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Pin weaviate-client version
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ChristianZaccaria
parent
980c7c244d
commit
920c2a3e12
5 changed files with 18 additions and 25 deletions
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@ -500,7 +500,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
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api=Api.vector_io,
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adapter_type="weaviate",
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provider_type="remote::weaviate",
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pip_packages=["weaviate-client"],
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pip_packages=["weaviate-client>=4.16.5"],
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module="llama_stack.providers.remote.vector_io.weaviate",
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config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig",
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provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",
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@ -96,9 +96,9 @@ class WeaviateIndex(EmbeddingIndex):
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k: Limit of number of results to return
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score_threshold: Minimum similarity score threshold
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Returns:
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QueryChunksResponse with chunks and scores
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QueryChunksResponse with chunks and scores.
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"""
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log.info(
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log.debug(
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f"WEAVIATE VECTOR SEARCH CALLED: embedding_shape={embedding.shape}, k={k}, threshold={score_threshold}"
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)
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sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)
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@ -135,7 +135,7 @@ class WeaviateIndex(EmbeddingIndex):
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chunks.append(chunk)
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scores.append(score)
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log.info(f"WEAVIATE VECTOR SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}")
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log.debug(f"WEAVIATE VECTOR SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}")
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return QueryChunksResponse(chunks=chunks, scores=scores)
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async def delete(self, chunk_ids: list[str] | None = None) -> None:
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@ -166,7 +166,7 @@ class WeaviateIndex(EmbeddingIndex):
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Returns:
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QueryChunksResponse with chunks and scores
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"""
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log.info(f"WEAVIATE KEYWORD SEARCH CALLED: query='{query_string}', k={k}, threshold={score_threshold}")
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log.debug(f"WEAVIATE KEYWORD SEARCH CALLED: query='{query_string}', k={k}, threshold={score_threshold}")
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sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)
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collection = self.client.collections.get(sanitized_collection_name)
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@ -199,7 +199,7 @@ class WeaviateIndex(EmbeddingIndex):
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chunks.append(chunk)
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scores.append(score)
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log.info(f"WEAVIATE KEYWORD SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}.")
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log.debug(f"WEAVIATE KEYWORD SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}.")
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return QueryChunksResponse(chunks=chunks, scores=scores)
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async def query_hybrid(
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@ -223,7 +223,7 @@ class WeaviateIndex(EmbeddingIndex):
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Returns:
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QueryChunksResponse with combined results
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"""
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log.info(
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log.debug(
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f"WEAVIATE HYBRID SEARCH CALLED: query='{query_string}', embedding_shape={embedding.shape}, k={k}, threshold={score_threshold}, reranker={reranker_type}"
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)
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sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)
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@ -265,11 +265,10 @@ class WeaviateIndex(EmbeddingIndex):
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if score < score_threshold:
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continue
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log.info(f"Document {chunk.metadata.get('document_id')} has score {score}")
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chunks.append(chunk)
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scores.append(score)
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log.info(f"WEAVIATE HYBRID SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}")
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log.debug(f"WEAVIATE HYBRID SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}")
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return QueryChunksResponse(chunks=chunks, scores=scores)
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@ -297,7 +296,7 @@ class WeaviateVectorIOAdapter(
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def _get_client(self) -> weaviate.WeaviateClient:
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if "localhost" in self.config.weaviate_cluster_url:
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log.info("using Weaviate locally in container")
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log.info("Using Weaviate locally in container")
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host, port = self.config.weaviate_cluster_url.split(":")
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key = "local_test"
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client = weaviate.connect_to_local(
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@ -25,8 +25,8 @@ classifiers = [
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]
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dependencies = [
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"aiohttp",
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"fastapi>=0.115.0,<1.0", # server
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"fire", # for MCP in LLS client
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"fastapi>=0.115.0,<1.0", # server
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"fire", # for MCP in LLS client
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"httpx",
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"huggingface-hub>=0.34.0,<1.0",
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"jinja2>=3.1.6",
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@ -43,13 +43,12 @@ dependencies = [
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"tiktoken",
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"pillow",
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"h11>=0.16.0",
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"python-multipart>=0.0.20", # For fastapi Form
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"uvicorn>=0.34.0", # server
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"opentelemetry-sdk>=1.30.0", # server
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"python-multipart>=0.0.20", # For fastapi Form
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"uvicorn>=0.34.0", # server
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"opentelemetry-sdk>=1.30.0", # server
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"opentelemetry-exporter-otlp-proto-http>=1.30.0", # server
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"aiosqlite>=0.21.0", # server - for metadata store
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"asyncpg", # for metadata store
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"weaviate-client>=4.16.5",
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"aiosqlite>=0.21.0", # server - for metadata store
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"asyncpg", # for metadata store
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]
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[project.optional-dependencies]
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@ -23,13 +23,13 @@ pymilvus_mock.AnnSearchRequest = MagicMock
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with patch.dict("sys.modules", {"pymilvus": pymilvus_mock}):
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from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex
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# This test is a unit test for the MilvusIndex class. This should only contain
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# This test is a unit test for the MilvusVectorIOAdapter class. This should only contain
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# tests which are specific to this class. More general (API-level) tests should be placed in
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# tests/integration/vector_io/
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#
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# How to run this test:
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#
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# pytest tests/unit/providers/vector_io/remote/test_milvus.py \
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# pytest tests/unit/providers/vector_io/test_milvus.py \
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# -v -s --tb=short --disable-warnings --asyncio-mode=auto
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MILVUS_PROVIDER = "milvus"
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@ -324,6 +324,3 @@ async def test_query_hybrid_search_default_rrf(
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call_args = mock_milvus_client.hybrid_search.call_args
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ranker = call_args[1]["ranker"]
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assert ranker is not None
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# TODO: Write tests for the MilvusVectorIOAdapter class.
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2
uv.lock
generated
2
uv.lock
generated
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@ -1777,7 +1777,6 @@ dependencies = [
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{ name = "termcolor" },
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{ name = "tiktoken" },
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{ name = "uvicorn" },
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{ name = "weaviate-client" },
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]
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[package.optional-dependencies]
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@ -1905,7 +1904,6 @@ requires-dist = [
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{ name = "termcolor" },
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{ name = "tiktoken" },
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{ name = "uvicorn", specifier = ">=0.34.0" },
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{ name = "weaviate-client", specifier = ">=4.16.5" },
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]
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provides-extras = ["ui"]
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