99 commits

Author	SHA1	Message	Date
ashwinb	9324e902f1	refactor(responses): move stuff into some utils and add unit tests (#3158) ... # What does this PR do? Refactors the OpenAI response conversion utilities by moving helper functions from `openai_responses.py` to `utils.py`. Adds unit tests.	2025-08-15 00:05:36 +00:00
ashwinb	47d5af703c	chore(responses): Refactor Responses Impl to be civilized (#3138) ... # What does this PR do? Refactors the OpenAI responses implementation by extracting streaming and tool execution logic into separate modules. This improves code organization by: 1. Creating a new `StreamingResponseOrchestrator` class in `streaming.py` to handle the streaming response generation logic 2. Moving tool execution functionality to a dedicated `ToolExecutor` class in `tool_executor.py` ## Test Plan Existing tests	2025-08-15 00:05:35 +00:00
Derek Higgins	c15cc7ed77	fix: use ChatCompletionMessageFunctionToolCall (#3142) ... The OpenAI compatibility layer was incorrectly importing ChatCompletionMessageToolCallParam instead of the ChatCompletionMessageFunctionToolCall class. This caused "Cannot instantiate typing.Union" errors when processing agent requests with tool calls. Closes: #3141 Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-08-14 10:27:00 -07:00
Ashwin Bharambe	ee7631b6cf	Revert "feat: add batches API with OpenAI compatibility" (#3149) ... Reverts llamastack/llama-stack#3088 The PR broke integration tests.	2025-08-14 10:08:54 -07:00
Matthew Farrellee	de692162af	feat: add batches API with OpenAI compatibility (#3088) ... Add complete batches API implementation with protocol, providers, and tests: Core Infrastructure: - Add batches API protocol using OpenAI Batch types directly - Add Api.batches enum value and protocol mapping in resolver - Add OpenAI "batch" file purpose support - Include proper error handling (ConflictError, ResourceNotFoundError) Reference Provider: - Add ReferenceBatchesImpl with full CRUD operations (create, retrieve, cancel, list) - Implement background batch processing with configurable concurrency - Add SQLite KVStore backend for persistence - Support /v1/chat/completions endpoint with request validation Comprehensive Test Suite: - Add unit tests for provider implementation with validation - Add integration tests for end-to-end batch processing workflows - Add error handling tests for validation, malformed inputs, and edge cases Configuration: - Add max_concurrent_batches and max_concurrent_requests_per_batch options - Add provider documentation with sample configurations Test with - ``` $ uv run llama stack build --image-type venv --providers inference=YOU_PICK,files=inline::localfs,batches=inline::reference --run & $ LLAMA_STACK_CONFIG=http://localhost:8321 uv run pytest tests/unit/providers/batches tests/integration/batches --text-model YOU_PICK ``` addresses #3066	2025-08-14 09:42:02 -04:00
Ashwin Bharambe	e1e161553c	feat(responses): add MCP argument streaming and content part events (#3136) ... # What does this PR do? Adds content part streaming events to the OpenAI-compatible Responses API to support more granular streaming of response content. This introduces: 1. New schema types for content parts: `OpenAIResponseContentPart` with variants for text output and refusals 2. New streaming event types: - `OpenAIResponseObjectStreamResponseContentPartAdded` for when content parts begin - `OpenAIResponseObjectStreamResponseContentPartDone` for when content parts complete 3. Implementation in the reference provider to emit these events during streaming responses. Also emits MCP arguments just like function call ones. ## Test Plan Updated existing streaming tests to verify content part events are properly emitted	2025-08-13 16:34:26 -07:00
Ashwin Bharambe	5b312a80b9	feat(responses): improve streaming for function calls (#3124) ... Emit streaming events for function calls ## Test Plan Improved the test case	2025-08-13 11:23:27 -07:00
Francisco Arceo	92aca434a7	fix: Fix list_sessions() (#3114) ... # What does this PR do? 1. Updates `AgentPersistence.list_sessions()` to properly filter out `Turn` keys from `Session` keys. 2. Adds a suite of unit tests to confirm the `list_sessions()` behavior and tests the failed sample in https://github.com/meta-llama/llama-stack/issues/3048 ## Fixes https://github.com/meta-llama/llama-stack/issues/3048 ## Test Plan Unit tests added. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-08-13 07:46:26 -07:00
Varsha	e3928e6a29	feat: Implement hybrid search in Milvus (#2644) ... # What does this PR do? This PR implements hybrid search for Milvus DB based on the inbuilt milvus support. To test: ``` pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s --tb=long --disable-warnings --asyncio-mode=auto ``` Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-08-07 09:42:03 +02:00
Varsha	1f0766308d	feat: Add openAI compatible APIs to Qdrant (#2465) ... # What does this PR do? Adds support to Vector store Open AI APIs in Qdrant. <!-- If resolving an issue, uncomment and update the line below --> Closes #2463 ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com> Co-authored-by: ehhuang <ehhuang@users.noreply.github.com> Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>	2025-08-01 00:41:34 -04:00
Francisco Arceo	33cca26154	chore: Enabling Integration tests for Weaviate (#2882) ... # What does this PR do? This PR (1) enables the files API for Weaviate and (2) enables integration tests for Weaviate, which adds a docker container to the github action. This PR also handles a couple of edge cases for in creating the collection and ensuring the tests all pass. ## Test Plan CI enabled --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-31 20:29:50 -04:00
Matthew Farrellee	218c89fff1	feat: Add clear error message when API key is missing (#2992) ... # What does this PR do? Improve user experience by providing specific guidance when no API key is available, showing both provider data header and config options with the correct field name for each provider. Also adds comprehensive test coverage for API key resolution scenarios. addresses #2990 for providers using litellm openai mixin ## Test Plan `./scripts/unit-tests.sh tests/unit/providers/inference/test_litellm_openai_mixin.py`	2025-07-31 16:33:16 -04:00
Ashwin Bharambe	2665f00102	chore(rename): move llama_stack.distribution to llama_stack.core (#2975) ... We would like to rename the term `template` to `distribution`. To prepare for that, this is a precursor. cc @leseb	2025-07-30 23:30:53 -07:00
Matthew Farrellee	968fc132d3	fix(openai-compat): restrict developer/assistant/system/tool messages to text-only content (#2932) ... **What:** - Added OpenAIChatCompletionTextOnlyMessageContent type for text-only content validation - Modified OpenAISystemMessageParam, OpenAIAssistantMessageParam, OpenAIDeveloperMessageParam, and OpenAIToolMessageParam to use text-only content type instead of mixed content - OpenAIUserMessageParam unchanged - still accepts both text and images - Updated OpenAPI spec files to reflect text-only content restrictions in schemas closes #2894 **Why:** - Enforces OpenAI API compatibility by restricting image content to user messages only - Prevents API misuse where images might be sent in message types that don't support them - Aligns with OpenAI's actual API behavior where only user messages can contain multimodal content - Improves type safety and validation at the API boundary **Test plan:** - Added comprehensive parametrized tests covering all 5 OpenAI message types - Tests verify text string acceptance for all message types - Tests verify text list acceptance for all message types - Tests verify image rejection for system/assistant/developer/tool messages (ValidationError expected) - Tests verify user messages still accept images (backward compatibility maintained)	2025-07-28 10:36:34 -07:00
Matthew Farrellee	60bb5e307e	feat(openai): add configurable base_url support with OPENAI_BASE_URL env var (#2919) ... # What does this PR do? - Add base_url field to OpenAIConfig with default "https://api.openai.com/v1" - Update sample_run_config to support OPENAI_BASE_URL environment variable - Modify get_base_url() to return configured base_url instead of hardcoded value - Add comprehensive test suite covering: - Default base URL behavior - Custom base URL from config - Environment variable override - Config precedence over environment variables - Client initialization with configured URL - Model availability checks using configured URL This enables users to configure custom OpenAI-compatible API endpoints via environment variables or configuration files. Closes #2910 ## Test Plan run unit tests	2025-07-28 10:16:02 -07:00
Christian Zaccaria	c48dcafc77	fix: Fix unit tests CI and failing tests (#2928) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> - Added `set -e` to the beginning of the unit test script to ensure the script exits on failure and correctly fails the CI when tests do not pass. - Fixed all unit tests that were silently failing in the CI. - Fixed Python 3.13 unit test CI failing silently. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> Closes #2877 ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> - **Previously:** Unit tests passing in CI eventhough it failed 11 tests -> [CI-run](4683681501 (step):4:2097) - **Made the fix. Now, ensuring CI fails as expected on test failures:** Unit tests failing in CI with 1 failed test -> [CI-run](4684234247 (step):4:1506) - This PR shows the CI passing and all unit tests passing.	2025-07-28 10:07:26 -07:00
Francisco Arceo	9e77be1f72	chore: Fix chroma unit tests (#2896) ... # What does this PR do? Enable Chroma inline unit tests and fix integration tests. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-25 10:12:14 -04:00
Mustafa Elbehery	6ab5760a1b	chore(test): migrate unit tests from unittest to pytest nvidia test safety (#2793) ... This PR replaces unittest with pytest. Part of https://github.com/meta-llama/llama-stack/issues/2680 cc @leseb Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>	2025-07-24 09:41:07 -07:00
Sarthak Deshpande	cd8715d327	chore: Added openai compatible vector io endpoints for chromadb (#2489) ... # What does this PR do? This PR implements the openai compatible endpoints for chromadb Closes #2462 ## Test Plan Ran ollama llama stack server and ran the command `pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2` 8 failed, 27 passed, 8 skipped, 1 xfailed The failed ones are regarding files api --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> Co-authored-by: sarthakdeshpande <sarthak.deshpande@engati.com> Co-authored-by: Francisco Javier Arceo <farceo@redhat.com> Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>	2025-07-23 13:51:58 -07:00
Matthew Farrellee	e1ed152779	chore: create OpenAIMixin for inference providers with an OpenAI-compat API that need to implement openai_* methods (#2835) ... # What does this PR do? add an `OpenAIMixin` for use by inference providers who remote endpoints support an OpenAI compatible API. use is demonstrated by refactoring - OpenAIInferenceAdapter - NVIDIAInferenceAdapter (adds embedding support) - LlamaCompatInferenceAdapter ## Test Plan existing unit and integration tests	2025-07-23 06:49:40 -04:00
IAN MILLER	9e6860b9cf	fix: remove @pytest.mark.asyncio from test_get_raw_document_text.py (#2840) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> The pre-commit workflow was failing in the main branch and removing `@pytest.mark.asyncio `from `test_get_raw_document_text.py` fixed that. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->	2025-07-21 09:14:34 -07:00
Ondrej Metelka	89c49eb003	feat: Allow application/yaml as mime_type (#2575) ... # What does this PR do? Allow application/yaml as mime_type for documents. ## Test Plan Added unit tests.	2025-07-21 15:43:32 +02:00
Mustafa Elbehery	fe6af7dc8b	chore(test): migrate unit tests from unittest to pytest nvidia test f… (#2794) ... This PR replaces unittest with pytest. Part of https://github.com/meta-llama/llama-stack/issues/2680 cc @leseb Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>	2025-07-18 12:32:19 +02:00
Mustafa Elbehery	3cdf748a8e	chore(test): migrate unit tests from unittest to pytest for nvidia datastore (#2790) ... This PR replaces unittest with pytest. Part of https://github.com/meta-llama/llama-stack/issues/2680 cc @leseb Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>	2025-07-18 11:52:47 +02:00
Mustafa Elbehery	55713abe7d	chore(test): migrate unit tests from unittest to pytest nvidia test p… (#2792) ... This PR replaces unittest with pytest. Part of https://github.com/meta-llama/llama-stack/issues/2680 cc @leseb Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>	2025-07-18 11:49:45 +02:00
Charlie Doern	d7cc38e934	fix: remove async test markers (fix pre-commit) (#2808) ... # What does this PR do? some async test markers are in the codebase causing pre-commit to fail due to #2744 remove these pytest fixtures ## Test Plan pre-commit passes Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-07-17 21:35:28 -07:00
Matthew Farrellee	477bcd4d09	feat: allow dynamic model registration for nvidia inference provider (#2726) ... # What does this PR do? let's users register models available at https://integrate.api.nvidia.com/v1/models that isn't already in llama_stack/providers/remote/inference/nvidia/models.py ## Test Plan 1. run the nvidia distro 2. register a model from https://integrate.api.nvidia.com/v1/models that isn't already know, as of this writing nvidia/llama-3.1-nemotron-ultra-253b-v1 is a good example 3. perform inference w/ the model	2025-07-17 12:11:30 -07:00
Sergey Yedrikov	30be1fd8b7	fix: SQLiteVecIndex.create(..., bank_id="test_bank.123") - bank_id with a dot - leads to sqlite3.OperationalError (#2770) (#2771) ... # What does this PR do? Resolves https://github.com/meta-llama/llama-stack/issues/2770. It replaces characters in SQLite table names that are not alphanumeric or underscores with underscores and quotes the table names with square brackets in SQL statements. Closes #[2770] ## Test Plan I added a ".123" suffix to the bank_id on the following line ``` index = await SQLiteVecIndex.create(dimension=embedding_dimension, db_path=db_path, bank_id="test_bank.123") ``` in tests/unit/providers/vector_io/test_sqlite_vec.py, which, without the fix in place, demonstrates the issue.	2025-07-16 08:25:44 -07:00
Matthew Farrellee	a3e249807b	chore: remove vision model URL workarounds and simplify client creation (#2775) ... The vision models are now available at the standard URL, so the workaround code has been removed. This also simplifies the codebase by eliminating the need for per-model client caching. - Remove special URL handling for meta/llama-3.2-11b/90b-vision-instruct models - Convert _get_client method to _client property for cleaner API - Remove unnecessary lru_cache decorator and functools import - Simplify client creation logic to use single base URL for all models	2025-07-16 07:10:04 -07:00
Varsha	4ae5656c2f	feat: Implement keyword search in milvus (#2231) ... # What does this PR do? This PR adds the keyword search implementation for Milvus. Along with the implementation for remote Milvus, the tests require us to start a Milvus containers locally. In order to verify the implementation, run: ``` pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s --tb=short --disable-warnings --asyncio-mode=auto ``` You can also test the changes using the below script: ``` #!/usr/bin/env python3 import asyncio import os import uuid from typing import List from llama_stack_client import ( Agent, AgentEventLogger, LlamaStackClient, RAGDocument ) class MilvusRAGDemo: def __init__(self, base_url: str = "http://localhost:8321/"): self.client = LlamaStackClient(base_url=base_url) self.vector_db_id = f"milvus_rag_demo_{uuid.uuid4().hex[:8]}" self.model_id = None self.embedding_model_id = None self.embedding_dimension = None def setup_models(self): """Get available models and select appropriate ones for LLM and embeddings.""" models = self.client.models.list() # Select embedding model embedding_models = [m for m in models if m.model_type == "embedding"] if not embedding_models: raise ValueError("No embedding models found") self.embedding_model_id = embedding_models[0].identifier self.embedding_dimension = embedding_models[0].metadata["embedding_dimension"] def register_vector_db(self): print(f"Registering Milvus vector database: {self.vector_db_id}") response = self.client.vector_dbs.register( vector_db_id=self.vector_db_id, embedding_model=self.embedding_model_id, embedding_dimension=self.embedding_dimension, provider_id="milvus-remote", # Use remote Milvus ) print(f"Vector database registered successfully") return response def insert_documents(self): """Insert sample documents into the vector database.""" print("\nInserting sample documents...") # Sample documents about different topics documents = [ RAGDocument( document_id="ai_ml_basics", content=""" Artificial Intelligence (AI) and Machine Learning (ML) are transforming the world. AI refers to the simulation of human intelligence in machines, while ML is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed. Deep learning, a subset of ML, uses neural networks with multiple layers to process complex patterns in data. Key concepts in AI/ML include: - Supervised Learning: Training with labeled data - Unsupervised Learning: Finding patterns in unlabeled data - Reinforcement Learning: Learning through trial and error - Neural Networks: Computing systems inspired by biological brains """, mime_type="text/plain", metadata={"topic": "technology", "category": "ai_ml"}, ), ] # Insert documents with chunking self.client.tool_runtime.rag_tool.insert( documents=documents, vector_db_id=self.vector_db_id, chunk_size_in_tokens=200, # Smaller chunks for better granularity ) print(f"Inserted {len(documents)} documents with chunking") def test_keyword_search(self): """Test keyword-based search using BM25.""" queries = [ "neural networks", "Python frameworks", "data cleaning", ] for query in queries: response = self.client.vector_io.query( vector_db_id=self.vector_db_id, query=query, params={ "mode": "keyword", # Keyword search "max_chunks": 3, "score_threshold": 0.0, } ) for i, (chunk, score) in enumerate(zip(response.chunks, response.scores)): print(f" {i+1}. Score: {score:.4f}") print(f" Content: {chunk.content[:100]}...") print(f" Metadata: {chunk.metadata}") def run_demo(self): try: self.setup_models() self.register_vector_db() self.insert_documents() self.test_keyword_search() except Exception as e: print(f"Error during demo: {e}") raise def main(): """Main function to run the demo.""" # Check if Llama Stack server is running demo = MilvusRAGDemo() try: demo.run_demo() except Exception as e: print(f"Demo failed: {e}") if __name__ == "__main__": main() ``` [//]: # (## Documentation) --------- Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-07-14 19:39:55 -04:00
Matthew Farrellee	f731f369a2	feat: add infrastructure to allow inference model discovery (#2710) ... # What does this PR do? inference providers each have a static list of supported / known models. some also have access to a dynamic list of currently available models. this change gives prodivers using the ModelRegistryHelper the ability to combine their static and dynamic lists. for instance, OpenAIInferenceAdapter can implement ``` def query_available_models(self) -> list[str]: return [entry.model for entry in self.openai_client.models.list()] ``` to augment its static list w/ a current list from openai. ## Test Plan scripts/unit-test.sh	2025-07-14 11:38:53 -07:00
Derek Higgins	a7ed86181c	fix(faiss): Delete file contents from kvstore (#2686) ... Remove both the metadata and content from the kvstore when a file is being removed from the vector store. Closes: #2685 Also add faiss provider to openai_vector_stores test suite --------- Signed-off-by: Derek Higgins <derekh@redhat.com> Co-authored-by: raghotham <rsm@meta.com>	2025-07-14 13:58:23 -04:00
Matthew Farrellee	68e7978c88	chore: block network access from unit tests (#2732) ... # What does this PR do? this blocks network access for all `tests/unit/` tests. `tests/integration/` are untouched. it also introduces an `allow_network` marker to explicitly allow network access. ## Test Plan `./scripts/unit-tests.sh`	2025-07-12 16:53:54 -07:00
Ben Browning	51d9fd4808	fix: Don't cache clients for passthrough auth providers (#2728) ... # What does this PR do? Some of our inference providers support passthrough authentication via `x-llamastack-provider-data` header values. This fixes the providers that support passthrough auth to not cache their clients to the backend providers (mostly OpenAI client instances) so that the client connecting to Llama Stack has to provide those auth values on each and every request. ## Test Plan I added some unit tests to ensure we're not caching clients across requests for all the fixed providers in this PR. ``` uv run pytest -sv tests/unit/providers/inference/test_inference_client_caching.py ``` I also ran some of our OpenAI compatible API integration tests for each of the changed providers, just to ensure they still work. Note that these providers don't actually pass all these tests (for unrelated reasons due to quirks of the Groq and Together SaaS services), but enough of the tests passed to confirm the clients are still working as intended. ### Together ``` ENABLE_TOGETHER="together" \ uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv \ tests/integration/inference/test_openai_completion.py \ --text-model "together/meta-llama/Llama-3.1-8B-Instruct" ``` ### OpenAI ``` ENABLE_OPENAI="openai" \ uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv \ tests/integration/inference/test_openai_completion.py \ --text-model "openai/gpt-4o-mini" ``` ### Groq ``` ENABLE_GROQ="groq" \ uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv \ tests/integration/inference/test_openai_completion.py \ --text-model "groq/meta-llama/Llama-3.1-8B-Instruct" ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-07-11 13:38:27 -07:00
Matthew Farrellee	30b2e6a495	chore: default to pytest asyncio-mode=auto (#2730) ... # What does this PR do? previously, developers who ran `./scripts/unit-tests.sh` would get `asyncio-mode=auto`, which meant `@pytest.mark.asyncio` and `@pytest_asyncio.fixture` were redundent. developers who ran `pytest` directly would get pytest's default (strict mode), would run into errors leading them to add `@pytest.mark.asyncio` / `@pytest_asyncio.fixture` to their code. with this change - - `asyncio_mode=auto` is included in `pyproject.toml` making behavior consistent for all invocations of pytest - removes all redundant `@pytest_asyncio.fixture` and `@pytest.mark.asyncio` - for good measure, requires `pytest>=8.4` and `pytest-asyncio>=1.0` ## Test Plan - `./scripts/unit-tests.sh` - `uv run pytest tests/unit`	2025-07-11 13:00:24 -07:00
Francisco Arceo	6a6b66ae4f	chore: Adding unit tests for OpenAI vector stores and migrating SQLite-vec registry to kvstore (#2665) ... # What does this PR do? This PR refactors and the VectorIO backend logic for `sqlite-vec` and adds unit tests and fixtures to make it easy to test both `sqlite-vec` and `milvus`. Key changes: - `sqlite-vec` migrated to `kvstore` registry - added in-memory cache for sqlite-vec to be consistent with `milvus` - default fixtures moved to `conftest.py` - removed redundant tests from sqlite`-vec` - made `test_vector_io_openai_vector_stores.py` more easily extensible ## Test Plan Unit tests added testing inline providers. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-10 14:22:13 -04:00
Francisco Arceo	83c89265e0	chore: Adding unit tests for Milvus and OpenAI compatibility (#2640) ... # What does this PR do? - Enabling Unit tests for Milvus to start to test OpenAI compatibility and fixing a few bugs. - Also fixed an inconsistency in the Milvus config between remote and inline. - Added pymilvus to extras for testing in CI I'm going to refactor this later to include the other inline providers so that we can catch issues sooner. I have another PR where I've been testing to find other bugs in the implementation (and required changes drafted here: https://github.com/meta-llama/llama-stack/pull/2617). ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-08 00:50:16 -07:00
Derek Higgins	f77d4d91f5	fix: handle encoding errors when adding files to vector store (#2574) ... - Add try-catch block around data.decode() to handle UnicodeDecodeError - Implement UTF-8 fallback when detected encoding fails - Return empty string when both encodings fail - add unit tests Fixes #2572: UnicodeDecodeError when uploading files with problematic encodings Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-07-04 12:10:18 +02:00
Francisco Arceo	ea80ea63ac	chore: Updating chunk id generation to ensure uniqueness (#2618) ... # What does this PR do? This handles an edge case for `generate_chunk_id` if the concatenation of the `document_id` and `chunk_text` combination are not unique. Adding the window location ensures uniqueness. ## Test Plan Added unit test Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-04 10:26:35 +05:30
Akram Ben Aissi	f4950f4ef0	fix: AccessDeniedError leads to HTTP 500 instead of error 403 (#2595) ... Resolves access control error visibility issues where 500 errors were returned instead of proper 403 responses with actionable error messages. • Enhance AccessDeniedError with detailed context and improve exception handling • Enhanced AccessDeniedError class to include user, action, and resource context - Added constructor parameters for action, resource, and user - Generate detailed error messages showing user principal, attributes, and attempted resource - Backward compatible with existing usage (falls back to generic message) • Updated exception handling in server.py - Import AccessDeniedError from access_control module - Return proper 403 status codes with detailed error messages - Separate handling for PermissionError (generic) vs AccessDeniedError (detailed) • Enhanced error context at raise sites - Updated routing_tables/common.py to pass action, resource, and user context - Updated agents persistence to include context in access denied errors - Provides better debugging information for access control issues • Added comprehensive unit tests - Created tests/unit/server/test_server.py with 13 test cases - Covers AccessDeniedError with and without context - Tests all exception types (ValidationError, BadRequestError, AuthenticationRequiredError, etc.) - Validates proper HTTP status codes and error message formats # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan ``` server: port: 8321 access_policy: - permit: principal: admin actions: [create, read, delete] when: user with admin in groups - permit: actions: [read] when: user with system:authenticated in roles ``` then: ``` curl --request POST --url http://localhost:8321/v1/vector-dbs \ --header "Authorization: Bearer your-bearer" \ --data '{ "vector_db_id": "my_demo_vector_db", "embedding_model": "ibm-granite/granite-embedding-125m-english", "embedding_dimension": 768, "provider_id": "milvus" }' ``` depending if user is in group admin or not, you should get the `AccessDeniedError`. Before this PR, this was leading to an error 500 and `Traceback` displayed in the logs. After the PR, logs display a simpler error (unless DEBUG logging is set) and a 403 Forbidden error is returned on the HTTP side. --------- Signed-off-by: Akram Ben Aissi <<akram.benaissi@gmail.com>>	2025-07-03 10:50:49 -07:00
Sébastien Han	ac5fd57387	chore: remove nested imports (#2515) ... # What does this PR do? * Given that our API packages use "import *" in `__init.py__` we don't need to do `from llama_stack.apis.models.models` but simply from llama_stack.apis.models. The decision to use `import *` is debatable and should probably be revisited at one point. * Remove unneeded Ruff F401 rule * Consolidate Ruff F403 rule in the pyprojectfrom llama_stack.apis.models.models Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-26 08:01:05 +05:30
Ben Browning	2d9fd041eb	fix: annotations list and web_search_preview in Responses (#2520) ... # What does this PR do? These are a couple of fixes to get an example LangChain app working with our OpenAI Responses API implementation. The Responses API spec requires an annotations array in `output[*].content[*].annotations` and we were not providing one. So, this adds that as an empty list, even though we don't do anything to populate it yet. This prevents an error from client libraries like Langchain that expect this field to always exist, even if an empty list. The other fix is `web_search_preview` is a valid name for the web search tool in the Responses API, but we only responded to `web_search` or `web_search_preview_2025_03_11`. ## Test Plan The existing Responses unit tests were expanded to test these cases, via: ``` pytest -sv tests/unit/providers/agents/meta_reference/test_openai_responses.py ``` The existing test_openai_responses.py integration tests still pass with this change, tested as below with Fireworks: ``` uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv tests/integration/agents/test_openai_responses.py \ --text-model accounts/fireworks/models/llama4-scout-instruct-basic ``` Lastly, this example LangChain app now works with Llama stack (tested with Ollama in the starter template in this case). This LangChain code is using the example snippets for using Responses API at https://python.langchain.com/docs/integrations/chat/openai/#responses-api ```python from langchain_openai import ChatOpenAI llm = ChatOpenAI( base_url="http://localhost:8321/v1/openai/v1", api_key="fake", model="ollama/meta-llama/Llama-3.2-3B-Instruct", ) tool = {"type": "web_search_preview"} llm_with_tools = llm.bind_tools([tool]) response = llm_with_tools.invoke("What was a positive news story from today?") print(response.content) ``` Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-26 07:59:33 +05:30
Francisco Arceo	82f13fe83e	feat: Add ChunkMetadata to Chunk (#2497) ... # What does this PR do? Adding `ChunkMetadata` so we can properly delete embeddings later. More specifically, this PR refactors and extends the chunk metadata handling in the vector database and introduces a distinction between metadata used for model context and backend-only metadata required for chunk management, storage, and retrieval. It also improves chunk ID generation and propagation throughout the stack, enhances test coverage, and adds new utility modules. ```python class ChunkMetadata(BaseModel): """ `ChunkMetadata` is backend metadata for a `Chunk` that is used to store additional information about the chunk that will NOT be inserted into the context during inference, but is required for backend functionality. Use `metadata` in `Chunk` for metadata that will be used during inference. """ document_id: str | None = None chunk_id: str | None = None source: str | None = None created_timestamp: int | None = None updated_timestamp: int | None = None chunk_window: str | None = None chunk_tokenizer: str | None = None chunk_embedding_model: str | None = None chunk_embedding_dimension: int | None = None content_token_count: int | None = None metadata_token_count: int | None = None ``` Eventually we can migrate the document_id out of the `metadata` field. I've introduced the changes so that `ChunkMetadata` is backwards compatible with `metadata`. <!-- If resolving an issue, uncomment and update the line below --> Closes https://github.com/meta-llama/llama-stack/issues/2501 ## Test Plan Added unit tests --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-06-25 15:55:23 -04:00
ehhuang	d3b60507d7	feat: support auth attributes in inference/responses stores (#2389) ... # What does this PR do? Inference/Response stores now store user attributes when inserting, and respects them when fetching. ## Test Plan pytest tests/unit/utils/test_sqlstore.py	2025-06-20 10:24:45 -07:00
Ihar Hrachyshka	a2f054607d	fix: cancel scheduler tasks on shutdown (#2130) ... # What does this PR do? Scheduler: cancel tasks on shutdown. Otherwise the currently running tasks will never exit (before they actually complete), which means the process can't be properly shut down (only with SIGKILL). Ideally, we let tasks know that they are about to shutdown and give them some time to do so; but in the lack of the mechanism, it's better to cancel than linger forever. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan Start a long running task (e.g. torchtune or external kfp-provider training). Ctr-C the process in TTY. Confirm it exits in reasonable time. ``` ^CINFO: Shutting down INFO: Waiting for application shutdown. 13:32:26.187 - INFO - Shutting down 13:32:26.187 - INFO - Shutting down DatasetsRoutingTable 13:32:26.187 - INFO - Shutting down DatasetIORouter 13:32:26.187 - INFO - Shutting down TorchtuneKFPPostTrainingImpl Traceback (most recent call last): File "/opt/homebrew/Cellar/python@3.12/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/homebrew/Cellar/python@3.12/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/base_events.py", line 687, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ asyncio.exceptions.CancelledError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "/Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/executor_main.py", line 109, in <module> executor_main() File "/Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/executor_main.py", line 101, in executor_main output_file = executor.execute() ^^^^^^^^^^^^^^^^^^ File "/Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/executor.py", line 361, in execute result = self.func(**func_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/var/folders/45/1q1rx6cn7jbcn2ty852w0g_r0000gn/T/tmp.RKpPrvTWDD/ephemeral_component.py", line 118, in component asyncio.run(recipe.setup()) File "/opt/homebrew/Cellar/python@3.12/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/runners.py", line 194, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/homebrew/Cellar/python@3.12/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/runners.py", line 123, in run raise KeyboardInterrupt() KeyboardInterrupt 13:32:31.219 - ERROR - Task 'component' finished with status FAILURE ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ INFO 2025-05-09 13:32:31,221 llama_stack.providers.utils.scheduler:221 scheduler: Job test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa: Pipeline [1m[95m'test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa'[1m[0m finished with status [1m[91mFAILURE[1m[0m. Inner task failed: [1m[96m'component'[1m[0m. ERROR 2025-05-09 13:32:31,223 llama_stack_provider_kfp_trainer.scheduler:54 scheduler: Job test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa failed. ╭───────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────╮ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/src/llama_stack_provider_kfp_trainer/scheduler.py:45 │ │ in do │ │ │ │ 42 │ │ │ │ │ 43 │ │ │ job.status = JobStatus.running │ │ 44 │ │ │ try: │ │ ❱ 45 │ │ │ │ artifacts = self._to_artifacts(job.handler().output) │ │ 46 │ │ │ │ for artifact in artifacts: │ │ 47 │ │ │ │ │ on_artifact_collected_cb(artifact) │ │ 48 │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/base_compon │ │ ent.py:101 in __call__ │ │ │ │ 98 │ │ │ │ f'{self.name}() missing {len(missing_arguments)} required ' │ │ 99 │ │ │ │ f'{argument_or_arguments}: {arguments}.') │ │ 100 │ │ │ │ ❱ 101 │ │ return pipeline_task.PipelineTask( │ │ 102 │ │ │ component_spec=self.component_spec, │ │ 103 │ │ │ args=task_inputs, │ │ 104 │ │ │ execute_locally=pipeline_context.Pipeline.get_default_pipeline() is │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/pipeline_ta │ │ sk.py:187 in __init__ │ │ │ │ 184 │ │ ]) │ │ 185 │ │ │ │ 186 │ │ if execute_locally: │ │ ❱ 187 │ │ │ self._execute_locally(args=args) │ │ 188 │ │ │ 189 │ def _execute_locally(self, args: Dict[str, Any]) -> None: │ │ 190 │ │ """Execute the pipeline task locally. │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/pipeline_ta │ │ sk.py:197 in _execute_locally │ │ │ │ 194 │ │ from kfp.local import task_dispatcher │ │ 195 │ │ │ │ 196 │ │ if self.pipeline_spec is not None: │ │ ❱ 197 │ │ │ self._outputs = pipeline_orchestrator.run_local_pipeline( │ │ 198 │ │ │ │ pipeline_spec=self.pipeline_spec, │ │ 199 │ │ │ │ arguments=args, │ │ 200 │ │ │ ) │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/local/pipeline_ │ │ orchestrator.py:43 in run_local_pipeline │ │ │ │ 40 │ │ │ 41 │ # validate and access all global state in this function, not downstream │ │ 42 │ config.LocalExecutionConfig.validate() │ │ ❱ 43 │ return _run_local_pipeline_implementation( │ │ 44 │ │ pipeline_spec=pipeline_spec, │ │ 45 │ │ arguments=arguments, │ │ 46 │ │ raise_on_error=config.LocalExecutionConfig.instance.raise_on_error, │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/local/pipeline_ │ │ orchestrator.py:108 in _run_local_pipeline_implementation │ │ │ │ 105 │ │ │ ) │ │ 106 │ │ return outputs │ │ 107 │ elif dag_status == status.Status.FAILURE: │ │ ❱ 108 │ │ log_and_maybe_raise_for_failure( │ │ 109 │ │ │ pipeline_name=pipeline_name, │ │ 110 │ │ │ fail_stack=fail_stack, │ │ 111 │ │ │ raise_on_error=raise_on_error, │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/local/pipeline_ │ │ orchestrator.py:137 in log_and_maybe_raise_for_failure │ │ │ │ 134 │ │ logging_utils.format_task_name(task_name) for task_name in fail_stack) │ │ 135 │ msg = f'Pipeline {pipeline_name_with_color} finished with status │ │ {status_with_color}. Inner task failed: {task_chain_with_color}.' │ │ 136 │ if raise_on_error: │ │ ❱ 137 │ │ raise RuntimeError(msg) │ │ 138 │ with logging_utils.local_logger_context(): │ │ 139 │ │ logging.error(msg) │ │ 140 │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ RuntimeError: Pipeline [1m[95m'test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa'[1m[0m finished with status [1m[91mFAILURE[1m[0m. Inner task failed: [1m[96m'component'[1m[0m. INFO 2025-05-09 13:32:31,266 llama_stack.distribution.server.server:136 server: Shutting down DistributionInspectImpl INFO 2025-05-09 13:32:31,266 llama_stack.distribution.server.server:136 server: Shutting down ProviderImpl INFO: Application shutdown complete. INFO: Finished server process [26648] ``` [//]: # (## Documentation) Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>	2025-06-19 17:01:33 +02:00
ehhuang	db2cd9e8f3	feat: support filters in file search (#2472) ... # What does this PR do? Move to use vector_stores.search for file search tool in Responses, which supports filters. closes #2435 ## Test Plan Added e2e test with fitlers. myenv ❯ llama stack run llama_stack/templates/fireworks/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search and filters' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.3-70B-Instruct	2025-06-18 21:50:55 -07:00
Sumit Jaiswal	90d03552d4	feat: To add health check for faiss inline vector_io provider (#2319) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> To add health check for faiss inline vector_io provider. I tried adding `async def health(self) -> HealthResponse:` like in inference provider, but it didn't worked for `inline->vector_io->faiss` provider. And via debug logs, I understood the critical issue, that the health responses are being stored with the API name as the key, not as a nested dictionary with provider IDs. This means that all providers of the same API type (e.g., "vector_io") will share the same health response, and only the last one processed will be visible in the API response. I've created a patch file that fixes this issue by: - Storing the original get_providers_health method - Creating a patched version that correctly maps health responses to providers - Applying the patch to the `ProviderImpl` class Not an expert, so please let me know, if there can be any other workaround using which I can get the health status updated directly from `faiss.py`. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> Added unit tests to test the provider patch implementation in the PR. Adding a screenshot with the FAISS inline vector_io health status as "OK" 	2025-06-18 17:56:25 +02:00
Varsha	2e8054bede	feat: Implement hybrid search in SQLite-vec (#2312) ... # What does this PR do? Add support for hybrid search mode in SQLite-vec provider, which combines keyword and vector search for better results. The implementation: - Adds hybrid search mode as a new option alongside vector and keyword search - Implements query_hybrid method in SQLiteVecIndex that: - First performs keyword search to get candidate matches - Then applies vector similarity search on those candidates - Updates documentation to reflect the new search mode This change improves search quality by leveraging both semantic similarity and keyword matching, while maintaining backward compatibility with existing vector and keyword search modes. ## Test Plan ``` pytest tests/unit/providers/vector_io/test_sqlite_vec.py -v -s --tb=short /Users/vnarsing/miniconda3/envs/stack-client/lib/python3.10/site-packages/pytest_asyncio/plugin.py:217: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset. The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session" warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET)) =============================================================================================== test session starts =============================================================================================== platform darwin -- Python 3.10.16, pytest-8.3.5, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.10.16', 'Platform': 'macOS-14.7.6-arm64-arm-64bit', 'Packages': {'pytest': '8.3.5', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'anyio': '4.8.0', 'asyncio': '0.26.0', 'nbval': '0.11.0', 'cov': '6.1.1'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: html-4.1.1, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, anyio-4.8.0, asyncio-0.26.0, nbval-0.11.0, cov-6.1.1 asyncio: mode=strict, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 10 items tests/unit/providers/vector_io/test_sqlite_vec.py::test_add_chunks PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_vector PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_full_text_search PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_full_text_search_k_greater_than_results PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_no_keyword_matches PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_score_threshold PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_different_embedding PASSED ``` --------- Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-06-13 15:54:06 -04:00
Ibrahim Haroon	a34cef925b	fix(faiss): handle case where distance is 0 by setting d to minimum positive… (#2387) ... # What does this PR do? Adds try-catch to faiss `query_vector` function for when the distance between the query embedding and an embedding within the vector db is 0 (identical vectors). Catches `ZeroDivisionError` and then appends `(1.0 / sys.float_info.min)` to `scores` to represent maximum similarity. <!-- If resolving an issue, uncomment and update the line below --> Closes [#2381] ## Test Plan Checkout this PR Execute this code and there will no longer be a `ZeroDivisionError` exception ``` from llama_stack_client import LlamaStackClient base_url = "http://localhost:8321" client = LlamaStackClient(base_url=base_url) models = client.models.list() embedding_model = ( em := next(m for m in models if m.model_type == "embedding") ).identifier embedding_dimension = 384 _ = client.vector_dbs.register( vector_db_id="foo_db", embedding_model=embedding_model, embedding_dimension=embedding_dimension, provider_id="faiss", ) chunk = { "content": "foo", "mime_type": "text/plain", "metadata": { "document_id": "foo-id" } } client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk]) client.vector_io.query(vector_db_id="foo_db", query="foo") ``` ### Running unit tests `uv run pytest tests/unit/rag/test_rag_query.py -v` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com> Co-authored-by: Ben Browning <bbrownin@redhat.com>	2025-06-07 16:09:46 -04:00
Sumit Jaiswal	33ecefd284	feat: To add health status check for remote VLLM (#2303) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> To add health status check for remote VLLM <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> PR includes the unit test to test the added health check implementation feature.	2025-06-06 15:33:12 -04:00