tokens: int
+
+
+class RerankResponseMeta(TypedDict, total=False):
+    api_version: dict
+    billed_units: RerankBilledUnits
+    tokens: RerankTokens
+
+
 class RerankResponse(BaseModel):
     id: str
     results: List[dict]  # Contains index and relevance_score
-    meta: Optional[dict] = None  # Contains api_version and billed_units
+    meta: Optional[RerankResponseMeta] = None  # Contains api_version and billed_units
 
     # Define private attributes using PrivateAttr
     _hidden_params: dict = PrivateAttr(default_factory=dict)

litellm/types/router.py

@@ -150,6 +150,8 @@ class GenericLiteLLMParams(BaseModel):
     max_retries: Optional[int] = None
     organization: Optional[str] = None  # for openai orgs
     configurable_clientside_auth_params: CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS = None
+    ## LOGGING PARAMS ##
+    litellm_trace_id: Optional[str] = None
     ## UNIFIED PROJECT/REGION ##
     region_name: Optional[str] = None
     ## VERTEX AI ##
@@ -186,6 +188,8 @@ class GenericLiteLLMParams(BaseModel):
             None  # timeout when making stream=True calls, if str, pass in as os.environ/
         ),
         organization: Optional[str] = None,  # for openai orgs
+        ## LOGGING PARAMS ##
+        litellm_trace_id: Optional[str] = None,
         ## UNIFIED PROJECT/REGION ##
         region_name: Optional[str] = None,
         ## VERTEX AI ##

litellm/types/utils.py

@@ -1334,6 +1334,7 @@ class ResponseFormatChunk(TypedDict, total=False):
 
 all_litellm_params = [
     "metadata",
+    "litellm_trace_id",
     "tags",
     "acompletion",
     "aimg_generation",
@@ -1523,6 +1524,7 @@ StandardLoggingPayloadStatus = Literal["success", "failure"]
 
 class StandardLoggingPayload(TypedDict):
     id: str
+    trace_id: str  # Trace multiple LLM calls belonging to same overall request (e.g. fallbacks/retries)
     call_type: str
     response_cost: float
     response_cost_failure_debug_info: Optional[

litellm/utils.py

@@ -527,6 +527,7 @@ def function_setup(  # noqa: PLR0915
             messages=messages,
             stream=stream,
             litellm_call_id=kwargs["litellm_call_id"],
+            litellm_trace_id=kwargs.get("litellm_trace_id"),
             function_id=function_id or "",
             call_type=call_type,
             start_time=start_time,
@@ -2056,6 +2057,7 @@ def get_litellm_params(
     azure_ad_token_provider=None,
     user_continue_message=None,
     base_model=None,
+    litellm_trace_id=None,
 ):
     litellm_params = {
         "acompletion": acompletion,
@@ -2084,6 +2086,7 @@ def get_litellm_params(
         "user_continue_message": user_continue_message,
         "base_model": base_model
         or _get_base_model_from_litellm_call_metadata(metadata=metadata),
+        "litellm_trace_id": litellm_trace_id,
     }
 
     return litellm_params

model_prices_and_context_window.json

@@ -2986,19 +2986,19 @@
         "supports_function_calling": true
     },
     "vertex_ai/imagegeneration@006": {
-        "cost_per_image": 0.020,
+        "output_cost_per_image": 0.020,
         "litellm_provider": "vertex_ai-image-models",
         "mode": "image_generation",
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
     },
     "vertex_ai/imagen-3.0-generate-001": {
-        "cost_per_image": 0.04,
+        "output_cost_per_image": 0.04,
         "litellm_provider": "vertex_ai-image-models",
         "mode": "image_generation",
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
     },
     "vertex_ai/imagen-3.0-fast-generate-001": {
-        "cost_per_image": 0.02,
+        "output_cost_per_image": 0.02,
         "litellm_provider": "vertex_ai-image-models",
         "mode": "image_generation",
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
@@ -5620,6 +5620,13 @@
         "litellm_provider": "bedrock",
         "mode": "image_generation"
     },
+    "stability.stable-image-ultra-v1:0": {
+        "max_tokens": 77, 
+        "max_input_tokens": 77, 
+        "output_cost_per_image": 0.14,
+        "litellm_provider": "bedrock",
+        "mode": "image_generation"
+    },
     "sagemaker/meta-textgeneration-llama-2-7b": {
         "max_tokens": 4096, 
         "max_input_tokens": 4096, 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.52.6"
+version = "1.52.9"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -91,7 +91,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.52.6"
+version = "1.52.9"
 version_files = [
     "pyproject.toml:^version"
 ]

tests/llm_translation/base_llm_unit_tests.py

@@ -13,8 +13,11 @@ sys.path.insert(
 import litellm
 from litellm.exceptions import BadRequestError
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
-from litellm.utils import CustomStreamWrapper
+from litellm.utils import (
-
+    CustomStreamWrapper,
+    get_supported_openai_params,
+    get_optional_params,
+)
 
 # test_example.py
 from abc import ABC, abstractmethod
@@ -45,6 +48,9 @@ class BaseLLMChatTest(ABC):
         )
         assert response is not None
 
+        # for OpenAI the content contains the JSON schema, so we need to assert that the content is not None
+        assert response.choices[0].message.content is not None
+
     def test_message_with_name(self):
         base_completion_call_args = self.get_base_completion_call_args()
         messages = [
@@ -79,6 +85,49 @@ class BaseLLMChatTest(ABC):
 
         print(response)
 
+        # OpenAI guarantees that the JSON schema is returned in the content
+        # relevant issue: https://github.com/BerriAI/litellm/issues/6741
+        assert response.choices[0].message.content is not None
+
+    def test_json_response_format_stream(self):
+        """
+        Test that the JSON response format with streaming is supported by the LLM API
+        """
+        base_completion_call_args = self.get_base_completion_call_args()
+        litellm.set_verbose = True
+
+        messages = [
+            {
+                "role": "system",
+                "content": "Your output should be a JSON object with no additional properties.  ",
+            },
+            {
+                "role": "user",
+                "content": "Respond with this in json. city=San Francisco, state=CA, weather=sunny, temp=60",
+            },
+        ]
+
+        response = litellm.completion(
+            **base_completion_call_args,
+            messages=messages,
+            response_format={"type": "json_object"},
+            stream=True,
+        )
+
+        print(response)
+
+        content = ""
+        for chunk in response:
+            content += chunk.choices[0].delta.content or ""
+
+        print("content=", content)
+
+        # OpenAI guarantees that the JSON schema is returned in the content
+        # relevant issue: https://github.com/BerriAI/litellm/issues/6741
+        # we need to assert that the JSON schema was returned in the content, (for Anthropic we were returning it as part of the tool call)
+        assert content is not None
+        assert len(content) > 0
+
     @pytest.fixture
     def pdf_messages(self):
         import base64

tests/llm_translation/base_rerank_unit_tests.py

@@ -0,0 +1,115 @@
+import asyncio
+import httpx
+import json
+import pytest
+import sys
+from typing import Any, Dict, List
+from unittest.mock import MagicMock, Mock, patch
+import os
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import litellm
+from litellm.exceptions import BadRequestError
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.utils import (
+    CustomStreamWrapper,
+    get_supported_openai_params,
+    get_optional_params,
+)
+
+# test_example.py
+from abc import ABC, abstractmethod
+
+
+def assert_response_shape(response, custom_llm_provider):
+    expected_response_shape = {"id": str, "results": list, "meta": dict}
+
+    expected_results_shape = {"index": int, "relevance_score": float}
+
+    expected_meta_shape = {"api_version": dict, "billed_units": dict}
+
+    expected_api_version_shape = {"version": str}
+
+    expected_billed_units_shape = {"search_units": int}
+
+    assert isinstance(response.id, expected_response_shape["id"])
+    assert isinstance(response.results, expected_response_shape["results"])
+    for result in response.results:
+        assert isinstance(result["index"], expected_results_shape["index"])
+        assert isinstance(
+            result["relevance_score"], expected_results_shape["relevance_score"]
+        )
+    assert isinstance(response.meta, expected_response_shape["meta"])
+
+    if custom_llm_provider == "cohere":
+
+        assert isinstance(
+            response.meta["api_version"], expected_meta_shape["api_version"]
+        )
+        assert isinstance(
+            response.meta["api_version"]["version"],
+            expected_api_version_shape["version"],
+        )
+        assert isinstance(
+            response.meta["billed_units"], expected_meta_shape["billed_units"]
+        )
+        assert isinstance(
+            response.meta["billed_units"]["search_units"],
+            expected_billed_units_shape["search_units"],
+        )
+
+
+class BaseLLMRerankTest(ABC):
+    """
+    Abstract base test class that enforces a common test across all test classes.
+    """
+
+    @abstractmethod
+    def get_base_rerank_call_args(self) -> dict:
+        """Must return the base rerank call args"""
+        pass
+
+    @abstractmethod
+    def get_custom_llm_provider(self) -> litellm.LlmProviders:
+        """Must return the custom llm provider"""
+        pass
+
+    @pytest.mark.asyncio()
+    @pytest.mark.parametrize("sync_mode", [True, False])
+    async def test_basic_rerank(self, sync_mode):
+        rerank_call_args = self.get_base_rerank_call_args()
+        custom_llm_provider = self.get_custom_llm_provider()
+        if sync_mode is True:
+            response = litellm.rerank(
+                **rerank_call_args,
+                query="hello",
+                documents=["hello", "world"],
+                top_n=3,
+            )
+
+            print("re rank response: ", response)
+
+            assert response.id is not None
+            assert response.results is not None
+
+            assert_response_shape(
+                response=response, custom_llm_provider=custom_llm_provider.value
+            )
+        else:
+            response = await litellm.arerank(
+                **rerank_call_args,
+                query="hello",
+                documents=["hello", "world"],
+                top_n=3,
+            )
+
+            print("async re rank response: ", response)
+
+            assert response.id is not None
+            assert response.results is not None
+
+            assert_response_shape(
+                response=response, custom_llm_provider=custom_llm_provider.value
+            )

tests/llm_translation/test_anthropic_completion.py

@@ -33,8 +33,10 @@ from litellm import (
 )
 from litellm.adapters.anthropic_adapter import anthropic_adapter
 from litellm.types.llms.anthropic import AnthropicResponse
-
+from litellm.types.utils import GenericStreamingChunk, ChatCompletionToolCallChunk
+from litellm.types.llms.openai import ChatCompletionToolCallFunctionChunk
 from litellm.llms.anthropic.common_utils import process_anthropic_headers
+from litellm.llms.anthropic.chat.handler import AnthropicChatCompletion
 from httpx import Headers
 from base_llm_unit_tests import BaseLLMChatTest
 
@@ -694,3 +696,91 @@ class TestAnthropicCompletion(BaseLLMChatTest):
             assert _document_validation["type"] == "document"
             assert _document_validation["source"]["media_type"] == "application/pdf"
             assert _document_validation["source"]["type"] == "base64"
+
+
+def test_convert_tool_response_to_message_with_values():
+    """Test converting a tool response with 'values' key to a message"""
+    tool_calls = [
+        ChatCompletionToolCallChunk(
+            id="test_id",
+            type="function",
+            function=ChatCompletionToolCallFunctionChunk(
+                name="json_tool_call",
+                arguments='{"values": {"name": "John", "age": 30}}',
+            ),
+            index=0,
+        )
+    ]
+
+    message = AnthropicChatCompletion._convert_tool_response_to_message(
+        tool_calls=tool_calls
+    )
+
+    assert message is not None
+    assert message.content == '{"name": "John", "age": 30}'
+
+
+def test_convert_tool_response_to_message_without_values():
+    """
+    Test converting a tool response without 'values' key to a message
+
+    Anthropic API returns the JSON schema in the tool call, OpenAI Spec expects it in the message. This test ensures that the tool call is converted to a message correctly.
+
+    Relevant issue: https://github.com/BerriAI/litellm/issues/6741
+    """
+    tool_calls = [
+        ChatCompletionToolCallChunk(
+            id="test_id",
+            type="function",
+            function=ChatCompletionToolCallFunctionChunk(
+                name="json_tool_call", arguments='{"name": "John", "age": 30}'
+            ),
+            index=0,
+        )
+    ]
+
+    message = AnthropicChatCompletion._convert_tool_response_to_message(
+        tool_calls=tool_calls
+    )
+
+    assert message is not None
+    assert message.content == '{"name": "John", "age": 30}'
+
+
+def test_convert_tool_response_to_message_invalid_json():
+    """Test converting a tool response with invalid JSON"""
+    tool_calls = [
+        ChatCompletionToolCallChunk(
+            id="test_id",
+            type="function",
+            function=ChatCompletionToolCallFunctionChunk(
+                name="json_tool_call", arguments="invalid json"
+            ),
+            index=0,
+        )
+    ]
+
+    message = AnthropicChatCompletion._convert_tool_response_to_message(
+        tool_calls=tool_calls
+    )
+
+    assert message is not None
+    assert message.content == "invalid json"
+
+
+def test_convert_tool_response_to_message_no_arguments():
+    """Test converting a tool response with no arguments"""
+    tool_calls = [
+        ChatCompletionToolCallChunk(
+            id="test_id",
+            type="function",
+            function=ChatCompletionToolCallFunctionChunk(name="json_tool_call"),
+            index=0,
+        )
+    ]
+
+    message = AnthropicChatCompletion._convert_tool_response_to_message(
+        tool_calls=tool_calls
+    )
+
+    assert message is None

tests/llm_translation/test_jina_ai.py

@@ -0,0 +1,23 @@
+import json
+import os
+import sys
+from datetime import datetime
+from unittest.mock import AsyncMock
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+
+
+from base_rerank_unit_tests import BaseLLMRerankTest
+import litellm
+
+
+class TestJinaAI(BaseLLMRerankTest):
+    def get_custom_llm_provider(self) -> litellm.LlmProviders:
+        return litellm.LlmProviders.JINA_AI
+
+    def get_base_rerank_call_args(self) -> dict:
+        return {
+            "model": "jina_ai/jina-reranker-v2-base-multilingual",
+        }

tests/llm_translation/test_optional_params.py

@@ -923,9 +923,22 @@ def test_watsonx_text_top_k():
     assert optional_params["top_k"] == 10
 
 
+
 def test_together_ai_model_params():
     optional_params = get_optional_params(
         model="together_ai", custom_llm_provider="together_ai", logprobs=1
     )
     print(optional_params)
     assert optional_params["logprobs"] == 1
+
+def test_forward_user_param():
+    from litellm.utils import get_supported_openai_params, get_optional_params
+
+    model = "claude-3-5-sonnet-20240620"
+    optional_params = get_optional_params(
+        model=model,
+        user="test_user",
+        custom_llm_provider="anthropic",
+    )
+
+    assert optional_params["metadata"]["user_id"] == "test_user"

tests/llm_translation/test_vertex.py

@@ -16,6 +16,7 @@ import pytest
 import litellm
 from litellm import get_optional_params
 from litellm.llms.custom_httpx.http_handler import HTTPHandler
+import httpx
 
 
 def test_completion_pydantic_obj_2():
@@ -1317,3 +1318,39 @@ def test_image_completion_request(image_url):
         mock_post.assert_called_once()
         print("mock_post.call_args.kwargs['json']", mock_post.call_args.kwargs["json"])
         assert mock_post.call_args.kwargs["json"] == expected_request_body
+
+
+@pytest.mark.parametrize(
+    "model, expected_url",
+    [
+        (
+            "textembedding-gecko@001",
+            "https://us-central1-aiplatform.googleapis.com/v1/projects/project-id/locations/us-central1/publishers/google/models/textembedding-gecko@001:predict",
+        ),
+        (
+            "123456789",
+            "https://us-central1-aiplatform.googleapis.com/v1/projects/project-id/locations/us-central1/endpoints/123456789:predict",
+        ),
+    ],
+)
+def test_vertex_embedding_url(model, expected_url):
+    """
+    Test URL generation for embedding models, including numeric model IDs (fine-tuned models
+
+    Relevant issue: https://github.com/BerriAI/litellm/issues/6482
+
+    When a fine-tuned embedding model is used, the URL is different from the standard one.
+    """
+    from litellm.llms.vertex_ai_and_google_ai_studio.common_utils import _get_vertex_url
+
+    url, endpoint = _get_vertex_url(
+        mode="embedding",
+        model=model,
+        stream=False,
+        vertex_project="project-id",
+        vertex_location="us-central1",
+        vertex_api_version="v1",
+    )
+
+    assert url == expected_url
+    assert endpoint == "predict"

tests/local_testing/test_amazing_vertex_completion.py

@@ -18,6 +18,8 @@ import json
 import os
 import tempfile
 from unittest.mock import AsyncMock, MagicMock, patch
+from respx import MockRouter
+import httpx
 
 import pytest
 
@@ -973,6 +975,7 @@ async def test_partner_models_httpx(model, sync_mode):
         data = {
             "model": model,
             "messages": messages,
+            "timeout": 10,
         }
         if sync_mode:
             response = litellm.completion(**data)
@@ -986,6 +989,8 @@ async def test_partner_models_httpx(model, sync_mode):
         assert isinstance(response._hidden_params["response_cost"], float)
     except litellm.RateLimitError as e:
         pass
+    except litellm.Timeout as e:
+        pass
     except litellm.InternalServerError as e:
         pass
     except Exception as e:
@@ -3051,3 +3056,70 @@ def test_custom_api_base(api_base):
         assert url == api_base + ":"
     else:
         assert url == test_endpoint
+
+
+@pytest.mark.asyncio
+@pytest.mark.respx
+async def test_vertexai_embedding_finetuned(respx_mock: MockRouter):
+    """
+    Tests that:
+    - Request URL and body are correctly formatted for Vertex AI embeddings
+    - Response is properly parsed into litellm's embedding response format
+    """
+    load_vertex_ai_credentials()
+    litellm.set_verbose = True
+
+    # Test input
+    input_text = ["good morning from litellm", "this is another item"]
+
+    # Expected request/response
+    expected_url = "https://us-central1-aiplatform.googleapis.com/v1/projects/633608382793/locations/us-central1/endpoints/1004708436694269952:predict"
+    expected_request = {
+        "instances": [
+            {"inputs": "good morning from litellm"},
+            {"inputs": "this is another item"},
+        ],
+        "parameters": {},
+    }
+
+    mock_response = {
+        "predictions": [
+            [[-0.000431762, -0.04416759, -0.03443353]],  # Truncated embedding vector
+            [[-0.000431762, -0.04416759, -0.03443353]],  # Truncated embedding vector
+        ],
+        "deployedModelId": "2275167734310371328",
+        "model": "projects/633608382793/locations/us-central1/models/snowflake-arctic-embed-m-long-1731622468876",
+        "modelDisplayName": "snowflake-arctic-embed-m-long-1731622468876",
+        "modelVersionId": "1",
+    }
+
+    # Setup mock request
+    mock_request = respx_mock.post(expected_url).mock(
+        return_value=httpx.Response(200, json=mock_response)
+    )
+
+    # Make request
+    response = await litellm.aembedding(
+        vertex_project="633608382793",
+        model="vertex_ai/1004708436694269952",
+        input=input_text,
+    )
+
+    # Assert request was made correctly
+    assert mock_request.called
+    request_body = json.loads(mock_request.calls[0].request.content)
+    print("\n\nrequest_body", request_body)
+    print("\n\nexpected_request", expected_request)
+    assert request_body == expected_request
+
+    # Assert response structure
+    assert response is not None
+    assert hasattr(response, "data")
+    assert len(response.data) == len(input_text)
+
+    # Assert embedding structure
+    for embedding in response.data:
+        assert "embedding" in embedding
+        assert isinstance(embedding["embedding"], list)
+        assert len(embedding["embedding"]) > 0
+        assert all(isinstance(x, float) for x in embedding["embedding"])

tests/local_testing/test_aws_secret_manager.py

@@ -0,0 +1,139 @@
+# What is this?
+
+import asyncio
+import os
+import sys
+import traceback
+
+from dotenv import load_dotenv
+
+import litellm.types
+import litellm.types.utils
+
+
+load_dotenv()
+import io
+
+import sys
+import os
+
+# Ensure the project root is in the Python path
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")))
+
+print("Python Path:", sys.path)
+print("Current Working Directory:", os.getcwd())
+
+
+from typing import Optional
+from unittest.mock import MagicMock, patch
+
+import pytest
+import uuid
+import json
+from litellm.secret_managers.aws_secret_manager_v2 import AWSSecretsManagerV2
+
+
+def check_aws_credentials():
+    """Helper function to check if AWS credentials are set"""
+    required_vars = ["AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY", "AWS_REGION_NAME"]
+    missing_vars = [var for var in required_vars if not os.getenv(var)]
+    if missing_vars:
+        pytest.skip(f"Missing required AWS credentials: {', '.join(missing_vars)}")
+
+
+@pytest.mark.asyncio
+async def test_write_and_read_simple_secret():
+    """Test writing and reading a simple string secret"""
+    check_aws_credentials()
+
+    secret_manager = AWSSecretsManagerV2()
+    test_secret_name = f"litellm_test_{uuid.uuid4().hex[:8]}"
+    test_secret_value = "test_value_123"
+
+    try:
+        # Write secret
+        write_response = await secret_manager.async_write_secret(
+            secret_name=test_secret_name,
+            secret_value=test_secret_value,
+            description="LiteLLM Test Secret",
+        )
+
+        print("Write Response:", write_response)
+
+        assert write_response is not None
+        assert "ARN" in write_response
+        assert "Name" in write_response
+        assert write_response["Name"] == test_secret_name
+
+        # Read secret back
+        read_value = await secret_manager.async_read_secret(
+            secret_name=test_secret_name
+        )
+
+        print("Read Value:", read_value)
+
+        assert read_value == test_secret_value
+    finally:
+        # Cleanup: Delete the secret
+        delete_response = await secret_manager.async_delete_secret(
+            secret_name=test_secret_name
+        )
+        print("Delete Response:", delete_response)
+        assert delete_response is not None
+
+
+@pytest.mark.asyncio
+async def test_write_and_read_json_secret():
+    """Test writing and reading a JSON structured secret"""
+    check_aws_credentials()
+
+    secret_manager = AWSSecretsManagerV2()
+    test_secret_name = f"litellm_test_{uuid.uuid4().hex[:8]}_json"
+    test_secret_value = {
+        "api_key": "test_key",
+        "model": "gpt-4",
+        "temperature": 0.7,
+        "metadata": {"team": "ml", "project": "litellm"},
+    }
+
+    try:
+        # Write JSON secret
+        write_response = await secret_manager.async_write_secret(
+            secret_name=test_secret_name,
+            secret_value=json.dumps(test_secret_value),
+            description="LiteLLM JSON Test Secret",
+        )
+
+        print("Write Response:", write_response)
+
+        # Read and parse JSON secret
+        read_value = await secret_manager.async_read_secret(
+            secret_name=test_secret_name
+        )
+        parsed_value = json.loads(read_value)
+
+        print("Read Value:", read_value)
+
+        assert parsed_value == test_secret_value
+        assert parsed_value["api_key"] == "test_key"
+        assert parsed_value["metadata"]["team"] == "ml"
+    finally:
+        # Cleanup: Delete the secret
+        delete_response = await secret_manager.async_delete_secret(
+            secret_name=test_secret_name
+        )
+        print("Delete Response:", delete_response)
+        assert delete_response is not None
+
+
+@pytest.mark.asyncio
+async def test_read_nonexistent_secret():
+    """Test reading a secret that doesn't exist"""
+    check_aws_credentials()
+
+    secret_manager = AWSSecretsManagerV2()
+    nonexistent_secret = f"litellm_nonexistent_{uuid.uuid4().hex}"
+
+    response = await secret_manager.async_read_secret(secret_name=nonexistent_secret)
+
+    assert response is None

tests/local_testing/test_completion.py

@@ -24,7 +24,7 @@ from litellm import RateLimitError, Timeout, completion, completion_cost, embedd
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
 from litellm.llms.prompt_templates.factory import anthropic_messages_pt
 
-# litellm.num_retries = 3
+# litellm.num_retries=3
 
 litellm.cache = None
 litellm.success_callback = []

tests/local_testing/test_cost_calc.py

@@ -10,7 +10,7 @@ import os
 
 sys.path.insert(
     0, os.path.abspath("../..")
-)  # Adds the parent directory to the system path
+)  # Adds the parent directory to the system-path
 from typing import Literal
 
 import pytest

tests/local_testing/test_custom_callback_input.py

@@ -1624,3 +1624,55 @@ async def test_standard_logging_payload_stream_usage(sync_mode):
             print(f"standard_logging_object usage: {built_response.usage}")
     except litellm.InternalServerError:
         pass
+
+
+def test_standard_logging_retries():
+    """
+    know if a request was retried.
+    """
+    from litellm.types.utils import StandardLoggingPayload
+    from litellm.router import Router
+
+    customHandler = CompletionCustomHandler()
+    litellm.callbacks = [customHandler]
+
+    router = Router(
+        model_list=[
+            {
+                "model_name": "gpt-3.5-turbo",
+                "litellm_params": {
+                    "model": "openai/gpt-3.5-turbo",
+                    "api_key": "test-api-key",
+                },
+            }
+        ]
+    )
+
+    with patch.object(
+        customHandler, "log_failure_event", new=MagicMock()
+    ) as mock_client:
+        try:
+            router.completion(
+                model="gpt-3.5-turbo",
+                messages=[{"role": "user", "content": "Hey, how's it going?"}],
+                num_retries=1,
+                mock_response="litellm.RateLimitError",
+            )
+        except litellm.RateLimitError:
+            pass
+
+        assert mock_client.call_count == 2
+        assert (
+            mock_client.call_args_list[0].kwargs["kwargs"]["standard_logging_object"][
+                "trace_id"
+            ]
+            is not None
+        )
+        assert (
+            mock_client.call_args_list[0].kwargs["kwargs"]["standard_logging_object"][
+                "trace_id"
+            ]
+            == mock_client.call_args_list[1].kwargs["kwargs"][
+                "standard_logging_object"
+            ]["trace_id"]
+        )

tests/local_testing/test_exceptions.py

@@ -58,6 +58,7 @@ async def test_content_policy_exception_azure():
     except litellm.ContentPolicyViolationError as e:
         print("caught a content policy violation error! Passed")
         print("exception", e)
+        assert e.response is not None
         assert e.litellm_debug_info is not None
         assert isinstance(e.litellm_debug_info, str)
         assert len(e.litellm_debug_info) > 0
@@ -1152,3 +1153,24 @@ async def test_exception_with_headers_httpx(
         if exception_raised is False:
             print(resp)
         assert exception_raised
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model", ["azure/chatgpt-v-2", "openai/gpt-3.5-turbo"])
+async def test_bad_request_error_contains_httpx_response(model):
+    """
+    Test that the BadRequestError contains the httpx response
+
+    Relevant issue: https://github.com/BerriAI/litellm/issues/6732
+    """
+    try:
+        await litellm.acompletion(
+            model=model,
+            messages=[{"role": "user", "content": "Hello world"}],
+            bad_arg="bad_arg",
+        )
+        pytest.fail("Expected to raise BadRequestError")
+    except litellm.BadRequestError as e:
+        print("e.response", e.response)
+        print("vars(e.response)", vars(e.response))
+        assert e.response is not None

tests/local_testing/test_get_llm_provider.py

@@ -157,7 +157,7 @@ def test_get_llm_provider_jina_ai():
     model, custom_llm_provider, dynamic_api_key, api_base = litellm.get_llm_provider(
         model="jina_ai/jina-embeddings-v3",
     )
-    assert custom_llm_provider == "openai_like"
+    assert custom_llm_provider == "jina_ai"
     assert api_base == "https://api.jina.ai/v1"
     assert model == "jina-embeddings-v3"
 

tests/local_testing/test_get_model_info.py

@@ -89,11 +89,16 @@ def test_get_model_info_ollama_chat():
                 "template": "tools",
             }
         ),
-    ):
+    ) as mock_client:
         info = OllamaConfig().get_model_info("mistral")
-        print("info", info)
         assert info["supports_function_calling"] is True
 
         info = get_model_info("ollama/mistral")
-        print("info", info)
+
         assert info["supports_function_calling"] is True
+
+        mock_client.assert_called()
+
+        print(mock_client.call_args.kwargs)
+
+        assert mock_client.call_args.kwargs["json"]["name"] == "mistral"

tests/local_testing/test_router_fallbacks.py

@@ -1138,9 +1138,9 @@ async def test_router_content_policy_fallbacks(
     router = Router(
         model_list=[
             {
-                "model_name": "claude-2",
+                "model_name": "claude-2.1",
                 "litellm_params": {
-                    "model": "claude-2",
+                    "model": "claude-2.1",
                     "api_key": "",
                     "mock_response": mock_response,
                 },
@@ -1164,7 +1164,7 @@ async def test_router_content_policy_fallbacks(
             {
                 "model_name": "my-general-model",
                 "litellm_params": {
-                    "model": "claude-2",
+                    "model": "claude-2.1",
                     "api_key": "",
                     "mock_response": Exception("Should not have called this."),
                 },
@@ -1172,14 +1172,14 @@ async def test_router_content_policy_fallbacks(
             {
                 "model_name": "my-context-window-model",
                 "litellm_params": {
-                    "model": "claude-2",
+                    "model": "claude-2.1",
                     "api_key": "",
                     "mock_response": Exception("Should not have called this."),
                 },
             },
         ],
         content_policy_fallbacks=(
-            [{"claude-2": ["my-fallback-model"]}]
+            [{"claude-2.1": ["my-fallback-model"]}]
             if fallback_type == "model-specific"
             else None
         ),
@@ -1190,12 +1190,12 @@ async def test_router_content_policy_fallbacks(
 
     if sync_mode is True:
         response = router.completion(
-            model="claude-2",
+            model="claude-2.1",
             messages=[{"role": "user", "content": "Hey, how's it going?"}],
         )
     else:
         response = await router.acompletion(
-            model="claude-2",
+            model="claude-2.1",
             messages=[{"role": "user", "content": "Hey, how's it going?"}],
         )
 
@@ -1455,3 +1455,46 @@ async def test_router_fallbacks_default_and_model_specific_fallbacks(sync_mode):
     assert isinstance(
         exc_info.value, litellm.AuthenticationError
     ), f"Expected AuthenticationError, but got {type(exc_info.value).__name__}"
+
+
+@pytest.mark.asyncio
+async def test_router_disable_fallbacks_dynamically():
+    from litellm.router import run_async_fallback
+
+    router = Router(
+        model_list=[
+            {
+                "model_name": "bad-model",
+                "litellm_params": {
+                    "model": "openai/my-bad-model",
+                    "api_key": "my-bad-api-key",
+                },
+            },
+            {
+                "model_name": "good-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": os.getenv("OPENAI_API_KEY"),
+                },
+            },
+        ],
+        fallbacks=[{"bad-model": ["good-model"]}],
+        default_fallbacks=["good-model"],
+    )
+
+    with patch.object(
+        router,
+        "log_retry",
+        new=MagicMock(return_value=None),
+    ) as mock_client:
+        try:
+            resp = await router.acompletion(
+                model="bad-model",
+                messages=[{"role": "user", "content": "Hey, how's it going?"}],
+                disable_fallbacks=True,
+            )
+            print(resp)
+        except Exception as e:
+            print(e)
+
+        mock_client.assert_not_called()

tests/local_testing/test_router_utils.py

@@ -14,6 +14,7 @@ from litellm.router import Deployment, LiteLLM_Params, ModelInfo
 from concurrent.futures import ThreadPoolExecutor
 from collections import defaultdict
 from dotenv import load_dotenv
+from unittest.mock import patch, MagicMock, AsyncMock
 
 load_dotenv()
 
@@ -83,3 +84,93 @@ def test_returned_settings():
     except Exception:
         print(traceback.format_exc())
         pytest.fail("An error occurred - " + traceback.format_exc())
+
+
+from litellm.types.utils import CallTypes
+
+
+def test_update_kwargs_before_fallbacks_unit_test():
+    router = Router(
+        model_list=[
+            {
+                "model_name": "gpt-3.5-turbo",
+                "litellm_params": {
+                    "model": "azure/chatgpt-v-2",
+                    "api_key": "bad-key",
+                    "api_version": os.getenv("AZURE_API_VERSION"),
+                    "api_base": os.getenv("AZURE_API_BASE"),
+                },
+            }
+        ],
+    )
+
+    kwargs = {"messages": [{"role": "user", "content": "write 1 sentence poem"}]}
+
+    router._update_kwargs_before_fallbacks(
+        model="gpt-3.5-turbo",
+        kwargs=kwargs,
+    )
+
+    assert kwargs["litellm_trace_id"] is not None
+
+
+@pytest.mark.parametrize(
+    "call_type",
+    [
+        CallTypes.acompletion,
+        CallTypes.atext_completion,
+        CallTypes.aembedding,
+        CallTypes.arerank,
+        CallTypes.atranscription,
+    ],
+)
+@pytest.mark.asyncio
+async def test_update_kwargs_before_fallbacks(call_type):
+
+    router = Router(
+        model_list=[
+            {
+                "model_name": "gpt-3.5-turbo",
+                "litellm_params": {
+                    "model": "azure/chatgpt-v-2",
+                    "api_key": "bad-key",
+                    "api_version": os.getenv("AZURE_API_VERSION"),
+                    "api_base": os.getenv("AZURE_API_BASE"),
+                },
+            }
+        ],
+    )
+
+    if call_type.value.startswith("a"):
+        with patch.object(router, "async_function_with_fallbacks") as mock_client:
+            if call_type.value == "acompletion":
+                input_kwarg = {
+                    "messages": [{"role": "user", "content": "Hello, how are you?"}],
+                }
+            elif (
+                call_type.value == "atext_completion"
+                or call_type.value == "aimage_generation"
+            ):
+                input_kwarg = {
+                    "prompt": "Hello, how are you?",
+                }
+            elif call_type.value == "aembedding" or call_type.value == "arerank":
+                input_kwarg = {
+                    "input": "Hello, how are you?",
+                }
+            elif call_type.value == "atranscription":
+                input_kwarg = {
+                    "file": "path/to/file",
+                }
+            else:
+                input_kwarg = {}
+
+            await getattr(router, call_type.value)(
+                model="gpt-3.5-turbo",
+                **input_kwarg,
+            )
+
+            mock_client.assert_called_once()
+
+            print(mock_client.call_args.kwargs)
+            assert mock_client.call_args.kwargs["litellm_trace_id"] is not None

tests/local_testing/test_secret_manager.py

@@ -15,22 +15,29 @@ sys.path.insert(
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
 import pytest
-
+import litellm
 from litellm.llms.AzureOpenAI.azure import get_azure_ad_token_from_oidc
 from litellm.llms.bedrock.chat import BedrockConverseLLM, BedrockLLM
-from litellm.secret_managers.aws_secret_manager import load_aws_secret_manager
+from litellm.secret_managers.aws_secret_manager_v2 import AWSSecretsManagerV2
-from litellm.secret_managers.main import get_secret
+from litellm.secret_managers.main import (
+    get_secret,
+    _should_read_secret_from_secret_manager,
+)
 
 
-@pytest.mark.skip(reason="AWS Suspended Account")
 def test_aws_secret_manager():
-    load_aws_secret_manager(use_aws_secret_manager=True)
+    import json
+
+    AWSSecretsManagerV2.load_aws_secret_manager(use_aws_secret_manager=True)
 
     secret_val = get_secret("litellm_master_key")
 
     print(f"secret_val: {secret_val}")
 
-    assert secret_val == "sk-1234"
+    # cast json to dict
+    secret_val = json.loads(secret_val)
+
+    assert secret_val["litellm_master_key"] == "sk-1234"
 
 
 def redact_oidc_signature(secret_val):
@@ -240,3 +247,71 @@ def test_google_secret_manager_read_in_memory():
     )
     print("secret_val: {}".format(secret_val))
     assert secret_val == "lite-llm"
+
+
+def test_should_read_secret_from_secret_manager():
+    """
+    Test that _should_read_secret_from_secret_manager returns correct values based on access mode
+    """
+    from litellm.proxy._types import KeyManagementSettings
+
+    # Test when secret manager client is None
+    litellm.secret_manager_client = None
+    litellm._key_management_settings = KeyManagementSettings()
+    assert _should_read_secret_from_secret_manager() is False
+
+    # Test with secret manager client and read_only access
+    litellm.secret_manager_client = "dummy_client"
+    litellm._key_management_settings = KeyManagementSettings(access_mode="read_only")
+    assert _should_read_secret_from_secret_manager() is True
+
+    # Test with secret manager client and read_and_write access
+    litellm._key_management_settings = KeyManagementSettings(
+        access_mode="read_and_write"
+    )
+    assert _should_read_secret_from_secret_manager() is True
+
+    # Test with secret manager client and write_only access
+    litellm._key_management_settings = KeyManagementSettings(access_mode="write_only")
+    assert _should_read_secret_from_secret_manager() is False
+
+    # Reset global variables
+    litellm.secret_manager_client = None
+    litellm._key_management_settings = KeyManagementSettings()
+
+
+def test_get_secret_with_access_mode():
+    """
+    Test that get_secret respects access mode settings
+    """
+    from litellm.proxy._types import KeyManagementSettings
+
+    # Set up test environment
+    test_secret_name = "TEST_SECRET_KEY"
+    test_secret_value = "test_secret_value"
+    os.environ[test_secret_name] = test_secret_value
+
+    # Test with write_only access (should read from os.environ)
+    litellm.secret_manager_client = "dummy_client"
+    litellm._key_management_settings = KeyManagementSettings(access_mode="write_only")
+    assert get_secret(test_secret_name) == test_secret_value
+
+    # Test with no KeyManagementSettings but secret_manager_client set
+    litellm.secret_manager_client = "dummy_client"
+    litellm._key_management_settings = KeyManagementSettings()
+    assert _should_read_secret_from_secret_manager() is True
+
+    # Test with read_only access
+    litellm._key_management_settings = KeyManagementSettings(access_mode="read_only")
+    assert _should_read_secret_from_secret_manager() is True
+
+    # Test with read_and_write access
+    litellm._key_management_settings = KeyManagementSettings(
+        access_mode="read_and_write"
+    )
+    assert _should_read_secret_from_secret_manager() is True
+
+    # Reset global variables
+    litellm.secret_manager_client = None
+    litellm._key_management_settings = KeyManagementSettings()
+    del os.environ[test_secret_name]

tests/local_testing/test_stream_chunk_builder.py

@@ -184,12 +184,11 @@ def test_stream_chunk_builder_litellm_usage_chunks():
         {"role": "assistant", "content": "uhhhh\n\n\nhmmmm.....\nthinking....\n"},
         {"role": "user", "content": "\nI am waiting...\n\n...\n"},
     ]
-    # make a regular gemini call
 
     usage: litellm.Usage = Usage(
-        completion_tokens=64,
+        completion_tokens=27,
         prompt_tokens=55,
-        total_tokens=119,
+        total_tokens=82,
         completion_tokens_details=None,
         prompt_tokens_details=None,
     )

tests/local_testing/test_streaming.py

@@ -718,7 +718,7 @@ async def test_acompletion_claude_2_stream():
     try:
         litellm.set_verbose = True
         response = await litellm.acompletion(
-            model="claude-2",
+            model="claude-2.1",
             messages=[{"role": "user", "content": "hello from litellm"}],
             stream=True,
         )
@@ -3274,7 +3274,7 @@ def test_completion_claude_3_function_call_with_streaming():
     ],  #  "claude-3-opus-20240229"
 )  #
 @pytest.mark.asyncio
-async def test_acompletion_claude_3_function_call_with_streaming(model):
+async def test_acompletion_function_call_with_streaming(model):
     litellm.set_verbose = True
     tools = [
         {
@@ -3335,6 +3335,8 @@ async def test_acompletion_claude_3_function_call_with_streaming(model):
         # raise Exception("it worked! ")
     except litellm.InternalServerError as e:
         pytest.skip(f"InternalServerError - {str(e)}")
+    except litellm.ServiceUnavailableError:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 

tests/proxy_unit_tests/test_key_generate_prisma.py

@@ -3451,3 +3451,90 @@ async def test_user_api_key_auth_db_unavailable_not_allowed():
             request=request,
             api_key="Bearer sk-123456789",
         )
+
+
+## E2E Virtual Key + Secret Manager Tests #########################################
+
+
+@pytest.mark.asyncio
+async def test_key_generate_with_secret_manager_call(prisma_client):
+    """
+    Generate a key
+    assert it exists in the secret manager
+
+    delete the key
+    assert it is deleted from the secret manager
+    """
+    from litellm.secret_managers.aws_secret_manager_v2 import AWSSecretsManagerV2
+    from litellm.proxy._types import KeyManagementSystem, KeyManagementSettings
+
+    litellm.set_verbose = True
+
+    #### Test Setup ############################################################
+    aws_secret_manager_client = AWSSecretsManagerV2()
+    litellm.secret_manager_client = aws_secret_manager_client
+    litellm._key_management_system = KeyManagementSystem.AWS_SECRET_MANAGER
+    litellm._key_management_settings = KeyManagementSettings(
+        store_virtual_keys=True,
+    )
+    general_settings = {
+        "key_management_system": "aws_secret_manager",
+        "key_management_settings": {
+            "store_virtual_keys": True,
+        },
+    }
+
+    setattr(litellm.proxy.proxy_server, "general_settings", general_settings)
+    setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
+    setattr(litellm.proxy.proxy_server, "prisma_client", prisma_client)
+    await litellm.proxy.proxy_server.prisma_client.connect()
+    ############################################################################
+
+    # generate new key
+    key_alias = f"test_alias_secret_manager_key-{uuid.uuid4()}"
+    spend = 100
+    max_budget = 400
+    models = ["fake-openai-endpoint"]
+    new_key = await generate_key_fn(
+        data=GenerateKeyRequest(
+            key_alias=key_alias, spend=spend, max_budget=max_budget, models=models
+        ),
+        user_api_key_dict=UserAPIKeyAuth(
+            user_role=LitellmUserRoles.PROXY_ADMIN,
+            api_key="sk-1234",
+            user_id="1234",
+        ),
+    )
+
+    generated_key = new_key.key
+    print(generated_key)
+
+    await asyncio.sleep(2)
+
+    # read from the secret manager
+    result = await aws_secret_manager_client.async_read_secret(secret_name=key_alias)
+
+    # Assert the correct key is stored in the secret manager
+    print("response from AWS Secret Manager")
+    print(result)
+    assert result == generated_key
+
+    # delete the key
+    await delete_key_fn(
+        data=KeyRequest(keys=[generated_key]),
+        user_api_key_dict=UserAPIKeyAuth(
+            user_role=LitellmUserRoles.PROXY_ADMIN, api_key="sk-1234", user_id="1234"
+        ),
+    )
+
+    await asyncio.sleep(2)
+
+    # Assert the key is deleted from the secret manager
+    result = await aws_secret_manager_client.async_read_secret(secret_name=key_alias)
+    assert result is None
+
+    # cleanup
+    setattr(litellm.proxy.proxy_server, "general_settings", {})
+
+
+################################################################################

tests/proxy_unit_tests/test_proxy_server.py

@@ -1500,6 +1500,31 @@ async def test_add_callback_via_key_litellm_pre_call_utils(
         assert new_data["failure_callback"] == expected_failure_callbacks
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "disable_fallbacks_set",
+    [
+        True,
+        False,
+    ],
+)
+async def test_disable_fallbacks_by_key(disable_fallbacks_set):
+    from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup
+
+    key_metadata = {"disable_fallbacks": disable_fallbacks_set}
+    existing_data = {
+        "model": "azure/chatgpt-v-2",
+        "messages": [{"role": "user", "content": "write 1 sentence poem"}],
+    }
+    data = LiteLLMProxyRequestSetup.add_key_level_controls(
+        key_metadata=key_metadata,
+        data=existing_data,
+        _metadata_variable_name="metadata",
+    )
+
+    assert data["disable_fallbacks"] == disable_fallbacks_set
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "callback_type, expected_success_callbacks, expected_failure_callbacks",