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"""Service for interacting with the Qdrant vector database.
Provides functionality to search for similar content chunks based on embeddings.
"""
from typing import List, Dict, Any, Optional
from qdrant_client import models
from src.config.database import get_qdrant_client
from src.config.settings import settings
from src.utils.embedding import get_embedding
from src.utils.logger import get_logger
logger = get_logger(__name__)
class VectorService:
"""Handles interactions with the Qdrant vector database."""
@staticmethod
async def search_similar_chunks(
query_text: str,
top_k: int = 5,
filters: Optional[Dict[str, Any]] = None
) -> List[Dict[str, Any]]:
"""
Embeds the query text and searches Qdrant for similar content chunks.
Args:
query_text: The text to query for.
top_k: The number of top similar chunks to retrieve.
filters: Optional Qdrant filters to apply (e.g., {"chapter": "Module 1"}).
Returns:
A list of dictionaries, where each dictionary represents a content chunk
and its metadata.
"""
qdrant_client = get_qdrant_client()
# Get embedding for the query text
query_vector = get_embedding(query_text)
search_params = models.SearchParams(hnsw_ef=128, exact=False)
# Build query filter from provided filters
qdrant_filter: Optional[models.Filter] = None
if filters:
must_clauses = []
for key, value in filters.items():
must_clauses.append(models.FieldCondition(
key=key,
range=models.KeywordRange(exact=value)
))
qdrant_filter = models.Filter(must=must_clauses)
try:
search_result = await qdrant_client.search(
collection_name=settings.qdrant_collection_name,
query_vector=query_vector,
query_filter=qdrant_filter,
limit=top_k,
search_params=search_params,
append_payload=True, # Ensure payload (metadata) is returned
)
chunks = []
for hit in search_result:
if hit.payload:
chunks.append(hit.payload)
logger.info(f"Found {len(chunks)} similar chunks for query: '{query_text[:50]}...'")
return chunks
except Exception as e:
logger.error(f"Error searching Qdrant: {e}")
raise |