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"""
Vector Storage Module
Handles storing chunks and embeddings in Qdrant vector database.
"""
import numpy as np
from typing import List
from pathlib import Path
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
class VectorStorage:
"""Handles vector storage operations with Qdrant."""
def __init__(self, base_db_path: Path):
"""
Initialize the vector storage.
Args:
base_db_path: Base path for storing Qdrant databases
"""
self.base_db_path = base_db_path
async def store_in_qdrant(self, chunks: List[str], embeddings: np.ndarray, doc_id: str):
"""
Store chunks and embeddings in Qdrant.
Args:
chunks: List of text chunks
embeddings: Corresponding embeddings array
doc_id: Document identifier
"""
if len(chunks) != embeddings.shape[0]:
raise ValueError(f"Chunk count ({len(chunks)}) doesn't match embedding count ({embeddings.shape[0]})")
collection_name = f"{doc_id}_collection"
db_path = self.base_db_path / f"{collection_name}.db"
client = QdrantClient(path=str(db_path))
print(f"💾 Storing {len(chunks)} vectors in collection: {collection_name}")
try:
# Create or recreate collection
await self._setup_collection(client, collection_name, embeddings.shape[1])
# Prepare and upload points
await self._upload_points(client, collection_name, chunks, embeddings, doc_id)
print(f"✅ Successfully stored all vectors in Qdrant")
finally:
client.close()
async def _setup_collection(self, client: QdrantClient, collection_name: str, embedding_dim: int):
"""
Set up Qdrant collection, recreating if it exists.
Args:
client: Qdrant client
collection_name: Name of the collection
embedding_dim: Dimension of embeddings
"""
# Delete existing collection if it exists
try:
client.delete_collection(collection_name)
print(f"🗑️ Deleted existing collection: {collection_name}")
except Exception:
pass # Collection might not exist
# Create new collection
client.create_collection(
collection_name=collection_name,
vectors_config=VectorParams(
size=embedding_dim,
distance=Distance.COSINE
)
)
print(f"✅ Created new collection: {collection_name}")
async def _upload_points(self, client: QdrantClient, collection_name: str,
chunks: List[str], embeddings: np.ndarray, doc_id: str):
"""
Upload points to Qdrant collection in batches.
Args:
client: Qdrant client
collection_name: Name of the collection
chunks: Text chunks
embeddings: Embedding vectors
doc_id: Document identifier
"""
# Prepare points
points = []
for i in range(len(chunks)):
points.append(
PointStruct(
id=i,
vector=embeddings[i].tolist(),
payload={
"text": chunks[i],
"chunk_id": i,
"doc_id": doc_id,
"char_count": len(chunks[i]),
"word_count": len(chunks[i].split())
}
)
)
# Upload in batches to handle large documents
batch_size = 100
total_batches = (len(points) + batch_size - 1) // batch_size
for i in range(0, len(points), batch_size):
batch = points[i:i + batch_size]
batch_num = (i // batch_size) + 1
print(f" Uploading batch {batch_num}/{total_batches} ({len(batch)} points)")
client.upsert(collection_name=collection_name, points=batch)
print(f"✅ Uploaded {len(points)} points in {total_batches} batches")
def collection_exists(self, doc_id: str) -> bool:
"""
Check if a collection exists for the given document ID.
Args:
doc_id: Document identifier
Returns:
bool: True if collection exists, False otherwise
"""
collection_name = f"{doc_id}_collection"
db_path = self.base_db_path / f"{collection_name}.db"
return db_path.exists()
def get_collection_info(self, doc_id: str) -> dict:
"""
Get information about a collection.
Args:
doc_id: Document identifier
Returns:
dict: Collection information
"""
collection_name = f"{doc_id}_collection"
db_path = self.base_db_path / f"{collection_name}.db"
if not db_path.exists():
return {
"collection_name": collection_name,
"exists": False,
"path": str(db_path)
}
try:
client = QdrantClient(path=str(db_path))
try:
collection_info = client.get_collection(collection_name)
return {
"collection_name": collection_name,
"exists": True,
"path": str(db_path),
"vectors_count": collection_info.vectors_count,
"status": collection_info.status
}
finally:
client.close()
except Exception as e:
return {
"collection_name": collection_name,
"exists": True,
"path": str(db_path),
"error": str(e)
}
def delete_collection(self, doc_id: str) -> bool:
"""
Delete a collection and its database file.
Args:
doc_id: Document identifier
Returns:
bool: True if successfully deleted, False otherwise
"""
collection_name = f"{doc_id}_collection"
db_path = self.base_db_path / f"{collection_name}.db"
try:
if db_path.exists():
# Try to delete collection properly first
try:
client = QdrantClient(path=str(db_path))
client.delete_collection(collection_name)
client.close()
except Exception:
pass # Collection might not exist or be corrupted
# Remove database directory
import shutil
shutil.rmtree(db_path, ignore_errors=True)
print(f"🗑️ Deleted collection: {collection_name}")
return True
except Exception as e:
print(f"❌ Error deleting collection {collection_name}: {e}")
return False
return True # Nothing to delete
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