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chore: Updating how default embedding model is set in stack (#3818)
# What does this PR do?
Refactor setting default vector store provider and embedding model to
use an optional `vector_stores` config in the `StackRunConfig` and clean
up code to do so (had to add back in some pieces of VectorDB). Also
added remote Qdrant and Weaviate to starter distro (based on other PR
where inference providers were added for UX).
New config is simply (default for Starter distro):
```yaml
vector_stores:
default_provider_id: faiss
default_embedding_model:
provider_id: sentence-transformers
model_id: nomic-ai/nomic-embed-text-v1.5
```
## Test Plan
CI and Unit tests.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
This commit is contained in:
Francisco Arceo
GitHub
parent
2c43285e22
commit
48581bf651
48 changed files with 973 additions and 818 deletions
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@ -13,12 +13,6 @@ async def get_adapter_impl(config: MilvusVectorIOConfig, deps: dict[Api, Provide
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from .milvus import MilvusVectorIOAdapter
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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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)
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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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@ -14,13 +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.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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@ -74,46 +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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@ -144,11 +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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@ -167,12 +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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@ -210,12 +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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@ -308,7 +266,6 @@ 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,
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files_api: Files | None,
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) -> None:
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super().__init__(files_api=files_api, kvstore=None)
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@ -316,7 +273,6 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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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_db_store = None
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self.metadata_collection_name = "openai_vector_stores_metadata"
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@ -355,10 +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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@ -395,12 +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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@ -408,10 +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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