diff --git a/docs/my-website/docs/completion/knowledgebase.md b/docs/my-website/docs/completion/knowledgebase.md
index ee0e30867853..e772f4fe9556 100644
--- a/docs/my-website/docs/completion/knowledgebase.md
+++ b/docs/my-website/docs/completion/knowledgebase.md
@@ -412,6 +412,219 @@ This is sent to: `https://bedrock-agent-runtime.{aws_region}.amazonaws.com/knowl
This process happens automatically whenever you include the `vector_store_ids` parameter in your request.
+## Accessing Search Results (Citations)
+
+When using vector stores, LiteLLM automatically returns search results in `provider_specific_fields`. This allows you to show users citations for the AI's response.
+
+### Key Concept
+
+Search results are always in: `response.choices[0].message.provider_specific_fields["search_results"]`
+
+For streaming: Results appear in the **final chunk** when `finish_reason == "stop"`
+
+### Non-Streaming Example
+
+
+**Non-Streaming Response with search results:**
+
+```json
+{
+ "id": "chatcmpl-abc123",
+ "choices": [{
+ "index": 0,
+ "message": {
+ "role": "assistant",
+ "content": "LiteLLM is a platform...",
+ "provider_specific_fields": {
+ "search_results": [{
+ "search_query": "What is litellm?",
+ "data": [{
+ "score": 0.95,
+ "content": [{"text": "...", "type": "text"}],
+ "filename": "litellm-docs.md",
+ "file_id": "doc-123"
+ }]
+ }]
+ }
+ },
+ "finish_reason": "stop"
+ }]
+}
+```
+
+
+
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+ base_url="http://localhost:4000",
+ api_key="your-litellm-api-key"
+)
+
+response = client.chat.completions.create(
+ model="claude-3-5-sonnet",
+ messages=[{"role": "user", "content": "What is litellm?"}],
+ tools=[{"type": "file_search", "vector_store_ids": ["T37J8R4WTM"]}]
+)
+
+# Get AI response
+print(response.choices[0].message.content)
+
+# Get search results (citations)
+search_results = response.choices[0].message.provider_specific_fields.get("search_results", [])
+
+for result_page in search_results:
+ for idx, item in enumerate(result_page['data'], 1):
+ print(f"[{idx}] {item.get('filename', 'Unknown')} (score: {item['score']:.2f})")
+```
+
+
+
+
+
+```typescript
+import OpenAI from 'openai';
+
+const client = new OpenAI({
+ baseURL: 'http://localhost:4000',
+ apiKey: process.env.LITELLM_API_KEY
+});
+
+const response = await client.chat.completions.create({
+ model: 'claude-3-5-sonnet',
+ messages: [{ role: 'user', content: 'What is litellm?' }],
+ tools: [{ type: 'file_search', vector_store_ids: ['T37J8R4WTM'] }]
+});
+
+// Get AI response
+console.log(response.choices[0].message.content);
+
+// Get search results (citations)
+const message = response.choices[0].message as any;
+const searchResults = message.provider_specific_fields?.search_results || [];
+
+searchResults.forEach((page: any) => {
+ page.data.forEach((item: any, idx: number) => {
+ console.log(`[${idx + 1}] ${item.filename || 'Unknown'} (${item.score.toFixed(2)})`);
+ });
+});
+```
+
+
+
+
+### Streaming Example
+
+**Streaming Response with search results (final chunk):**
+
+```json
+{
+ "id": "chatcmpl-abc123",
+ "choices": [{
+ "index": 0,
+ "delta": {
+ "provider_specific_fields": {
+ "search_results": [{
+ "search_query": "What is litellm?",
+ "data": [{
+ "score": 0.95,
+ "content": [{"text": "...", "type": "text"}],
+ "filename": "litellm-docs.md",
+ "file_id": "doc-123"
+ }]
+ }]
+ }
+ },
+ "finish_reason": "stop"
+ }]
+}
+```
+
+
+
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+ base_url="http://localhost:4000",
+ api_key="your-litellm-api-key"
+)
+
+stream = client.chat.completions.create(
+ model="claude-3-5-sonnet",
+ messages=[{"role": "user", "content": "What is litellm?"}],
+ tools=[{"type": "file_search", "vector_store_ids": ["T37J8R4WTM"]}],
+ stream=True
+)
+
+for chunk in stream:
+ # Stream content
+ if chunk.choices[0].delta.content:
+ print(chunk.choices[0].delta.content, end="", flush=True)
+
+ # Get citations in final chunk
+ if chunk.choices[0].finish_reason == "stop":
+ search_results = getattr(chunk.choices[0].delta, 'provider_specific_fields', {}).get('search_results', [])
+ if search_results:
+ print("\n\nSources:")
+ for page in search_results:
+ for idx, item in enumerate(page['data'], 1):
+ print(f" [{idx}] {item.get('filename', 'Unknown')} ({item['score']:.2f})")
+```
+
+
+
+
+
+```typescript
+import OpenAI from 'openai';
+
+const stream = await client.chat.completions.create({
+ model: 'claude-3-5-sonnet',
+ messages: [{ role: 'user', content: 'What is litellm?' }],
+ tools: [{ type: 'file_search', vector_store_ids: ['T37J8R4WTM'] }],
+ stream: true
+});
+
+for await (const chunk of stream) {
+ // Stream content
+ if (chunk.choices[0]?.delta?.content) {
+ process.stdout.write(chunk.choices[0].delta.content);
+ }
+
+ // Get citations in final chunk
+ if (chunk.choices[0]?.finish_reason === 'stop') {
+ const searchResults = (chunk.choices[0].delta as any).provider_specific_fields?.search_results || [];
+ if (searchResults.length > 0) {
+ console.log('\n\nSources:');
+ searchResults.forEach((page: any) => {
+ page.data.forEach((item: any, idx: number) => {
+ console.log(` [${idx + 1}] ${item.filename || 'Unknown'} (${item.score.toFixed(2)})`);
+ });
+ });
+ }
+ }
+}
+```
+
+
+
+
+### Search Result Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `search_query` | string | The query used to search the vector store |
+| `data` | array | Array of search results |
+| `data[].score` | float | Relevance score (0-1, higher is more relevant) |
+| `data[].content` | array | Content chunks with `text` and `type` |
+| `data[].filename` | string | Name of the source file (optional) |
+| `data[].file_id` | string | Identifier for the source file (optional) |
+| `data[].attributes` | object | Provider-specific metadata (optional) |
+
## API Reference
### LiteLLM Completion Knowledge Base Parameters
diff --git a/docs/my-website/docs/providers/bedrock_vector_store.md b/docs/my-website/docs/providers/bedrock_vector_store.md
index 779c4fd0417d..39e1aec5ab83 100644
--- a/docs/my-website/docs/providers/bedrock_vector_store.md
+++ b/docs/my-website/docs/providers/bedrock_vector_store.md
@@ -138,7 +138,14 @@ print(response.choices[0].message.content)
-Futher Reading Vector Stores:
+## Accessing Search Results
+
+See how to access vector store search results in your response:
+- [Accessing Search Results (Non-Streaming & Streaming)](../completion/knowledgebase#accessing-search-results-citations)
+
+## Further Reading
+
+Vector Stores:
- [Always on Vector Stores](https://docs.litellm.ai/docs/completion/knowledgebase#always-on-for-a-model)
- [Listing available vector stores on litellm proxy](https://docs.litellm.ai/docs/completion/knowledgebase#listing-available-vector-stores)
- [How LiteLLM Vector Stores Work](https://docs.litellm.ai/docs/completion/knowledgebase#how-it-works)
\ No newline at end of file
diff --git a/docs/my-website/docs/providers/openai.md b/docs/my-website/docs/providers/openai.md
index 3fad78dc80e4..a26b987dd1a9 100644
--- a/docs/my-website/docs/providers/openai.md
+++ b/docs/my-website/docs/providers/openai.md
@@ -836,4 +836,10 @@ response = completion(
model="gpt-5-pro",
messages=[{"role": "user", "content": "Solve this complex reasoning problem..."}]
)
-```
\ No newline at end of file
+```
+
+## Video Generation
+
+LiteLLM supports OpenAI's video generation models including Sora.
+
+For detailed documentation on video generation, see [OpenAI Video Generation →](./openai/video_generation.md)
\ No newline at end of file
diff --git a/docs/my-website/docs/providers/openai/video_generation.md b/docs/my-website/docs/providers/openai/video_generation.md
new file mode 100644
index 000000000000..4dc18da77bd2
--- /dev/null
+++ b/docs/my-website/docs/providers/openai/video_generation.md
@@ -0,0 +1,143 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# OpenAI Video Generation
+
+LiteLLM supports OpenAI's video generation models including Sora.
+
+## Quick Start
+
+### Required API Keys
+
+```python
+import os
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+```
+
+### Basic Usage
+
+```python
+from litellm import video_generation, video_retrieval
+import os
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+# Generate a video
+response = video_generation(
+ prompt="A cat playing with a ball of yarn in a sunny garden",
+ model="sora-2",
+ seconds="8",
+ size="720x1280"
+)
+
+print(f"Video ID: {response.id}")
+print(f"Status: {response.status}")
+
+# Download video content when ready
+video_bytes = video_retrieval(
+ video_id=response.id,
+ model="sora-2"
+)
+
+# Save to file
+with open("generated_video.mp4", "wb") as f:
+ f.write(video_bytes)
+```
+
+## Supported Models
+
+| Model Name | Description | Max Duration | Supported Sizes |
+|------------|-------------|--------------|-----------------|
+| sora-2 | OpenAI's latest video generation model | 8 seconds | 720x1280, 1280x720 |
+
+## Video Generation Parameters
+
+- `prompt` (required): Text description of the desired video
+- `model` (optional): Model to use, defaults to "sora-2"
+- `seconds` (optional): Video duration in seconds (e.g., "8", "16")
+- `size` (optional): Video dimensions (e.g., "720x1280", "1280x720")
+- `input_reference` (optional): Reference image for video editing
+- `user` (optional): User identifier for tracking
+
+## Video Content Retrieval
+
+```python
+# Download video content
+video_bytes = video_retrieval(
+ video_id="video_1234567890",
+ model="sora-2"
+)
+
+# Save to file
+with open("video.mp4", "wb") as f:
+ f.write(video_bytes)
+```
+
+## Complete Workflow
+
+```python
+import litellm
+import time
+
+def generate_and_download_video(prompt):
+ # Step 1: Generate video
+ response = litellm.video_generation(
+ prompt=prompt,
+ model="sora-2",
+ seconds="8",
+ size="720x1280"
+ )
+
+ video_id = response.id
+ print(f"Video ID: {video_id}")
+
+ # Step 2: Wait for processing (in practice, poll status)
+ time.sleep(30)
+
+ # Step 3: Download video
+ video_bytes = litellm.video_retrieval(
+ video_id=video_id,
+ model="sora-2"
+ )
+
+ # Step 4: Save to file
+ with open(f"video_{video_id}.mp4", "wb") as f:
+ f.write(video_bytes)
+
+ return f"video_{video_id}.mp4"
+
+# Usage
+video_file = generate_and_download_video(
+ "A cat playing with a ball of yarn in a sunny garden"
+)
+```
+
+## Video Editing with Reference Images
+
+```python
+# Video editing with reference image
+response = litellm.video_generation(
+ prompt="Make the cat jump higher",
+ input_reference="path/to/image.jpg", # Reference image
+ model="sora-2",
+ seconds="8"
+)
+
+print(f"Video ID: {response.id}")
+```
+
+## Error Handling
+
+```python
+from litellm.exceptions import BadRequestError, AuthenticationError
+
+try:
+ response = video_generation(
+ prompt="A cat playing with a ball of yarn",
+ model="sora-2"
+ )
+except AuthenticationError as e:
+ print(f"Authentication failed: {e}")
+except BadRequestError as e:
+ print(f"Bad request: {e}")
+```
diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md
index 3b6562b51aef..6f0210f270b8 100644
--- a/docs/my-website/docs/providers/vertex.md
+++ b/docs/my-website/docs/providers/vertex.md
@@ -12,7 +12,7 @@ import TabItem from '@theme/TabItem';
| Provider Route on LiteLLM | `vertex_ai/` |
| Link to Provider Doc | [Vertex AI ↗](https://cloud.google.com/vertex-ai) |
| Base URL | 1. Regional endpoints
`https://{vertex_location}-aiplatform.googleapis.com/`
2. Global endpoints (limited availability)
`https://aiplatform.googleapis.com/`|
-| Supported Operations | [`/chat/completions`](#sample-usage), `/completions`, [`/embeddings`](#embedding-models), [`/audio/speech`](#text-to-speech-apis), [`/fine_tuning`](#fine-tuning-apis), [`/batches`](#batch-apis), [`/files`](#batch-apis), [`/images`](#image-generation-models) |
+| Supported Operations | [`/chat/completions`](#sample-usage), `/completions`, [`/embeddings`](#embedding-models), [`/audio/speech`](#text-to-speech-apis), [`/fine_tuning`](#fine-tuning-apis), [`/batches`](#batch-apis), [`/files`](#batch-apis), [`/images`](#image-generation-models), [`/rerank`](#rerank-api) |
@@ -3114,3 +3114,101 @@ Once that's done, when you deploy the new container in the Google Cloud Run serv
s/o @[Darien Kindlund](https://www.linkedin.com/in/kindlund/) for this tutorial
+
+## **Rerank API**
+
+Vertex AI supports reranking through the Discovery Engine API, providing semantic ranking capabilities for document retrieval.
+
+### Setup
+
+Set your Google Cloud project ID:
+
+```bash
+export VERTEXAI_PROJECT="your-project-id"
+```
+
+### Usage
+
+```python
+from litellm import rerank
+
+# Using the latest model (recommended)
+response = rerank(
+ model="vertex_ai/semantic-ranker-default@latest",
+ query="What is Google Gemini?",
+ documents=[
+ "Gemini is a cutting edge large language model created by Google.",
+ "The Gemini zodiac symbol often depicts two figures standing side-by-side.",
+ "Gemini is a constellation that can be seen in the night sky."
+ ],
+ top_n=2,
+ return_documents=True # Set to False for ID-only responses
+)
+
+# Using specific model versions
+response_v003 = rerank(
+ model="vertex_ai/semantic-ranker-default-003",
+ query="What is Google Gemini?",
+ documents=documents,
+ top_n=2
+)
+
+print(response.results)
+```
+
+### Parameters
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `model` | string | Model name (e.g., `vertex_ai/semantic-ranker-default@latest`) |
+| `query` | string | Search query |
+| `documents` | list | Documents to rank |
+| `top_n` | int | Number of top results to return |
+| `return_documents` | bool | Return full content (True) or IDs only (False) |
+
+### Supported Models
+
+- `semantic-ranker-default@latest`
+- `semantic-ranker-fast@latest`
+- `semantic-ranker-default-003`
+- `semantic-ranker-default-002`
+
+For detailed model specifications, see the [Google Cloud ranking API documentation](https://cloud.google.com/generative-ai-app-builder/docs/ranking#rank_or_rerank_a_set_of_records_according_to_a_query).
+
+### Proxy Usage
+
+Add to your `config.yaml`:
+
+```yaml
+model_list:
+ - model_name: semantic-ranker-default@latest
+ litellm_params:
+ model: vertex_ai/semantic-ranker-default@latest
+ vertex_ai_project: "your-project-id"
+ vertex_ai_location: "us-central1"
+ vertex_ai_credentials: "path/to/service-account.json"
+```
+
+Start the proxy:
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+Test with curl:
+
+```bash
+curl http://0.0.0.0:4000/rerank \
+ -H "Authorization: Bearer sk-1234" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "semantic-ranker-default@latest",
+ "query": "What is Google Gemini?",
+ "documents": [
+ "Gemini is a cutting edge large language model created by Google.",
+ "The Gemini zodiac symbol often depicts two figures standing side-by-side.",
+ "Gemini is a constellation that can be seen in the night sky."
+ ],
+ "top_n": 2
+ }'
+```
diff --git a/docs/my-website/docs/rerank.md b/docs/my-website/docs/rerank.md
index cad647183846..72f35c6ceceb 100644
--- a/docs/my-website/docs/rerank.md
+++ b/docs/my-website/docs/rerank.md
@@ -121,4 +121,5 @@ curl http://0.0.0.0:4000/rerank \
| HuggingFace| [Usage](../docs/providers/huggingface_rerank) |
| Infinity| [Usage](../docs/providers/infinity) |
| vLLM| [Usage](../docs/providers/vllm#rerank-endpoint) |
-| DeepInfra| [Usage](../docs/providers/deepinfra#rerank-endpoint) |
\ No newline at end of file
+| DeepInfra| [Usage](../docs/providers/deepinfra#rerank-endpoint) |
+| Vertex AI| [Usage](../docs/providers/vertex#rerank-api) |
\ No newline at end of file
diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py
index 3b37e14b8969..4e81206122f0 100644
--- a/enterprise/litellm_enterprise/integrations/prometheus.py
+++ b/enterprise/litellm_enterprise/integrations/prometheus.py
@@ -1,6 +1,7 @@
# used for /metrics endpoint on LiteLLM Proxy
#### What this does ####
# On success, log events to Prometheus
+import os
import sys
from datetime import datetime, timedelta
from typing import (
@@ -2211,7 +2212,13 @@ def _mount_metrics_endpoint(premium_user: bool):
)
# Create metrics ASGI app
- metrics_app = make_asgi_app()
+ if 'PROMETHEUS_MULTIPROC_DIR' in os.environ:
+ from prometheus_client import CollectorRegistry, multiprocess
+ registry = CollectorRegistry()
+ multiprocess.MultiProcessCollector(registry)
+ metrics_app = make_asgi_app(registry)
+ else:
+ metrics_app = make_asgi_app()
# Mount the metrics app to the app
app.mount("/metrics", metrics_app)
@@ -2354,7 +2361,6 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]:
}
"""
- from litellm.router_utils.pattern_match_deployments import PatternMatchRouter
from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name
configured_tags = litellm.custom_prometheus_tags
@@ -2362,7 +2368,6 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]:
return {}
result: Dict[str, str] = {}
- pattern_router = PatternMatchRouter()
for configured_tag in configured_tags:
label_name = _sanitize_prometheus_label_name(f"tag_{configured_tag}")
diff --git a/litellm/__init__.py b/litellm/__init__.py
index 53168dd60140..84ebbaf7a420 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -104,7 +104,7 @@
# Register async client cleanup to prevent resource leaks
register_async_client_cleanup()
####################################################
-if set_verbose == True:
+if set_verbose:
_turn_on_debug()
####################################################
### Callbacks /Logging / Success / Failure Handlers #####
@@ -977,6 +977,9 @@ def add_known_models():
####### IMAGE GENERATION MODELS ###################
openai_image_generation_models = ["dall-e-2", "dall-e-3"]
+####### VIDEO GENERATION MODELS ###################
+openai_video_generation_models = ["sora-2"]
+
from .timeout import timeout
from .cost_calculator import completion_cost
from litellm.litellm_core_utils.litellm_logging import Logging, modify_integration
@@ -1071,6 +1074,7 @@ def add_known_models():
from .llms.jina_ai.rerank.transformation import JinaAIRerankConfig
from .llms.deepinfra.rerank.transformation import DeepinfraRerankConfig
from .llms.nvidia_nim.rerank.transformation import NvidiaNimRerankConfig
+from .llms.vertex_ai.rerank.transformation import VertexAIRerankConfig
from .llms.clarifai.chat.transformation import ClarifaiConfig
from .llms.ai21.chat.transformation import AI21ChatConfig, AI21ChatConfig as AI21Config
from .llms.meta_llama.chat.transformation import LlamaAPIConfig
@@ -1202,7 +1206,6 @@ def add_known_models():
OpenAIOSeriesConfig,
)
-from .llms.snowflake.chat.transformation import SnowflakeConfig
from .llms.gradient_ai.chat.transformation import GradientAIConfig
openaiOSeriesConfig = OpenAIOSeriesConfig()
@@ -1238,7 +1241,6 @@ def add_known_models():
from .llms.baseten.chat import BasetenConfig
from .llms.sambanova.chat import SambanovaConfig
from .llms.sambanova.embedding.transformation import SambaNovaEmbeddingConfig
-from .llms.ai21.chat.transformation import AI21ChatConfig
from .llms.fireworks_ai.chat.transformation import FireworksAIConfig
from .llms.fireworks_ai.completion.transformation import FireworksAITextCompletionConfig
from .llms.fireworks_ai.audio_transcription.transformation import (
@@ -1325,6 +1327,7 @@ def add_known_models():
from .assistants.main import *
from .batches.main import *
from .images.main import *
+from .videos.main 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/constants.py b/litellm/constants.py
index 24657c900144..9b657f928b65 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -17,6 +17,7 @@
DEFAULT_NUM_WORKERS_LITELLM_PROXY = int(
os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1)
)
+DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE = int(os.getenv("DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE", 1))
DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512))
SQS_SEND_MESSAGE_ACTION = "SendMessage"
SQS_API_VERSION = "2012-11-05"
@@ -261,6 +262,7 @@
"high": 10,
}
DEFAULT_IMAGE_ENDPOINT_MODEL = "dall-e-2"
+DEFAULT_VIDEO_ENDPOINT_MODEL = "sora-2"
LITELLM_CHAT_PROVIDERS = [
"openai",
diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index af504edfb4ed..247c0b43bce9 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -870,6 +870,29 @@ def completion_cost( # noqa: PLR0915
size=size,
optional_params=optional_params,
)
+ elif (
+ call_type == CallTypes.create_video.value
+ or call_type == CallTypes.acreate_video.value
+ ):
+ ### VIDEO GENERATION COST CALCULATION ###
+ if completion_response is not None and hasattr(completion_response, 'usage'):
+ usage_obj = completion_response.usage
+ duration_seconds = usage_obj.get('duration_seconds')
+
+ if duration_seconds is not None:
+ # Calculate cost based on video duration using video-specific cost calculation
+ from litellm.llms.openai.cost_calculation import video_generation_cost
+ return video_generation_cost(
+ model=model,
+ duration_seconds=duration_seconds,
+ custom_llm_provider=custom_llm_provider
+ )
+ # Fallback to default video cost calculation if no duration available
+ return default_video_cost_calculator(
+ model=model,
+ duration_seconds=0.0, # Default to 0 if no duration available
+ custom_llm_provider=custom_llm_provider
+ )
elif (
call_type == CallTypes.speech.value
or call_type == CallTypes.aspeech.value
@@ -1290,6 +1313,80 @@ def default_image_cost_calculator(
return cost_info["input_cost_per_pixel"] * height * width * n
+def default_video_cost_calculator(
+ model: str,
+ duration_seconds: float,
+ custom_llm_provider: Optional[str] = None,
+) -> float:
+ """
+ Default video cost calculator for video generation
+
+ Args:
+ model (str): Model name
+ duration_seconds (float): Duration of the generated video in seconds
+ custom_llm_provider (Optional[str]): Custom LLM provider
+
+ Returns:
+ float: Cost in USD for the video generation
+
+ Raises:
+ Exception: If model pricing not found in cost map
+ """
+ # Build model names for cost lookup
+ base_model_name = model
+ model_name_without_custom_llm_provider: Optional[str] = None
+ if custom_llm_provider and model.startswith(f"{custom_llm_provider}/"):
+ model_name_without_custom_llm_provider = model.replace(
+ f"{custom_llm_provider}/", ""
+ )
+ base_model_name = f"{custom_llm_provider}/{model_name_without_custom_llm_provider}"
+
+ verbose_logger.debug(
+ f"Looking up cost for video model: {base_model_name}"
+ )
+
+ model_without_provider = model.split('/')[-1]
+
+ # Try model with provider first, fall back to base model name
+ cost_info: Optional[dict] = None
+ models_to_check: List[Optional[str]] = [
+ base_model_name,
+ model,
+ model_without_provider,
+ model_name_without_custom_llm_provider,
+ ]
+ for _model in models_to_check:
+ if _model is not None and _model in litellm.model_cost:
+ cost_info = litellm.model_cost[_model]
+ break
+
+ # If still not found, try with custom_llm_provider prefix
+ if cost_info is None and custom_llm_provider:
+ prefixed_model = f"{custom_llm_provider}/{model}"
+ if prefixed_model in litellm.model_cost:
+ cost_info = litellm.model_cost[prefixed_model]
+ if cost_info is None:
+ raise Exception(
+ f"Model not found in cost map. Tried checking {models_to_check}"
+ )
+
+ # Check for video-specific cost per second first
+ video_cost_per_second = cost_info.get("output_cost_per_video_per_second")
+ if video_cost_per_second is not None:
+ return video_cost_per_second * duration_seconds
+
+ # Fallback to general output cost per second
+ output_cost_per_second = cost_info.get("output_cost_per_second")
+ if output_cost_per_second is not None:
+ return output_cost_per_second * duration_seconds
+
+ # If no cost information found, return 0
+ verbose_logger.info(
+ f"No cost information found for video model {model}. Please add pricing to model_prices_and_context_window.json"
+ )
+ return 0.0
+
+
def batch_cost_calculator(
usage: Usage,
model: str,
diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py
index ee7e771faa60..615e64b97f99 100644
--- a/litellm/integrations/custom_logger.py
+++ b/litellm/integrations/custom_logger.py
@@ -204,6 +204,19 @@ async def async_post_call_success_deployment_hook(
"""
pass
+ async def async_post_call_streaming_deployment_hook(
+ self,
+ request_data: dict,
+ response_chunk: Any,
+ call_type: Optional[CallTypes],
+ ) -> Optional[Any]:
+ """
+ Allow modifying streaming chunks just before they're returned to the user.
+
+ This is called for each streaming chunk in the response.
+ """
+ pass
+
#### Fallback Events - router/proxy only ####
async def log_model_group_rate_limit_error(
self, exception: Exception, original_model_group: Optional[str], kwargs: dict
diff --git a/litellm/integrations/sqs.py b/litellm/integrations/sqs.py
index 8a2ebf8d3445..c03f134b6dfc 100644
--- a/litellm/integrations/sqs.py
+++ b/litellm/integrations/sqs.py
@@ -7,6 +7,8 @@
from __future__ import annotations
import asyncio
+import base64
+import json
import traceback
from typing import List, Optional
@@ -27,31 +29,37 @@
from litellm.types.utils import StandardLoggingPayload
from .custom_batch_logger import CustomBatchLogger
+from litellm.litellm_core_utils.app_crypto import AppCrypto
class SQSLogger(CustomBatchLogger, BaseAWSLLM):
- """Batching logger that writes logs to an AWS SQS queue."""
+ """Batching logger that writes logs to an AWS SQS queue, optionally encrypting the payload."""
def __init__(
- self,
- sqs_queue_url: Optional[str] = None,
- sqs_region_name: Optional[str] = None,
- sqs_api_version: Optional[str] = None,
- sqs_use_ssl: bool = True,
- sqs_verify: Optional[bool] = None,
- sqs_endpoint_url: Optional[str] = None,
- sqs_aws_access_key_id: Optional[str] = None,
- sqs_aws_secret_access_key: Optional[str] = None,
- sqs_aws_session_token: Optional[str] = None,
- sqs_aws_session_name: Optional[str] = None,
- sqs_aws_profile_name: Optional[str] = None,
- sqs_aws_role_name: Optional[str] = None,
- sqs_aws_web_identity_token: Optional[str] = None,
- sqs_aws_sts_endpoint: Optional[str] = None,
- sqs_flush_interval: Optional[int] = DEFAULT_SQS_FLUSH_INTERVAL_SECONDS,
- sqs_batch_size: Optional[int] = DEFAULT_SQS_BATCH_SIZE,
- sqs_config=None,
- **kwargs,
+ self,
+ # --- Standard SQS params ---
+ sqs_queue_url: Optional[str] = None,
+ sqs_region_name: Optional[str] = None,
+ sqs_api_version: Optional[str] = None,
+ sqs_use_ssl: bool = True,
+ sqs_verify: Optional[bool] = None,
+ sqs_endpoint_url: Optional[str] = None,
+ sqs_aws_access_key_id: Optional[str] = None,
+ sqs_aws_secret_access_key: Optional[str] = None,
+ sqs_aws_session_token: Optional[str] = None,
+ sqs_aws_session_name: Optional[str] = None,
+ sqs_aws_profile_name: Optional[str] = None,
+ sqs_aws_role_name: Optional[str] = None,
+ sqs_aws_web_identity_token: Optional[str] = None,
+ sqs_aws_sts_endpoint: Optional[str] = None,
+ sqs_flush_interval: Optional[int] = DEFAULT_SQS_FLUSH_INTERVAL_SECONDS,
+ sqs_batch_size: Optional[int] = DEFAULT_SQS_BATCH_SIZE,
+ sqs_config=None,
+ # --- 🔐 Application-level encryption params ---
+ sqs_aws_use_application_level_encryption: bool = False,
+ sqs_app_encryption_key_b64: Optional[str] = None,
+ sqs_app_encryption_aad: Optional[str] = None,
+ **kwargs,
) -> None:
try:
verbose_logger.debug(
@@ -77,7 +85,11 @@ def __init__(
sqs_aws_role_name=sqs_aws_role_name,
sqs_aws_web_identity_token=sqs_aws_web_identity_token,
sqs_aws_sts_endpoint=sqs_aws_sts_endpoint,
+ sqs_aws_use_application_level_encryption=sqs_aws_use_application_level_encryption,
+ sqs_app_encryption_key_b64=sqs_app_encryption_key_b64,
+ sqs_app_encryption_aad=sqs_app_encryption_aad,
sqs_config=sqs_config,
+ **kwargs,
)
asyncio.create_task(self.periodic_flush())
@@ -95,7 +107,6 @@ def __init__(
)
self.log_queue: List[StandardLoggingPayload] = []
-
BaseAWSLLM.__init__(self)
except Exception as e:
@@ -118,6 +129,9 @@ def _init_sqs_params(
sqs_aws_role_name: Optional[str] = None,
sqs_aws_web_identity_token: Optional[str] = None,
sqs_aws_sts_endpoint: Optional[str] = None,
+ sqs_aws_use_application_level_encryption: bool = False,
+ sqs_app_encryption_key_b64: Optional[str] = None,
+ sqs_app_encryption_aad: Optional[str] = None,
sqs_config=None,
) -> None:
litellm.aws_sqs_callback_params = litellm.aws_sqs_callback_params or {}
@@ -179,6 +193,27 @@ def _init_sqs_params(
litellm.aws_sqs_callback_params.get("sqs_aws_sts_endpoint") or sqs_aws_sts_endpoint
)
+ self.sqs_aws_use_application_level_encryption = (
+ litellm.aws_sqs_callback_params.get("sqs_aws_use_application_level_encryption", False)
+ or sqs_aws_use_application_level_encryption
+ )
+ self.sqs_app_encryption_key_b64 = (
+ litellm.aws_sqs_callback_params.get("sqs_app_encryption_key_b64")
+ or sqs_app_encryption_key_b64
+ )
+ self.sqs_app_encryption_aad = (
+ litellm.aws_sqs_callback_params.get("sqs_app_encryption_aad")
+ or sqs_app_encryption_aad
+ )
+ self.app_crypto: Optional[AppCrypto] = None
+ if self.sqs_aws_use_application_level_encryption:
+ if not self.sqs_app_encryption_key_b64:
+ raise ValueError("sqs_app_encryption_key_b64 is required when encryption is enabled.")
+ key = base64.b64decode(self.sqs_app_encryption_key_b64)
+ self.app_crypto = AppCrypto(key)
+ verbose_logger.debug(
+ "SQSLogger: Application-level encryption enabled."
+ )
self.sqs_config = litellm.aws_sqs_callback_params.get("sqs_config") or sqs_config
async def async_log_success_event(
@@ -256,11 +291,21 @@ async def async_send_message(self, payload: StandardLoggingPayload) -> None:
if self.sqs_queue_url is None:
raise ValueError("sqs_queue_url not set")
- json_string = safe_dumps(payload)
+ json_data = json.loads(safe_dumps(payload))
+ if self.app_crypto:
+ aad_bytes = (
+ self.sqs_app_encryption_aad.encode("utf-8")
+ if self.sqs_app_encryption_aad
+ else None
+ )
+ encrypted = self.app_crypto.encrypt_json(json_data, aad=aad_bytes)
+ json_string = json.dumps({"__encrypted__": True, "payload": encrypted})
+ else:
+ json_string = safe_dumps(payload)
body = (
- f"Action={SQS_SEND_MESSAGE_ACTION}&Version={SQS_API_VERSION}&MessageBody="
- + quote(json_string, safe="")
+ f"Action={SQS_SEND_MESSAGE_ACTION}&Version={SQS_API_VERSION}&MessageBody="
+ + quote(json_string, safe="")
)
headers = {
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 8ef160dd7834..b411a217575e 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
@@ -5,7 +5,7 @@
It searches the vector store for relevant context and appends it to the messages.
"""
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, cast
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast
import litellm
import litellm.vector_stores
@@ -88,6 +88,8 @@ async def async_get_chat_completion_prompt(
return model, messages, non_default_params
modified_messages: List[AllMessageValues] = messages.copy()
+ all_search_results: List[VectorStoreSearchResponse] = []
+
for vector_store_to_run in vector_stores_to_run:
# Get vector store id from the vector store config
@@ -104,6 +106,8 @@ async def async_get_chat_completion_prompt(
verbose_logger.debug(f"search_response: {search_response}")
+ # Store search results for later use in citations
+ all_search_results.append(search_response)
# Process search results and append as context
modified_messages = self._append_search_results_to_messages(
@@ -115,6 +119,10 @@ async def async_get_chat_completion_prompt(
num_results = 0
num_results = len(search_response.get("data", []) or [])
verbose_logger.debug(f"Vector store search completed. Added context from {num_results} results")
+
+ # Store search results as-is (already in OpenAI-compatible format)
+ if litellm_logging_obj and all_search_results:
+ litellm_logging_obj.model_call_details["search_results"] = all_search_results
return model, modified_messages, non_default_params
@@ -194,3 +202,109 @@ def _append_search_results_to_messages(
return modified_messages
return messages
+
+ async def async_post_call_success_deployment_hook(
+ self,
+ request_data: dict,
+ response: Any,
+ call_type: Optional[Any],
+ ) -> Optional[Any]:
+ """
+ Add search results to the response after successful LLM call.
+
+ This hook adds the vector store search results (already in OpenAI-compatible format)
+ to the response's provider_specific_fields.
+ """
+ try:
+ verbose_logger.debug("VectorStorePreCallHook.async_post_call_success_deployment_hook called")
+
+ # Get logging object from request_data
+ litellm_logging_obj = request_data.get("litellm_logging_obj")
+ if not litellm_logging_obj:
+ verbose_logger.debug("No litellm_logging_obj in request_data")
+ return None
+
+ verbose_logger.debug(f"model_call_details keys: {list(litellm_logging_obj.model_call_details.keys())}")
+
+ # Get search results from model_call_details (already in OpenAI format)
+ search_results: Optional[List[VectorStoreSearchResponse]] = (
+ litellm_logging_obj.model_call_details.get("search_results")
+ )
+
+ verbose_logger.debug(f"Search results found: {search_results is not None}")
+
+ if not search_results:
+ verbose_logger.debug("No search results found")
+ return None
+
+ # Add search results to response object
+ if hasattr(response, "choices") and response.choices:
+ for choice in response.choices:
+ if hasattr(choice, "message") and choice.message:
+ # Get existing provider_specific_fields or create new dict
+ provider_fields = getattr(choice.message, "provider_specific_fields", None) or {}
+
+ # Add search results (already in OpenAI-compatible format)
+ provider_fields["search_results"] = search_results
+
+ # Set the provider_specific_fields
+ setattr(choice.message, "provider_specific_fields", provider_fields)
+
+ verbose_logger.debug(f"Added {len(search_results)} search results to response")
+
+ # Return modified response
+ return response
+
+ except Exception as e:
+ verbose_logger.exception(f"Error adding search results to response: {str(e)}")
+ # Don't fail the request if search results fail to be added
+ return None
+
+ async def async_post_call_streaming_deployment_hook(
+ self,
+ request_data: dict,
+ response_chunk: Any,
+ call_type: Optional[Any],
+ ) -> Optional[Any]:
+ """
+ Add search results to the final streaming chunk.
+
+ This hook is called for the final streaming chunk, allowing us to add
+ search results to the stream before it's returned to the user.
+ """
+ try:
+ verbose_logger.debug("VectorStorePreCallHook.async_post_call_streaming_deployment_hook called")
+
+ # Get search results from model_call_details (already in OpenAI format)
+ search_results: Optional[List[VectorStoreSearchResponse]] = (
+ request_data.get("search_results")
+ )
+
+ verbose_logger.debug(f"Search results found for streaming chunk: {search_results is not None}")
+
+ if not search_results:
+ verbose_logger.debug("No search results found for streaming chunk")
+ return response_chunk
+
+ # Add search results to streaming chunk
+ if hasattr(response_chunk, "choices") and response_chunk.choices:
+ for choice in response_chunk.choices:
+ if hasattr(choice, "delta") and choice.delta:
+ # Get existing provider_specific_fields or create new dict
+ provider_fields = getattr(choice.delta, "provider_specific_fields", None) or {}
+
+ # Add search results (already in OpenAI-compatible format)
+ provider_fields["search_results"] = search_results
+
+ # Set the provider_specific_fields
+ choice.delta.provider_specific_fields = provider_fields
+
+ verbose_logger.debug(f"Added {len(search_results)} search results to streaming chunk")
+
+ # Return modified chunk
+ return response_chunk
+
+ except Exception as e:
+ verbose_logger.exception(f"Error adding search results to streaming chunk: {str(e)}")
+ # Don't fail the request if search results fail to be added
+ return response_chunk
diff --git a/litellm/litellm_core_utils/app_crypto.py b/litellm/litellm_core_utils/app_crypto.py
new file mode 100644
index 000000000000..bb5043b2b7b3
--- /dev/null
+++ b/litellm/litellm_core_utils/app_crypto.py
@@ -0,0 +1,31 @@
+import base64
+import json
+import os
+
+from cryptography.hazmat.primitives.ciphers.aead import AESGCM
+
+class AppCrypto:
+ def __init__(self, master_key: bytes):
+ if len(master_key) != 32:
+ raise ValueError("Master key must be 32 bytes for AES-256-GCM")
+ self.key = master_key
+
+ def encrypt_json(self, data: dict, aad: bytes | None = None) -> dict:
+ aes = AESGCM(self.key)
+ nonce = os.urandom(12)
+ plaintext = json.dumps(data).encode("utf-8")
+ ct = aes.encrypt(nonce, plaintext, aad)
+ ciphertext, tag = ct[:-16], ct[-16:]
+ return {
+ "nonce": base64.b64encode(nonce).decode(),
+ "ciphertext": base64.b64encode(ciphertext).decode(),
+ "tag": base64.b64encode(tag).decode(),
+ }
+
+ def decrypt_json(self, enc: dict, aad: bytes | None = None) -> dict:
+ aes = AESGCM(self.key)
+ nonce = base64.b64decode(enc["nonce"])
+ ct = base64.b64decode(enc["ciphertext"])
+ tag = base64.b64decode(enc["tag"])
+ data = aes.decrypt(nonce, ct + tag, aad)
+ return json.loads(data.decode())
\ No newline at end of file
diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py
index 275f2a63a1ba..fb25c5ed8403 100644
--- a/litellm/litellm_core_utils/get_llm_provider_logic.py
+++ b/litellm/litellm_core_utils/get_llm_provider_logic.py
@@ -279,6 +279,7 @@ def get_llm_provider( # noqa: PLR0915
or "ft:gpt-3.5-turbo" in model
or "ft:gpt-4" in model # catches ft:gpt-4-0613, ft:gpt-4o
or model in litellm.openai_image_generation_models
+ or model in litellm.openai_video_generation_models
):
custom_llm_provider = "openai"
elif model in litellm.open_ai_text_completion_models:
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index 85f88a13f2f2..773ff29e3717 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -699,6 +699,9 @@ def get_custom_logger_for_prompt_management(
self.model_call_details["prompt_integration"] = (
vector_store_custom_logger.__class__.__name__
)
+ # Add to global callbacks so post-call hooks are invoked
+ if vector_store_custom_logger and vector_store_custom_logger not in litellm.callbacks:
+ litellm.logging_callback_manager.add_litellm_callback(vector_store_custom_logger)
return vector_store_custom_logger
return None
diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index 1daf543cfcb4..64223a9ba4e8 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.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,
@@ -1520,6 +1522,43 @@ def set_logging_event_loop(self, loop):
"""
self.logging_loop = loop
+ async def _call_post_streaming_deployment_hook(self, chunk):
+ """
+ Call the post-call streaming deployment hook for callbacks.
+
+ This allows callbacks to modify streaming chunks before they're returned.
+ """
+ try:
+ import litellm
+ from litellm.integrations.custom_logger import CustomLogger
+ from litellm.types.utils import CallTypes
+
+ # Get request kwargs from logging object
+ request_data = self.logging_obj.model_call_details
+ call_type_str = self.logging_obj.call_type
+
+ try:
+ typed_call_type = CallTypes(call_type_str)
+ except ValueError:
+ typed_call_type = None
+
+ # Call hooks for all callbacks
+ for callback in litellm.callbacks:
+ if isinstance(callback, CustomLogger) and hasattr(callback, "async_post_call_streaming_deployment_hook"):
+ result = await callback.async_post_call_streaming_deployment_hook(
+ request_data=request_data,
+ response_chunk=chunk,
+ call_type=typed_call_type,
+ )
+ if result is not None:
+ chunk = result
+
+ return chunk
+ except Exception as e:
+ from litellm._logging import verbose_logger
+ verbose_logger.exception(f"Error in post-call streaming deployment hook: {str(e)}")
+ return chunk
+
def cache_streaming_response(self, processed_chunk, cache_hit: bool):
"""
Caches the streaming response
@@ -1825,6 +1864,11 @@ async def __anext__(self): # noqa: PLR0915
if self.sent_last_chunk is True and self.stream_options is None:
usage = calculate_total_usage(chunks=self.chunks)
processed_chunk._hidden_params["usage"] = usage
+
+ # Call post-call streaming deployment hook for final chunk
+ if self.sent_last_chunk is True:
+ processed_chunk = await self._call_post_streaming_deployment_hook(processed_chunk)
+
return processed_chunk
raise StopAsyncIteration
else: # temporary patch for non-aiohttp async calls
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
index 47263dc17483..ecad7a500111 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
@@ -31,7 +31,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
sent_first_chunk: bool = False
sent_content_block_start: bool = False
sent_content_block_finish: bool = False
- current_content_block_type: Literal["text", "tool_use"] = "text"
+ current_content_block_type: Literal["text", "tool_use", "thinking"] = "text"
sent_last_message: bool = False
holding_chunk: Optional[Any] = None
holding_stop_reason_chunk: Optional[Any] = None
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
index 7de2a1e1c66f..4a01191b1c7d 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
@@ -13,6 +13,8 @@
from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingChoice
+from litellm.types.utils import StreamingChoices
+
from litellm.types.llms.anthropic import (
AllAnthropicToolsValues,
AnthopicMessagesAssistantMessageParam,
@@ -21,10 +23,14 @@
AnthropicMessagesToolChoice,
AnthropicMessagesUserMessageParam,
AnthropicResponseContentBlockText,
+ AnthropicResponseContentBlockRedactedThinking,
+ AnthropicResponseContentBlockThinking,
AnthropicResponseContentBlockToolUse,
ContentBlockDelta,
ContentJsonBlockDelta,
ContentTextBlockDelta,
+ ContentThinkingBlockDelta,
+ ContentThinkingSignatureBlockDelta,
MessageBlockDelta,
MessageDelta,
UsageDelta,
@@ -39,9 +45,11 @@
ChatCompletionAssistantToolCall,
ChatCompletionImageObject,
ChatCompletionImageUrlObject,
+ ChatCompletionRedactedThinkingBlock,
ChatCompletionRequest,
ChatCompletionSystemMessage,
ChatCompletionTextObject,
+ ChatCompletionThinkingBlock,
ChatCompletionToolCallFunctionChunk,
ChatCompletionToolChoiceFunctionParam,
ChatCompletionToolChoiceObjectParam,
@@ -103,7 +111,6 @@ def translate_completion_input_params(
def translate_completion_output_params(
self, response: ModelResponse
) -> Optional[AnthropicMessagesResponse]:
-
return LiteLLMAnthropicMessagesAdapter().translate_openai_response_to_anthropic(
response=response
)
@@ -227,6 +234,7 @@ def translate_anthropic_messages_to_openai( # noqa: PLR0915
## ASSISTANT MESSAGE ##
assistant_message_str: Optional[str] = None
tool_calls: List[ChatCompletionAssistantToolCall] = []
+ thinking_blocks: List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]] = []
if m["role"] == "assistant":
if isinstance(m.get("content"), str):
assistant_message_str = str(m.get("content", ""))
@@ -253,14 +261,33 @@ def translate_anthropic_messages_to_openai( # noqa: PLR0915
function=function_chunk,
)
)
+ elif content.get("type") == "thinking":
+ thinking_block = ChatCompletionThinkingBlock(
+ type="thinking",
+ thinking=content.get("thinking") or "",
+ signature=content.get("signature") or "",
+ cache_control=content.get("cache_control", {})
+ )
+ thinking_blocks.append(thinking_block)
+ elif content.get("type") == "redacted_thinking":
+ redacted_thinking_block = ChatCompletionRedactedThinkingBlock(
+ type="redacted_thinking",
+ data=content.get("data") or "",
+ cache_control=content.get("cache_control", {})
+ )
+ thinking_blocks.append(redacted_thinking_block)
+
- if assistant_message_str is not None or len(tool_calls) > 0:
+ if assistant_message_str is not None or len(tool_calls) > 0 or len(thinking_blocks) > 0:
assistant_message = ChatCompletionAssistantMessage(
role="assistant",
content=assistant_message_str,
+ thinking_blocks=thinking_blocks if len(thinking_blocks) > 0 else None,
)
if len(tool_calls) > 0:
assistant_message["tool_calls"] = tool_calls
+ if len(thinking_blocks) > 0:
+ assistant_message["thinking_blocks"] = thinking_blocks # type: ignore
new_messages.append(assistant_message)
return new_messages
@@ -313,6 +340,7 @@ def translate_anthropic_to_openai(
"""
This is used by the beta Anthropic Adapter, for translating anthropic `/v1/messages` requests to the openai format.
"""
+ # Debug: Processing Anthropic message request
new_messages: List[AllMessageValues] = []
## CONVERT ANTHROPIC MESSAGES TO OPENAI
@@ -383,14 +411,37 @@ def translate_anthropic_to_openai(
def _translate_openai_content_to_anthropic(
self, choices: List[Choices]
) -> List[
- Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse]
+ Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking]
]:
new_content: List[
Union[
- AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse
+ AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking
]
] = []
for choice in choices:
+ # Handle thinking blocks first
+ if hasattr(choice.message, 'thinking_blocks') and choice.message.thinking_blocks:
+ for thinking_block in choice.message.thinking_blocks:
+ if thinking_block.get("type") == "thinking":
+ thinking_value = thinking_block.get("thinking", "")
+ signature_value = thinking_block.get("signature", "")
+ new_content.append(
+ AnthropicResponseContentBlockThinking(
+ type="thinking",
+ thinking=str(thinking_value) if thinking_value is not None else "",
+ signature=str(signature_value) if signature_value is not None else None,
+ )
+ )
+ elif thinking_block.get("type") == "redacted_thinking":
+ data_value = thinking_block.get("data", "")
+ new_content.append(
+ AnthropicResponseContentBlockRedactedThinking(
+ type="redacted_thinking",
+ data=str(data_value) if data_value is not None else "",
+ )
+ )
+
+ # Handle tool calls
if (
choice.message.tool_calls is not None
and len(choice.message.tool_calls) > 0
@@ -404,12 +455,41 @@ def _translate_openai_content_to_anthropic(
input=json.loads(tool_call.function.arguments) if tool_call.function.arguments else {},
)
)
+ # Handle text content
elif choice.message.content is not None:
new_content.append(
AnthropicResponseContentBlockText(
type="text", text=choice.message.content
)
)
+ elif choice.message.thinking_blocks is not None:
+ for thinking_block in choice.message.thinking_blocks:
+ if "thinking" in thinking_block and "signature" in thinking_block:
+ thinking = thinking_block.get("thinking")
+ signature = thinking_block.get("signature")
+
+ assert isinstance(thinking, str)
+ assert isinstance(signature, str) or signature is None
+
+ new_content.append(
+ AnthropicResponseContentBlockThinking(
+ type="thinking",
+ thinking=thinking,
+ signature=signature,
+ )
+ )
+ elif "data" in thinking_block:
+ data = thinking_block.get("data")
+
+ assert isinstance(data, str)
+
+ new_content.append(
+ AnthropicResponseContentBlockRedactedThinking(
+ type="redacted_thinking",
+ data=data,
+ )
+ )
+
return new_content
@@ -453,9 +533,9 @@ def translate_openai_response_to_anthropic(
return translated_obj
def _translate_streaming_openai_chunk_to_anthropic_content_block(
- self, choices: List[OpenAIStreamingChoice]
+ self, choices: List[Union[OpenAIStreamingChoice, StreamingChoices]]
) -> Tuple[
- Literal["text", "tool_use"],
+ Literal["text", "tool_use", "thinking"],
"ContentBlockContentBlockDict",
]:
from litellm._uuid import uuid
@@ -476,17 +556,41 @@ def _translate_streaming_openai_chunk_to_anthropic_content_block(
name=choice.delta.tool_calls[0].function.name or "",
input={},
)
+ elif (
+ isinstance(choice, StreamingChoices) and hasattr(choice.delta, "thinking_blocks")
+ ):
+ thinking_blocks = choice.delta.thinking_blocks or []
+ if len(thinking_blocks) > 0:
+ thinking_block = thinking_blocks[0]
+ if thinking_block["type"] == "thinking":
+ thinking = thinking_block.get("thinking") or ""
+ signature = thinking_block.get("signature") or ""
+
+ assert isinstance(thinking, str)
+ assert isinstance(signature, str)
+
+ if thinking and signature:
+ raise ValueError("Both `thinking` and `signature` in a single streaming chunk isn't supported.")
+
+ return "thinking", ChatCompletionThinkingBlock(
+ type="thinking",
+ thinking=thinking,
+ signature=signature
+ )
+
return "text", TextBlock(type="text", text="")
def _translate_streaming_openai_chunk_to_anthropic(
- self, choices: List[OpenAIStreamingChoice]
+ self, choices: List[Union[OpenAIStreamingChoice, StreamingChoices]]
) -> Tuple[
- Literal["text_delta", "input_json_delta"],
- Union[ContentTextBlockDelta, ContentJsonBlockDelta],
+ Literal["text_delta", "input_json_delta", "thinking_delta", "signature_delta"],
+ Union[ContentTextBlockDelta, ContentJsonBlockDelta, ContentThinkingBlockDelta, ContentThinkingSignatureBlockDelta],
]:
text: str = ""
+ reasoning_content: str = ""
+ reasoning_signature: str = ""
partial_json: Optional[str] = None
for choice in choices:
if choice.delta.content is not None and len(choice.delta.content) > 0:
@@ -499,10 +603,32 @@ def _translate_streaming_openai_chunk_to_anthropic(
and tool.function.arguments is not None
):
partial_json += tool.function.arguments
+ elif isinstance(choice, StreamingChoices) and hasattr(choice.delta, "thinking_blocks"):
+ thinking_blocks = choice.delta.thinking_blocks or []
+ if len(thinking_blocks) > 0:
+ for thinking_block in thinking_blocks:
+ if thinking_block["type"] == "thinking":
+ thinking = thinking_block.get("thinking") or ""
+ signature = thinking_block.get("signature") or ""
+
+ assert isinstance(thinking, str)
+ assert isinstance(signature, str)
+
+ reasoning_content += thinking
+ reasoning_signature += signature
+
+ if reasoning_content and reasoning_signature:
+ raise ValueError("Both `reasoning` and `signature` in a single streaming chunk isn't supported.")
+
+
if partial_json is not None:
return "input_json_delta", ContentJsonBlockDelta(
type="input_json_delta", partial_json=partial_json
)
+ elif reasoning_content:
+ return "thinking_delta", ContentThinkingBlockDelta(type="thinking_delta", thinking=reasoning_content)
+ elif reasoning_signature:
+ return "signature_delta", ContentThinkingSignatureBlockDelta(type="signature_delta", signature=reasoning_signature)
else:
return "text_delta", ContentTextBlockDelta(type="text_delta", text=text)
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
index 46ba96f2605c..e04a1aef5e88 100644
--- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
@@ -2,7 +2,7 @@
import httpx
-from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj, verbose_logger
from litellm.llms.base_llm.anthropic_messages.transformation import (
BaseAnthropicMessagesConfig,
)
@@ -94,6 +94,7 @@ def transform_anthropic_messages_request(
status_code=400,
)
####### get required params for all anthropic messages requests ######
+ verbose_logger.info(f"🔍 TRANSFORMATION DEBUG - Messages: {messages}")
anthropic_messages_request: AnthropicMessagesRequest = AnthropicMessagesRequest(
messages=messages,
max_tokens=max_tokens,
diff --git a/litellm/llms/base_llm/videos/transformation.py b/litellm/llms/base_llm/videos/transformation.py
new file mode 100644
index 000000000000..060e2e81bd27
--- /dev/null
+++ b/litellm/llms/base_llm/videos/transformation.py
@@ -0,0 +1,155 @@
+import types
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+
+import httpx
+from httpx._types import RequestFiles
+
+from litellm.types.videos.main import VideoCreateOptionalRequestParams
+from litellm.types.responses.main import *
+from litellm.types.router import GenericLiteLLMParams
+
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+ from litellm.types.videos.main import VideoObject as _VideoObject
+
+ from ..chat.transformation import BaseLLMException as _BaseLLMException
+
+ LiteLLMLoggingObj = _LiteLLMLoggingObj
+ BaseLLMException = _BaseLLMException
+ VideoObject = _VideoObject
+else:
+ LiteLLMLoggingObj = Any
+ BaseLLMException = Any
+ VideoObject = Any
+
+
+class BaseVideoConfig(ABC):
+ def __init__(self):
+ pass
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not k.startswith("_abc")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ @abstractmethod
+ def get_supported_openai_params(self, model: str) -> list:
+ pass
+
+ @abstractmethod
+ def map_openai_params(
+ self,
+ video_create_optional_params: VideoCreateOptionalRequestParams,
+ model: str,
+ drop_params: bool,
+ ) -> Dict:
+ pass
+
+ @abstractmethod
+ def validate_environment(
+ self,
+ headers: dict,
+ model: str,
+ api_key: Optional[str] = None,
+ ) -> dict:
+ return {}
+
+ @abstractmethod
+ def get_complete_url(
+ self,
+ model: str,
+ api_base: Optional[str],
+ litellm_params: dict,
+ ) -> str:
+ """
+ OPTIONAL
+
+ Get the complete url for the request
+
+ Some providers need `model` in `api_base`
+ """
+ if api_base is None:
+ raise ValueError("api_base is required")
+ return api_base
+
+ @abstractmethod
+ def transform_video_create_request(
+ self,
+ model: str,
+ prompt: str,
+ video_create_optional_request_params: Dict,
+ litellm_params: GenericLiteLLMParams,
+ headers: dict,
+ ) -> Tuple[Dict, RequestFiles]:
+ pass
+
+ @abstractmethod
+ def transform_video_create_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> VideoObject:
+ pass
+
+ @abstractmethod
+ def transform_video_content_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> bytes:
+ pass
+
+ @abstractmethod
+ def transform_video_remix_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> VideoObject:
+ pass
+
+ @abstractmethod
+ def transform_video_list_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> Dict[str, Any]:
+ pass
+
+ @abstractmethod
+ def transform_video_delete_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> VideoObject:
+ pass
+
+ def get_error_class(
+ self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+ ) -> BaseLLMException:
+ from ..chat.transformation import BaseLLMException
+
+ raise BaseLLMException(
+ status_code=status_code,
+ message=error_message,
+ headers=headers,
+ )
diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py
index 8211addaf954..bc0466388f97 100644
--- a/litellm/llms/bedrock/base_aws_llm.py
+++ b/litellm/llms/bedrock/base_aws_llm.py
@@ -984,11 +984,20 @@ def get_request_headers(
raise ImportError(
"Missing boto3 to call bedrock. Run 'pip install boto3'."
)
+
+ # Filter headers for AWS signature calculation
+ # AWS SigV4 only includes specific headers in signature calculation
+ aws_signature_headers = self._filter_headers_for_aws_signature(headers)
sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
request = AWSRequest(
- method="POST", url=endpoint_url, data=data, headers=headers
+ method="POST", url=endpoint_url, data=data, headers=aws_signature_headers
)
sigv4.add_auth(request)
+
+ # Add back all original headers (including forwarded ones) after signature calculation
+ for header_name, header_value in headers.items():
+ request.headers[header_name] = header_value
+
if (
extra_headers is not None and "Authorization" in extra_headers
): # prevent sigv4 from overwriting the auth header
@@ -997,6 +1006,25 @@ def get_request_headers(
return prepped
+ def _filter_headers_for_aws_signature(self, headers: dict) -> dict:
+ """
+ Filter headers to only include those that AWS SigV4 includes in signature calculation.
+ This Fixes forwarded client headers from breaking the signature calculation.
+ """
+ aws_signature_headers = {}
+ aws_headers = {
+ 'host', 'content-type', 'date', 'x-amz-date', 'x-amz-security-token',
+ 'x-amz-content-sha256', 'x-amz-algorithm', 'x-amz-credential',
+ 'x-amz-signedheaders', 'x-amz-signature'
+ }
+
+ for header_name, header_value in headers.items():
+ header_lower = header_name.lower()
+ if header_lower in aws_headers or header_lower.startswith('x-amz-') or header_lower.startswith('x-amzn-'):
+ aws_signature_headers[header_name] = header_value
+
+ return aws_signature_headers
+
def _sign_request(
self,
service_name: Literal["bedrock", "sagemaker"],
diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
index 4fa8517a0907..be782d35766a 100644
--- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
+++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
@@ -111,7 +111,6 @@ def transform_anthropic_messages_request(
litellm_params=litellm_params,
headers=headers,
)
-
#########################################################
############## BEDROCK Invoke SPECIFIC TRANSFORMATION ###
#########################################################
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index d7b7987b6707..28da83fe5b17 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -44,6 +44,7 @@
from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
+from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
HTTPHandler,
@@ -81,6 +82,7 @@
VectorStoreSearchOptionalRequestParams,
VectorStoreSearchResponse,
)
+from litellm.types.videos.main import VideoObject
from litellm.utils import (
CustomStreamWrapper,
ImageResponse,
@@ -1823,7 +1825,7 @@ def response_api_handler(
headers=headers,
json=data,
timeout=timeout
- or response_api_optional_request_params.get("timeout"),
+ or float(response_api_optional_request_params.get("timeout", 0)),
stream=stream,
)
if fake_stream is True:
@@ -1851,7 +1853,7 @@ def response_api_handler(
headers=headers,
json=data,
timeout=timeout
- or response_api_optional_request_params.get("timeout"),
+ or float(response_api_optional_request_params.get("timeout", 0)),
)
except Exception as e:
raise self._handle_error(
@@ -1944,7 +1946,7 @@ async def async_response_api_handler(
headers=headers,
json=data,
timeout=timeout
- or response_api_optional_request_params.get("timeout"),
+ or float(response_api_optional_request_params.get("timeout", 0)),
stream=stream,
)
@@ -1974,7 +1976,7 @@ async def async_response_api_handler(
headers=headers,
json=data,
timeout=timeout
- or response_api_optional_request_params.get("timeout"),
+ or float(response_api_optional_request_params.get("timeout", 0)),
)
except Exception as e:
@@ -3279,6 +3281,7 @@ def _handle_error(
BaseGoogleGenAIGenerateContentConfig,
BaseAnthropicMessagesConfig,
BaseBatchesConfig,
+ BaseVideoConfig,
BaseOCRConfig,
"BasePassthroughConfig",
],
@@ -3782,6 +3785,712 @@ async def async_image_generation_handler(
return model_response
+ ###### VIDEO GENERATION HANDLER ######
+ def video_generation_handler(
+ self,
+ model: str,
+ prompt: str,
+ video_generation_provider_config: BaseVideoConfig,
+ video_generation_optional_request_params: Dict,
+ custom_llm_provider: str,
+ litellm_params: GenericLiteLLMParams,
+ logging_obj: LiteLLMLoggingObj,
+ timeout: Union[float, httpx.Timeout],
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+ _is_async: bool = False,
+ fake_stream: bool = False,
+ litellm_metadata: Optional[Dict[str, Any]] = None,
+ api_key: Optional[str] = None,
+ ) -> Union[
+ VideoObject,
+ Coroutine[Any, Any, VideoObject],
+ ]:
+ """
+ Handles video generation requests.
+ When _is_async=True, returns a coroutine instead of making the call directly.
+ """
+ if _is_async:
+ # Return the async coroutine if called with _is_async=True
+ return self.async_video_generation_handler(
+ model=model,
+ prompt=prompt,
+ video_generation_provider_config=video_generation_provider_config,
+ video_generation_optional_request_params=video_generation_optional_request_params,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=logging_obj,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ timeout=timeout,
+ 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):
+ sync_httpx_client = _get_httpx_client(
+ params={"ssl_verify": litellm_params.get("ssl_verify", None)}
+ )
+ else:
+ sync_httpx_client = client
+
+ headers = video_generation_provider_config.validate_environment(
+ api_key=api_key,
+ headers=video_generation_optional_request_params.get("extra_headers", {})
+ or {},
+ model=model,
+ )
+
+ if extra_headers:
+ headers.update(extra_headers)
+
+ api_base = video_generation_provider_config.get_complete_url(
+ model=model,
+ api_base=litellm_params.get("api_base", None),
+ litellm_params=dict(litellm_params),
+ )
+
+ data, files = video_generation_provider_config.transform_video_create_request(
+ model=model,
+ prompt=prompt,
+ video_create_optional_request_params=video_generation_optional_request_params,
+ litellm_params=litellm_params,
+ headers=headers,
+ )
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input=prompt,
+ api_key="",
+ additional_args={
+ "complete_input_dict": data,
+ "api_base": api_base,
+ "headers": headers,
+ },
+ )
+
+ try:
+ # Use JSON when no files, otherwise use form data with files
+ if files and len(files) > 0:
+ # Use multipart/form-data when files are present
+ response = sync_httpx_client.post(
+ url=api_base,
+ headers=headers,
+ data=data,
+ files=files,
+ timeout=timeout,
+ )
+
+ # --- END MOCK VIDEO RESPONSE ---
+ else:
+ response = sync_httpx_client.post(
+ url=api_base,
+ headers=headers,
+ json=data,
+ timeout=timeout,
+ )
+
+ except Exception as e:
+ raise self._handle_error(
+ e=e,
+ provider_config=video_generation_provider_config,
+ )
+
+ return video_generation_provider_config.transform_video_create_response(
+ model=model,
+ raw_response=response,
+ logging_obj=logging_obj,
+ )
+
+ async def async_video_generation_handler(
+ self,
+ model: str,
+ prompt: str,
+ video_generation_provider_config: "BaseVideoConfig",
+ video_generation_optional_request_params: Dict,
+ custom_llm_provider: str,
+ litellm_params: GenericLiteLLMParams,
+ logging_obj: LiteLLMLoggingObj,
+ timeout: Union[float, httpx.Timeout],
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+ fake_stream: bool = False,
+ litellm_metadata: Optional[Dict[str, Any]] = None,
+ api_key: Optional[str] = None,
+ ) -> VideoObject:
+ """
+ Async version of the video generation handler.
+ Uses async HTTP client to make requests.
+ """
+ if client is None or not isinstance(client, AsyncHTTPHandler):
+ async_httpx_client = get_async_httpx_client(
+ llm_provider=litellm.LlmProviders(custom_llm_provider),
+ params={"ssl_verify": litellm_params.get("ssl_verify", None)},
+ )
+ else:
+ async_httpx_client = client
+
+ headers = video_generation_provider_config.validate_environment(
+ api_key=api_key,
+ headers=video_generation_optional_request_params.get("extra_headers", {})
+ or {},
+ model=model,
+ )
+
+ if extra_headers:
+ headers.update(extra_headers)
+
+ api_base = video_generation_provider_config.get_complete_url(
+ model=model,
+ api_base=litellm_params.get("api_base", None),
+ litellm_params=dict(litellm_params),
+ )
+
+ data, files = video_generation_provider_config.transform_video_create_request(
+ model=model,
+ prompt=prompt,
+ video_create_optional_request_params=video_generation_optional_request_params,
+ litellm_params=litellm_params,
+ headers=headers,
+ )
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input=prompt,
+ api_key="",
+ additional_args={
+ "complete_input_dict": data,
+ "api_base": api_base,
+ "headers": headers,
+ },
+ )
+
+ try:
+ # Use JSON when no files, otherwise use form data with files
+ if files is None or len(files) == 0:
+ response = await async_httpx_client.post(
+ url=api_base,
+ headers=headers,
+ json=data,
+ timeout=timeout,
+ )
+ else:
+ response = await async_httpx_client.post(
+ url=api_base,
+ headers=headers,
+ data=data,
+ files=files,
+ timeout=timeout,
+ )
+
+ except Exception as e:
+ raise self._handle_error(
+ e=e,
+ provider_config=video_generation_provider_config,
+ )
+
+ return video_generation_provider_config.transform_video_create_response(
+ model=model,
+ raw_response=response,
+ logging_obj=logging_obj,
+ )
+
+ ###### VIDEO CONTENT HANDLER ######
+ def video_content_handler(
+ self,
+ video_id: str,
+ model: str,
+ video_content_provider_config: BaseVideoConfig,
+ custom_llm_provider: str,
+ litellm_params: GenericLiteLLMParams,
+ logging_obj: LiteLLMLoggingObj,
+ timeout: Union[float, httpx.Timeout],
+ extra_headers: Optional[Dict[str, Any]] = None,
+ api_key: Optional[str] = None,
+ client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+ _is_async: bool = False,
+ ) -> Union[bytes, Coroutine[Any, Any, bytes]]:
+ """
+ Handle video content download requests.
+ """
+ if _is_async:
+ return self.async_video_content_handler(
+ video_id=video_id,
+ model=model,
+ video_content_provider_config=video_content_provider_config,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=logging_obj,
+ timeout=timeout,
+ extra_headers=extra_headers,
+ api_key=api_key,
+ client=client,
+ )
+
+ if client is None or not isinstance(client, HTTPHandler):
+ sync_httpx_client = _get_httpx_client(
+ params={"ssl_verify": litellm_params.get("ssl_verify", None)}
+ )
+ else:
+ sync_httpx_client = client
+
+ headers = video_content_provider_config.validate_environment(
+ headers=extra_headers or {},
+ model=model,
+ api_key=api_key,
+ )
+
+ if extra_headers:
+ headers.update(extra_headers)
+
+ api_base = video_content_provider_config.get_complete_url(
+ model=model,
+ api_base=litellm_params.get("api_base", None),
+ litellm_params=dict(litellm_params),
+ )
+
+ # Construct the URL for video content download
+ url = f"{api_base.rstrip('/')}/{video_id}/content"
+
+ # Add variant query parameter if provided
+ params = { "video_id": video_id }
+
+ try:
+ # Make the GET request to download content
+ response = sync_httpx_client.get(
+ url=url,
+ headers=headers,
+ params=params,
+ )
+
+ # Transform the response using the provider config
+ return video_content_provider_config.transform_video_content_response(
+ model=model,
+ raw_response=response,
+ logging_obj=logging_obj,
+ )
+
+ except Exception as e:
+ raise self._handle_error(
+ e=e,
+ provider_config=video_content_provider_config,
+ )
+
+ async def async_video_content_handler(
+ self,
+ video_id: str,
+ model: str,
+ video_content_provider_config: BaseVideoConfig,
+ custom_llm_provider: str,
+ litellm_params: GenericLiteLLMParams,
+ logging_obj: LiteLLMLoggingObj,
+ timeout: Union[float, httpx.Timeout],
+ extra_headers: Optional[Dict[str, Any]] = None,
+ api_key: Optional[str] = None,
+ client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+ ) -> bytes:
+ """
+ Async version of the video content download handler.
+ """
+ if client is None or not isinstance(client, AsyncHTTPHandler):
+ async_httpx_client = get_async_httpx_client(
+ llm_provider=litellm.LlmProviders(custom_llm_provider),
+ params={"ssl_verify": litellm_params.get("ssl_verify", None)},
+ )
+ else:
+ async_httpx_client = client
+
+ headers = video_content_provider_config.validate_environment(
+ headers=extra_headers or {},
+ model=model,
+ api_key=api_key,
+ )
+
+ if extra_headers:
+ headers.update(extra_headers)
+
+ api_base = video_content_provider_config.get_complete_url(
+ model=model,
+ api_base=litellm_params.get("api_base", None),
+ litellm_params=dict(litellm_params),
+ )
+
+ # Construct the URL for video content download
+ url = f"{api_base.rstrip('/')}/{video_id}/content"
+
+ params = {
+ "video_id": video_id,
+ }
+
+ try:
+ # Make the GET request to download content
+ response = await async_httpx_client.get(
+ url=url,
+ headers=headers,
+ params=params,
+ )
+
+ # Transform the response using the provider config
+ return video_content_provider_config.transform_video_content_response(
+ model=model,
+ raw_response=response,
+ logging_obj=logging_obj,
+ )
+
+ except Exception as e:
+ raise self._handle_error(
+ e=e,
+ provider_config=video_content_provider_config,
+ )
+
+ def video_remix_handler(
+ self,
+ video_id: str,
+ prompt: str,
+ model: str,
+ video_remix_provider_config: BaseVideoConfig,
+ custom_llm_provider: str,
+ litellm_params,
+ logging_obj,
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ timeout: Optional[float] = None,
+ _is_async: bool = False,
+ client=None,
+ ):
+ """
+ Handler for video remix requests.
+ """
+ if _is_async:
+ return self.async_video_remix_handler(
+ video_id=video_id,
+ prompt=prompt,
+ model=model,
+ video_remix_provider_config=video_remix_provider_config,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=logging_obj,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ timeout=timeout,
+ client=client,
+ )
+ else:
+ # For sync calls, we'll use the async handler in a sync context
+ import asyncio
+ return asyncio.run(
+ self.async_video_remix_handler(
+ video_id=video_id,
+ prompt=prompt,
+ model=model,
+ video_remix_provider_config=video_remix_provider_config,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=logging_obj,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ timeout=timeout,
+ client=client,
+ )
+ )
+
+ async def async_video_remix_handler(
+ self,
+ video_id: str,
+ prompt: str,
+ model: str,
+ video_remix_provider_config: BaseVideoConfig,
+ custom_llm_provider: str,
+ litellm_params,
+ logging_obj,
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ timeout: Optional[float] = None,
+ client=None,
+ ):
+ """
+ Async version of the video remix handler.
+ """
+ if client is None or not isinstance(client, AsyncHTTPHandler):
+ async_httpx_client = get_async_httpx_client(
+ llm_provider=litellm.LlmProviders(custom_llm_provider),
+ params={"ssl_verify": litellm_params.get("ssl_verify", None)},
+ )
+ else:
+ async_httpx_client = client
+
+ headers = video_remix_provider_config.validate_environment(
+ api_key=litellm_params.get("api_key"),
+ headers=extra_headers or {},
+ model=model,
+ )
+
+ if extra_headers:
+ headers.update(extra_headers)
+
+ api_base = video_remix_provider_config.get_complete_url(
+ model=model,
+ api_base=litellm_params.get("api_base", None),
+ litellm_params=dict(litellm_params),
+ )
+
+ # Construct the URL for video remix
+ url = f"{api_base.rstrip('/')}/{video_id}/remix"
+
+ # Prepare the request data
+ data = {
+ "prompt": prompt,
+ }
+
+ # Add any extra body parameters
+ if extra_body:
+ data.update(extra_body)
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input=prompt,
+ api_key="",
+ additional_args={
+ "complete_input_dict": data,
+ "api_base": url,
+ "headers": headers,
+ "video_id": video_id,
+ },
+ )
+
+ try:
+ response = await async_httpx_client.post(
+ url=url,
+ headers=headers,
+ json=data,
+ timeout=timeout,
+ )
+
+ return video_remix_provider_config.transform_video_remix_response(
+ model=model,
+ raw_response=response,
+ logging_obj=logging_obj,
+ )
+
+ except Exception as e:
+ raise self._handle_error(
+ e=e,
+ provider_config=video_remix_provider_config,
+ )
+
+ def video_list_handler(
+ self,
+ after: Optional[str],
+ limit: Optional[int],
+ order: Optional[str],
+ model: str,
+ video_list_provider_config,
+ custom_llm_provider: str,
+ litellm_params,
+ logging_obj,
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_query: Optional[Dict[str, Any]] = None,
+ timeout: Optional[float] = None,
+ _is_async: bool = False,
+ client=None,
+ ):
+ """
+ Handler for video list requests.
+ """
+ if _is_async:
+ return self.async_video_list_handler(
+ after=after,
+ limit=limit,
+ order=order,
+ model=model,
+ video_list_provider_config=video_list_provider_config,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=logging_obj,
+ extra_headers=extra_headers,
+ extra_query=extra_query,
+ timeout=timeout,
+ client=client,
+ )
+ else:
+ # For sync calls, we'll use the async handler in a sync context
+ import asyncio
+ return asyncio.run(
+ self.async_video_list_handler(
+ after=after,
+ limit=limit,
+ order=order,
+ model=model,
+ video_list_provider_config=video_list_provider_config,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=logging_obj,
+ extra_headers=extra_headers,
+ extra_query=extra_query,
+ timeout=timeout,
+ client=client,
+ )
+ )
+
+ async def async_video_list_handler(
+ self,
+ after: Optional[str],
+ limit: Optional[int],
+ order: Optional[str],
+ model: str,
+ video_list_provider_config: BaseVideoConfig,
+ custom_llm_provider: str,
+ litellm_params,
+ logging_obj,
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_query: Optional[Dict[str, Any]] = None,
+ timeout: Optional[float] = None,
+ client=None,
+ ):
+ """
+ Async version of the video list handler.
+ """
+ if client is None or not isinstance(client, AsyncHTTPHandler):
+ async_httpx_client = get_async_httpx_client(
+ llm_provider=litellm.LlmProviders(custom_llm_provider),
+ params={"ssl_verify": litellm_params.get("ssl_verify", None)},
+ )
+ else:
+ async_httpx_client = client
+
+ headers = video_list_provider_config.validate_environment(
+ api_key=litellm_params.get("api_key"),
+ headers=extra_headers or {},
+ model=model,
+ )
+
+ if extra_headers:
+ headers.update(extra_headers)
+
+ api_base = video_list_provider_config.get_complete_url(
+ model=model,
+ api_base=litellm_params.get("api_base", None),
+ litellm_params=dict(litellm_params),
+ )
+
+ # Prepare query parameters
+ params = {}
+ if after is not None:
+ params["after"] = after
+ if limit is not None:
+ params["limit"] = limit
+ if order is not None:
+ params["order"] = order
+
+ # Add any extra query parameters
+ if extra_query:
+ params.update(extra_query)
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input="",
+ api_key="",
+ additional_args={
+ "api_base": api_base,
+ "headers": headers,
+ "params": params,
+ },
+ )
+
+ try:
+ response = await async_httpx_client.get(
+ url=api_base,
+ headers=headers,
+ params=params,
+ )
+
+ return video_list_provider_config.transform_video_list_response(
+ model=model,
+ raw_response=response,
+ logging_obj=logging_obj,
+ )
+
+ except Exception as e:
+ raise self._handle_error(
+ e=e,
+ provider_config=video_list_provider_config,
+ )
+
+ async def async_video_delete_handler(
+ self,
+ video_id: str,
+ model: str,
+ video_delete_provider_config: BaseVideoConfig,
+ custom_llm_provider: str,
+ litellm_params,
+ logging_obj,
+ extra_headers: Optional[Dict[str, Any]] = None,
+ timeout: Optional[float] = None,
+ client=None,
+ ):
+ """
+ Async version of the video delete handler.
+ """
+ if client is None or not isinstance(client, AsyncHTTPHandler):
+ async_httpx_client = get_async_httpx_client(
+ llm_provider=litellm.LlmProviders(custom_llm_provider),
+ params={"ssl_verify": litellm_params.get("ssl_verify", None)},
+ )
+ else:
+ async_httpx_client = client
+
+ headers = video_delete_provider_config.validate_environment(
+ api_key=litellm_params.get("api_key"),
+ headers=extra_headers or {},
+ model=model,
+ )
+
+ if extra_headers:
+ headers.update(extra_headers)
+
+ api_base = video_delete_provider_config.get_complete_url(
+ model=model,
+ api_base=litellm_params.get("api_base", None),
+ litellm_params=dict(litellm_params),
+ )
+
+ # Construct the URL for video delete
+ url = f"{api_base.rstrip('/')}/{video_id}"
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input="",
+ api_key="",
+ additional_args={
+ "api_base": url,
+ "headers": headers,
+ "video_id": video_id,
+ },
+ )
+
+ try:
+ response = await async_httpx_client.delete(
+ url=url,
+ headers=headers,
+ timeout=timeout,
+ )
+
+ return video_delete_provider_config.transform_video_delete_response(
+ model=model,
+ raw_response=response,
+ logging_obj=logging_obj,
+ )
+
+ except Exception as e:
+ raise self._handle_error(
+ e=e,
+ provider_config=video_delete_provider_config,
+ )
+
###### VECTOR STORE HANDLER ######
async def async_vector_store_search_handler(
self,
diff --git a/litellm/llms/gemini/google_genai/transformation.py b/litellm/llms/gemini/google_genai/transformation.py
index 94dfea5f58a9..2d5857690297 100644
--- a/litellm/llms/gemini/google_genai/transformation.py
+++ b/litellm/llms/gemini/google_genai/transformation.py
@@ -317,5 +317,20 @@ def transform_generate_content_response(
)
logging_obj.model_call_details["httpx_response"] = raw_response
+ response = self.convert_citation_sources_to_citations(response)
return GenerateContentResponse(**response)
+
+ def convert_citation_sources_to_citations(self, response: Dict) -> Dict:
+ """
+ Convert citation sources to citations.
+ API's camelCase citationSources becomes the SDK's snake_case citations
+ """
+ if "candidates" in response:
+ for candidate in response["candidates"]:
+ if "citationMetadata" in candidate and isinstance(candidate["citationMetadata"], dict):
+ citation_metadata = candidate["citationMetadata"]
+ # Transform citationSources to citations to match expected schema
+ if "citationSources" in citation_metadata:
+ citation_metadata["citations"] = citation_metadata.pop("citationSources")
+ return response
\ No newline at end of file
diff --git a/litellm/llms/openai/cost_calculation.py b/litellm/llms/openai/cost_calculation.py
index 229f75f26574..65a50224bb8d 100644
--- a/litellm/llms/openai/cost_calculation.py
+++ b/litellm/llms/openai/cost_calculation.py
@@ -120,3 +120,47 @@ def cost_per_second(
completion_cost = 0.0
return prompt_cost, completion_cost
+
+
+def video_generation_cost(
+ model: str,
+ duration_seconds: float,
+ custom_llm_provider: Optional[str] = None
+) -> float:
+ """
+ Calculates the cost for video generation based on duration in seconds.
+
+ Input:
+ - model: str, the model name without provider prefix
+ - duration_seconds: float, the duration of the generated video in seconds
+ - custom_llm_provider: str, the custom llm provider
+
+ Returns:
+ float - total_cost_in_usd
+ """
+ ## GET MODEL INFO
+ model_info = get_model_info(
+ model=model, custom_llm_provider=custom_llm_provider or "openai"
+ )
+
+ # Check for video-specific cost per second
+ video_cost_per_second = model_info.get("output_cost_per_video_per_second")
+ if video_cost_per_second is not None:
+ verbose_logger.debug(
+ f"For model={model} - output_cost_per_video_per_second: {video_cost_per_second}; duration: {duration_seconds}"
+ )
+ return video_cost_per_second * duration_seconds
+
+ # Fallback to general output cost per second
+ output_cost_per_second = model_info.get("output_cost_per_second")
+ if output_cost_per_second is not None:
+ verbose_logger.debug(
+ f"For model={model} - output_cost_per_second: {output_cost_per_second}; duration: {duration_seconds}"
+ )
+ return output_cost_per_second * duration_seconds
+
+ # If no cost information found, return 0
+ verbose_logger.warning(
+ f"No cost information found for video model {model}. Please add pricing to model_prices_and_context_window.json"
+ )
+ return 0.0
diff --git a/litellm/llms/openai/videos/transformation.py b/litellm/llms/openai/videos/transformation.py
new file mode 100644
index 000000000000..ea856cc28e72
--- /dev/null
+++ b/litellm/llms/openai/videos/transformation.py
@@ -0,0 +1,258 @@
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, IO
+from io import BufferedReader
+
+import httpx
+from httpx._types import RequestFiles
+
+from litellm.types.videos.main import VideoCreateOptionalRequestParams
+from litellm.types.llms.openai import CreateVideoRequest
+from litellm.types.videos.main import VideoResponse
+from litellm.types.router import GenericLiteLLMParams
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.videos.main import VideoObject
+import litellm
+
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+ from ...base_llm.videos.transformation import BaseVideoConfig as _BaseVideoConfig
+ from ...base_llm.chat.transformation import BaseLLMException as _BaseLLMException
+
+ LiteLLMLoggingObj = _LiteLLMLoggingObj
+ BaseVideoConfig = _BaseVideoConfig
+ BaseLLMException = _BaseLLMException
+else:
+ LiteLLMLoggingObj = Any
+ BaseVideoConfig = Any
+ BaseLLMException = Any
+
+
+class OpenAIVideoConfig(BaseVideoConfig):
+ """
+ Configuration class for OpenAI video generation.
+ """
+
+ def __init__(self):
+ super().__init__()
+
+ def get_supported_openai_params(self, model: str) -> list:
+ """
+ Get the list of supported OpenAI parameters for video generation.
+ """
+ return [
+ "model",
+ "prompt",
+ "input_reference",
+ "seconds",
+ "size",
+ "user",
+ "extra_headers",
+ ]
+
+ def map_openai_params(
+ self,
+ video_create_optional_params: VideoCreateOptionalRequestParams,
+ model: str,
+ drop_params: bool,
+ ) -> Dict:
+ """No mapping applied since inputs are in OpenAI spec already"""
+ return dict(video_create_optional_params)
+
+ def validate_environment(
+ self,
+ headers: dict,
+ model: str,
+ api_key: Optional[str] = None,
+ ) -> dict:
+ api_key = (
+ api_key
+ or litellm.api_key
+ or litellm.openai_key
+ or get_secret_str("OPENAI_API_KEY")
+ )
+ headers.update(
+ {
+ "Authorization": f"Bearer {api_key}",
+ }
+ )
+ return headers
+
+ def get_complete_url(
+ self,
+ model: str,
+ api_base: Optional[str],
+ litellm_params: dict,
+ ) -> str:
+ """
+ Get the complete URL for OpenAI video generation.
+ """
+ if api_base is None:
+ api_base = "https://api.openai.com/v1"
+
+ return f"{api_base.rstrip('/')}/videos"
+
+ def transform_video_create_request(
+ self,
+ model: str,
+ prompt: str,
+ video_create_optional_request_params: Dict,
+ litellm_params: GenericLiteLLMParams,
+ headers: dict,
+ ) -> Tuple[Dict, RequestFiles]:
+ """
+ Transform the video creation request for OpenAI API.
+ """
+ # Remove model and extra_headers from optional params as they're handled separately
+ video_create_optional_request_params = {
+ k: v for k, v in video_create_optional_request_params.items()
+ if k not in ["model", "extra_headers"]
+ }
+
+ # Create the request data
+ video_create_request = CreateVideoRequest(
+ model=model,
+ prompt=prompt,
+ **video_create_optional_request_params
+ )
+
+ # Handle file uploads
+ files_list: List[Tuple[str, Tuple[str, Union[IO[bytes], bytes, str], str]]] = []
+
+ # Handle input_reference parameter if provided
+ _input_reference = video_create_optional_request_params.get("input_reference")
+ if _input_reference is not None:
+ if isinstance(_input_reference, BufferedReader):
+ files_list.append(
+ ("input_reference", (_input_reference.name, _input_reference, "image/png"))
+ )
+ elif isinstance(_input_reference, str):
+ # Handle file path - open the file
+ try:
+ with open(_input_reference, "rb") as f:
+ files_list.append(
+ ("input_reference", (f.name, f.read(), "image/png"))
+ )
+ except Exception as e:
+ raise ValueError(f"Could not open input_reference file {_input_reference}: {e}")
+ else:
+ # Handle file-like object
+ files_list.append(
+ ("input_reference", ("input_reference.png", _input_reference, "image/png"))
+ )
+
+ # Convert to dict for JSON serialization
+ data = dict(video_create_request)
+
+ return data, files_list
+
+ def transform_video_create_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> VideoObject:
+ """
+ Transform the OpenAI video creation response.
+ """
+ response_data = raw_response.json()
+
+ # Transform the response data
+
+ video_obj = VideoObject(**response_data)
+
+ # Create usage object with duration information for cost calculation
+ # Video generation API doesn't provide usage, so we create one with duration
+ usage_data = {}
+ if video_obj:
+ if hasattr(video_obj, 'seconds') and video_obj.seconds:
+ try:
+ usage_data["duration_seconds"] = float(video_obj.seconds)
+ except (ValueError, TypeError):
+ pass
+ # Create the response
+ video_obj.usage = usage_data
+
+
+ return video_obj
+
+ def transform_video_content_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> bytes:
+ """
+ Transform the OpenAI video content download response.
+ Returns raw video content as bytes.
+ """
+ # For video content download, return the raw content as bytes
+ return raw_response.content
+
+ def transform_video_remix_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> VideoObject:
+ """
+ Transform the OpenAI video remix response.
+ """
+ response_data = raw_response.json()
+
+ # Transform the response data
+ video_obj = VideoObject(**response_data)
+
+ # Create usage object with duration information for cost calculation
+ # Video remix API doesn't provide usage, so we create one with duration
+ usage_data = {}
+ if video_obj:
+ if hasattr(video_obj, 'seconds') and video_obj.seconds:
+ try:
+ usage_data["duration_seconds"] = float(video_obj.seconds)
+ except (ValueError, TypeError):
+ pass
+ # Create the response
+ video_obj.usage = usage_data
+
+ return video_obj
+
+ def transform_video_list_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> Dict[str, Any]:
+ """
+ Transform the OpenAI video list response.
+ """
+ response_data = raw_response.json()
+ video_response = VideoResponse(**response_data)
+ # Convert VideoResponse object to dictionary to match base class return type
+ return video_response.model_dump()
+
+ def transform_video_delete_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> VideoObject:
+ """
+ Transform the OpenAI video delete response.
+ """
+ response_data = raw_response.json()
+
+ # Transform the response data
+ video_obj = VideoObject(**response_data)
+
+ return video_obj
+
+ def get_error_class(
+ self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+ ) -> BaseLLMException:
+ from ...base_llm.chat.transformation import BaseLLMException
+
+ raise BaseLLMException(
+ status_code=status_code,
+ message=error_message,
+ headers=headers,
+ )
diff --git a/litellm/llms/sagemaker/completion/handler.py b/litellm/llms/sagemaker/completion/handler.py
index 3d4108776cae..2a30dc5ef381 100644
--- a/litellm/llms/sagemaker/completion/handler.py
+++ b/litellm/llms/sagemaker/completion/handler.py
@@ -17,12 +17,12 @@
CustomStreamWrapper,
EmbeddingResponse,
ModelResponse,
- Usage,
get_secret,
)
from ..common_utils import AWSEventStreamDecoder, SagemakerError
from .transformation import SagemakerConfig
+from ..embedding.transformation import SagemakerEmbeddingConfig
sagemaker_config = SagemakerConfig()
@@ -578,7 +578,7 @@ def embedding(
logger_fn=None,
):
"""
- Supports Huggingface Jumpstart embeddings like GPT-6B
+ Supports both Huggingface Jumpstart embeddings and Voyage models
"""
### BOTO3 INIT
import boto3
@@ -625,8 +625,11 @@ def embedding(
): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in
inference_params[k] = v
- #### HF EMBEDDING LOGIC
- data = json.dumps({"inputs": input}).encode("utf-8")
+ #### EMBEDDING LOGIC
+ # Transform request based on model type
+ provider_config = SagemakerEmbeddingConfig.get_model_config(model)
+ request_data = provider_config.transform_embedding_request(model, input, optional_params, {})
+ data = json.dumps(request_data).encode("utf-8")
## LOGGING
request_str = f"""
@@ -670,40 +673,27 @@ def embedding(
)
print_verbose(f"raw model_response: {response}")
- if "embedding" not in response:
- raise SagemakerError(
- status_code=500, message="embedding not found in response"
- )
- embeddings = response["embedding"]
-
- if not isinstance(embeddings, list):
- raise SagemakerError(
- status_code=422,
- message=f"Response not in expected format - {embeddings}",
- )
-
- output_data = []
- for idx, embedding in enumerate(embeddings):
- output_data.append(
- {"object": "embedding", "index": idx, "embedding": embedding}
- )
-
- model_response.object = "list"
- model_response.data = output_data
- model_response.model = model
-
- input_tokens = 0
- for text in input:
- input_tokens += len(encoding.encode(text))
-
- setattr(
- model_response,
- "usage",
- Usage(
- prompt_tokens=input_tokens,
- completion_tokens=0,
- total_tokens=input_tokens,
- ),
+
+ # Transform response based on model type
+ from httpx import Response as HttpxResponse
+
+ # Create a mock httpx Response object for the transformation
+ mock_response = HttpxResponse(
+ status_code=200,
+ content=json.dumps(response).encode('utf-8'),
+ headers={"content-type": "application/json"}
+ )
+
+ model_response = EmbeddingResponse()
+
+ # Use the request_data that was already transformed above
+ return provider_config.transform_embedding_response(
+ model=model,
+ raw_response=mock_response,
+ model_response=model_response,
+ logging_obj=logging_obj,
+ api_key=None,
+ request_data=request_data,
+ optional_params=optional_params,
+ litellm_params=litellm_params or {}
)
-
- return model_response
diff --git a/litellm/llms/sagemaker/completion/transformation.py b/litellm/llms/sagemaker/completion/transformation.py
index 0747a1fe7bad..42202bbf079c 100644
--- a/litellm/llms/sagemaker/completion/transformation.py
+++ b/litellm/llms/sagemaker/completion/transformation.py
@@ -8,6 +8,7 @@
import time
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+
from httpx._models import Headers, Response
import litellm
@@ -277,3 +278,5 @@ def validate_environment(
headers = {"Content-Type": "application/json", **headers}
return headers
+
+
diff --git a/litellm/llms/sagemaker/embedding/transformation.py b/litellm/llms/sagemaker/embedding/transformation.py
new file mode 100644
index 000000000000..bd8abc5e01a0
--- /dev/null
+++ b/litellm/llms/sagemaker/embedding/transformation.py
@@ -0,0 +1,154 @@
+"""
+Translate from OpenAI's `/v1/embeddings` to Sagemaker's `/invoke`
+
+In the Huggingface TGI format.
+"""
+
+from typing import TYPE_CHECKING, Any, List, Optional, Union
+
+if TYPE_CHECKING:
+ from litellm.types.llms.openai import AllEmbeddingInputValues
+
+from httpx._models import Headers, Response
+
+from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.types.utils import Usage, EmbeddingResponse
+from litellm.llms.voyage.embedding.transformation import VoyageEmbeddingConfig
+
+from ..common_utils import SagemakerError
+
+
+class SagemakerEmbeddingConfig(BaseEmbeddingConfig):
+ """
+ SageMaker embedding configuration factory for supporting embedding parameters
+ """
+
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def get_model_config(cls, model: str) -> "BaseEmbeddingConfig":
+ """
+ Factory method to get the appropriate embedding config based on model type
+
+ Args:
+ model: The model name
+
+ Returns:
+ Appropriate embedding config instance
+ """
+ if "voyage" in model.lower():
+ return VoyageEmbeddingConfig()
+ else:
+ return cls()
+
+ def get_supported_openai_params(self, model: str) -> List[str]:
+ # Check if this is an embedding model
+ if "voyage" in model.lower():
+ return VoyageEmbeddingConfig().get_supported_openai_params(model)
+ else:
+ return []
+
+ def map_openai_params(
+ self,
+ non_default_params: dict,
+ optional_params: dict,
+ model: str,
+ drop_params: bool,
+ ) -> dict:
+
+ return optional_params
+
+ def get_error_class(
+ self, error_message: str, status_code: int, headers: Union[dict, Headers]
+ ) -> BaseLLMException:
+ return SagemakerError(
+ message=error_message, status_code=status_code, headers=headers
+ )
+
+ def transform_embedding_request(
+ self,
+ model: str,
+ input: "AllEmbeddingInputValues",
+ optional_params: dict,
+ headers: dict,
+ ) -> dict:
+ """
+ Transform embedding request for Hugging Face models on SageMaker
+ """
+ # HF models expect "inputs" field (plural)
+ return {"inputs": input, **optional_params}
+
+ def transform_embedding_response(
+ self,
+ model: str,
+ raw_response: Response,
+ model_response: "EmbeddingResponse",
+ logging_obj: Any,
+ api_key: Optional[str] = None,
+ request_data: dict = {},
+ optional_params: dict = {},
+ litellm_params: dict = {},
+ ) -> "EmbeddingResponse":
+ """
+ Transform embedding response for Hugging Face models on SageMaker
+ """
+ try:
+ response_data = raw_response.json()
+ except Exception as e:
+ raise SagemakerError(
+ message=f"Failed to parse response: {str(e)}",
+ status_code=raw_response.status_code
+ )
+
+ if "embedding" not in response_data:
+ raise SagemakerError(
+ status_code=500, message="HF response missing 'embedding' field"
+ )
+ embeddings = response_data["embedding"]
+
+ if not isinstance(embeddings, list):
+ raise SagemakerError(
+ status_code=422,
+ message=f"HF response not in expected format - {embeddings}",
+ )
+
+ output_data = []
+ for idx, embedding in enumerate(embeddings):
+ output_data.append(
+ {"object": "embedding", "index": idx, "embedding": embedding}
+ )
+
+ model_response.object = "list"
+ model_response.data = output_data
+ model_response.model = model
+
+ # Calculate usage from request data
+ input_texts = request_data.get("inputs", [])
+ input_tokens = 0
+ for text in input_texts:
+ input_tokens += len(text.split()) # Simple word count fallback
+
+ model_response.usage = Usage(
+ prompt_tokens=input_tokens,
+ completion_tokens=0,
+ total_tokens=input_tokens,
+ )
+
+ return model_response
+
+ def validate_environment(
+ self,
+ headers: dict,
+ model: str,
+ messages: List[Any],
+ optional_params: dict,
+ litellm_params: dict,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ ) -> dict:
+ """
+ Validate environment for SageMaker embeddings
+ """
+ return {"Content-Type": "application/json"}
diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py
index 3d313456d19e..28211fff6c92 100644
--- a/litellm/llms/vertex_ai/gemini/transformation.py
+++ b/litellm/llms/vertex_ai/gemini/transformation.py
@@ -302,26 +302,27 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915
)
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),
+ if block["type"] == "thinking":
+ 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,
+ 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:
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 cd7ebaca7904..67b722e92cd1 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
@@ -215,6 +215,15 @@ def __init__(
@classmethod
def get_config(cls):
return super().get_config()
+
+ def _supports_penalty_parameters(self, model: str) -> bool:
+ unsupported_models = ["gemini-2.5-pro-preview-06-05"]
+
+ for pattern in unsupported_models:
+ if model in pattern:
+ return False
+
+ return True
def get_supported_openai_params(self, model: str) -> List[str]:
supported_params = [
@@ -229,8 +238,6 @@ def get_supported_openai_params(self, model: str) -> List[str]:
"response_format",
"n",
"stop",
- "frequency_penalty",
- "presence_penalty",
"extra_headers",
"seed",
"logprobs",
@@ -239,6 +246,11 @@ def get_supported_openai_params(self, model: str) -> List[str]:
"parallel_tool_calls",
"web_search_options",
]
+
+ # Add penalty parameters only for non-preview models
+ if self._supports_penalty_parameters(model):
+ supported_params.extend(["frequency_penalty", "presence_penalty"])
+
if supports_reasoning(model):
supported_params.append("reasoning_effort")
supported_params.append("thinking")
@@ -680,8 +692,12 @@ def map_openai_params( # noqa: PLR0915
)
elif param == "frequency_penalty":
optional_params["frequency_penalty"] = value
+ if self._supports_penalty_parameters(model):
+ optional_params["frequency_penalty"] = value
elif param == "presence_penalty":
optional_params["presence_penalty"] = value
+ if self._supports_penalty_parameters(model):
+ optional_params["presence_penalty"] = value
elif param == "logprobs":
optional_params["responseLogprobs"] = value
elif param == "top_logprobs":
diff --git a/litellm/llms/vertex_ai/rerank/handler.py b/litellm/llms/vertex_ai/rerank/handler.py
new file mode 100644
index 000000000000..3df719819dd6
--- /dev/null
+++ b/litellm/llms/vertex_ai/rerank/handler.py
@@ -0,0 +1,5 @@
+"""
+Vertex AI Rerank - uses `llm_http_handler.py` to make httpx requests
+
+Request/Response transformation is handled in `transformation.py`
+"""
diff --git a/litellm/llms/vertex_ai/rerank/transformation.py b/litellm/llms/vertex_ai/rerank/transformation.py
new file mode 100644
index 000000000000..966368bc3cca
--- /dev/null
+++ b/litellm/llms/vertex_ai/rerank/transformation.py
@@ -0,0 +1,227 @@
+"""
+Translates from Cohere's `/v1/rerank` input format to Vertex AI Discovery Engine's `/rank` input format.
+
+Why separate file? Make it easy to see how transformation works
+"""
+
+from typing import Any, Dict, List, Optional, Union
+
+import httpx
+
+import litellm
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
+from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.rerank import RerankResponse, RerankResponseMeta, RerankBilledUnits, RerankResponseResult
+
+
+
+class VertexAIRerankConfig(BaseRerankConfig, VertexBase):
+ """
+ Configuration for Vertex AI Discovery Engine Rerank API
+
+ Reference: https://cloud.google.com/generative-ai-app-builder/docs/ranking#rank_or_rerank_a_set_of_records_according_to_a_query
+ """
+
+ def __init__(self) -> None:
+ super().__init__()
+
+ def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+ """
+ Get the complete URL for the Vertex AI Discovery Engine ranking API
+ """
+ # Get project ID from environment or litellm config
+ project_id = (
+ get_secret_str("VERTEXAI_PROJECT")
+ or litellm.vertex_project
+ )
+
+ if not project_id:
+ raise ValueError(
+ "Vertex AI project ID is required. Please set 'VERTEXAI_PROJECT' or 'litellm.vertex_project'"
+ )
+
+ return f"https://discoveryengine.googleapis.com/v1/projects/{project_id}/locations/global/rankingConfigs/default_ranking_config:rank"
+
+ def validate_environment(
+ self,
+ headers: dict,
+ model: str,
+ api_key: Optional[str] = None,
+ ) -> dict:
+ """
+ Validate and set up authentication for Vertex AI Discovery Engine API
+ """
+ # Get credentials and project info
+ vertex_credentials = self.get_vertex_ai_credentials({})
+ vertex_project = self.get_vertex_ai_project({})
+
+ # Get access token using the base class method
+ access_token, project_id = self._ensure_access_token(
+ credentials=vertex_credentials,
+ project_id=vertex_project,
+ custom_llm_provider="vertex_ai",
+ )
+
+ default_headers = {
+ "Authorization": f"Bearer {access_token}",
+ "Content-Type": "application/json",
+ "X-Goog-User-Project": project_id,
+ }
+
+ # If 'Authorization' is provided in headers, it overrides the default.
+ if "Authorization" in headers:
+ default_headers["Authorization"] = headers["Authorization"]
+
+ # Merge other headers, overriding any default ones except Authorization
+ return {**default_headers, **headers}
+
+ def transform_rerank_request(
+ self,
+ model: str,
+ optional_rerank_params: Dict,
+ headers: dict,
+ ) -> dict:
+ """
+ Transform the request from Cohere format to Vertex AI Discovery Engine format
+ """
+ if "query" not in optional_rerank_params:
+ raise ValueError("query is required for Vertex AI rerank")
+ if "documents" not in optional_rerank_params:
+ raise ValueError("documents is required for Vertex AI rerank")
+
+ query = optional_rerank_params["query"]
+ documents = optional_rerank_params["documents"]
+ top_n = optional_rerank_params.get("top_n", None)
+ return_documents = optional_rerank_params.get("return_documents", True)
+
+ # Convert documents to records format
+ records = []
+ for idx, document in enumerate(documents):
+ if isinstance(document, str):
+ content = document
+ title = " ".join(document.split()[:3]) # First 3 words as title
+ else:
+ # Handle dict format
+ content = document.get("text", str(document))
+ title = document.get("title", " ".join(content.split()[:3]))
+
+ records.append({
+ "id": str(idx),
+ "title": title,
+ "content": content
+ })
+
+ request_data = {
+ "model": model,
+ "query": query,
+ "records": records
+ }
+
+ if top_n is not None:
+ request_data["topN"] = top_n
+
+ # Map return_documents to ignoreRecordDetailsInResponse
+ # When return_documents is False, we want to ignore record details (return only IDs)
+ request_data["ignoreRecordDetailsInResponse"] = not return_documents
+
+ return request_data
+
+ def transform_rerank_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ model_response: RerankResponse,
+ logging_obj: LiteLLMLoggingObj,
+ api_key: Optional[str] = None,
+ request_data: dict = {},
+ optional_params: dict = {},
+ litellm_params: dict = {},
+ ) -> RerankResponse:
+ """
+ Transform Vertex AI Discovery Engine response to Cohere format
+ """
+ try:
+ raw_response_json = raw_response.json()
+ except Exception as e:
+ raise ValueError(f"Failed to parse response: {e}")
+
+ # Extract records from response
+ records = raw_response_json.get("records", [])
+
+ # Convert to Cohere format
+ results = []
+ for record in records:
+ # Handle both cases: with full details and with only IDs
+ if "score" in record:
+ # Full response with score and details
+ results.append({
+ "index": int(record["id"]),
+ "relevance_score": record.get("score", 0.0)
+ })
+ else:
+ # Response with only IDs (when ignoreRecordDetailsInResponse=true)
+ # We can't provide a relevance score, so we'll use a default
+ results.append({
+ "index": int(record["id"]),
+ "relevance_score": 1.0 # Default score when details are ignored
+ })
+
+ # Sort by relevance score (descending)
+ results.sort(key=lambda x: x["relevance_score"], reverse=True)
+
+ # Create response in Cohere format
+ # Convert results to proper RerankResponseResult objects
+ rerank_results = []
+ for result in results:
+ rerank_results.append(RerankResponseResult(
+ index=result["index"],
+ relevance_score=result["relevance_score"]
+ ))
+
+ # Create meta object
+ meta = RerankResponseMeta(
+ billed_units=RerankBilledUnits(
+ search_units=len(records)
+ )
+ )
+
+ return RerankResponse(
+ id=f"vertex_ai_rerank_{model}",
+ results=rerank_results,
+ meta=meta
+ )
+
+ def get_supported_cohere_rerank_params(self, model: str) -> list:
+ return [
+ "query",
+ "documents",
+ "top_n",
+ "return_documents",
+ ]
+
+ def map_cohere_rerank_params(
+ self,
+ non_default_params: dict,
+ model: str,
+ drop_params: bool,
+ query: str,
+ documents: List[Union[str, Dict[str, Any]]],
+ custom_llm_provider: Optional[str] = None,
+ top_n: Optional[int] = None,
+ rank_fields: Optional[List[str]] = None,
+ return_documents: Optional[bool] = True,
+ max_chunks_per_doc: Optional[int] = None,
+ max_tokens_per_doc: Optional[int] = None,
+ ) -> Dict:
+ """
+ Map Cohere rerank params to Vertex AI format
+ """
+ return {
+ "query": query,
+ "documents": documents,
+ "top_n": top_n,
+ "return_documents": return_documents,
+ }
+
diff --git a/litellm/llms/xai/cost_calculator.py b/litellm/llms/xai/cost_calculator.py
index 62a48080d1c6..e78721ef6e9e 100644
--- a/litellm/llms/xai/cost_calculator.py
+++ b/litellm/llms/xai/cost_calculator.py
@@ -1,17 +1,19 @@
"""
Helper util for handling XAI-specific cost calculation
-- e.g.: reasoning tokens for grok models
+- Uses the generic cost calculator which already handles tiered pricing correctly
+- Handles XAI-specific reasoning token billing (billed as part of completion tokens)
"""
-from typing import Tuple, Union
+from typing import Tuple
from litellm.types.utils import Usage
-from litellm.utils import get_model_info
+from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
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.
+ Uses the generic cost calculator for all pricing logic, with XAI-specific reasoning token handling.
Input:
- model: str, the model name without provider prefix
@@ -20,35 +22,27 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
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"))
-
+ # XAI-specific completion cost calculation
# 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
+ reasoning_tokens = int(getattr(usage.completion_tokens_details, "reasoning_tokens", 0) or 0)
+
+ total_completion_tokens = completion_tokens + reasoning_tokens
+
+ modified_usage = Usage(
+ prompt_tokens=usage.prompt_tokens,
+ completion_tokens=total_completion_tokens,
+ total_tokens=usage.total_tokens,
+ prompt_tokens_details=usage.prompt_tokens_details,
+ completion_tokens_details=None
+ )
+
+ prompt_cost, completion_cost = generic_cost_per_token(
+ model=model,
+ usage=modified_usage,
+ custom_llm_provider="xai"
+ )
return prompt_cost, completion_cost
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 8cab321c4dd6..c9433168ccfe 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -12170,6 +12170,44 @@
"supports_vision": true,
"tool_use_system_prompt_tokens": 159
},
+ "global.anthropic.claude-haiku-4-5-20251001-v1:0": {
+ "cache_creation_input_token_cost": 1.25e-06,
+ "cache_read_input_token_cost": 1e-07,
+ "input_cost_per_token": 1e-06,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
+ "output_cost_per_token": 5e-06,
+ "supports_assistant_prefill": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
+ "global.anthropic.claude-haiku-4-5-20251001-v1:0": {
+ "cache_creation_input_token_cost": 1.25e-06,
+ "cache_read_input_token_cost": 1e-07,
+ "input_cost_per_token": 1e-06,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
+ "output_cost_per_token": 5e-06,
+ "supports_assistant_prefill": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
"gpt-3.5-turbo": {
"input_cost_per_token": 0.5e-06,
"litellm_provider": "openai",
@@ -20583,6 +20621,25 @@
"supports_vision": true,
"tool_use_system_prompt_tokens": 346
},
+ "au.anthropic.claude-haiku-4-5-20251001-v1:0": {
+ "cache_creation_input_token_cost": 1.375e-06,
+ "cache_read_input_token_cost": 1.1e-07,
+ "input_cost_per_token": 1.1e-06,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
+ "output_cost_per_token": 5.5e-06,
+ "supports_assistant_prefill": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
"us.anthropic.claude-opus-4-20250514-v1:0": {
"cache_creation_input_token_cost": 1.875e-05,
"cache_read_input_token_cost": 1.5e-06,
@@ -23609,5 +23666,12 @@
"supports_tool_choice": true,
"supports_vision": true,
"supports_web_search": true
+ },
+ "sora-2": {
+ "input_cost_per_second": 0.0,
+ "litellm_provider": "openai",
+ "mode": "video_generation",
+ "output_cost_per_second": 0.0,
+ "supports_video_generation": true
}
}
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2012-90188715a5858c8c.js b/litellm/proxy/_experimental/out/_next/static/chunks/2012-90188715a5858c8c.js
new file mode 100644
index 000000000000..3e83fbe886f9
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/2012-90188715a5858c8c.js
@@ -0,0 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/xz5haJZkuXOIRFpqcqwGL/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/xJpMbkw-PrXOjhVmfa1qv/_buildManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/xz5haJZkuXOIRFpqcqwGL/_buildManifest.js
rename to litellm/proxy/_experimental/out/_next/static/xJpMbkw-PrXOjhVmfa1qv/_buildManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/xz5haJZkuXOIRFpqcqwGL/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/xJpMbkw-PrXOjhVmfa1qv/_ssgManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/xz5haJZkuXOIRFpqcqwGL/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/xJpMbkw-PrXOjhVmfa1qv/_ssgManifest.js
diff --git a/ui/litellm-dashboard/out/api-reference.html b/litellm/proxy/_experimental/out/api-reference.html
similarity index 94%
rename from ui/litellm-dashboard/out/api-reference.html
rename to litellm/proxy/_experimental/out/api-reference.html
index 8db773177023..cfd0aa527288 100644
--- a/ui/litellm-dashboard/out/api-reference.html
+++ b/litellm/proxy/_experimental/out/api-reference.html
@@ -1 +1 @@
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+LiteLLM Dashboard
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diff --git a/litellm/proxy/_experimental/out/api-reference.txt b/litellm/proxy/_experimental/out/api-reference.txt
index 72bfcd77d55b..7a3ee9a89715 100644
--- a/litellm/proxy/_experimental/out/api-reference.txt
+++ b/litellm/proxy/_experimental/out/api-reference.txt
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diff --git a/litellm/proxy/_experimental/out/guardrails.html b/litellm/proxy/_experimental/out/guardrails.html
new file mode 100644
index 000000000000..764c80cbeccb
--- /dev/null
+++ b/litellm/proxy/_experimental/out/guardrails.html
@@ -0,0 +1,5 @@
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+LiteLLM Dashboard
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+LiteLLM Dashboard
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diff --git a/litellm/proxy/_experimental/out/guardrails.txt b/litellm/proxy/_experimental/out/guardrails.txt
index c2520db58644..2bb4394d9ea3 100644
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diff --git a/litellm/proxy/_experimental/out/index.html b/litellm/proxy/_experimental/out/index.html
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diff --git a/litellm/proxy/_experimental/out/model-hub/index.html b/litellm/proxy/_experimental/out/model-hub.html
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rename from litellm/proxy/_experimental/out/model-hub/index.html
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index 75a10d2d7b5b..f2856d201f5f 100644
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/organizations/index.html b/litellm/proxy/_experimental/out/virtual-keys.html
similarity index 58%
rename from litellm/proxy/_experimental/out/organizations/index.html
rename to litellm/proxy/_experimental/out/virtual-keys.html
index fed63453a0c6..2ec3a2eff3d3 100644
--- a/litellm/proxy/_experimental/out/organizations/index.html
+++ b/litellm/proxy/_experimental/out/virtual-keys.html
@@ -1 +1 @@
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\ No newline at end of file
+LiteLLM Dashboard
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/virtual-keys.txt b/litellm/proxy/_experimental/out/virtual-keys.txt
index f0ad6b96ba1e..fafaea414e10 100644
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+0:["xJpMbkw-PrXOjhVmfa1qv",[[["",{"children":["(dashboard)",{"children":["virtual-keys",{"children":["__PAGE__",{}]}]}]},"$undefined","$undefined",true],["",{"children":["(dashboard)",{"children":["virtual-keys",{"children":["__PAGE__",{},[["$L1",["$","$L2",null,{"props":{"params":{},"searchParams":{}},"Component":"$3"}],null],null],null]},[null,["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children","(dashboard)","children","virtual-keys","children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","notFoundStyles":"$undefined"}]],null]},[[null,["$","$L6",null,{"children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children","(dashboard)","children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":"404"}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[]}],"params":"$7"}]],null],null]},[[[["$","link","0",{"rel":"stylesheet","href":"/litellm-asset-prefix/_next/static/css/349654da14372cd9.css","precedence":"next","crossOrigin":"$undefined"}],["$","link","1",{"rel":"stylesheet","href":"/litellm-asset-prefix/_next/static/css/646842864d761e6f.css","precedence":"next","crossOrigin":"$undefined"}]],["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_1c856b","children":["$","$L8",null,{"children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":"$9","children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":"$a","children":"404"}],["$","div",null,{"style":"$b","children":["$","h2",null,{"style":"$c","children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[]}]}]}]}]],null],null],["$Ld",null]]]]
d:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","link","5",{"rel":"icon","href":"./favicon.ico"}],["$","meta","6",{"name":"next-size-adjust"}]]
1:null
diff --git a/litellm/proxy/_experimental/out/virtual-keys/index.html b/litellm/proxy/_experimental/out/virtual-keys/index.html
deleted file mode 100644
index e1b00e717793..000000000000
--- a/litellm/proxy/_experimental/out/virtual-keys/index.html
+++ /dev/null
@@ -1 +0,0 @@
-LiteLLM Dashboard
\ No newline at end of file
diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index d205d73da8eb..15503a541c3b 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -779,10 +779,10 @@ class KeyRequestBase(GenerateRequestBase):
allowed_routes: Optional[list] = []
allowed_passthrough_routes: Optional[list] = None
rpm_limit_type: Optional[
- Literal["guaranteed_throughput", "best_effort_throughput"]
+ Literal["guaranteed_throughput", "best_effort_throughput", "dynamic"]
] = None # raise an error if 'guaranteed_throughput' is set and we're overallocating rpm
tpm_limit_type: Optional[
- Literal["guaranteed_throughput", "best_effort_throughput"]
+ Literal["guaranteed_throughput", "best_effort_throughput", "dynamic"]
] = None # raise an error if 'guaranteed_throughput' is set and we're overallocating tpm
diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py
index 2ca45b55ec71..c0459c9c6977 100644
--- a/litellm/proxy/hooks/parallel_request_limiter_v3.py
+++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py
@@ -24,6 +24,7 @@
from litellm import DualCache
from litellm._logging import verbose_proxy_logger
+from litellm.constants import DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE
from litellm.integrations.custom_logger import CustomLogger
from litellm.proxy._types import UserAPIKeyAuth
from litellm.types.llms.openai import BaseLiteLLMOpenAIResponseObject
@@ -545,24 +546,90 @@ async def should_rate_limit(
)
return rate_limit_response
- async def async_pre_call_hook(
+ def _should_enforce_rate_limit(
+ self,
+ limit_type: Optional[str],
+ model_has_failures: bool,
+ ) -> bool:
+ """
+ Determine if rate limit should be enforced based on limit type and model health.
+
+ Args:
+ limit_type: Type of rate limit ("dynamic", "guaranteed_throughput", "best_effort_throughput", or None)
+ model_has_failures: Whether the model has recent failures
+
+ Returns:
+ True if rate limit should be enforced, False otherwise
+ """
+ if limit_type == "dynamic":
+ # Dynamic mode: only enforce if model has failures
+ return model_has_failures
+ # All other modes (including None): always enforce
+ return True
+
+ def _get_enforced_limit(
+ self,
+ limit_value: Optional[int],
+ limit_type: Optional[str],
+ model_has_failures: bool,
+ ) -> Optional[int]:
+ """
+ Get the rate limit value to enforce based on limit type and model health.
+
+ Args:
+ limit_value: The configured limit value
+ limit_type: Type of rate limit ("dynamic", "guaranteed_throughput", "best_effort_throughput", or None)
+ model_has_failures: Whether the model has recent failures
+
+ Returns:
+ The limit value if it should be enforced, None otherwise
+ """
+ if limit_value is None:
+ return None
+
+ if self._should_enforce_rate_limit(
+ limit_type=limit_type,
+ model_has_failures=model_has_failures,
+ ):
+ return limit_value
+
+ return None
+
+ def _is_dynamic_rate_limiting_enabled(
+ self,
+ rpm_limit_type: Optional[str],
+ tpm_limit_type: Optional[str],
+ ) -> bool:
+ """
+ Check if dynamic rate limiting is enabled for either RPM or TPM.
+
+ Args:
+ rpm_limit_type: RPM rate limit type
+ tpm_limit_type: TPM rate limit type
+
+ Returns:
+ True if dynamic mode is enabled for either limit type
+ """
+ return rpm_limit_type == "dynamic" or tpm_limit_type == "dynamic"
+
+ def _create_rate_limit_descriptors(
self,
user_api_key_dict: UserAPIKeyAuth,
- cache: DualCache,
data: dict,
- call_type: str,
- ):
+ rpm_limit_type: Optional[str],
+ tpm_limit_type: Optional[str],
+ model_has_failures: bool,
+ ) -> List[RateLimitDescriptor]:
"""
- Pre-call hook to check rate limits before making the API call.
+ Create all rate limit descriptors for the request.
+
+ Returns list of descriptors for API key, user, team, team member, end user, and model-specific limits.
"""
from litellm.proxy.auth.auth_utils import (
get_key_model_rpm_limit,
get_key_model_tpm_limit,
)
-
- verbose_proxy_logger.debug("Inside Rate Limit Pre-Call Hook")
-
- # Create rate limit descriptors
+
descriptors = []
# API Key rate limits
@@ -576,10 +643,18 @@ async def async_pre_call_hook(
key="api_key",
value=user_api_key_dict.api_key,
rate_limit={
- "requests_per_unit": user_api_key_dict.rpm_limit,
- "tokens_per_unit": user_api_key_dict.tpm_limit,
+ "requests_per_unit": self._get_enforced_limit(
+ limit_value=user_api_key_dict.rpm_limit,
+ limit_type=rpm_limit_type,
+ model_has_failures=model_has_failures,
+ ),
+ "tokens_per_unit": self._get_enforced_limit(
+ limit_value=user_api_key_dict.tpm_limit,
+ limit_type=tpm_limit_type,
+ model_has_failures=model_has_failures,
+ ),
"max_parallel_requests": user_api_key_dict.max_parallel_requests,
- "window_size": self.window_size, # 1 minute window
+ "window_size": self.window_size,
},
)
)
@@ -687,6 +762,104 @@ async def async_pre_call_hook(
},
)
)
+
+ return descriptors
+
+ async def _check_model_has_recent_failures(
+ self,
+ model: str,
+ parent_otel_span: Optional[Span] = None,
+ ) -> bool:
+ """
+ Check if any deployment for this model has recent failures by using
+ the router's existing failure tracking.
+
+ Returns True if any deployment has failures in the current minute.
+ """
+ from litellm.proxy.proxy_server import llm_router
+ from litellm.router_utils.router_callbacks.track_deployment_metrics import (
+ get_deployment_failures_for_current_minute,
+ )
+
+ if llm_router is None:
+ return False
+
+ try:
+ # Get all deployments for this model
+ model_list = llm_router.get_model_list(model_name=model)
+ if not model_list:
+ return False
+
+ # Check each deployment's failure count
+ for deployment in model_list:
+ deployment_id = deployment.get("model_info", {}).get("id")
+ if not deployment_id:
+ continue
+
+ # Use router's existing failure tracking
+ failure_count = get_deployment_failures_for_current_minute(
+ litellm_router_instance=llm_router,
+ deployment_id=deployment_id,
+ )
+
+ if failure_count > DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE:
+ verbose_proxy_logger.debug(
+ f"[Dynamic Rate Limit] Deployment {deployment_id} has {failure_count} failures "
+ f"in current minute - enforcing rate limits for model {model}"
+ )
+ return True
+
+ verbose_proxy_logger.debug(
+ f"[Dynamic Rate Limit] No failures detected for model {model} - allowing dynamic exceeding"
+ )
+ return False
+
+ except Exception as e:
+ verbose_proxy_logger.debug(
+ f"Error checking model failure status: {str(e)}, defaulting to enforce limits"
+ )
+ # Fail safe: enforce limits if we can't check
+ return True
+
+ async def async_pre_call_hook(
+ self,
+ user_api_key_dict: UserAPIKeyAuth,
+ cache: DualCache,
+ data: dict,
+ call_type: str,
+ ):
+ """
+ Pre-call hook to check rate limits before making the API call.
+ Supports dynamic rate limiting based on deployment health.
+ """
+ verbose_proxy_logger.debug("Inside Rate Limit Pre-Call Hook")
+
+ # Get rate limit types from metadata
+ metadata = user_api_key_dict.metadata or {}
+ rpm_limit_type = metadata.get("rpm_limit_type")
+ tpm_limit_type = metadata.get("tpm_limit_type")
+
+ # For dynamic mode, check if the model has recent failures
+ model_has_failures = False
+ requested_model = data.get("model", None)
+
+ if self._is_dynamic_rate_limiting_enabled(
+ rpm_limit_type=rpm_limit_type,
+ tpm_limit_type=tpm_limit_type,
+ ) and requested_model:
+ model_has_failures = await self._check_model_has_recent_failures(
+ model=requested_model,
+ parent_otel_span=user_api_key_dict.parent_otel_span,
+ )
+
+ # Create rate limit descriptors
+ descriptors = self._create_rate_limit_descriptors(
+ user_api_key_dict=user_api_key_dict,
+ data=data,
+ rpm_limit_type=rpm_limit_type,
+ tpm_limit_type=tpm_limit_type,
+ model_has_failures=model_has_failures,
+ )
# Only check rate limits if we have descriptors with actual limits
if descriptors:
diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py
index aa4864333962..2e9701df835a 100644
--- a/litellm/proxy/management_endpoints/key_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/key_management_endpoints.py
@@ -827,8 +827,8 @@ async def generate_key_fn(
- model_max_budget: Optional[Dict[str, BudgetConfig]] - Model-specific budgets {"gpt-4": {"budget_limit": 0.0005, "time_period": "30d"}}}. IF null or {} then no model specific budget.
- model_rpm_limit: Optional[dict] - key-specific model rpm limit. Example - {"text-davinci-002": 1000, "gpt-3.5-turbo": 1000}. IF null or {} then no model specific rpm limit.
- model_tpm_limit: Optional[dict] - key-specific model tpm limit. Example - {"text-davinci-002": 1000, "gpt-3.5-turbo": 1000}. IF null or {} then no model specific tpm limit.
- - tpm_limit_type: Optional[str] - Type of tpm limit. Options: "best_effort_throughput" (no error if we're overallocating tpm), "guaranteed_throughput" (raise an error if we're overallocating tpm). Defaults to "best_effort_throughput".
- - rpm_limit_type: Optional[str] - Type of rpm limit. Options: "best_effort_throughput" (no error if we're overallocating rpm), "guaranteed_throughput" (raise an error if we're overallocating rpm). Defaults to "best_effort_throughput".
+ - tpm_limit_type: Optional[str] - Type of tpm limit. Options: "best_effort_throughput" (no error if we're overallocating tpm), "guaranteed_throughput" (raise an error if we're overallocating tpm), "dynamic" (dynamically exceed limit when no 429 errors). Defaults to "best_effort_throughput".
+ - rpm_limit_type: Optional[str] - Type of rpm limit. Options: "best_effort_throughput" (no error if we're overallocating rpm), "guaranteed_throughput" (raise an error if we're overallocating rpm), "dynamic" (dynamically exceed limit when no 429 errors). Defaults to "best_effort_throughput".
- allowed_cache_controls: Optional[list] - List of allowed cache control values. Example - ["no-cache", "no-store"]. See all values - https://docs.litellm.ai/docs/proxy/caching#turn-on--off-caching-per-request
- blocked: Optional[bool] - Whether the key is blocked.
- rpm_limit: Optional[int] - Specify rpm limit for a given key (Requests per minute)
@@ -980,8 +980,8 @@ async def generate_service_account_key_fn(
- model_max_budget: Optional[Dict[str, BudgetConfig]] - Model-specific budgets {"gpt-4": {"budget_limit": 0.0005, "time_period": "30d"}}}. IF null or {} then no model specific budget.
- model_rpm_limit: Optional[dict] - key-specific model rpm limit. Example - {"text-davinci-002": 1000, "gpt-3.5-turbo": 1000}. IF null or {} then no model specific rpm limit.
- model_tpm_limit: Optional[dict] - key-specific model tpm limit. Example - {"text-davinci-002": 1000, "gpt-3.5-turbo": 1000}. IF null or {} then no model specific tpm limit.
- - tpm_limit_type: Optional[str] - TPM rate limit type - "best_effort_throughput" or "guaranteed_throughput"
- - rpm_limit_type: Optional[str] - RPM rate limit type - "best_effort_throughput" or "guaranteed_throughput"
+ - tpm_limit_type: Optional[str] - TPM rate limit type - "best_effort_throughput", "guaranteed_throughput", or "dynamic"
+ - rpm_limit_type: Optional[str] - RPM rate limit type - "best_effort_throughput", "guaranteed_throughput", or "dynamic"
- allowed_cache_controls: Optional[list] - List of allowed cache control values. Example - ["no-cache", "no-store"]. See all values - https://docs.litellm.ai/docs/proxy/caching#turn-on--off-caching-per-request
- blocked: Optional[bool] - Whether the key is blocked.
- rpm_limit: Optional[int] - Specify rpm limit for a given key (Requests per minute)
@@ -1263,8 +1263,8 @@ async def update_key_fn(
- rpm_limit: Optional[int] - Requests per minute limit
- model_rpm_limit: Optional[dict] - Model-specific RPM limits {"gpt-4": 100, "claude-v1": 200}
- model_tpm_limit: Optional[dict] - Model-specific TPM limits {"gpt-4": 100000, "claude-v1": 200000}
- - tpm_limit_type: Optional[str] - TPM rate limit type - "best_effort_throughput" or "guaranteed_throughput"
- - rpm_limit_type: Optional[str] - RPM rate limit type - "best_effort_throughput" or "guaranteed_throughput"
+ - tpm_limit_type: Optional[str] - TPM rate limit type - "best_effort_throughput", "guaranteed_throughput", or "dynamic"
+ - rpm_limit_type: Optional[str] - RPM rate limit type - "best_effort_throughput", "guaranteed_throughput", or "dynamic"
- allowed_cache_controls: Optional[list] - List of allowed cache control values
- duration: Optional[str] - Key validity duration ("30d", "1h", etc.)
- permissions: Optional[dict] - Key-specific permissions
diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml
index cca136811f7c..fc32eb65f271 100644
--- a/litellm/proxy/proxy_config.yaml
+++ b/litellm/proxy/proxy_config.yaml
@@ -22,4 +22,37 @@ model_list:
litellm_params:
model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
aws_region_name: us-east-1
- custom_llm_provider: bedrock
\ No newline at end of file
+ custom_llm_provider: bedrock
+ - model_name: fake-openai-endpoint
+ - model_name: bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0
+ litellm_params:
+ model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
+ #########################################################
+ ########## batch specific params ########################
+ s3_bucket_name: litellm-proxy
+ s3_region_name: us-west-2
+ s3_access_key_id: os.environ/AWS_ACCESS_KEY_ID
+ s3_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
+ aws_batch_role_arn: arn:aws:iam::888602223428:role/service-role/AmazonBedrockExecutionRoleForAgents_BB9HNW6V4CV
+ model_info:
+ mode: batch
+ - model_name: anthropic/*
+ litellm_params:
+ model: anthropic/*
+ api_key: os.environ/ANTHROPIC_API_KEY
+ - model_name: openai/*
+ litellm_params:
+ model: openai/*
+ api_key: os.environ/OPENAI_API_KEY
+ - model_name: gemini/*
+ litellm_params:
+ model: gemini/*
+ api_key: os.environ/GEMINI_API_KEY
+ - model_name: vertex-gemini-2.5-flash
+ litellm_params:
+ model: vertex_ai/gemini-2.5-flash
+ vertex_project: "your-gcp-project-id"
+ vertex_location: "us-central1"
+ vertex_credentials: "path/to/your/service-account-key.json"
+
+
diff --git a/litellm/router.py b/litellm/router.py
index 842f6522660a..1b691678bd64 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -429,8 +429,7 @@ def __init__( # noqa: PLR0915
self.model_name_to_deployment_indices: Dict[str, List[int]] = {}
if model_list is not None:
- # Build model index immediately to enable O(1) lookups from the start
- self._build_model_id_to_deployment_index_map(model_list)
+ # set_model_list will build indices automatically
self.set_model_list(model_list)
self.healthy_deployments: List = self.model_list # type: ignore
for m in model_list:
@@ -5211,8 +5210,8 @@ def set_model_list(self, model_list: list):
)
self.model_names = {m["model_name"] for m in model_list}
- # Build model_name index for O(1) lookups
- self._build_model_name_index(self.model_list)
+ # Note: model_name_to_deployment_indices is already built incrementally
+ # by _create_deployment -> _add_model_to_list_and_index_map
def _add_deployment(self, deployment: Deployment) -> Deployment:
import os
@@ -5435,6 +5434,26 @@ def _update_deployment_indices_after_removal(
# Remove the deleted model from index
if model_id in self.model_id_to_deployment_index_map:
del self.model_id_to_deployment_index_map[model_id]
+
+ # Update model_name_to_deployment_indices
+ for model_name, indices in list(self.model_name_to_deployment_indices.items()):
+ # Remove the deleted index
+ if removal_idx in indices:
+ indices.remove(removal_idx)
+
+ # Decrement all indices greater than removal_idx
+ updated_indices = []
+ for idx in indices:
+ if idx > removal_idx:
+ updated_indices.append(idx - 1)
+ else:
+ updated_indices.append(idx)
+
+ # Update or remove the entry
+ if len(updated_indices) > 0:
+ self.model_name_to_deployment_indices[model_name] = updated_indices
+ else:
+ del self.model_name_to_deployment_indices[model_name]
def _add_model_to_list_and_index_map(
self, model: dict, model_id: Optional[str] = None
diff --git a/litellm/types/llms/anthropic.py b/litellm/types/llms/anthropic.py
index 02c6f2cf8cf8..582d238e21c7 100644
--- a/litellm/types/llms/anthropic.py
+++ b/litellm/types/llms/anthropic.py
@@ -1,10 +1,10 @@
from enum import Enum
-from typing import Any, Dict, Iterable, List, Optional, Union
+from typing import Dict, Iterable, List, Optional, Union
-from pydantic import BaseModel, validator
+from pydantic import BaseModel
from typing_extensions import Literal, Required, TypedDict
-from .openai import ChatCompletionCachedContent, ChatCompletionThinkingBlock
+from .openai import ChatCompletionCachedContent, ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock
class AnthropicMessagesToolChoice(TypedDict, total=False):
@@ -104,6 +104,7 @@ class AnthropicMessagesToolUseParam(TypedDict, total=False):
AnthropicMessagesTextParam,
AnthropicMessagesToolUseParam,
ChatCompletionThinkingBlock,
+ ChatCompletionRedactedThinkingBlock,
]
@@ -156,6 +157,36 @@ class CitationsObject(TypedDict):
enabled: bool
+class AnthropicCitationPageLocation(TypedDict, total=False):
+ """
+ Anthropic citation for page-based references.
+ Used when citing from documents with page numbers.
+ """
+ type: Literal["page_location"]
+ cited_text: str # The exact text being cited (not counted towards output tokens)
+ document_index: int # Index referencing the cited document
+ document_title: Optional[str] # Title of the cited document
+ start_page_number: int # 1-indexed starting page
+ end_page_number: int # Exclusive ending page
+
+
+class AnthropicCitationCharLocation(TypedDict, total=False):
+ """
+ Anthropic citation for character-based references.
+ Used when citing from text with character positions.
+ """
+ type: Literal["char_location"]
+ cited_text: str # The exact text being cited (not counted towards output tokens)
+ document_index: int # Index referencing the cited document
+ document_title: Optional[str] # Title of the cited document
+ start_char_index: int # Starting character index for the citation
+ end_char_index: int # Ending character index for the citation
+
+
+# Union type for all citation formats
+AnthropicCitation = Union[AnthropicCitationPageLocation, AnthropicCitationCharLocation]
+
+
class AnthropicMessagesDocumentParam(TypedDict, total=False):
type: Required[Literal["document"]]
source: Required[
@@ -264,11 +295,29 @@ class ContentJsonBlockDelta(TypedDict):
partial_json: str
+class ContentThinkingBlockDelta(TypedDict):
+ """
+ "delta": {"type": "thinking_delta", "thinking": "Let me solve this step by step:"}}
+ """
+
+ type: Literal["thinking_delta"]
+ thinking: str
+
+
+class ContentThinkingSignatureBlockDelta(TypedDict):
+ """
+ "delta": {"type": "signature_delta", "signature": "EqQBCgIYAhIM1gbcDa9GJwZA2b3hGgxBdjrkzLoky3dl1pkiMOYds..."}}
+ """
+
+ type: Literal["signature_delta"]
+ signature: str
+
+
class ContentBlockDelta(TypedDict):
type: Literal["content_block_delta"]
index: int
delta: Union[
- ContentTextBlockDelta, ContentJsonBlockDelta, ContentCitationsBlockDelta
+ ContentTextBlockDelta, ContentJsonBlockDelta, ContentCitationsBlockDelta, ContentThinkingBlockDelta, ContentThinkingSignatureBlockDelta
]
@@ -311,7 +360,7 @@ class ContentBlockStartText(TypedDict):
content_block: TextBlock
-ContentBlockContentBlockDict = Union[ToolUseBlock, TextBlock]
+ContentBlockContentBlockDict = Union[ToolUseBlock, TextBlock, ChatCompletionThinkingBlock]
ContentBlockStart = Union[ContentBlockStartToolUse, ContentBlockStartText]
@@ -384,6 +433,17 @@ class AnthropicResponseContentBlockToolUse(BaseModel):
input: dict
+class AnthropicResponseContentBlockThinking(BaseModel):
+ type: Literal["thinking"]
+ thinking: str
+ signature: Optional[str]
+
+
+class AnthropicResponseContentBlockRedactedThinking(BaseModel):
+ type: Literal["redacted_thinking"]
+ data: str
+
+
class AnthropicResponseUsageBlock(BaseModel):
input_tokens: int
output_tokens: int
@@ -403,7 +463,7 @@ class AnthropicResponse(BaseModel):
"""Conversational role of the generated message. This will always be "assistant"."""
content: List[
- Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse]
+ Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking]
]
"""Content generated by the model."""
diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py
index 56971b91e5d2..31fe74df5cd1 100644
--- a/litellm/types/llms/openai.py
+++ b/litellm/types/llms/openai.py
@@ -674,7 +674,7 @@ class OpenAIChatCompletionAssistantMessage(TypedDict, total=False):
class ChatCompletionAssistantMessage(OpenAIChatCompletionAssistantMessage, total=False):
cache_control: ChatCompletionCachedContent
- thinking_blocks: Optional[List[ChatCompletionThinkingBlock]]
+ thinking_blocks: Optional[List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]]]
class ChatCompletionToolMessage(TypedDict):
@@ -1831,3 +1831,88 @@ class OpenAIMcpServerTool(TypedDict, total=False):
require_approval: str
allowed_tools: Optional[List[str]]
headers: Optional[Dict[str, str]]
+
+
+# Video Generation Types
+class CreateVideoRequest(TypedDict, total=False):
+ """
+ CreateVideoRequest for OpenAI video generation API
+
+ Required Params:
+ prompt: str - Text prompt that describes the video to generate
+
+ Optional Params:
+ input_reference: Optional[str] - Optional image reference that guides generation
+ model: Optional[str] - The video generation model to use (defaults to sora-2)
+ seconds: Optional[str] - Clip duration in seconds (defaults to 4 seconds)
+ size: Optional[str] - Output resolution formatted as width x height (defaults to 720x1280)
+ user: Optional[str] - A unique identifier representing your end-user
+ extra_headers: Optional[Dict[str, str]] - Additional headers
+ extra_body: Optional[Dict[str, str]] - Additional body parameters
+ timeout: Optional[float] - Request timeout
+ """
+ prompt: Required[str]
+ input_reference: Optional[str]
+ model: Optional[str]
+ seconds: Optional[str]
+ size: Optional[str]
+ user: Optional[str]
+ extra_headers: Optional[Dict[str, str]]
+ extra_body: Optional[Dict[str, str]]
+ timeout: Optional[float]
+
+
+class OpenAIVideoObject(BaseModel):
+ """OpenAI Video Object representing a video generation job."""
+ id: str
+ """Unique identifier for the video job."""
+
+ object: Literal["video"]
+ """The object type, which is always 'video'."""
+
+ status: str
+ """Current lifecycle status of the video job."""
+
+ created_at: int
+ """Unix timestamp (seconds) for when the job was created."""
+
+ completed_at: Optional[int] = None
+ """Unix timestamp (seconds) for when the job completed, if finished."""
+
+ expires_at: Optional[int] = None
+ """Unix timestamp (seconds) for when the downloadable assets expire, if set."""
+
+ error: Optional[Dict[str, Any]] = None
+ """Error payload that explains why generation failed, if applicable."""
+
+ progress: Optional[int] = None
+ """Approximate completion percentage for the generation task."""
+
+ remixed_from_video_id: Optional[str] = None
+ """Identifier of the source video if this video is a remix."""
+
+ seconds: Optional[str] = None
+ """Duration of the generated clip in seconds."""
+
+ size: Optional[str] = None
+ """The resolution of the generated video."""
+
+ model: Optional[str] = None
+ """The video generation model that produced the job."""
+
+ _hidden_params: Dict[str, Any] = {}
+
+ def __contains__(self, key):
+ return hasattr(self, key)
+
+ def get(self, key, default=None):
+ return getattr(self, key, default)
+
+ def __getitem__(self, key):
+ return getattr(self, key)
+
+ def json(self, **kwargs): # type: ignore
+ try:
+ return self.model_dump(**kwargs)
+ except Exception:
+ return self.dict()
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index 05e3a5e342bd..a8e6c3c636d2 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -274,6 +274,24 @@ class CallTypes(Enum):
file_content = "file_content"
create_fine_tuning_job = "create_fine_tuning_job"
acreate_fine_tuning_job = "acreate_fine_tuning_job"
+
+ #########################################################
+ # Video Generation Call Types
+ #########################################################
+ create_video = "create_video"
+ acreate_video = "acreate_video"
+ avideo_retrieve = "avideo_retrieve"
+ video_retrieve = "video_retrieve"
+ avideo_content = "avideo_content"
+ video_content = "video_content"
+ video_remix = "video_remix"
+ avideo_remix = "avideo_remix"
+ video_list = "video_list"
+ avideo_list = "avideo_list"
+ video_retrieve_job = "video_retrieve_job"
+ avideo_retrieve_job = "avideo_retrieve_job"
+ video_delete = "video_delete"
+ avideo_delete = "avideo_delete"
acancel_fine_tuning_job = "acancel_fine_tuning_job"
cancel_fine_tuning_job = "cancel_fine_tuning_job"
alist_fine_tuning_jobs = "alist_fine_tuning_jobs"
@@ -1149,7 +1167,6 @@ def __init__(self, **kwargs):
super().__init__(**kwargs)
-from openai.types.chat import ChatCompletionChunk
class ModelResponseBase(OpenAIObject):
diff --git a/litellm/types/videos/main.py b/litellm/types/videos/main.py
new file mode 100644
index 000000000000..f33e1d5a4411
--- /dev/null
+++ b/litellm/types/videos/main.py
@@ -0,0 +1,89 @@
+from typing import Any, Dict, List, Literal, Optional
+from typing_extensions import TypedDict
+
+from pydantic import BaseModel
+from litellm.types.utils import FileTypes
+
+
+class VideoObject(BaseModel):
+ """Represents a generated video object."""
+ id: str
+ object: Literal["video"]
+ status: str
+ created_at: int
+ completed_at: Optional[int] = None
+ expires_at: Optional[int] = None
+ error: Optional[Dict[str, Any]] = None
+ progress: Optional[int] = None
+ remixed_from_video_id: Optional[str] = None
+ seconds: Optional[str] = None
+ size: Optional[str] = None
+ model: Optional[str] = None
+ usage: Optional[Dict[str, Any]] = None
+ _hidden_params: Dict[str, Any] = {}
+
+ def __contains__(self, key):
+ # Define custom behavior for the 'in' operator
+ return hasattr(self, key)
+
+ def get(self, key, default=None):
+ # Custom .get() method to access attributes with a default value if the attribute doesn't exist
+ return getattr(self, key, default)
+
+ def __getitem__(self, key):
+ # Allow dictionary-style access to attributes
+ return getattr(self, key)
+
+ def json(self, **kwargs): # type: ignore
+ try:
+ return self.model_dump(**kwargs)
+ except Exception:
+ # if using pydantic v1
+ return self.dict()
+
+
+
+
+class VideoResponse(BaseModel):
+ """Response object for video generation requests."""
+ data: List[VideoObject]
+ hidden_params: Dict[str, Any] = {}
+
+ def __contains__(self, key):
+ return hasattr(self, key)
+
+ def get(self, key, default=None):
+ return getattr(self, key, default)
+
+ def __getitem__(self, key):
+ return getattr(self, key)
+
+ def json(self, **kwargs): # type: ignore
+ try:
+ return self.model_dump(**kwargs)
+ except Exception:
+ return self.dict()
+
+
+class VideoCreateOptionalRequestParams(TypedDict, total=False):
+ """
+ TypedDict for Optional parameters supported by OpenAI's video creation API.
+
+ Params here: https://platform.openai.com/docs/api-reference/videos/create
+ """
+ input_reference: Optional[FileTypes] # File reference for input image
+ model: Optional[str]
+ seconds: Optional[str]
+ size: Optional[str]
+ user: Optional[str]
+ extra_headers: Optional[Dict[str, str]]
+ extra_body: Optional[Dict[str, str]]
+
+
+class VideoCreateRequestParams(VideoCreateOptionalRequestParams, total=False):
+ """
+ TypedDict for request parameters supported by OpenAI's video creation API.
+
+ Params here: https://platform.openai.com/docs/api-reference/videos/create
+ """
+ prompt: str
diff --git a/litellm/utils.py b/litellm/utils.py
index dd42f4aebf1a..55067d9ed772 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -262,6 +262,7 @@
from litellm.llms.base_llm.image_variations.transformation import (
BaseImageVariationConfig,
)
+from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig
from litellm.llms.base_llm.realtime.transformation import BaseRealtimeConfig
from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
@@ -1509,7 +1510,7 @@ async def wrapper_async(*args, **kwargs): # noqa: PLR0915
)
# Only run if call_type is a valid value in CallTypes
if call_type in [ct.value for ct in CallTypes]:
- await async_post_call_success_deployment_hook(
+ result = await async_post_call_success_deployment_hook(
request_data=kwargs,
response=result,
call_type=CallTypes(call_type),
@@ -4949,6 +4950,7 @@ def _get_model_info_helper( # noqa: PLR0915
"output_cost_per_token_above_200k_tokens", None
),
output_cost_per_second=_model_info.get("output_cost_per_second", None),
+ output_cost_per_video_per_second=_model_info.get("output_cost_per_video_per_second", None),
output_cost_per_image=_model_info.get("output_cost_per_image", None),
output_vector_size=_model_info.get("output_vector_size", None),
citation_cost_per_token=_model_info.get(
@@ -7215,6 +7217,9 @@ def get_provider_embedding_config(
return litellm.OVHCloudEmbeddingConfig()
elif litellm.LlmProviders.COMETAPI == provider:
return litellm.CometAPIEmbeddingConfig()
+ elif litellm.LlmProviders.SAGEMAKER == provider:
+ from litellm.llms.sagemaker.embedding.transformation import SagemakerEmbeddingConfig
+ return SagemakerEmbeddingConfig.get_model_config(model)
return None
@staticmethod
@@ -7244,6 +7249,8 @@ def get_provider_rerank_config(
return litellm.DeepinfraRerankConfig()
elif litellm.LlmProviders.NVIDIA_NIM == provider:
return litellm.NvidiaNimRerankConfig()
+ elif litellm.LlmProviders.VERTEX_AI == provider:
+ return litellm.VertexAIRerankConfig()
return litellm.CohereRerankConfig()
@staticmethod
@@ -7544,6 +7551,20 @@ def get_provider_image_generation_config(
return LiteLLMProxyImageGenerationConfig()
return None
+ @staticmethod
+ def get_provider_video_config(
+ model: str,
+ provider: LlmProviders,
+ ) -> Optional[BaseVideoConfig]:
+ if LlmProviders.OPENAI == provider:
+ from litellm.llms.openai.videos.transformation import (
+ OpenAIVideoConfig,
+ )
+
+ return OpenAIVideoConfig()
+ return None
+
+
@staticmethod
def get_provider_realtime_config(
model: str,
diff --git a/litellm/videos/main.py b/litellm/videos/main.py
new file mode 100644
index 000000000000..2e7b25e9e152
--- /dev/null
+++ b/litellm/videos/main.py
@@ -0,0 +1,904 @@
+import asyncio
+import contextvars
+from functools import partial
+from typing import Any, Coroutine, Literal, Optional, Union, overload, Dict
+
+import json
+import litellm
+from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
+from litellm.types.videos.main import (
+ VideoCreateOptionalRequestParams,
+ VideoObject,
+ VideoResponse,
+)
+from litellm.videos.utils import VideoGenerationRequestUtils
+from litellm.constants import DEFAULT_VIDEO_ENDPOINT_MODEL, request_timeout as DEFAULT_REQUEST_TIMEOUT
+from litellm.main import base_llm_http_handler
+from litellm.utils import client, ProviderConfigManager
+from litellm.types.utils import FileTypes, CallTypes
+from litellm.types.router import GenericLiteLLMParams
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
+from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
+
+#################### Initialize provider clients ####################
+llm_http_handler: BaseLLMHTTPHandler = BaseLLMHTTPHandler()
+
+##### Video Generation #######################
+@client
+async def avideo_generation(
+ prompt: str,
+ model: Optional[str] = None,
+ input_reference: Optional[FileTypes] = None,
+ seconds: Optional[str] = None,
+ size: Optional[str] = None,
+ user: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ custom_llm_provider=None,
+ # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+ # The extra values given here take precedence over values defined on the client or passed to this method.
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_query: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ **kwargs,
+) -> VideoObject:
+ """
+ Asynchronously calls the `video_generation` function with the given arguments and keyword arguments.
+
+ Parameters:
+ - `prompt` (str): Text prompt that describes the video to generate
+ - `model` (Optional[str]): The video generation model to use
+ - `input_reference` (Optional[FileTypes]): Optional image reference that guides generation
+ - `seconds` (Optional[str]): Clip duration in seconds
+ - `size` (Optional[str]): Output resolution formatted as width x height
+ - `user` (Optional[str]): A unique identifier representing your end-user
+ - `timeout` (int): Request timeout in seconds
+ - `custom_llm_provider` (Optional[str]): The LLM provider to use
+ - `extra_headers` (Optional[Dict[str, Any]]): Additional headers
+ - `extra_query` (Optional[Dict[str, Any]]): Additional query parameters
+ - `extra_body` (Optional[Dict[str, Any]]): Additional body parameters
+ - `kwargs` (dict): Additional keyword arguments
+
+ Returns:
+ - `response` (VideoResponse): The response returned by the `video_generation` function.
+ """
+ local_vars = locals()
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["async_call"] = True
+
+ # get custom llm provider so we can use this for mapping exceptions
+ if custom_llm_provider is None:
+ _, custom_llm_provider, _, _ = litellm.get_llm_provider(
+ model=model or DEFAULT_VIDEO_ENDPOINT_MODEL, api_base=local_vars.get("api_base", None)
+ )
+
+ func = partial(
+ video_generation,
+ prompt=prompt,
+ model=model,
+ input_reference=input_reference,
+ seconds=seconds,
+ size=size,
+ user=user,
+ timeout=timeout,
+ custom_llm_provider=custom_llm_provider,
+ extra_headers=extra_headers,
+ extra_query=extra_query,
+ extra_body=extra_body,
+ **kwargs,
+ )
+
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response
+
+ return response
+ except Exception as e:
+ raise litellm.exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs=local_vars,
+ extra_kwargs=kwargs,
+ )
+
+
+# fmt: off
+
+# Overload for when avideo_generation=True (returns Coroutine)
+@overload
+def video_generation(
+ prompt: str,
+ model: Optional[str] = None,
+ input_reference: Optional[str] = None,
+ size: Optional[str] = None,
+ user: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ api_version: Optional[str] = None,
+ custom_llm_provider=None,
+ *,
+ avideo_generation: Literal[True],
+ **kwargs,
+) -> Coroutine[Any, Any, VideoObject]:
+ ...
+
+
+@overload
+def video_generation(
+ prompt: str,
+ model: Optional[str] = None,
+ input_reference: Optional[str] = None,
+ seconds: Optional[str] = None,
+ size: Optional[str] = None,
+ user: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ api_version: Optional[str] = None,
+ custom_llm_provider=None,
+ *,
+ avideo_generation: Literal[False] = False,
+ **kwargs,
+) -> VideoObject:
+ ...
+
+# fmt: on
+
+
+@client
+def video_generation( # noqa: PLR0915
+ prompt: str,
+ model: Optional[str] = None,
+ input_reference: Optional[FileTypes] = None,
+ seconds: Optional[str] = None,
+ size: Optional[str] = None,
+ user: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ custom_llm_provider=None,
+ # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+ # The extra values given here take precedence over values defined on the client or passed to this method.
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_query: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ **kwargs,
+) -> Union[
+ VideoObject,
+ Coroutine[Any, Any, VideoObject],
+]:
+ """
+ Maps the https://api.openai.com/v1/videos endpoint.
+
+ Currently supports OpenAI
+ """
+ local_vars = locals()
+ try:
+ litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
+ litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
+ _is_async = kwargs.pop("async_call", False) is True
+
+ # Check for mock response first
+ mock_response = kwargs.get("mock_response", None)
+ if mock_response is not None:
+ if isinstance(mock_response, str):
+ mock_response = json.loads(mock_response)
+
+ response = VideoObject(**mock_response)
+ return response
+
+ # get llm provider logic
+ litellm_params = GenericLiteLLMParams(**kwargs)
+ model, custom_llm_provider, _, _ = get_llm_provider(
+ model=model or DEFAULT_VIDEO_ENDPOINT_MODEL,
+ custom_llm_provider=custom_llm_provider,
+ )
+
+ # get provider config
+ video_generation_provider_config: Optional[BaseVideoConfig] = (
+ ProviderConfigManager.get_provider_video_config(
+ model=model,
+ provider=litellm.LlmProviders(custom_llm_provider),
+ )
+ )
+
+ if video_generation_provider_config is None:
+ raise ValueError(f"image edit is not supported for {custom_llm_provider}")
+
+ local_vars.update(kwargs)
+ # Get VideoGenerationOptionalRequestParams with only valid parameters
+ video_generation_optional_params: VideoCreateOptionalRequestParams = (
+ VideoGenerationRequestUtils.get_requested_video_generation_optional_param(local_vars)
+ )
+
+ # Get optional parameters for the responses API
+ video_generation_request_params: Dict = (
+ VideoGenerationRequestUtils.get_optional_params_video_generation(
+ model=model,
+ video_generation_provider_config=video_generation_provider_config,
+ video_generation_optional_params=video_generation_optional_params,
+ )
+ )
+
+ # Pre Call logging
+ litellm_logging_obj.update_environment_variables(
+ model=model,
+ user=user,
+ optional_params=dict(video_generation_request_params),
+ litellm_params={
+ "litellm_call_id": litellm_call_id,
+ **video_generation_request_params,
+ },
+ custom_llm_provider=custom_llm_provider,
+ )
+
+ # Set the correct call type for video generation
+ litellm_logging_obj.call_type = CallTypes.create_video.value
+
+ # Call the handler with _is_async flag instead of directly calling the async handler
+ return base_llm_http_handler.video_generation_handler(
+ model=model,
+ prompt=prompt,
+ video_generation_provider_config=video_generation_provider_config,
+ video_generation_optional_request_params=video_generation_request_params,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=litellm_logging_obj,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
+ _is_async=_is_async,
+ client=kwargs.get("client"),
+ )
+
+ except Exception as e:
+ raise litellm.exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs=local_vars,
+ extra_kwargs=kwargs,
+ )
+
+
+@client
+def video_content(
+ video_id: str,
+ model: Optional[str] = None,
+ api_base: Optional[str] = None,
+ timeout: Optional[float] = None,
+ custom_llm_provider: Optional[str] = None,
+ # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+ # The extra values given here take precedence over values defined on the client or passed to this method.
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_query: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ **kwargs,
+) -> Union[
+ bytes,
+ Coroutine[Any, Any, bytes],
+]:
+ """
+ Download video content from OpenAI's video API.
+
+ Args:
+ video_id (str): The identifier of the video whose content to download.
+ model (Optional[str]): The model to use. If not provided, will be auto-detected.
+ api_key (Optional[str]): The API key to use for authentication.
+ api_base (Optional[str]): The base URL for the API.
+ timeout (Optional[float]): The timeout for the request in seconds.
+ custom_llm_provider (Optional[str]): The LLM provider to use. If not provided, will be auto-detected.
+ variant (Optional[str]): Which downloadable asset to return. Defaults to the MP4 video.
+ extra_headers (Optional[Dict[str, Any]]): Additional headers to include in the request.
+ extra_query (Optional[Dict[str, Any]]): Additional query parameters.
+ extra_body (Optional[Dict[str, Any]]): Additional body parameters.
+
+ Returns:
+ bytes: The raw video content as bytes.
+
+ Example:
+ ```python
+ import litellm
+
+ # Download video content
+ video_bytes = litellm.video_content(
+ video_id="video_123",
+ custom_llm_provider="openai"
+ )
+
+ # Save to file
+ with open("video.mp4", "wb") as f:
+ f.write(video_bytes)
+ ```
+ """
+ local_vars = locals()
+ try:
+ litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
+ litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
+ _is_async = kwargs.pop("async_call", False) is True
+
+ # get llm provider logic
+ litellm_params = GenericLiteLLMParams(**kwargs)
+ model, custom_llm_provider, _, _ = get_llm_provider(
+ model=model or "sora-2", # Default model for video content
+ custom_llm_provider=custom_llm_provider,
+ )
+
+ # get provider config
+ video_provider_config: Optional[BaseVideoConfig] = (
+ ProviderConfigManager.get_provider_video_config(
+ model=model,
+ provider=litellm.LlmProviders(custom_llm_provider),
+ )
+ )
+
+ if video_provider_config is None:
+ raise ValueError(f"video support download is not supported for {custom_llm_provider}")
+
+ local_vars.update(kwargs)
+ # For video content download, we don't need complex optional parameter handling
+ # Just pass the basic parameters that are relevant for content download
+ video_content_request_params: Dict = {
+ "video_id": video_id,
+ }
+
+ # Pre Call logging
+ litellm_logging_obj.update_environment_variables(
+ model=model,
+ user=kwargs.get("user"),
+ optional_params=dict(video_content_request_params),
+ litellm_params={
+ "litellm_call_id": litellm_call_id,
+ **video_content_request_params,
+ },
+ custom_llm_provider=custom_llm_provider,
+ )
+
+ # Call the handler with _is_async flag instead of directly calling the async handler
+ return base_llm_http_handler.video_content_handler(
+ video_id=video_id,
+ model=model,
+ video_content_provider_config=video_provider_config,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=litellm_logging_obj,
+ timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
+ extra_headers=extra_headers,
+ client=kwargs.get("client"),
+ _is_async=_is_async,
+ )
+
+ except Exception as e:
+ raise litellm.exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs=local_vars,
+ extra_kwargs=kwargs,
+ )
+
+
+##### Video Content Download #######################
+@client
+async def avideo_content(
+ video_id: str,
+ model: Optional[str] = None,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ timeout: Optional[float] = None,
+ custom_llm_provider: Optional[str] = None,
+ # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+ # The extra values given here take precedence over values defined on the client or passed to this method.
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_query: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ **kwargs,
+) -> bytes:
+ """
+ Asynchronously download video content.
+
+ Parameters:
+ - `video_id` (str): The identifier of the video whose content to download
+ - `model` (Optional[str]): The model to use. If not provided, will be auto-detected
+ - `api_key` (Optional[str]): The API key to use for authentication
+ - `api_base` (Optional[str]): The base URL for the API
+ - `timeout` (Optional[float]): The timeout for the request in seconds
+ - `custom_llm_provider` (Optional[str]): The LLM provider to use
+ - `extra_headers` (Optional[Dict[str, Any]]): Additional headers
+ - `extra_query` (Optional[Dict[str, Any]]): Additional query parameters
+ - `extra_body` (Optional[Dict[str, Any]]): Additional body parameters
+ - `kwargs` (dict): Additional keyword arguments
+
+ Returns:
+ - `bytes`: The raw video content as bytes
+ """
+ local_vars = locals()
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["async_call"] = True
+
+ # get custom llm provider so we can use this for mapping exceptions
+ if custom_llm_provider is None:
+ _, custom_llm_provider, _, _ = litellm.get_llm_provider(
+ model=model or DEFAULT_VIDEO_ENDPOINT_MODEL, api_base=api_base
+ )
+
+ func = partial(
+ video_content,
+ video_id=video_id,
+ model=model,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ custom_llm_provider=custom_llm_provider,
+ extra_headers=extra_headers,
+ extra_query=extra_query,
+ extra_body=extra_body,
+ **kwargs,
+ )
+
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response
+
+ return response
+ except Exception as e:
+ raise litellm.exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs=local_vars,
+ extra_kwargs=kwargs,
+ )
+
+##### Video Remix #######################
+@client
+async def avideo_remix(
+ video_id: str,
+ prompt: str,
+ model: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ custom_llm_provider=None,
+ # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+ # The extra values given here take precedence over values defined on the client or passed to this method.
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_query: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ **kwargs,
+) -> VideoObject:
+ """
+ Asynchronously calls the `video_remix` function with the given arguments and keyword arguments.
+
+ Parameters:
+ - `video_id` (str): The identifier of the completed video to remix
+ - `prompt` (str): Updated text prompt that directs the remix generation
+ - `model` (Optional[str]): The video generation model to use
+ - `timeout` (int): Request timeout in seconds
+ - `custom_llm_provider` (Optional[str]): The LLM provider to use
+ - `extra_headers` (Optional[Dict[str, Any]]): Additional headers
+ - `extra_query` (Optional[Dict[str, Any]]): Additional query parameters
+ - `extra_body` (Optional[Dict[str, Any]]): Additional body parameters
+ - `kwargs` (dict): Additional keyword arguments
+
+ Returns:
+ - `response` (VideoObject): The response returned by the `video_remix` function.
+ """
+ local_vars = locals()
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["async_call"] = True
+
+ # get custom llm provider so we can use this for mapping exceptions
+ if custom_llm_provider is None:
+ _, custom_llm_provider, _, _ = litellm.get_llm_provider(
+ model=model or DEFAULT_VIDEO_ENDPOINT_MODEL, api_base=local_vars.get("api_base", None)
+ )
+
+ func = partial(
+ video_remix,
+ video_id=video_id,
+ prompt=prompt,
+ model=model,
+ timeout=timeout,
+ custom_llm_provider=custom_llm_provider,
+ extra_headers=extra_headers,
+ extra_query=extra_query,
+ extra_body=extra_body,
+ **kwargs,
+ )
+
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response
+
+ return response
+ except Exception as e:
+ raise litellm.exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs=local_vars,
+ extra_kwargs=kwargs,
+ )
+
+
+# fmt: off
+
+# Overload for when avideo_remix=True (returns Coroutine)
+@overload
+def video_remix(
+ video_id: str,
+ prompt: str,
+ model: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ api_version: Optional[str] = None,
+ custom_llm_provider=None,
+ *,
+ avideo_remix: Literal[True],
+ **kwargs,
+) -> Coroutine[Any, Any, VideoObject]:
+ ...
+
+
+@overload
+def video_remix(
+ video_id: str,
+ prompt: str,
+ model: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ api_version: Optional[str] = None,
+ custom_llm_provider=None,
+ *,
+ avideo_remix: Literal[False] = False,
+ **kwargs,
+) -> VideoObject:
+ ...
+
+# fmt: on
+
+
+@client
+def video_remix( # noqa: PLR0915
+ video_id: str,
+ prompt: str,
+ model: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ custom_llm_provider=None,
+ # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+ # The extra values given here take precedence over values defined on the client or passed to this method.
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_query: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ **kwargs,
+) -> Union[
+ VideoObject,
+ Coroutine[Any, Any, VideoObject],
+]:
+ """
+ Maps the https://api.openai.com/v1/videos/{video_id}/remix endpoint.
+
+ Currently supports OpenAI
+ """
+ local_vars = locals()
+ try:
+ litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
+ litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
+ _is_async = kwargs.pop("async_call", False) is True
+
+ # Check for mock response first
+ mock_response = kwargs.get("mock_response", None)
+ if mock_response is not None:
+ if isinstance(mock_response, str):
+ mock_response = json.loads(mock_response)
+
+ response = VideoObject(**mock_response)
+ return response
+
+ # get llm provider logic
+ litellm_params = GenericLiteLLMParams(**kwargs)
+ model, custom_llm_provider, _, _ = get_llm_provider(
+ model=model or DEFAULT_VIDEO_ENDPOINT_MODEL,
+ custom_llm_provider=custom_llm_provider,
+ )
+
+ # get provider config
+ video_remix_provider_config: Optional[BaseVideoConfig] = (
+ ProviderConfigManager.get_provider_video_config(
+ model=model,
+ provider=litellm.LlmProviders(custom_llm_provider),
+ )
+ )
+
+ if video_remix_provider_config is None:
+ raise ValueError(f"video remix is not supported for {custom_llm_provider}")
+
+ local_vars.update(kwargs)
+ # For video remix, we need the video_id and prompt
+ video_remix_request_params: Dict = {
+ "video_id": video_id,
+ "prompt": prompt,
+ }
+
+ # Pre Call logging
+ litellm_logging_obj.update_environment_variables(
+ model=model,
+ user=kwargs.get("user"),
+ optional_params=dict(video_remix_request_params),
+ litellm_params={
+ "litellm_call_id": litellm_call_id,
+ **video_remix_request_params,
+ },
+ custom_llm_provider=custom_llm_provider,
+ )
+
+ # Set the correct call type for video remix
+ litellm_logging_obj.call_type = CallTypes.video_remix.value
+
+ # Call the handler with _is_async flag instead of directly calling the async handler
+ return base_llm_http_handler.video_remix_handler(
+ video_id=video_id,
+ prompt=prompt,
+ model=model,
+ video_remix_provider_config=video_remix_provider_config,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=litellm_logging_obj,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
+ _is_async=_is_async,
+ client=kwargs.get("client"),
+ )
+
+ except Exception as e:
+ raise litellm.exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs=local_vars,
+ extra_kwargs=kwargs,
+ )
+
+
+##### Video List #######################
+@client
+async def avideo_list(
+ after: Optional[str] = None,
+ limit: Optional[int] = None,
+ order: Optional[str] = None,
+ model: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ custom_llm_provider=None,
+ # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+ # The extra values given here take precedence over values defined on the client or passed to this method.
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_query: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ **kwargs,
+) -> Dict[str, Any]:
+ """
+ Asynchronously calls the `video_list` function with the given arguments and keyword arguments.
+
+ Parameters:
+ - `after` (Optional[str]): Identifier for the last item from the previous pagination request
+ - `limit` (Optional[int]): Number of items to retrieve
+ - `order` (Optional[str]): Sort order of results by timestamp. Use asc for ascending order or desc for descending order
+ - `model` (Optional[str]): The video generation model to use
+ - `timeout` (int): Request timeout in seconds
+ - `custom_llm_provider` (Optional[str]): The LLM provider to use
+ - `extra_headers` (Optional[Dict[str, Any]]): Additional headers
+ - `extra_query` (Optional[Dict[str, Any]]): Additional query parameters
+ - `extra_body` (Optional[Dict[str, Any]]): Additional body parameters
+ - `kwargs` (dict): Additional keyword arguments
+
+ Returns:
+ - `response` (Dict[str, Any]): The response returned by the `video_list` function.
+ """
+ local_vars = locals()
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["async_call"] = True
+
+ # get custom llm provider so we can use this for mapping exceptions
+ if custom_llm_provider is None:
+ _, custom_llm_provider, _, _ = litellm.get_llm_provider(
+ model=model or DEFAULT_VIDEO_ENDPOINT_MODEL, api_base=local_vars.get("api_base", None)
+ )
+
+ func = partial(
+ video_list,
+ after=after,
+ limit=limit,
+ order=order,
+ model=model,
+ timeout=timeout,
+ custom_llm_provider=custom_llm_provider,
+ extra_headers=extra_headers,
+ extra_query=extra_query,
+ extra_body=extra_body,
+ **kwargs,
+ )
+
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response
+
+ return response
+ except Exception as e:
+ raise litellm.exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs=local_vars,
+ extra_kwargs=kwargs,
+ )
+
+
+# fmt: off
+
+# Overload for when avideo_list=True (returns Coroutine)
+@overload
+def video_list(
+ after: Optional[str] = None,
+ limit: Optional[int] = None,
+ order: Optional[str] = None,
+ model: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ api_version: Optional[str] = None,
+ custom_llm_provider=None,
+ *,
+ avideo_list: Literal[True],
+ **kwargs,
+) -> Coroutine[Any, Any, Dict[str, Any]]:
+ ...
+
+
+@overload
+def video_list(
+ after: Optional[str] = None,
+ limit: Optional[int] = None,
+ order: Optional[str] = None,
+ model: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ api_version: Optional[str] = None,
+ custom_llm_provider=None,
+ *,
+ avideo_list: Literal[False] = False,
+ **kwargs,
+) -> Dict[str, Any]:
+ ...
+
+# fmt: on
+
+
+@client
+def video_list( # noqa: PLR0915
+ after: Optional[str] = None,
+ limit: Optional[int] = None,
+ order: Optional[str] = None,
+ model: Optional[str] = None,
+ timeout=600, # default to 10 minutes
+ custom_llm_provider=None,
+ # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+ # The extra values given here take precedence over values defined on the client or passed to this method.
+ extra_headers: Optional[Dict[str, Any]] = None,
+ extra_query: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ **kwargs,
+) -> Union[
+ Dict[str, Any],
+ Coroutine[Any, Any, Dict[str, Any]],
+]:
+ """
+ Maps the https://api.openai.com/v1/videos endpoint.
+
+ Currently supports OpenAI
+ """
+ local_vars = locals()
+ try:
+ litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
+ litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
+ _is_async = kwargs.pop("async_call", False) is True
+
+ # Check for mock response first
+ mock_response = kwargs.get("mock_response", None)
+ if mock_response is not None:
+ if isinstance(mock_response, str):
+ mock_response = json.loads(mock_response)
+ return mock_response
+
+ # get llm provider logic
+ litellm_params = GenericLiteLLMParams(**kwargs)
+ model, custom_llm_provider, _, _ = get_llm_provider(
+ model=model or DEFAULT_VIDEO_ENDPOINT_MODEL,
+ custom_llm_provider=custom_llm_provider,
+ )
+
+ # get provider config
+ video_list_provider_config: Optional[BaseVideoConfig] = (
+ ProviderConfigManager.get_provider_video_config(
+ model=model,
+ provider=litellm.LlmProviders(custom_llm_provider),
+ )
+ )
+
+ if video_list_provider_config is None:
+ raise ValueError(f"video list is not supported for {custom_llm_provider}")
+
+ local_vars.update(kwargs)
+ # For video list, we need the query parameters
+ video_list_request_params: Dict = {
+ "after": after,
+ "limit": limit,
+ "order": order,
+ }
+
+ # Pre Call logging
+ litellm_logging_obj.update_environment_variables(
+ model=model,
+ user=kwargs.get("user"),
+ optional_params=dict(video_list_request_params),
+ litellm_params={
+ "litellm_call_id": litellm_call_id,
+ **video_list_request_params,
+ },
+ custom_llm_provider=custom_llm_provider,
+ )
+
+ # Set the correct call type for video list
+ litellm_logging_obj.call_type = CallTypes.video_list.value
+
+ # Call the handler with _is_async flag instead of directly calling the async handler
+ return base_llm_http_handler.video_list_handler(
+ after=after,
+ limit=limit,
+ order=order,
+ model=model,
+ video_list_provider_config=video_list_provider_config,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=litellm_logging_obj,
+ extra_headers=extra_headers,
+ extra_query=extra_query,
+ timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
+ _is_async=_is_async,
+ client=kwargs.get("client"),
+ )
+
+ except Exception as e:
+ raise litellm.exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs=local_vars,
+ extra_kwargs=kwargs,
+ )
diff --git a/litellm/videos/utils.py b/litellm/videos/utils.py
new file mode 100644
index 000000000000..7cfccab6720d
--- /dev/null
+++ b/litellm/videos/utils.py
@@ -0,0 +1,74 @@
+from typing import Any, Dict, cast, get_type_hints
+
+import litellm
+from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
+from litellm.types.videos.main import VideoCreateOptionalRequestParams
+
+
+class VideoGenerationRequestUtils:
+ """Helper utils for constructing video generation requests"""
+
+ @staticmethod
+ def get_optional_params_video_generation(
+ model: str,
+ video_generation_provider_config: BaseVideoConfig,
+ video_generation_optional_params: VideoCreateOptionalRequestParams,
+ ) -> Dict:
+ """
+ Get optional parameters for the video generation API.
+
+ Args:
+ model: The model name
+ video_generation_provider_config: The provider configuration for video generation API
+ video_generation_optional_params: The optional parameters for video generation
+
+ Returns:
+ A dictionary of supported parameters for the video generation API
+ """
+ # Get supported parameters for the model
+ supported_params = video_generation_provider_config.get_supported_openai_params(model)
+
+ # Check for unsupported parameters
+ unsupported_params = [
+ param
+ for param in video_generation_optional_params
+ if param not in supported_params
+ ]
+
+ if unsupported_params:
+ raise litellm.UnsupportedParamsError(
+ model=model,
+ message=(
+ f"The following parameters are not supported for model {model}: "
+ f"{', '.join(unsupported_params)}"
+ ),
+ )
+
+ # Map parameters to provider-specific format
+ mapped_params = video_generation_provider_config.map_openai_params(
+ video_create_optional_params=video_generation_optional_params,
+ model=model,
+ drop_params=litellm.drop_params,
+ )
+
+ return mapped_params
+
+ @staticmethod
+ def get_requested_video_generation_optional_param(
+ params: Dict[str, Any],
+ ) -> VideoCreateOptionalRequestParams:
+ """
+ Filter parameters to only include those defined in VideoCreateOptionalRequestParams.
+
+ Args:
+ params: Dictionary of parameters to filter
+
+ Returns:
+ VideoCreateOptionalRequestParams instance with only the valid parameters
+ """
+ valid_keys = get_type_hints(VideoCreateOptionalRequestParams).keys()
+ filtered_params = {
+ k: v for k, v in params.items() if k in valid_keys and v is not None
+ }
+
+ return cast(VideoCreateOptionalRequestParams, filtered_params)
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 8cab321c4dd6..2d987c24dbb1 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -12170,6 +12170,25 @@
"supports_vision": true,
"tool_use_system_prompt_tokens": 159
},
+ "global.anthropic.claude-haiku-4-5-20251001-v1:0": {
+ "cache_creation_input_token_cost": 1.25e-06,
+ "cache_read_input_token_cost": 1e-07,
+ "input_cost_per_token": 1e-06,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
+ "output_cost_per_token": 5e-06,
+ "supports_assistant_prefill": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
"gpt-3.5-turbo": {
"input_cost_per_token": 0.5e-06,
"litellm_provider": "openai",
@@ -20583,6 +20602,25 @@
"supports_vision": true,
"tool_use_system_prompt_tokens": 346
},
+ "au.anthropic.claude-haiku-4-5-20251001-v1:0": {
+ "cache_creation_input_token_cost": 1.375e-06,
+ "cache_read_input_token_cost": 1.1e-07,
+ "input_cost_per_token": 1.1e-06,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
+ "output_cost_per_token": 5.5e-06,
+ "supports_assistant_prefill": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
"us.anthropic.claude-opus-4-20250514-v1:0": {
"cache_creation_input_token_cost": 1.875e-05,
"cache_read_input_token_cost": 1.5e-06,
@@ -23491,7 +23529,9 @@
"max_tokens": 2e6,
"mode": "chat",
"input_cost_per_token": 0.2e-06,
+ "input_cost_per_token_above_128k_tokens": 0.4e-06,
"output_cost_per_token": 0.5e-06,
+ "output_cost_per_token_above_128k_tokens": 1e-06,
"cache_read_input_token_cost": 0.05e-06,
"source": "https://docs.x.ai/docs/models",
"supports_function_calling": true,
@@ -23507,7 +23547,9 @@
"max_tokens": 2e6,
"mode": "chat",
"input_cost_per_token": 0.2e-06,
+ "input_cost_per_token_above_128k_tokens": 0.4e-06,
"output_cost_per_token": 0.5e-06,
+ "output_cost_per_token_above_128k_tokens": 1e-06,
"source": "https://docs.x.ai/docs/models",
"supports_function_calling": true,
"supports_tool_choice": true,
@@ -23515,12 +23557,14 @@
},
"xai/grok-4-0709": {
"input_cost_per_token": 3e-06,
+ "input_cost_per_token_above_128k_tokens": 6e-06,
"litellm_provider": "xai",
"max_input_tokens": 256000,
"max_output_tokens": 256000,
"max_tokens": 256000,
"mode": "chat",
"output_cost_per_token": 1.5e-05,
+ "output_cost_per_token_above_128k_tokens": 30e-06,
"source": "https://docs.x.ai/docs/models",
"supports_function_calling": true,
"supports_reasoning": true,
@@ -23529,12 +23573,14 @@
},
"xai/grok-4-latest": {
"input_cost_per_token": 3e-06,
+ "input_cost_per_token_above_128k_tokens": 6e-06,
"litellm_provider": "xai",
"max_input_tokens": 256000,
"max_output_tokens": 256000,
"max_tokens": 256000,
"mode": "chat",
"output_cost_per_token": 1.5e-05,
+ "output_cost_per_token_above_128k_tokens": 30e-06,
"source": "https://docs.x.ai/docs/models",
"supports_function_calling": true,
"supports_reasoning": true,
@@ -23609,5 +23655,21 @@
"supports_tool_choice": true,
"supports_vision": true,
"supports_web_search": true
+ },
+ "openai/sora-2": {
+ "litellm_provider": "openai",
+ "mode": "video_generation",
+ "output_cost_per_video_per_second": 0.10,
+ "source": "https://platform.openai.com/docs/api-reference/videos",
+ "supported_modalities": [
+ "text"
+ ],
+ "supported_output_modalities": [
+ "video"
+ ],
+ "supported_resolutions": [
+ "720x1280",
+ "1280x720"
+ ]
}
}
diff --git a/tests/llm_translation/test_unit_test_bedrock_invoke.py b/tests/llm_translation/test_unit_test_bedrock_invoke.py
index 5ed0b4aae321..4a970ff15676 100644
--- a/tests/llm_translation/test_unit_test_bedrock_invoke.py
+++ b/tests/llm_translation/test_unit_test_bedrock_invoke.py
@@ -216,3 +216,58 @@ def test_transform_request_meta_llama(bedrock_transformer):
print("transformed request for invoke meta llama=", json.dumps(result, indent=4))
expected_result = {"prompt": "Hello", "max_gen_len": 2048}
assert result == expected_result
+
+
+def test_filter_headers_for_aws_signature():
+ """Test that header filtering works correctly for AWS signature calculation"""
+ from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
+
+ # Create a test instance
+ aws_llm = BaseAWSLLM()
+
+ # Test headers including both AWS and non-AWS headers
+ test_headers = {
+ "Content-Type": "application/json",
+ "Host": "bedrock-runtime.us-east-1.amazonaws.com",
+ "x-amz-date": "20240101T120000Z",
+ "x-amz-security-token": "test-token",
+ "x-custom-header": "custom-value",
+ "x-litellm-user-id": "user123",
+ "x-forwarded-for": "192.168.1.1",
+ "authorization": "Bearer test-token",
+ "user-agent": "test-agent",
+ "x-envoy-expected-rq-timeout-ms": "300000",
+ "x-envoy-external-address": "10.105.1.156"
+ }
+
+ # Filter headers for AWS signature
+ filtered_headers = aws_llm._filter_headers_for_aws_signature(test_headers)
+
+ # Verify that only AWS-related headers are included
+ expected_aws_headers = {
+ "Content-Type": "application/json",
+ "Host": "bedrock-runtime.us-east-1.amazonaws.com",
+ "x-amz-date": "20240101T120000Z",
+ "x-amz-security-token": "test-token"
+ }
+
+ assert filtered_headers == expected_aws_headers, f"Expected {expected_aws_headers}, got {filtered_headers}"
+
+ # Verify that non-AWS headers are excluded
+ excluded_headers = ["x-custom-header", "x-litellm-user-id", "x-forwarded-for", "user-agent",
+ "x-envoy-expected-rq-timeout-ms", "x-envoy-external-address"]
+ for header in excluded_headers:
+ assert header not in filtered_headers, f"Header {header} should not be in filtered headers"
+
+ # Test with empty headers
+ empty_filtered = aws_llm._filter_headers_for_aws_signature({})
+ assert empty_filtered == {}
+
+ # Test with only non-AWS headers
+ non_aws_headers = {
+ "x-custom-trace": "trace-123",
+ "x-user-context": "premium",
+ "x-request-source": "mobile-app"
+ }
+ filtered_non_aws = aws_llm._filter_headers_for_aws_signature(non_aws_headers)
+ assert filtered_non_aws == {}
diff --git a/tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py b/tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py
index 60eb53ed6af5..5b8a99bff7f8 100644
--- a/tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py
+++ b/tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py
@@ -118,7 +118,7 @@ async def test_e2e_bedrock_knowledgebase_retrieval_with_completion(setup_vector_
@pytest.mark.asyncio
async def test_e2e_bedrock_knowledgebase_retrieval_with_llm_api_call(setup_vector_store_registry):
"""
- Test that the Bedrock Knowledge Base Hook works when making a real llm api call
+ Test that the Bedrock Knowledge Base Hook works when making a real llm api call and returns citations.
"""
# Init client
@@ -132,9 +132,85 @@ async def test_e2e_bedrock_knowledgebase_retrieval_with_llm_api_call(setup_vecto
],
client=async_client
)
+ print("OPENAI RESPONSE:", json.dumps(dict(response), indent=4, default=str))
assert response is not None
+
+ # Check that search_results are present in provider_specific_fields
+ assert hasattr(response.choices[0].message, "provider_specific_fields")
+ provider_fields = response.choices[0].message.provider_specific_fields
+ assert provider_fields is not None
+ assert "search_results" in provider_fields
+ search_results = provider_fields["search_results"]
+ assert search_results is not None
+ assert len(search_results) > 0
+
+ # Check search result structure (OpenAI-compatible format)
+ first_search_result = search_results[0]
+ assert "object" in first_search_result
+ assert first_search_result["object"] == "vector_store.search_results.page"
+ assert "data" in first_search_result
+ assert len(first_search_result["data"]) > 0
+
+ # Check individual result structure
+ first_result = first_search_result["data"][0]
+ assert "score" in first_result
+ assert "content" in first_result
+ print(f"Search results returned: {len(search_results)}")
+ print(f"First search result has {len(first_search_result['data'])} items")
+
+
+@pytest.mark.asyncio
+async def test_e2e_bedrock_knowledgebase_retrieval_with_llm_api_call_streaming(setup_vector_store_registry):
+ """
+ Test that the Bedrock Knowledge Base Hook works with streaming and returns search_results in chunks.
+ """
+
+ # Init client
+ # litellm._turn_on_debug()
+ async_client = AsyncHTTPHandler()
+ response = await litellm.acompletion(
+ model="anthropic/claude-3-5-haiku-latest",
+ messages=[{"role": "user", "content": "what is litellm?"}],
+ vector_store_ids = [
+ "T37J8R4WTM"
+ ],
+ stream=True,
+ client=async_client
+ )
+
+ # Collect chunks
+ chunks = []
+ search_results_found = False
+ async for chunk in response:
+ chunks.append(chunk)
+ print(f"Chunk: {chunk}")
+
+ # Check if this chunk has search_results in provider_specific_fields
+ if hasattr(chunk, "choices") and chunk.choices:
+ for choice in chunk.choices:
+ if hasattr(choice, "delta") and choice.delta:
+ provider_fields = getattr(choice.delta, "provider_specific_fields", None)
+ if provider_fields and "search_results" in provider_fields:
+ search_results = provider_fields["search_results"]
+ print(f"Found search_results in streaming chunk: {len(search_results)} results")
+
+ # Verify structure
+ assert search_results is not None
+ assert len(search_results) > 0
+
+ first_search_result = search_results[0]
+ assert "object" in first_search_result
+ assert first_search_result["object"] == "vector_store.search_results.page"
+ assert "data" in first_search_result
+ assert len(first_search_result["data"]) > 0
+
+ search_results_found = True
+
+ print(f"Total chunks received: {len(chunks)}")
+ assert len(chunks) > 0
+ assert search_results_found, "search_results should be present in streaming chunks"
@pytest.mark.asyncio
diff --git a/tests/logging_callback_tests/test_sqs_logger.py b/tests/logging_callback_tests/test_sqs_logger.py
index 38d730712f3c..511d2d222732 100644
--- a/tests/logging_callback_tests/test_sqs_logger.py
+++ b/tests/logging_callback_tests/test_sqs_logger.py
@@ -1,6 +1,8 @@
import asyncio
+import base64
import json
-from unittest.mock import AsyncMock, MagicMock
+import os
+from unittest.mock import AsyncMock, MagicMock, patch
from urllib.parse import unquote
import litellm
@@ -9,6 +11,8 @@
from litellm.integrations.sqs import SQSLogger
from litellm.types.utils import StandardLoggingPayload
+from litellm.integrations.sqs import AppCrypto
+
@pytest.mark.asyncio
async def test_async_sqs_logger_flush():
@@ -134,3 +138,157 @@ async def test_async_sqs_logger_error_flush():
assert len(payload_data["messages"]) == 1
assert payload_data["messages"][0]["role"] == "user"
assert payload_data["messages"][0]["content"] == "hello"
+
+
+
+# =============================================================================
+# 📥 Logging Queue Tests
+# =============================================================================
+
+@pytest.mark.asyncio
+async def test_async_log_success_event_adds_to_queue(monkeypatch):
+ monkeypatch.setattr("litellm.aws_sqs_callback_params", {})
+ logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2")
+
+ fake_payload = {"some": "data"}
+ await logger.async_log_success_event(
+ {"standard_logging_object": fake_payload}, None, None, None
+ )
+ assert fake_payload in logger.log_queue
+
+
+@pytest.mark.asyncio
+async def test_async_log_failure_event_adds_to_queue(monkeypatch):
+ monkeypatch.setattr("litellm.aws_sqs_callback_params", {})
+ logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2")
+
+ fake_payload = {"fail": True}
+ await logger.async_log_failure_event(
+ {"standard_logging_object": fake_payload}, None, None, None
+ )
+ assert fake_payload in logger.log_queue
+
+
+
+# =============================================================================
+# 🧾 async_send_batch Tests
+# =============================================================================
+
+@pytest.mark.asyncio
+async def test_async_send_batch_triggers_tasks(monkeypatch):
+ monkeypatch.setattr("litellm.aws_sqs_callback_params", {})
+ logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2")
+ logger.async_send_message = AsyncMock()
+
+ logger.log_queue = [{"log": 1}, {"log": 2}]
+ await logger.async_send_batch()
+
+ assert logger.async_send_message.await_count == 0 # uses create_task internally
+
+
+
+# =============================================================================
+# 🔐 AppCrypto Tests
+# =============================================================================
+
+def test_appcrypto_encrypt_decrypt_roundtrip():
+ key = os.urandom(32)
+ crypto = AppCrypto(key)
+ data = {"event": "test", "value": 42}
+ aad = b"context"
+ enc = crypto.encrypt_json(data, aad=aad)
+ dec = crypto.decrypt_json(enc, aad=aad)
+ assert dec == data
+
+
+def test_appcrypto_invalid_key_length():
+ with pytest.raises(ValueError, match="32 bytes"):
+ AppCrypto(b"short")
+
+
+# =============================================================================
+# 🪣 SQSLogger Initialization Tests
+# =============================================================================
+
+def test_sqs_logger_init_without_encryption(monkeypatch):
+ monkeypatch.setattr("litellm.aws_sqs_callback_params", {})
+ # Patch asyncio.create_task to avoid RuntimeError
+ monkeypatch.setattr(asyncio, "create_task", MagicMock())
+ logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2")
+ assert logger.sqs_queue_url == "https://example.com"
+ assert logger.app_crypto is None
+
+
+def test_sqs_logger_init_with_encryption(monkeypatch):
+ monkeypatch.setattr("litellm.aws_sqs_callback_params", {})
+ monkeypatch.setattr(asyncio, "create_task", MagicMock())
+ key_b64 = base64.b64encode(os.urandom(32)).decode()
+
+ logger = SQSLogger(
+ sqs_queue_url="https://example.com",
+ sqs_region_name="us-west-2",
+ sqs_aws_use_application_level_encryption=True,
+ sqs_app_encryption_key_b64=key_b64,
+ sqs_app_encryption_aad="tenant=bill",
+ )
+ assert logger.app_crypto is not None
+ assert logger.sqs_app_encryption_aad == "tenant=bill"
+
+
+def test_sqs_logger_init_with_encryption_missing_key(monkeypatch):
+ monkeypatch.setattr("litellm.aws_sqs_callback_params", {})
+ monkeypatch.setattr(asyncio, "create_task", MagicMock())
+ with pytest.raises(ValueError, match="required when encryption is enabled"):
+ SQSLogger(
+ sqs_queue_url="https://example.com",
+ sqs_region_name="us-west-2",
+ sqs_aws_use_application_level_encryption=True,
+ )
+
+
+# =============================================================================
+# 📥 Logging Queue Tests
+# =============================================================================
+
+@pytest.mark.asyncio
+async def test_async_log_success_event_adds_to_queue(monkeypatch):
+ monkeypatch.setattr("litellm.aws_sqs_callback_params", {})
+ monkeypatch.setattr(asyncio, "create_task", MagicMock())
+ logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2")
+
+ fake_payload = {"some": "data"}
+ await logger.async_log_success_event(
+ {"standard_logging_object": fake_payload}, None, None, None
+ )
+ assert fake_payload in logger.log_queue
+
+
+@pytest.mark.asyncio
+async def test_async_log_failure_event_adds_to_queue(monkeypatch):
+ monkeypatch.setattr("litellm.aws_sqs_callback_params", {})
+ monkeypatch.setattr(asyncio, "create_task", MagicMock())
+ logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2")
+
+ fake_payload = {"fail": True}
+ await logger.async_log_failure_event(
+ {"standard_logging_object": fake_payload}, None, None, None
+ )
+ assert fake_payload in logger.log_queue
+
+
+# =============================================================================
+# 🧾 async_send_batch Tests
+# =============================================================================
+
+@pytest.mark.asyncio
+async def test_async_send_batch_triggers_tasks(monkeypatch):
+ monkeypatch.setattr("litellm.aws_sqs_callback_params", {})
+ monkeypatch.setattr(asyncio, "create_task", MagicMock())
+ logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2")
+
+ logger.async_send_message = AsyncMock()
+ logger.log_queue = [{"log": 1}, {"log": 2}]
+
+ await logger.async_send_batch()
+ # It uses asyncio.create_task() so direct await count = 0 is expected
+ asyncio.create_task.assert_called()
\ No newline at end of file
diff --git a/tests/router_unit_tests/test_router_index_management.py b/tests/router_unit_tests/test_router_index_management.py
index 0c313e9ef4eb..90d98b8ab0a8 100644
--- a/tests/router_unit_tests/test_router_index_management.py
+++ b/tests/router_unit_tests/test_router_index_management.py
@@ -18,25 +18,38 @@ def router(self):
"""Create a router instance for testing"""
return Router(model_list=[])
- def test_update_deployment_indices_after_removal(self, router):
- """Test _update_deployment_indices_after_removal function"""
- # Setup: Add models to router with proper structure
+ def test_deletion_updates_model_name_indices(self, router):
+ """Test that deleting a deployment updates model_name_to_deployment_indices correctly"""
router.model_list = [
- {"model": "test1", "model_info": {"id": "model-1"}},
- {"model": "test2", "model_info": {"id": "model-2"}},
- {"model": "test3", "model_info": {"id": "model-3"}}
+ {"model_name": "gpt-3.5", "model_info": {"id": "model-1"}},
+ {"model_name": "gpt-4", "model_info": {"id": "model-2"}},
+ {"model_name": "gpt-4", "model_info": {"id": "model-3"}},
+ {"model_name": "claude", "model_info": {"id": "model-4"}}
]
- router.model_id_to_deployment_index_map = {"model-1": 0, "model-2": 1, "model-3": 2}
+ router.model_id_to_deployment_index_map = {
+ "model-1": 0, "model-2": 1, "model-3": 2, "model-4": 3
+ }
+ router.model_name_to_deployment_indices = {
+ "gpt-3.5": [0],
+ "gpt-4": [1, 2],
+ "claude": [3]
+ }
- # Test: Remove model-2 (index 1)
+ # Remove one of the duplicate gpt-4 deployments
router._update_deployment_indices_after_removal(model_id="model-2", removal_idx=1)
- # Verify: model-2 is removed from index
- assert "model-2" not in router.model_id_to_deployment_index_map
- # Verify: model-3 index is updated (2 -> 1)
- assert router.model_id_to_deployment_index_map["model-3"] == 1
- # Verify: model-1 index remains unchanged
- assert router.model_id_to_deployment_index_map["model-1"] == 0
+ # Verify indices are shifted correctly
+ assert router.model_name_to_deployment_indices["gpt-3.5"] == [0]
+ assert router.model_name_to_deployment_indices["gpt-4"] == [1] # was [1,2], removed 1, shifted 2->1
+ assert router.model_name_to_deployment_indices["claude"] == [2] # was [3], shifted to [2]
+
+ # Remove the last gpt-4 deployment
+ router._update_deployment_indices_after_removal(model_id="model-3", removal_idx=1)
+
+ # Verify gpt-4 is removed from dict when no deployments remain
+ assert "gpt-4" not in router.model_name_to_deployment_indices
+ assert router.model_name_to_deployment_indices["gpt-3.5"] == [0]
+ assert router.model_name_to_deployment_indices["claude"] == [1]
def test_build_model_id_to_deployment_index_map(self, router):
"""Test _build_model_id_to_deployment_index_map function"""
diff --git a/tests/test_litellm/google_genai/test_google_genai_handler.py b/tests/test_litellm/google_genai/test_google_genai_handler.py
index a199f086fe69..89b53ca06d55 100644
--- a/tests/test_litellm/google_genai/test_google_genai_handler.py
+++ b/tests/test_litellm/google_genai/test_google_genai_handler.py
@@ -217,4 +217,112 @@ async def test_stream_transformation_error_async():
contents=[{"role": "user", "parts": [{"text": "Hello"}]}],
litellm_params={}, # Empty dict for params
stream=True
- )
\ No newline at end of file
+ )
+
+
+def test_citation_metadata_transformation():
+ """
+ Test that citationMetadata.citationSources is properly transformed to citationMetadata.citations
+ to avoid Pydantic validation errors.
+ """
+ from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig
+ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+ from unittest.mock import MagicMock
+ import httpx
+
+ # Create a mock response with citationMetadata.citationSources (the problematic format)
+ mock_response_data = {
+ "candidates": [
+ {
+ "content": {
+ "parts": [
+ {
+ "text": "This is a video analysis response with citation metadata."
+ }
+ ],
+ "role": "model"
+ },
+ "finishReason": "STOP",
+ "index": 0,
+ "safetyRatings": [],
+ "citationMetadata": {
+ "citationSources": [
+ {
+ "startIndex": 5848,
+ "endIndex": 5900,
+ "uri": "https://example.com/video-source",
+ "license": "MIT",
+ "title": "Video Analysis Source",
+ "publicationDate": "2024-01-15"
+ },
+ {
+ "startIndex": 6200,
+ "endIndex": 6250,
+ "uri": "https://another-source.com/reference",
+ "license": "CC-BY",
+ "title": "Another Reference",
+ "publicationDate": "2024-02-01"
+ }
+ ]
+ }
+ }
+ ],
+ "usageMetadata": {
+ "promptTokenCount": 150,
+ "candidatesTokenCount": 200,
+ "totalTokenCount": 350
+ },
+ "responseId": "test-response-123"
+ }
+
+ # Create mock httpx response
+ mock_httpx_response = MagicMock(spec=httpx.Response)
+ mock_httpx_response.json.return_value = mock_response_data
+ mock_httpx_response.status_code = 200
+ mock_httpx_response.headers = {}
+
+ # Create logging object
+ logging_obj = LiteLLMLoggingObj(
+ model="gemini-2.5-flash",
+ messages=[],
+ stream=False,
+ call_type="generate_content",
+ start_time=1234567890,
+ litellm_call_id="test-call-123",
+ function_id="test-function-123"
+ )
+
+ # Create GoogleGenAI config
+ config = GoogleGenAIConfig()
+
+ # Test the transformation
+ try:
+ result = config.transform_generate_content_response(
+ model="gemini-2.5-flash",
+ raw_response=mock_httpx_response,
+ logging_obj=logging_obj
+ )
+
+ # Verify the transformation worked
+ assert result is not None
+
+ # Check that citationSources was transformed to citations
+ if hasattr(result, 'candidates') and result.candidates:
+ candidate = result.candidates[0]
+ if hasattr(candidate, 'citationMetadata') and candidate.citationMetadata:
+ # The citationMetadata should now have 'citations' instead of 'citationSources'
+ citation_metadata = candidate.citationMetadata
+
+ # Check that citations field exists
+ assert hasattr(citation_metadata, 'citations'), "citations field should exist after transformation"
+
+ # Verify the citations data is preserved
+ if hasattr(citation_metadata, 'citations') and citation_metadata.citations:
+ assert len(citation_metadata.citations) == 2, "Should have 2 citations"
+ assert citation_metadata.citations[0]['uri'] == "https://example.com/video-source"
+ assert citation_metadata.citations[1]['uri'] == "https://another-source.com/reference"
+
+ print("✅ Citation metadata transformation test passed!")
+
+ except Exception as e:
+ pytest.fail(f"Citation metadata transformation failed: {e}")
\ No newline at end of file
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py
index e5dba275bda9..aea267cd6544 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py
@@ -70,6 +70,188 @@ def test_translate_streaming_openai_chunk_to_anthropic_content_block():
}
+def test_translate_streaming_openai_chunk_to_anthropic_thinking_content_block():
+ choices = [
+ StreamingChoices(
+ finish_reason=None,
+ index=0,
+ delta=Delta(
+ reasoning_content="I need to summar",
+ thinking_blocks=[
+ {
+ "type": "thinking",
+ "thinking": "I need to summar",
+ "signature": None,
+ }
+ ],
+ provider_specific_fields={
+ "thinking_blocks": [
+ {
+ "type": "thinking",
+ "thinking": "I need to summar",
+ "signature": None,
+ }
+ ]
+ },
+ content="",
+ role="assistant",
+ function_call=None,
+ tool_calls=None,
+ audio=None,
+ ),
+ logprobs=None,
+ )
+ ]
+
+ (
+ block_type,
+ content_block_start,
+ ) = LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic_content_block(
+ choices=choices
+ )
+
+ assert block_type == "thinking"
+ assert content_block_start == {
+ "type": "thinking",
+ "thinking": "I need to summar",
+ "signature": "",
+ }
+
+
+def test_translate_streaming_openai_chunk_to_anthropic_thinking_signature_block():
+ choices = [
+ StreamingChoices(
+ finish_reason=None,
+ index=0,
+ delta=Delta(
+ reasoning_content="",
+ thinking_blocks=[
+ {
+ "type": "thinking",
+ "thinking": None,
+ "signature": "sigsig",
+ }
+ ],
+ provider_specific_fields={
+ "thinking_blocks": [
+ {
+ "type": "thinking",
+ "thinking": None,
+ "signature": "sigsig",
+ }
+ ]
+ },
+ content="",
+ role="assistant",
+ function_call=None,
+ tool_calls=None,
+ audio=None,
+ ),
+ logprobs=None,
+ )
+ ]
+
+ (
+ block_type,
+ content_block_start,
+ ) = LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic_content_block(
+ choices=choices
+ )
+
+ assert block_type == "thinking"
+ assert content_block_start == {
+ "type": "thinking",
+ "thinking": "",
+ "signature": "sigsig",
+ }
+
+
+def test_translate_streaming_openai_chunk_to_anthropic_raises_when_thinking_and_signature_content_block():
+ choices = [
+ StreamingChoices(
+ finish_reason=None,
+ index=0,
+ delta=Delta(
+ reasoning_content="",
+ thinking_blocks=[
+ {
+ "type": "thinking",
+ "thinking": "I need to summar",
+ "signature": "sigsig",
+ }
+ ],
+ provider_specific_fields={
+ "thinking_blocks": [
+ {
+ "type": "thinking",
+ "thinking": "I need to summar",
+ "signature": "sigsig",
+ }
+ ]
+ },
+ content="",
+ role="assistant",
+ function_call=None,
+ tool_calls=None,
+ audio=None,
+ ),
+ logprobs=None,
+ )
+ ]
+
+ with pytest.raises(ValueError):
+ LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic_content_block(
+ choices=choices
+ )
+
+
+def test_translate_anthropic_messages_to_openai_thinking_blocks():
+ """Test that tool result messages are placed before user messages in the conversation order."""
+
+ anthropic_messages = [
+ AnthropicMessagesUserMessageParam(
+ role="user",
+ content=[{"type": "text", "text": "What's the weather in Boston?"}]
+ ),
+ AnthopicMessagesAssistantMessageParam(
+ role="assistant",
+ content=[
+ {
+ "type": "thinking",
+ "thinking": "I will call the get_weather tool.",
+ "signature": "sigsig"
+ },
+ {
+ "type": "redacted_thinking",
+ "data": "REDACTED",
+ },
+ {
+ "type": "tool_use",
+ "id": "toolu_01234",
+ "name": "get_weather",
+ "input": {"location": "Boston"}
+ }
+ ]
+ ),
+ ]
+
+ adapter = LiteLLMAnthropicMessagesAdapter()
+ result = adapter.translate_anthropic_messages_to_openai(messages=anthropic_messages)
+
+ assert len(result) == 2
+ assert result[1]["role"] == "assistant"
+ assert "thinking_blocks" in result[1]
+ assert len(result[1]["thinking_blocks"]) == 2
+ assert result[1]["thinking_blocks"][0]["type"] == "thinking"
+ assert result[1]["thinking_blocks"][0]["thinking"] == "I will call the get_weather tool."
+ assert result[1]["thinking_blocks"][0]["signature"] == "sigsig"
+ assert result[1]["thinking_blocks"][1]["type"] == "redacted_thinking"
+ assert result[1]["thinking_blocks"][1]["data"] == "REDACTED"
+ assert "tool_calls" in result[1]
+ assert len(result[1]["tool_calls"]) == 1
+ assert result[1]["tool_calls"][0]["id"] == "toolu_01234"
+
+
def test_translate_anthropic_messages_to_openai_tool_message_placement():
"""Test that tool result messages are placed before user messages in the conversation order."""
@@ -192,3 +374,165 @@ def test_translate_streaming_openai_chunk_to_anthropic_with_partial_json():
assert type_of_content == "input_json_delta"
assert content_block_delta["type"] == "input_json_delta"
assert content_block_delta["partial_json"] == ': "San '
+
+
+
+def test_translate_openai_content_to_anthropic_thinking_and_redacted_thinking():
+ openai_choices = [
+ Choices(
+ message=Message(
+ role="assistant",
+ content=None,
+ thinking_blocks=[
+ {
+ "type": "thinking",
+ "thinking": "I need to summar",
+ "signature": "sigsig",
+ },
+ {
+ "type": "redacted_thinking",
+ "data": "REDACTED"
+ }
+ ]
+ )
+ )
+ ]
+
+ adapter = LiteLLMAnthropicMessagesAdapter()
+ result = adapter._translate_openai_content_to_anthropic(choices=openai_choices)
+
+ assert len(result) == 2
+ assert result[0].type == "thinking"
+ assert result[0].thinking == "I need to summar"
+ assert result[0].signature == "sigsig"
+ assert result[1].type == "redacted_thinking"
+ assert result[1].data == "REDACTED"
+
+
+def test_translate_streaming_openai_chunk_to_anthropic_with_thinking():
+ choices = [
+ StreamingChoices(
+ finish_reason=None,
+ index=0,
+ delta=Delta(
+ reasoning_content="I need to summar",
+ thinking_blocks=[
+ {
+ "type": "thinking",
+ "thinking": "I need to summar",
+ "signature": None,
+ }
+ ],
+ provider_specific_fields={
+ "thinking_blocks": [
+ {
+ "type": "thinking",
+ "thinking": "I need to summar",
+ "signature": None,
+ }
+ ]
+ },
+ content="",
+ role="assistant",
+ function_call=None,
+ tool_calls=None,
+ audio=None,
+ ),
+ logprobs=None,
+ )
+ ]
+
+ (
+ type_of_content,
+ content_block_delta,
+ ) = LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic(
+ choices=choices
+ )
+
+ assert type_of_content == "thinking_delta"
+ assert content_block_delta["type"] == "thinking_delta"
+ assert content_block_delta["thinking"] == "I need to summar"
+
+
+def test_translate_streaming_openai_chunk_to_anthropic_with_thinking():
+ choices = [
+ StreamingChoices(
+ finish_reason=None,
+ index=0,
+ delta=Delta(
+ reasoning_content="",
+ thinking_blocks=[
+ {
+ "type": "thinking",
+ "thinking": None,
+ "signature": "sigsig",
+ }
+ ],
+ provider_specific_fields={
+ "thinking_blocks": [
+ {
+ "type": "thinking",
+ "thinking": None,
+ "signature": "sigsig",
+ }
+ ]
+ },
+ content="",
+ role="assistant",
+ function_call=None,
+ tool_calls=None,
+ audio=None,
+ ),
+ logprobs=None,
+ )
+ ]
+
+ (
+ type_of_content,
+ content_block_delta,
+ ) = LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic(
+ choices=choices
+ )
+
+ assert type_of_content == "signature_delta"
+ assert content_block_delta["type"] == "signature_delta"
+ assert content_block_delta["signature"] == "sigsig"
+
+
+def test_translate_streaming_openai_chunk_to_anthropic_raises_when_thinking_and_signature():
+ choices = [
+ StreamingChoices(
+ finish_reason=None,
+ index=0,
+ delta=Delta(
+ reasoning_content="",
+ thinking_blocks=[
+ {
+ "type": "thinking",
+ "thinking": "I need to summar",
+ "signature": "sigsig",
+ }
+ ],
+ provider_specific_fields={
+ "thinking_blocks": [
+ {
+ "type": "thinking",
+ "thinking": "I need to summar",
+ "signature": "sigsig",
+ }
+ ]
+ },
+ content="",
+ role="assistant",
+ function_call=None,
+ tool_calls=None,
+ audio=None,
+ ),
+ logprobs=None,
+ )
+ ]
+
+ with pytest.raises(ValueError):
+ LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic(
+ choices=choices
+ )
diff --git a/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py b/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py
new file mode 100644
index 000000000000..989a06f80b43
--- /dev/null
+++ b/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py
@@ -0,0 +1,376 @@
+"""
+Test cases for SageMaker Voyage embedding model integration
+
+This module tests the factory pattern implementation for Voyage embedding models
+deployed on AWS SageMaker endpoints, including parameter handling, request/response
+transformation, and model type detection.
+"""
+
+import json
+import os
+import sys
+from unittest.mock import MagicMock, patch
+
+import httpx
+import pytest
+
+sys.path.insert(0, os.path.abspath("../../../../.."))
+
+from litellm import embedding
+from litellm.llms.sagemaker.embedding.transformation import SagemakerEmbeddingConfig
+from litellm.llms.voyage.embedding.transformation import VoyageEmbeddingConfig
+from litellm.types.utils import EmbeddingResponse, Usage
+
+
+class TestSagemakerEmbeddingFactory:
+ """Test the factory pattern for SageMaker embedding configurations"""
+
+ def test_get_model_config_voyage_model(self):
+ """Test that Voyage models return VoyageEmbeddingConfig"""
+ config = SagemakerEmbeddingConfig.get_model_config("voyage-3-5-embedding")
+
+ assert isinstance(config, VoyageEmbeddingConfig)
+ assert config.get_supported_openai_params("voyage-3-5-embedding") == ["encoding_format", "dimensions"]
+
+ def test_get_model_config_hf_model(self):
+ """Test that non-Voyage models return base SagemakerEmbeddingConfig"""
+ config = SagemakerEmbeddingConfig.get_model_config("sentence-transformers-model")
+
+ assert isinstance(config, SagemakerEmbeddingConfig)
+ assert config.get_supported_openai_params("sentence-transformers-model") == []
+
+ def test_get_model_config_case_insensitive(self):
+ """Test that model detection is case insensitive"""
+ config1 = SagemakerEmbeddingConfig.get_model_config("VOYAGE-3-5-embedding")
+ config2 = SagemakerEmbeddingConfig.get_model_config("Voyage-3-5-Embedding")
+ config3 = SagemakerEmbeddingConfig.get_model_config("voyage-3-5-embedding")
+
+ assert isinstance(config1, VoyageEmbeddingConfig)
+ assert isinstance(config2, VoyageEmbeddingConfig)
+ assert isinstance(config3, VoyageEmbeddingConfig)
+
+
+class TestVoyageEmbeddingConfig:
+ """Test Voyage-specific embedding configuration"""
+
+ def setup_method(self):
+ self.config = VoyageEmbeddingConfig()
+
+ def test_get_supported_openai_params(self):
+ """Test supported parameters for Voyage models"""
+ params = self.config.get_supported_openai_params("voyage-3-5-embedding")
+ assert params == ["encoding_format", "dimensions"]
+
+ def test_map_openai_params_encoding_format(self):
+ """Test mapping of encoding_format parameter"""
+ result = self.config.map_openai_params(
+ non_default_params={"encoding_format": "float"},
+ optional_params={},
+ model="voyage-3-5-embedding",
+ drop_params=False
+ )
+ assert result == {"encoding_format": "float"}
+
+ def test_map_openai_params_dimensions(self):
+ """Test mapping of dimensions parameter to output_dimension"""
+ result = self.config.map_openai_params(
+ non_default_params={"dimensions": 1024},
+ optional_params={},
+ model="voyage-3-5-embedding",
+ drop_params=False
+ )
+ assert result == {"output_dimension": 1024}
+
+ def test_map_openai_params_unsupported_encoding(self):
+ """Test handling of unsupported encoding_format values - VoyageEmbeddingConfig passes through without validation"""
+ result = self.config.map_openai_params(
+ non_default_params={"encoding_format": "invalid"},
+ optional_params={},
+ model="voyage-3-5-embedding",
+ drop_params=False
+ )
+ assert result == {"encoding_format": "invalid"}
+
+ def test_map_openai_params_drop_unsupported(self):
+ """Test that VoyageEmbeddingConfig doesn't drop parameters based on drop_params flag"""
+ result = self.config.map_openai_params(
+ non_default_params={"encoding_format": "invalid", "dimensions": 512},
+ optional_params={},
+ model="voyage-3-5-embedding",
+ drop_params=True
+ )
+ assert result == {"encoding_format": "invalid", "output_dimension": 512}
+
+ def test_transform_embedding_request(self):
+ """Test Voyage request transformation"""
+ result = self.config.transform_embedding_request(
+ model="voyage-3-5-embedding",
+ input=["Hello", "World"],
+ optional_params={"encoding_format": "float"},
+ headers={}
+ )
+ expected = {
+ "input": ["Hello", "World"],
+ "model": "voyage-3-5-embedding",
+ "encoding_format": "float"
+ }
+ assert result == expected
+
+ def test_transform_embedding_response(self):
+ """Test Voyage response transformation to OpenAI format"""
+ # Mock Voyage response
+ voyage_response = {
+ "data": [
+ {
+ "object": "embedding",
+ "embedding": [0.1, 0.2, 0.3],
+ "index": 0
+ },
+ {
+ "object": "embedding",
+ "embedding": [0.4, 0.5, 0.6],
+ "index": 1
+ }
+ ],
+ "object": "list",
+ "model": "voyage-3-5-embedding",
+ "usage": {"total_tokens": 10}
+ }
+
+ # Create mock httpx Response
+ mock_response = httpx.Response(
+ status_code=200,
+ content=json.dumps(voyage_response).encode('utf-8'),
+ headers={"content-type": "application/json"}
+ )
+
+ model_response = EmbeddingResponse()
+ result = self.config.transform_embedding_response(
+ model="voyage-3-5-embedding",
+ raw_response=mock_response,
+ model_response=model_response,
+ logging_obj=None,
+ request_data={"input": ["Hello", "World"]}
+ )
+
+ # Verify response structure
+ assert result.object == "list"
+ assert result.model == "voyage-3-5-embedding"
+ assert len(result.data) == 2
+ assert result.data[0]["object"] == "embedding"
+ assert result.data[0]["index"] == 0
+ assert result.data[0]["embedding"] == [0.1, 0.2, 0.3]
+ assert result.data[1]["object"] == "embedding"
+ assert result.data[1]["index"] == 1
+ assert result.data[1]["embedding"] == [0.4, 0.5, 0.6]
+ assert isinstance(result.usage, Usage)
+ assert result.usage.total_tokens == 10
+
+
+class TestHFSagemakerEmbeddingConfig:
+ """Test Hugging Face embedding configuration"""
+
+ def setup_method(self):
+ self.config = SagemakerEmbeddingConfig()
+
+ def test_get_supported_openai_params_hf(self):
+ """Test that HF models don't support embedding parameters"""
+ params = self.config.get_supported_openai_params("sentence-transformers-model")
+ assert params == []
+
+ def test_transform_embedding_request_hf(self):
+ """Test HF request transformation"""
+ result = self.config.transform_embedding_request(
+ model="sentence-transformers-model",
+ input=["Hello", "World"],
+ optional_params={},
+ headers={}
+ )
+ expected = {
+ "inputs": ["Hello", "World"]
+ }
+ assert result == expected
+
+ def test_transform_embedding_response_hf(self):
+ """Test HF response transformation to OpenAI format"""
+ # Mock HF response
+ hf_response = {
+ "embedding": [
+ [0.1, 0.2, 0.3],
+ [0.4, 0.5, 0.6]
+ ]
+ }
+
+ # Create mock httpx Response
+ mock_response = httpx.Response(
+ status_code=200,
+ content=json.dumps(hf_response).encode('utf-8'),
+ headers={"content-type": "application/json"}
+ )
+
+ model_response = EmbeddingResponse()
+ result = self.config.transform_embedding_response(
+ model="sentence-transformers-model",
+ raw_response=mock_response,
+ model_response=model_response,
+ logging_obj=None,
+ request_data={"inputs": ["Hello", "World"]}
+ )
+
+ # Verify response structure
+ assert result.object == "list"
+ assert result.model == "sentence-transformers-model"
+ assert len(result.data) == 2
+ assert result.data[0]["object"] == "embedding"
+ assert result.data[0]["index"] == 0
+ assert result.data[0]["embedding"] == [0.1, 0.2, 0.3]
+ assert result.data[1]["object"] == "embedding"
+ assert result.data[1]["index"] == 1
+ assert result.data[1]["embedding"] == [0.4, 0.5, 0.6]
+ assert isinstance(result.usage, Usage)
+
+
+class TestSagemakerEmbeddingIntegration:
+ """Integration tests for SageMaker embedding with factory pattern"""
+
+ def test_voyage_embedding_request_format(self):
+ """Test that Voyage models use correct request format"""
+ with patch('litellm.llms.sagemaker.completion.handler.SagemakerLLM.embedding') as mock_embedding:
+ # Mock the actual SageMaker call to avoid AWS credentials
+ mock_embedding.return_value = EmbeddingResponse(
+ object="list",
+ data=[
+ {"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]},
+ {"object": "embedding", "index": 1, "embedding": [0.4, 0.5, 0.6]}
+ ],
+ model="voyage-3-5-embedding",
+ usage=Usage(prompt_tokens=10, completion_tokens=0, total_tokens=10)
+ )
+
+ # Test Voyage model
+ response = embedding(
+ model="sagemaker/voyage-3-5-embedding-endpoint",
+ input=["Hello", "World"],
+ encoding_format="float",
+ dimensions=1024
+ )
+
+ # Verify the request was made with correct format
+ mock_embedding.assert_called_once()
+ call_args = mock_embedding.call_args
+ assert call_args[1]["model"] == "voyage-3-5-embedding-endpoint"
+ assert call_args[1]["input"] == ["Hello", "World"]
+ # Check that the parameters are in the optional_params
+ optional_params = call_args[1].get("optional_params", {})
+ assert optional_params.get("encoding_format") == "float"
+ assert optional_params.get("output_dimension") == 1024 # dimensions is mapped to output_dimension
+
+ def test_hf_embedding_request_format(self):
+ """Test that HF models use correct request format"""
+ with patch('litellm.llms.sagemaker.completion.handler.SagemakerLLM.embedding') as mock_embedding:
+ # Mock the actual SageMaker call to avoid AWS credentials
+ mock_embedding.return_value = EmbeddingResponse(
+ object="list",
+ data=[
+ {"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]},
+ {"object": "embedding", "index": 1, "embedding": [0.4, 0.5, 0.6]}
+ ],
+ model="sentence-transformers-model",
+ usage=Usage(prompt_tokens=10, completion_tokens=0, total_tokens=10)
+ )
+
+ # Test HF model with drop_params=True to ignore unsupported parameters
+ response = embedding(
+ model="sagemaker/sentence-transformers-endpoint",
+ input=["Hello", "World"],
+ encoding_format="float", # Should be ignored
+ dimensions=1024, # Should be ignored
+ drop_params=True
+ )
+
+ # Verify the request was made
+ mock_embedding.assert_called_once()
+ call_args = mock_embedding.call_args
+ assert call_args[1]["model"] == "sentence-transformers-endpoint"
+ assert call_args[1]["input"] == ["Hello", "World"]
+ # HF models should ignore these parameters in optional_params
+ optional_params = call_args[1].get("optional_params", {})
+ assert "encoding_format" not in optional_params or optional_params["encoding_format"] is None
+ assert "dimensions" not in optional_params or optional_params["dimensions"] is None
+
+ def test_parameter_validation_voyage(self):
+ """Test parameter validation for Voyage models"""
+ # Test valid parameters
+ config = VoyageEmbeddingConfig()
+ result = config.map_openai_params(
+ non_default_params={"encoding_format": "float", "dimensions": 512},
+ optional_params={},
+ model="voyage-3-5-embedding",
+ drop_params=False
+ )
+ assert result == {"encoding_format": "float", "output_dimension": 512}
+
+ def test_parameter_validation_hf(self):
+ """Test parameter validation for HF models"""
+ # Test that HF models ignore embedding parameters
+ config = SagemakerEmbeddingConfig()
+ result = config.map_openai_params(
+ non_default_params={"encoding_format": "float", "dimensions": 512},
+ optional_params={},
+ model="sentence-transformers-model",
+ drop_params=False
+ )
+ assert result == {} # HF models should ignore these parameters
+
+
+class TestErrorHandling:
+ """Test error handling in the embedding integration"""
+
+ def test_voyage_response_missing_data(self):
+ """Test handling of Voyage response missing data field"""
+ config = VoyageEmbeddingConfig()
+
+ # Mock response without data field
+ mock_response = httpx.Response(
+ status_code=200,
+ content=json.dumps({"object": "list"}).encode('utf-8'),
+ headers={"content-type": "application/json"}
+ )
+
+ model_response = EmbeddingResponse()
+
+ # VoyageEmbeddingConfig doesn't validate for missing data field, it just sets it to None
+ result = config.transform_embedding_response(
+ model="voyage-3-5-embedding",
+ raw_response=mock_response,
+ model_response=model_response,
+ logging_obj=None,
+ request_data={"input": ["Hello"]}
+ )
+ assert result.data is None
+
+ def test_hf_response_missing_embedding(self):
+ """Test handling of HF response missing embedding field"""
+ config = SagemakerEmbeddingConfig()
+
+ # Mock response without embedding field
+ mock_response = httpx.Response(
+ status_code=200,
+ content=json.dumps({"object": "list"}).encode('utf-8'),
+ headers={"content-type": "application/json"}
+ )
+
+ model_response = EmbeddingResponse()
+
+ with pytest.raises(Exception, match="HF response missing 'embedding' field"):
+ config.transform_embedding_response(
+ model="sentence-transformers-model",
+ raw_response=mock_response,
+ model_response=model_response,
+ logging_obj=None,
+ request_data={"inputs": ["Hello"]}
+ )
+
+
+if __name__ == "__main__":
+ pytest.main([__file__])
diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
index 5ae6cf3da283..971c71696d14 100644
--- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
+++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
@@ -1151,3 +1151,57 @@ def test_vertex_ai_map_google_maps_tool_with_location():
assert retrieval_config["latLng"]["latitude"] == 37.7749
assert retrieval_config["latLng"]["longitude"] == -122.4194
assert retrieval_config["languageCode"] == "en_US"
+
+def test_vertex_ai_penalty_parameters_validation():
+ """
+ Test that penalty parameters are properly validated for different Gemini models.
+
+ This test ensures that:
+ 1. Models that don't support penalty parameters (like preview models) filter them out
+ 2. Models that support penalty parameters include them in the request
+ 3. Appropriate warnings are logged for unsupported models
+ """
+ v = VertexGeminiConfig()
+
+ # Test cases: (model_name, should_support_penalty_params)
+ test_cases = [
+ ("gemini-2.5-pro-preview-06-05", False), # Preview model - should not support
+ ]
+
+ for model, should_support in test_cases:
+ # Test _supports_penalty_parameters method
+ assert v._supports_penalty_parameters(model) == should_support, \
+ f"Model {model} penalty support should be {should_support}"
+
+ # Test get_supported_openai_params method
+ supported_params = v.get_supported_openai_params(model)
+ has_penalty_params = "frequency_penalty" in supported_params and "presence_penalty" in supported_params
+ assert has_penalty_params == should_support, \
+ f"Model {model} should {'include' if should_support else 'exclude'} penalty params in supported list"
+
+ # Test parameter mapping for unsupported model
+ model = "gemini-2.5-pro-preview-06-05"
+ non_default_params = {
+ "temperature": 0.7,
+ "frequency_penalty": 0.5,
+ "presence_penalty": 0.3,
+ "max_tokens": 100
+ }
+
+ optional_params = {}
+ result = v.map_openai_params(
+ non_default_params=non_default_params,
+ optional_params=optional_params,
+ model=model,
+ drop_params=False
+ )
+
+ # Penalty parameters should be filtered out for unsupported models
+ assert "frequency_penalty" not in result, "frequency_penalty should be filtered out for unsupported model"
+ assert "presence_penalty" not in result, "presence_penalty should be filtered out for unsupported model"
+
+ # Other parameters should still be included
+ assert "temperature" in result, "temperature should still be included"
+ assert "max_output_tokens" in result, "max_output_tokens should still be included"
+ assert result["temperature"] == 0.7
+ assert result["max_output_tokens"] == 100
\ No newline at end of file
diff --git a/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_integration.py b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_integration.py
new file mode 100644
index 000000000000..1acdadf541a5
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_integration.py
@@ -0,0 +1,218 @@
+"""
+Integration tests for Vertex AI rerank functionality.
+These tests demonstrate end-to-end usage of the Vertex AI rerank feature.
+"""
+import os
+from unittest.mock import MagicMock, patch
+
+import httpx
+import pytest
+
+from litellm.llms.vertex_ai.rerank.transformation import VertexAIRerankConfig
+
+
+class TestVertexAIRerankIntegration:
+ def setup_method(self):
+ self.config = VertexAIRerankConfig()
+ self.model = "semantic-ranker-default@latest"
+
+ @patch('litellm.llms.vertex_ai.rerank.transformation.VertexAIRerankConfig._ensure_access_token')
+ def test_end_to_end_rerank_flow(self, mock_ensure_access_token):
+ """Test complete rerank flow from request to response."""
+ # Mock authentication
+ mock_ensure_access_token.return_value = ("test-access-token", "test-project-123")
+
+ # Test documents
+ documents = [
+ "Gemini is a cutting edge large language model created by Google.",
+ "The Gemini zodiac symbol often depicts two figures standing side-by-side.",
+ "Gemini is a constellation that can be seen in the night sky.",
+ "Google's Gemini AI model represents a significant advancement in artificial intelligence technology."
+ ]
+ query = "What is Google Gemini?"
+
+ # Step 1: Test request transformation
+ with patch.object(self.config, 'get_vertex_ai_credentials', return_value=None), \
+ patch.object(self.config, 'get_vertex_ai_project', return_value="test-project-123"):
+
+ # Validate environment
+ headers = self.config.validate_environment(
+ headers={},
+ model=self.model,
+ api_key=None
+ )
+
+ # Transform request
+ request_data = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params={
+ "query": query,
+ "documents": documents,
+ "top_n": 2,
+ "return_documents": True
+ },
+ headers=headers
+ )
+
+ # Verify request structure
+ assert request_data["model"] == self.model
+ assert request_data["query"] == query
+ assert request_data["topN"] == 2
+ assert request_data["ignoreRecordDetailsInResponse"] == False
+ assert len(request_data["records"]) == 4
+
+ # Verify record structure
+ for i, record in enumerate(request_data["records"]):
+ assert record["id"] == str(i) # 0-based indexing
+ assert "title" in record
+ assert "content" in record
+ assert record["content"] == documents[i]
+
+ # Step 2: Test response transformation
+ # Mock Vertex AI Discovery Engine response
+ mock_response_data = {
+ "records": [
+ {
+ "id": "3",
+ "score": 0.95,
+ "title": "Google's Gemini AI model",
+ "content": "Google's Gemini AI model represents a significant advancement in artificial intelligence technology."
+ },
+ {
+ "id": "0",
+ "score": 0.92,
+ "title": "Gemini is a",
+ "content": "Gemini is a cutting edge large language model created by Google."
+ }
+ ]
+ }
+
+ mock_response = MagicMock(spec=httpx.Response)
+ mock_response.json.return_value = mock_response_data
+ mock_response.text = '{"records": [{"id": "3", "score": 0.95, "title": "Google\'s Gemini AI model", "content": "Google\'s Gemini AI model represents a significant advancement in artificial intelligence technology."}, {"id": "0", "score": 0.92, "title": "Gemini is a", "content": "Gemini is a cutting edge large language model created by Google."}]}'
+
+ mock_logging = MagicMock()
+
+ # Transform response
+ from litellm.types.rerank import RerankResponse
+ model_response = RerankResponse()
+
+ result = self.config.transform_rerank_response(
+ model=self.model,
+ raw_response=mock_response,
+ model_response=model_response,
+ logging_obj=mock_logging,
+ )
+
+ # Verify response structure
+ assert result.id == f"vertex_ai_rerank_{self.model}"
+ assert len(result.results) == 2
+
+ # Results should be sorted by relevance score (descending)
+ assert result.results[0]["index"] == 3 # Highest score
+ assert result.results[0]["relevance_score"] == 0.95
+ assert result.results[1]["index"] == 0 # Second highest score
+ assert result.results[1]["relevance_score"] == 0.92
+
+ # Verify metadata
+ assert result.meta["billed_units"]["search_units"] == 2
+
+ def test_return_documents_false_flow(self):
+ """Test rerank flow when return_documents=False (ID-only response)."""
+ documents = ["doc1", "doc2", "doc3"]
+ query = "test query"
+
+ # Transform request with return_documents=False
+ request_data = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params={
+ "query": query,
+ "documents": documents,
+ "return_documents": False
+ },
+ headers={}
+ )
+
+ # Verify ignoreRecordDetailsInResponse is True
+ assert request_data["ignoreRecordDetailsInResponse"] == True
+
+ # Mock response with only IDs
+ mock_response_data = {
+ "records": [
+ {"id": "1"},
+ {"id": "0"},
+ {"id": "2"}
+ ]
+ }
+
+ mock_response = MagicMock(spec=httpx.Response)
+ mock_response.json.return_value = mock_response_data
+ mock_response.text = '{"records": [{"id": "1"}, {"id": "0"}, {"id": "2"}]}'
+
+ mock_logging = MagicMock()
+
+ # Transform response
+ from litellm.types.rerank import RerankResponse
+ model_response = RerankResponse()
+
+ result = self.config.transform_rerank_response(
+ model=self.model,
+ raw_response=mock_response,
+ model_response=model_response,
+ logging_obj=mock_logging,
+ )
+
+ # Verify response structure with default scores
+ assert len(result.results) == 3
+ for result_item in result.results:
+ assert result_item["relevance_score"] == 1.0 # Default score when details are ignored
+ assert "index" in result_item
+
+ def test_document_title_generation(self):
+ """Test that document titles are generated correctly from content."""
+ documents = [
+ "This is a very long document with many words that should be truncated to only the first three words for the title",
+ "Short doc",
+ "Another document with multiple words here and more content"
+ ]
+
+ request_data = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params={
+ "query": "test query",
+ "documents": documents
+ },
+ headers={}
+ )
+
+ # Verify title generation
+ assert request_data["records"][0]["title"] == "This is a" # First 3 words
+ assert request_data["records"][1]["title"] == "Short doc" # Less than 3 words
+ assert request_data["records"][2]["title"] == "Another document with" # First 3 words
+
+ def test_dictionary_document_handling(self):
+ """Test handling of dictionary-format documents."""
+ documents = [
+ {"text": "Gemini is a cutting edge large language model created by Google.", "title": "Custom Title 1"},
+ {"text": "The Gemini zodiac symbol often depicts two figures standing side-by-side."},
+ {"text": "Gemini is a constellation that can be seen in the night sky.", "title": "Custom Title 3"}
+ ]
+
+ request_data = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params={
+ "query": "test query",
+ "documents": documents
+ },
+ headers={}
+ )
+
+ # Verify custom titles are used when provided
+ assert request_data["records"][0]["title"] == "Custom Title 1"
+ assert request_data["records"][1]["title"] == "The Gemini zodiac" # Generated from first 3 words
+ assert request_data["records"][2]["title"] == "Custom Title 3"
+
+ # Verify content is extracted correctly
+ assert request_data["records"][0]["content"] == "Gemini is a cutting edge large language model created by Google."
+ assert request_data["records"][1]["content"] == "The Gemini zodiac symbol often depicts two figures standing side-by-side."
+ assert request_data["records"][2]["content"] == "Gemini is a constellation that can be seen in the night sky."
diff --git a/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py
new file mode 100644
index 000000000000..9a1029b1726c
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py
@@ -0,0 +1,353 @@
+"""
+Tests for Vertex AI rerank transformation functionality.
+Based on the test patterns from other rerank providers and the current Vertex AI implementation.
+"""
+import json
+import os
+from unittest.mock import MagicMock, patch
+
+import httpx
+import pytest
+
+from litellm.llms.vertex_ai.rerank.transformation import VertexAIRerankConfig
+from litellm.types.rerank import RerankResponse
+
+
+class TestVertexAIRerankTransform:
+ def setup_method(self):
+ self.config = VertexAIRerankConfig()
+ self.model = "semantic-ranker-default@latest"
+
+ def test_get_complete_url(self):
+ """Test URL generation for Vertex AI Discovery Engine rerank API."""
+ # Test with project ID from environment
+ with patch.dict(os.environ, {"VERTEXAI_PROJECT": "test-project-123"}):
+ url = self.config.get_complete_url(api_base=None, model=self.model)
+ expected_url = "https://discoveryengine.googleapis.com/v1/projects/test-project-123/locations/global/rankingConfigs/default_ranking_config:rank"
+ assert url == expected_url
+
+ # Test with litellm.vertex_project
+ with patch.dict(os.environ, {}, clear=True):
+ import litellm
+ # Set vertex_project attribute if it doesn't exist
+ if not hasattr(litellm, 'vertex_project'):
+ litellm.vertex_project = None
+ original_project = litellm.vertex_project
+ litellm.vertex_project = "litellm-project-456"
+ try:
+ url = self.config.get_complete_url(api_base=None, model=self.model)
+ expected_url = "https://discoveryengine.googleapis.com/v1/projects/litellm-project-456/locations/global/rankingConfigs/default_ranking_config:rank"
+ assert url == expected_url
+ finally:
+ litellm.vertex_project = original_project
+
+ # Test error when no project ID is available
+ with patch.dict(os.environ, {}, clear=True):
+ import litellm
+ # Set vertex_project to None to ensure no project ID is available
+ if not hasattr(litellm, 'vertex_project'):
+ litellm.vertex_project = None
+ original_project = litellm.vertex_project
+ litellm.vertex_project = None
+ try:
+ with pytest.raises(ValueError, match="Vertex AI project ID is required"):
+ self.config.get_complete_url(api_base=None, model=self.model)
+ finally:
+ litellm.vertex_project = original_project
+
+ @patch('litellm.llms.vertex_ai.rerank.transformation.VertexAIRerankConfig._ensure_access_token')
+ def test_validate_environment(self, mock_ensure_access_token):
+ """Test environment validation and header setup."""
+ # Mock the authentication
+ mock_ensure_access_token.return_value = ("test-access-token", "test-project-123")
+
+ # Mock the credential and project methods
+ with patch.object(self.config, 'get_vertex_ai_credentials', return_value=None), \
+ patch.object(self.config, 'get_vertex_ai_project', return_value="test-project-123"):
+
+ headers = self.config.validate_environment(
+ headers={},
+ model=self.model,
+ api_key=None
+ )
+
+ expected_headers = {
+ "Authorization": "Bearer test-access-token",
+ "Content-Type": "application/json",
+ "X-Goog-User-Project": "test-project-123"
+ }
+ assert headers == expected_headers
+
+ def test_transform_rerank_request_basic(self):
+ """Test basic request transformation for Vertex AI Discovery Engine format."""
+ optional_params = {
+ "query": "What is Google Gemini?",
+ "documents": [
+ "Gemini is a cutting edge large language model created by Google.",
+ "The Gemini zodiac symbol often depicts two figures standing side-by-side."
+ ],
+ "top_n": 2
+ }
+
+ request_data = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params=optional_params,
+ headers={}
+ )
+
+ # Verify basic structure
+ assert request_data["model"] == self.model
+ assert request_data["query"] == "What is Google Gemini?"
+ assert request_data["topN"] == 2
+ assert "records" in request_data
+ assert len(request_data["records"]) == 2
+
+ # Verify record structure
+ for i, record in enumerate(request_data["records"]):
+ assert "id" in record
+ assert "title" in record
+ assert "content" in record
+ assert record["id"] == str(i) # 0-based indexing
+ assert len(record["title"].split()) <= 3 # First 3 words as title
+
+ def test_transform_rerank_request_with_dict_documents(self):
+ """Test request transformation with dictionary documents."""
+ optional_params = {
+ "query": "What is Google Gemini?",
+ "documents": [
+ {"text": "Gemini is a cutting edge large language model created by Google.", "title": "Custom Title 1"},
+ {"text": "The Gemini zodiac symbol often depicts two figures standing side-by-side."}
+ ]
+ }
+
+ request_data = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params=optional_params,
+ headers={}
+ )
+
+ # Verify record structure with custom titles
+ assert request_data["records"][0]["title"] == "Custom Title 1"
+ assert request_data["records"][1]["title"] == "The Gemini zodiac" # First 3 words
+
+ def test_transform_rerank_request_return_documents_mapping(self):
+ """Test return_documents to ignoreRecordDetailsInResponse mapping."""
+ # Test return_documents=True (default)
+ optional_params_true = {
+ "query": "test query",
+ "documents": ["doc1", "doc2"],
+ "return_documents": True
+ }
+
+ request_data_true = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params=optional_params_true,
+ headers={}
+ )
+ assert request_data_true["ignoreRecordDetailsInResponse"] == False
+
+ # Test return_documents=False
+ optional_params_false = {
+ "query": "test query",
+ "documents": ["doc1", "doc2"],
+ "return_documents": False
+ }
+
+ request_data_false = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params=optional_params_false,
+ headers={}
+ )
+ assert request_data_false["ignoreRecordDetailsInResponse"] == True
+
+ # Test return_documents not specified (should default to True)
+ optional_params_default = {
+ "query": "test query",
+ "documents": ["doc1", "doc2"]
+ }
+
+ request_data_default = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params=optional_params_default,
+ headers={}
+ )
+ assert request_data_default["ignoreRecordDetailsInResponse"] == False
+
+ def test_transform_rerank_request_missing_required_params(self):
+ """Test that transform_rerank_request handles missing required parameters."""
+ # Test missing query
+ with pytest.raises(ValueError, match="query is required for Vertex AI rerank"):
+ self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params={"documents": ["doc1"]},
+ headers={}
+ )
+
+ # Test missing documents
+ with pytest.raises(ValueError, match="documents is required for Vertex AI rerank"):
+ self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params={"query": "test query"},
+ headers={}
+ )
+
+ def test_transform_rerank_response_success(self):
+ """Test successful response transformation."""
+ # Mock Vertex AI Discovery Engine response format
+ response_data = {
+ "records": [
+ {
+ "id": "1",
+ "score": 0.98,
+ "title": "The Science of a Blue Sky",
+ "content": "The sky appears blue due to a phenomenon called Rayleigh scattering."
+ },
+ {
+ "id": "0",
+ "score": 0.64,
+ "title": "The Color of the Sky: A Poem",
+ "content": "A canvas stretched across the day, Where sunlight learns to dance and play."
+ }
+ ]
+ }
+
+ # Create mock httpx response
+ mock_response = MagicMock(spec=httpx.Response)
+ mock_response.json.return_value = response_data
+ mock_response.text = json.dumps(response_data)
+
+ # Create mock logging object
+ mock_logging = MagicMock()
+
+ model_response = RerankResponse()
+
+ result = self.config.transform_rerank_response(
+ model=self.model,
+ raw_response=mock_response,
+ model_response=model_response,
+ logging_obj=mock_logging,
+ )
+
+ # Verify response structure
+ assert result.id == f"vertex_ai_rerank_{self.model}"
+ assert len(result.results) == 2
+ assert result.results[0]["index"] == 1 # Converted back to 0-based index
+ assert result.results[0]["relevance_score"] == 0.98
+ assert result.results[1]["index"] == 0
+ assert result.results[1]["relevance_score"] == 0.64
+
+ # Verify metadata
+ assert result.meta["billed_units"]["search_units"] == 2
+
+ def test_transform_rerank_response_with_ignore_record_details(self):
+ """Test response transformation when ignoreRecordDetailsInResponse=true."""
+ # Mock response with only IDs (when ignoreRecordDetailsInResponse=true)
+ response_data = {
+ "records": [
+ {"id": "1"},
+ {"id": "0"}
+ ]
+ }
+
+ mock_response = MagicMock(spec=httpx.Response)
+ mock_response.json.return_value = response_data
+ mock_response.text = json.dumps(response_data)
+
+ mock_logging = MagicMock()
+ model_response = RerankResponse()
+
+ result = self.config.transform_rerank_response(
+ model=self.model,
+ raw_response=mock_response,
+ model_response=model_response,
+ logging_obj=mock_logging,
+ )
+
+ # Verify response structure with default scores
+ assert len(result.results) == 2
+ assert result.results[0]["index"] == 1 # 0-based index
+ assert result.results[0]["relevance_score"] == 1.0 # Default score
+ assert result.results[1]["index"] == 0
+ assert result.results[1]["relevance_score"] == 1.0
+
+ def test_transform_rerank_response_json_error(self):
+ """Test response transformation with JSON parsing error."""
+ mock_response = MagicMock(spec=httpx.Response)
+ mock_response.json.side_effect = json.JSONDecodeError("Invalid JSON", "doc", 0)
+ mock_response.text = "Invalid JSON response"
+
+ mock_logging = MagicMock()
+ model_response = RerankResponse()
+
+ with pytest.raises(ValueError, match="Failed to parse response"):
+ self.config.transform_rerank_response(
+ model=self.model,
+ raw_response=mock_response,
+ model_response=model_response,
+ logging_obj=mock_logging,
+ )
+
+ def test_get_supported_cohere_rerank_params(self):
+ """Test getting supported parameters for Vertex AI rerank."""
+ supported_params = self.config.get_supported_cohere_rerank_params(self.model)
+ expected_params = ["query", "documents", "top_n", "return_documents"]
+ assert supported_params == expected_params
+
+ def test_map_cohere_rerank_params(self):
+ """Test parameter mapping for Vertex AI rerank."""
+ params = self.config.map_cohere_rerank_params(
+ non_default_params={"documents": ["doc1", "doc2"]},
+ model=self.model,
+ drop_params=False,
+ query="test query",
+ documents=["doc1", "doc2"],
+ top_n=2,
+ return_documents=True
+ )
+
+ expected_params = {
+ "query": "test query",
+ "documents": ["doc1", "doc2"],
+ "top_n": 2,
+ "return_documents": True
+ }
+ assert params == expected_params
+
+ def test_title_generation_from_content(self):
+ """Test that titles are generated correctly from document content."""
+ optional_params = {
+ "query": "test query",
+ "documents": [
+ "This is a very long document with many words that should be truncated to only the first three words for the title",
+ "Short doc",
+ "Another document with multiple words here"
+ ]
+ }
+
+ request_data = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params=optional_params,
+ headers={}
+ )
+
+ # Verify title generation
+ assert request_data["records"][0]["title"] == "This is a" # First 3 words
+ assert request_data["records"][1]["title"] == "Short doc" # Less than 3 words
+ assert request_data["records"][2]["title"] == "Another document with" # First 3 words
+
+ def test_record_id_generation(self):
+ """Test that record IDs are generated correctly with 0-based indexing."""
+ optional_params = {
+ "query": "test query",
+ "documents": ["doc1", "doc2", "doc3", "doc4"]
+ }
+
+ request_data = self.config.transform_rerank_request(
+ model=self.model,
+ optional_rerank_params=optional_params,
+ headers={}
+ )
+
+ # Verify 0-based indexing
+ for i, record in enumerate(request_data["records"]):
+ assert record["id"] == str(i)
diff --git a/tests/test_litellm/llms/xai/test_xai_cost_calculator.py b/tests/test_litellm/llms/xai/test_xai_cost_calculator.py
index 5a26d808c5de..440941735449 100644
--- a/tests/test_litellm/llms/xai/test_xai_cost_calculator.py
+++ b/tests/test_litellm/llms/xai/test_xai_cost_calculator.py
@@ -24,8 +24,16 @@ class TestXAICostCalculator:
def setup_method(self):
"""Set up test environment."""
- os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
- litellm.model_cost = litellm.get_model_cost_map(url="")
+ # Load the main model cost map directly to ensure we have the latest pricing
+ import json
+ try:
+ with open("model_prices_and_context_window.json", "r") as f:
+ model_cost_map = json.load(f)
+ litellm.model_cost = model_cost_map
+ except FileNotFoundError:
+ # Fallback to default behavior
+ os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+ litellm.model_cost = litellm.get_model_cost_map(url="")
def test_basic_cost_calculation(self):
"""Test basic cost calculation without reasoning tokens."""
@@ -187,3 +195,128 @@ def test_edge_case_large_reasoning_tokens(self):
assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10)
assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10)
+
+ def test_tiered_pricing_above_128k_tokens(self):
+ """Test tiered pricing for tokens above 128k."""
+ # Test with grok-4-fast-reasoning which has tiered pricing
+ usage = Usage(
+ prompt_tokens=150000, # Above 128k threshold
+ completion_tokens=100000, # Above 128k threshold
+ total_tokens=250000,
+ completion_tokens_details=CompletionTokensDetailsWrapper(
+ accepted_prediction_tokens=0,
+ audio_tokens=0,
+ reasoning_tokens=50000, # Total completion tokens = 100000 + 50000 = 150000 > 128k
+ rejected_prediction_tokens=0,
+ text_tokens=None,
+ ),
+ )
+
+ prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-fast-reasoning", usage=usage)
+
+ # Expected costs for grok-4-fast-reasoning with tiered pricing:
+ # Input: 150000 tokens * $0.4e-6 (ALL tokens at tiered rate since input > 128k) = $0.06
+ # Completion: (100000 + 50000) tokens * $1e-6 (tiered rate since input > 128k) = $0.15
+ expected_prompt_cost = 150000 * 0.4e-6
+ expected_completion_cost = (100000 + 50000) * 1e-6
+
+ assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10)
+ assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10)
+
+ def test_tiered_pricing_below_128k_tokens(self):
+ """Test that regular pricing is used for tokens below 128k threshold."""
+ # Test with grok-4-fast-reasoning which has tiered pricing
+ usage = Usage(
+ prompt_tokens=100000, # Below 128k threshold
+ completion_tokens=50000,
+ total_tokens=150000,
+ completion_tokens_details=CompletionTokensDetailsWrapper(
+ accepted_prediction_tokens=0,
+ audio_tokens=0,
+ reasoning_tokens=10000,
+ rejected_prediction_tokens=0,
+ text_tokens=None,
+ ),
+ )
+
+ prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-fast-reasoning", usage=usage)
+
+ # Expected costs for grok-4-fast-reasoning with regular pricing:
+ # Input: 100000 tokens * $0.2e-6 (regular rate) = $0.02
+ # Completion: (50000 + 10000) tokens * $0.5e-6 (regular rate) = $0.03
+ expected_prompt_cost = 100000 * 0.2e-6
+ expected_completion_cost = (50000 + 10000) * 0.5e-6
+
+ assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10)
+ assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10)
+
+ def test_tiered_pricing_grok_4_latest(self):
+ """Test tiered pricing for grok-4-latest model."""
+ usage = Usage(
+ prompt_tokens=200000, # Above 128k threshold
+ completion_tokens=100000,
+ total_tokens=300000,
+ completion_tokens_details=CompletionTokensDetailsWrapper(
+ accepted_prediction_tokens=0,
+ audio_tokens=0,
+ reasoning_tokens=50000,
+ rejected_prediction_tokens=0,
+ text_tokens=None,
+ ),
+ )
+
+ prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-latest", usage=usage)
+
+ # Expected costs for grok-4-latest with tiered pricing:
+ # Input: 200000 tokens * $6e-6 (ALL tokens at tiered rate since input > 128k) = $1.2
+ # Completion: (100000 + 50000) tokens * $30e-6 (tiered rate since input > 128k) = $4.5
+ expected_prompt_cost = 200000 * 6e-6
+ expected_completion_cost = (100000 + 50000) * 30e-6
+
+ assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10)
+ assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10)
+
+ def test_tiered_pricing_output_tokens_below_128k(self):
+ """Test that output tokens get tiered rate when input tokens > 128k, even if output tokens < 128k."""
+ usage = Usage(
+ prompt_tokens=150000, # Above 128k threshold
+ completion_tokens=50000, # Below 128k threshold
+ total_tokens=200000,
+ completion_tokens_details=CompletionTokensDetailsWrapper(
+ accepted_prediction_tokens=0,
+ audio_tokens=0,
+ reasoning_tokens=10000, # Total completion tokens = 50000 + 10000 = 60000 < 128k
+ rejected_prediction_tokens=0,
+ text_tokens=None,
+ ),
+ )
+
+ prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-fast-reasoning", usage=usage)
+
+ # Expected costs for grok-4-fast-reasoning:
+ # Input: 150000 tokens * $0.4e-6 (ALL tokens at tiered rate since input > 128k) = $0.06
+ # Completion: (50000 + 10000) tokens * $1e-6 (tiered rate since input > 128k) = $0.06
+ expected_prompt_cost = 150000 * 0.4e-6
+ expected_completion_cost = (50000 + 10000) * 1e-6
+
+ assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10)
+ assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10)
+
+ def test_tiered_pricing_model_without_tiered_pricing(self):
+ """Test that models without tiered pricing use regular pricing even above 128k."""
+ usage = Usage(
+ prompt_tokens=150000, # Above 128k threshold
+ completion_tokens=50000,
+ total_tokens=200000,
+ )
+
+ prompt_cost, completion_cost = cost_per_token(model="grok-3-mini", usage=usage)
+
+ # grok-3-mini doesn't have tiered pricing, so should use regular rates:
+ # Input: 150000 tokens * $3e-7 (regular rate) = $0.045
+ # Completion: 50000 tokens * $5e-7 (regular rate) = $0.025
+ expected_prompt_cost = 150000 * 3e-7
+ expected_completion_cost = 50000 * 5e-7
+
+ assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10)
+ assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10)
diff --git a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py
index 511eb5bbb891..a8aae3e1a687 100644
--- a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py
+++ b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py
@@ -954,6 +954,93 @@ async def mock_should_rate_limit(descriptors, **kwargs):
assert team_member_descriptor["rate_limit"]["tokens_per_unit"] == 1000, "Team member TPM limit should be set"
+@pytest.mark.asyncio
+async def test_dynamic_rate_limiting_v3():
+ """
+ Test that dynamic rate limiting only enforces limits when model has failures.
+
+ When rpm_limit_type is set to "dynamic":
+ - If model has no failures, rate limits should NOT be enforced (allow exceeding)
+ - If model has failures above threshold, rate limits SHOULD be enforced
+ """
+ _api_key = "sk-12345"
+ _api_key_hash = hash_token(_api_key)
+ model = "gpt-3.5-turbo"
+
+ # Set a low RPM limit to make testing easier
+ user_api_key_dict = UserAPIKeyAuth(
+ api_key=_api_key_hash,
+ rpm_limit=2,
+ metadata={"rpm_limit_type": "dynamic"},
+ )
+
+ local_cache = DualCache()
+ parallel_request_handler = _PROXY_MaxParallelRequestsHandler(
+ internal_usage_cache=InternalUsageCache(local_cache)
+ )
+
+ # Mock should_rate_limit to track if limits are enforced
+ captured_descriptors = []
+
+ async def mock_should_rate_limit(descriptors, **kwargs):
+ captured_descriptors.clear()
+ captured_descriptors.extend(descriptors)
+ return {"overall_code": "OK", "statuses": []}
+
+ parallel_request_handler.should_rate_limit = mock_should_rate_limit
+
+ # Test 1: No failures - rate limits should NOT be enforced (rpm_limit should be None)
+ async def mock_check_no_failures(*args, **kwargs):
+ return False
+
+ parallel_request_handler._check_model_has_recent_failures = mock_check_no_failures
+
+ await parallel_request_handler.async_pre_call_hook(
+ user_api_key_dict=user_api_key_dict,
+ cache=local_cache,
+ data={"model": model},
+ call_type="",
+ )
+
+ # Find the API key descriptor
+ api_key_descriptor = None
+ for descriptor in captured_descriptors:
+ if descriptor["key"] == "api_key":
+ api_key_descriptor = descriptor
+ break
+
+ assert api_key_descriptor is not None, "API key descriptor should be present"
+ assert (
+ api_key_descriptor["rate_limit"]["requests_per_unit"] is None
+ ), "RPM limit should be None when dynamic mode and no failures"
+
+ # Test 2: With failures - rate limits SHOULD be enforced (rpm_limit should be set)
+ async def mock_check_with_failures(*args, **kwargs):
+ return True
+
+ parallel_request_handler._check_model_has_recent_failures = mock_check_with_failures
+ captured_descriptors.clear()
+
+ await parallel_request_handler.async_pre_call_hook(
+ user_api_key_dict=user_api_key_dict,
+ cache=local_cache,
+ data={"model": model},
+ call_type="",
+ )
+
+ # Find the API key descriptor again
+ api_key_descriptor = None
+ for descriptor in captured_descriptors:
+ if descriptor["key"] == "api_key":
+ api_key_descriptor = descriptor
+ break
+
+ assert api_key_descriptor is not None, "API key descriptor should be present"
+ assert (
+ api_key_descriptor["rate_limit"]["requests_per_unit"] == 2
+ ), "RPM limit should be enforced when dynamic mode and failures detected"
+
+
@pytest.mark.asyncio
async def test_async_increment_tokens_with_ttl_preservation():
"""
diff --git a/tests/test_litellm/test_video_generation.py b/tests/test_litellm/test_video_generation.py
new file mode 100644
index 000000000000..109f8629510a
--- /dev/null
+++ b/tests/test_litellm/test_video_generation.py
@@ -0,0 +1,382 @@
+import json
+import os
+import sys
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+sys.path.insert(
+ 0, os.path.abspath("../..")
+) # Adds the parent directory to the system path
+
+import litellm
+from litellm.types.videos.main import VideoObject, VideoResponse
+from litellm.videos.main import video_generation, avideo_generation
+from litellm.llms.openai.videos.transformation import OpenAIVideoConfig
+from litellm.cost_calculator import default_video_cost_calculator
+
+
+class TestVideoGeneration:
+ """Test suite for video generation functionality."""
+
+ def test_video_generation_basic(self):
+ """Test basic video generation functionality."""
+ # Mock the video generation response
+ mock_response = VideoObject(
+ id="video_123",
+ object="video",
+ status="queued",
+ created_at=1712697600,
+ model="sora-2",
+ size="720x1280",
+ seconds="8"
+ )
+
+ with patch('litellm.videos.main.base_llm_http_handler') as mock_handler:
+ mock_handler.video_generation_handler.return_value = mock_response
+
+ response = video_generation(
+ prompt="Show them running around the room",
+ model="sora-2",
+ seconds="8",
+ size="720x1280"
+ )
+
+ assert isinstance(response, VideoObject)
+ assert response.id == "video_123"
+ assert response.model == "sora-2"
+ assert response.size == "720x1280"
+ assert response.seconds == "8"
+
+ def test_video_generation_with_mock_response(self):
+ """Test video generation with mock response."""
+ mock_data = {
+ "id": "video_456",
+ "object": "video",
+ "status": "completed",
+ "created_at": 1712697600,
+ "completed_at": 1712697660,
+ "model": "sora-2",
+ "size": "1280x720",
+ "seconds": "10"
+ }
+
+ response = video_generation(
+ prompt="A beautiful sunset over the ocean",
+ model="sora-2",
+ seconds="10",
+ size="1280x720",
+ mock_response=mock_data
+ )
+
+ assert isinstance(response, VideoObject)
+ assert response.id == "video_456"
+ assert response.status == "completed"
+ assert response.model == "sora-2"
+ assert response.size == "1280x720"
+ assert response.seconds == "10"
+
+ def test_video_generation_async(self):
+ """Test async video generation functionality."""
+ mock_response = VideoObject(
+ id="video_async_123",
+ object="video",
+ status="processing",
+ created_at=1712697600,
+ model="sora-2",
+ progress=50
+ )
+
+ with patch('litellm.videos.main.base_llm_http_handler') as mock_handler:
+ mock_handler.video_generation_handler.return_value = mock_response
+
+ import asyncio
+
+ async def test_async():
+ response = await avideo_generation(
+ prompt="A cat playing with a ball",
+ model="sora-2",
+ seconds="5",
+ size="720x1280"
+ )
+ return response
+
+ response = asyncio.run(test_async())
+
+ assert isinstance(response, VideoObject)
+ assert response.id == "video_async_123"
+ assert response.status == "processing"
+ assert response.progress == 50
+
+ def test_video_generation_parameter_validation(self):
+ """Test video generation parameter validation."""
+ # Test with minimal required parameters
+ response = video_generation(
+ prompt="Test video",
+ model="sora-2",
+ mock_response={"id": "test", "object": "video", "status": "queued", "created_at": 1712697600}
+ )
+
+ assert isinstance(response, VideoObject)
+ assert response.id == "test"
+
+ def test_video_generation_error_handling(self):
+ """Test video generation error handling."""
+ with patch('litellm.videos.main.base_llm_http_handler') as mock_handler:
+ mock_handler.video_generation_handler.side_effect = Exception("API Error")
+
+ with pytest.raises(Exception):
+ video_generation(
+ prompt="Test video",
+ model="sora-2"
+ )
+
+ def test_video_generation_provider_config(self):
+ """Test video generation provider configuration."""
+ config = OpenAIVideoConfig()
+
+ # Test supported parameters
+ supported_params = config.get_supported_openai_params("sora-2")
+ assert "prompt" in supported_params
+ assert "model" in supported_params
+ assert "seconds" in supported_params
+ assert "size" in supported_params
+
+ def test_video_generation_request_transformation(self):
+ """Test video generation request transformation."""
+ config = OpenAIVideoConfig()
+
+ # Test request transformation
+ data, files = config.transform_video_create_request(
+ model="sora-2",
+ prompt="Test video prompt",
+ video_create_optional_request_params={
+ "seconds": "8",
+ "size": "720x1280"
+ },
+ litellm_params=MagicMock(),
+ headers={}
+ )
+
+ assert data["model"] == "sora-2"
+ assert data["prompt"] == "Test video prompt"
+ assert data["seconds"] == "8"
+ assert data["size"] == "720x1280"
+ assert files == []
+
+ def test_video_generation_response_transformation(self):
+ """Test video generation response transformation."""
+ config = OpenAIVideoConfig()
+
+ # Mock HTTP response
+ mock_http_response = MagicMock()
+ mock_http_response.json.return_value = {
+ "id": "video_789",
+ "object": "video",
+ "status": "completed",
+ "created_at": 1712697600,
+ "model": "sora-2",
+ "size": "1280x720",
+ "seconds": "12"
+ }
+
+ response = config.transform_video_create_response(
+ model="sora-2",
+ raw_response=mock_http_response,
+ logging_obj=MagicMock()
+ )
+
+ assert isinstance(response, VideoObject)
+ assert response.id == "video_789"
+ assert response.status == "completed"
+ assert response.model == "sora-2"
+
+ def test_video_generation_cost_calculation(self):
+ """Test video generation cost calculation."""
+ # Load the local model cost map instead of online
+ import json
+ with open("model_prices_and_context_window.json", "r") as f:
+ litellm.model_cost = json.load(f)
+
+ # Test with sora-2 model
+ cost = default_video_cost_calculator(
+ model="openai/sora-2",
+ duration_seconds=10.0,
+ custom_llm_provider="openai"
+ )
+
+ # Should calculate cost based on duration (10 seconds * $0.10 per second = $1.00)
+ assert cost == 1.0
+
+ def test_video_generation_cost_calculation_unknown_model(self):
+ """Test video generation cost calculation for unknown model."""
+ with pytest.raises(Exception, match="Model not found in cost map"):
+ default_video_cost_calculator(
+ model="unknown-model",
+ duration_seconds=5.0,
+ custom_llm_provider="openai"
+ )
+
+ def test_video_generation_with_files(self):
+ """Test video generation with file uploads."""
+ config = OpenAIVideoConfig()
+
+ # Mock file data
+ mock_file = MagicMock()
+ mock_file.read.return_value = b"fake_image_data"
+
+ data, files = config.transform_video_create_request(
+ model="sora-2",
+ prompt="Test video with image",
+ video_create_optional_request_params={
+ "input_reference": mock_file,
+ "seconds": "8",
+ "size": "720x1280"
+ },
+ litellm_params=MagicMock(),
+ headers={}
+ )
+
+ assert data["model"] == "sora-2"
+ assert data["prompt"] == "Test video with image"
+ assert len(files) > 0 # Should have files when input_reference is provided
+
+ def test_video_generation_environment_validation(self):
+ """Test video generation environment validation."""
+ config = OpenAIVideoConfig()
+
+ # Test environment validation
+ headers = config.validate_environment(
+ headers={},
+ model="sora-2",
+ api_key="test-api-key"
+ )
+
+ assert "Authorization" in headers
+ assert headers["Authorization"] == "Bearer test-api-key"
+
+ def test_video_generation_url_generation(self):
+ """Test video generation URL generation."""
+ config = OpenAIVideoConfig()
+
+ # Test URL generation
+ url = config.get_complete_url(
+ model="sora-2",
+ api_base="https://api.openai.com/v1",
+ litellm_params={}
+ )
+
+ assert url == "https://api.openai.com/v1/videos"
+
+ def test_video_generation_parameter_mapping(self):
+ """Test video generation parameter mapping."""
+ config = OpenAIVideoConfig()
+
+ # Test parameter mapping
+ mapped_params = config.map_openai_params(
+ video_create_optional_params={
+ "seconds": "8",
+ "size": "720x1280",
+ "user": "test-user"
+ },
+ model="sora-2",
+ drop_params=False
+ )
+
+ assert mapped_params["seconds"] == "8"
+ assert mapped_params["size"] == "720x1280"
+ assert mapped_params["user"] == "test-user"
+
+ def test_video_generation_unsupported_parameters(self):
+ """Test video generation with unsupported parameters."""
+ from litellm.videos.utils import VideoGenerationRequestUtils
+
+ # Test unsupported parameter detection
+ with pytest.raises(litellm.UnsupportedParamsError):
+ VideoGenerationRequestUtils.get_optional_params_video_generation(
+ model="sora-2",
+ video_generation_provider_config=OpenAIVideoConfig(),
+ video_generation_optional_params={
+ "unsupported_param": "value"
+ }
+ )
+
+ def test_video_generation_request_utils(self):
+ """Test video generation request utilities."""
+ from litellm.videos.utils import VideoGenerationRequestUtils
+
+ # Test parameter filtering
+ params = {
+ "prompt": "Test video",
+ "model": "sora-2",
+ "seconds": "8",
+ "size": "720x1280",
+ "user": "test-user",
+ "invalid_param": "should_be_filtered"
+ }
+
+ filtered_params = VideoGenerationRequestUtils.get_requested_video_generation_optional_param(params)
+
+ # Should only contain valid parameters
+ assert "prompt" not in filtered_params # prompt is required, not optional
+ assert "seconds" in filtered_params
+ assert "size" in filtered_params
+ assert "user" in filtered_params
+ assert "invalid_param" not in filtered_params
+ # Note: model is included in the filtered params as it's part of the TypedDict
+
+ def test_video_generation_types(self):
+ """Test video generation type definitions."""
+ # Test VideoObject
+ video_obj = VideoObject(
+ id="test_id",
+ object="video",
+ status="completed",
+ created_at=1712697600,
+ model="sora-2"
+ )
+
+ assert video_obj.id == "test_id"
+ assert video_obj.object == "video"
+ assert video_obj.status == "completed"
+
+ # Test dictionary-like access
+ assert video_obj["id"] == "test_id"
+ assert video_obj["status"] == "completed"
+ assert "id" in video_obj
+ assert video_obj.get("id") == "test_id"
+ assert video_obj.get("nonexistent", "default") == "default"
+
+ # Test JSON serialization
+ json_data = video_obj.json()
+ assert json_data["id"] == "test_id"
+ assert json_data["object"] == "video"
+
+ def test_video_generation_response_types(self):
+ """Test video generation response types."""
+ # Test VideoResponse
+ video_obj = VideoObject(
+ id="test_id",
+ object="video",
+ status="completed",
+ created_at=1712697600
+ )
+
+ response = VideoResponse(data=[video_obj])
+
+ assert len(response.data) == 1
+ assert response.data[0].id == "test_id"
+
+ # Test dictionary-like access
+ assert response["data"][0]["id"] == "test_id"
+ assert "data" in response
+ assert response.get("data")[0]["id"] == "test_id"
+
+ # Test JSON serialization
+ json_data = response.json()
+ assert len(json_data["data"]) == 1
+ assert json_data["data"][0]["id"] == "test_id"
+
+
+if __name__ == "__main__":
+ pytest.main([__file__])
diff --git a/ui/litellm-dashboard/out/_next/static/chunks/2344-5605d5481b598d8f.js b/ui/litellm-dashboard/out/_next/static/chunks/2344-5605d5481b598d8f.js
deleted file mode 100644
index 34995612a9d4..000000000000
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L,B,R=i()(S)?d:S,z=null!==(L=null!==(B=(0,T.zT)(M,_,!0))&&void 0!==B?B:R)&&void 0!==L?L:0;I=(0,T.qz)({barGap:f,barCategoryGap:h,bandSize:z!==D?z:D,sizeList:g[k],maxBarSize:R}),z!==D&&(I=I.map(function(t){return ee(ee({},t),{},{position:ee(ee({},t.position),{},{offset:t.position.offset-z/2})})}))}var U=n&&n.type&&n.type.getComposedData;U&&b.push({props:ee(ee({},U(ee(ee({},P),{},{displayedData:x,props:t,dataKey:j,item:n,bandSize:D,barPosition:I,offset:o,stackedData:C,layout:s,dataStartIndex:u,dataEndIndex:c}))),{},(en(y={key:n.key||"item-".concat(l)},v,P[v]),en(y,m,P[m]),en(y,"animationId",a),y)),childIndex:(0,A.$R)(n,t.children),item:n})}),b},E=function(t,e){var r=t.props,i=t.dataStartIndex,a=t.dataEndIndex,u=t.updateId;if(!(0,A.TT)({props:r}))return null;var c=r.children,l=r.layout,s=r.stackOffset,f=r.data,h=r.reverseStackOrder,y=eg(l),v=y.numericAxisName,m=y.cateAxisName,b=(0,A.NN)(c,o),x=(0,T.wh)(f,b,"".concat(v,"Id"),"".concat(m,"Id"),s,h),O=p.reduce(function(t,e){var n="".concat(e.axisType,"Map");return ee(ee({},t),{},en({},n,ey(r,ee(ee({},e),{},{graphicalItems:b,stackGroups:e.axisType===v&&x,dataStartIndex:i,dataEndIndex:a}))))},{}),w=eb(ee(ee({},O),{},{props:r,graphicalItems:b}),null==e?void 0:e.legendBBox);Object.keys(O).forEach(function(t){O[t]=d(r,O[t],w,t.replace("Map",""),n)});var j=ev(O["".concat(m,"Map")]),S=g(r,ee(ee({},O),{},{dataStartIndex:i,dataEndIndex:a,updateId:u,graphicalItems:b,stackGroups:x,offset:w}));return ee(ee({formattedGraphicalItems:S,graphicalItems:b,offset:w,stackGroups:x},j),O)};return e=function(t){(function(t,e){if("function"!=typeof e&&null!==e)throw TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),Object.defineProperty(t,"prototype",{writable:!1}),e&&t6(t,e)})(l,t);var e,o,a=(e=function(){if("undefined"==typeof Reflect||!Reflect.construct||Reflect.construct.sham)return!1;if("function"==typeof 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