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cd6f412 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import logging
import chromadb
# from langchain_community.vectorstores import Chroma
from langchain_chroma import Chroma
from langchain_core.embeddings import Embeddings
# from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_huggingface import HuggingFaceEmbeddings
from pathlib import Path
from typing import List
from langchain_core.documents import Document
from ..config import settings
logger = logging.getLogger(__name__)
# Use a local, open-source embedding model for cost-effectiveness and privacy.
EMBEDDING_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
# Define the path for the persistent ChromaDB store
CHROMA_PATH = "data/vector_store"
class VectorStoreService:
def __init__(self):
"""Initializes the VectorStoreService."""
self.client = chromadb.PersistentClient(path=CHROMA_PATH)
self.embedding_function = self._get_embedding_function()
self.collection_name = "insucompass_kb"
# Get or create the collection
self.collection = self.client.get_or_create_collection(
name=self.collection_name,
embedding_function=None # LangChain's wrapper handles this
)
self.langchain_chroma = Chroma(
client=self.client,
collection_name=self.collection_name,
embedding_function=self.embedding_function,
)
logger.info(f"ChromaDB service initialized. Collection '{self.collection_name}' at {CHROMA_PATH}")
def _get_embedding_function(self) -> Embeddings:
"""Initializes and returns the embedding model."""
logger.info(f"Loading embedding model: {EMBEDDING_MODEL_NAME}")
# Specify 'mps' for Apple Silicon, 'cuda' for NVIDIA, or 'cpu'
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
return HuggingFaceEmbeddings(
model_name=EMBEDDING_MODEL_NAME,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
def add_documents(self, documents: List[Document]) -> List[str]:
"""
Adds a list of documents to the Chroma vector store.
Args:
documents: A list of LangChain Document objects.
Returns:
A list of vector IDs for the added documents.
"""
if not documents:
logger.warning("No documents provided to add to the vector store.")
return []
logger.info(f"Adding {len(documents)} documents to the vector store...")
try:
vector_ids = self.langchain_chroma.add_documents(documents)
logger.info(f"Successfully added {len(documents)} documents.")
return vector_ids
except Exception as e:
logger.error(f"Failed to add documents to vector store: {e}")
raise
def get_retriever(self, search_kwargs={'k': 5}):
"""Returns a LangChain retriever for the vector store."""
return self.langchain_chroma.as_retriever(search_type="mmr", search_kwargs=search_kwargs)
# Singleton instance
vector_store_service = VectorStoreService() |