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"""Vector store implementation using ChromaDB."""
import logging
import os
from typing import List, Dict, Optional, Any, Callable
from pathlib import Path
import chromadb
from chromadb.config import Settings as ChromaSettings
from chromadb.utils import embedding_functions
from openai import OpenAI
from src.core.config import get_settings
from src.retrieval.embeddings import get_embedding_generator
logger = logging.getLogger(__name__)
class VectorStore:
"""Vector store for document embeddings using ChromaDB."""
def __init__(
self,
collection_name: Optional[str] = None,
persist_directory: Optional[str] = None,
):
"""Initialize the vector store."""
self.settings = get_settings()
self.collection_name = collection_name or self.settings.chroma_collection_name
self.persist_directory = persist_directory or self.settings.chroma_db_path
# Ensure directory exists
os.makedirs(self.persist_directory, exist_ok=True)
# Initialize ChromaDB client
self.client = chromadb.PersistentClient(
path=self.persist_directory,
settings=ChromaSettings(
anonymized_telemetry=False,
allow_reset=True,
),
)
# Get or create collection
# Use OpenAI embedding function (supports OpenRouter via base_url)
embedding_kwargs = {
"api_key": self.settings.openai_api_key,
"model_name": self.settings.openai_embedding_model,
}
# Add base_url if configured (for OpenRouter)
if self.settings.openai_base_url:
embedding_kwargs["api_base"] = self.settings.openai_base_url
# Add OpenRouter headers if configured
headers = {}
if self.settings.openrouter_http_referer:
headers["HTTP-Referer"] = self.settings.openrouter_http_referer
if self.settings.openrouter_title:
headers["X-Title"] = self.settings.openrouter_title
if headers:
embedding_kwargs["default_headers"] = headers
embedding_fn = embedding_functions.OpenAIEmbeddingFunction(**embedding_kwargs)
try:
self.collection = self.client.get_collection(
name=self.collection_name,
embedding_function=embedding_fn,
)
logger.info(f"Loaded existing collection: {self.collection_name}")
except Exception:
self.collection = self.client.create_collection(
name=self.collection_name,
embedding_function=embedding_fn,
metadata={"hnsw:space": "cosine"},
)
logger.info(f"Created new collection: {self.collection_name}")
def add_documents(
self,
documents: List[str],
metadatas: Optional[List[Dict[str, Any]]] = None,
ids: Optional[List[str]] = None,
) -> List[str]:
"""
Add documents to the vector store.
Args:
documents: List of document texts
metadatas: Optional list of metadata dictionaries
ids: Optional list of document IDs
Returns:
List of document IDs
"""
if not documents:
return []
if ids is None:
import uuid
ids = [str(uuid.uuid4()) for _ in documents]
if metadatas is None:
metadatas = [{}] * len(documents)
try:
self.collection.add(
documents=documents,
metadatas=metadatas,
ids=ids,
)
logger.info(f"Added {len(documents)} documents to vector store")
return ids
except Exception as e:
logger.error(f"Error adding documents: {e}")
raise
def search(
self,
query: str,
n_results: int = 5,
filter: Optional[Dict[str, Any]] = None,
) -> Dict[str, Any]:
"""
Search for similar documents.
Args:
query: Query text
n_results: Number of results to return
filter: Optional metadata filter
Returns:
Dictionary with 'documents', 'metadatas', 'distances', and 'ids'
"""
try:
results = self.collection.query(
query_texts=[query],
n_results=n_results,
where=filter,
)
return {
"documents": results["documents"][0] if results["documents"] else [],
"metadatas": results["metadatas"][0] if results["metadatas"] else [],
"distances": results["distances"][0] if results["distances"] else [],
"ids": results["ids"][0] if results["ids"] else [],
}
except Exception as e:
logger.error(f"Error searching vector store: {e}")
raise
def get_by_ids(self, ids: List[str]) -> Dict[str, Any]:
"""
Get documents by their IDs.
Args:
ids: List of document IDs
Returns:
Dictionary with 'documents', 'metadatas', and 'ids'
"""
try:
results = self.collection.get(ids=ids)
return {
"documents": results["documents"],
"metadatas": results["metadatas"],
"ids": results["ids"],
}
except Exception as e:
logger.error(f"Error getting documents by IDs: {e}")
raise
def delete(self, ids: Optional[List[str]] = None, filter: Optional[Dict[str, Any]] = None) -> None:
"""
Delete documents from the vector store.
Args:
ids: Optional list of document IDs to delete
filter: Optional metadata filter for deletion
"""
try:
if ids:
self.collection.delete(ids=ids)
logger.info(f"Deleted {len(ids)} documents")
elif filter:
self.collection.delete(where=filter)
logger.info("Deleted documents matching filter")
else:
logger.warning("No IDs or filter provided for deletion")
except Exception as e:
logger.error(f"Error deleting documents: {e}")
raise
def update(
self,
ids: List[str],
documents: Optional[List[str]] = None,
metadatas: Optional[List[Dict[str, Any]]] = None,
) -> None:
"""
Update documents in the vector store.
Args:
ids: List of document IDs to update
documents: Optional new document texts
metadatas: Optional new metadata
"""
try:
self.collection.update(
ids=ids,
documents=documents,
metadatas=metadatas,
)
logger.info(f"Updated {len(ids)} documents")
except Exception as e:
logger.error(f"Error updating documents: {e}")
raise
def count(self) -> int:
"""Get the total number of documents in the collection."""
try:
return self.collection.count()
except Exception as e:
logger.error(f"Error counting documents: {e}")
return 0
def reset(self) -> None:
"""Reset the collection (delete all documents)."""
try:
self.client.delete_collection(name=self.collection_name)
# Recreate collection with same embedding configuration
embedding_kwargs = {
"api_key": self.settings.openai_api_key,
"model_name": self.settings.openai_embedding_model,
}
# Add base_url if configured (for OpenRouter)
if self.settings.openai_base_url:
embedding_kwargs["api_base"] = self.settings.openai_base_url
# Add OpenRouter headers if configured
headers = {}
if self.settings.openrouter_http_referer:
headers["HTTP-Referer"] = self.settings.openrouter_http_referer
if self.settings.openrouter_title:
headers["X-Title"] = self.settings.openrouter_title
if headers:
embedding_kwargs["default_headers"] = headers
embedding_fn = embedding_functions.OpenAIEmbeddingFunction(**embedding_kwargs)
self.collection = self.client.create_collection(
name=self.collection_name,
embedding_function=embedding_fn,
metadata={"hnsw:space": "cosine"},
)
logger.info("Reset vector store collection")
except Exception as e:
logger.error(f"Error resetting collection: {e}")
raise
# Global instance
_vector_store: Optional[VectorStore] = None
def get_vector_store() -> VectorStore:
"""Get or create the global vector store instance."""
global _vector_store
if _vector_store is None:
_vector_store = VectorStore()
return _vector_store
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