feat: Inference Migration Using FastAPI router factory #4445
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This pull request has merge conflicts that must be resolved before it can be merged. @JayDi11a please rebase it. https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork |
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thanks a lot for your contribution, you didn't pick the easiest one, please see the tests failures. |
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Why are we introducing so many changes to the demo_script?
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I wrote it up in the forum-llama-stack-core channel. But here is the list of what needed to be done to fix the demo script:
Vector DB API Change ([demo_script.py:21-23](vscode-webview://01gn3kfohjdkuq986mnfpbqrs203d8vlfvp0a49o8qe60jf1pkr3/demo_script.py#L21-L23))
1. Removed client.vector_dbs.register() - this API no longer exists
2. The vector store doesn't need to be explicitly registered; it's handled automatically
RAG Tool Insert API Mismatch ([demo_script.py:35-42](vscode-webview://01gn3kfohjdkuq986mnfpbqrs203d8vlfvp0a49o8qe60jf1pkr3/demo_script.py#L35-L42))
Changed from client.tool_runtime.rag_tool.insert() to using client.post() directly
1. The client library expects vector_db_id but the server requires vector_store_id in the request body
2. Used direct POST to work around this client/server API mismatch (exposes a deeper issue with client and server maintenance)
Tool Configuration for Agent ([demo_script.py:48-53](vscode-webview://01gn3kfohjdkuq986mnfpbqrs203d8vlfvp0a49o8qe60jf1pkr3/demo_script.py#L48-L53))
Changed from using builtin::rag/knowledge_search to the correct format
1. Used type: "file_search" with vector_store_ids parameter
2. This matches the OpenAI-compatible API format
Event Logger Output ([demo_script.py:69-73](vscode-webview://01gn3kfohjdkuq986mnfpbqrs203d8vlfvp0a49o8qe60jf1pkr3/demo_script.py#L69-L73))
1. Added check for log objects that might not have a .print() method
Falls back to regular print() for string outputs
These fixes still seem to hold for client v.040 as well.
| """Request model for listing chat completions.""" | ||
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| after: str | None = Field(default=None, description="The ID of the last chat completion to return.") | ||
| limit: int | None = Field(default=20, description="The maximum number of chat completions to return.") |
| class GetChatCompletionRequest(BaseModel): | ||
| """Request model for getting a chat completion.""" | ||
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| completion_id: str = Field(..., description="ID of the chat completion.") |
| ..., | ||
| description="The search query to rank items against. Can be a string, text content part, or image content part. The input must not exceed the model's max input token length.", | ||
| ) | ||
| items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam] = Field( |
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missing min_length=1 for non-empty list
| description="List of items to rerank. Each item can be a string, text content part, or image content part. Each input must not exceed the model's max input token length.", | ||
| ) | ||
| max_num_results: int | None = Field( | ||
| default=None, description="Maximum number of results to return. Default: returns all." |
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missing ge=1 when provided
Simplify the Ollama model selection logic in the detailed tutorial. Changes: - Replace complex custom_metadata filtering with simple ID check - Use direct 'ollama' in model ID check instead of metadata lookup - Makes code more concise and easier to understand This aligns with the simplified approach used in the updated demo_script.py.
Update the agent examples to use the latest API methods. Changes: - Simplify model selection (already applied in previous commit) - Use response.output_text instead of response.output_message.content - Use direct print(event) instead of event.print() for streaming This aligns the tutorial with the current Agent API implementation.
Modernize the RAG agent example to use the latest Vector Stores API. Changes: - Replace deprecated VectorDB API with Vector Stores API - Use file upload and vector_stores.create() instead of rag_tool.insert() - Download files via requests and upload to Llama Stack - Update to use file_search tool type with vector_store_ids - Simplify model selection with Ollama preference - Improve logging and user feedback - Update event logging to handle both old and new API - Add note about known server routing issues This provides a more accurate example using current Llama Stack APIs.
Fix conformance test failures by explicitly defining both application/json and text/event-stream media types in the 200 responses for streaming endpoints (/chat/completions and /completions). Changes: - Updated fastapi_routes.py to include explicit response schemas for both media types - Regenerated OpenAPI specs with proper 200 responses - Regenerated Stainless config This fixes the "response-success-status-removed" conformance errors while maintaining the dynamic streaming/non-streaming behavior. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
BREAKING CHANGE: Chunk.chunk_id field was made optional in commit 53af013 to support backward compatibility with legacy data. This changes the OpenAPI schema but maintains API compatibility.
…dpoints Updated the InferenceRouter.list_chat_completions and InferenceRouter.get_chat_completion methods to accept the new request object parameters (ListChatCompletionsRequest and GetChatCompletionRequest) instead of individual parameters. This fixes a SQLite parameter binding error where Pydantic request objects were being passed directly to SQL queries. The router now unpacks request object fields when calling the underlying store. Fixes the following error: sqlite3.ProgrammingError: Error binding parameter 1: type 'ListChatCompletionsRequest' is not supported 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Added missing exports for GetChatCompletionRequest and ListChatCompletionsRequest to llama_stack_api/__init__.py. These request types are needed by InferenceRouter and must be importable from the top-level llama_stack_api package. Fixes import error: ImportError: cannot import name 'GetChatCompletionRequest' from 'llama_stack_api' 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
FastAPI routers need to explicitly return StreamingResponse for
streaming endpoints. The old webmethod system had middleware that
automatically wrapped AsyncIterator results in SSE format, but the
new router system requires explicit handling.
Changes:
- Added _create_sse_event() helper to format data as SSE events
- Added _sse_generator() to convert async generators to SSE format
- Updated openai_chat_completion() to return StreamingResponse when stream=True
- Updated openai_completion() to return StreamingResponse when stream=True
Fixes streaming errors:
TypeError("'async_generator' object is not iterable")
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Upgrade llama-stack-client from >=0.3.0 to >=0.4.0 to ensure compatibility with recently added APIs (admin, vector_io parameter changes, beta namespace, and tool_runtime authorization parameter). This resolves test failures caused by client SDK being out of sync with server API changes from router migrations. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This reverts commit df24d26.
The FastAPI router was wrapping streaming responses in StreamingResponse, which broke library-mode usage. The library client expects to iterate over the raw AsyncIterator returned by the implementation, not a StreamingResponse wrapper. This fix removes the StreamingResponse wrapping and returns the raw implementation result directly. This matches the pattern used by the old @webmethod system and allows both HTTP and library modes to work correctly: - HTTP mode: FastAPI automatically handles AsyncIterator as streaming - Library mode: Client can iterate over the raw AsyncIterator Fixes the 3 failing integration tests: - test_streaming_tool_calls (vllm) - test_image_chat_completion_streaming (ollama-vision) - similar streaming tests (ollama base)
…Response" This reverts commit 14fb7cc. The raw AsyncIterator approach broke HTTP streaming. Need to investigate a different solution that works for both HTTP and library modes.
Move schema_utils imports to the beginning of __init__.py, right after the version constant. This aligns with PR llamastack#4667 and ensures decorators like @json_schema_type and @webmethod are available before other modules that depend on them are imported. Changes: - Moved schema_utils imports from line 450 to line 25 - Added schema_utils to __all__ exports list - Added noqa: I001 comment to preserve import order This addresses the maintainer feedback in PR review.
Address maintainer feedback to keep rerank endpoint at v1alpha level
instead of v1, matching the original webmethod implementation.
Changes:
- Moved rerank from inference.methods to alpha.subresources.inference in generate_config.py
- Changed inference router to use absolute paths instead of prefix
- Updated all v1 endpoints to use f"/{LLAMA_STACK_API_V1}/..." format
- Added rerank endpoint with f"/{LLAMA_STACK_API_V1ALPHA}/inference/rerank"
- Regenerated OpenAPI specs (48 paths, 67 operations)
This aligns with PR review feedback and maintains API versioning consistency.
Revert accidental version downgrade from 0.4.0.dev0 to 0.3.5 in src/llama_stack_api/__init__.py. This maintains version consistency with the main branch.
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Known Issue: Streaming Incompatibility Between HTTP and Library ModesUPDATE: Root cause identified! The issue is in how library client routes are registered, not in the router implementation itself. The ProblemThe current FastAPI router implementation works for HTTP mode but breaks library mode streaming. Root Cause AnalysisWith @webmethod (old system): # routes.py line 151 (old version)
func = getattr(impl, legacy_route.name) # Gets implementation method directly
With FastAPI router (current system): # routes.py line 106 (current)
func = route.endpoint # Gets router endpoint wrapper
The BugIn func = route.endpoint # ❌ Gets router wrapper functionShould be: func = getattr(impl, route.name) # ✅ Gets implementation methodWhy This Worked BeforeThe @webmethod system had two separate code paths:
The FastAPI router unintentionally merged these paths because library client route registration uses Proper FixThe fix should be in # In initialize_route_impls(), around line 106:
# Instead of:
func = route.endpoint
# Use the implementation method directly for library mode:
# Extract the method name from the route and get it from impl
# This requires mapping route paths back to implementation method namesImpactThis is a systemic issue affecting ALL migrated APIs, not just Inference:
Recommended Solution
cc @leseb @r-bit-rry - This is actually a route registration bug, not a router implementation issue. The fix should be in the route initialization logic that builds the library client's route map. |
…library client The library client was calling router wrapper functions which return StreamingResponse objects for HTTP mode. This broke library mode streaming because StreamingResponse is not an async iterator. The fix extracts the method name from the router endpoint and gets the corresponding implementation method directly, matching the behavior of the old @webmethod system. This fixes library mode streaming for ALL migrated APIs (not just Inference): - test_streaming_tool_calls (vllm) - test_image_chat_completion_streaming (ollama-vision) - other streaming tests in library mode Router wrappers correctly return StreamingResponse for HTTP mode while library client now gets raw AsyncIterator from implementation methods. Fixes llamastack#4445
✅ Fixed: Streaming Library Mode BugUpdate: The root cause has been identified and fixed in commit f73f36a! The Bug (Now Fixed!)The library client was calling router wrapper functions instead of implementation methods. This caused streaming to fail in library mode because:
The FixFile: # OLD (BROKEN):
func = route.endpoint # Gets router wrapper → returns StreamingResponse
# NEW (FIXED):
method_name = route.endpoint.__name__
func = getattr(impl, method_name, None) # Gets implementation → returns AsyncIteratorThis makes FastAPI routers work exactly like the old
Tests Passing
ImpactThis fix resolves streaming issues for ALL migrated APIs, not just Inference:
The Inference API router implementation was correct all along - the bug was in how routes were registered for library client mode. cc @leseb @r-bit-rry - The streaming issue is now resolved! 🎉 |
Response to Review FeedbackThank you @leseb and @r-bit-rry for the detailed reviews! I've addressed the key issues: ✅ Fixed: Version Consistency (pyproject.toml)Comment: "revert" on pyproject.toml:61 Response: Fixed in commit 15d05b4. The version has been restored to
✅ Fixed: Import Order (init.py)Comment: "no no no, please revert" on init.py Response: Fixed in commit ebd11bc. Moved ✅ Fixed: API Versioning (generate_config.py + openapi.yml)Comment: "this is not correct, please revert" on generate_config.py Response: Fixed in commit 3226302:
📋 Pending: Docstring Cleanup (models.py)Comment: "please remove ALL the Question for maintainer: The
I'm happy to do either - just want to clarify the preference before making such a large change. 🔍 Regarding Large models.py FileComment from @r-bit-rry: "15x larger than any other API's models file" Context: The Inference API has significantly more types than other APIs because it implements the full OpenAI-compatible API surface:
These types aren't duplicated from elsewhere - they're the OpenAI-compatible types specific to the Inference API. Other APIs (batches, models, datasets) have much simpler request/response patterns. ✅ Fixed: Streaming Bug in Library ModeMajor breakthrough: Discovered and fixed the root cause in commit f73f36a! The issue wasn't in the router implementation - it was in Fix: Changed from This should resolve the 3 failing integration tests (ollama-vision, ollama base, vllm streaming tests). Ready for re-review once the docstring approach is clarified! 🚀 |
## What does this PR do? This PR migrates the Inference API to the FastAPI router system, building on the work done in PR #4191. This continues the effort to move away from the legacy `@webmethod` decorator system to explicit FastAPI routers. ### Changes - **Inference API Migration**: Migrated the Inference API to use FastAPI routers following the established API package structure pattern - **SSE Streaming Support**: Added SSE utilities for streaming inference endpoints (chat completions, completions) - **OpenAPI Spec Updates**: Updated OpenAPI specifications and Stainless config for the new router structure - **Documentation Updates**: Updated tutorial examples to use the new Agent API patterns ### Implementation Details - Protocol definitions and models live in `llama_stack_api/inference/` - FastAPI router implementation follows the established pattern from other migrated APIs - The `/v1alpha/inference/rerank` endpoint is properly configured in the Stainless config - Explicit 200 responses added for streaming endpoints to properly document SSE behavior This represents an incremental migration of the Inference API to the router system while maintaining full backward compatibility. ## Test Plan 1. Verify routes are preserved: ```bash curl http://localhost:8321/v1/inspect/routes | jq '.data[] | select(.route | contains("inference") or contains("chat") or contains("completion") or contains("embedding"))' ``` 2. Run unit tests: ```bash uv run pytest tests/unit/core/ -v ``` 3. Run integration tests: ```bash uv run pytest tests/integration/inference/ -vv --stack-config=http://localhost:8321 ``` --- Co-authored-by: Gerald Trotman <gtrotman@redhat.com> (@JayDi11a) This PR supersedes #4445 with a clean, rebased history. BREAKING CHANGES: update OpenAIEmbeddingsRequestWithExtraBody to support token array --------- Co-authored-by: Gerald Trotman <gtrotman@redhat.com> Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com> Co-authored-by: Happy <yesreply@happy.engineering>
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What does this PR do?
This commit builds on top of the work done in PR #4191 instantiating APIs to register FastAPI router factories and migrating away from the legacy @webmethod decorator system. The implementation primarily focuses on the migration of the Inference API which updates the server and OpenAPI generation while maintaining the existing routing and inspection.
The Inference API has been migrated to adopt the same new API Package Structure as the migrated Batches AI migration, i.e., protocol definitions and models live in llama_stack_api/inference. The FastAPI router implementation lives in llama_stack/core/server/routers/inference maintaining the established pattern of API contracts and server routing logic.
The nuances of migrating the Inference API include fixing model chunking where chunk_id aren't uniform across 100+ models and adding a sync for chunk id and meta data and an overall effort for backwards compatibility including content types. Last but not least, the Stainless config needed to be updated for the /v1/inference/rerank path.
This implementation represents an incremental migrating of the Inference API to the router system while maintaining full backward compatibility with existing webmethod-based APIs.
Test Plan
Run this from the command line and the same routes should be upheld:
curl http://localhost:8321/v1/inspect/routes | jq '.data[] | select(.route | contains("inference") or contains("chat") or contains("completion") or contains("embedding"))'Since the inference unit tests only import types and not routing logic and the types are reimported, unit testing didn't need modification. Therefore:
Lastly, after the completion of each migration, the server gets tested against the
demo_script.pyin the getting started documentation as well as theinference.py,agent.py, andrag_agent.pyexamples created from thedetailed_tutorial.mdxdocumentation.