12 commits

Author	SHA1	Message	Date
Varsha Prasad Narsing	a879b6c12e	feat: use kv store for metadata ... Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-06-26 22:19:36 -07:00
Varsha Prasad Narsing	61bddfe70e	feat: Add openAI compatible APIs to QDrant ... Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-06-26 22:19:36 -07:00
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
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
Francisco Arceo	f328436831	feat: Enable ingestion of precomputed embeddings (#2317)	2025-05-31 04:03:37 -06:00
Varsha	e92301f2d7	feat(sqlite-vec): enable keyword search for sqlite-vec (#1439) ... # What does this PR do? This PR introduces support for keyword based FTS5 search with BM25 relevance scoring. It makes changes to the existing EmbeddingIndex base class in order to support a search_mode and query_str parameter, that can be used for keyword based search implementations. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan run ``` pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto ``` Output: ``` pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto /Users/vnarsing/miniconda3/envs/stack-client/lib/python3.10/site-packages/pytest_asyncio/plugin.py:207: 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-8.3.4, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.10.16', 'Platform': 'macOS-14.7.4-arm64-arm-64bit', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: html-4.1.1, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0 asyncio: mode=auto, asyncio_default_fixture_loop_scope=None collected 7 items llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_add_chunks PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks_vector PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks_fts PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_register_vector_db PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_unregister_vector_db PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED ``` For reference, with the implementation, the fts table looks like below: ``` Chunk ID: 9fbc39ce-c729-64a2-260f-c5ec9bb2a33e, Content: Sentence 0 from document 0 Chunk ID: 94062914-3e23-44cf-1e50-9e25821ba882, Content: Sentence 1 from document 0 Chunk ID: e6cfd559-4641-33ba-6ce1-7038226495eb, Content: Sentence 2 from document 0 Chunk ID: 1383af9b-f1f0-f417-4de5-65fe9456cc20, Content: Sentence 3 from document 0 Chunk ID: 2db19b1a-de14-353b-f4e1-085e8463361c, Content: Sentence 4 from document 0 Chunk ID: 9faf986a-f028-7714-068a-1c795e8f2598, Content: Sentence 5 from document 0 Chunk ID: ef593ead-5a4a-392f-7ad8-471a50f033e8, Content: Sentence 6 from document 0 Chunk ID: e161950f-021f-7300-4d05-3166738b94cf, Content: Sentence 7 from document 0 Chunk ID: 90610fc4-67c1-e740-f043-709c5978867a, Content: Sentence 8 from document 0 Chunk ID: 97712879-6fff-98ad-0558-e9f42e6b81d3, Content: Sentence 9 from document 0 Chunk ID: aea70411-51df-61ba-d2f0-cb2b5972c210, Content: Sentence 0 from document 1 Chunk ID: b678a463-7b84-92b8-abb2-27e9a1977e3c, Content: Sentence 1 from document 1 Chunk ID: 27bd63da-909c-1606-a109-75bdb9479882, Content: Sentence 2 from document 1 Chunk ID: a2ad49ad-f9be-5372-e0c7-7b0221d0b53e, Content: Sentence 3 from document 1 Chunk ID: cac53bcd-1965-082a-c0f4-ceee7323fc70, Content: Sentence 4 from document 1 ``` Query results: Result 1: Sentence 5 from document 0 Result 2: Sentence 5 from document 1 Result 3: Sentence 5 from document 2 [//]: # (## Documentation) --------- Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-05-21 15:24:24 -04:00
Francisco Arceo	9e1ddf2b53	chore: Updating sqlite-vec to make non-blocking calls (#1762) ... # What does this PR do? This PR updates the sqlite-vec database calls to be non-blocking. Note that each operation creates a new connection, which incurs some performance overhead but is reasonable given [SQLite's threading and connections constraints](https://www.sqlite.org/threadsafe.html). Summary of changes: - Refactored `SQLiteVecIndex` class to store database path instead of connection object - Added `_create_sqlite_connection()` helper function to create connections on demand - Ensured proper connection closure in all database operations - Fixed test fixtures to use a file-based SQLite database for thread-safety - Updated the `SQLiteVecVectorIOAdapter` class to handle per-operation connections This PR helps chip away at https://github.com/meta-llama/llama-stack/issues/1489 ## Test Plan sqlite-vec unit tests passed locally as well as a test script using the client as a library. ## Misc FYI @varshaprasad96 @kevincogan Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-03-23 17:25:44 -07:00
Daniele Martinoli	cca9bd6cc3	feat: Qdrant inline provider (#1273) ... # What does this PR do? Removed local execution option from the remote Qdrant provider and introduced an explicit inline provider for the embedded execution. Updated the ollama template to include this option: this part can be reverted in case we don't want to have two default `vector_io` providers. (Closes #1082) ## Test Plan Build and run an ollama distro: ```bash llama stack build --template ollama --image-type conda llama stack run --image-type conda ollama ``` Run one of the sample ingestionapplicatinos like [rag_with_vector_db.py](https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py), but replace this line: ```py selected_vector_provider = vector_providers[0] ``` with the following, to use the `qdrant` provider: ```py selected_vector_provider = vector_providers[1] ``` After running the test code, verify the timestamp of the Qdrant store: ```bash % ls -ltr ~/.llama/distributions/ollama/qdrant.db/collection/test_vector_db_* total 784 -rw-r--r--@ 1 dmartino staff 401408 Feb 26 10:07 storage.sqlite ``` [//]: # (## Documentation) --------- Signed-off-by: Daniele Martinoli <dmartino@redhat.com> Co-authored-by: Francisco Arceo <farceo@redhat.com>	2025-03-18 14:04:21 -07:00
Ben Browning	00570fde31	chore: Get sqlite_vec and vector_store unit tests passing (#1413)	2025-03-05 13:20:13 -05:00
Ashwin Bharambe	dd0db8038b	refactor(test): unify vector_io tests and make them configurable (#1398) ... ## Test Plan `LLAMA_STACK_CONFIG=inference=sentence-transformers,vector_io=sqlite-vec pytest -s -v test_vector_io.py --embedding-model all-miniLM-L6-V2 --inference-model='' --vision-inference-model=''` ``` test_vector_io.py::test_vector_db_retrieve[txt=:vis=:emb=all-miniLM-L6-V2] PASSED test_vector_io.py::test_vector_db_register[txt=:vis=:emb=all-miniLM-L6-V2] PASSED test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case0] PASSED test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case1] PASSED test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case2] PASSED test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case3] PASSED test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case4] PASSED ``` Same thing with: - LLAMA_STACK_CONFIG=inference=sentence-transformers,vector_io=faiss - LLAMA_STACK_CONFIG=fireworks (Note that ergonomics will soon be improved re: cmd-line options and env variables)	2025-03-04 13:37:45 -08:00