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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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@ -12,11 +12,6 @@ from .config import WeaviateVectorIOConfig
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async def get_adapter_impl(config: WeaviateVectorIOConfig, deps: dict[Api, ProviderSpec]):
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from .weaviate import WeaviateVectorIOAdapter
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impl = WeaviateVectorIOAdapter(
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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 = WeaviateVectorIOAdapter(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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@ -21,11 +21,7 @@ class WeaviateVectorIOConfig(BaseModel):
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)
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@classmethod
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def sample_run_config(
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cls,
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__distro_dir__: str,
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**kwargs: Any,
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) -> dict[str, Any]:
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def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
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return {
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"weaviate_api_key": None,
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"weaviate_cluster_url": "${env.WEAVIATE_CLUSTER_URL:=localhost:8080}",
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@ -16,7 +16,6 @@ from llama_stack.apis.common.content_types import InterleavedContent
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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
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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 Chunk, QueryChunksResponse, VectorIO
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from llama_stack.core.request_headers import NeedsRequestProviderData
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@ -24,9 +23,7 @@ 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.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 (
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OpenAIVectorStoreMixin,
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)
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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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RERANKER_TYPE_RRF,
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ChunkForDeletion,
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@ -48,12 +45,7 @@ OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_conten
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class WeaviateIndex(EmbeddingIndex):
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def __init__(
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self,
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client: weaviate.WeaviateClient,
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collection_name: str,
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kvstore: KVStore | None = None,
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):
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def __init__(self, client: weaviate.WeaviateClient, collection_name: str, kvstore: KVStore | None = None):
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self.client = client
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self.collection_name = sanitize_collection_name(collection_name, weaviate_format=True)
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self.kvstore = kvstore
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@ -108,9 +100,7 @@ class WeaviateIndex(EmbeddingIndex):
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try:
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results = collection.query.near_vector(
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near_vector=embedding.tolist(),
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limit=k,
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return_metadata=wvc.query.MetadataQuery(distance=True),
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near_vector=embedding.tolist(), limit=k, return_metadata=wvc.query.MetadataQuery(distance=True)
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)
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except Exception as e:
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log.error(f"Weaviate client vector search failed: {e}")
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@ -153,12 +143,7 @@ class WeaviateIndex(EmbeddingIndex):
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collection = self.client.collections.get(sanitized_collection_name)
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collection.data.delete_many(where=Filter.by_property("id").contains_any(chunk_ids))
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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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Performs BM25-based keyword search using Weaviate's built-in full-text search.
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Args:
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@ -175,9 +160,7 @@ class WeaviateIndex(EmbeddingIndex):
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# Perform BM25 keyword search on chunk_content field
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try:
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results = collection.query.bm25(
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query=query_string,
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limit=k,
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return_metadata=wvc.query.MetadataQuery(score=True),
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query=query_string, limit=k, return_metadata=wvc.query.MetadataQuery(score=True)
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)
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except Exception as e:
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log.error(f"Weaviate client keyword search failed: {e}")
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@ -274,23 +257,11 @@ class WeaviateIndex(EmbeddingIndex):
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return QueryChunksResponse(chunks=chunks, scores=scores)
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class WeaviateVectorIOAdapter(
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OpenAIVectorStoreMixin,
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VectorIO,
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NeedsRequestProviderData,
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VectorDBsProtocolPrivate,
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):
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def __init__(
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self,
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config: WeaviateVectorIOConfig,
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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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class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProviderData, VectorDBsProtocolPrivate):
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def __init__(self, config: WeaviateVectorIOConfig, inference_api: Inference, files_api: Files | None) -> 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.inference_api = inference_api
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self.models_api = models_api
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self.client_cache = {}
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self.cache = {}
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self.vector_db_store = None
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@ -301,10 +272,7 @@ class WeaviateVectorIOAdapter(
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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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host=host,
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port=port,
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)
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client = weaviate.connect_to_local(host=host, port=port)
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else:
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log.info("Using Weaviate remote cluster with URL")
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key = f"{self.config.weaviate_cluster_url}::{self.config.weaviate_api_key}"
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@ -334,15 +302,9 @@ class WeaviateVectorIOAdapter(
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for raw in stored:
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vector_db = VectorDB.model_validate_json(raw)
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client = self._get_client()
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idx = WeaviateIndex(
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client=client,
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collection_name=vector_db.identifier,
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kvstore=self.kvstore,
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)
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idx = WeaviateIndex(client=client, collection_name=vector_db.identifier, kvstore=self.kvstore)
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self.cache[vector_db.identifier] = VectorDBWithIndex(
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vector_db=vector_db,
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index=idx,
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inference_api=self.inference_api,
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vector_db=vector_db, index=idx, inference_api=self.inference_api
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)
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# Load OpenAI vector stores metadata into cache
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@ -354,10 +316,7 @@ class WeaviateVectorIOAdapter(
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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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client = self._get_client()
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sanitized_collection_name = sanitize_collection_name(vector_db.identifier, weaviate_format=True)
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# Create collection if it doesn't exist
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@ -366,17 +325,12 @@ class WeaviateVectorIOAdapter(
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name=sanitized_collection_name,
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vectorizer_config=wvc.config.Configure.Vectorizer.none(),
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properties=[
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wvc.config.Property(
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name="chunk_content",
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data_type=wvc.config.DataType.TEXT,
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),
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wvc.config.Property(name="chunk_content", data_type=wvc.config.DataType.TEXT),
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],
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)
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self.cache[vector_db.identifier] = VectorDBWithIndex(
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vector_db,
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WeaviateIndex(client=client, collection_name=sanitized_collection_name),
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self.inference_api,
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vector_db, WeaviateIndex(client=client, collection_name=sanitized_collection_name), self.inference_api
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)
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async def unregister_vector_db(self, vector_db_id: str) -> None:
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@ -412,12 +366,7 @@ class WeaviateVectorIOAdapter(
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self.cache[vector_db_id] = index
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return index
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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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@ -425,10 +374,7 @@ class WeaviateVectorIOAdapter(
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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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