Qdrant Integration and Vector Infrastructure

Qdrant serves as the vector backbone of the semantic layer. Each stored vector is associated with a structured payload containing:

• document_id
• original_text
• metadata
• indexed_at

This design ensures that semantic retrieval is not detached from document context. Payloads eliminate unnecessary round-trips to primary databases and allow immediate reconstruction of matching content.

The collection configuration defines cosine distance as the similarity metric and configures HNSW graph parameters for balanced recall and latency. These parameters are tunable based on corpus size and expected throughput.

The integration supports batch upserts, streaming ingestion, and delta-based reprocessing, ensuring scalability and safe incremental updates.

Example in Practice: Upserting Vectors to Qdrant

from qdrant_client.models import PointStruct
import uuid
from datetime import datetime

points_batch = []
for vector, chunk, meta in zip(vectors, chunks_to_process, metadata_list):
    point = PointStruct(
        id=str(uuid.uuid4()),
        vector=vector,
        payload={
            "document_id": meta["doc_id"],
            "original_text": chunk,
            "text_length": len(chunk),
            "indexed_at": str(datetime.now()),
            "language": "ne"
        }
    )
    points_batch.append(point)

# Batch upsert via Qdrant client without waiting to prevent pipeline blocking
qdrant_client.upsert(
    collection_name="documents", 
    points=points_batch, 
    wait=False
)