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update resolver to only pass vector_stores section of run config
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> Using Router only from VectorDBs Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> removing model_api from vector store providers Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> fix test Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> updating integration tests Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> special handling for replay mode for available providers Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo
parent
24a1430c8b
commit
accc4c437e
46 changed files with 397 additions and 702 deletions
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@ -4,27 +4,14 @@
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack.core.datatypes import StackRunConfig
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from llama_stack.providers.datatypes import Api, ProviderSpec
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from .config import ChromaVectorIOConfig
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async def get_adapter_impl(
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config: ChromaVectorIOConfig, deps: dict[Api, ProviderSpec], run_config: StackRunConfig | None = None
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):
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async def get_adapter_impl(config: ChromaVectorIOConfig, deps: dict[Api, ProviderSpec]):
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from .chroma import ChromaVectorIOAdapter
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vector_stores_config = None
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if run_config and run_config.vector_stores:
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vector_stores_config = run_config.vector_stores
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impl = ChromaVectorIOAdapter(
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config,
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deps[Api.inference],
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deps[Api.models],
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deps.get(Api.files),
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vector_stores_config,
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)
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impl = ChromaVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
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await impl.initialize()
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return impl
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@ -13,25 +13,15 @@ from numpy.typing import NDArray
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from llama_stack.apis.files import Files
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from llama_stack.apis.inference import Inference, InterleavedContent
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from llama_stack.apis.models import Models
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from llama_stack.apis.vector_dbs import VectorDB
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from llama_stack.apis.vector_io import (
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Chunk,
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QueryChunksResponse,
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VectorIO,
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)
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from llama_stack.core.datatypes import VectorStoresConfig
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from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
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from llama_stack.log import get_logger
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from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
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from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig
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from llama_stack.providers.utils.kvstore import kvstore_impl
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from llama_stack.providers.utils.kvstore.api import KVStore
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from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
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from llama_stack.providers.utils.memory.vector_store import (
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ChunkForDeletion,
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EmbeddingIndex,
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VectorDBWithIndex,
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)
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from llama_stack.providers.utils.memory.vector_store import ChunkForDeletion, EmbeddingIndex, VectorDBWithIndex
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from .config import ChromaVectorIOConfig as RemoteChromaVectorIOConfig
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@ -70,19 +60,13 @@ class ChromaIndex(EmbeddingIndex):
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ids = [f"{c.metadata.get('document_id', '')}:{c.chunk_id}" for c in chunks]
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await maybe_await(
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self.collection.add(
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documents=[chunk.model_dump_json() for chunk in chunks],
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embeddings=embeddings,
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ids=ids,
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)
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self.collection.add(documents=[chunk.model_dump_json() for chunk in chunks], embeddings=embeddings, ids=ids)
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)
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async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
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results = await maybe_await(
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self.collection.query(
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query_embeddings=[embedding.tolist()],
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n_results=k,
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include=["documents", "distances"],
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query_embeddings=[embedding.tolist()], n_results=k, include=["documents", "distances"]
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)
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)
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distances = results["distances"][0]
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@ -110,12 +94,7 @@ class ChromaIndex(EmbeddingIndex):
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async def delete(self):
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await maybe_await(self.client.delete_collection(self.collection.name))
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async def query_keyword(
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self,
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query_string: str,
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k: int,
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score_threshold: float,
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) -> QueryChunksResponse:
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async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
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raise NotImplementedError("Keyword search is not supported in Chroma")
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async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]) -> None:
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@ -140,16 +119,12 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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self,
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config: RemoteChromaVectorIOConfig | InlineChromaVectorIOConfig,
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inference_api: Inference,
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models_apis: Models,
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files_api: Files | None,
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vector_stores_config: VectorStoresConfig | None = None,
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) -> None:
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super().__init__(files_api=files_api, kvstore=None)
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log.info(f"Initializing ChromaVectorIOAdapter with url: {config}")
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self.config = config
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self.inference_api = inference_api
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self.models_api = models_apis
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self.vector_stores_config = vector_stores_config
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self.client = None
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self.cache = {}
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self.vector_db_store = None
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@ -176,14 +151,10 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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# Clean up mixin resources (file batch tasks)
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await super().shutdown()
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async def register_vector_db(
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self,
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vector_db: VectorDB,
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) -> None:
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async def register_vector_db(self, vector_db: VectorDB) -> None:
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collection = await maybe_await(
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self.client.get_or_create_collection(
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name=vector_db.identifier,
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metadata={"vector_db": vector_db.model_dump_json()},
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name=vector_db.identifier, metadata={"vector_db": vector_db.model_dump_json()}
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)
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)
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self.cache[vector_db.identifier] = VectorDBWithIndex(
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@ -198,12 +169,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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await self.cache[vector_db_id].index.delete()
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del self.cache[vector_db_id]
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async def insert_chunks(
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self,
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vector_db_id: str,
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chunks: list[Chunk],
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ttl_seconds: int | None = None,
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) -> None:
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async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
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index = await self._get_and_cache_vector_db_index(vector_db_id)
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if index is None:
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raise ValueError(f"Vector DB {vector_db_id} not found in Chroma")
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@ -211,10 +177,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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await index.insert_chunks(chunks)
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async def query_chunks(
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self,
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vector_db_id: str,
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query: InterleavedContent,
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params: dict[str, Any] | None = None,
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self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
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) -> QueryChunksResponse:
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index = await self._get_and_cache_vector_db_index(vector_db_id)
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@ -22,7 +22,6 @@ class ChromaVectorIOConfig(BaseModel):
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return {
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"url": url,
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"kvstore": SqliteKVStoreConfig.sample_run_config(
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__distro_dir__=__distro_dir__,
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db_name="chroma_remote_registry.db",
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__distro_dir__=__distro_dir__, db_name="chroma_remote_registry.db"
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),
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}
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@ -4,28 +4,15 @@
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack.core.datatypes import StackRunConfig
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from llama_stack.providers.datatypes import Api, ProviderSpec
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from .config import MilvusVectorIOConfig
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async def get_adapter_impl(
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config: MilvusVectorIOConfig, deps: dict[Api, ProviderSpec], run_config: StackRunConfig | None = None
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):
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async def get_adapter_impl(config: MilvusVectorIOConfig, deps: dict[Api, ProviderSpec]):
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from .milvus import MilvusVectorIOAdapter
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vector_stores_config = None
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if run_config and run_config.vector_stores:
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vector_stores_config = run_config.vector_stores
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assert isinstance(config, MilvusVectorIOConfig), f"Unexpected config type: {type(config)}"
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impl = MilvusVectorIOAdapter(
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config,
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deps[Api.inference],
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deps[Api.models],
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deps.get(Api.files),
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vector_stores_config,
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)
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impl = MilvusVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
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await impl.initialize()
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return impl
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@ -29,7 +29,6 @@ class MilvusVectorIOConfig(BaseModel):
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"uri": "${env.MILVUS_ENDPOINT}",
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"token": "${env.MILVUS_TOKEN}",
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"kvstore": SqliteKVStoreConfig.sample_run_config(
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__distro_dir__=__distro_dir__,
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db_name="milvus_remote_registry.db",
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__distro_dir__=__distro_dir__, db_name="milvus_remote_registry.db"
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),
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}
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@ -14,14 +14,8 @@ from pymilvus import AnnSearchRequest, DataType, Function, FunctionType, MilvusC
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from llama_stack.apis.common.errors import VectorStoreNotFoundError
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from llama_stack.apis.files import Files
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from llama_stack.apis.inference import Inference, InterleavedContent
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from llama_stack.apis.models import Models
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from llama_stack.apis.vector_dbs import VectorDB
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from llama_stack.apis.vector_io import (
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Chunk,
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QueryChunksResponse,
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VectorIO,
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)
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from llama_stack.core.datatypes import VectorStoresConfig
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from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
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from llama_stack.log import get_logger
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from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
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from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
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@ -75,46 +69,23 @@ class MilvusIndex(EmbeddingIndex):
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logger.info(f"Creating new collection {self.collection_name} with nullable sparse field")
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# Create schema for vector search
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schema = self.client.create_schema()
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schema.add_field(
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field_name="chunk_id",
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datatype=DataType.VARCHAR,
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is_primary=True,
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max_length=100,
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)
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schema.add_field(field_name="chunk_id", datatype=DataType.VARCHAR, is_primary=True, max_length=100)
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schema.add_field(
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field_name="content",
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datatype=DataType.VARCHAR,
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max_length=65535,
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enable_analyzer=True, # Enable text analysis for BM25
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)
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schema.add_field(
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field_name="vector",
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datatype=DataType.FLOAT_VECTOR,
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dim=len(embeddings[0]),
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)
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schema.add_field(
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field_name="chunk_content",
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datatype=DataType.JSON,
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)
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schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=len(embeddings[0]))
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schema.add_field(field_name="chunk_content", datatype=DataType.JSON)
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# Add sparse vector field for BM25 (required by the function)
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schema.add_field(
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field_name="sparse",
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datatype=DataType.SPARSE_FLOAT_VECTOR,
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)
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schema.add_field(field_name="sparse", datatype=DataType.SPARSE_FLOAT_VECTOR)
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# Create indexes
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index_params = self.client.prepare_index_params()
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index_params.add_index(
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field_name="vector",
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index_type="FLAT",
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metric_type="COSINE",
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)
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index_params.add_index(field_name="vector", index_type="FLAT", metric_type="COSINE")
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# Add index for sparse field (required by BM25 function)
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index_params.add_index(
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field_name="sparse",
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index_type="SPARSE_INVERTED_INDEX",
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metric_type="BM25",
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)
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index_params.add_index(field_name="sparse", index_type="SPARSE_INVERTED_INDEX", metric_type="BM25")
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# Add BM25 function for full-text search
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bm25_function = Function(
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@ -145,11 +116,7 @@ class MilvusIndex(EmbeddingIndex):
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}
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)
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try:
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await asyncio.to_thread(
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self.client.insert,
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self.collection_name,
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data=data,
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)
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await asyncio.to_thread(self.client.insert, self.collection_name, data=data)
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except Exception as e:
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logger.error(f"Error inserting chunks into Milvus collection {self.collection_name}: {e}")
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raise e
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@ -168,12 +135,7 @@ class MilvusIndex(EmbeddingIndex):
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scores = [res["distance"] for res in search_res[0]]
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return QueryChunksResponse(chunks=chunks, scores=scores)
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async def query_keyword(
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self,
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query_string: str,
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k: int,
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score_threshold: float,
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) -> QueryChunksResponse:
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async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
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"""
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Perform BM25-based keyword search using Milvus's built-in full-text search.
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"""
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@ -211,12 +173,7 @@ class MilvusIndex(EmbeddingIndex):
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# Fallback to simple text search
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return await self._fallback_keyword_search(query_string, k, score_threshold)
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async def _fallback_keyword_search(
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self,
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query_string: str,
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k: int,
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score_threshold: float,
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) -> QueryChunksResponse:
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async def _fallback_keyword_search(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
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"""
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Fallback to simple text search when BM25 search is not available.
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"""
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@ -309,17 +266,13 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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self,
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config: RemoteMilvusVectorIOConfig | InlineMilvusVectorIOConfig,
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inference_api: Inference,
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models_api: Models | None,
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files_api: Files | None,
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vector_stores_config: VectorStoresConfig | None = None,
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) -> None:
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super().__init__(files_api=files_api, kvstore=None)
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self.config = config
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self.cache = {}
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self.client = None
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self.inference_api = inference_api
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self.models_api = models_api
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self.vector_stores_config = vector_stores_config
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self.vector_db_store = None
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self.metadata_collection_name = "openai_vector_stores_metadata"
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@ -358,10 +311,7 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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# Clean up mixin resources (file batch tasks)
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await super().shutdown()
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async def register_vector_db(
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self,
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vector_db: VectorDB,
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) -> None:
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async def register_vector_db(self, vector_db: VectorDB) -> None:
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if isinstance(self.config, RemoteMilvusVectorIOConfig):
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consistency_level = self.config.consistency_level
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else:
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|
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@ -398,12 +348,7 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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await self.cache[vector_db_id].index.delete()
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del self.cache[vector_db_id]
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async def insert_chunks(
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self,
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vector_db_id: str,
|
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chunks: list[Chunk],
|
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ttl_seconds: int | None = None,
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) -> None:
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async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
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index = await self._get_and_cache_vector_db_index(vector_db_id)
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if not index:
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raise VectorStoreNotFoundError(vector_db_id)
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|
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@ -411,10 +356,7 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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await index.insert_chunks(chunks)
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async def query_chunks(
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self,
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vector_db_id: str,
|
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query: InterleavedContent,
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params: dict[str, Any] | None = None,
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self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
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) -> QueryChunksResponse:
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index = await self._get_and_cache_vector_db_index(vector_db_id)
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if not index:
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|
|
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|
|
@ -4,26 +4,14 @@
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# This source code is licensed under the terms described in the LICENSE file in
|
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# the root directory of this source tree.
|
||||
|
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from llama_stack.core.datatypes import StackRunConfig
|
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from llama_stack.providers.datatypes import Api, ProviderSpec
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|
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from .config import PGVectorVectorIOConfig
|
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|
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|
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async def get_adapter_impl(
|
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config: PGVectorVectorIOConfig, deps: dict[Api, ProviderSpec], run_config: StackRunConfig | None = None
|
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):
|
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async def get_adapter_impl(config: PGVectorVectorIOConfig, deps: dict[Api, ProviderSpec]):
|
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from .pgvector import PGVectorVectorIOAdapter
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|
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vector_stores_config = None
|
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if run_config and run_config.vector_stores:
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vector_stores_config = run_config.vector_stores
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impl = PGVectorVectorIOAdapter(
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config,
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deps[Api.inference],
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deps[Api.models],
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deps.get(Api.files),
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vector_stores_config,
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)
|
||||
impl = PGVectorVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
|
||||
await impl.initialize()
|
||||
return impl
|
||||
|
|
|
|||
|
|
@ -8,10 +8,7 @@ from typing import Any
|
|||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from llama_stack.providers.utils.kvstore.config import (
|
||||
KVStoreConfig,
|
||||
SqliteKVStoreConfig,
|
||||
)
|
||||
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
|
||||
from llama_stack.schema_utils import json_schema_type
|
||||
|
||||
|
||||
|
|
@ -42,7 +39,6 @@ class PGVectorVectorIOConfig(BaseModel):
|
|||
"user": user,
|
||||
"password": password,
|
||||
"kvstore": SqliteKVStoreConfig.sample_run_config(
|
||||
__distro_dir__=__distro_dir__,
|
||||
db_name="pgvector_registry.db",
|
||||
__distro_dir__=__distro_dir__, db_name="pgvector_registry.db"
|
||||
),
|
||||
}
|
||||
|
|
|
|||
|
|
@ -16,27 +16,15 @@ from pydantic import BaseModel, TypeAdapter
|
|||
from llama_stack.apis.common.errors import VectorStoreNotFoundError
|
||||
from llama_stack.apis.files import Files
|
||||
from llama_stack.apis.inference import Inference, InterleavedContent
|
||||
from llama_stack.apis.models import Models
|
||||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import (
|
||||
Chunk,
|
||||
QueryChunksResponse,
|
||||
VectorIO,
|
||||
)
|
||||
from llama_stack.core.datatypes import VectorStoresConfig
|
||||
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
|
||||
from llama_stack.log import get_logger
|
||||
from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
|
||||
from llama_stack.providers.utils.inference.prompt_adapter import (
|
||||
interleaved_content_as_str,
|
||||
)
|
||||
from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
|
||||
from llama_stack.providers.utils.kvstore import kvstore_impl
|
||||
from llama_stack.providers.utils.kvstore.api import KVStore
|
||||
from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
|
||||
from llama_stack.providers.utils.memory.vector_store import (
|
||||
ChunkForDeletion,
|
||||
EmbeddingIndex,
|
||||
VectorDBWithIndex,
|
||||
)
|
||||
from llama_stack.providers.utils.memory.vector_store import ChunkForDeletion, EmbeddingIndex, VectorDBWithIndex
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator, sanitize_collection_name
|
||||
|
||||
from .config import PGVectorVectorIOConfig
|
||||
|
|
@ -206,12 +194,7 @@ class PGVectorIndex(EmbeddingIndex):
|
|||
|
||||
return QueryChunksResponse(chunks=chunks, scores=scores)
|
||||
|
||||
async def query_keyword(
|
||||
self,
|
||||
query_string: str,
|
||||
k: int,
|
||||
score_threshold: float,
|
||||
) -> QueryChunksResponse:
|
||||
async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
|
||||
"""
|
||||
Performs keyword-based search using PostgreSQL's full-text search with ts_rank scoring.
|
||||
|
||||
|
|
@ -318,7 +301,7 @@ class PGVectorIndex(EmbeddingIndex):
|
|||
"""Remove a chunk from the PostgreSQL table."""
|
||||
chunk_ids = [c.chunk_id for c in chunks_for_deletion]
|
||||
with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
|
||||
cur.execute(f"DELETE FROM {self.table_name} WHERE id = ANY(%s)", (chunk_ids,))
|
||||
cur.execute(f"DELETE FROM {self.table_name} WHERE id = ANY(%s)", (chunk_ids))
|
||||
|
||||
def get_pgvector_search_function(self) -> str:
|
||||
return self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION[self.distance_metric]
|
||||
|
|
@ -342,18 +325,11 @@ class PGVectorIndex(EmbeddingIndex):
|
|||
|
||||
class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
|
||||
def __init__(
|
||||
self,
|
||||
config: PGVectorVectorIOConfig,
|
||||
inference_api: Inference,
|
||||
models_api: Models,
|
||||
files_api: Files | None = None,
|
||||
vector_stores_config: VectorStoresConfig | None = None,
|
||||
self, config: PGVectorVectorIOConfig, inference_api: Inference, files_api: Files | None = None
|
||||
) -> None:
|
||||
super().__init__(files_api=files_api, kvstore=None)
|
||||
self.config = config
|
||||
self.inference_api = inference_api
|
||||
self.models_api = models_api
|
||||
self.vector_stores_config = vector_stores_config
|
||||
self.conn = None
|
||||
self.cache = {}
|
||||
self.vector_db_store = None
|
||||
|
|
@ -410,11 +386,7 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
|
|||
vector_db=vector_db, dimension=vector_db.embedding_dimension, conn=self.conn, kvstore=self.kvstore
|
||||
)
|
||||
await pgvector_index.initialize()
|
||||
index = VectorDBWithIndex(
|
||||
vector_db,
|
||||
index=pgvector_index,
|
||||
inference_api=self.inference_api,
|
||||
)
|
||||
index = VectorDBWithIndex(vector_db, index=pgvector_index, inference_api=self.inference_api)
|
||||
self.cache[vector_db.identifier] = index
|
||||
|
||||
async def unregister_vector_db(self, vector_db_id: str) -> None:
|
||||
|
|
@ -427,20 +399,12 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
|
|||
assert self.kvstore is not None
|
||||
await self.kvstore.delete(key=f"{VECTOR_DBS_PREFIX}{vector_db_id}")
|
||||
|
||||
async def insert_chunks(
|
||||
self,
|
||||
vector_db_id: str,
|
||||
chunks: list[Chunk],
|
||||
ttl_seconds: int | None = None,
|
||||
) -> None:
|
||||
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
|
||||
index = await self._get_and_cache_vector_db_index(vector_db_id)
|
||||
await index.insert_chunks(chunks)
|
||||
|
||||
async def query_chunks(
|
||||
self,
|
||||
vector_db_id: str,
|
||||
query: InterleavedContent,
|
||||
params: dict[str, Any] | None = None,
|
||||
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
|
||||
) -> QueryChunksResponse:
|
||||
index = await self._get_and_cache_vector_db_index(vector_db_id)
|
||||
return await index.query_chunks(query, params)
|
||||
|
|
|
|||
|
|
@ -4,27 +4,14 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from llama_stack.core.datatypes import StackRunConfig
|
||||
from llama_stack.providers.datatypes import Api, ProviderSpec
|
||||
|
||||
from .config import QdrantVectorIOConfig
|
||||
|
||||
|
||||
async def get_adapter_impl(
|
||||
config: QdrantVectorIOConfig, deps: dict[Api, ProviderSpec], run_config: StackRunConfig | None = None
|
||||
):
|
||||
async def get_adapter_impl(config: QdrantVectorIOConfig, deps: dict[Api, ProviderSpec]):
|
||||
from .qdrant import QdrantVectorIOAdapter
|
||||
|
||||
vector_stores_config = None
|
||||
if run_config and run_config.vector_stores:
|
||||
vector_stores_config = run_config.vector_stores
|
||||
|
||||
impl = QdrantVectorIOAdapter(
|
||||
config,
|
||||
deps[Api.inference],
|
||||
deps[Api.models],
|
||||
deps.get(Api.files),
|
||||
vector_stores_config,
|
||||
)
|
||||
impl = QdrantVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
|
||||
await impl.initialize()
|
||||
return impl
|
||||
|
|
|
|||
|
|
@ -8,10 +8,7 @@ from typing import Any
|
|||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from llama_stack.providers.utils.kvstore.config import (
|
||||
KVStoreConfig,
|
||||
SqliteKVStoreConfig,
|
||||
)
|
||||
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
|
||||
from llama_stack.schema_utils import json_schema_type
|
||||
|
||||
|
||||
|
|
@ -34,7 +31,6 @@ class QdrantVectorIOConfig(BaseModel):
|
|||
return {
|
||||
"api_key": "${env.QDRANT_API_KEY:=}",
|
||||
"kvstore": SqliteKVStoreConfig.sample_run_config(
|
||||
__distro_dir__=__distro_dir__,
|
||||
db_name="qdrant_registry.db",
|
||||
__distro_dir__=__distro_dir__, db_name="qdrant_registry.db"
|
||||
),
|
||||
}
|
||||
|
|
|
|||
|
|
@ -16,7 +16,6 @@ from qdrant_client.models import PointStruct
|
|||
from llama_stack.apis.common.errors import VectorStoreNotFoundError
|
||||
from llama_stack.apis.files import Files
|
||||
from llama_stack.apis.inference import Inference, InterleavedContent
|
||||
from llama_stack.apis.models import Models
|
||||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import (
|
||||
Chunk,
|
||||
|
|
@ -25,17 +24,12 @@ from llama_stack.apis.vector_io import (
|
|||
VectorStoreChunkingStrategy,
|
||||
VectorStoreFileObject,
|
||||
)
|
||||
from llama_stack.core.datatypes import VectorStoresConfig
|
||||
from llama_stack.log import get_logger
|
||||
from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
|
||||
from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
|
||||
from llama_stack.providers.utils.kvstore import kvstore_impl
|
||||
from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
|
||||
from llama_stack.providers.utils.memory.vector_store import (
|
||||
ChunkForDeletion,
|
||||
EmbeddingIndex,
|
||||
VectorDBWithIndex,
|
||||
)
|
||||
from llama_stack.providers.utils.memory.vector_store import ChunkForDeletion, EmbeddingIndex, VectorDBWithIndex
|
||||
|
||||
from .config import QdrantVectorIOConfig as RemoteQdrantVectorIOConfig
|
||||
|
||||
|
|
@ -100,8 +94,7 @@ class QdrantIndex(EmbeddingIndex):
|
|||
chunk_ids = [convert_id(c.chunk_id) for c in chunks_for_deletion]
|
||||
try:
|
||||
await self.client.delete(
|
||||
collection_name=self.collection_name,
|
||||
points_selector=models.PointIdsList(points=chunk_ids),
|
||||
collection_name=self.collection_name, points_selector=models.PointIdsList(points=chunk_ids)
|
||||
)
|
||||
except Exception as e:
|
||||
log.error(f"Error deleting chunks from Qdrant collection {self.collection_name}: {e}")
|
||||
|
|
@ -134,12 +127,7 @@ class QdrantIndex(EmbeddingIndex):
|
|||
|
||||
return QueryChunksResponse(chunks=chunks, scores=scores)
|
||||
|
||||
async def query_keyword(
|
||||
self,
|
||||
query_string: str,
|
||||
k: int,
|
||||
score_threshold: float,
|
||||
) -> QueryChunksResponse:
|
||||
async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
|
||||
raise NotImplementedError("Keyword search is not supported in Qdrant")
|
||||
|
||||
async def query_hybrid(
|
||||
|
|
@ -162,17 +150,13 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
|
|||
self,
|
||||
config: RemoteQdrantVectorIOConfig | InlineQdrantVectorIOConfig,
|
||||
inference_api: Inference,
|
||||
models_api: Models,
|
||||
files_api: Files | None = None,
|
||||
vector_stores_config: VectorStoresConfig | None = None,
|
||||
) -> None:
|
||||
super().__init__(files_api=files_api, kvstore=None)
|
||||
self.config = config
|
||||
self.client: AsyncQdrantClient = None
|
||||
self.cache = {}
|
||||
self.inference_api = inference_api
|
||||
self.models_api = models_api
|
||||
self.vector_stores_config = vector_stores_config
|
||||
self.vector_db_store = None
|
||||
self._qdrant_lock = asyncio.Lock()
|
||||
|
||||
|
|
@ -187,11 +171,7 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
|
|||
|
||||
for vector_db_data in stored_vector_dbs:
|
||||
vector_db = VectorDB.model_validate_json(vector_db_data)
|
||||
index = VectorDBWithIndex(
|
||||
vector_db,
|
||||
QdrantIndex(self.client, vector_db.identifier),
|
||||
self.inference_api,
|
||||
)
|
||||
index = VectorDBWithIndex(vector_db, QdrantIndex(self.client, vector_db.identifier), self.inference_api)
|
||||
self.cache[vector_db.identifier] = index
|
||||
self.openai_vector_stores = await self._load_openai_vector_stores()
|
||||
|
||||
|
|
@ -200,18 +180,13 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
|
|||
# Clean up mixin resources (file batch tasks)
|
||||
await super().shutdown()
|
||||
|
||||
async def register_vector_db(
|
||||
self,
|
||||
vector_db: VectorDB,
|
||||
) -> None:
|
||||
async def register_vector_db(self, vector_db: VectorDB) -> None:
|
||||
assert self.kvstore is not None
|
||||
key = f"{VECTOR_DBS_PREFIX}{vector_db.identifier}"
|
||||
await self.kvstore.set(key=key, value=vector_db.model_dump_json())
|
||||
|
||||
index = VectorDBWithIndex(
|
||||
vector_db=vector_db,
|
||||
index=QdrantIndex(self.client, vector_db.identifier),
|
||||
inference_api=self.inference_api,
|
||||
vector_db=vector_db, index=QdrantIndex(self.client, vector_db.identifier), inference_api=self.inference_api
|
||||
)
|
||||
|
||||
self.cache[vector_db.identifier] = index
|
||||
|
|
@ -243,12 +218,7 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
|
|||
self.cache[vector_db_id] = index
|
||||
return index
|
||||
|
||||
async def insert_chunks(
|
||||
self,
|
||||
vector_db_id: str,
|
||||
chunks: list[Chunk],
|
||||
ttl_seconds: int | None = None,
|
||||
) -> None:
|
||||
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
|
||||
index = await self._get_and_cache_vector_db_index(vector_db_id)
|
||||
if not index:
|
||||
raise VectorStoreNotFoundError(vector_db_id)
|
||||
|
|
@ -256,10 +226,7 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
|
|||
await index.insert_chunks(chunks)
|
||||
|
||||
async def query_chunks(
|
||||
self,
|
||||
vector_db_id: str,
|
||||
query: InterleavedContent,
|
||||
params: dict[str, Any] | None = None,
|
||||
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
|
||||
) -> QueryChunksResponse:
|
||||
index = await self._get_and_cache_vector_db_index(vector_db_id)
|
||||
if not index:
|
||||
|
|
|
|||
|
|
@ -4,27 +4,14 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from llama_stack.core.datatypes import StackRunConfig
|
||||
from llama_stack.providers.datatypes import Api, ProviderSpec
|
||||
|
||||
from .config import WeaviateVectorIOConfig
|
||||
|
||||
|
||||
async def get_adapter_impl(
|
||||
config: WeaviateVectorIOConfig, deps: dict[Api, ProviderSpec], run_config: StackRunConfig | None = None
|
||||
):
|
||||
async def get_adapter_impl(config: WeaviateVectorIOConfig, deps: dict[Api, ProviderSpec]):
|
||||
from .weaviate import WeaviateVectorIOAdapter
|
||||
|
||||
vector_stores_config = None
|
||||
if run_config and run_config.vector_stores:
|
||||
vector_stores_config = run_config.vector_stores
|
||||
|
||||
impl = WeaviateVectorIOAdapter(
|
||||
config,
|
||||
deps[Api.inference],
|
||||
deps[Api.models],
|
||||
deps.get(Api.files),
|
||||
vector_stores_config,
|
||||
)
|
||||
impl = WeaviateVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
|
||||
await impl.initialize()
|
||||
return impl
|
||||
|
|
|
|||
|
|
@ -8,10 +8,7 @@ from typing import Any
|
|||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from llama_stack.providers.utils.kvstore.config import (
|
||||
KVStoreConfig,
|
||||
SqliteKVStoreConfig,
|
||||
)
|
||||
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
|
||||
from llama_stack.schema_utils import json_schema_type
|
||||
|
||||
|
||||
|
|
@ -22,16 +19,11 @@ class WeaviateVectorIOConfig(BaseModel):
|
|||
kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None)
|
||||
|
||||
@classmethod
|
||||
def sample_run_config(
|
||||
cls,
|
||||
__distro_dir__: str,
|
||||
**kwargs: Any,
|
||||
) -> dict[str, Any]:
|
||||
def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
|
||||
return {
|
||||
"weaviate_api_key": None,
|
||||
"weaviate_cluster_url": "${env.WEAVIATE_CLUSTER_URL:=localhost:8080}",
|
||||
"kvstore": SqliteKVStoreConfig.sample_run_config(
|
||||
__distro_dir__=__distro_dir__,
|
||||
db_name="weaviate_registry.db",
|
||||
__distro_dir__=__distro_dir__, db_name="weaviate_registry.db"
|
||||
),
|
||||
}
|
||||
|
|
|
|||
|
|
@ -16,18 +16,14 @@ from llama_stack.apis.common.content_types import InterleavedContent
|
|||
from llama_stack.apis.common.errors import VectorStoreNotFoundError
|
||||
from llama_stack.apis.files import Files
|
||||
from llama_stack.apis.inference import Inference
|
||||
from llama_stack.apis.models import Models
|
||||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
|
||||
from llama_stack.core.datatypes import VectorStoresConfig
|
||||
from llama_stack.core.request_headers import NeedsRequestProviderData
|
||||
from llama_stack.log import get_logger
|
||||
from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
|
||||
from llama_stack.providers.utils.kvstore import kvstore_impl
|
||||
from llama_stack.providers.utils.kvstore.api import KVStore
|
||||
from llama_stack.providers.utils.memory.openai_vector_store_mixin import (
|
||||
OpenAIVectorStoreMixin,
|
||||
)
|
||||
from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
|
||||
from llama_stack.providers.utils.memory.vector_store import (
|
||||
RERANKER_TYPE_RRF,
|
||||
ChunkForDeletion,
|
||||
|
|
@ -49,12 +45,7 @@ OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_conten
|
|||
|
||||
|
||||
class WeaviateIndex(EmbeddingIndex):
|
||||
def __init__(
|
||||
self,
|
||||
client: weaviate.WeaviateClient,
|
||||
collection_name: str,
|
||||
kvstore: KVStore | None = None,
|
||||
):
|
||||
def __init__(self, client: weaviate.WeaviateClient, collection_name: str, kvstore: KVStore | None = None):
|
||||
self.client = client
|
||||
self.collection_name = sanitize_collection_name(collection_name, weaviate_format=True)
|
||||
self.kvstore = kvstore
|
||||
|
|
@ -109,9 +100,7 @@ class WeaviateIndex(EmbeddingIndex):
|
|||
|
||||
try:
|
||||
results = collection.query.near_vector(
|
||||
near_vector=embedding.tolist(),
|
||||
limit=k,
|
||||
return_metadata=wvc.query.MetadataQuery(distance=True),
|
||||
near_vector=embedding.tolist(), limit=k, return_metadata=wvc.query.MetadataQuery(distance=True)
|
||||
)
|
||||
except Exception as e:
|
||||
log.error(f"Weaviate client vector search failed: {e}")
|
||||
|
|
@ -154,12 +143,7 @@ class WeaviateIndex(EmbeddingIndex):
|
|||
collection = self.client.collections.get(sanitized_collection_name)
|
||||
collection.data.delete_many(where=Filter.by_property("id").contains_any(chunk_ids))
|
||||
|
||||
async def query_keyword(
|
||||
self,
|
||||
query_string: str,
|
||||
k: int,
|
||||
score_threshold: float,
|
||||
) -> QueryChunksResponse:
|
||||
async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
|
||||
"""
|
||||
Performs BM25-based keyword search using Weaviate's built-in full-text search.
|
||||
Args:
|
||||
|
|
@ -176,9 +160,7 @@ class WeaviateIndex(EmbeddingIndex):
|
|||
# Perform BM25 keyword search on chunk_content field
|
||||
try:
|
||||
results = collection.query.bm25(
|
||||
query=query_string,
|
||||
limit=k,
|
||||
return_metadata=wvc.query.MetadataQuery(score=True),
|
||||
query=query_string, limit=k, return_metadata=wvc.query.MetadataQuery(score=True)
|
||||
)
|
||||
except Exception as e:
|
||||
log.error(f"Weaviate client keyword search failed: {e}")
|
||||
|
|
@ -275,25 +257,11 @@ class WeaviateIndex(EmbeddingIndex):
|
|||
return QueryChunksResponse(chunks=chunks, scores=scores)
|
||||
|
||||
|
||||
class WeaviateVectorIOAdapter(
|
||||
OpenAIVectorStoreMixin,
|
||||
VectorIO,
|
||||
NeedsRequestProviderData,
|
||||
VectorDBsProtocolPrivate,
|
||||
):
|
||||
def __init__(
|
||||
self,
|
||||
config: WeaviateVectorIOConfig,
|
||||
inference_api: Inference,
|
||||
models_api: Models,
|
||||
files_api: Files | None,
|
||||
vector_stores_config: VectorStoresConfig | None = None,
|
||||
) -> None:
|
||||
class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProviderData, VectorDBsProtocolPrivate):
|
||||
def __init__(self, config: WeaviateVectorIOConfig, inference_api: Inference, files_api: Files | None) -> None:
|
||||
super().__init__(files_api=files_api, kvstore=None)
|
||||
self.config = config
|
||||
self.inference_api = inference_api
|
||||
self.models_api = models_api
|
||||
self.vector_stores_config = vector_stores_config
|
||||
self.client_cache = {}
|
||||
self.cache = {}
|
||||
self.vector_db_store = None
|
||||
|
|
@ -304,10 +272,7 @@ class WeaviateVectorIOAdapter(
|
|||
log.info("Using Weaviate locally in container")
|
||||
host, port = self.config.weaviate_cluster_url.split(":")
|
||||
key = "local_test"
|
||||
client = weaviate.connect_to_local(
|
||||
host=host,
|
||||
port=port,
|
||||
)
|
||||
client = weaviate.connect_to_local(host=host, port=port)
|
||||
else:
|
||||
log.info("Using Weaviate remote cluster with URL")
|
||||
key = f"{self.config.weaviate_cluster_url}::{self.config.weaviate_api_key}"
|
||||
|
|
@ -337,15 +302,9 @@ class WeaviateVectorIOAdapter(
|
|||
for raw in stored:
|
||||
vector_db = VectorDB.model_validate_json(raw)
|
||||
client = self._get_client()
|
||||
idx = WeaviateIndex(
|
||||
client=client,
|
||||
collection_name=vector_db.identifier,
|
||||
kvstore=self.kvstore,
|
||||
)
|
||||
idx = WeaviateIndex(client=client, collection_name=vector_db.identifier, kvstore=self.kvstore)
|
||||
self.cache[vector_db.identifier] = VectorDBWithIndex(
|
||||
vector_db=vector_db,
|
||||
index=idx,
|
||||
inference_api=self.inference_api,
|
||||
vector_db=vector_db, index=idx, inference_api=self.inference_api
|
||||
)
|
||||
|
||||
# Load OpenAI vector stores metadata into cache
|
||||
|
|
@ -357,10 +316,7 @@ class WeaviateVectorIOAdapter(
|
|||
# Clean up mixin resources (file batch tasks)
|
||||
await super().shutdown()
|
||||
|
||||
async def register_vector_db(
|
||||
self,
|
||||
vector_db: VectorDB,
|
||||
) -> None:
|
||||
async def register_vector_db(self, vector_db: VectorDB) -> None:
|
||||
client = self._get_client()
|
||||
sanitized_collection_name = sanitize_collection_name(vector_db.identifier, weaviate_format=True)
|
||||
# Create collection if it doesn't exist
|
||||
|
|
@ -369,17 +325,12 @@ class WeaviateVectorIOAdapter(
|
|||
name=sanitized_collection_name,
|
||||
vectorizer_config=wvc.config.Configure.Vectorizer.none(),
|
||||
properties=[
|
||||
wvc.config.Property(
|
||||
name="chunk_content",
|
||||
data_type=wvc.config.DataType.TEXT,
|
||||
),
|
||||
wvc.config.Property(name="chunk_content", data_type=wvc.config.DataType.TEXT),
|
||||
],
|
||||
)
|
||||
|
||||
self.cache[vector_db.identifier] = VectorDBWithIndex(
|
||||
vector_db,
|
||||
WeaviateIndex(client=client, collection_name=sanitized_collection_name),
|
||||
self.inference_api,
|
||||
vector_db, WeaviateIndex(client=client, collection_name=sanitized_collection_name), self.inference_api
|
||||
)
|
||||
|
||||
async def unregister_vector_db(self, vector_db_id: str) -> None:
|
||||
|
|
@ -415,12 +366,7 @@ class WeaviateVectorIOAdapter(
|
|||
self.cache[vector_db_id] = index
|
||||
return index
|
||||
|
||||
async def insert_chunks(
|
||||
self,
|
||||
vector_db_id: str,
|
||||
chunks: list[Chunk],
|
||||
ttl_seconds: int | None = None,
|
||||
) -> None:
|
||||
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
|
||||
index = await self._get_and_cache_vector_db_index(vector_db_id)
|
||||
if not index:
|
||||
raise VectorStoreNotFoundError(vector_db_id)
|
||||
|
|
@ -428,10 +374,7 @@ class WeaviateVectorIOAdapter(
|
|||
await index.insert_chunks(chunks)
|
||||
|
||||
async def query_chunks(
|
||||
self,
|
||||
vector_db_id: str,
|
||||
query: InterleavedContent,
|
||||
params: dict[str, Any] | None = None,
|
||||
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
|
||||
) -> QueryChunksResponse:
|
||||
index = await self._get_and_cache_vector_db_index(vector_db_id)
|
||||
if not index:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue