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/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/providers/vertex_batch_passthrough.md b/docs/my-website/docs/providers/vertex_batch_passthrough.md new file mode 100644 index 000000000000..8c0f7d463e3c --- /dev/null +++ b/docs/my-website/docs/providers/vertex_batch_passthrough.md @@ -0,0 +1,160 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Vertex AI Batch Passthrough + +LiteLLM supports Vertex AI batch prediction jobs through passthrough endpoints, allowing you to create and manage batch jobs directly through the proxy server. + +## Features + +- **Batch Job Creation**: Create batch prediction jobs using Vertex AI models +- **Cost Tracking**: Automatic cost calculation and usage tracking for batch operations +- **Status Monitoring**: Track job status and retrieve results +- **Model Support**: Works with all supported Vertex AI models (Gemini, Text Embedding) + +## Cost Tracking Support + +| Feature | Supported | Notes | +|---------|-----------|-------| +| Cost Tracking | ✅ | Automatic cost calculation for batch operations | +| Usage Monitoring | ✅ | Track token usage and costs across batch jobs | +| Logging | ✅ | Supported | + +## Quick Start + +1. **Configure your model** in the proxy configuration: + +```yaml +model_list: + - model_name: gemini-1.5-flash + litellm_params: + model: vertex_ai/gemini-1.5-flash + vertex_project: your-project-id + vertex_location: us-central1 + vertex_credentials: path/to/service-account.json +``` + +2. **Create a batch job**: + +```bash +curl -X POST "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs" \ + -H "Authorization: Bearer your-api-key" \ + -H "Content-Type: application/json" \ + -d '{ + "displayName": "my-batch-job", + "model": "projects/your-project/locations/us-central1/publishers/google/models/gemini-1.5-flash", + "inputConfig": { + "gcsSource": { + "uris": ["gs://my-bucket/input.jsonl"] + }, + "instancesFormat": "jsonl" + }, + "outputConfig": { + "gcsDestination": { + "outputUriPrefix": "gs://my-bucket/output/" + }, + "predictionsFormat": "jsonl" + } + }' +``` + +3. **Monitor job status**: + +```bash +curl -X GET "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs/job-id" \ + -H "Authorization: Bearer your-api-key" +``` + +## Model Configuration + +When configuring models for batch operations, use these naming conventions: + +- **`model_name`**: Base model name (e.g., `gemini-1.5-flash`) +- **`model`**: Full LiteLLM identifier (e.g., `vertex_ai/gemini-1.5-flash`) + +## Supported Models + +- `gemini-1.5-flash` / `vertex_ai/gemini-1.5-flash` +- `gemini-1.5-pro` / `vertex_ai/gemini-1.5-pro` +- `gemini-2.0-flash` / `vertex_ai/gemini-2.0-flash` +- `gemini-2.0-pro` / `vertex_ai/gemini-2.0-pro` + +## Advanced Usage + +### Batch Job with Custom Parameters + +```bash +curl -X POST "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs" \ + -H "Authorization: Bearer your-api-key" \ + -H "Content-Type: application/json" \ + -d '{ + "displayName": "advanced-batch-job", + "model": "projects/your-project/locations/us-central1/publishers/google/models/gemini-1.5-pro", + "inputConfig": { + "gcsSource": { + "uris": ["gs://my-bucket/advanced-input.jsonl"] + }, + "instancesFormat": "jsonl" + }, + "outputConfig": { + "gcsDestination": { + "outputUriPrefix": "gs://my-bucket/advanced-output/" + }, + "predictionsFormat": "jsonl" + }, + "labels": { + "environment": "production", + "team": "ml-engineering" + } + }' +``` + +### List All Batch Jobs + +```bash +curl -X GET "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs" \ + -H "Authorization: Bearer your-api-key" +``` + +### Cancel a Batch Job + +```bash +curl -X POST "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs/job-id:cancel" \ + -H "Authorization: Bearer your-api-key" +``` + +## Cost Tracking Details + +LiteLLM provides comprehensive cost tracking for Vertex AI batch operations: + +- **Token Usage**: Tracks input and output tokens for each batch request +- **Cost Calculation**: Automatically calculates costs based on current Vertex AI pricing +- **Usage Aggregation**: Aggregates costs across all requests in a batch job +- **Real-time Monitoring**: Monitor costs as batch jobs progress + +The cost tracking works seamlessly with the `generateContent` API and provides detailed insights into your batch processing expenses. + +## Error Handling + +Common error scenarios and their solutions: + +| Error | Description | Solution | +|-------|-------------|----------| +| `INVALID_ARGUMENT` | Invalid model or configuration | Verify model name and project settings | +| `PERMISSION_DENIED` | Insufficient permissions | Check Vertex AI IAM roles | +| `RESOURCE_EXHAUSTED` | Quota exceeded | Check Vertex AI quotas and limits | +| `NOT_FOUND` | Job or resource not found | Verify job ID and project configuration | + +## Best Practices + +1. **Use appropriate batch sizes**: Balance between processing efficiency and resource usage +2. **Monitor job status**: Regularly check job status to handle failures promptly +3. **Set up alerts**: Configure monitoring for job completion and failures +4. **Optimize costs**: Use cost tracking to identify optimization opportunities +5. **Test with small batches**: Validate your setup with small test batches first + +## Related Documentation + +- [Vertex AI Provider Documentation](./vertex.md) +- [General Batches API Documentation](../batches.md) +- [Cost Tracking and Monitoring](../observability/telemetry.md) diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md index 4d51aa34dc3a..7d2389383d15 100644 --- a/docs/my-website/docs/proxy/deploy.md +++ b/docs/my-website/docs/proxy/deploy.md @@ -788,7 +788,7 @@ docker run --name litellm-proxy \ ## Platform-specific Guide - ### Terraform-based ECS Deployment 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/docs/my-website/release_notes/v1.78.0-stable/index.md b/docs/my-website/release_notes/v1.78.0-stable/index.md index e9a471f45b57..63d5eaca0b02 100644 --- a/docs/my-website/release_notes/v1.78.0-stable/index.md +++ b/docs/my-website/release_notes/v1.78.0-stable/index.md @@ -40,7 +40,7 @@ import TabItem from '@theme/TabItem'; docker run \ -e STORE_MODEL_IN_DB=True \ -p 4000:4000 \ -ghcr.io/berriai/litellm:v1.78.0.rc.1 +ghcr.io/berriai/litellm:v1.78.0.rc.2 ``` @@ -48,7 +48,7 @@ ghcr.io/berriai/litellm:v1.78.0.rc.1 ``` showLineNumbers title="pip install litellm" -pip install litellm==1.78.0.rc.1 +pip install litellm==1.78.0.rc.2 ``` 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/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py index 4b1bb024ac6a..d4ee4042b1ae 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -57,7 +57,6 @@ async def check_batch_cost(self): "file_purpose": "batch", } ) - completed_jobs = [] for job in jobs: @@ -139,7 +138,7 @@ async def check_batch_cost(self): custom_llm_provider = deployment_info.litellm_params.custom_llm_provider litellm_model_name = deployment_info.litellm_params.model - _, llm_provider, _, _ = get_llm_provider( + model_name, llm_provider, _, _ = get_llm_provider( model=litellm_model_name, custom_llm_provider=custom_llm_provider, ) @@ -148,9 +147,9 @@ async def check_batch_cost(self): await calculate_batch_cost_and_usage( file_content_dictionary=file_content_as_dict, custom_llm_provider=llm_provider, # type: ignore + model_name=model_name, ) ) - logging_obj = LiteLLMLogging( model=batch_models[0], messages=[{"role": "user", "content": ""}], diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 1da3fa405aec..8af7c212f523 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.2.26" +version = "0.2.27" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.2.26" +version = "0.2.27" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index e461c88efd6d..e6ed15d7df04 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1067,6 +1067,7 @@ 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 diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index 814851e560b5..027c0b219c88 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -1,5 +1,5 @@ import json -from typing import Any, List, Literal, Tuple +from typing import Any, List, Literal, Tuple, Optional import litellm from litellm._logging import verbose_logger @@ -10,21 +10,22 @@ async def calculate_batch_cost_and_usage( file_content_dictionary: List[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai"], + model_name: Optional[str] = None, ) -> Tuple[float, Usage, List[str]]: """ Calculate the cost and usage of a batch """ - # Calculate costs and usage batch_cost = _batch_cost_calculator( custom_llm_provider=custom_llm_provider, file_content_dictionary=file_content_dictionary, + model_name=model_name, ) batch_usage = _get_batch_job_total_usage_from_file_content( file_content_dictionary=file_content_dictionary, custom_llm_provider=custom_llm_provider, + model_name=model_name, ) - - batch_models = _get_batch_models_from_file_content(file_content_dictionary) + batch_models = _get_batch_models_from_file_content(file_content_dictionary, model_name) return batch_cost, batch_usage, batch_models @@ -32,6 +33,7 @@ async def calculate_batch_cost_and_usage( async def _handle_completed_batch( batch: Batch, custom_llm_provider: Literal["openai", "azure", "vertex_ai"], + model_name: Optional[str] = None, ) -> Tuple[float, Usage, List[str]]: """Helper function to process a completed batch and handle logging""" # Get batch results @@ -43,23 +45,28 @@ async def _handle_completed_batch( batch_cost = _batch_cost_calculator( custom_llm_provider=custom_llm_provider, file_content_dictionary=file_content_dictionary, + model_name=model_name, ) batch_usage = _get_batch_job_total_usage_from_file_content( file_content_dictionary=file_content_dictionary, custom_llm_provider=custom_llm_provider, + model_name=model_name, ) - batch_models = _get_batch_models_from_file_content(file_content_dictionary) + batch_models = _get_batch_models_from_file_content(file_content_dictionary, model_name) return batch_cost, batch_usage, batch_models def _get_batch_models_from_file_content( file_content_dictionary: List[dict], + model_name: Optional[str] = None, ) -> List[str]: """ Get the models from the file content """ + if model_name: + return [model_name] batch_models = [] for _item in file_content_dictionary: if _batch_response_was_successful(_item): @@ -73,12 +80,18 @@ def _get_batch_models_from_file_content( def _batch_cost_calculator( file_content_dictionary: List[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + model_name: Optional[str] = None, ) -> float: """ Calculate the cost of a batch based on the output file id """ - if custom_llm_provider == "vertex_ai": - raise ValueError("Vertex AI does not support file content retrieval") + # Handle Vertex AI with specialized method + if custom_llm_provider == "vertex_ai" and model_name: + batch_cost, _ = calculate_vertex_ai_batch_cost_and_usage(file_content_dictionary, model_name) + verbose_logger.debug("vertex_ai_total_cost=%s", batch_cost) + return batch_cost + + # For other providers, use the existing logic total_cost = _get_batch_job_cost_from_file_content( file_content_dictionary=file_content_dictionary, custom_llm_provider=custom_llm_provider, @@ -87,6 +100,87 @@ def _batch_cost_calculator( return total_cost +def calculate_vertex_ai_batch_cost_and_usage( + vertex_ai_batch_responses: List[dict], + model_name: Optional[str] = None, +) -> Tuple[float, Usage]: + """ + Calculate both cost and usage from Vertex AI batch responses + """ + total_cost = 0.0 + total_tokens = 0 + prompt_tokens = 0 + completion_tokens = 0 + + for response in vertex_ai_batch_responses: + if response.get("status") == "JOB_STATE_SUCCEEDED": # Check if response was successful + # Transform Vertex AI response to OpenAI format if needed + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig + from litellm import ModelResponse + from litellm.litellm_core_utils.litellm_logging import Logging + from litellm.types.utils import CallTypes + from litellm._uuid import uuid + import httpx + import time + + # Create required arguments for the transformation method + model_response = ModelResponse() + + # Ensure model_name is not None + actual_model_name = model_name or "gemini-2.5-flash" + + # Create a real LiteLLM logging object + logging_obj = Logging( + model=actual_model_name, + messages=[{"role": "user", "content": "batch_request"}], + stream=False, + call_type=CallTypes.aretrieve_batch, + start_time=time.time(), + litellm_call_id="batch_" + str(uuid.uuid4()), + function_id="batch_processing", + litellm_trace_id=str(uuid.uuid4()), + kwargs={"optional_params": {}} + ) + + # Add the optional_params attribute that the Vertex AI transformation expects + logging_obj.optional_params = {} + raw_response = httpx.Response(200) # Mock response object + + openai_format_response = VertexGeminiConfig()._transform_google_generate_content_to_openai_model_response( + completion_response=response["response"], + model_response=model_response, + model=actual_model_name, + logging_obj=logging_obj, + raw_response=raw_response, + ) + + # Calculate cost using existing function + cost = litellm.completion_cost( + completion_response=openai_format_response, + custom_llm_provider="vertex_ai", + call_type=CallTypes.aretrieve_batch.value, + ) + total_cost += cost + + # Extract usage from the transformed response + if hasattr(openai_format_response, 'usage') and openai_format_response.usage: + usage = openai_format_response.usage + else: + # Fallback: create usage from response dict + response_dict = openai_format_response.dict() if hasattr(openai_format_response, 'dict') else {} + usage = _get_batch_job_usage_from_response_body(response_dict) + + total_tokens += usage.total_tokens + prompt_tokens += usage.prompt_tokens + completion_tokens += usage.completion_tokens + + return total_cost, Usage( + total_tokens=total_tokens, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ) + + async def _get_batch_output_file_content_as_dictionary( batch: Batch, custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", @@ -157,10 +251,17 @@ def _get_batch_job_cost_from_file_content( def _get_batch_job_total_usage_from_file_content( file_content_dictionary: List[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + model_name: Optional[str] = None, ) -> Usage: """ Get the tokens of a batch job from the file content """ + # Handle Vertex AI with specialized method + if custom_llm_provider == "vertex_ai" and model_name: + _, batch_usage = calculate_vertex_ai_batch_cost_and_usage(file_content_dictionary, model_name) + return batch_usage + + # For other providers, use the existing logic total_tokens: int = 0 prompt_tokens: int = 0 completion_tokens: int = 0 diff --git a/litellm/constants.py b/litellm/constants.py index 54ac3e6d6b84..4c336033241c 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" 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/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index eafcab885577..7369cdf92845 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/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/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/proxy/_experimental/out/_next/static/chunks/1052-6c4e848aed27b319.js b/litellm/proxy/_experimental/out/_next/static/chunks/1052-bdb89c881be4619e.js similarity index 99% rename from litellm/proxy/_experimental/out/_next/static/chunks/1052-6c4e848aed27b319.js rename to litellm/proxy/_experimental/out/_next/static/chunks/1052-bdb89c881be4619e.js index 8ab8f6ab8aad..d760fa17b653 100644 --- a/litellm/proxy/_experimental/out/_next/static/chunks/1052-6c4e848aed27b319.js +++ b/litellm/proxy/_experimental/out/_next/static/chunks/1052-bdb89c881be4619e.js @@ -1 +1 @@ -"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[1052],{19046:function(e,t,s){s.d(t,{Dx:function(){return r.Z},Zb:function(){return a.Z},oi:function(){return l.Z},xv:function(){return n.Z},zx:function(){return o.Z}});var o=s(20831),a=s(12514),n=s(84264),l=s(49566),r=s(96761)},31052:function(e,t,s){s.d(t,{Z:function(){return eA}});var o=s(57437),a=s(2265),n=s(243),l=s(19046),r=s(93837),i=s(64482),c=s(65319),d=s(93192),m=s(52787),g=s(89970),p=s(87908),u=s(82680),x=s(73002),h=s(26433),f=s(19250);async function b(e,t,s,o,a,n,l,r,i,c,d,m,g,p){console.log=function(){},console.log("isLocal:",!1);let u=(0,f.getProxyBaseUrl)(),x={};a&&a.length>0&&(x["x-litellm-tags"]=a.join(","));let b=new h.ZP.OpenAI({apiKey:o,baseURL:u,dangerouslyAllowBrowser:!0,defaultHeaders:x});try{let a;let x=Date.now(),h=!1,f=g&&g.length>0?[{type:"mcp",server_label:"litellm",server_url:"".concat(u,"/mcp"),require_approval:"never",allowed_tools:g,headers:{"x-litellm-api-key":"Bearer ".concat(o)}}]:void 0;for await(let o of(await b.chat.completions.create({model:s,stream:!0,stream_options:{include_usage:!0},litellm_trace_id:c,messages:e,...d?{vector_store_ids:d}:{},...m?{guardrails:m}:{},...f?{tools:f,tool_choice:"auto"}:{}},{signal:n}))){var v,y,j,w,S,N,k,P;console.log("Stream chunk:",o);let e=null===(v=o.choices[0])||void 0===v?void 0:v.delta;if(console.log("Delta content:",null===(j=o.choices[0])||void 0===j?void 0:null===(y=j.delta)||void 0===y?void 0:y.content),console.log("Delta reasoning content:",null==e?void 0:e.reasoning_content),!h&&((null===(S=o.choices[0])||void 0===S?void 0:null===(w=S.delta)||void 0===w?void 0:w.content)||e&&e.reasoning_content)&&(h=!0,a=Date.now()-x,console.log("First token received! 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n=e.graphicalItems,r=e.stackGroups,o=e.offset,a=e.updateId,u=e.dataStartIndex,c=e.dataEndIndex,l=t.barSize,s=t.layout,f=t.barGap,h=t.barCategoryGap,d=t.maxBarSize,y=eg(s),v=y.numericAxisName,m=y.cateAxisName,g=!!n&&!!n.length&&n.some(function(t){var e=(0,A.Gf)(t&&t.type);return e&&e.indexOf("Bar")>=0})&&(0,T.pt)({barSize:l,stackGroups:r}),b=[];return n.forEach(function(n,l){var y,x=el(t.data,{graphicalItems:[n],dataStartIndex:u,dataEndIndex:c}),w=n.props,j=w.dataKey,S=w.maxBarSize,E=n.props["".concat(v,"Id")],k=n.props["".concat(m,"Id")],P=p.reduce(function(t,r){var o,i=e["".concat(r.axisType,"Map")],a=n.props["".concat(r.axisType,"Id")];i&&i[a]||"zAxis"===r.axisType||(0,O.Z)(!1);var u=i[a];return ee(ee({},t),{},(en(o={},r.axisType,u),en(o,"".concat(r.axisType,"Ticks"),(0,T.uY)(u)),o))},{}),M=P[m],_=P["".concat(m,"Ticks")],C=r&&r[E]&&r[E].hasStack&&(0,T.O3)(n,r[E].stackGroups),N=(0,A.Gf)(n.type).indexOf("Bar")>=0,D=(0,T.zT)(M,_),I=[];if(N){var 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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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base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(w,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(w,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(b,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,t="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:h}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(w,'",\n input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(w,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(b,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(w,'",\n prompt="').concat(r,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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azure_endpoint="').concat(f,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(_||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(f,'"\n)');switch(u){case a.KP.CHAT:{let e=Object.keys(v).length>0,t="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:h}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(w,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(w,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(b,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,t="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:h}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(w,'",\n input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(w,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(b,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(w,'",\n prompt="').concat(r,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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- 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/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index 5b22b2746c9a..c7695704330a 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -18,14 +18,19 @@ ImageResponse, ModelResponse, TextCompletionResponse, + Choices, ) +from litellm.types.utils import SpecialEnums if TYPE_CHECKING: from ..success_handler import PassThroughEndpointLogging - from ..types import EndpointType + from litellm.types.utils import LiteLLMBatch else: PassThroughEndpointLogging = Any - EndpointType = Any + LiteLLMBatch = Any + +# Define EndpointType locally to avoid import issues +EndpointType = Any class VertexPassthroughLoggingHandler: @@ -204,6 +209,17 @@ def vertex_passthrough_handler( "result": litellm_prediction_response, "kwargs": kwargs, } + elif "batchPredictionJobs" in url_route: + return VertexPassthroughLoggingHandler.batch_prediction_jobs_handler( + httpx_response=httpx_response, + logging_obj=logging_obj, + url_route=url_route, + result=result, + start_time=start_time, + end_time=end_time, + cache_hit=cache_hit, + **kwargs, + ) else: return { "result": None, @@ -324,6 +340,38 @@ def extract_model_from_url(url: str) -> str: return match.group(1) return "unknown" + @staticmethod + def extract_model_name_from_vertex_path(vertex_model_path: str) -> str: + """ + Extract the actual model name from a Vertex AI model path. + + Examples: + - publishers/google/models/gemini-2.5-flash -> gemini-2.5-flash + - projects/PROJECT_ID/locations/LOCATION/models/MODEL_ID -> MODEL_ID + + Args: + vertex_model_path: The full Vertex AI model path + + Returns: + The extracted model name for use with LiteLLM + """ + # Handle publishers/google/models/ format + if "publishers/" in vertex_model_path and "models/" in vertex_model_path: + # Extract everything after the last models/ + parts = vertex_model_path.split("models/") + if len(parts) > 1: + return parts[-1] + + # Handle projects/PROJECT_ID/locations/LOCATION/models/MODEL_ID format + elif "projects/" in vertex_model_path and "models/" in vertex_model_path: + # Extract everything after the last models/ + parts = vertex_model_path.split("models/") + if len(parts) > 1: + return parts[-1] + + # If no recognized pattern, return the original path + return vertex_model_path + @staticmethod def _get_vertex_publisher_or_api_spec_from_url(url: str) -> Optional[str]: # Check for specific Vertex AI partner publishers @@ -388,7 +436,7 @@ def _create_vertex_response_logging_payload_for_generate_content( end_time: datetime, logging_obj: LiteLLMLoggingObj, custom_llm_provider: str, - ): + ) -> dict: """ Create the standard logging object for Vertex passthrough generateContent (streaming and non-streaming) @@ -412,3 +460,255 @@ def _create_vertex_response_logging_payload_for_generate_content( logging_obj.model_call_details["model"] = logging_obj.model logging_obj.model_call_details["custom_llm_provider"] = custom_llm_provider return kwargs + + @staticmethod + def batch_prediction_jobs_handler( + httpx_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + url_route: str, + result: str, + start_time: datetime, + end_time: datetime, + cache_hit: bool, + **kwargs, + ) -> PassThroughEndpointLoggingTypedDict: + """ + Handle batch prediction jobs passthrough logging. + Creates a managed object for cost tracking when batch job is successfully created. + """ + from litellm.llms.vertex_ai.batches.transformation import VertexAIBatchTransformation + from litellm._uuid import uuid + import base64 + + try: + _json_response = httpx_response.json() + + # Only handle successful batch job creation (POST requests) + if httpx_response.status_code == 200 and "name" in _json_response: + # Transform Vertex AI response to LiteLLM batch format + litellm_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( + response=_json_response + ) + + # Extract batch ID and model from the response + batch_id = VertexAIBatchTransformation._get_batch_id_from_vertex_ai_batch_response(_json_response) + model_name = _json_response.get("model", "unknown") + + # Create unified object ID for tracking + # Format: base64(litellm_proxy;model_id:{};llm_batch_id:{}) + actual_model_id = VertexPassthroughLoggingHandler.get_actual_model_id_from_router(model_name) + + unified_id_string = SpecialEnums.LITELLM_MANAGED_BATCH_COMPLETE_STR.value.format(actual_model_id, batch_id) + unified_object_id = base64.urlsafe_b64encode(unified_id_string.encode()).decode().rstrip("=") + + # Store the managed object for cost tracking + # This will be picked up by check_batch_cost polling mechanism + VertexPassthroughLoggingHandler._store_batch_managed_object( + unified_object_id=unified_object_id, + batch_object=litellm_batch_response, + model_object_id=batch_id, + logging_obj=logging_obj, + **kwargs, + ) + + # Create a batch job response for logging + litellm_model_response = ModelResponse() + litellm_model_response.id = str(uuid.uuid4()) + litellm_model_response.model = model_name + litellm_model_response.object = "batch_prediction_job" + litellm_model_response.created = int(start_time.timestamp()) + + # Add batch-specific metadata to indicate this is a pending batch job + litellm_model_response.choices = [Choices( + finish_reason="batch_pending", + index=0, + message={ + "role": "assistant", + "content": f"Batch prediction job {batch_id} created and is pending. Status will be updated when the batch completes.", + "tool_calls": None, + "function_call": None, + "provider_specific_fields": { + "batch_job_id": batch_id, + "batch_job_state": "JOB_STATE_PENDING", + "unified_object_id": unified_object_id + } + } + )] + + # Set response cost to 0 initially (will be updated when batch completes) + response_cost = 0.0 + kwargs["response_cost"] = response_cost + kwargs["model"] = model_name + kwargs["batch_id"] = batch_id + kwargs["unified_object_id"] = unified_object_id + kwargs["batch_job_state"] = "JOB_STATE_PENDING" + + logging_obj.model = model_name + logging_obj.model_call_details["model"] = logging_obj.model + logging_obj.model_call_details["response_cost"] = response_cost + logging_obj.model_call_details["batch_id"] = batch_id + + return { + "result": litellm_model_response, + "kwargs": kwargs, + } + else: + # Handle non-successful responses + litellm_model_response = ModelResponse() + litellm_model_response.id = str(uuid.uuid4()) + litellm_model_response.model = "vertex_ai_batch" + litellm_model_response.object = "batch_prediction_job" + litellm_model_response.created = int(start_time.timestamp()) + + # Add error-specific metadata + litellm_model_response.choices = [Choices( + finish_reason="batch_error", + index=0, + message={ + "role": "assistant", + "content": f"Batch prediction job creation failed. Status: {httpx_response.status_code}", + "tool_calls": None, + "function_call": None, + "provider_specific_fields": { + "batch_job_state": "JOB_STATE_FAILED", + "status_code": httpx_response.status_code + } + } + )] + + kwargs["response_cost"] = 0.0 + kwargs["model"] = "vertex_ai_batch" + kwargs["batch_job_state"] = "JOB_STATE_FAILED" + + return { + "result": litellm_model_response, + "kwargs": kwargs, + } + + except Exception as e: + verbose_proxy_logger.error(f"Error in batch_prediction_jobs_handler: {e}") + # Return basic response on error + litellm_model_response = ModelResponse() + litellm_model_response.id = str(uuid.uuid4()) + litellm_model_response.model = "vertex_ai_batch" + litellm_model_response.object = "batch_prediction_job" + litellm_model_response.created = int(start_time.timestamp()) + + # Add error-specific metadata + litellm_model_response.choices = [Choices( + finish_reason="batch_error", + index=0, + message={ + "role": "assistant", + "content": f"Error creating batch prediction job: {str(e)}", + "tool_calls": None, + "function_call": None, + "provider_specific_fields": { + "batch_job_state": "JOB_STATE_FAILED", + "error": str(e) + } + } + )] + + kwargs["response_cost"] = 0.0 + kwargs["model"] = "vertex_ai_batch" + kwargs["batch_job_state"] = "JOB_STATE_FAILED" + + return { + "result": litellm_model_response, + "kwargs": kwargs, + } + + @staticmethod + def _store_batch_managed_object( + unified_object_id: str, + batch_object: LiteLLMBatch, + model_object_id: str, + logging_obj: LiteLLMLoggingObj, + **kwargs, + ) -> None: + """ + Store batch managed object for cost tracking. + This will be picked up by the check_batch_cost polling mechanism. + """ + try: + # Get the managed files hook from the logging object + # This is a bit of a hack, but we need access to the proxy logging system + from litellm.proxy.proxy_server import proxy_logging_obj + + managed_files_hook = proxy_logging_obj.get_proxy_hook("managed_files") + if managed_files_hook is not None and hasattr(managed_files_hook, 'store_unified_object_id'): + # Create a mock user API key dict for the managed object storage + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy._types import LitellmUserRoles + user_api_key_dict = UserAPIKeyAuth( + user_id=kwargs.get("user_id", "default-user"), + api_key="", + team_id=None, + team_alias=None, + user_role=LitellmUserRoles.CUSTOMER, # Use proper enum value + user_email=None, + max_budget=None, + spend=0.0, # Set to 0.0 instead of None + models=[], # Set to empty list instead of None + tpm_limit=None, + rpm_limit=None, + budget_duration=None, + budget_reset_at=None, + max_parallel_requests=None, + allowed_model_region=None, + metadata={}, # Set to empty dict instead of None + key_alias=None, + permissions={}, # Set to empty dict instead of None + model_max_budget={}, # Set to empty dict instead of None + model_spend={}, # Set to empty dict instead of None + ) + + # Store the unified object for batch cost tracking + import asyncio + asyncio.create_task( + managed_files_hook.store_unified_object_id( + unified_object_id=unified_object_id, + file_object=batch_object, + litellm_parent_otel_span=None, + model_object_id=model_object_id, + file_purpose="batch", + user_api_key_dict=user_api_key_dict, + ) + ) + + verbose_proxy_logger.info( + f"Stored batch managed object with unified_object_id={unified_object_id}, batch_id={model_object_id}" + ) + else: + verbose_proxy_logger.warning("Managed files hook not available, cannot store batch object for cost tracking") + + except Exception as e: + verbose_proxy_logger.error(f"Error storing batch managed object: {e}") + + @staticmethod + def get_actual_model_id_from_router(model_name: str) -> str: + from litellm.proxy.proxy_server import llm_router + + if llm_router is not None: + # Try to find the model in the router by the extracted model name + extracted_model_name = VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path(model_name) + + # Use the existing get_model_ids method from router + model_ids = llm_router.get_model_ids(model_name=extracted_model_name) + if model_ids and len(model_ids) > 0: + # Use the first model ID found + actual_model_id = model_ids[0] + verbose_proxy_logger.info(f"Found model ID in router: {actual_model_id}") + return actual_model_id + else: + # Fallback to constructed model name + actual_model_id = extracted_model_name + verbose_proxy_logger.warning(f"Model not found in router, using constructed name: {actual_model_id}") + return actual_model_id + else: + # Fallback if router is not available + extracted_model_name = VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path(model_name) + verbose_proxy_logger.warning(f"Router not available, using constructed model name: {extracted_model_name}") + return extracted_model_name + diff --git a/litellm/proxy/pass_through_endpoints/success_handler.py b/litellm/proxy/pass_through_endpoints/success_handler.py index a819c429f103..0c0ade848eaa 100644 --- a/litellm/proxy/pass_through_endpoints/success_handler.py +++ b/litellm/proxy/pass_through_endpoints/success_handler.py @@ -40,6 +40,7 @@ def __init__(self): "predict", "rawPredict", "streamRawPredict", + "batchPredictionJobs", ] # Anthropic diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 338fa98118c8..6c49159ef0ec 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -1,16 +1,33 @@ model_list: - - model_name: db-openai-endpoint + - model_name: bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0 litellm_params: - model: openai/gm - api_key: hi - api_base: https://exampleopenaiendpoint-production.up.railway.app/ - + 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" -litellm_settings: - callbacks: ["dynamic_rate_limiter_v3"] - priority_reservation: - "prod": 0.9 # 90% reserved for production (9 RPM) - "dev": 0.1 # 10% reserved for development (1 RPM) - priority_reservation_settings: - default_priority: 0.2 # Weight (0%) assigned to keys without explicit priority metadata - saturation_threshold: 0.50 # A model is saturated if it has hit 50% of its RPM limit \ No newline at end of file + diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 8f5a046be030..c37c7c3fde6d 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -4034,7 +4034,7 @@ async def initialize_scheduled_background_jobs( ### CHECK BATCH COST ### if llm_router is not None: try: - from litellm_enterprise.proxy.common_utils.check_batch_cost import ( + from enterprise.litellm_enterprise.proxy.common_utils.check_batch_cost import ( CheckBatchCost, ) @@ -4048,8 +4048,12 @@ async def initialize_scheduled_background_jobs( "interval", seconds=proxy_batch_polling_interval, # these can run infrequently, as batch jobs take time to complete ) + verbose_proxy_logger.info("Batch cost check job scheduled successfully") - except Exception: + except Exception as e: + verbose_proxy_logger.error( + f"Failed to setup batch cost checking: {e}" + ) verbose_proxy_logger.debug( "Checking batch cost for LiteLLM Managed Files is an Enterprise Feature. Skipping..." ) diff --git a/litellm/router.py b/litellm/router.py index 5972b06f01a1..9bbca54c6b95 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: @@ -5156,8 +5155,8 @@ 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 @@ -5380,6 +5379,26 @@ 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..a48d9a29911a 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): diff --git a/litellm/utils.py b/litellm/utils.py index 5861d703a344..d502056f607b 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -1507,7 +1507,7 @@ 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), @@ -7211,6 +7211,9 @@ def get_provider_embedding_config( return VolcEngineEmbeddingConfig() elif litellm.LlmProviders.OVHCLOUD == provider: return litellm.OVHCloudEmbeddingConfig() + elif litellm.LlmProviders.SAGEMAKER == provider: + from litellm.llms.sagemaker.embedding.transformation import SagemakerEmbeddingConfig + return SagemakerEmbeddingConfig.get_model_config(model) return None @staticmethod @@ -7240,6 +7243,8 @@ 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 diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 91a23b7f00fa..e990d34a716c 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -23136,7 +23136,9 @@ "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, @@ -23152,7 +23154,9 @@ "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, @@ -23160,12 +23164,14 @@ }, "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, @@ -23174,12 +23180,14 @@ }, "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, diff --git a/poetry.lock b/poetry.lock index 8b679ffaa663..1fc0b3afcada 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.2.0 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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database migration tool for SQLAlchemy." optional = true -python-versions = ">=3.9" -groups = ["main"] -markers = "python_version >= \"3.10\" and extra == \"mlflow\"" +python-versions = ">=3.10" files = [ - {file = "alembic-1.16.5-py3-none-any.whl", hash = "sha256:e845dfe090c5ffa7b92593ae6687c5cb1a101e91fa53868497dbd79847f9dbe3"}, - {file = "alembic-1.16.5.tar.gz", hash = "sha256:a88bb7f6e513bd4301ecf4c7f2206fe93f9913f9b48dac3b78babde2d6fe765e"}, + {file = "alembic-1.17.0-py3-none-any.whl", hash = "sha256:80523bc437d41b35c5db7e525ad9d908f79de65c27d6a5a5eab6df348a352d99"}, + {file = "alembic-1.17.0.tar.gz", hash = "sha256:4652a0b3e19616b57d652b82bfa5e38bf5dbea0813eed971612671cb9e90c0fe"}, ] [package.dependencies] @@ -350,7 +174,6 @@ version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" -groups = ["main", "dev", "proxy-dev"] files = [ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = 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"pytest-mypy-plugins", "pytest-xdist[psutil]"] +tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] [[package]] name = "azure-core" @@ -474,8 +268,6 @@ version = "1.33.0" description = "Microsoft Azure Core Library for Python" optional = false python-versions = ">=3.8" -groups = ["main", "proxy-dev"] -markers = "python_full_version == \"3.8.*\" or platform_python_implementation == \"PyPy\" and python_version < \"3.10\"" files = [ {file = "azure_core-1.33.0-py3-none-any.whl", hash = "sha256:9b5b6d0223a1d38c37500e6971118c1e0f13f54951e6893968b38910bc9cda8f"}, {file = "azure_core-1.33.0.tar.gz", hash = "sha256:f367aa07b5e3005fec2c1e184b882b0b039910733907d001c20fb08ebb8c0eb9"}, @@ -490,36 +282,12 @@ typing-extensions = ">=4.6.0" aio = ["aiohttp (>=3.0)"] tracing = ["opentelemetry-api (>=1.26,<2.0)"] -[[package]] -name = "azure-core" -version = "1.35.1" -description = "Microsoft Azure Core Library for Python" -optional = false -python-versions = ">=3.9" -groups = ["main", "proxy-dev"] -markers = "python_version >= \"3.9\" and platform_python_implementation != \"PyPy\" or python_version >= \"3.10\"" -files = [ - {file = "azure_core-1.35.1-py3-none-any.whl", hash = "sha256:12da0c9e08e48e198f9158b56ddbe33b421477e1dc98c2e1c8f9e254d92c468b"}, - {file = "azure_core-1.35.1.tar.gz", hash = "sha256:435d05d6df0fff2f73fb3c15493bb4721ede14203f1ff1382aa6b6b2bdd7e562"}, -] - -[package.dependencies] -requests = ">=2.21.0" -six = ">=1.11.0" -typing-extensions = ">=4.6.0" - -[package.extras] -aio = ["aiohttp (>=3.0)"] -tracing = ["opentelemetry-api (>=1.26,<2.0)"] - [[package]] name = "azure-identity" version = "1.21.0" description = "Microsoft Azure Identity Library for Python" optional = false python-versions = ">=3.8" -groups = ["main", "proxy-dev"] -markers = "python_full_version == \"3.8.*\" or platform_python_implementation == \"PyPy\" and python_version < \"3.10\"" files = [ {file = "azure_identity-1.21.0-py3-none-any.whl", hash = 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">=1.2.0" -typing-extensions = ">=4.0.0" - [[package]] name = "azure-keyvault-secrets" version = "4.9.0" description = "Microsoft Azure Key Vault Secrets Client Library for Python" optional = true python-versions = ">=3.8" -groups = ["main"] -markers = "(python_full_version == \"3.8.*\" or platform_python_implementation == \"PyPy\") and extra == \"extra-proxy\" and python_version < \"3.10\"" files = [ {file = "azure_keyvault_secrets-4.9.0-py3-none-any.whl", hash = "sha256:33c7e2aca2cc2092cebc8c6e96eca36a5cc30c767e16ea429c5fa21270e9fba6"}, {file = "azure_keyvault_secrets-4.9.0.tar.gz", hash = "sha256:2a03bb2ffd9a0d6c8ad1c330d9d0310113985a9de06607ece378fd72a5889fe1"}, @@ -570,32 +316,12 @@ azure-core = ">=1.31.0" isodate = ">=0.6.1" typing-extensions = ">=4.0.1" -[[package]] -name = "azure-keyvault-secrets" -version = "4.10.0" -description = "Microsoft Corporation Key Vault Secrets Client Library for Python" -optional = true -python-versions = ">=3.9" -groups = ["main"] -markers = 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"jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] type = ["pytest-mypy"] [extras] @@ -9596,6 +6418,6 @@ semantic-router = ["semantic-router"] utils = ["numpydoc"] [metadata] -lock-version = "2.1" +lock-version = "2.0" python-versions = ">=3.8.1,<4.0, !=3.9.7" -content-hash = "3ea68de15459ee89fa79e91864be3510201096d789203588770c3b522a929e11" +content-hash = "ee4b9e40ff989a3944b42791100faaf200ea24c7000889b7c0ecc02906dcc5e7" diff --git a/pyproject.toml b/pyproject.toml index 67ffe767799e..0a379ec752bb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm" -version = "1.78.0" +version = "1.78.1" description = "Library to easily interface with LLM API providers" authors = ["BerriAI"] license = "MIT" @@ -59,7 +59,7 @@ websockets = {version = "^13.1.0", optional = true} boto3 = {version = "1.36.0", optional = true} redisvl = {version = "^0.4.1", optional = true, markers = "python_version >= '3.9' and python_version < '3.14'"} mcp = {version = "^1.10.0", optional = true, python = ">=3.10"} -litellm-proxy-extras = {version = "0.2.26", optional = true} +litellm-proxy-extras = {version = "0.2.27", optional = true} rich = {version = "13.7.1", optional = true} litellm-enterprise = {version = "0.1.20", optional = true} diskcache = {version = "^5.6.1", optional = true} @@ -157,7 +157,7 @@ requires = ["poetry-core", "wheel"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "1.78.0" +version = "1.78.1" version_files = [ "pyproject.toml:^version" ] diff --git a/requirements.txt b/requirements.txt index d869d7f574ef..67cac4f4b01d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -43,7 +43,7 @@ sentry_sdk==2.21.0 # for sentry error handling detect-secrets==1.5.0 # Enterprise - secret detection / masking in LLM requests cryptography==44.0.1 tzdata==2025.1 # IANA time zone database -litellm-proxy-extras==0.2.26 # for proxy extras - e.g. prisma migrations +litellm-proxy-extras==0.2.27 # for proxy extras - e.g. prisma migrations ### LITELLM PACKAGE DEPENDENCIES python-dotenv==1.0.1 # for env tiktoken==0.8.0 # for calculating usage 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 04ea92149917..584d3995f44e 100644 --- a/tests/router_unit_tests/test_router_index_management.py +++ b/tests/router_unit_tests/test_router_index_management.py @@ -16,25 +16,38 @@ 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} - - # Test: Remove model-2 (index 1) + 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] + } + + # 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/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..fbeb15019bd6 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 +374,225 @@ 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 + ) +def test_translate_anthropic_messages_with_thinking_blocks_to_openai(): + """Test that thinking blocks are properly converted from Anthropic to OpenAI format""" + + # Test data: Anthropic format with thinking blocks + anthropic_message = { + "role": "assistant", + "content": [ + { + "type": "thinking", + "thinking": "I need to recall basic geography knowledge. France is a country in Western Europe, and Paris has been its capital for centuries. This is a straightforward factual question.", + "signature": "abc123def456" + }, + { + "type": "text", + "text": "The capital of France is Paris." + } + ] + } + + adapter = LiteLLMAnthropicMessagesAdapter() + anthropic_messages = [anthropic_message] + openai_messages = adapter.translate_anthropic_messages_to_openai(anthropic_messages) + + # Verify thinking blocks are preserved + assert "thinking_blocks" in openai_messages[0], "Thinking blocks should be present in OpenAI format" + assert len(openai_messages[0]["thinking_blocks"]) == 1, "Should have one thinking block" + assert openai_messages[0]["thinking_blocks"][0]["type"] == "thinking", "Thinking block type should be 'thinking'" + assert openai_messages[0]["thinking_blocks"][0]["thinking"] == "I need to recall basic geography knowledge. France is a country in Western Europe, and Paris has been its capital for centuries. This is a straightforward factual question.", "Thinking content should match" + assert openai_messages[0]["thinking_blocks"][0]["signature"] == "abc123def456", "Signature should match" + assert openai_messages[0]["content"] == "The capital of France is Paris.", "Text content should be preserved" + + +def test_translate_anthropic_messages_with_redacted_thinking_blocks_to_openai(): + """Test that redacted thinking blocks are properly converted from Anthropic to OpenAI format""" + + # Test data: Anthropic format with redacted thinking blocks + anthropic_message = { + "role": "assistant", + "content": [ + { + "type": "redacted_thinking", + "data": "EmwKAhgBEgy3va3pzix/LafPsn4aDFIT2Xlxh0L5L8rLVyIwxtE3rAFBa8cr3qpPkNRj2YfWXGmKDxH4mPnZ5sQ7vB9URj2pLmN3kF8/dW5hR7xJ0aP1oLs9yTcMnKVf2wRpEGjH9XZaBt4UvDcPrQ..." + }, + { + "type": "text", + "text": "The capital of France is Paris." + } + ] + } + + adapter = LiteLLMAnthropicMessagesAdapter() + anthropic_messages = [anthropic_message] + openai_messages = adapter.translate_anthropic_messages_to_openai(anthropic_messages) + + # Verify redacted thinking blocks are preserved + assert "thinking_blocks" in openai_messages[0], "Thinking blocks should be present in OpenAI format" + assert len(openai_messages[0]["thinking_blocks"]) == 1, "Should have one thinking block" + assert openai_messages[0]["thinking_blocks"][0]["type"] == "redacted_thinking", "Thinking block type should be 'redacted_thinking'" + assert openai_messages[0]["thinking_blocks"][0]["data"] == "EmwKAhgBEgy3va3pzix/LafPsn4aDFIT2Xlxh0L5L8rLVyIwxtE3rAFBa8cr3qpPkNRj2YfWXGmKDxH4mPnZ5sQ7vB9URj2pLmN3kF8/dW5hR7xJ0aP1oLs9yTcMnKVf2wRpEGjH9XZaBt4UvDcPrQ...", "Data should match" + assert openai_messages[0]["content"] == "The capital of France is Paris.", "Text content should be preserved" 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/auth/test_route_checks.py b/tests/test_litellm/proxy/auth/test_route_checks.py index 0c72c8bb1630..7d00b812a5cc 100644 --- a/tests/test_litellm/proxy/auth/test_route_checks.py +++ b/tests/test_litellm/proxy/auth/test_route_checks.py @@ -156,6 +156,31 @@ def test_virtual_key_llm_api_route_includes_passthrough_prefix(route): assert result is True +@pytest.mark.parametrize( + "route", + [ + "/v1beta/models/gemini-2.5-flash:countTokens", + "/v1beta/models/gemini-2.0-flash:generateContent", + "/v1beta/models/gemini-1.5-pro:streamGenerateContent", + "/models/gemini-2.5-flash:countTokens", + "/models/gemini-2.0-flash:generateContent", + "/models/gemini-1.5-pro:streamGenerateContent", + ], +) +def test_virtual_key_llm_api_routes_allows_google_routes(route): + """ + Test that virtual keys with llm_api_routes permission can access Google AI Studio routes. + """ + + valid_token = UserAPIKeyAuth(user_id="test_user", allowed_routes=["llm_api_routes"]) + + result = RouteChecks.is_virtual_key_allowed_to_call_route( + route=route, valid_token=valid_token + ) + + assert result is True + + def test_virtual_key_allowed_routes_with_multiple_litellm_routes_member_names(): """Test that virtual key works with multiple LiteLLMRoutes member names in allowed_routes""" 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/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py b/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py new file mode 100644 index 000000000000..a8479394fc41 --- /dev/null +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py @@ -0,0 +1,535 @@ +""" +Test cases for Vertex AI passthrough batch prediction functionality +""" +import base64 +import json +import pytest +from unittest.mock import Mock, patch, MagicMock +from datetime import datetime +from typing import Dict, Any + +from litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler import ( + VertexPassthroughLoggingHandler, +) +from litellm.types.utils import SpecialEnums +from litellm.types.llms.openai import BatchJobStatus + + +class TestVertexAIBatchPassthroughHandler: + """Test cases for Vertex AI batch prediction passthrough functionality""" + + @pytest.fixture + def mock_httpx_response(self): + """Mock httpx response for batch job creation""" + mock_response = Mock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "name": "projects/test-project/locations/us-central1/batchPredictionJobs/123456789", + "displayName": "litellm-vertex-batch-test", + "model": "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash", + "createTime": "2024-01-01T00:00:00Z", + "state": "JOB_STATE_PENDING", + "inputConfig": { + "gcsSource": { + "uris": ["gs://test-bucket/input.jsonl"] + }, + "instancesFormat": "jsonl" + }, + "outputConfig": { + "gcsDestination": { + "outputUriPrefix": "gs://test-bucket/output/" + }, + "predictionsFormat": "jsonl" + } + } + return mock_response + + @pytest.fixture + def mock_logging_obj(self): + """Mock logging object""" + mock = Mock() + mock.litellm_call_id = "test-call-id-123" + mock.model_call_details = {} + mock.optional_params = {} + return mock + + @pytest.fixture + def mock_managed_files_hook(self): + """Mock managed files hook""" + mock_hook = Mock() + mock_hook.afile_content.return_value = Mock(content=b'{"test": "data"}') + return mock_hook + + def test_batch_prediction_jobs_handler_success(self, mock_httpx_response, mock_logging_obj): + """Test successful batch job creation and tracking""" + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.verbose_proxy_logger') as mock_logger: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler.get_actual_model_id_from_router') as mock_get_model_id: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler._store_batch_managed_object') as mock_store: + with patch('litellm.llms.vertex_ai.batches.transformation.VertexAIBatchTransformation') as mock_transformation: + + # Setup mocks + mock_get_model_id.return_value = "vertex_ai/gemini-1.5-flash" + mock_transformation.transform_vertex_ai_batch_response_to_openai_batch_response.return_value = { + "id": "123456789", + "object": "batch", + "status": "validating", + "created_at": 1704067200, + "input_file_id": "file-123", + "output_file_id": "file-456", + "error_file_id": None, + "completion_window": "24hrs" + } + mock_transformation._get_batch_id_from_vertex_ai_batch_response.return_value = "123456789" + + # Test the handler + result = VertexPassthroughLoggingHandler.batch_prediction_jobs_handler( + httpx_response=mock_httpx_response, + logging_obj=mock_logging_obj, + url_route="/v1/projects/test-project/locations/us-central1/batchPredictionJobs", + result="success", + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + user_api_key_dict={"user_id": "test-user"} + ) + + # Verify the result + assert result is not None + assert "kwargs" in result + assert result["kwargs"]["model"] == "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash" + assert result["kwargs"]["batch_id"] == "123456789" + + # Verify mocks were called + mock_get_model_id.assert_called_once() + mock_store.assert_called_once() + + def test_batch_prediction_jobs_handler_failure(self, mock_logging_obj): + """Test batch job creation failure handling""" + # Mock failed response + mock_httpx_response = Mock() + mock_httpx_response.status_code = 400 + mock_httpx_response.json.return_value = {"error": "Invalid request"} + + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.verbose_proxy_logger') as mock_logger: + # Test the handler with failed response + result = VertexPassthroughLoggingHandler.batch_prediction_jobs_handler( + httpx_response=mock_httpx_response, + logging_obj=mock_logging_obj, + url_route="/v1/projects/test-project/locations/us-central1/batchPredictionJobs", + result="error", + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + user_api_key_dict={"user_id": "test-user"} + ) + + # Should return None for failed responses + assert result is None + + def test_get_actual_model_id_from_router_with_router(self): + """Test getting model ID when router is available""" + with patch('litellm.proxy.proxy_server.llm_router') as mock_router: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path') as mock_extract: + + # Setup mocks + mock_router.get_model_ids.return_value = ["vertex_ai/gemini-1.5-flash"] + mock_extract.return_value = "gemini-1.5-flash" + + # Test the method + result = VertexPassthroughLoggingHandler.get_actual_model_id_from_router( + "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash" + ) + + # Verify result + assert result == "vertex_ai/gemini-1.5-flash" + mock_router.get_model_ids.assert_called_once_with(model_name="gemini-1.5-flash") + + def test_get_actual_model_id_from_router_without_router(self): + """Test getting model ID when router is not available""" + with patch('litellm.proxy.proxy_server.llm_router', None): + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path') as mock_extract: + + # Setup mocks + mock_extract.return_value = "gemini-1.5-flash" + + # Test the method + result = VertexPassthroughLoggingHandler.get_actual_model_id_from_router( + "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash" + ) + + # Verify result + assert result == "gemini-1.5-flash" + + def test_get_actual_model_id_from_router_model_not_found(self): + """Test getting model ID when model is not found in router""" + with patch('litellm.proxy.proxy_server.llm_router') as mock_router: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path') as mock_extract: + + # Setup mocks - router returns empty list + mock_router.get_model_ids.return_value = [] + mock_extract.return_value = "gemini-1.5-flash" + + # Test the method + result = VertexPassthroughLoggingHandler.get_actual_model_id_from_router( + "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash" + ) + + # Verify result - should fallback to extracted model name + assert result == "gemini-1.5-flash" + + def test_unified_object_id_generation(self): + """Test unified object ID generation for batch tracking""" + model_id = "vertex_ai/gemini-1.5-flash" + batch_id = "123456789" + + # Generate the expected unified ID + unified_id_string = SpecialEnums.LITELLM_MANAGED_BATCH_COMPLETE_STR.value.format(model_id, batch_id) + expected_unified_id = base64.urlsafe_b64encode(unified_id_string.encode()).decode().rstrip("=") + + # Test the generation + actual_unified_id = base64.urlsafe_b64encode(unified_id_string.encode()).decode().rstrip("=") + + assert actual_unified_id == expected_unified_id + assert isinstance(actual_unified_id, str) + assert len(actual_unified_id) > 0 + + def test_store_batch_managed_object(self, mock_logging_obj, mock_managed_files_hook): + """Test storing batch managed object for cost tracking""" + with patch('litellm.proxy.proxy_server.proxy_logging_obj') as mock_proxy_logging_obj: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.verbose_proxy_logger') as mock_logger: + + # Setup mock proxy logging obj + mock_proxy_logging_obj.get_proxy_hook.return_value = mock_managed_files_hook + + # Test data + unified_object_id = "test-unified-id" + batch_object = { + "id": "123456789", + "object": "batch", + "status": "validating" + } + model_object_id = "123456789" + + # Test the method + VertexPassthroughLoggingHandler._store_batch_managed_object( + unified_object_id=unified_object_id, + batch_object=batch_object, + model_object_id=model_object_id, + logging_obj=mock_logging_obj, + user_api_key_dict={"user_id": "test-user"} + ) + + # Verify the managed files hook was called + mock_managed_files_hook.store_unified_object_id.assert_called_once() + + def test_batch_cost_calculation_integration(self): + """Test integration with batch cost calculation""" + from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage + + # Mock Vertex AI batch responses + vertex_ai_batch_responses = [ + { + "status": "JOB_STATE_SUCCEEDED", + "response": { + "candidates": [ + { + "content": { + "parts": [ + {"text": "Hello, world!"} + ] + } + } + ], + "usageMetadata": { + "promptTokenCount": 10, + "candidatesTokenCount": 5, + "totalTokenCount": 15 + } + } + } + ] + + with patch('litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexGeminiConfig') as mock_config: + with patch('litellm.completion_cost') as mock_completion_cost: + + # Setup mocks + mock_config.return_value._transform_google_generate_content_to_openai_model_response.return_value = Mock( + usage=Mock(total_tokens=15, prompt_tokens=10, completion_tokens=5) + ) + mock_completion_cost.return_value = 0.001 + + # Test the cost calculation + total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + vertex_ai_batch_responses, + model_name="gemini-1.5-flash" + ) + + # Verify results + assert total_cost == 0.001 + assert usage.total_tokens == 15 + assert usage.prompt_tokens == 10 + assert usage.completion_tokens == 5 + + def test_batch_response_transformation(self): + """Test transformation of Vertex AI batch responses to OpenAI format""" + from litellm.llms.vertex_ai.batches.transformation import VertexAIBatchTransformation + + # Mock Vertex AI batch response + vertex_ai_response = { + "name": "projects/test-project/locations/us-central1/batchPredictionJobs/123456789", + "displayName": "test-batch", + "model": "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash", + "createTime": "2024-01-01T00:00:00.000Z", + "state": "JOB_STATE_SUCCEEDED" + } + + # Test transformation + result = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( + vertex_ai_response + ) + + # Verify the transformation + assert result["id"] == "123456789" + assert result["object"] == "batch" + assert result["status"] == "completed" # JOB_STATE_SUCCEEDED should map to completed + + def test_batch_id_extraction(self): + """Test extraction of batch ID from Vertex AI response""" + from litellm.llms.vertex_ai.batches.transformation import VertexAIBatchTransformation + + # Test various batch ID formats + test_cases = [ + "projects/123/locations/us-central1/batchPredictionJobs/456789", + "projects/abc/locations/europe-west1/batchPredictionJobs/def123", + "batchPredictionJobs/999", + "invalid-format" + ] + + expected_results = ["456789", "def123", "999", "invalid-format"] + + for test_case, expected in zip(test_cases, expected_results): + result = VertexAIBatchTransformation._get_batch_id_from_vertex_ai_batch_response( + {"name": test_case} + ) + assert result == expected + + def test_model_name_extraction_from_vertex_path(self): + """Test extraction of model name from Vertex AI path""" + from litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler import ( + VertexPassthroughLoggingHandler + ) + + # Test various model path formats + test_cases = [ + "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash", + "projects/abc/locations/europe-west1/publishers/google/models/gemini-2.0-flash", + "publishers/google/models/gemini-pro", + "invalid-path" + ] + + expected_results = ["gemini-1.5-flash", "gemini-2.0-flash", "gemini-pro", "invalid-path"] + + for test_case, expected in zip(test_cases, expected_results): + result = VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path(test_case) + assert result == expected + + @pytest.mark.asyncio + async def test_batch_completion_workflow(self, mock_httpx_response, mock_logging_obj, mock_managed_files_hook): + """Test the complete batch completion workflow""" + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.verbose_proxy_logger') as mock_logger: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler.get_actual_model_id_from_router') as mock_get_model_id: + with patch('litellm.proxy.proxy_server.proxy_logging_obj') as mock_proxy_logging_obj: + mock_proxy_logging_obj.get_proxy_hook.return_value = mock_managed_files_hook + with patch('litellm.llms.vertex_ai.batches.transformation.VertexAIBatchTransformation') as mock_transformation: + + # Setup mocks + mock_get_model_id.return_value = "vertex_ai/gemini-1.5-flash" + mock_transformation.transform_vertex_ai_batch_response_to_openai_batch_response.return_value = { + "id": "123456789", + "object": "batch", + "status": "completed", + "created_at": 1704067200, + "input_file_id": "file-123", + "output_file_id": "file-456", + "error_file_id": None, + "completion_window": "24hrs" + } + mock_transformation._get_batch_id_from_vertex_ai_batch_response.return_value = "123456789" + + # Test the complete workflow + result = VertexPassthroughLoggingHandler.batch_prediction_jobs_handler( + httpx_response=mock_httpx_response, + logging_obj=mock_logging_obj, + url_route="/v1/projects/test-project/locations/us-central1/batchPredictionJobs", + result="success", + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + user_api_key_dict={"user_id": "test-user"} + ) + + # Verify the complete workflow + assert result is not None + assert "kwargs" in result + assert result["kwargs"]["model"] == "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash" + assert result["kwargs"]["batch_id"] == "123456789" + + # Verify all mocks were called + mock_get_model_id.assert_called_once() + mock_transformation.transform_vertex_ai_batch_response_to_openai_batch_response.assert_called_once() + # Note: store_unified_object_id is called asynchronously, so we can't easily verify it in this test + + +class TestVertexAIBatchCostCalculation: + """Test cases for Vertex AI batch cost calculation functionality""" + + def test_calculate_vertex_ai_batch_cost_and_usage_success(self): + """Test successful batch cost and usage calculation""" + from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage + + # Mock successful batch responses + vertex_ai_batch_responses = [ + { + "status": "JOB_STATE_SUCCEEDED", + "response": { + "candidates": [ + { + "content": { + "parts": [ + {"text": "Hello, world!"} + ] + } + } + ], + "usageMetadata": { + "promptTokenCount": 10, + "candidatesTokenCount": 5, + "totalTokenCount": 15 + } + } + }, + { + "status": "JOB_STATE_SUCCEEDED", + "response": { + "candidates": [ + { + "content": { + "parts": [ + {"text": "How are you?"} + ] + } + } + ], + "usageMetadata": { + "promptTokenCount": 8, + "candidatesTokenCount": 3, + "totalTokenCount": 11 + } + } + } + ] + + with patch('litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexGeminiConfig') as mock_config: + with patch('litellm.completion_cost') as mock_completion_cost: + + # Setup mocks + mock_model_response = Mock() + mock_model_response.usage = Mock(total_tokens=15, prompt_tokens=10, completion_tokens=5) + mock_config.return_value._transform_google_generate_content_to_openai_model_response.return_value = mock_model_response + mock_completion_cost.return_value = 0.001 + + # Test the calculation + total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + vertex_ai_batch_responses, + model_name="gemini-1.5-flash" + ) + + # Verify results + assert total_cost == 0.002 # 2 responses * 0.001 each + assert usage.total_tokens == 30 # 15 + 15 + assert usage.prompt_tokens == 20 # 10 + 10 + assert usage.completion_tokens == 10 # 5 + 5 + + def test_calculate_vertex_ai_batch_cost_and_usage_with_failed_responses(self): + """Test batch cost calculation with some failed responses""" + from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage + + # Mock batch responses with some failures + vertex_ai_batch_responses = [ + { + "status": "JOB_STATE_SUCCEEDED", + "response": { + "candidates": [ + { + "content": { + "parts": [ + {"text": "Hello, world!"} + ] + } + } + ], + "usageMetadata": { + "promptTokenCount": 10, + "candidatesTokenCount": 5, + "totalTokenCount": 15 + } + } + }, + { + "status": "JOB_STATE_FAILED", # Failed response + "response": None + }, + { + "status": "JOB_STATE_SUCCEEDED", + "response": { + "candidates": [ + { + "content": { + "parts": [ + {"text": "How are you?"} + ] + } + } + ], + "usageMetadata": { + "promptTokenCount": 8, + "candidatesTokenCount": 3, + "totalTokenCount": 11 + } + } + } + ] + + with patch('litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexGeminiConfig') as mock_config: + with patch('litellm.completion_cost') as mock_completion_cost: + + # Setup mocks + mock_model_response = Mock() + mock_model_response.usage = Mock(total_tokens=15, prompt_tokens=10, completion_tokens=5) + mock_config.return_value._transform_google_generate_content_to_openai_model_response.return_value = mock_model_response + mock_completion_cost.return_value = 0.001 + + # Test the calculation + total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + vertex_ai_batch_responses, + model_name="gemini-1.5-flash" + ) + + # Verify results - should only process successful responses + assert total_cost == 0.002 # 2 successful responses * 0.001 each + assert usage.total_tokens == 30 # 15 + 15 + assert usage.prompt_tokens == 20 # 10 + 10 + assert usage.completion_tokens == 10 # 5 + 5 + + def test_calculate_vertex_ai_batch_cost_and_usage_empty_responses(self): + """Test batch cost calculation with empty response list""" + from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage + + # Test with empty list + total_cost, usage = calculate_vertex_ai_batch_cost_and_usage([], model_name="gemini-1.5-flash") + + # Verify results + assert total_cost == 0.0 + assert usage.total_tokens == 0 + assert usage.prompt_tokens == 0 + assert usage.completion_tokens == 0 diff --git a/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx b/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx index ab288c369838..680f66702bc3 100644 --- a/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx +++ b/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx @@ -37,6 +37,7 @@ import ChatImageRenderer from "./ChatImageRenderer"; import { createChatMultimodalMessage, createChatDisplayMessage } from "./ChatImageUtils"; import SessionManagement from "./SessionManagement"; import MCPEventsDisplay, { MCPEvent } from "./MCPEventsDisplay"; +import { SearchResultsDisplay } from "./SearchResultsDisplay"; import { ApiOutlined, KeyOutlined, @@ -436,6 +437,25 @@ const ChatUI: React.FC = ({ accessToken, token, userRole, userID, d }); }; + const updateSearchResults = (searchResults: any[]) => { + console.log("Received search results:", searchResults); + setChatHistory((prevHistory) => { + const lastMessage = prevHistory[prevHistory.length - 1]; + + if (lastMessage && lastMessage.role === "assistant") { + console.log("Updating message with search results"); + const updatedMessage = { + ...lastMessage, + searchResults, + }; + + return [...prevHistory.slice(0, prevHistory.length - 1), updatedMessage]; + } + + return prevHistory; + }); + }; + const handleResponseId = (responseId: string) => { console.log("Received response ID for session management:", responseId); if (useApiSessionManagement) { @@ -687,6 +707,7 @@ const ChatUI: React.FC = ({ accessToken, token, userRole, userID, d selectedGuardrails.length > 0 ? selectedGuardrails : undefined, selectedMCPTools, // Pass the selected tools array updateChatImageUI, // Pass the image callback + updateSearchResults, // Pass the search results callback ); } else if (endpointType === EndpointType.IMAGE) { // For image generation @@ -1101,6 +1122,11 @@ const ChatUI: React.FC = ({ accessToken, token, userRole, userID, d )} + {/* Show search results at the start of assistant messages */} + {message.role === "assistant" && message.searchResults && ( + + )} +
>({}); + + if (!searchResults || searchResults.length === 0) { + return null; + } + + const toggleResult = (pageIndex: number, resultIndex: number) => { + const key = `${pageIndex}-${resultIndex}`; + setExpandedResults((prev) => ({ + ...prev, + [key]: !prev[key], + })); + }; + + const totalResults = searchResults.reduce((sum, page) => sum + page.data.length, 0); + + return ( +
+ + + {isExpanded && ( +
+
+ {searchResults.map((resultPage, pageIndex) => ( +
+
+ Query: + "{resultPage.search_query}" + • + {resultPage.data.length} result{resultPage.data.length !== 1 ? 's' : ''} +
+ +
+ {resultPage.data.map((result, resultIndex) => { + const isResultExpanded = expandedResults[`${pageIndex}-${resultIndex}`] || false; + + return ( +
+
toggleResult(pageIndex, resultIndex)} + > +
+ + + + + + {result.filename || result.file_id || `Result ${resultIndex + 1}`} + + + {result.score.toFixed(3)} + +
+
+ + {isResultExpanded && ( +
+
+ {result.content.map((content, contentIndex) => ( +
+
+ {content.text} +
+
+ ))} + + {result.attributes && Object.keys(result.attributes).length > 0 && ( +
+
Metadata:
+
+ {Object.entries(result.attributes).map(([key, value]) => ( +
+ {key}: + {String(value)} +
+ ))} +
+
+ )} +
+
+ )} +
+ ); + })} +
+
+ ))} +
+
+ )} +
+ ); +} + diff --git a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx index 44aea48986bb..7baad24e48c4 100644 --- a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx +++ b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx @@ -1,6 +1,7 @@ import openai from "openai"; import { ChatCompletionMessageParam } from "openai/resources/chat/completions"; import { TokenUsage } from "../ResponseMetrics"; +import { VectorStoreSearchResponse } from "../types"; import { getProxyBaseUrl } from "@/components/networking"; export async function makeOpenAIChatCompletionRequest( @@ -18,6 +19,7 @@ export async function makeOpenAIChatCompletionRequest( guardrails?: string[], selectedMCPTools?: string[], onImageGenerated?: (imageUrl: string, model?: string) => void, + onSearchResults?: (searchResults: VectorStoreSearchResponse[]) => void, ) { // base url should be the current base_url const isLocal = process.env.NODE_ENV === "development"; @@ -127,6 +129,12 @@ export async function makeOpenAIChatCompletionRequest( fullReasoningContent += reasoningContent; } + // Check for search results in provider_specific_fields + if (delta && delta.provider_specific_fields?.search_results && onSearchResults) { + console.log("Search results found:", delta.provider_specific_fields.search_results); + onSearchResults(delta.provider_specific_fields.search_results); + } + // Check for usage data using type assertion const chunkWithUsage = chunk as any; if (chunkWithUsage.usage && onUsageData) { diff --git a/ui/litellm-dashboard/src/components/chat_ui/types.ts b/ui/litellm-dashboard/src/components/chat_ui/types.ts index 454256e56cb1..e8a98473cf40 100644 --- a/ui/litellm-dashboard/src/components/chat_ui/types.ts +++ b/ui/litellm-dashboard/src/components/chat_ui/types.ts @@ -56,6 +56,20 @@ export interface StreamingResponse { usage?: Usage; } +export interface VectorStoreSearchResult { + score: number; + content: Array<{ text: string; type: string }>; + file_id?: string; + filename?: string; + attributes?: Record; +} + +export interface VectorStoreSearchResponse { + object: string; + search_query: string; + data: VectorStoreSearchResult[]; +} + export interface MessageType { role: string; content: string | MultimodalContent[]; @@ -75,6 +89,7 @@ export interface MessageType { url: string; detail: string; }; + searchResults?: VectorStoreSearchResponse[]; } export interface MultimodalContent { diff --git a/ui/litellm-dashboard/src/components/common_components/RateLimitTypeFormItem.tsx b/ui/litellm-dashboard/src/components/common_components/RateLimitTypeFormItem.tsx index 63a2bf92dd29..e3524bb8f493 100644 --- a/ui/litellm-dashboard/src/components/common_components/RateLimitTypeFormItem.tsx +++ b/ui/litellm-dashboard/src/components/common_components/RateLimitTypeFormItem.tsx @@ -85,11 +85,21 @@ export const RateLimitTypeFormItem: React.FC = ({
+ ) : ( <> + )}