"""
Qdrant adapter implementation for VectorDatabase protocol.
Main adapter class that coordinates Qdrant vector database operations.
"""
import logging
import os
import uuid
from qdrant_client import AsyncQdrantClient
from qdrant_client.models import Distance, VectorParams
from src.core.exceptions import DatabaseConnectionError, VectorStoreError
logger = logging.getLogger(__name__)
class QdrantAdapter:
"""
Qdrant implementation of the VectorDatabase protocol.
Uses AsyncQdrantClient for native async vector search operations.
"""
def __init__(self, url: str | None = None, api_key: str | None = None):
"""Initialize Qdrant adapter with connection parameters"""
self.url = url or os.getenv("QDRANT_URL", "http://localhost:6333")
self.api_key = api_key or os.getenv("QDRANT_API_KEY")
self.client: AsyncQdrantClient = AsyncQdrantClient(url=self.url, api_key=self.api_key)
self.batch_size = 100 # Qdrant can handle larger batches
# Collection names
self.CRAWLED_PAGES = "crawled_pages"
self.CODE_EXAMPLES = "code_examples"
self.SOURCES = "sources"
async def initialize(self) -> None:
"""Initialize Qdrant client and create collections if needed"""
# Create collections if they don't exist
await self._ensure_collections()
async def _ensure_collections(self) -> None:
"""Ensure all required collections exist"""
collections = [
(self.CRAWLED_PAGES, 1536), # OpenAI embedding size
(self.CODE_EXAMPLES, 1536),
(self.SOURCES, 1536), # OpenAI embedding size for consistency
]
for collection_name, vector_size in collections:
try:
await self.client.get_collection(collection_name)
except Exception:
# Collection doesn't exist, create it
try:
await self.client.create_collection(
collection_name=collection_name,
vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
)
except VectorStoreError as create_error:
logger.warning(
"Could not create collection %s: %s",
collection_name,
create_error,
)
except Exception as create_error:
logger.exception(
"Unexpected error creating collection %s: %s",
collection_name,
create_error,
)
except Exception:
# Collection doesn't exist or other error, try to create it
try:
await self.client.create_collection(
collection_name=collection_name,
vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
)
except VectorStoreError as create_error:
logger.warning(
"Could not create collection %s: %s",
collection_name,
create_error,
)
except Exception as create_error:
logger.exception(
"Unexpected error creating collection %s: %s",
collection_name,
create_error,
)
def _generate_point_id(self, url: str, chunk_number: int) -> str:
"""Generate a deterministic UUID for a document point"""
id_string = f"{url}_{chunk_number}"
# Use uuid5 to generate a deterministic UUID from the URL and chunk number
return str(uuid.uuid5(uuid.NAMESPACE_URL, id_string))
