+
+
+**Default and Recommended for Production** - Best performance with minimal latency overhead.
+
+Picks a deployment based on the provided **Requests per minute (rpm) or Tokens per minute (tpm)**
+
+If `rpm` or `tpm` is not provided, it randomly picks a deployment
+
+You can also set a `weight` param, to specify which model should get picked when.
+
+
+
+
+##### **LiteLLM Proxy Config.yaml**
+
+```yaml
+model_list:
+ - model_name: gpt-3.5-turbo
+ litellm_params:
+ model: azure/chatgpt-v-2
+ api_key: os.environ/AZURE_API_KEY
+ api_version: os.environ/AZURE_API_VERSION
+ api_base: os.environ/AZURE_API_BASE
+ rpm: 900
+ - model_name: gpt-3.5-turbo
+ litellm_params:
+ model: azure/chatgpt-functioncalling
+ api_key: os.environ/AZURE_API_KEY
+ api_version: os.environ/AZURE_API_VERSION
+ api_base: os.environ/AZURE_API_BASE
+ rpm: 10
+```
+
+##### **Python SDK**
+
+```python
+from litellm import Router
+import asyncio
+
+model_list = [{ # list of model deployments
+ "model_name": "gpt-3.5-turbo", # model alias
+ "litellm_params": { # params for litellm completion/embedding call
+ "model": "azure/chatgpt-v-2", # actual model name
+ "api_key": os.getenv("AZURE_API_KEY"),
+ "api_version": os.getenv("AZURE_API_VERSION"),
+ "api_base": os.getenv("AZURE_API_BASE"),
+ "rpm": 900, # requests per minute for this API
+ }
+}, {
+ "model_name": "gpt-3.5-turbo",
+ "litellm_params": { # params for litellm completion/embedding call
+ "model": "azure/chatgpt-functioncalling",
+ "api_key": os.getenv("AZURE_API_KEY"),
+ "api_version": os.getenv("AZURE_API_VERSION"),
+ "api_base": os.getenv("AZURE_API_BASE"),
+ "rpm": 10,
+ }
+},]
+
+# init router
+router = Router(model_list=model_list, routing_strategy="simple-shuffle")
+async def router_acompletion():
+ response = await router.acompletion(
+ model="gpt-3.5-turbo",
+ messages=[{"role": "user", "content": "Hey, how's it going?"}]
+ )
+ print(response)
+ return response
+
+asyncio.run(router_acompletion())
+```
+
+
+
+
+##### **LiteLLM Proxy Config.yaml**
+
+```yaml
+model_list:
+ - model_name: gpt-3.5-turbo
+ litellm_params:
+ model: azure/chatgpt-v-2
+ api_key: os.environ/AZURE_API_KEY
+ api_version: os.environ/AZURE_API_VERSION
+ api_base: os.environ/AZURE_API_BASE
+ weight: 9
+ - model_name: gpt-3.5-turbo
+ litellm_params:
+ model: azure/chatgpt-functioncalling
+ api_key: os.environ/AZURE_API_KEY
+ api_version: os.environ/AZURE_API_VERSION
+ api_base: os.environ/AZURE_API_BASE
+ weight: 1
+```
+
+##### **Python SDK**
+
+```python
+from litellm import Router
+import asyncio
+
+model_list = [{
+ "model_name": "gpt-3.5-turbo", # model alias
+ "litellm_params": {
+ "model": "azure/chatgpt-v-2", # actual model name
+ "api_key": os.getenv("AZURE_API_KEY"),
+ "api_version": os.getenv("AZURE_API_VERSION"),
+ "api_base": os.getenv("AZURE_API_BASE"),
+ "weight": 9, # pick this 90% of the time
+ }
+}, {
+ "model_name": "gpt-3.5-turbo",
+ "litellm_params": {
+ "model": "azure/chatgpt-functioncalling",
+ "api_key": os.getenv("AZURE_API_KEY"),
+ "api_version": os.getenv("AZURE_API_VERSION"),
+ "api_base": os.getenv("AZURE_API_BASE"),
+ "weight": 1,
+ }
+}]
+
+# init router
+router = Router(model_list=model_list, routing_strategy="simple-shuffle")
+async def router_acompletion():
+ response = await router.acompletion(
+ model="gpt-3.5-turbo",
+ messages=[{"role": "user", "content": "Hey, how's it going?"}]
+ )
+ print(response)
+ return response
+
+asyncio.run(router_acompletion())
+```
+
+
+
+
+
+> [!WARNING]
+**Usage-based routing is not recommended for production due to performance impacts.** Use `simple-shuffle` (default) for optimal performance in high-traffic scenarios. Usage-based routing adds significant latency due to Redis operations for tracking usage across deployments.
+
+
**🎉 NEW** This is an async implementation of usage-based-routing.
**Filters out deployment if tpm/rpm limit exceeded** - If you pass in the deployment's tpm/rpm limits.
@@ -209,7 +351,7 @@ router = Router(model_list=model_list,
redis_host=os.environ["REDIS_HOST"],
redis_password=os.environ["REDIS_PASSWORD"],
redis_port=os.environ["REDIS_PORT"],
- routing_strategy="usage-based-routing-v2" # 👈 KEY CHANGE
+ routing_strategy="simple-shuffle" # 👈 RECOMMENDED - best performance
enable_pre_call_checks=True, # enables router rate limits for concurrent calls
)
@@ -241,7 +383,7 @@ model_list:
rpm: 1000
router_settings:
- routing_strategy: usage-based-routing-v2 # 👈 KEY CHANGE
+ routing_strategy: simple-shuffle # 👈 RECOMMENDED - best performance
redis_host:
redis_password:
redis_port:
@@ -365,143 +507,7 @@ router_settings:
```
-
-**Default** Picks a deployment based on the provided **Requests per minute (rpm) or Tokens per minute (tpm)**
-
-If `rpm` or `tpm` is not provided, it randomly picks a deployment
-
-You can also set a `weight` param, to specify which model should get picked when.
-
-
-
-
-##### **LiteLLM Proxy Config.yaml**
-
-```yaml
-model_list:
- - model_name: gpt-3.5-turbo
- litellm_params:
- model: azure/chatgpt-v-2
- api_key: os.environ/AZURE_API_KEY
- api_version: os.environ/AZURE_API_VERSION
- api_base: os.environ/AZURE_API_BASE
- rpm: 900
- - model_name: gpt-3.5-turbo
- litellm_params:
- model: azure/chatgpt-functioncalling
- api_key: os.environ/AZURE_API_KEY
- api_version: os.environ/AZURE_API_VERSION
- api_base: os.environ/AZURE_API_BASE
- rpm: 10
-```
-
-##### **Python SDK**
-
-```python
-from litellm import Router
-import asyncio
-
-model_list = [{ # list of model deployments
- "model_name": "gpt-3.5-turbo", # model alias
- "litellm_params": { # params for litellm completion/embedding call
- "model": "azure/chatgpt-v-2", # actual model name
- "api_key": os.getenv("AZURE_API_KEY"),
- "api_version": os.getenv("AZURE_API_VERSION"),
- "api_base": os.getenv("AZURE_API_BASE"),
- "rpm": 900, # requests per minute for this API
- }
-}, {
- "model_name": "gpt-3.5-turbo",
- "litellm_params": { # params for litellm completion/embedding call
- "model": "azure/chatgpt-functioncalling",
- "api_key": os.getenv("AZURE_API_KEY"),
- "api_version": os.getenv("AZURE_API_VERSION"),
- "api_base": os.getenv("AZURE_API_BASE"),
- "rpm": 10,
- }
-},]
-
-# init router
-router = Router(model_list=model_list, routing_strategy="simple-shuffle")
-async def router_acompletion():
- response = await router.acompletion(
- model="gpt-3.5-turbo",
- messages=[{"role": "user", "content": "Hey, how's it going?"}]
- )
- print(response)
- return response
-
-asyncio.run(router_acompletion())
-```
-
-
-
-
-##### **LiteLLM Proxy Config.yaml**
-
-```yaml
-model_list:
- - model_name: gpt-3.5-turbo
- litellm_params:
- model: azure/chatgpt-v-2
- api_key: os.environ/AZURE_API_KEY
- api_version: os.environ/AZURE_API_VERSION
- api_base: os.environ/AZURE_API_BASE
- weight: 9
- - model_name: gpt-3.5-turbo
- litellm_params:
- model: azure/chatgpt-functioncalling
- api_key: os.environ/AZURE_API_KEY
- api_version: os.environ/AZURE_API_VERSION
- api_base: os.environ/AZURE_API_BASE
- weight: 1
-```
-
-
-##### **Python SDK**
-
-```python
-from litellm import Router
-import asyncio
-
-model_list = [{
- "model_name": "gpt-3.5-turbo", # model alias
- "litellm_params": {
- "model": "azure/chatgpt-v-2", # actual model name
- "api_key": os.getenv("AZURE_API_KEY"),
- "api_version": os.getenv("AZURE_API_VERSION"),
- "api_base": os.getenv("AZURE_API_BASE"),
- "weight": 9, # pick this 90% of the time
- }
-}, {
- "model_name": "gpt-3.5-turbo",
- "litellm_params": {
- "model": "azure/chatgpt-functioncalling",
- "api_key": os.getenv("AZURE_API_KEY"),
- "api_version": os.getenv("AZURE_API_VERSION"),
- "api_base": os.getenv("AZURE_API_BASE"),
- "weight": 1,
- }
-}]
-
-# init router
-router = Router(model_list=model_list, routing_strategy="simple-shuffle")
-async def router_acompletion():
- response = await router.acompletion(
- model="gpt-3.5-turbo",
- messages=[{"role": "user", "content": "Hey, how's it going?"}]
- )
- print(response)
- return response
-
-asyncio.run(router_acompletion())
-```
-
-
-
-
-
This will route to the deployment with the lowest TPM usage for that minute.
diff --git a/docs/my-website/docs/scheduler.md b/docs/my-website/docs/scheduler.md
index 2b0a582626c..9b84c374e3b 100644
--- a/docs/my-website/docs/scheduler.md
+++ b/docs/my-website/docs/scheduler.md
@@ -41,7 +41,7 @@ router = Router(
},
],
timeout=2, # timeout request if takes > 2s
- routing_strategy="usage-based-routing-v2",
+ routing_strategy="simple-shuffle", # recommended for best performance
polling_interval=0.03 # poll queue every 3ms if no healthy deployments
)
diff --git a/docs/my-website/img/create_team_member_rate_limits.png b/docs/my-website/img/create_team_member_rate_limits.png
new file mode 100644
index 00000000000..0c5eba04461
Binary files /dev/null and b/docs/my-website/img/create_team_member_rate_limits.png differ
diff --git a/docs/my-website/img/release_notes/team_member_rate_limits.png b/docs/my-website/img/release_notes/team_member_rate_limits.png
new file mode 100644
index 00000000000..ec0affb1271
Binary files /dev/null and b/docs/my-website/img/release_notes/team_member_rate_limits.png differ
diff --git a/docs/my-website/release_notes/v1.74.15-stable/index.md b/docs/my-website/release_notes/v1.74.15-stable/index.md
index dd748f18ffa..9807a00b7e7 100644
--- a/docs/my-website/release_notes/v1.74.15-stable/index.md
+++ b/docs/my-website/release_notes/v1.74.15-stable/index.md
@@ -86,9 +86,9 @@ This is great to central AI Platform teams looking to track how they are helping
| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Cost per Image |
| ----------- | -------------------------------------- | -------------- | ------------------- | -------------------- | -------------- |
| OpenRouter | `openrouter/x-ai/grok-4` | 256k | $3 | $15 | N/A |
-| Google AI Studio | `gemini/imagen-4.0-generate-preview-06-06` | N/A | N/A | N/A | $0.04 |
-| Google AI Studio | `gemini/imagen-4.0-ultra-generate-preview-06-06` | N/A | N/A | N/A | $0.06 |
-| Google AI Studio | `gemini/imagen-4.0-fast-generate-preview-06-06` | N/A | N/A | N/A | $0.02 |
+| Google AI Studio | `gemini/imagen-4.0-generate-001` | N/A | N/A | N/A | $0.04 |
+| Google AI Studio | `gemini/imagen-4.0-ultra-generate-001` | N/A | N/A | N/A | $0.06 |
+| Google AI Studio | `gemini/imagen-4.0-fast-generate-001` | N/A | N/A | N/A | $0.02 |
| Google AI Studio | `gemini/imagen-3.0-generate-002` | N/A | N/A | N/A | $0.04 |
| Google AI Studio | `gemini/imagen-3.0-generate-001` | N/A | N/A | N/A | $0.04 |
| Google AI Studio | `gemini/imagen-3.0-fast-generate-001` | N/A | N/A | N/A | $0.02 |
diff --git a/docs/my-website/release_notes/v1.75.8/index.md b/docs/my-website/release_notes/v1.75.8/index.md
index 2e90f26c3ad..d7d4f37c4ee 100644
--- a/docs/my-website/release_notes/v1.75.8/index.md
+++ b/docs/my-website/release_notes/v1.75.8/index.md
@@ -1,5 +1,5 @@
---
-title: "[PRE-RELEASE]v1.75.8"
+title: "v1.75.8-stable - Team Member Rate Limits"
slug: "v1-75-8"
date: 2025-08-16T10:00:00
authors:
@@ -28,7 +28,7 @@ import TabItem from '@theme/TabItem';
docker run \
-e STORE_MODEL_IN_DB=True \
-p 4000:4000 \
-ghcr.io/berriai/litellm:v1.75.8
+ghcr.io/berriai/litellm:v1.75.8-stable
```
@@ -52,6 +52,22 @@ pip install litellm==1.75.8
---
+## Team Member Rate Limits
+
+
+
+ LiteLLM MCP Architecture: Use MCP tools with all LiteLLM supported models
+
+
+
+This release adds support for setting rate limits on individual members (including machine users) within a team. Teams can now give each agent its own rate limits—so that heavy-traffic agents don’t impact other agents or human users.
+
+Agents can authenticate with LiteLLM using JWT and the same team role as human users, while still enforcing per-agent rate limits.
+
+
## New Models / Updated Models
#### New Model Support
diff --git a/docs/my-website/release_notes/v1.76.1-stable/index.md b/docs/my-website/release_notes/v1.76.1-stable/index.md
new file mode 100644
index 00000000000..4437b7f5799
--- /dev/null
+++ b/docs/my-website/release_notes/v1.76.1-stable/index.md
@@ -0,0 +1,269 @@
+---
+title: "v1.76.1-stable - Gemini 2.5 Flash Image"
+slug: "v1-76-1"
+date: 2025-08-30T10:00:00
+authors:
+ - name: Krrish Dholakia
+ title: CEO, LiteLLM
+ url: https://www.linkedin.com/in/krish-d/
+ image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+ - name: Ishaan Jaffer
+ title: CTO, LiteLLM
+ url: https://www.linkedin.com/in/reffajnaahsi/
+ image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+## Deploy this version
+
+
+
+
+``` showLineNumbers title="docker run litellm"
+docker run \
+-e STORE_MODEL_IN_DB=True \
+-p 4000:4000 \
+ghcr.io/berriai/litellm:v1.76.1
+```
+
+
+
+
+``` showLineNumbers title="pip install litellm"
+pip install litellm==1.76.1
+```
+
+
+
+
+---
+
+## Key Highlights
+
+- **Major Performance Improvements** - 6.5x faster LiteLLM Python SDK completion with fastuuid integration.
+- **New Model Support** - Gemini 2.5 Flash Image Preview, Grok Code Fast, and GPT Realtime models
+- **Enhanced Provider Support** - DeepSeek-v3.1 pricing on Fireworks AI, Vercel AI Gateway, and improved Anthropic/GitHub Copilot integration
+- **MCP Improvements** - Better connection testing and SSE MCP tools bug fixes
+
+## Major Changes
+- Added support for using Gemini 2.5 Flash Image Preview with /chat/completions. **🚨 Warning** If you were using `gemini-2.0-flash-exp-image-generation` please follow this migration guide.
+ [Gemini Image Generation Migration Guide](../../docs/extras/gemini_img_migration)
+---
+
+## Performance Improvements
+
+This release includes significant performance optimizations:
+
+- **6.5x faster LiteLLM Python SDK Completion** - Major performance boost for completion operations - [PR #13990](https://github.com/BerriAI/litellm/pull/13990)
+- **fastuuid Integration** - 2.1x faster UUID generation with +80 RPS improvement for /chat/completions and other LLM endpoints - [PR #13992](https://github.com/BerriAI/litellm/pull/13992), [PR #14016](https://github.com/BerriAI/litellm/pull/14016)
+- **Optimized Request Logging** - Don't print request params by default for +50 RPS improvement - [PR #14015](https://github.com/BerriAI/litellm/pull/14015)
+- **Cache Performance** - 21% speedup in InMemoryCache.evict_cache and 45% speedup in `_is_debugging_on` function - [PR #14012](https://github.com/BerriAI/litellm/pull/14012), [PR #13988](https://github.com/BerriAI/litellm/pull/13988)
+
+---
+
+## New Models / Updated Models
+
+#### New Model Support
+
+| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
+| ----------- | -------------------------------------- | -------------- | ------------------- | -------------------- | -------- |
+| Google | `gemini-2.5-flash-image-preview` | 1M | $0.30 | $2.50 | Chat completions + image generation ($0.039/image) |
+| X.AI | `xai/grok-code-fast` | 256K | $0.20 | $1.50 | Code generation |
+| OpenAI | `gpt-realtime` | 32K | $4.00 | $16.00 | Real-time conversation + audio |
+| Vercel AI Gateway | `vercel_ai_gateway/openai/o3` | 200K | $2.00 | $8.00 | Advanced reasoning |
+| Vercel AI Gateway | `vercel_ai_gateway/openai/o3-mini` | 200K | $1.10 | $4.40 | Efficient reasoning |
+| Vercel AI Gateway | `vercel_ai_gateway/openai/o4-mini` | 200K | $1.10 | $4.40 | Latest mini model |
+| DeepInfra | `deepinfra/zai-org/GLM-4.5` | 131K | $0.55 | $2.00 | Chat completions |
+| Perplexity | `perplexity/codellama-34b-instruct` | 16K | $0.35 | $1.40 | Code generation |
+| Fireworks AI | `fireworks_ai/accounts/fireworks/models/deepseek-v3p1` | 128K | $0.56 | $1.68 | Chat completions |
+
+**Additional Models Added:** Various other Vercel AI Gateway models were added too. See [models.litellm.ai](https://models.litellm.ai) for the full list.
+
+#### Features
+
+- **[Google Gemini](../../docs/providers/gemini)**
+ - Added support for `gemini-2.5-flash-image-preview` with image return capability - [PR #13979](https://github.com/BerriAI/litellm/pull/13979), [PR #13983](https://github.com/BerriAI/litellm/pull/13983)
+ - Support for requests with only system prompt - [PR #14010](https://github.com/BerriAI/litellm/pull/14010)
+ - Fixed invalid model name error for Gemini Imagen models - [PR #13991](https://github.com/BerriAI/litellm/pull/13991)
+- **[X.AI](../../docs/providers/xai)**
+ - Added `xai/grok-code-fast` model family support - [PR #14054](https://github.com/BerriAI/litellm/pull/14054)
+ - Fixed frequency_penalty parameter for grok-4 models - [PR #14078](https://github.com/BerriAI/litellm/pull/14078)
+- **[OpenAI](../../docs/providers/openai)**
+ - Added support for gpt-realtime models - [PR #14082](https://github.com/BerriAI/litellm/pull/14082)
+ - Support for reasoning and reasoning_effort parameters by default - [PR #12865](https://github.com/BerriAI/litellm/pull/12865)
+- **[Fireworks AI](../../docs/providers/fireworks_ai)**
+ - Added DeepSeek-v3.1 pricing - [PR #13958](https://github.com/BerriAI/litellm/pull/13958)
+- **[DeepInfra](../../docs/providers/deepinfra)**
+ - Fixed reasoning_effort setting for DeepSeek-V3.1 - [PR #14053](https://github.com/BerriAI/litellm/pull/14053)
+- **[GitHub Copilot](../../docs/providers/github_copilot)**
+ - Added support for thinking and reasoning_effort parameters - [PR #13691](https://github.com/BerriAI/litellm/pull/13691)
+ - Added image headers support - [PR #13955](https://github.com/BerriAI/litellm/pull/13955)
+- **[Anthropic](../../docs/providers/anthropic)**
+ - Support for custom Anthropic-compatible API endpoints - [PR #13945](https://github.com/BerriAI/litellm/pull/13945)
+ - Fixed /messages fallback from Anthropic API to Bedrock API - [PR #13946](https://github.com/BerriAI/litellm/pull/13946)
+- **[Nebius](../../docs/providers/nebius)**
+ - Expanded provider models and normalized model IDs - [PR #13965](https://github.com/BerriAI/litellm/pull/13965)
+- **[Vertex AI](../../docs/providers/vertex)**
+ - Fixed Vertex Mistral streaming issues - [PR #13952](https://github.com/BerriAI/litellm/pull/13952)
+ - Fixed anyOf corner cases for Gemini tool calls - [PR #12797](https://github.com/BerriAI/litellm/pull/12797)
+- **[Bedrock](../../docs/providers/bedrock)**
+ - Fixed structure output issues - [PR #14005](https://github.com/BerriAI/litellm/pull/14005)
+- **[OpenRouter](../../docs/providers/openrouter)**
+ - Added GPT-5 family models pricing - [PR #13536](https://github.com/BerriAI/litellm/pull/13536)
+
+#### New Provider Support
+
+- **[Vercel AI Gateway](../../docs/providers/vercel_ai_gateway)**
+ - New provider support added - [PR #13144](https://github.com/BerriAI/litellm/pull/13144)
+- **[DataRobot](../../docs/providers/datarobot)**
+ - Added provider documentation - [PR #14038](https://github.com/BerriAI/litellm/pull/14038), [PR #14074](https://github.com/BerriAI/litellm/pull/14074)
+
+---
+
+## LLM API Endpoints
+
+#### Features
+
+- **[Images API](../../docs/image_generation)**
+ - Support for multiple images in OpenAI images/edits endpoint - [PR #13916](https://github.com/BerriAI/litellm/pull/13916)
+ - Allow using dynamic `api_key` for image generation requests - [PR #14007](https://github.com/BerriAI/litellm/pull/14007)
+- **[Responses API](../../docs/response_api)**
+ - Fixed `/responses` endpoint ignoring extra_headers in GitHub Copilot - [PR #13775](https://github.com/BerriAI/litellm/pull/13775)
+ - Added support for new web_search tool - [PR #14083](https://github.com/BerriAI/litellm/pull/14083)
+- **[Azure Passthrough](../../docs/providers/azure/azure)**
+ - Fixed Azure Passthrough request with streaming - [PR #13831](https://github.com/BerriAI/litellm/pull/13831)
+
+#### Bugs
+
+- **General**
+ - Fixed handling of None metadata in batch requests - [PR #13996](https://github.com/BerriAI/litellm/pull/13996)
+ - Fixed token_counter with special token input - [PR #13374](https://github.com/BerriAI/litellm/pull/13374)
+ - Removed incorrect web search support for azure/gpt-4.1 family - [PR #13566](https://github.com/BerriAI/litellm/pull/13566)
+
+---
+
+## [MCP Gateway](../../docs/mcp)
+
+#### Features
+
+- **SSE MCP Tools**
+ - Bug fix for adding SSE MCP tools - improved connection testing when adding MCPs - [PR #14048](https://github.com/BerriAI/litellm/pull/14048)
+
+[Read More](../../docs/mcp)
+
+---
+
+## Management Endpoints / UI
+
+#### Features
+
+- **Team Management**
+ - Allow setting Team Member RPM/TPM limits when creating a team - [PR #13943](https://github.com/BerriAI/litellm/pull/13943)
+- **UI Improvements**
+ - Fixed Next.js Security Vulnerabilities in UI Dashboard - [PR #14084](https://github.com/BerriAI/litellm/pull/14084)
+ - Fixed collapsible navbar design - [PR #14075](https://github.com/BerriAI/litellm/pull/14075)
+
+#### Bugs
+
+- **Authentication**
+ - Fixed Virtual keys with llm_api type causing Internal Server Error for /anthropic/* and other LLM passthrough routes - [PR #14046](https://github.com/BerriAI/litellm/pull/14046)
+
+---
+
+## Logging / Guardrail Integrations
+
+#### Features
+
+- **[Langfuse OTEL](../../docs/proxy/logging#langfuse)**
+ - Allow using LANGFUSE_OTEL_HOST for configuring host - [PR #14013](https://github.com/BerriAI/litellm/pull/14013)
+- **[Braintrust](../../docs/proxy/logging#braintrust)**
+ - Added span name metadata feature - [PR #13573](https://github.com/BerriAI/litellm/pull/13573)
+ - Fixed tests to reference moved attributes in `braintrust_logging` module - [PR #13978](https://github.com/BerriAI/litellm/pull/13978)
+- **[OpenMeter](../../docs/proxy/logging#openmeter)**
+ - Set user from token user_id for OpenMeter integration - [PR #13152](https://github.com/BerriAI/litellm/pull/13152)
+
+#### New Guardrail Support
+
+- **[Noma Security](../../docs/proxy/guardrails)**
+ - Added Noma Security guardrail support - [PR #13572](https://github.com/BerriAI/litellm/pull/13572)
+- **[Pangea](../../docs/proxy/guardrails)**
+ - Updated Pangea Guardrail to support new AIDR endpoint - [PR #13160](https://github.com/BerriAI/litellm/pull/13160)
+
+---
+
+## Performance / Loadbalancing / Reliability improvements
+
+#### Features
+
+- **Caching**
+ - Verify if cache entry has expired prior to serving it to client - [PR #13933](https://github.com/BerriAI/litellm/pull/13933)
+ - Fixed error saving latency as timedelta on Redis - [PR #14040](https://github.com/BerriAI/litellm/pull/14040)
+- **Router**
+ - Refactored router to choose weights by 'weight', 'rpm', 'tpm' in one loop for simple_shuffle - [PR #13562](https://github.com/BerriAI/litellm/pull/13562)
+- **Logging**
+ - Fixed LoggingWorker graceful shutdown to prevent CancelledError warnings - [PR #14050](https://github.com/BerriAI/litellm/pull/14050)
+ - Enhanced logging for containers to log on files both with usual format and json format - [PR #13394](https://github.com/BerriAI/litellm/pull/13394)
+
+#### Bugs
+
+- **Dependencies**
+ - Bumped `orjson` version to "3.11.2" - [PR #13969](https://github.com/BerriAI/litellm/pull/13969)
+
+---
+
+## General Proxy Improvements
+
+#### Features
+
+- **AWS**
+ - Add support for AWS assume_role with a session token - [PR #13919](https://github.com/BerriAI/litellm/pull/13919)
+- **OCI Provider**
+ - Added oci_key_file as an optional_parameter - [PR #14036](https://github.com/BerriAI/litellm/pull/14036)
+- **Configuration**
+ - Allow configuration to set threshold before request entry in spend log gets truncated - [PR #14042](https://github.com/BerriAI/litellm/pull/14042)
+ - Enhanced proxy_config configuration: add support for existing configmap in Helm charts - [PR #14041](https://github.com/BerriAI/litellm/pull/14041)
+- **Docker**
+ - Added back supervisor to non-root image - [PR #13922](https://github.com/BerriAI/litellm/pull/13922)
+
+
+---
+
+## New Contributors
+* @ArthurRenault made their first contribution in [PR #13922](https://github.com/BerriAI/litellm/pull/13922)
+* @stevenmanton made their first contribution in [PR #13919](https://github.com/BerriAI/litellm/pull/13919)
+* @uc4w6c made their first contribution in [PR #13914](https://github.com/BerriAI/litellm/pull/13914)
+* @nielsbosma made their first contribution in [PR #13573](https://github.com/BerriAI/litellm/pull/13573)
+* @Yuki-Imajuku made their first contribution in [PR #13567](https://github.com/BerriAI/litellm/pull/13567)
+* @codeflash-ai[bot] made their first contribution in [PR #13988](https://github.com/BerriAI/litellm/pull/13988)
+* @ColeFrench made their first contribution in [PR #13978](https://github.com/BerriAI/litellm/pull/13978)
+* @dttran-glo made their first contribution in [PR #13969](https://github.com/BerriAI/litellm/pull/13969)
+* @manascb1344 made their first contribution in [PR #13965](https://github.com/BerriAI/litellm/pull/13965)
+* @DorZion made their first contribution in [PR #13572](https://github.com/BerriAI/litellm/pull/13572)
+* @edwardsamuel made their first contribution in [PR #13536](https://github.com/BerriAI/litellm/pull/13536)
+* @blahgeek made their first contribution in [PR #13374](https://github.com/BerriAI/litellm/pull/13374)
+* @Deviad made their first contribution in [PR #13394](https://github.com/BerriAI/litellm/pull/13394)
+* @XSAM made their first contribution in [PR #13775](https://github.com/BerriAI/litellm/pull/13775)
+* @KRRT7 made their first contribution in [PR #14012](https://github.com/BerriAI/litellm/pull/14012)
+* @ikaadil made their first contribution in [PR #13991](https://github.com/BerriAI/litellm/pull/13991)
+* @timelfrink made their first contribution in [PR #13691](https://github.com/BerriAI/litellm/pull/13691)
+* @qidu made their first contribution in [PR #13562](https://github.com/BerriAI/litellm/pull/13562)
+* @nagyv made their first contribution in [PR #13243](https://github.com/BerriAI/litellm/pull/13243)
+* @xywei made their first contribution in [PR #12885](https://github.com/BerriAI/litellm/pull/12885)
+* @ericgtkb made their first contribution in [PR #12797](https://github.com/BerriAI/litellm/pull/12797)
+* @NoWall57 made their first contribution in [PR #13945](https://github.com/BerriAI/litellm/pull/13945)
+* @lmwang9527 made their first contribution in [PR #14050](https://github.com/BerriAI/litellm/pull/14050)
+* @WilsonSunBritten made their first contribution in [PR #14042](https://github.com/BerriAI/litellm/pull/14042)
+* @Const-antine made their first contribution in [PR #14041](https://github.com/BerriAI/litellm/pull/14041)
+* @dmvieira made their first contribution in [PR #14040](https://github.com/BerriAI/litellm/pull/14040)
+* @gotsysdba made their first contribution in [PR #14036](https://github.com/BerriAI/litellm/pull/14036)
+* @moshemorad made their first contribution in [PR #14005](https://github.com/BerriAI/litellm/pull/14005)
+* @joshualipman123 made their first contribution in [PR #13144](https://github.com/BerriAI/litellm/pull/13144)
+
+---
+
+## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.76.0-nightly...v1.76.1)**
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 7827649d2d9..19ec9ffc0b2 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -40,6 +40,7 @@ const sidebars = {
"proxy/guardrails/guardrails_ai",
"proxy/guardrails/lakera_ai",
"proxy/guardrails/model_armor",
+ "proxy/guardrails/noma_security",
"proxy/guardrails/openai_moderation",
"proxy/guardrails/pangea",
"proxy/guardrails/pillar_security",
@@ -463,6 +464,7 @@ const sidebars = {
"providers/replicate",
"providers/togetherai",
"providers/v0",
+ "providers/vercel_ai_gateway",
"providers/morph",
"providers/lambda_ai",
"providers/novita",
@@ -481,6 +483,7 @@ const sidebars = {
"providers/dashscope",
"providers/bytez",
"providers/oci",
+ "providers/datarobot",
],
},
{
@@ -492,6 +495,7 @@ const sidebars = {
"guides/finetuned_models",
"guides/security_settings",
"completion/audio",
+ "completion/image_generation_chat",
"completion/web_search",
"completion/document_understanding",
"completion/vision",
diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py
index a2d781fa1c4..efee1a7783e 100644
--- a/enterprise/litellm_enterprise/integrations/prometheus.py
+++ b/enterprise/litellm_enterprise/integrations/prometheus.py
@@ -95,13 +95,14 @@ class PrometheusLogger(CustomLogger):
self.litellm_llm_api_time_to_first_token_metric = self._histogram_factory(
"litellm_llm_api_time_to_first_token_metric",
"Time to first token for a models LLM API call",
- labelnames=[
- "model",
- "hashed_api_key",
- "api_key_alias",
- "team",
- "team_alias",
- ],
+ # labelnames=[
+ # "model",
+ # "hashed_api_key",
+ # "api_key_alias",
+ # "team",
+ # "team_alias",
+ # ],
+ labelnames=self.get_labels_for_metric("litellm_llm_api_time_to_first_token_metric"),
buckets=LATENCY_BUCKETS,
)
@@ -109,15 +110,7 @@ class PrometheusLogger(CustomLogger):
self.litellm_spend_metric = self._counter_factory(
"litellm_spend_metric",
"Total spend on LLM requests",
- labelnames=[
- "end_user",
- "hashed_api_key",
- "api_key_alias",
- "model",
- "team",
- "team_alias",
- "user",
- ],
+ labelnames=self.get_labels_for_metric("litellm_spend_metric"),
)
# Counter for total_output_tokens
@@ -243,25 +236,18 @@ class PrometheusLogger(CustomLogger):
labelnames=["api_provider"],
)
- # Get all keys
- _logged_llm_labels = [
- UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value,
- UserAPIKeyLabelNames.MODEL_ID.value,
- UserAPIKeyLabelNames.API_BASE.value,
- UserAPIKeyLabelNames.API_PROVIDER.value,
- ]
-
# Metric for deployment state
self.litellm_deployment_state = self._gauge_factory(
"litellm_deployment_state",
"LLM Deployment Analytics - The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage",
- labelnames=_logged_llm_labels,
+ labelnames=self.get_labels_for_metric("litellm_deployment_state")
)
self.litellm_deployment_cooled_down = self._counter_factory(
"litellm_deployment_cooled_down",
"LLM Deployment Analytics - Number of times a deployment has been cooled down by LiteLLM load balancing logic. exception_status is the status of the exception that caused the deployment to be cooled down",
- labelnames=_logged_llm_labels + [EXCEPTION_STATUS],
+ # labelnames=_logged_llm_labels + [EXCEPTION_STATUS],
+ labelnames=self.get_labels_for_metric("litellm_deployment_cooled_down")
)
self.litellm_deployment_success_responses = self._counter_factory(
@@ -327,6 +313,7 @@ class PrometheusLogger(CustomLogger):
documentation="deprecated - use litellm_proxy_total_requests_metric. Total number of LLM calls to litellm - track total per API Key, team, user",
labelnames=self.get_labels_for_metric("litellm_requests_metric"),
)
+
except Exception as e:
print_verbose(f"Got exception on init prometheus client {str(e)}")
raise e
diff --git a/litellm/__init__.py b/litellm/__init__.py
index c405d3cdeb2..79865c83513 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -226,6 +226,7 @@ vertex_location: Optional[str] = None
predibase_tenant_id: Optional[str] = None
togetherai_api_key: Optional[str] = None
cloudflare_api_key: Optional[str] = None
+vercel_ai_gateway_key: Optional[str] = None
baseten_key: Optional[str] = None
llama_api_key: Optional[str] = None
aleph_alpha_key: Optional[str] = None
@@ -542,6 +543,7 @@ hyperbolic_models: Set = set()
recraft_models: Set = set()
cometapi_models: Set = set()
oci_models: Set = set()
+vercel_ai_gateway_models: Set = set()
def is_bedrock_pricing_only_model(key: str) -> bool:
@@ -599,6 +601,8 @@ def add_known_models():
empower_models.add(key)
elif value.get("litellm_provider") == "openrouter":
openrouter_models.add(key)
+ elif value.get("litellm_provider") == "vercel_ai_gateway":
+ vercel_ai_gateway_models.add(key)
elif value.get("litellm_provider") == "datarobot":
datarobot_models.add(key)
elif value.get("litellm_provider") == "vertex_ai-text-models":
@@ -835,6 +839,7 @@ model_list = list(
| recraft_models
| cometapi_models
| oci_models
+ | vercel_ai_gateway_models
)
model_list_set = set(model_list)
@@ -853,6 +858,7 @@ models_by_provider: dict = {
"together_ai": together_ai_models,
"baseten": baseten_models,
"openrouter": openrouter_models,
+ "vercel_ai_gateway": vercel_ai_gateway_models,
"datarobot": datarobot_models,
"vertex_ai": vertex_chat_models | vertex_text_models | vertex_anthropic_models | vertex_vision_models | vertex_language_models | vertex_deepseek_models,
"ai21": ai21_models,
@@ -1247,6 +1253,7 @@ from .llms.oci.chat.transformation import OCIChatConfig
from .llms.morph.chat.transformation import MorphChatConfig
from .llms.lambda_ai.chat.transformation import LambdaAIChatConfig
from .llms.hyperbolic.chat.transformation import HyperbolicChatConfig
+from .llms.vercel_ai_gateway.chat.transformation import VercelAIGatewayConfig
from .main import * # type: ignore
from .integrations import *
from .llms.custom_httpx.async_client_cleanup import close_litellm_async_clients
@@ -1254,6 +1261,7 @@ from .exceptions import (
AuthenticationError,
InvalidRequestError,
BadRequestError,
+ ImageFetchError,
NotFoundError,
RateLimitError,
ServiceUnavailableError,
@@ -1278,7 +1286,6 @@ from .router import Router
from .assistants.main import *
from .batches.main import *
from .images.main import *
-from .vector_stores import *
from .batch_completion.main import * # type: ignore
from .rerank_api.main import *
from .llms.anthropic.experimental_pass_through.messages.handler import *
diff --git a/litellm/_logging.py b/litellm/_logging.py
index 8c23994f92a..73902d2fc5a 100644
--- a/litellm/_logging.py
+++ b/litellm/_logging.py
@@ -108,6 +108,7 @@ verbose_router_logger.addHandler(handler)
verbose_proxy_logger.addHandler(handler)
verbose_logger.addHandler(handler)
+
def _suppress_loggers():
"""Suppress noisy loggers at INFO level"""
# Suppress httpx request logging at INFO level
@@ -120,6 +121,7 @@ def _suppress_loggers():
apscheduler_scheduler_logger = logging.getLogger("apscheduler.scheduler")
apscheduler_scheduler_logger.setLevel(logging.WARNING)
+
# Call the suppression function
_suppress_loggers()
@@ -187,6 +189,4 @@ def _is_debugging_on() -> bool:
"""
Returns True if debugging is on
"""
- if verbose_logger.isEnabledFor(logging.DEBUG) or set_verbose is True:
- return True
- return False
+ return verbose_logger.isEnabledFor(logging.DEBUG) or set_verbose is True
diff --git a/litellm/caching/in_memory_cache.py b/litellm/caching/in_memory_cache.py
index 47f911894a3..63869474d47 100644
--- a/litellm/caching/in_memory_cache.py
+++ b/litellm/caching/in_memory_cache.py
@@ -112,14 +112,15 @@ class InMemoryCache(BaseCache):
- 3. the size of in-memory cache is bounded
"""
- for key in list(self.ttl_dict.keys()):
- if self._is_key_expired(key):
- self._remove_key(key)
+ current_time = time.time()
+ expired_keys = [key for key, ttl in self.ttl_dict.items() if current_time > ttl]
+ for key in expired_keys:
+ self._remove_key(key)
- # de-reference the removed item
- # https://www.geeksforgeeks.org/diagnosing-and-fixing-memory-leaks-in-python/
- # One of the most common causes of memory leaks in Python is the retention of objects that are no longer being used.
- # This can occur when an object is referenced by another object, but the reference is never removed.
+ # de-reference the removed item
+ # https://www.geeksforgeeks.org/diagnosing-and-fixing-memory-leaks-in-python/
+ # One of the most common causes of memory leaks in Python is the retention of objects that are no longer being used.
+ # This can occur when an object is referenced by another object, but the reference is never removed.
def allow_ttl_override(self, key: str) -> bool:
"""
diff --git a/litellm/caching/s3_cache.py b/litellm/caching/s3_cache.py
index 15f7a5c1e16..180964605f6 100644
--- a/litellm/caching/s3_cache.py
+++ b/litellm/caching/s3_cache.py
@@ -13,6 +13,7 @@ import asyncio
import json
from functools import partial
from typing import Optional
+from datetime import datetime, timezone, timedelta
from litellm._logging import print_verbose, verbose_logger
@@ -69,11 +70,9 @@ class S3Cache(BaseCache):
if ttl is not None:
cache_control = f"immutable, max-age={ttl}, s-maxage={ttl}"
- import datetime
# Calculate expiration time
- expiration_time = datetime.datetime.now() + datetime.timedelta(seconds=ttl)
-
+ expiration_time = datetime.now(timezone.utc) + timedelta(seconds=ttl)
# Upload the data to S3 with the calculated expiration time
self.s3_client.put_object(
Bucket=self.bucket_name,
@@ -126,6 +125,13 @@ class S3Cache(BaseCache):
)
if cached_response is not None:
+ if "Expires" in cached_response:
+ expires_time = cached_response['Expires']
+ current_time = datetime.now(expires_time.tzinfo)
+
+ if current_time > expires_time:
+ return None
+
# cached_response is in `b{} convert it to ModelResponse
cached_response = (
cached_response["Body"].read().decode("utf-8")
diff --git a/litellm/constants.py b/litellm/constants.py
index 72cc52fdf21..0655473301f 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -14,6 +14,7 @@ DEFAULT_S3_BATCH_SIZE = int(os.getenv("DEFAULT_S3_BATCH_SIZE", 512))
DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int(
os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10)
)
+DEFAULT_NUM_WORKERS_LITELLM_PROXY = int(os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 4))
DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512))
SQS_SEND_MESSAGE_ACTION = "SendMessage"
SQS_API_VERSION = "2012-11-05"
@@ -157,6 +158,7 @@ NON_LLM_CONNECTION_TIMEOUT = int(
os.getenv("NON_LLM_CONNECTION_TIMEOUT", 15)
) # timeout for adjacent services (e.g. jwt auth)
MAX_EXCEPTION_MESSAGE_LENGTH = int(os.getenv("MAX_EXCEPTION_MESSAGE_LENGTH", 2000))
+MAX_STRING_LENGTH_PROMPT_IN_DB = int(os.getenv("MAX_STRING_LENGTH_PROMPT_IN_DB", 1000))
BEDROCK_MAX_POLICY_SIZE = int(os.getenv("BEDROCK_MAX_POLICY_SIZE", 75))
REPLICATE_POLLING_DELAY_SECONDS = float(
os.getenv("REPLICATE_POLLING_DELAY_SECONDS", 0.5)
@@ -288,6 +290,7 @@ LITELLM_CHAT_PROVIDERS = [
"oci",
"morph",
"lambda_ai",
+ "vercel_ai_gateway",
]
LITELLM_EMBEDDING_PROVIDERS_SUPPORTING_INPUT_ARRAY_OF_TOKENS = [
@@ -420,6 +423,7 @@ openai_compatible_endpoints: List = [
"https://api.morphllm.com/v1",
"https://api.lambda.ai/v1",
"https://api.hyperbolic.xyz/v1",
+ "https://ai-gateway.vercel.sh/v1",
]
@@ -462,6 +466,7 @@ openai_compatible_providers: List = [
"morph",
"lambda_ai",
"hyperbolic",
+ "vercel_ai_gateway",
"aiml",
]
openai_text_completion_compatible_providers: List = (
@@ -486,190 +491,247 @@ _openai_like_providers: List = [
"watsonx",
] # private helper. similar to openai but require some custom auth / endpoint handling, so can't use the openai sdk
# well supported replicate llms
-replicate_models: set = set([
- # llama replicate supported LLMs
- "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf",
- "a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52",
- "meta/codellama-13b:1c914d844307b0588599b8393480a3ba917b660c7e9dfae681542b5325f228db",
- # Vicuna
- "replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b",
- "joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe",
- # Flan T-5
- "daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f",
- # Others
- "replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5",
- "replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad",
-])
+replicate_models: set = set(
+ [
+ # llama replicate supported LLMs
+ "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf",
+ "a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52",
+ "meta/codellama-13b:1c914d844307b0588599b8393480a3ba917b660c7e9dfae681542b5325f228db",
+ # Vicuna
+ "replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b",
+ "joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe",
+ # Flan T-5
+ "daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f",
+ # Others
+ "replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5",
+ "replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad",
+ ]
+)
-clarifai_models: set = set([
- "clarifai/meta.Llama-3.Llama-3-8B-Instruct",
- "clarifai/gcp.generate.gemma-1_1-7b-it",
- "clarifai/mistralai.completion.mixtral-8x22B",
- "clarifai/cohere.generate.command-r-plus",
- "clarifai/databricks.drbx.dbrx-instruct",
- "clarifai/mistralai.completion.mistral-large",
- "clarifai/mistralai.completion.mistral-medium",
- "clarifai/mistralai.completion.mistral-small",
- "clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1",
- "clarifai/gcp.generate.gemma-2b-it",
- "clarifai/gcp.generate.gemma-7b-it",
- "clarifai/deci.decilm.deciLM-7B-instruct",
- "clarifai/mistralai.completion.mistral-7B-Instruct",
- "clarifai/gcp.generate.gemini-pro",
- "clarifai/anthropic.completion.claude-v1",
- "clarifai/anthropic.completion.claude-instant-1_2",
- "clarifai/anthropic.completion.claude-instant",
- "clarifai/anthropic.completion.claude-v2",
- "clarifai/anthropic.completion.claude-2_1",
- "clarifai/meta.Llama-2.codeLlama-70b-Python",
- "clarifai/meta.Llama-2.codeLlama-70b-Instruct",
- "clarifai/openai.completion.gpt-3_5-turbo-instruct",
- "clarifai/meta.Llama-2.llama2-7b-chat",
- "clarifai/meta.Llama-2.llama2-13b-chat",
- "clarifai/meta.Llama-2.llama2-70b-chat",
- "clarifai/openai.chat-completion.gpt-4-turbo",
- "clarifai/microsoft.text-generation.phi-2",
- "clarifai/meta.Llama-2.llama2-7b-chat-vllm",
- "clarifai/upstage.solar.solar-10_7b-instruct",
- "clarifai/openchat.openchat.openchat-3_5-1210",
- "clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B",
- "clarifai/gcp.generate.text-bison",
- "clarifai/meta.Llama-2.llamaGuard-7b",
- "clarifai/fblgit.una-cybertron.una-cybertron-7b-v2",
- "clarifai/openai.chat-completion.GPT-4",
- "clarifai/openai.chat-completion.GPT-3_5-turbo",
- "clarifai/ai21.complete.Jurassic2-Grande",
- "clarifai/ai21.complete.Jurassic2-Grande-Instruct",
- "clarifai/ai21.complete.Jurassic2-Jumbo-Instruct",
- "clarifai/ai21.complete.Jurassic2-Jumbo",
- "clarifai/ai21.complete.Jurassic2-Large",
- "clarifai/cohere.generate.cohere-generate-command",
- "clarifai/wizardlm.generate.wizardCoder-Python-34B",
- "clarifai/wizardlm.generate.wizardLM-70B",
- "clarifai/tiiuae.falcon.falcon-40b-instruct",
- "clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat",
- "clarifai/gcp.generate.code-gecko",
- "clarifai/gcp.generate.code-bison",
- "clarifai/mistralai.completion.mistral-7B-OpenOrca",
- "clarifai/mistralai.completion.openHermes-2-mistral-7B",
- "clarifai/wizardlm.generate.wizardLM-13B",
- "clarifai/huggingface-research.zephyr.zephyr-7B-alpha",
- "clarifai/wizardlm.generate.wizardCoder-15B",
- "clarifai/microsoft.text-generation.phi-1_5",
- "clarifai/databricks.Dolly-v2.dolly-v2-12b",
- "clarifai/bigcode.code.StarCoder",
- "clarifai/salesforce.xgen.xgen-7b-8k-instruct",
- "clarifai/mosaicml.mpt.mpt-7b-instruct",
- "clarifai/anthropic.completion.claude-3-opus",
- "clarifai/anthropic.completion.claude-3-sonnet",
- "clarifai/gcp.generate.gemini-1_5-pro",
- "clarifai/gcp.generate.imagen-2",
- "clarifai/salesforce.blip.general-english-image-caption-blip-2",
-])
+clarifai_models: set = set(
+ [
+ "clarifai/meta.Llama-3.Llama-3-8B-Instruct",
+ "clarifai/gcp.generate.gemma-1_1-7b-it",
+ "clarifai/mistralai.completion.mixtral-8x22B",
+ "clarifai/cohere.generate.command-r-plus",
+ "clarifai/databricks.drbx.dbrx-instruct",
+ "clarifai/mistralai.completion.mistral-large",
+ "clarifai/mistralai.completion.mistral-medium",
+ "clarifai/mistralai.completion.mistral-small",
+ "clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1",
+ "clarifai/gcp.generate.gemma-2b-it",
+ "clarifai/gcp.generate.gemma-7b-it",
+ "clarifai/deci.decilm.deciLM-7B-instruct",
+ "clarifai/mistralai.completion.mistral-7B-Instruct",
+ "clarifai/gcp.generate.gemini-pro",
+ "clarifai/anthropic.completion.claude-v1",
+ "clarifai/anthropic.completion.claude-instant-1_2",
+ "clarifai/anthropic.completion.claude-instant",
+ "clarifai/anthropic.completion.claude-v2",
+ "clarifai/anthropic.completion.claude-2_1",
+ "clarifai/meta.Llama-2.codeLlama-70b-Python",
+ "clarifai/meta.Llama-2.codeLlama-70b-Instruct",
+ "clarifai/openai.completion.gpt-3_5-turbo-instruct",
+ "clarifai/meta.Llama-2.llama2-7b-chat",
+ "clarifai/meta.Llama-2.llama2-13b-chat",
+ "clarifai/meta.Llama-2.llama2-70b-chat",
+ "clarifai/openai.chat-completion.gpt-4-turbo",
+ "clarifai/microsoft.text-generation.phi-2",
+ "clarifai/meta.Llama-2.llama2-7b-chat-vllm",
+ "clarifai/upstage.solar.solar-10_7b-instruct",
+ "clarifai/openchat.openchat.openchat-3_5-1210",
+ "clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B",
+ "clarifai/gcp.generate.text-bison",
+ "clarifai/meta.Llama-2.llamaGuard-7b",
+ "clarifai/fblgit.una-cybertron.una-cybertron-7b-v2",
+ "clarifai/openai.chat-completion.GPT-4",
+ "clarifai/openai.chat-completion.GPT-3_5-turbo",
+ "clarifai/ai21.complete.Jurassic2-Grande",
+ "clarifai/ai21.complete.Jurassic2-Grande-Instruct",
+ "clarifai/ai21.complete.Jurassic2-Jumbo-Instruct",
+ "clarifai/ai21.complete.Jurassic2-Jumbo",
+ "clarifai/ai21.complete.Jurassic2-Large",
+ "clarifai/cohere.generate.cohere-generate-command",
+ "clarifai/wizardlm.generate.wizardCoder-Python-34B",
+ "clarifai/wizardlm.generate.wizardLM-70B",
+ "clarifai/tiiuae.falcon.falcon-40b-instruct",
+ "clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat",
+ "clarifai/gcp.generate.code-gecko",
+ "clarifai/gcp.generate.code-bison",
+ "clarifai/mistralai.completion.mistral-7B-OpenOrca",
+ "clarifai/mistralai.completion.openHermes-2-mistral-7B",
+ "clarifai/wizardlm.generate.wizardLM-13B",
+ "clarifai/huggingface-research.zephyr.zephyr-7B-alpha",
+ "clarifai/wizardlm.generate.wizardCoder-15B",
+ "clarifai/microsoft.text-generation.phi-1_5",
+ "clarifai/databricks.Dolly-v2.dolly-v2-12b",
+ "clarifai/bigcode.code.StarCoder",
+ "clarifai/salesforce.xgen.xgen-7b-8k-instruct",
+ "clarifai/mosaicml.mpt.mpt-7b-instruct",
+ "clarifai/anthropic.completion.claude-3-opus",
+ "clarifai/anthropic.completion.claude-3-sonnet",
+ "clarifai/gcp.generate.gemini-1_5-pro",
+ "clarifai/gcp.generate.imagen-2",
+ "clarifai/salesforce.blip.general-english-image-caption-blip-2",
+ ]
+)
-huggingface_models: set = set([
- "meta-llama/Llama-2-7b-hf",
- "meta-llama/Llama-2-7b-chat-hf",
- "meta-llama/Llama-2-13b-hf",
- "meta-llama/Llama-2-13b-chat-hf",
- "meta-llama/Llama-2-70b-hf",
- "meta-llama/Llama-2-70b-chat-hf",
- "meta-llama/Llama-2-7b",
- "meta-llama/Llama-2-7b-chat",
- "meta-llama/Llama-2-13b",
- "meta-llama/Llama-2-13b-chat",
- "meta-llama/Llama-2-70b",
- "meta-llama/Llama-2-70b-chat",
-]) # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers
-empower_models = set([
- "empower/empower-functions",
- "empower/empower-functions-small",
-])
+huggingface_models: set = set(
+ [
+ "meta-llama/Llama-2-7b-hf",
+ "meta-llama/Llama-2-7b-chat-hf",
+ "meta-llama/Llama-2-13b-hf",
+ "meta-llama/Llama-2-13b-chat-hf",
+ "meta-llama/Llama-2-70b-hf",
+ "meta-llama/Llama-2-70b-chat-hf",
+ "meta-llama/Llama-2-7b",
+ "meta-llama/Llama-2-7b-chat",
+ "meta-llama/Llama-2-13b",
+ "meta-llama/Llama-2-13b-chat",
+ "meta-llama/Llama-2-70b",
+ "meta-llama/Llama-2-70b-chat",
+ ]
+) # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers
+empower_models = set(
+ [
+ "empower/empower-functions",
+ "empower/empower-functions-small",
+ ]
+)
-together_ai_models: set = set([
- # llama llms - chat
- "togethercomputer/llama-2-70b-chat",
- # llama llms - language / instruct
- "togethercomputer/llama-2-70b",
- "togethercomputer/LLaMA-2-7B-32K",
- "togethercomputer/Llama-2-7B-32K-Instruct",
- "togethercomputer/llama-2-7b",
- # falcon llms
- "togethercomputer/falcon-40b-instruct",
- "togethercomputer/falcon-7b-instruct",
- # alpaca
- "togethercomputer/alpaca-7b",
- # chat llms
- "HuggingFaceH4/starchat-alpha",
- # code llms
- "togethercomputer/CodeLlama-34b",
- "togethercomputer/CodeLlama-34b-Instruct",
- "togethercomputer/CodeLlama-34b-Python",
- "defog/sqlcoder",
- "NumbersStation/nsql-llama-2-7B",
- "WizardLM/WizardCoder-15B-V1.0",
- "WizardLM/WizardCoder-Python-34B-V1.0",
- # language llms
- "NousResearch/Nous-Hermes-Llama2-13b",
- "Austism/chronos-hermes-13b",
- "upstage/SOLAR-0-70b-16bit",
- "WizardLM/WizardLM-70B-V1.0",
-])
- # supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...)
+together_ai_models: set = set(
+ [
+ # llama llms - chat
+ "togethercomputer/llama-2-70b-chat",
+ # llama llms - language / instruct
+ "togethercomputer/llama-2-70b",
+ "togethercomputer/LLaMA-2-7B-32K",
+ "togethercomputer/Llama-2-7B-32K-Instruct",
+ "togethercomputer/llama-2-7b",
+ # falcon llms
+ "togethercomputer/falcon-40b-instruct",
+ "togethercomputer/falcon-7b-instruct",
+ # alpaca
+ "togethercomputer/alpaca-7b",
+ # chat llms
+ "HuggingFaceH4/starchat-alpha",
+ # code llms
+ "togethercomputer/CodeLlama-34b",
+ "togethercomputer/CodeLlama-34b-Instruct",
+ "togethercomputer/CodeLlama-34b-Python",
+ "defog/sqlcoder",
+ "NumbersStation/nsql-llama-2-7B",
+ "WizardLM/WizardCoder-15B-V1.0",
+ "WizardLM/WizardCoder-Python-34B-V1.0",
+ # language llms
+ "NousResearch/Nous-Hermes-Llama2-13b",
+ "Austism/chronos-hermes-13b",
+ "upstage/SOLAR-0-70b-16bit",
+ "WizardLM/WizardLM-70B-V1.0",
+ ]
+)
+# supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...)
-baseten_models: set = set([
- "qvv0xeq",
- "q841o8w",
- "31dxrj3",
-]) # FALCON 7B # WizardLM # Mosaic ML
+baseten_models: set = set(
+ [
+ "qvv0xeq",
+ "q841o8w",
+ "31dxrj3",
+ ]
+) # FALCON 7B # WizardLM # Mosaic ML
-featherless_ai_models: set = set([
- "featherless-ai/Qwerky-72B",
- "featherless-ai/Qwerky-QwQ-32B",
- "Qwen/Qwen2.5-72B-Instruct",
- "all-hands/openhands-lm-32b-v0.1",
- "Qwen/Qwen2.5-Coder-32B-Instruct",
- "deepseek-ai/DeepSeek-V3-0324",
- "mistralai/Mistral-Small-24B-Instruct-2501",
- "mistralai/Mistral-Nemo-Instruct-2407",
- "ProdeusUnity/Stellar-Odyssey-12b-v0.0",
-])
+featherless_ai_models: set = set(
+ [
+ "featherless-ai/Qwerky-72B",
+ "featherless-ai/Qwerky-QwQ-32B",
+ "Qwen/Qwen2.5-72B-Instruct",
+ "all-hands/openhands-lm-32b-v0.1",
+ "Qwen/Qwen2.5-Coder-32B-Instruct",
+ "deepseek-ai/DeepSeek-V3-0324",
+ "mistralai/Mistral-Small-24B-Instruct-2501",
+ "mistralai/Mistral-Nemo-Instruct-2407",
+ "ProdeusUnity/Stellar-Odyssey-12b-v0.0",
+ ]
+)
-nebius_models: set = set([
- "Qwen/Qwen3-235B-A22B",
- "Qwen/Qwen3-30B-A3B-fast",
- "Qwen/Qwen3-32B",
- "Qwen/Qwen3-14B",
- "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1",
- "deepseek-ai/DeepSeek-V3-0324",
- "deepseek-ai/DeepSeek-V3-0324-fast",
- "deepseek-ai/DeepSeek-R1",
- "deepseek-ai/DeepSeek-R1-fast",
- "meta-llama/Llama-3.3-70B-Instruct-fast",
- "Qwen/Qwen2.5-32B-Instruct-fast",
- "Qwen/Qwen2.5-Coder-32B-Instruct-fast",
-])
+nebius_models: set = set(
+ [
+ # deepseek models
+ "deepseek-ai/DeepSeek-R1-0528",
+ "deepseek-ai/DeepSeek-V3-0324",
+ "deepseek-ai/DeepSeek-V3",
+ "deepseek-ai/DeepSeek-R1",
+ "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
+ # google models
+ "google/gemma-2-2b-it",
+ "google/gemma-2-9b-it-fast",
+ # llama models
+ "meta-llama/Llama-3.3-70B-Instruct",
+ "meta-llama/Meta-Llama-3.1-70B-Instruct",
+ "meta-llama/Meta-Llama-3.1-8B-Instruct",
+ "meta-llama/Meta-Llama-3.1-405B-Instruct",
+ "NousResearch/Hermes-3-Llama-405B",
+ # microsoft models
+ "microsoft/phi-4",
+ # mistral models
+ "mistralai/Mistral-Nemo-Instruct-2407",
+ "mistralai/Devstral-Small-2505",
+ # moonshot models
+ "moonshotai/Kimi-K2-Instruct",
+ # nvidia models
+ "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1",
+ "nvidia/Llama-3_3-Nemotron-Super-49B-v1",
+ # openai models
+ "openai/gpt-oss-120b",
+ "openai/gpt-oss-20b",
+ # qwen models
+ "Qwen/Qwen3-Coder-480B-A35B-Instruct",
+ "Qwen/Qwen3-235B-A22B-Instruct-2507",
+ "Qwen/Qwen3-235B-A22B",
+ "Qwen/Qwen3-30B-A3B",
+ "Qwen/Qwen3-32B",
+ "Qwen/Qwen3-14B",
+ "Qwen/Qwen3-4B-fast",
+ "Qwen/Qwen2.5-Coder-7B",
+ "Qwen/Qwen2.5-Coder-32B-Instruct",
+ "Qwen/Qwen2.5-72B-Instruct",
+ "Qwen/QwQ-32B",
+ "Qwen/Qwen3-30B-A3B-Thinking-2507",
+ "Qwen/Qwen3-30B-A3B-Instruct-2507",
+ # zai models
+ "zai-org/GLM-4.5",
+ "zai-org/GLM-4.5-Air",
+ # other models
+ "aaditya/Llama3-OpenBioLLM-70B",
+ "ProdeusUnity/Stellar-Odyssey-12b-v0.0",
+ "all-hands/openhands-lm-32b-v0.1",
+ ]
+)
-dashscope_models: set = set([
- "qwen-turbo",
- "qwen-plus",
- "qwen-max",
- "qwen-turbo-latest",
- "qwen-plus-latest",
- "qwen-max-latest",
- "qwq-32b",
- "qwen3-235b-a22b",
- "qwen3-32b",
- "qwen3-30b-a3b",
-])
+dashscope_models: set = set(
+ [
+ "qwen-turbo",
+ "qwen-plus",
+ "qwen-max",
+ "qwen-turbo-latest",
+ "qwen-plus-latest",
+ "qwen-max-latest",
+ "qwq-32b",
+ "qwen3-235b-a22b",
+ "qwen3-32b",
+ "qwen3-30b-a3b",
+ ]
+)
-nebius_embedding_models: set = set([
- "BAAI/bge-en-icl",
- "BAAI/bge-multilingual-gemma2",
- "intfloat/e5-mistral-7b-instruct",
-])
+nebius_embedding_models: set = set(
+ [
+ "BAAI/bge-en-icl",
+ "BAAI/bge-multilingual-gemma2",
+ "intfloat/e5-mistral-7b-instruct",
+ ]
+)
BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[
"cohere",
@@ -684,20 +746,24 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[
]
open_ai_embedding_models: set = set(["text-embedding-ada-002"])
-cohere_embedding_models: set = set([
- "embed-v4.0",
- "embed-english-v3.0",
- "embed-english-light-v3.0",
- "embed-multilingual-v3.0",
- "embed-english-v2.0",
- "embed-english-light-v2.0",
- "embed-multilingual-v2.0",
-])
-bedrock_embedding_models: set = set([
- "amazon.titan-embed-text-v1",
- "cohere.embed-english-v3",
- "cohere.embed-multilingual-v3",
-])
+cohere_embedding_models: set = set(
+ [
+ "embed-v4.0",
+ "embed-english-v3.0",
+ "embed-english-light-v3.0",
+ "embed-multilingual-v3.0",
+ "embed-english-v2.0",
+ "embed-english-light-v2.0",
+ "embed-multilingual-v2.0",
+ ]
+)
+bedrock_embedding_models: set = set(
+ [
+ "amazon.titan-embed-text-v1",
+ "cohere.embed-english-v3",
+ "cohere.embed-multilingual-v3",
+ ]
+)
known_tokenizer_config = {
"mistralai/Mistral-7B-Instruct-v0.1": {
diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index 6c6a09cd73e..01f3e2472f8 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -57,6 +57,7 @@ from litellm.llms.vertex_ai.cost_calculator import (
cost_per_token as google_cost_per_token,
)
from litellm.llms.vertex_ai.cost_calculator import cost_router as google_cost_router
+from litellm.llms.xai.cost_calculator import cost_per_token as xai_cost_per_token
from litellm.responses.utils import ResponseAPILoggingUtils
from litellm.types.llms.openai import (
HttpxBinaryResponseContent,
@@ -341,6 +342,8 @@ def cost_per_token( # noqa: PLR0915
return deepseek_cost_per_token(model=model, usage=usage_block)
elif custom_llm_provider == "perplexity":
return perplexity_cost_per_token(model=model, usage=usage_block)
+ elif custom_llm_provider == "xai":
+ return xai_cost_per_token(model=model, usage=usage_block)
else:
model_info = _cached_get_model_info_helper(
model=model, custom_llm_provider=custom_llm_provider
@@ -675,9 +678,9 @@ def completion_cost( # noqa: PLR0915
or isinstance(completion_response, dict)
): # tts returns a custom class
if isinstance(completion_response, dict):
- usage_obj: Optional[Union[dict, Usage]] = (
- completion_response.get("usage", {})
- )
+ usage_obj: Optional[
+ Union[dict, Usage]
+ ] = completion_response.get("usage", {})
else:
usage_obj = getattr(completion_response, "usage", {})
if isinstance(usage_obj, BaseModel) and not _is_known_usage_objects(
@@ -1279,7 +1282,9 @@ class BaseTokenUsageProcessor:
not hasattr(combined, "completion_tokens_details")
or not combined.completion_tokens_details
):
- combined.completion_tokens_details = CompletionTokensDetailsWrapper()
+ combined.completion_tokens_details = (
+ CompletionTokensDetailsWrapper()
+ )
# Check what keys exist in the model's completion_tokens_details
for attr in usage.completion_tokens_details.model_fields:
diff --git a/litellm/exceptions.py b/litellm/exceptions.py
index 153230518cc..77fb9c1faef 100644
--- a/litellm/exceptions.py
+++ b/litellm/exceptions.py
@@ -153,6 +153,29 @@ class BadRequestError(openai.BadRequestError): # type: ignore
_message += f", LiteLLM Max Retries: {self.max_retries}"
return _message
+class ImageFetchError(BadRequestError):
+ def __init__(
+ self,
+ message,
+ model=None,
+ llm_provider=None,
+ response: Optional[httpx.Response] = None,
+ litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
+ body: Optional[dict] = None,
+ ):
+ super().__init__(
+ message=message,
+ model=model,
+ llm_provider=llm_provider,
+ response=response,
+ litellm_debug_info=litellm_debug_info,
+ max_retries=max_retries,
+ num_retries=num_retries,
+ body=body,
+ )
+
class UnprocessableEntityError(openai.UnprocessableEntityError): # type: ignore
def __init__(
diff --git a/litellm/google_genai/adapters/handler.py b/litellm/google_genai/adapters/handler.py
index 1f575f27591..dcf707ebd51 100644
--- a/litellm/google_genai/adapters/handler.py
+++ b/litellm/google_genai/adapters/handler.py
@@ -37,6 +37,10 @@ class GenerateContentToCompletionHandler:
completion_kwargs: Dict[str, Any] = dict(completion_request)
+ # feed metadata for custom callback
+ if extra_kwargs is not None and "metadata" in extra_kwargs:
+ completion_kwargs["metadata"] = extra_kwargs["metadata"]
+
if stream:
completion_kwargs["stream"] = stream
diff --git a/litellm/images/main.py b/litellm/images/main.py
index 953a8012e49..4993a48c724 100644
--- a/litellm/images/main.py
+++ b/litellm/images/main.py
@@ -1,7 +1,7 @@
import asyncio
import contextvars
from functools import partial
-from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast, overload
+from typing import Any, Coroutine, Dict, List, Literal, Optional, Union, cast, overload
import httpx
@@ -347,6 +347,7 @@ def image_generation( # noqa: PLR0915
raise ValueError(f"image generation config is not supported for {custom_llm_provider}")
return llm_http_handler.image_generation_handler(
+ api_key=api_key,
model=model,
prompt=prompt,
image_generation_provider_config=image_generation_config,
@@ -676,7 +677,7 @@ def image_variation(
@client
def image_edit(
- image: FileTypes,
+ image: Union[FileTypes, List[FileTypes]],
prompt: str,
model: Optional[str] = None,
mask: Optional[str] = None,
@@ -704,6 +705,9 @@ def image_edit(
litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
_is_async = kwargs.pop("async_call", False) is True
+ #add images / or return a single image
+ images = image if isinstance(image, list) else [image]
+
# get llm provider logic
litellm_params = GenericLiteLLMParams(**kwargs)
model, custom_llm_provider, _, _ = get_llm_provider(
@@ -752,7 +756,7 @@ def image_edit(
# Call the handler with _is_async flag instead of directly calling the async handler
return base_llm_http_handler.image_edit_handler(
model=model,
- image=image,
+ image=images,
prompt=prompt,
image_edit_provider_config=image_edit_provider_config,
image_edit_optional_request_params=image_edit_request_params,
@@ -778,7 +782,7 @@ def image_edit(
@client
async def aimage_edit(
- image: FileTypes,
+ image: Union[FileTypes, List[FileTypes]],
model: str,
prompt: str,
mask: Optional[str] = None,
@@ -818,9 +822,11 @@ async def aimage_edit(
model=model, api_base=local_vars.get("base_url", None)
)
+ images = image if isinstance(image, list) else [image]
+
func = partial(
image_edit,
- image=image,
+ image=images,
prompt=prompt,
mask=mask,
model=model,
diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py
index 5cf403adee4..5bc6afb6dbc 100644
--- a/litellm/integrations/braintrust_logging.py
+++ b/litellm/integrations/braintrust_logging.py
@@ -1,13 +1,11 @@
# What is this?
## Log success + failure events to Braintrust
-import copy
import os
from datetime import datetime
from typing import Dict, Optional
import httpx
-from pydantic import BaseModel
import litellm
from litellm import verbose_logger
@@ -24,7 +22,6 @@ API_BASE = "https://api.braintrustdata.com/v1"
def get_utc_datetime():
import datetime as dt
- from datetime import datetime
if hasattr(dt, "UTC"):
return datetime.now(dt.UTC) # type: ignore
@@ -45,9 +42,9 @@ class BraintrustLogger(CustomLogger):
"Authorization": "Bearer " + self.api_key,
"Content-Type": "application/json",
}
- self._project_id_cache: Dict[
- str, str
- ] = {} # Cache mapping project names to IDs
+ self._project_id_cache: Dict[str, str] = (
+ {}
+ ) # Cache mapping project names to IDs
self.global_braintrust_http_handler = get_async_httpx_client(
llm_provider=httpxSpecialProvider.LoggingCallback
)
@@ -108,43 +105,6 @@ class BraintrustLogger(CustomLogger):
except httpx.HTTPStatusError as e:
raise Exception(f"Failed to register project: {e.response.text}")
- @staticmethod
- def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict:
- """
- Adds metadata from proxy request headers to Braintrust logging if keys start with "braintrust_"
- and overwrites litellm_params.metadata if already included.
-
- For example if you want to append your trace to an existing `trace_id` via header, send
- `headers: { ..., langfuse_existing_trace_id: your-existing-trace-id }` via proxy request.
- """
- if litellm_params is None:
- return metadata
-
- if litellm_params.get("proxy_server_request") is None:
- return metadata
-
- if metadata is None:
- metadata = {}
-
- proxy_headers = (
- litellm_params.get("proxy_server_request", {}).get("headers", {}) or {}
- )
-
- for metadata_param_key in proxy_headers:
- if metadata_param_key.startswith("braintrust"):
- trace_param_key = metadata_param_key.replace("braintrust", "", 1)
- if trace_param_key in metadata:
- verbose_logger.warning(
- f"Overwriting Braintrust `{trace_param_key}` from request header"
- )
- else:
- verbose_logger.debug(
- f"Found Braintrust `{trace_param_key}` in request header"
- )
- metadata[trace_param_key] = proxy_headers.get(metadata_param_key)
-
- return metadata
-
async def create_default_project_and_experiment(self):
project = await self.global_braintrust_http_handler.post(
f"{self.api_base}/project", headers=self.headers, json={"name": "litellm"}
@@ -169,7 +129,9 @@ class BraintrustLogger(CustomLogger):
verbose_logger.debug("REACHES BRAINTRUST SUCCESS")
try:
litellm_call_id = kwargs.get("litellm_call_id")
+ standard_logging_object = kwargs.get("standard_logging_object", {})
prompt = {"messages": kwargs.get("messages")}
+
output = None
choices = []
if response_obj is not None and (
@@ -192,33 +154,13 @@ class BraintrustLogger(CustomLogger):
):
output = response_obj["data"]
- litellm_params = kwargs.get("litellm_params", {})
- metadata = (
- litellm_params.get("metadata", {}) or {}
- ) # if litellm_params['metadata'] == None
- metadata = self.add_metadata_from_header(litellm_params, metadata)
- clean_metadata = {}
- try:
- metadata = copy.deepcopy(
- metadata
- ) # Avoid modifying the original metadata
- except Exception:
- new_metadata = {}
- for key, value in metadata.items():
- if (
- isinstance(value, list)
- or isinstance(value, dict)
- or isinstance(value, str)
- or isinstance(value, int)
- or isinstance(value, float)
- ):
- new_metadata[key] = copy.deepcopy(value)
- metadata = new_metadata
+ litellm_params = kwargs.get("litellm_params", {}) or {}
+ dynamic_metadata = litellm_params.get("metadata", {}) or {}
# Get project_id from metadata or create default if needed
- project_id = metadata.get("project_id")
+ project_id = dynamic_metadata.get("project_id")
if project_id is None:
- project_name = metadata.get("project_name")
+ project_name = dynamic_metadata.get("project_name")
project_id = (
self.get_project_id_sync(project_name) if project_name else None
)
@@ -229,8 +171,9 @@ class BraintrustLogger(CustomLogger):
project_id = self.default_project_id
tags = []
- if isinstance(metadata, dict):
- for key, value in metadata.items():
+
+ if isinstance(dynamic_metadata, dict):
+ for key, value in dynamic_metadata.items():
# generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy
if (
litellm.langfuse_default_tags is not None
@@ -239,25 +182,12 @@ class BraintrustLogger(CustomLogger):
):
tags.append(f"{key}:{value}")
- # clean litellm metadata before logging
- if key in [
- "headers",
- "endpoint",
- "caching_groups",
- "previous_models",
- ]:
- continue
- else:
- clean_metadata[key] = value
+ if (
+ isinstance(value, str) and key not in standard_logging_object
+ ): # support logging dynamic metadata to braintrust
+ standard_logging_object[key] = value
cost = kwargs.get("response_cost", None)
- if cost is not None:
- clean_metadata["litellm_response_cost"] = cost
-
- # metadata.model is required for braintrust to calculate the "Estimated cost" metric
- litellm_model = kwargs.get("model", None)
- if litellm_model is not None:
- clean_metadata["model"] = litellm_model
metrics: Optional[dict] = None
usage_obj = getattr(response_obj, "usage", None)
@@ -274,12 +204,15 @@ class BraintrustLogger(CustomLogger):
"end": end_time.timestamp(),
}
+ # Allow metadata override for span name
+ span_name = dynamic_metadata.get("span_name", "Chat Completion")
+
request_data = {
"id": litellm_call_id,
"input": prompt["messages"],
- "metadata": clean_metadata,
+ "metadata": standard_logging_object,
"tags": tags,
- "span_attributes": {"name": "Chat Completion", "type": "llm"},
+ "span_attributes": {"name": span_name, "type": "llm"},
}
if choices is not None:
request_data["output"] = [choice.dict() for choice in choices]
@@ -309,6 +242,7 @@ class BraintrustLogger(CustomLogger):
verbose_logger.debug("REACHES BRAINTRUST SUCCESS")
try:
litellm_call_id = kwargs.get("litellm_call_id")
+ standard_logging_object = kwargs.get("standard_logging_object", {})
prompt = {"messages": kwargs.get("messages")}
output = None
choices = []
@@ -333,32 +267,12 @@ class BraintrustLogger(CustomLogger):
output = response_obj["data"]
litellm_params = kwargs.get("litellm_params", {})
- metadata = (
- litellm_params.get("metadata", {}) or {}
- ) # if litellm_params['metadata'] == None
- metadata = self.add_metadata_from_header(litellm_params, metadata)
- clean_metadata = {}
- new_metadata = {}
- for key, value in metadata.items():
- if (
- isinstance(value, list)
- or isinstance(value, str)
- or isinstance(value, int)
- or isinstance(value, float)
- ):
- new_metadata[key] = value
- elif isinstance(value, BaseModel):
- new_metadata[key] = value.model_dump_json()
- elif isinstance(value, dict):
- for k, v in value.items():
- if isinstance(v, datetime):
- value[k] = v.isoformat()
- new_metadata[key] = value
+ dynamic_metadata = litellm_params.get("metadata", {}) or {}
# Get project_id from metadata or create default if needed
- project_id = metadata.get("project_id")
+ project_id = dynamic_metadata.get("project_id")
if project_id is None:
- project_name = metadata.get("project_name")
+ project_name = dynamic_metadata.get("project_name")
project_id = (
await self.get_project_id_async(project_name)
if project_name
@@ -371,8 +285,9 @@ class BraintrustLogger(CustomLogger):
project_id = self.default_project_id
tags = []
- if isinstance(metadata, dict):
- for key, value in metadata.items():
+
+ if isinstance(dynamic_metadata, dict):
+ for key, value in dynamic_metadata.items():
# generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy
if (
litellm.langfuse_default_tags is not None
@@ -381,25 +296,12 @@ class BraintrustLogger(CustomLogger):
):
tags.append(f"{key}:{value}")
- # clean litellm metadata before logging
- if key in [
- "headers",
- "endpoint",
- "caching_groups",
- "previous_models",
- ]:
- continue
- else:
- clean_metadata[key] = value
+ if (
+ isinstance(value, str) and key not in standard_logging_object
+ ): # support logging dynamic metadata to braintrust
+ standard_logging_object[key] = value
cost = kwargs.get("response_cost", None)
- if cost is not None:
- clean_metadata["litellm_response_cost"] = cost
-
- # metadata.model is required for braintrust to calculate the "Estimated cost" metric
- litellm_model = kwargs.get("model", None)
- if litellm_model is not None:
- clean_metadata["model"] = litellm_model
metrics: Optional[dict] = None
usage_obj = getattr(response_obj, "usage", None)
@@ -426,13 +328,16 @@ class BraintrustLogger(CustomLogger):
- api_call_start_time.timestamp()
)
+ # Allow metadata override for span name
+ span_name = dynamic_metadata.get("span_name", "Chat Completion")
+
request_data = {
"id": litellm_call_id,
"input": prompt["messages"],
"output": output,
- "metadata": clean_metadata,
+ "metadata": standard_logging_object,
"tags": tags,
- "span_attributes": {"name": "Chat Completion", "type": "llm"},
+ "span_attributes": {"name": span_name, "type": "llm"},
}
if choices is not None:
request_data["output"] = [choice.dict() for choice in choices]
diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py
index 8b90e123371..fbe480be95f 100644
--- a/litellm/integrations/langfuse/langfuse_otel.py
+++ b/litellm/integrations/langfuse/langfuse_otel.py
@@ -141,6 +141,17 @@ class LangfuseOtelLogger(OpenTelemetry):
value = str(value)
safe_set_attribute(span, enum_attr.value, value)
+ @staticmethod
+ def _get_langfuse_otel_host() -> Optional[str]:
+ """
+ Returns the Langfuse OTEL host based on environment variables.
+
+ Returned in the following order of precedence:
+ 1. LANGFUSE_OTEL_HOST
+ 2. LANGFUSE_HOST
+ """
+ return os.environ.get("LANGFUSE_OTEL_HOST") or os.environ.get("LANGFUSE_HOST")
+
@staticmethod
def get_langfuse_otel_config() -> LangfuseOtelConfig:
"""
@@ -166,7 +177,7 @@ class LangfuseOtelLogger(OpenTelemetry):
)
# Determine endpoint - default to US cloud
- langfuse_host = os.environ.get("LANGFUSE_HOST", None)
+ langfuse_host = LangfuseOtelLogger._get_langfuse_otel_host()
if langfuse_host:
# If LANGFUSE_HOST is provided, construct OTEL endpoint from it
diff --git a/litellm/integrations/openmeter.py b/litellm/integrations/openmeter.py
index 19010daf831..b8fb64ec287 100644
--- a/litellm/integrations/openmeter.py
+++ b/litellm/integrations/openmeter.py
@@ -66,8 +66,18 @@ class OpenMeterLogger(CustomLogger):
}
user_param = kwargs.get("user", None) # end-user passed in via 'user' param
+
+ # If no user provided directly, try to get it from token user_id
if user_param is None:
- raise Exception("OpenMeter: user is required")
+ # Check if user_id is available from the API key metadata
+ litellm_params = kwargs.get("litellm_params", {})
+ metadata = litellm_params.get("metadata", {})
+ user_api_key_user_id = metadata.get("user_api_key_user_id", None)
+
+ if user_api_key_user_id is not None:
+ user_param = user_api_key_user_id
+ else:
+ raise Exception("OpenMeter: user is required")
# Ensure subject is always a string for OpenMeter API
subject = str(user_param)
diff --git a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py
index 59c378f8204..8ef160dd783 100644
--- a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py
+++ b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py
@@ -8,6 +8,7 @@ It searches the vector store for relevant context and appends it to the messages
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, cast
import litellm
+import litellm.vector_stores
from litellm._logging import verbose_logger
from litellm.integrations.custom_logger import CustomLogger
from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage
@@ -192,4 +193,4 @@ class VectorStorePreCallHook(CustomLogger):
modified_messages.insert(-1, cast(AllMessageValues, context_message))
return modified_messages
- return messages
\ No newline at end of file
+ return messages
diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py
index 2d511741ec6..2049480e264 100644
--- a/litellm/litellm_core_utils/get_llm_provider_logic.py
+++ b/litellm/litellm_core_utils/get_llm_provider_logic.py
@@ -249,6 +249,9 @@ def get_llm_provider( # noqa: PLR0915
elif endpoint == "https://api.hyperbolic.xyz/v1":
custom_llm_provider = "hyperbolic"
dynamic_api_key = get_secret_str("HYPERBOLIC_API_KEY")
+ elif endpoint == "https://ai-gateway.vercel.sh/v1":
+ custom_llm_provider = "vercel_ai_gateway"
+ dynamic_api_key = get_secret_str("VERCEL_AI_GATEWAY_API_KEY")
if api_base is not None and not isinstance(api_base, str):
raise Exception(
@@ -742,6 +745,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915
) = litellm.HyperbolicChatConfig()._get_openai_compatible_provider_info(
api_base, api_key
)
+ elif custom_llm_provider == "vercel_ai_gateway":
+ (
+ api_base,
+ dynamic_api_key,
+ ) = litellm.VercelAIGatewayConfig()._get_openai_compatible_provider_info(
+ api_base, api_key
+ )
elif custom_llm_provider == "aiml":
(
api_base,
diff --git a/litellm/litellm_core_utils/get_provider_specific_headers.py b/litellm/litellm_core_utils/get_provider_specific_headers.py
new file mode 100644
index 00000000000..cf9165cfda9
--- /dev/null
+++ b/litellm/litellm_core_utils/get_provider_specific_headers.py
@@ -0,0 +1,23 @@
+from typing import Dict, Optional
+
+from litellm.types.utils import ProviderSpecificHeader
+
+
+class ProviderSpecificHeaderUtils:
+ @staticmethod
+ def get_provider_specific_headers(
+ provider_specific_header: Optional[ProviderSpecificHeader],
+ custom_llm_provider: Optional[str],
+ ) -> Dict:
+ """
+ Get the provider specific headers for the given custom llm provider
+
+ Returns:
+ Optional[Dict]: The provider specific headers for the given custom llm provider
+ """
+ if (
+ provider_specific_header is not None
+ and provider_specific_header.get("custom_llm_provider") == custom_llm_provider
+ ):
+ return provider_specific_header.get("extra_headers", {})
+ return {}
\ No newline at end of file
diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py
index 6584defe5b7..86535943762 100644
--- a/litellm/litellm_core_utils/get_supported_openai_params.py
+++ b/litellm/litellm_core_utils/get_supported_openai_params.py
@@ -131,6 +131,8 @@ def get_supported_openai_params( # noqa: PLR0915
return litellm.AzureOpenAIConfig().get_supported_openai_params(model=model)
elif custom_llm_provider == "openrouter":
return litellm.OpenrouterConfig().get_supported_openai_params(model=model)
+ elif custom_llm_provider == "vercel_ai_gateway":
+ return litellm.VercelAIGatewayConfig().get_supported_openai_params(model=model)
elif custom_llm_provider == "mistral" or custom_llm_provider == "codestral":
# mistal and codestral api have the exact same params
if request_type == "chat_completion":
@@ -268,10 +270,9 @@ def get_supported_openai_params( # noqa: PLR0915
from litellm.llms.elevenlabs.audio_transcription.transformation import (
ElevenLabsAudioTranscriptionConfig,
)
- return (
- ElevenLabsAudioTranscriptionConfig().get_supported_openai_params(
- model=model
- )
+
+ return ElevenLabsAudioTranscriptionConfig().get_supported_openai_params(
+ model=model
)
elif custom_llm_provider in litellm._custom_providers:
if request_type == "chat_completion":
diff --git a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py
index 75bb699292e..21ff44ab082 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py
@@ -580,7 +580,9 @@ class StandardBuiltInToolCostTracking:
return WebSearchOptions(**kwargs.get("web_search_options", {}))
tools = StandardBuiltInToolCostTracking._get_tools_from_kwargs(
- kwargs, "web_search_preview"
+ kwargs=kwargs, tool_type="web_search_preview"
+ ) or StandardBuiltInToolCostTracking._get_tools_from_kwargs(
+ kwargs=kwargs, tool_type="web_search"
)
if tools:
# Look for web search tool in the tools array
@@ -612,6 +614,8 @@ class StandardBuiltInToolCostTracking:
def _is_web_search_tool_call(tool: Dict) -> bool:
if tool.get("type", None) == "web_search_preview":
return True
+ if tool.get("type", None) == "web_search":
+ return True
if "search_context_size" in tool:
return True
return False
diff --git a/litellm/litellm_core_utils/logging_worker.py b/litellm/litellm_core_utils/logging_worker.py
index faf8413bd57..3f83719dd32 100644
--- a/litellm/litellm_core_utils/logging_worker.py
+++ b/litellm/litellm_core_utils/logging_worker.py
@@ -57,9 +57,10 @@ class LoggingWorker:
finally:
self._queue.task_done()
- except asyncio.CancelledError as e:
- verbose_logger.exception(f"LoggingWorker cancelled: {e}")
- pass
+ except asyncio.CancelledError:
+ verbose_logger.debug("LoggingWorker cancelled during shutdown")
+ # Attempt to clear remaining items to prevent "never awaited" warnings
+ await self.clear_queue()
def enqueue(self, coroutine: Coroutine) -> None:
"""
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index 77cbe4c9a8e..2adddd52e74 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -3846,7 +3846,13 @@ def function_call_prompt(messages: list, functions: list):
function_added_to_prompt = False
for message in messages:
if "system" in message["role"]:
- message["content"] += f""" {function_prompt}"""
+ if isinstance(message["content"], str):
+ message["content"] += f""" {function_prompt}"""
+ else:
+ message["content"].append({
+ "type": "text",
+ "text": f""" {function_prompt}"""
+ })
function_added_to_prompt = True
if function_added_to_prompt is False:
diff --git a/litellm/litellm_core_utils/prompt_templates/image_handling.py b/litellm/litellm_core_utils/prompt_templates/image_handling.py
index a9ff14d6c82..4fa10e42111 100644
--- a/litellm/litellm_core_utils/prompt_templates/image_handling.py
+++ b/litellm/litellm_core_utils/prompt_templates/image_handling.py
@@ -17,7 +17,7 @@ in_memory_cache = InMemoryCache(max_size_in_memory=MAX_IMGS_IN_MEMORY)
def _process_image_response(response: Response, url: str) -> str:
if response.status_code != 200:
- raise Exception(
+ raise litellm.ImageFetchError(
f"Error: Unable to fetch image from URL. Status code: {response.status_code}, url={url}"
)
@@ -57,9 +57,11 @@ async def async_convert_url_to_base64(url: str) -> str:
try:
response = await client.get(url, follow_redirects=True)
return _process_image_response(response, url)
+ except litellm.ImageFetchError:
+ raise
except Exception:
pass
- raise Exception(
+ raise litellm.ImageFetchError(
f"Error: Unable to fetch image from URL after 3 attempts. url={url}"
)
@@ -74,10 +76,11 @@ def convert_url_to_base64(url: str) -> str:
try:
response = client.get(url, follow_redirects=True)
return _process_image_response(response, url)
+ except litellm.ImageFetchError:
+ raise
except Exception as e:
verbose_logger.exception(e)
- # print(e)
pass
- raise Exception(
- f"Error: Unable to fetch image from URL after 3 attempts. url={url}"
+ raise litellm.ImageFetchError(
+ f"Error: Unable to fetch image from URL after 3 attempts. url={url}",
)
diff --git a/litellm/litellm_core_utils/safe_json_dumps.py b/litellm/litellm_core_utils/safe_json_dumps.py
index 7ad0038ecb2..c714e36b5f9 100644
--- a/litellm/litellm_core_utils/safe_json_dumps.py
+++ b/litellm/litellm_core_utils/safe_json_dumps.py
@@ -1,5 +1,6 @@
import json
from typing import Any, Union
+
from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH
diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index b9433239271..01b2609d31d 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -20,7 +20,9 @@ from litellm.litellm_core_utils.redact_messages import LiteLLMLoggingObject
from litellm.litellm_core_utils.thread_pool_executor import executor
from litellm.types.llms.openai import ChatCompletionChunk
from litellm.types.router import GenericLiteLLMParams
-from litellm.types.utils import Delta
+from litellm.types.utils import (
+ Delta,
+)
from litellm.types.utils import GenericStreamingChunk as GChunk
from litellm.types.utils import (
ModelResponse,
@@ -35,6 +37,12 @@ from .exception_mapping_utils import exception_type
from .llm_response_utils.get_api_base import get_api_base
from .rules import Rules
+# Constants for special delta attribute names
+AUDIO_ATTRIBUTE = "audio"
+IMAGE_ATTRIBUTE = "image"
+TOOL_CALLS_ATTRIBUTE = "tool_calls"
+FUNCTION_CALL_ATTRIBUTE = "function_call"
+
def is_async_iterable(obj: Any) -> bool:
"""
@@ -766,6 +774,66 @@ class CustomStreamWrapper:
model_response.choices[0].delta = Delta(**_initial_delta)
return model_response
+ def _has_special_delta_content(self, model_response: ModelResponseStream) -> bool:
+ """
+ Check if the delta contains special content types (tool_calls, function_call, audio, or image).
+ """
+ if len(model_response.choices) == 0:
+ return False
+
+ delta = model_response.choices[0].delta
+
+ # Check for tool_calls or function_call
+ if getattr(delta, TOOL_CALLS_ATTRIBUTE, None) is not None or getattr(delta, FUNCTION_CALL_ATTRIBUTE, None) is not None:
+ return True
+
+ # Check for audio
+ if hasattr(delta, AUDIO_ATTRIBUTE) and getattr(delta, AUDIO_ATTRIBUTE, None) is not None:
+ return True
+
+ # Check for image
+ if hasattr(delta, IMAGE_ATTRIBUTE) and getattr(delta, IMAGE_ATTRIBUTE, None) is not None:
+ return True
+
+ return False
+
+ def _handle_special_delta_content(self, model_response: ModelResponseStream) -> ModelResponseStream:
+ """
+ Handle special delta content types by stripping role and returning the response.
+ """
+ return self.strip_role_from_delta(model_response)
+
+ def _has_special_delta_attribute(self, delta, attribute_name: str) -> bool:
+ """
+ Check if delta has a specific attribute and it's not None.
+ """
+ return delta is not None and getattr(delta, attribute_name, None) is not None
+
+ def _copy_delta_attribute(self, source_delta, target_delta, attribute_name: str) -> None:
+ """
+ Copy a specific attribute from source delta to target delta.
+ """
+ setattr(target_delta, attribute_name, getattr(source_delta, attribute_name))
+
+ def _has_any_special_delta_attributes(self, delta) -> bool:
+ """
+ Check if delta has any special attributes (audio, image).
+ """
+ special_attributes = [AUDIO_ATTRIBUTE, IMAGE_ATTRIBUTE]
+ for attribute in special_attributes:
+ if self._has_special_delta_attribute(delta, attribute):
+ return True
+ return False
+
+ def _handle_special_delta_attributes(self, delta, model_response: "ModelResponseStream") -> None:
+ """
+ Handle special delta attributes (audio, image) by copying them to model_response.
+ """
+ special_attributes = [AUDIO_ATTRIBUTE, IMAGE_ATTRIBUTE]
+ for attribute in special_attributes:
+ if self._has_special_delta_attribute(delta, attribute):
+ self._copy_delta_attribute(delta, model_response.choices[0].delta, attribute)
+
def return_processed_chunk_logic( # noqa
self,
completion_obj: Dict[str, Any],
@@ -888,20 +956,8 @@ class CustomStreamWrapper:
self.sent_last_chunk = True
return model_response
- elif (
- model_response.choices[0].delta.tool_calls is not None
- or model_response.choices[0].delta.function_call is not None
- ):
- model_response = self.strip_role_from_delta(model_response)
-
- return model_response
- elif (
- len(model_response.choices) > 0
- and hasattr(model_response.choices[0].delta, "audio")
- and model_response.choices[0].delta.audio is not None
- ):
- model_response = self.strip_role_from_delta(model_response)
- return model_response
+ elif self._has_special_delta_content(model_response):
+ return self._handle_special_delta_content(model_response)
else:
if hasattr(model_response, "usage"):
self.chunks.append(model_response)
@@ -1374,10 +1430,8 @@ class CustomStreamWrapper:
)
)
model_response.choices[0].delta = Delta()
- elif (
- delta is not None and getattr(delta, "audio", None) is not None
- ):
- model_response.choices[0].delta.audio = delta.audio
+ elif self._has_any_special_delta_attributes(delta):
+ self._handle_special_delta_attributes(delta, model_response)
else:
try:
delta = (
diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py
index 4df944edbaa..fab2c1e76ee 100644
--- a/litellm/litellm_core_utils/token_counter.py
+++ b/litellm/litellm_core_utils/token_counter.py
@@ -529,7 +529,7 @@ def _get_count_function(
encoding = tiktoken.get_encoding("cl100k_base")
def count_tokens(text: str) -> int:
- return len(encoding.encode(text))
+ return len(encoding.encode(text, disallowed_special=()))
else:
raise ValueError("Unsupported tokenizer type")
diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py
index 55aad673ced..ce196757f94 100644
--- a/litellm/llms/bedrock/base_aws_llm.py
+++ b/litellm/llms/bedrock/base_aws_llm.py
@@ -202,6 +202,7 @@ class BaseAWSLLM:
credentials, _cache_ttl = self._auth_with_aws_role(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
+ aws_session_token=aws_session_token,
aws_role_name=aws_role_name,
aws_session_name=aws_session_name,
)
@@ -554,6 +555,7 @@ class BaseAWSLLM:
self,
aws_access_key_id: Optional[str],
aws_secret_access_key: Optional[str],
+ aws_session_token: Optional[str],
aws_role_name: str,
aws_session_name: str,
) -> Tuple[Credentials, Optional[int]]:
@@ -614,6 +616,7 @@ class BaseAWSLLM:
"sts",
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
+ aws_session_token=aws_session_token,
)
sts_response = sts_client.assume_role(
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index c9d70088d04..2faea53901c 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -1257,6 +1257,10 @@ class BaseLLMHTTPHandler:
stream: Optional[bool] = False,
kwargs: Optional[Dict[str, Any]] = None,
) -> Union[AnthropicMessagesResponse, AsyncIterator]:
+ from litellm.litellm_core_utils.get_provider_specific_headers import (
+ ProviderSpecificHeaderUtils,
+ )
+
if client is None or not isinstance(client, AsyncHTTPHandler):
async_httpx_client = get_async_httpx_client(
llm_provider=litellm.LlmProviders.ANTHROPIC
@@ -1270,10 +1274,9 @@ class BaseLLMHTTPHandler:
Optional[litellm.types.utils.ProviderSpecificHeader],
kwargs.get("provider_specific_header", None),
)
- extra_headers = (
- provider_specific_header.get("extra_headers", {})
- if provider_specific_header
- else {}
+ extra_headers = ProviderSpecificHeaderUtils.get_provider_specific_headers(
+ provider_specific_header=provider_specific_header,
+ custom_llm_provider=custom_llm_provider,
)
(
headers,
@@ -2678,6 +2681,7 @@ class BaseLLMHTTPHandler:
_is_async: bool = False,
fake_stream: bool = False,
litellm_metadata: Optional[Dict[str, Any]] = None,
+ api_key: Optional[str] = None,
) -> Union[
ImageResponse,
Coroutine[Any, Any, ImageResponse],
@@ -2702,6 +2706,7 @@ class BaseLLMHTTPHandler:
client=client if isinstance(client, AsyncHTTPHandler) else None,
fake_stream=fake_stream,
litellm_metadata=litellm_metadata,
+ api_key=api_key,
)
if client is None or not isinstance(client, HTTPHandler):
@@ -2712,7 +2717,7 @@ class BaseLLMHTTPHandler:
sync_httpx_client = client
headers = image_generation_provider_config.validate_environment(
- api_key=litellm_params.get("api_key", None),
+ api_key=api_key,
headers=image_generation_optional_request_params.get("extra_headers", {})
or {},
model=model,
@@ -2795,6 +2800,7 @@ class BaseLLMHTTPHandler:
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
fake_stream: bool = False,
litellm_metadata: Optional[Dict[str, Any]] = None,
+ api_key: Optional[str] = None,
) -> ImageResponse:
"""
Async version of the image generation handler.
@@ -2809,7 +2815,7 @@ class BaseLLMHTTPHandler:
async_httpx_client = client
headers = image_generation_provider_config.validate_environment(
- api_key=litellm_params.get("api_key", None),
+ api_key=api_key,
headers=image_generation_optional_request_params.get("extra_headers", {})
or {},
model=model,
diff --git a/litellm/llms/deepinfra/chat/transformation.py b/litellm/llms/deepinfra/chat/transformation.py
index 0d446d39b92..09cdabcdd82 100644
--- a/litellm/llms/deepinfra/chat/transformation.py
+++ b/litellm/llms/deepinfra/chat/transformation.py
@@ -12,6 +12,9 @@ class DeepInfraConfig(OpenAIGPTConfig):
The class `DeepInfra` provides configuration for the DeepInfra's Chat Completions API interface. Below are the parameters:
"""
+ @property
+ def custom_llm_provider(self) -> Optional[str]:
+ return "deepinfra"
frequency_penalty: Optional[int] = None
function_call: Optional[Union[str, dict]] = None
@@ -53,7 +56,7 @@ class DeepInfraConfig(OpenAIGPTConfig):
return super().get_config()
def get_supported_openai_params(self, model: str):
- return [
+ supported_openai_params = [
"stream",
"frequency_penalty",
"function_call",
@@ -68,9 +71,16 @@ class DeepInfraConfig(OpenAIGPTConfig):
"top_p",
"response_format",
"tools",
- "tool_choice",
+ "tool_choice"
]
+ if litellm.supports_reasoning(
+ model=model,
+ custom_llm_provider=self.custom_llm_provider,
+ ):
+ supported_openai_params.append("reasoning_effort")
+ return supported_openai_params
+
def map_openai_params(
self,
non_default_params: dict,
diff --git a/litellm/llms/github_copilot/chat/transformation.py b/litellm/llms/github_copilot/chat/transformation.py
index 4526e6247b4..66227ac21d8 100644
--- a/litellm/llms/github_copilot/chat/transformation.py
+++ b/litellm/llms/github_copilot/chat/transformation.py
@@ -75,8 +75,36 @@ class GithubCopilotConfig(OpenAIConfig):
initiator = self._determine_initiator(messages)
validated_headers["X-Initiator"] = initiator
+ # Add Copilot-Vision-Request header if request contains images
+ if self._has_vision_content(messages):
+ validated_headers["Copilot-Vision-Request"] = "true"
+
return validated_headers
+ def get_supported_openai_params(self, model: str) -> list:
+ """
+ Get supported OpenAI parameters for GitHub Copilot.
+
+ For Claude models that support extended thinking (Claude 4 family and Claude 3-7), includes thinking and reasoning_effort parameters.
+ For other models, returns standard OpenAI parameters (which may include reasoning_effort for o-series models).
+ """
+ from litellm.utils import supports_reasoning
+
+ # Get base OpenAI parameters
+ base_params = super().get_supported_openai_params(model)
+
+ # Add Claude-specific parameters for models that support extended thinking
+ if "claude" in model.lower() and supports_reasoning(
+ model=model.lower(),
+ ):
+ if "thinking" not in base_params:
+ base_params.append("thinking")
+ # reasoning_effort is not included by parent for Claude models, so add it
+ if "reasoning_effort" not in base_params:
+ base_params.append("reasoning_effort")
+
+ return base_params
+
def _determine_initiator(self, messages: List[AllMessageValues]) -> str:
"""
Determine if request is user or agent initiated based on message roles.
@@ -87,3 +115,27 @@ class GithubCopilotConfig(OpenAIConfig):
if role in ["tool", "assistant"]:
return "agent"
return "user"
+
+ def _has_vision_content(self, messages: List[AllMessageValues]) -> bool:
+ """
+ Check if any message contains vision content (images).
+ Returns True if any message has content with vision-related types, otherwise False.
+
+ Checks for:
+ - image_url content type (OpenAI format)
+ - Content items with type 'image_url'
+ """
+ for message in messages:
+ content = message.get("content")
+ if isinstance(content, list):
+ # Check if any content item indicates vision content
+ for content_item in content:
+ if isinstance(content_item, dict):
+ # Check for image_url field (direct image URL)
+ if "image_url" in content_item:
+ return True
+ # Check for type field indicating image content
+ content_type = content_item.get("type")
+ if content_type == "image_url":
+ return True
+ return False
diff --git a/litellm/llms/github_copilot/common_utils.py b/litellm/llms/github_copilot/common_utils.py
index 4c9a4b6dad0..86fbb706e52 100644
--- a/litellm/llms/github_copilot/common_utils.py
+++ b/litellm/llms/github_copilot/common_utils.py
@@ -28,7 +28,6 @@ class GithubCopilotError(BaseLLMException):
)
-
class GetDeviceCodeError(GithubCopilotError):
pass
diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py
index 3e05473630d..915d2029afe 100644
--- a/litellm/llms/oci/chat/transformation.py
+++ b/litellm/llms/oci/chat/transformation.py
@@ -92,6 +92,22 @@ def load_private_key_from_str(key_str: str):
return key
+def load_private_key_from_file(file_path: str):
+ """Loads a private key from a file path"""
+ try:
+ with open(file_path, "r", encoding="utf-8") as f:
+ key_str = f.read().strip()
+ except FileNotFoundError:
+ raise FileNotFoundError(f"Private key file not found: {file_path}")
+ except OSError as e:
+ raise OSError(f"Failed to read private key file '{file_path}': {e}") from e
+
+ if not key_str:
+ raise ValueError(f"Private key file is empty: {file_path}")
+
+ return load_private_key_from_str(key_str)
+
+
def get_vendor_from_model(model: str) -> OCIVendors:
"""
Extracts the vendor from the model name.
@@ -237,10 +253,17 @@ class OCIChatConfig(BaseConfig):
oci_fingerprint = optional_params.get("oci_fingerprint")
oci_tenancy = optional_params.get("oci_tenancy")
oci_key = optional_params.get("oci_key")
+ oci_key_file = optional_params.get("oci_key_file")
- if not oci_user or not oci_fingerprint or not oci_tenancy or not oci_key:
+ if (
+ not oci_user
+ or not oci_fingerprint
+ or not oci_tenancy
+ or not (oci_key or oci_key_file)
+ ):
raise Exception(
- "Missing one of the following parameters: oci_user, oci_fingerprint, oci_tenancy, oci_key"
+ "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, "
+ "and at least one of oci_key or oci_key_file."
)
method = str(optional_params.get("method", "POST")).upper()
@@ -283,7 +306,17 @@ class OCIChatConfig(BaseConfig):
"Please install it with: pip install cryptography"
) from e
- private_key = load_private_key_from_str(oci_key)
+ private_key = (
+ load_private_key_from_str(oci_key)
+ if oci_key
+ else load_private_key_from_file(oci_key_file) if oci_key_file else None
+ )
+
+ if private_key is None:
+ raise Exception(
+ "Private key is required for OCI authentication. Please provide either oci_key or oci_key_file."
+ )
+
signature = private_key.sign(
signing_string.encode("utf-8"),
padding.PKCS1v15(),
@@ -334,17 +367,19 @@ class OCIChatConfig(BaseConfig):
oci_fingerprint = optional_params.get("oci_fingerprint")
oci_tenancy = optional_params.get("oci_tenancy")
oci_key = optional_params.get("oci_key")
+ oci_key_file = optional_params.get("oci_key_file")
oci_compartment_id = optional_params.get("oci_compartment_id")
if (
not oci_user
or not oci_fingerprint
or not oci_tenancy
- or not oci_key
+ or not (oci_key or oci_key_file)
or not oci_compartment_id
):
raise Exception(
- "Missing one of the following parameters: oci_user, oci_fingerprint, oci_tenancy, oci_key, oci_compartment_id"
+ "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, "
+ "and at least one of oci_key or oci_key_file."
)
if not api_base:
diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py
index d4ce4052a7e..c70fb97af74 100644
--- a/litellm/llms/ollama/chat/transformation.py
+++ b/litellm/llms/ollama/chat/transformation.py
@@ -137,6 +137,7 @@ class OllamaChatConfig(BaseConfig):
"tool_choice",
"functions",
"response_format",
+ "reasoning_effort",
]
def map_openai_params(
@@ -175,6 +176,8 @@ class OllamaChatConfig(BaseConfig):
if value.get("json_schema") and value["json_schema"].get("schema"):
optional_params["format"] = value["json_schema"]["schema"]
### FUNCTION CALLING LOGIC ###
+ if param == "reasoning_effort" and value is not None:
+ optional_params["think"] = True
if param == "tools":
## CHECK IF MODEL SUPPORTS TOOL CALLING ##
try:
@@ -212,9 +215,9 @@ class OllamaChatConfig(BaseConfig):
litellm.add_function_to_prompt = (
True # so that main.py adds the function call to the prompt
)
- optional_params[
- "functions_unsupported_model"
- ] = non_default_params.get("functions")
+ optional_params["functions_unsupported_model"] = (
+ non_default_params.get("functions")
+ )
non_default_params.pop("tool_choice", None) # causes ollama requests to hang
non_default_params.pop("functions", None) # causes ollama requests to hang
return optional_params
@@ -346,11 +349,31 @@ class OllamaChatConfig(BaseConfig):
## RESPONSE OBJECT
model_response.choices[0].finish_reason = "stop"
+ response_json_message = response_json.get("message")
+ if response_json_message is not None:
+ if "thinking" in response_json_message:
+ # remap 'thinking' to 'reasoning_content'
+ response_json_message["reasoning_content"] = response_json_message[
+ "thinking"
+ ]
+ del response_json_message["thinking"]
+ elif response_json_message.get("content") is not None:
+ # parse reasoning content from content
+ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
+ _parse_content_for_reasoning,
+ )
+
+ reasoning_content, content = _parse_content_for_reasoning(
+ response_json_message["content"]
+ )
+ response_json_message["reasoning_content"] = reasoning_content
+ response_json_message["content"] = content
+
if (
request_data.get("format", "") == "json"
and litellm_params.get("function_name") is not None
):
- function_call = json.loads(response_json["message"]["content"])
+ function_call = json.loads(response_json_message["content"])
message = litellm.Message(
content=None,
tool_calls=[
@@ -367,11 +390,13 @@ class OllamaChatConfig(BaseConfig):
"type": "function",
}
],
+ reasoning_content=response_json_message.get("reasoning_content"),
)
model_response.choices[0].message = message # type: ignore
model_response.choices[0].finish_reason = "tool_calls"
else:
- _message = litellm.Message(**response_json["message"])
+
+ _message = litellm.Message(**response_json_message)
model_response.choices[0].message = _message # type: ignore
model_response.created = int(time.time())
model_response.model = "ollama_chat/" + model
@@ -412,6 +437,9 @@ class OllamaChatConfig(BaseConfig):
class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
+ started_reasoning_content: bool = False
+ finished_reasoning_content: bool = False
+
def _is_function_call_complete(self, function_args: Union[str, dict]) -> bool:
if isinstance(function_args, dict):
return True
@@ -465,8 +493,38 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
if is_function_call_complete:
tool_call["id"] = str(uuid.uuid4())
+ # PROCESS REASONING CONTENT
+ reasoning_content: Optional[str] = None
+ content: Optional[str] = None
+ if chunk["message"].get("thinking") is not None:
+ if self.started_reasoning_content is False:
+ reasoning_content = chunk["message"].get("thinking")
+ self.started_reasoning_content = True
+ elif self.finished_reasoning_content is False:
+ reasoning_content = chunk["message"].get("thinking")
+ self.finished_reasoning_content = True
+ elif chunk["message"].get("content") is not None:
+ message_content = chunk["message"].get("content")
+ if "" in message_content:
+ message_content = message_content.replace("", "")
+
+ self.started_reasoning_content = True
+
+ if "" in message_content and self.started_reasoning_content:
+ message_content = message_content.replace("", "")
+ self.finished_reasoning_content = True
+
+ if (
+ self.started_reasoning_content
+ and not self.finished_reasoning_content
+ ):
+ reasoning_content = message_content
+ else:
+ content = message_content
+
delta = Delta(
- content=chunk["message"].get("content", ""),
+ content=content,
+ reasoning_content=reasoning_content,
tool_calls=tool_calls,
)
diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py
index 4f7be507cc2..5689864017f 100644
--- a/litellm/llms/ollama/completion/transformation.py
+++ b/litellm/llms/ollama/completion/transformation.py
@@ -19,13 +19,13 @@ from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMExcepti
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import AllMessageValues, ChatCompletionUsageBlock
from litellm.types.utils import (
+ Delta,
GenericStreamingChunk,
ModelInfoBase,
ModelResponse,
ModelResponseStream,
ProviderField,
StreamingChoices,
- Delta,
)
from ..common_utils import OllamaError, _convert_image
@@ -92,9 +92,9 @@ class OllamaConfig(BaseConfig):
repeat_penalty: Optional[float] = None
temperature: Optional[float] = None
seed: Optional[int] = None
- stop: Optional[
- list
- ] = None # stop is a list based on this - https://github.com/ollama/ollama/pull/442
+ stop: Optional[list] = (
+ None # stop is a list based on this - https://github.com/ollama/ollama/pull/442
+ )
tfs_z: Optional[float] = None
num_predict: Optional[int] = None
top_k: Optional[int] = None
@@ -154,6 +154,7 @@ class OllamaConfig(BaseConfig):
"stop",
"response_format",
"max_completion_tokens",
+ "reasoning_effort",
]
def map_openai_params(
@@ -166,19 +167,21 @@ class OllamaConfig(BaseConfig):
for param, value in non_default_params.items():
if param == "max_tokens" or param == "max_completion_tokens":
optional_params["num_predict"] = value
- if param == "stream":
+ elif param == "stream":
optional_params["stream"] = value
- if param == "temperature":
+ elif param == "temperature":
optional_params["temperature"] = value
- if param == "seed":
+ elif param == "seed":
optional_params["seed"] = value
- if param == "top_p":
+ elif param == "top_p":
optional_params["top_p"] = value
- if param == "frequency_penalty":
+ elif param == "frequency_penalty":
optional_params["frequency_penalty"] = value
- if param == "stop":
+ elif param == "stop":
optional_params["stop"] = value
- if param == "response_format" and isinstance(value, dict):
+ elif param == "reasoning_effort" and value is not None:
+ optional_params["think"] = True
+ elif param == "response_format" and isinstance(value, dict):
if value["type"] == "json_object":
optional_params["format"] = "json"
elif value["type"] == "json_schema":
@@ -258,12 +261,17 @@ class OllamaConfig(BaseConfig):
api_key: Optional[str] = None,
json_mode: Optional[bool] = None,
) -> ModelResponse:
+ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
+ _parse_content_for_reasoning,
+ )
+
response_json = raw_response.json()
## RESPONSE OBJECT
model_response.choices[0].finish_reason = "stop"
if request_data.get("format", "") == "json":
# Check if response field exists and is not empty before parsing JSON
response_text = response_json.get("response", "")
+
if not response_text or not response_text.strip():
# Handle empty response gracefully - set empty content
message = litellm.Message(content="")
@@ -288,7 +296,9 @@ class OllamaConfig(BaseConfig):
"id": f"call_{str(uuid.uuid4())}",
"function": {
"name": function_call["name"],
- "arguments": json.dumps(function_call["arguments"]),
+ "arguments": json.dumps(
+ function_call["arguments"]
+ ),
},
"type": "function",
}
@@ -305,11 +315,26 @@ class OllamaConfig(BaseConfig):
model_response.choices[0].finish_reason = "stop"
except json.JSONDecodeError:
# If JSON parsing fails, treat as regular text response
- message = litellm.Message(content=response_text)
+ ## output parse reasoning content from response_text
+ reasoning_content: Optional[str] = None
+ content: Optional[str] = None
+ if response_text is not None:
+ reasoning_content, content = _parse_content_for_reasoning(
+ response_text
+ )
+ message = litellm.Message(
+ content=content, reasoning_content=reasoning_content
+ )
model_response.choices[0].message = message # type: ignore
model_response.choices[0].finish_reason = "stop"
else:
- model_response.choices[0].message.content = response_json["response"] # type: ignore
+ response_text = response_json.get("response", "")
+ content = None
+ reasoning_content = None
+ if response_text is not None:
+ reasoning_content, content = _parse_content_for_reasoning(response_text)
+ model_response.choices[0].message.content = content # type: ignore
+ model_response.choices[0].message.reasoning_content = reasoning_content # type: ignore
model_response.created = int(time.time())
model_response.model = "ollama/" + model
_prompt = request_data.get("prompt", "")
@@ -434,12 +459,21 @@ class OllamaConfig(BaseConfig):
class OllamaTextCompletionResponseIterator(BaseModelResponseIterator):
+ def __init__(
+ self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False
+ ):
+ super().__init__(streaming_response, sync_stream, json_mode)
+ self.started_reasoning_content: bool = False
+ self.finished_reasoning_content: bool = False
+
def _handle_string_chunk(
self, str_line: str
) -> Union[GenericStreamingChunk, ModelResponseStream]:
return self.chunk_parser(json.loads(str_line))
- def chunk_parser(self, chunk: dict) -> Union[GenericStreamingChunk, ModelResponseStream]:
+ def chunk_parser(
+ self, chunk: dict
+ ) -> Union[GenericStreamingChunk, ModelResponseStream]:
try:
if "error" in chunk:
raise Exception(f"Ollama Error - {chunk}")
@@ -469,12 +503,42 @@ class OllamaTextCompletionResponseIterator(BaseModelResponseIterator):
)
elif chunk["response"]:
text = chunk["response"]
- return GenericStreamingChunk(
- text=text,
- is_finished=is_finished,
- finish_reason="stop",
+ reasoning_content: Optional[str] = None
+ content: Optional[str] = None
+ if text is not None:
+ if "" in text:
+ text = text.replace("", "")
+ self.started_reasoning_content = True
+ elif "" in text:
+ text = text.replace("", "")
+ self.finished_reasoning_content = True
+
+ if (
+ self.started_reasoning_content
+ and not self.finished_reasoning_content
+ ):
+ reasoning_content = text
+ else:
+ content = text
+
+ return ModelResponseStream(
+ choices=[
+ StreamingChoices(
+ index=0,
+ delta=Delta(
+ reasoning_content=reasoning_content, content=content
+ ),
+ )
+ ],
+ finish_reason=finish_reason,
usage=None,
)
+ # return GenericStreamingChunk(
+ # text=text,
+ # is_finished=is_finished,
+ # finish_reason="stop",
+ # usage=None,
+ # )
elif "thinking" in chunk and not chunk["response"]:
# Return reasoning content as ModelResponseStream so UIs can render it
thinking_content = chunk.get("thinking") or ""
diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py
index 9a8bb74d447..3902304a3b4 100644
--- a/litellm/llms/openai/chat/gpt_5_transformation.py
+++ b/litellm/llms/openai/chat/gpt_5_transformation.py
@@ -28,7 +28,18 @@ class OpenAIGPT5Config(OpenAIGPTConfig):
base_gpt_series_params.extend(gpt_5_only_params)
if not supports_tool_choice(model=model):
base_gpt_series_params.remove("tool_choice")
- return base_gpt_series_params
+
+ non_supported_params = [
+ "logprobs",
+ "top_p",
+ "presence_penalty",
+ "frequency_penalty",
+ "top_logprobs",
+ ]
+
+ return [
+ param for param in base_gpt_series_params if param not in non_supported_params
+ ]
def map_openai_params(
self,
diff --git a/litellm/llms/openai_like/chat/handler.py b/litellm/llms/openai_like/chat/handler.py
index ae0a3cc3551..821fc9b7f15 100644
--- a/litellm/llms/openai_like/chat/handler.py
+++ b/litellm/llms/openai_like/chat/handler.py
@@ -49,15 +49,8 @@ async def make_call(
model_response = ModelResponse(**response.json())
completion_stream = MockResponseIterator(model_response=model_response)
else:
- # Use aiter_text with explicit UTF-8 encoding to avoid ASCII encoding errors
- async def utf8_aiter_lines():
- async for line in response.aiter_text(encoding='utf-8'):
- for line_part in line.splitlines(keepends=True):
- if line_part.strip():
- yield line_part.rstrip('\r\n')
-
completion_stream = ModelResponseIterator(
- streaming_response=utf8_aiter_lines(), sync_stream=False
+ streaming_response=response.aiter_lines(), sync_stream=False
)
# LOGGING
logging_obj.post_call(
@@ -100,15 +93,8 @@ def make_sync_call(
model_response = ModelResponse(**response.json())
completion_stream = MockResponseIterator(model_response=model_response)
else:
- # Use iter_text with explicit UTF-8 encoding to avoid ASCII encoding errors
- def utf8_iter_lines():
- for line in response.iter_text(encoding='utf-8'):
- for line_part in line.splitlines(keepends=True):
- if line_part.strip():
- yield line_part.rstrip('\r\n')
-
completion_stream = ModelResponseIterator(
- streaming_response=utf8_iter_lines(), sync_stream=True
+ streaming_response=response.iter_lines(), sync_stream=True
)
# LOGGING
diff --git a/litellm/llms/vercel_ai_gateway/chat/transformation.py b/litellm/llms/vercel_ai_gateway/chat/transformation.py
new file mode 100644
index 00000000000..13a88377489
--- /dev/null
+++ b/litellm/llms/vercel_ai_gateway/chat/transformation.py
@@ -0,0 +1,112 @@
+"""
+Support for OpenAI's `/v1/chat/completions` endpoint.
+
+Calls done in OpenAI/openai.py as Vercel AI Gateway is openai-compatible.
+
+Docs: https://vercel.com/docs/ai-gateway
+"""
+
+from typing import List, Optional, Tuple, Union
+
+import httpx
+
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.types.llms.openai import AllMessageValues
+from litellm.secret_managers.main import get_secret_str
+import litellm
+
+from ...openai.chat.gpt_transformation import OpenAIGPTConfig
+from ..common_utils import VercelAIGatewayException
+
+
+class VercelAIGatewayConfig(OpenAIGPTConfig):
+ @property
+ def custom_llm_provider(self) -> Optional[str]:
+ return "vercel_ai_gateway"
+
+ def get_supported_openai_params(self, model: str) -> list:
+ base_params = super().get_supported_openai_params(model)
+ if "extra_body" not in base_params:
+ base_params.append("extra_body")
+ return base_params
+
+ def _get_openai_compatible_provider_info(
+ self, api_base: Optional[str], api_key: Optional[str]
+ ) -> Tuple[Optional[str], Optional[str]]:
+
+ api_base = (
+ api_base
+ or get_secret_str("VERCEL_AI_GATEWAY_API_BASE")
+ or "https://ai-gateway.vercel.sh/v1"
+ )
+ user_api_key = (
+ api_key
+ or get_secret_str("VERCEL_AI_GATEWAY_API_KEY")
+ or get_secret_str("VERCEL_OIDC_TOKEN")
+ )
+ return api_base, user_api_key
+
+ def map_openai_params(
+ self,
+ non_default_params: dict,
+ optional_params: dict,
+ model: str,
+ drop_params: bool,
+ ) -> dict:
+ mapped_openai_params = super().map_openai_params(
+ non_default_params, optional_params, model, drop_params
+ )
+
+ # Vercel AI Gateway-only parameters
+ extra_body = {}
+ provider_options = non_default_params.pop("providerOptions", None)
+
+ if provider_options is not None:
+ extra_body["providerOptions"] = provider_options
+
+ mapped_openai_params["extra_body"] = extra_body # openai client supports `extra_body` param
+ return mapped_openai_params
+
+ def transform_request(
+ self,
+ model: str,
+ messages: List[AllMessageValues],
+ optional_params: dict,
+ litellm_params: dict,
+ headers: dict,
+ ) -> dict:
+ """
+ Transform the overall request to be sent to the API.
+
+ Returns:
+ dict: The transformed request. Sent as the body of the API call.
+ """
+ return super().transform_request(
+ model, messages, optional_params, litellm_params, headers
+ )
+
+ def get_error_class(
+ self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+ ) -> BaseLLMException:
+ return VercelAIGatewayException(
+ message=error_message,
+ status_code=status_code,
+ headers=headers,
+ )
+
+ def get_models(
+ self, api_key: Optional[str] = None, api_base: Optional[str] = None
+ ) -> List[str]:
+ api_base, _ = self._get_openai_compatible_provider_info(api_base, api_key)
+
+ if api_base is None:
+ api_base = "https://ai-gateway.vercel.sh/v1"
+
+ models_url = f"{api_base}/models"
+ response = litellm.module_level_client.get(url=models_url)
+
+ if response.status_code != 200:
+ raise Exception(f"Failed to get models: {response.text}")
+
+ models = response.json()["data"]
+ return [model["id"] for model in models]
diff --git a/litellm/llms/vercel_ai_gateway/common_utils.py b/litellm/llms/vercel_ai_gateway/common_utils.py
new file mode 100644
index 00000000000..93e792be05e
--- /dev/null
+++ b/litellm/llms/vercel_ai_gateway/common_utils.py
@@ -0,0 +1,5 @@
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+
+
+class VercelAIGatewayException(BaseLLMException):
+ pass
diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py
index 4931631d75d..8588c3efa27 100644
--- a/litellm/llms/vertex_ai/common_utils.py
+++ b/litellm/llms/vertex_ai/common_utils.py
@@ -187,6 +187,25 @@ def _check_text_in_content(parts: List[PartType]) -> bool:
return has_text_param
+def _fix_enum_empty_strings(schema, depth=0):
+ """Fix empty strings in enum values by replacing them with None. Gemini doesn't accept empty strings in enums."""
+ if depth > DEFAULT_MAX_RECURSE_DEPTH:
+ raise ValueError(f"Max depth of {DEFAULT_MAX_RECURSE_DEPTH} exceeded while processing schema.")
+
+ if "enum" in schema and isinstance(schema["enum"], list):
+ schema["enum"] = [None if value == "" else value for value in schema["enum"]]
+
+ # Reuse existing recursion pattern from convert_anyof_null_to_nullable
+ properties = schema.get("properties", None)
+ if properties is not None:
+ for _, value in properties.items():
+ _fix_enum_empty_strings(value, depth=depth + 1)
+
+ items = schema.get("items", None)
+ if items is not None:
+ _fix_enum_empty_strings(items, depth=depth + 1)
+
+
def _build_vertex_schema(parameters: dict, add_property_ordering: bool = False):
"""
This is a modified version of https://github.com/google-gemini/generative-ai-python/blob/8f77cc6ac99937cd3a81299ecf79608b91b06bbb/google/generativeai/types/content_types.py#L419
@@ -215,6 +234,11 @@ def _build_vertex_schema(parameters: dict, add_property_ordering: bool = False):
# * https://github.com/pydantic/pydantic/discussions/4872
convert_anyof_null_to_nullable(parameters)
+ _convert_schema_types(parameters)
+
+ # Handle empty strings in enum values - Gemini doesn't accept empty strings in enums
+ _fix_enum_empty_strings(parameters)
+
# Handle empty items objects
process_items(parameters)
add_object_type(parameters)
@@ -254,9 +278,7 @@ def _filter_anyof_fields(schema_dict: Dict[str, Any]) -> Dict[str, Any]:
item["title"] = title
if description:
item["description"] = description
- return {"anyOf": any_of}
- else:
- return schema_dict
+ return {"anyOf": any_of}
return schema_dict
@@ -441,6 +463,47 @@ def _convert_vertex_datetime_to_openai_datetime(vertex_datetime: str) -> int:
return int(dt.timestamp())
+def _convert_schema_types(schema, depth=0):
+ """
+ Convert type arrays and lowercase types for Vertex AI compatibility.
+
+ Transforms OpenAI-style schemas to Vertex AI format by converting type arrays
+ like ["string", "number"] to anyOf format and converting all types to uppercase.
+ """
+ if depth > DEFAULT_MAX_RECURSE_DEPTH:
+ raise ValueError(
+ f"Max depth of {DEFAULT_MAX_RECURSE_DEPTH} exceeded while processing schema. Please check the schema for excessive nesting."
+ )
+
+ if not isinstance(schema, dict):
+ return
+
+
+ # Handle type field
+ if "type" in schema:
+ type_val = schema["type"]
+ if isinstance(type_val, list) and len(type_val) > 1:
+ # Convert ["string", "number"] -> {"anyOf": [{"type": "STRING"}, {"type": "NUMBER"}]}
+ schema["anyOf"] = [{"type": t} for t in type_val if isinstance(t, str)]
+ schema.pop("type")
+ elif isinstance(type_val, list) and len(type_val) == 1:
+ schema["type"] = type_val[0]
+ elif isinstance(type_val, str):
+ schema["type"] = type_val
+
+ # Recursively process nested properties, items, and anyOf
+ for key in ["properties", "items", "anyOf"]:
+ if key in schema:
+ value = schema[key]
+ if key == "properties" and isinstance(value, dict):
+ for prop_schema in value.values():
+ _convert_schema_types(prop_schema, depth + 1)
+ elif key == "items":
+ _convert_schema_types(value, depth + 1)
+ elif key == "anyOf" and isinstance(value, list):
+ for anyof_schema in value:
+ _convert_schema_types(anyof_schema, depth + 1)
+
def get_vertex_project_id_from_url(url: str) -> Optional[str]:
"""
Get the vertex project id from the url
diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py
index 85e3f15364b..267ca61ef5d 100644
--- a/litellm/llms/vertex_ai/gemini/transformation.py
+++ b/litellm/llms/vertex_ai/gemini/transformation.py
@@ -35,6 +35,7 @@ from litellm.types.llms.openai import (
ChatCompletionFileObject,
ChatCompletionImageObject,
ChatCompletionTextObject,
+ ChatCompletionUserMessage,
)
from litellm.types.llms.vertex_ai import *
from litellm.types.llms.vertex_ai import (
@@ -104,6 +105,64 @@ def _process_gemini_image(image_url: str, format: Optional[str] = None) -> PartT
raise e
+def _snake_to_camel(snake_str: str) -> str:
+ """Convert snake_case to camelCase"""
+ components = snake_str.split("_")
+ return components[0] + "".join(x.capitalize() for x in components[1:])
+
+
+def _camel_to_snake(camel_str: str) -> str:
+ """Convert camelCase to snake_case"""
+ import re
+
+ return re.sub(r"(? Optional[str]:
+ """
+ Get the equivalent key from available keys, checking both camelCase and snake_case variants
+ """
+ if key in available_keys:
+ return key
+
+ # Try camelCase version
+ camel_key = _snake_to_camel(key)
+ if camel_key in available_keys:
+ return camel_key
+
+ # Try snake_case version
+ snake_key = _camel_to_snake(key)
+ if snake_key in available_keys:
+ return snake_key
+
+ return None
+
+
+def check_if_part_exists_in_parts(
+ parts: List[PartType], part: PartType, excluded_keys: List[str] = []
+) -> bool:
+ """
+ Check if a part exists in a list of parts
+ Handles both camelCase and snake_case key variations (e.g., function_call vs functionCall)
+ """
+ keys_to_compare = set(part.keys()) - set(excluded_keys)
+ for p in parts:
+ p_keys = set(p.keys())
+ # Check if all keys in part have equivalent values in p
+ match_found = True
+ for key in keys_to_compare:
+ equivalent_key = _get_equivalent_key(key, p_keys)
+ if equivalent_key is None or p.get(equivalent_key, None) != part.get(
+ key, None
+ ):
+ match_found = False
+ break
+
+ if match_found:
+ return True
+ return False
+
+
def _gemini_convert_messages_with_history( # noqa: PLR0915
messages: List[AllMessageValues],
) -> List[ContentType]:
@@ -235,10 +294,33 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915
assistant_msg = ChatCompletionAssistantMessage(**msg_dict) # type: ignore
_message_content = assistant_msg.get("content", None)
reasoning_content = assistant_msg.get("reasoning_content", None)
+ thinking_blocks = assistant_msg.get("thinking_blocks")
if reasoning_content is not None:
assistant_content.append(
PartType(thought=True, text=reasoning_content)
)
+ if thinking_blocks is not None:
+ for block in thinking_blocks:
+ block_thinking_str = block.get("thinking")
+ block_signature = block.get("signature")
+ if (
+ block_thinking_str is not None
+ and block_signature is not None
+ ):
+ try:
+ assistant_content.append(
+ PartType(
+ thoughtSignature=block_signature,
+ **json.loads(block_thinking_str),
+ )
+ )
+ except Exception:
+ assistant_content.append(
+ PartType(
+ thoughtSignature=block_signature,
+ text=block_thinking_str,
+ )
+ )
if _message_content is not None and isinstance(_message_content, list):
_parts = []
for element in _message_content:
@@ -261,9 +343,17 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915
assistant_msg.get("tool_calls", []) is not None
or assistant_msg.get("function_call") is not None
): # support assistant tool invoke conversion
- assistant_content.extend(
- convert_to_gemini_tool_call_invoke(assistant_msg)
+ gemini_tool_call_parts = convert_to_gemini_tool_call_invoke(
+ assistant_msg
)
+ ## check if gemini_tool_call already exists in assistant_content
+ for gemini_tool_call_part in gemini_tool_call_parts:
+ if not check_if_part_exists_in_parts(
+ assistant_content,
+ gemini_tool_call_part,
+ excluded_keys=["thoughtSignature"],
+ ):
+ assistant_content.append(gemini_tool_call_part)
last_message_with_tool_calls = assistant_msg
msg_i += 1
@@ -476,6 +566,15 @@ async def async_transform_request_body(
)
+def _default_user_message_when_system_message_passed() -> ChatCompletionUserMessage:
+ """
+ Returns a default user message when a "system" message is passed in gemini fails.
+
+ This adds a blank user message to the messages list, to ensure that gemini doesn't fail the request.
+ """
+ return ChatCompletionUserMessage(content=".", role="user")
+
+
def _transform_system_message(
supports_system_message: bool, messages: List[AllMessageValues]
) -> Tuple[Optional[SystemInstructions], List[AllMessageValues]]:
@@ -510,6 +609,13 @@ def _transform_system_message(
messages.pop(idx)
if len(system_content_blocks) > 0:
+ #########################################################
+ # If no messages are passed in, add a blank user message
+ # Relevant Issue - https://github.com/BerriAI/litellm/issues/13769
+ #########################################################
+ if len(messages) == 0:
+ messages.append(_default_user_message_when_system_message_passed())
+ #########################################################
return SystemInstructions(parts=system_content_blocks), messages
return None, messages
diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
index c5c4f46c92f..37470a6ee09 100644
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -43,9 +43,11 @@ from litellm.types.llms.gemini import BidiGenerateContentServerMessage
from litellm.types.llms.openai import (
AllMessageValues,
ChatCompletionResponseMessage,
+ ChatCompletionThinkingBlock,
ChatCompletionToolCallChunk,
ChatCompletionToolCallFunctionChunk,
ChatCompletionToolParamFunctionChunk,
+ ImageURLObject,
OpenAIChatCompletionFinishReason,
)
from litellm.types.llms.vertex_ai import (
@@ -89,11 +91,12 @@ from .transformation import (
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
- from litellm.types.utils import ModelResponseStream
+ from litellm.types.utils import ModelResponseStream, StreamingChoices
LoggingClass = LiteLLMLoggingObj
else:
LoggingClass = Any
+ StreamingChoices = Any
class VertexAIBaseConfig:
@@ -461,7 +464,6 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
params["includeThoughts"] = True
if thinking_budget is not None and isinstance(thinking_budget, int):
params["thinkingBudget"] = thinking_budget
-
return params
def map_response_modalities(self, value: list) -> list:
@@ -774,8 +776,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
elif "inlineData" in part:
mime_type = part["inlineData"]["mimeType"]
data = part["inlineData"]["data"]
- # Check if inline data is audio - if so, exclude from text content
- if mime_type.startswith("audio/"):
+ # Check if inline data is audio or image - if so, exclude from text content
+ # Images and audio are now handled separately in their respective response fields
+ if mime_type.startswith("audio/") or mime_type.startswith("image/"):
continue
_content_str += "data:{};base64,{}".format(mime_type, data)
@@ -791,6 +794,38 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
return content_str, reasoning_content_str
+ def _extract_thinking_blocks_from_parts(
+ self, parts: List[HttpxPartType]
+ ) -> List[ChatCompletionThinkingBlock]:
+ """Extract thinking blocks from parts if present"""
+ thinking_blocks: List[ChatCompletionThinkingBlock] = []
+ for part in parts:
+ if "thoughtSignature" in part:
+ part_copy = part.copy()
+ part_copy.pop("thoughtSignature")
+ thinking_blocks.append(
+ ChatCompletionThinkingBlock(
+ type="thinking",
+ thinking=json.dumps(part_copy),
+ signature=part["thoughtSignature"],
+ )
+ )
+ return thinking_blocks
+
+ def _extract_image_response_from_parts(
+ self, parts: List[HttpxPartType]
+ ) -> Optional[ImageURLObject]:
+ """Extract image response from parts if present"""
+ for part in parts:
+ if "inlineData" in part:
+ mime_type = part["inlineData"]["mimeType"]
+ data = part["inlineData"]["data"]
+ if mime_type.startswith("image/"):
+ # Convert base64 data to data URI format
+ data_uri = f"data:{mime_type};base64,{data}"
+ return ImageURLObject(url=data_uri, detail="auto")
+ return None
+
def _extract_audio_response_from_parts(
self, parts: List[HttpxPartType]
) -> Optional[ChatCompletionAudioResponse]:
@@ -1109,6 +1144,80 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
web_search_requests = len(grounding_metadata)
return web_search_requests
+ @staticmethod
+ def _create_streaming_choice(
+ chat_completion_message: ChatCompletionResponseMessage,
+ candidate: Candidates,
+ idx: int,
+ tools: Optional[List[ChatCompletionToolCallChunk]],
+ functions: Optional[ChatCompletionToolCallFunctionChunk],
+ chat_completion_logprobs: Optional[ChoiceLogprobs],
+ image_response: Optional[ImageURLObject],
+ ) -> StreamingChoices:
+ """
+ Helper method to create a streaming choice object for Vertex AI
+ """
+ from litellm.types.utils import Delta, StreamingChoices
+
+ # create a streaming choice object
+ choice = StreamingChoices(
+ finish_reason=VertexGeminiConfig._check_finish_reason(
+ chat_completion_message, candidate.get("finishReason")
+ ),
+ index=candidate.get("index", idx),
+ delta=Delta(
+ content=chat_completion_message.get("content"),
+ reasoning_content=chat_completion_message.get("reasoning_content"),
+ tool_calls=tools,
+ image=image_response,
+ function_call=functions,
+ ),
+ logprobs=chat_completion_logprobs,
+ enhancements=None,
+ )
+ return choice
+
+ @staticmethod
+ def _extract_candidate_metadata(
+ candidate: Candidates,
+ ) -> Tuple[List[dict], List[dict], List, List]:
+ """
+ Extract metadata from a single candidate response.
+
+ Returns:
+ grounding_metadata: List[dict]
+ url_context_metadata: List[dict]
+ safety_ratings: List
+ citation_metadata: List
+ """
+ grounding_metadata: List[dict] = []
+ url_context_metadata: List[dict] = []
+ safety_ratings: List = []
+ citation_metadata: List = []
+
+ if "groundingMetadata" in candidate:
+ if isinstance(candidate["groundingMetadata"], list):
+ grounding_metadata.extend(candidate["groundingMetadata"]) # type: ignore
+ else:
+ grounding_metadata.append(candidate["groundingMetadata"]) # type: ignore
+
+ if "safetyRatings" in candidate:
+ safety_ratings.append(candidate["safetyRatings"])
+
+ if "citationMetadata" in candidate:
+ citation_metadata.append(candidate["citationMetadata"])
+
+ if "urlContextMetadata" in candidate:
+ # Add URL context metadata to grounding metadata
+ url_context_metadata.append(cast(dict, candidate["urlContextMetadata"]))
+
+ return (
+ grounding_metadata,
+ url_context_metadata,
+ safety_ratings,
+ citation_metadata,
+ )
+
@staticmethod
def _process_candidates(
_candidates: List[Candidates],
@@ -1131,6 +1240,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
grounding_metadata: List[dict] = []
url_context_metadata: List[dict] = []
+ image_response: Optional[ImageURLObject] = None
safety_ratings: List = []
citation_metadata: List = []
chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"}
@@ -1138,26 +1248,24 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
tools: Optional[List[ChatCompletionToolCallChunk]] = []
functions: Optional[ChatCompletionToolCallFunctionChunk] = None
cumulative_tool_call_index: int = 0
+ thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
for idx, candidate in enumerate(_candidates):
if "content" not in candidate:
continue
- if "groundingMetadata" in candidate:
- if isinstance(candidate["groundingMetadata"], list):
- grounding_metadata.extend(candidate["groundingMetadata"]) # type: ignore
- else:
- grounding_metadata.append(candidate["groundingMetadata"]) # type: ignore
+ # Extract metadata using helper function
+ (
+ candidate_grounding_metadata,
+ candidate_url_context_metadata,
+ candidate_safety_ratings,
+ candidate_citation_metadata,
+ ) = VertexGeminiConfig._extract_candidate_metadata(candidate)
- if "safetyRatings" in candidate:
- safety_ratings.append(candidate["safetyRatings"])
-
- if "citationMetadata" in candidate:
- citation_metadata.append(candidate["citationMetadata"])
-
- if "urlContextMetadata" in candidate:
- # Add URL context metadata to grounding metadata
- url_context_metadata.append(cast(dict, candidate["urlContextMetadata"]))
+ grounding_metadata.extend(candidate_grounding_metadata)
+ url_context_metadata.extend(candidate_url_context_metadata)
+ safety_ratings.extend(candidate_safety_ratings)
+ citation_metadata.extend(candidate_citation_metadata)
if "parts" in candidate["content"]:
(
@@ -1172,18 +1280,33 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
parts=candidate["content"]["parts"]
)
)
+ image_response = (
+ VertexGeminiConfig()._extract_image_response_from_parts(
+ parts=candidate["content"]["parts"]
+ )
+ )
+
+ thinking_blocks = (
+ VertexGeminiConfig()._extract_thinking_blocks_from_parts(
+ parts=candidate["content"]["parts"]
+ )
+ )
if audio_response is not None:
cast(Dict[str, Any], chat_completion_message)[
"audio"
] = audio_response
chat_completion_message["content"] = None # OpenAI spec
- elif content is not None:
+ if image_response is not None:
+ # Handle image response - combine with text content into structured format
+ cast(Dict[str, Any], chat_completion_message)[
+ "image"
+ ] = image_response
+ if content is not None:
chat_completion_message["content"] = content
if reasoning_content is not None:
chat_completion_message["reasoning_content"] = reasoning_content
-
(
functions,
tools,
@@ -1205,25 +1328,18 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
if functions is not None:
chat_completion_message["function_call"] = functions
- if isinstance(model_response, ModelResponseStream):
- from litellm.types.utils import Delta, StreamingChoices
+ if thinking_blocks is not None:
+ chat_completion_message["thinking_blocks"] = thinking_blocks # type: ignore
- # create a streaming choice object
- choice = StreamingChoices(
- finish_reason=VertexGeminiConfig._check_finish_reason(
- chat_completion_message, candidate.get("finishReason")
- ),
- index=candidate.get("index", idx),
- delta=Delta(
- content=chat_completion_message.get("content"),
- reasoning_content=chat_completion_message.get(
- "reasoning_content"
- ),
- tool_calls=tools,
- function_call=functions,
- ),
- logprobs=chat_completion_logprobs,
- enhancements=None,
+ if isinstance(model_response, ModelResponseStream):
+ choice = VertexGeminiConfig._create_streaming_choice(
+ chat_completion_message=chat_completion_message,
+ candidate=candidate,
+ idx=idx,
+ tools=tools,
+ functions=functions,
+ chat_completion_logprobs=chat_completion_logprobs,
+ image_response=image_response,
)
model_response.choices.append(choice)
elif isinstance(model_response, ModelResponse):
diff --git a/litellm/llms/xai/chat/transformation.py b/litellm/llms/xai/chat/transformation.py
index 5a488876cd9..78c20ac5731 100644
--- a/litellm/llms/xai/chat/transformation.py
+++ b/litellm/llms/xai/chat/transformation.py
@@ -31,7 +31,6 @@ class XAIChatConfig(OpenAIGPTConfig):
def get_supported_openai_params(self, model: str) -> list:
base_openai_params = [
- "frequency_penalty",
"logit_bias",
"logprobs",
"max_tokens",
@@ -50,8 +49,22 @@ class XAIChatConfig(OpenAIGPTConfig):
"web_search_options",
]
# for some reason, grok-3-mini does not support stop tokens
+ #########################################################
+ # stop tokens check
+ #########################################################
if self._supports_stop_reason(model):
base_openai_params.append("stop")
+
+
+ #########################################################
+ # frequency penalty check
+ #########################################################
+ if self._supports_frequency_penalty(model):
+ base_openai_params.append("frequency_penalty")
+
+ #########################################################
+ # reasoning check
+ #########################################################
try:
if litellm.supports_reasoning(
model=model, custom_llm_provider=self.custom_llm_provider
@@ -68,6 +81,18 @@ class XAIChatConfig(OpenAIGPTConfig):
elif "grok-4" in model:
return False
return True
+
+ def _supports_frequency_penalty(self, model: str) -> bool:
+ """
+ From manual testing grok-4 does not support `frequency_penalty`
+
+ When sent the model fails from xAI API
+ """
+ if "grok-4" in model:
+ return False
+ if "grok-code-fast" in model:
+ return False
+ return True
def map_openai_params(
self,
diff --git a/litellm/llms/xai/cost_calculator.py b/litellm/llms/xai/cost_calculator.py
new file mode 100644
index 00000000000..62a48080d1c
--- /dev/null
+++ b/litellm/llms/xai/cost_calculator.py
@@ -0,0 +1,54 @@
+"""
+Helper util for handling XAI-specific cost calculation
+- e.g.: reasoning tokens for grok models
+"""
+
+from typing import Tuple, Union
+
+from litellm.types.utils import Usage
+from litellm.utils import get_model_info
+
+
+def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
+ """
+ Calculates the cost per token for a given XAI model, prompt tokens, and completion tokens.
+
+ Input:
+ - model: str, the model name without provider prefix
+ - usage: LiteLLM Usage block, containing XAI-specific usage information
+
+ Returns:
+ Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
+ """
+ ## GET MODEL INFO
+ model_info = get_model_info(model=model, custom_llm_provider="xai")
+
+ def _safe_float_cast(
+ value: Union[str, int, float, None, object], default: float = 0.0
+ ) -> float:
+ """Safely cast a value to float with proper type handling for mypy."""
+ if value is None:
+ return default
+ try:
+ return float(value) # type: ignore
+ except (ValueError, TypeError):
+ return default
+
+ ## CALCULATE INPUT COST
+ input_cost_per_token = _safe_float_cast(model_info.get("input_cost_per_token"))
+ prompt_cost: float = (usage.prompt_tokens or 0) * input_cost_per_token
+
+ ## CALCULATE OUTPUT COST
+ output_cost_per_token = _safe_float_cast(model_info.get("output_cost_per_token"))
+
+ # For XAI models, completion is billed as (visible completion tokens + reasoning tokens)
+ completion_tokens = int(getattr(usage, "completion_tokens", 0) or 0)
+ reasoning_tokens = 0
+ if hasattr(usage, "completion_tokens_details") and usage.completion_tokens_details:
+ reasoning_tokens = int(
+ getattr(usage.completion_tokens_details, "reasoning_tokens", 0) or 0
+ )
+
+ completion_cost = (completion_tokens + reasoning_tokens) * output_cost_per_token
+
+ return prompt_cost, completion_cost
diff --git a/litellm/main.py b/litellm/main.py
index c8442d483e9..786a0196e5e 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -61,6 +61,9 @@ from litellm.exceptions import LiteLLMUnknownProvider
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.audio_utils.utils import get_audio_file_for_health_check
from litellm.litellm_core_utils.dd_tracing import tracer
+from litellm.litellm_core_utils.get_provider_specific_headers import (
+ ProviderSpecificHeaderUtils,
+)
from litellm.litellm_core_utils.health_check_utils import (
_create_health_check_response,
_filter_model_params,
@@ -1107,11 +1110,11 @@ def completion( # type: ignore # noqa: PLR0915
api_key=api_key,
)
- if (
- provider_specific_header is not None
- and provider_specific_header["custom_llm_provider"] == custom_llm_provider
- ):
- headers.update(provider_specific_header["extra_headers"])
+ if provider_specific_header is not None:
+ headers.update(ProviderSpecificHeaderUtils.get_provider_specific_headers(
+ provider_specific_header=provider_specific_header,
+ custom_llm_provider=custom_llm_provider,
+ ))
if model_response is not None and hasattr(model_response, "_hidden_params"):
model_response._hidden_params["custom_llm_provider"] = custom_llm_provider
@@ -1253,6 +1256,7 @@ def completion( # type: ignore # noqa: PLR0915
additional_drop_params=kwargs.get("additional_drop_params"),
remove_sensitive_keys=True,
add_provider_specific_params=True,
+ provider_config=provider_config,
)
if litellm.add_function_to_prompt and optional_params.get(
@@ -2169,8 +2173,18 @@ def completion( # type: ignore # noqa: PLR0915
or "https://api.anthropic.com/v1/complete"
)
- if api_base is not None and not api_base.endswith("/v1/complete"):
+ # Check if we should disable automatic URL suffix appending
+ disable_url_suffix = get_secret_bool("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX")
+ if (
+ api_base is not None
+ and not disable_url_suffix
+ and not api_base.endswith("/v1/complete")
+ ):
api_base += "/v1/complete"
+ elif disable_url_suffix:
+ verbose_logger.debug(
+ "LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX is set, skipping /v1/complete suffix"
+ )
response = base_llm_http_handler.completion(
model=model,
@@ -2206,8 +2220,18 @@ def completion( # type: ignore # noqa: PLR0915
or "https://api.anthropic.com/v1/messages"
)
- if api_base is not None and not api_base.endswith("/v1/messages"):
+ # Check if we should disable automatic URL suffix appending
+ disable_url_suffix = get_secret_bool("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX")
+ if (
+ api_base is not None
+ and not disable_url_suffix
+ and not api_base.endswith("/v1/messages")
+ ):
api_base += "/v1/messages"
+ elif disable_url_suffix:
+ verbose_logger.debug(
+ "LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX is set, skipping /v1/messages suffix"
+ )
response = anthropic_chat_completions.completion(
model=model,
@@ -2644,6 +2668,70 @@ def completion( # type: ignore # noqa: PLR0915
logging.post_call(
input=messages, api_key=openai.api_key, original_response=response
)
+ elif custom_llm_provider == "vercel_ai_gateway":
+ api_base = (
+ api_base
+ or litellm.api_base
+ or get_secret_str("VERCEL_AI_GATEWAY_API_BASE")
+ or "https://ai-gateway.vercel.sh/v1"
+ )
+
+ api_key = (
+ api_key
+ or litellm.api_key
+ or get_secret("VERCEL_AI_GATEWAY_API_KEY")
+ )
+
+ vercel_site_url = get_secret("VERCEL_SITE_URL") or "https://litellm.ai"
+ vercel_app_name = get_secret("VERCEL_APP_NAME") or "liteLLM"
+
+ vercel_headers = {
+ "http-referer": vercel_site_url,
+ "x-title": vercel_app_name,
+ }
+
+ _headers = headers or litellm.headers
+ if _headers:
+ vercel_headers.update(_headers)
+
+ headers = vercel_headers
+
+ ## Load Config
+ config = litellm.VercelAIGatewayConfig.get_config()
+ for k, v in config.items():
+ if k == "extra_body":
+ # we use openai 'extra_body' to pass vercel specific params - providerOptions
+ if "extra_body" in optional_params:
+ optional_params[k].update(v)
+ else:
+ optional_params[k] = v
+ elif k not in optional_params:
+ optional_params[k] = v
+
+ data = {"model": model, "messages": messages, **optional_params}
+
+ ## COMPLETION CALL
+ response = base_llm_http_handler.completion(
+ model=model,
+ stream=stream,
+ messages=messages,
+ acompletion=acompletion,
+ api_base=api_base,
+ model_response=model_response,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ custom_llm_provider="vercel_ai_gateway",
+ timeout=timeout,
+ headers=headers,
+ encoding=encoding,
+ api_key=api_key,
+ logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
+ client=client,
+ )
+ ## LOGGING
+ logging.post_call(
+ input=messages, api_key=openai.api_key, original_response=response
+ )
elif (
custom_llm_provider == "together_ai"
or ("togethercomputer" in model)
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index a29d03f2c6c..57309554621 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -1479,6 +1479,70 @@
"supports_system_messages": true,
"supports_tool_choice": true
},
+ "gpt-realtime": {
+ "max_tokens": 4096,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 4e-06,
+ "cache_read_input_token_cost": 0.4e-06,
+ "output_cost_per_token": 16e-06,
+ "input_cost_per_audio_token": 32e-06,
+ "output_cost_per_audio_token": 64e-06,
+ "cache_creation_input_audio_token_cost": 0.4e-06,
+ "input_cost_per_image": 5e-06,
+ "litellm_provider": "openai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_audio_input": true,
+ "supports_audio_output": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supported_endpoints": [
+ "/v1/realtime"
+ ],
+ "supported_modalities": [
+ "text",
+ "image",
+ "audio"
+ ],
+ "supported_output_modalities": [
+ "text",
+ "audio"
+ ]
+ },
+ "gpt-realtime-2025-08-28": {
+ "max_tokens": 4096,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 4e-06,
+ "cache_read_input_token_cost": 0.4e-06,
+ "output_cost_per_token": 16e-06,
+ "input_cost_per_audio_token": 32e-06,
+ "output_cost_per_audio_token": 64e-06,
+ "cache_creation_input_audio_token_cost": 0.4e-06,
+ "input_cost_per_image": 5e-06,
+ "litellm_provider": "openai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_audio_input": true,
+ "supports_audio_output": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supported_endpoints": [
+ "/v1/realtime"
+ ],
+ "supported_modalities": [
+ "text",
+ "image",
+ "audio"
+ ],
+ "supported_output_modalities": [
+ "text",
+ "audio"
+ ]
+ },
"gpt-4o-realtime-preview-2024-10-01": {
"max_tokens": 4096,
"max_input_tokens": 128000,
@@ -2658,12 +2722,7 @@
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_native_streaming": true,
- "supports_web_search": true,
- "search_context_cost_per_query": {
- "search_context_size_low": 0.03,
- "search_context_size_medium": 0.035,
- "search_context_size_high": 0.05
- }
+ "supports_web_search": false
},
"azure/gpt-4.1-2025-04-14": {
"max_tokens": 32768,
@@ -2696,12 +2755,7 @@
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_native_streaming": true,
- "supports_web_search": true,
- "search_context_cost_per_query": {
- "search_context_size_low": 0.03,
- "search_context_size_medium": 0.035,
- "search_context_size_high": 0.05
- }
+ "supports_web_search": false
},
"azure/gpt-4.1-mini": {
"max_tokens": 32768,
@@ -2734,12 +2788,7 @@
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_native_streaming": true,
- "supports_web_search": true,
- "search_context_cost_per_query": {
- "search_context_size_low": 0.025,
- "search_context_size_medium": 0.0275,
- "search_context_size_high": 0.03
- }
+ "supports_web_search": false
},
"azure/gpt-4.1-mini-2025-04-14": {
"max_tokens": 32768,
@@ -2772,12 +2821,7 @@
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_native_streaming": true,
- "supports_web_search": true,
- "search_context_cost_per_query": {
- "search_context_size_low": 0.025,
- "search_context_size_medium": 0.0275,
- "search_context_size_high": 0.03
- }
+ "supports_web_search": false
},
"azure/gpt-4.1-nano": {
"max_tokens": 32768,
@@ -5618,6 +5662,48 @@
"supports_tool_choice": true,
"supports_web_search": true
},
+ "xai/grok-code-fast-1": {
+ "max_tokens": 256000,
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "input_cost_per_token": 0.2e-06,
+ "output_cost_per_token": 1.5e-06,
+ "cache_read_input_token_cost": 0.02e-06,
+ "litellm_provider": "xai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_reasoning": true,
+ "source": "https://docs.x.ai/docs/models"
+ },
+ "xai/grok-code-fast": {
+ "max_tokens": 256000,
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "input_cost_per_token": 0.2e-06,
+ "output_cost_per_token": 1.5e-06,
+ "cache_read_input_token_cost": 0.02e-06,
+ "litellm_provider": "xai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_reasoning": true,
+ "source": "https://docs.x.ai/docs/models"
+ },
+ "xai/grok-code-fast-1-0825": {
+ "max_tokens": 256000,
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "input_cost_per_token": 0.2e-06,
+ "output_cost_per_token": 1.5e-06,
+ "cache_read_input_token_cost": 0.02e-06,
+ "litellm_provider": "xai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_reasoning": true,
+ "source": "https://docs.x.ai/docs/models"
+ },
"xai/grok-4": {
"max_tokens": 256000,
"max_input_tokens": 256000,
@@ -5731,16 +5817,6 @@
"supports_response_schema": true,
"supports_tool_choice": true
},
- "groq/llama3-8b-8192": {
- "max_tokens": 8192,
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
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@@ -5821,17 +5897,6 @@
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@@ -7929,6 +7994,54 @@
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@@ -8245,6 +8358,54 @@
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@@ -10102,18 +10263,6 @@
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@@ -10126,12 +10275,6 @@
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@@ -10838,36 +10981,18 @@
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@@ -11511,9 +11636,9 @@
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@@ -11528,9 +11653,9 @@
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@@ -11570,6 +11695,40 @@
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@@ -11811,6 +11970,165 @@
"mode": "chat",
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@@ -14733,10 +15051,10 @@
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},
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@@ -14744,10 +15062,10 @@
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@@ -14755,10 +15073,10 @@
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@@ -14801,10 +15119,10 @@
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},
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@@ -15095,236 +15425,16 @@
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+ "max_output_tokens": 8192,
+ "max_input_tokens": 8192,
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+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
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+ "vercel_ai_gateway/openai/text-embedding-3-large": {
+ "max_tokens": 0,
+ "input_cost_per_token": 1.3e-07,
+ "output_cost_per_token": 0.0,
+ "max_output_tokens": 0,
+ "max_input_tokens": 0,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "embedding"
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+ "vercel_ai_gateway/google/gemini-2.0-flash-lite": {
+ "max_tokens": 1048576,
+ "input_cost_per_token": 7.5e-08,
+ "output_cost_per_token": 3e-07,
+ "max_output_tokens": 8192,
+ "max_input_tokens": 1048576,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/mistral/ministral-3b": {
+ "max_tokens": 128000,
+ "input_cost_per_token": 4e-08,
+ "output_cost_per_token": 4e-08,
+ "max_output_tokens": 4000,
+ "max_input_tokens": 128000,
+ "litellm_provider": "vercel_ai_gateway",
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+ "vercel_ai_gateway/perplexity/sonar-reasoning-pro": {
+ "max_tokens": 127000,
+ "input_cost_per_token": 2e-06,
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+ "max_input_tokens": 127000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/google/gemini-embedding-001": {
+ "max_tokens": 0,
+ "input_cost_per_token": 1.5e-07,
+ "output_cost_per_token": 0.0,
+ "max_output_tokens": 0,
+ "max_input_tokens": 0,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "embedding"
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+ "vercel_ai_gateway/anthropic/claude-3-haiku": {
+ "max_tokens": 200000,
+ "input_cost_per_token": 2.5e-07,
+ "output_cost_per_token": 1.25e-06,
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+ "max_input_tokens": 200000,
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+ "cache_creation_input_token_cost": 3e-07,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/openai/o1": {
+ "max_tokens": 200000,
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+ "max_input_tokens": 200000,
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+ "cache_creation_input_token_cost": 0.0,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/deepseek/deepseek-r1": {
+ "max_tokens": 128000,
+ "input_cost_per_token": 5.5e-07,
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+ "max_output_tokens": 8192,
+ "max_input_tokens": 128000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/mistral/mistral-embed": {
+ "max_tokens": 0,
+ "input_cost_per_token": 1e-07,
+ "output_cost_per_token": 0.0,
+ "max_output_tokens": 0,
+ "max_input_tokens": 0,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/openai/gpt-4.1-mini": {
+ "max_tokens": 1047576,
+ "input_cost_per_token": 4e-07,
+ "output_cost_per_token": 1.6e-06,
+ "max_output_tokens": 32768,
+ "max_input_tokens": 1047576,
+ "cache_read_input_token_cost": 1e-07,
+ "cache_creation_input_token_cost": 0.0,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/openai/gpt-4o-mini": {
+ "max_tokens": 128000,
+ "input_cost_per_token": 1.5e-07,
+ "output_cost_per_token": 6e-07,
+ "max_output_tokens": 16384,
+ "max_input_tokens": 128000,
+ "cache_read_input_token_cost": 7.5e-08,
+ "cache_creation_input_token_cost": 0.0,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/alibaba/qwen-3-14b": {
+ "max_tokens": 40960,
+ "input_cost_per_token": 8e-08,
+ "output_cost_per_token": 2.4e-07,
+ "max_output_tokens": 16384,
+ "max_input_tokens": 40960,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/anthropic/claude-4-opus": {
+ "max_tokens": 200000,
+ "input_cost_per_token": 1.5e-05,
+ "output_cost_per_token": 7.5e-05,
+ "max_output_tokens": 32000,
+ "max_input_tokens": 200000,
+ "cache_read_input_token_cost": 1.5e-06,
+ "cache_creation_input_token_cost": 1.875e-05,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/mistral/mistral-saba-24b": {
+ "max_tokens": 32768,
+ "input_cost_per_token": 7.9e-07,
+ "output_cost_per_token": 7.9e-07,
+ "max_output_tokens": 32768,
+ "max_input_tokens": 32768,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/perplexity/sonar-reasoning": {
+ "max_tokens": 127000,
+ "input_cost_per_token": 1e-06,
+ "output_cost_per_token": 5e-06,
+ "max_output_tokens": 8000,
+ "max_input_tokens": 127000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/anthropic/claude-3.5-haiku": {
+ "max_tokens": 200000,
+ "input_cost_per_token": 8e-07,
+ "output_cost_per_token": 4e-06,
+ "max_output_tokens": 8192,
+ "max_input_tokens": 200000,
+ "cache_read_input_token_cost": 8e-08,
+ "cache_creation_input_token_cost": 1e-06,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/cohere/command-a": {
+ "max_tokens": 256000,
+ "input_cost_per_token": 2.5e-06,
+ "output_cost_per_token": 1e-05,
+ "max_output_tokens": 8000,
+ "max_input_tokens": 256000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/google/gemma-2-9b": {
+ "max_tokens": 8192,
+ "input_cost_per_token": 2e-07,
+ "output_cost_per_token": 2e-07,
+ "max_output_tokens": 8192,
+ "max_input_tokens": 8192,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/meta/llama-3.2-3b": {
+ "max_tokens": 128000,
+ "input_cost_per_token": 1.5e-07,
+ "output_cost_per_token": 1.5e-07,
+ "max_output_tokens": 8192,
+ "max_input_tokens": 128000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/openai/gpt-4.1-nano": {
+ "max_tokens": 1047576,
+ "input_cost_per_token": 1e-07,
+ "output_cost_per_token": 4e-07,
+ "max_output_tokens": 32768,
+ "max_input_tokens": 1047576,
+ "cache_read_input_token_cost": 2.5e-08,
+ "cache_creation_input_token_cost": 0.0,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/anthropic/claude-4-sonnet": {
+ "max_tokens": 200000,
+ "input_cost_per_token": 3e-06,
+ "output_cost_per_token": 1.5e-05,
+ "max_output_tokens": 64000,
+ "max_input_tokens": 200000,
+ "cache_read_input_token_cost": 3e-07,
+ "cache_creation_input_token_cost": 3.75e-06,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/perplexity/sonar": {
+ "max_tokens": 127000,
+ "input_cost_per_token": 1e-06,
+ "output_cost_per_token": 1e-06,
+ "max_output_tokens": 8000,
+ "max_input_tokens": 127000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/meta/llama-4-maverick": {
+ "max_tokens": 131072,
+ "input_cost_per_token": 2e-07,
+ "output_cost_per_token": 6e-07,
+ "max_output_tokens": 8192,
+ "max_input_tokens": 131072,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/openai/text-embedding-ada-002": {
+ "max_tokens": 0,
+ "input_cost_per_token": 1e-07,
+ "output_cost_per_token": 0.0,
+ "max_output_tokens": 0,
+ "max_input_tokens": 0,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "embedding"
+ },
+ "vercel_ai_gateway/xai/grok-3-mini": {
+ "max_tokens": 131072,
+ "input_cost_per_token": 3e-07,
+ "output_cost_per_token": 5e-07,
+ "max_output_tokens": 131072,
+ "max_input_tokens": 131072,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/cohere/embed-v4.0": {
+ "max_tokens": 0,
+ "input_cost_per_token": 1.2e-07,
+ "output_cost_per_token": 0.0,
+ "max_output_tokens": 0,
+ "max_input_tokens": 0,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/meta/llama-3.3-70b": {
+ "max_tokens": 128000,
+ "input_cost_per_token": 7.2e-07,
+ "output_cost_per_token": 7.2e-07,
+ "max_output_tokens": 8192,
+ "max_input_tokens": 128000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/cohere/command-r-plus": {
+ "max_tokens": 128000,
+ "input_cost_per_token": 2.5e-06,
+ "output_cost_per_token": 1e-05,
+ "max_output_tokens": 4096,
+ "max_input_tokens": 128000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/openai/gpt-3.5-turbo-instruct": {
+ "max_tokens": 8192,
+ "input_cost_per_token": 1.5e-06,
+ "output_cost_per_token": 2e-06,
+ "max_output_tokens": 4096,
+ "max_input_tokens": 8192,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/mistral/devstral-small": {
+ "max_tokens": 128000,
+ "input_cost_per_token": 7e-08,
+ "output_cost_per_token": 2.8e-07,
+ "max_output_tokens": 128000,
+ "max_input_tokens": 128000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/anthropic/claude-3.7-sonnet": {
+ "max_tokens": 200000,
+ "input_cost_per_token": 3e-06,
+ "output_cost_per_token": 1.5e-05,
+ "max_output_tokens": 64000,
+ "max_input_tokens": 200000,
+ "cache_read_input_token_cost": 3e-07,
+ "cache_creation_input_token_cost": 3.75e-06,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/google/gemini-2.0-flash": {
+ "max_tokens": 1048576,
+ "input_cost_per_token": 1.5e-07,
+ "output_cost_per_token": 6e-07,
+ "max_output_tokens": 8192,
+ "max_input_tokens": 1048576,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/mistral/pixtral-12b": {
+ "max_tokens": 128000,
+ "input_cost_per_token": 1.5e-07,
+ "output_cost_per_token": 1.5e-07,
+ "max_output_tokens": 4000,
+ "max_input_tokens": 128000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/mistral/magistral-small": {
+ "max_tokens": 128000,
+ "input_cost_per_token": 5e-07,
+ "output_cost_per_token": 1.5e-06,
+ "max_output_tokens": 64000,
+ "max_input_tokens": 128000,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/moonshotai/kimi-k2": {
+ "max_tokens": 131072,
+ "input_cost_per_token": 5.5e-07,
+ "output_cost_per_token": 2.2e-06,
+ "max_output_tokens": 16384,
+ "max_input_tokens": 131072,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/alibaba/qwen-3-32b": {
+ "max_tokens": 40960,
+ "input_cost_per_token": 1e-07,
+ "output_cost_per_token": 3e-07,
+ "max_output_tokens": 16384,
+ "max_input_tokens": 40960,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
+ "vercel_ai_gateway/openai/gpt-4.1": {
+ "max_tokens": 1047576,
+ "input_cost_per_token": 2e-06,
+ "output_cost_per_token": 8e-06,
+ "max_output_tokens": 32768,
+ "max_input_tokens": 1047576,
+ "cache_read_input_token_cost": 5e-07,
+ "cache_creation_input_token_cost": 0.0,
+ "litellm_provider": "vercel_ai_gateway",
+ "mode": "chat"
+ },
"oci/meta.llama-4-maverick-17b-128e-instruct-fp8": {
"max_tokens": 512000,
"max_input_tokens": 512000,
@@ -19747,4 +20942,4 @@
"notes": "DALL-E 2 via AI/ML API - Reliable text-to-image generation"
}
}
-}
+}
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
index 6624eb7e64e..048b25fa35a 100644
--- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
+++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
@@ -1,5 +1,5 @@
import importlib
-from typing import Optional, Dict
+from typing import Dict, List, Optional
from fastapi import APIRouter, Depends, Query, Request
@@ -21,9 +21,10 @@ router = APIRouter(
)
if MCP_AVAILABLE:
+ from litellm.experimental_mcp_client.client import MCPTool
from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
- global_mcp_server_manager,
_convert_protocol_version_to_enum,
+ global_mcp_server_manager,
)
from litellm.proxy._experimental.mcp_server.server import (
ListMCPToolsRestAPIResponseObject,
@@ -100,7 +101,9 @@ if MCP_AVAILABLE:
"message": "Successfully retrieved tools"
}
"""
- from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import MCPRequestHandler
+ from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import (
+ MCPRequestHandler,
+ )
try:
# Extract auth headers from request
@@ -172,10 +175,11 @@ if MCP_AVAILABLE:
"""
REST API to call a specific MCP tool with the provided arguments
"""
- from litellm.proxy.proxy_server import add_litellm_data_to_request, proxy_config
- from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException
from fastapi import HTTPException
+ from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException
+ from litellm.proxy.proxy_server import add_litellm_data_to_request, proxy_config
+
try:
data = await request.json()
data = await add_litellm_data_to_request(
@@ -230,13 +234,19 @@ if MCP_AVAILABLE:
from litellm.proxy.management_endpoints.mcp_management_endpoints import (
NewMCPServerRequest,
)
- @router.post("/test/connection")
- async def test_connection(
- request: NewMCPServerRequest,
- ):
+
+ async def _execute_with_mcp_client(request: NewMCPServerRequest, operation):
"""
- Test if we can connect to the provided MCP server before adding it
+ Common helper to create MCP client, execute operation, and ensure proper cleanup.
+
+ Args:
+ request: MCP server configuration
+ operation: Async function that takes a client and returns the operation result
+
+ Returns:
+ Operation result or error response
"""
+ client = None
try:
client = global_mcp_server_manager._create_mcp_client(
server=MCPServer(
@@ -250,12 +260,31 @@ if MCP_AVAILABLE:
),
mcp_auth_header=None,
)
-
- await client.connect()
+
+ return await operation(client)
+
except Exception as e:
- verbose_logger.error(f"Error in test_connection: {e}", exc_info=True)
+ verbose_logger.error(f"Error in MCP operation: {e}", exc_info=True)
return {"status": "error", "message": "An internal error has occurred."}
- return {"status": "ok"}
+ finally:
+ # Ensure client is properly disconnected before response is sent
+ if client is not None:
+ try:
+ await client.disconnect()
+ except Exception as e:
+ verbose_logger.warning(f"Error disconnecting MCP client: {e}")
+ @router.post("/test/connection")
+ async def test_connection(
+ request: NewMCPServerRequest,
+ ):
+ """
+ Test if we can connect to the provided MCP server before adding it
+ """
+ async def _test_connection_operation(client):
+ await client.connect()
+ return {"status": "ok"}
+
+ return await _execute_with_mcp_client(request, _test_connection_operation)
@router.post("/test/tools/list")
@@ -266,25 +295,13 @@ if MCP_AVAILABLE:
"""
Preview tools available from MCP server before adding it
"""
- try:
- client = global_mcp_server_manager._create_mcp_client(
- server=MCPServer(
- server_id=request.server_id or "",
- name=request.alias or request.server_name or "",
- url=request.url,
- transport=request.transport,
- spec_version=_convert_protocol_version_to_enum(request.spec_version),
- auth_type=request.auth_type,
- mcp_info=request.mcp_info,
- ),
- mcp_auth_header=None,
- )
- list_tools_result = await client.list_tools()
- except Exception as e:
- verbose_logger.error(f"Error in test_tools_list: {e}", exc_info=True)
- return {"status": "error", "message": "An internal error has occurred."}
- return {
- "tools": list_tools_result,
- "error": None,
- "message": "Successfully retrieved tools"
- }
+ async def _list_tools_operation(client):
+ list_tools_result: List[MCPTool] = await client.list_tools()
+ model_dumped_tools: List[dict] = [tool.model_dump() for tool in list_tools_result]
+ return {
+ "tools": model_dumped_tools,
+ "error": None,
+ "message": "Successfully retrieved tools"
+ }
+
+ return await _execute_with_mcp_client(request, _list_tools_operation)
diff --git a/litellm/proxy/_experimental/out/_next/static/0rzQKhrKTur56aOgaxVrq/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_buildManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/0rzQKhrKTur56aOgaxVrq/_buildManifest.js
rename to litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_buildManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/0rzQKhrKTur56aOgaxVrq/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_ssgManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/0rzQKhrKTur56aOgaxVrq/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/117-a0da667066d322b6.js b/litellm/proxy/_experimental/out/_next/static/chunks/117-6a9f6a591033d4a0.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/117-a0da667066d322b6.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/117-6a9f6a591033d4a0.js
index 21cf536ca55..54b5be82634 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/117-a0da667066d322b6.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/117-6a9f6a591033d4a0.js
@@ -1,2 +1,2 @@
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s=r.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED.Dispatcher,f=Symbol.for("react.element"),d=Symbol.for("react.lazy"),p=Symbol.iterator,h=Array.isArray,y=Object.getPrototypeOf,_=Object.prototype,v=new WeakMap;function b(e,t,n,r){this.status=e,this.value=t,this.reason=n,this._response=r}function g(e){switch(e.status){case"resolved_model":E(e);break;case"resolved_module":w(e)}switch(e.status){case"fulfilled":return e.value;case"pending":case"blocked":case"cyclic":throw e;default:throw e.reason}}function m(e,t){for(var n=0;nh?(_=h,h=3,p++):(_=0,h=3);continue;case 2:44===(m=d[p++])?h=4:v=v<<4|(96d.length&&(m=-1)}var O=d.byteOffset+p;if(-1{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,endpointType:m,selectedModel:p,selectedSdk:u}=e,g="session"===n?s:r,x=window.location.origin,h=i||"Your prompt here",f=h.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),_=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),b={};l.length>0&&(b.tags=l),c.length>0&&(b.vector_stores=c),d.length>0&&(b.guardrails=d);let v=p||"your-model-name",j="azure"===u?'import openai\n\nclient = openai.AzureOpenAI(\n api_key="'.concat(g||"YOUR_LITELLM_API_KEY",'",\n azure_endpoint="').concat(x,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(g||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(x,'"\n)');switch(m){case a.KP.CHAT:{let e=Object.keys(b).length>0,n="";if(e){let e=JSON.stringify({metadata:b},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=_.length>0?_:[{role:"user",content:h}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(v,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(v,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(f,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(b).length>0,n="";if(e){let e=JSON.stringify({metadata:b},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=_.length>0?_:[{role:"user",content:h}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(v,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(v,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(f,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===u?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(v,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===u?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/162-76acc071cdf524f9.js b/litellm/proxy/_experimental/out/_next/static/chunks/162-76acc071cdf524f9.js
deleted file mode 100644
index dd149327dfa..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/162-76acc071cdf524f9.js
+++ /dev/null
@@ -1 +0,0 @@
-"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[162],{36724:function(e,t,n){n.d(t,{Dx:function(){return i.Z},Zb:function(){return s.Z},xv:function(){return r.Z},zx:function(){return a.Z}});var a=n(20831),s=n(12514),r=n(84264),i=n(96761)},19130:function(e,t,n){n.d(t,{RM:function(){return s.Z},SC:function(){return l.Z},iA:function(){return a.Z},pj:function(){return r.Z},ss:function(){return i.Z},xs:function(){return o.Z}});var a=n(21626),s=n(97214),r=n(28241),i=n(58834),o=n(69552),l=n(71876)},88658:function(e,t,n){n.d(t,{L:function(){return s}});var a=n(49817);let s=e=>{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,endpointType:m,selectedModel:p,selectedSdk:u}=e,g="session"===n?s:r,x=window.location.origin,h=i||"Your prompt here",f=h.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),_=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),b={};l.length>0&&(b.tags=l),c.length>0&&(b.vector_stores=c),d.length>0&&(b.guardrails=d);let v=p||"your-model-name",j="azure"===u?'import openai\n\nclient = openai.AzureOpenAI(\n api_key="'.concat(g||"YOUR_LITELLM_API_KEY",'",\n azure_endpoint="').concat(x,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(g||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(x,'"\n)');switch(m){case a.KP.CHAT:{let e=Object.keys(b).length>0,n="";if(e){let e=JSON.stringify({metadata:b},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=_.length>0?_:[{role:"user",content:h}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(v,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(v,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(f,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(b).length>0,n="";if(e){let e=JSON.stringify({metadata:b},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=_.length>0?_:[{role:"user",content:h}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(v,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(v,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(f,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===u?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(v,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===u?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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