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Make qdrant the default vector db (#1285)

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* Make qdrant the default vector db

---------

Co-authored-by: Pablo Orgaz <pabloogc@gmail.com>
Co-authored-by: lopagela <lpglm@orange.fr>
This commit is contained in:
Iván Martínez
2023-11-20 16:19:22 +01:00
committed by GitHub
parent f1cbff0fb7
commit 510caa576b
9 changed files with 122 additions and 91 deletions
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33
private_gpt/components/vector_store/vector_store_component.py
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@@ -1,8 +1,6 @@
import logging
import typing
import chromadb
from chromadb.config import Settings as ChromaSettings
from injector import inject, singleton
from llama_index import VectorStoreIndex
from llama_index.indices.vector_store import VectorIndexRetriever
@@ -43,6 +41,18 @@ class VectorStoreComponent:
def __init__(self, settings: Settings) -> None:
match settings.vectorstore.database:
case "chroma":
try:
import chromadb # type: ignore
from chromadb.config import ( # type: ignore
Settings as ChromaSettings,
)
except ImportError as e:
raise ImportError(
"'chromadb' is not installed."
"To use PrivateGPT with Chroma, install the 'chroma' extra."
"`poetry install --extras chroma`"
) from e
chroma_settings = ChromaSettings(anonymized_telemetry=False)
chroma_client = chromadb.PersistentClient(
path=str((local_data_path / "chroma_db").absolute()),
@@ -60,15 +70,9 @@ class VectorStoreComponent:
)
case "qdrant":
try:
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient # type: ignore
except ImportError as e:
raise ImportError(
"'qdrant_client' is not installed."
"To use PrivateGPT with Qdrant, install the 'qdrant' extra."
"`poetry install --extras qdrant`"
) from e
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
if settings.qdrant is None:
logger.info(
"Qdrant config not found. Using default settings."
@@ -99,11 +103,16 @@ class VectorStoreComponent:
context_filter: ContextFilter | None = None,
similarity_top_k: int = 2,
) -> VectorIndexRetriever:
# TODO this 'where' is specific to chromadb. Implement other vector stores
# This way we support qdrant (using doc_ids) and chroma (using where clause)
return VectorIndexRetriever(
index=index,
similarity_top_k=similarity_top_k,
doc_ids=context_filter.docs_ids if context_filter else None,
vector_store_kwargs={
"where": _chromadb_doc_id_metadata_filter(context_filter)
},
)
def close(self) -> None:
if hasattr(self.vector_store.client, "close"):
self.vector_store.client.close()