Qdrant

High-performance vector search with Qdrant—Rust engine, advanced filtering.

Quick Start

skill-seekers scrape --format qdrant --config configs/react.json

Setup

pip install qdrant-client

Python Example

from qdrant_client import QdrantClient
import json

# Connect
client = QdrantClient(host="localhost", port=6333)

# Load and upload
with open("output/react-qdrant.json") as f:
    data = json.load(f)
    
client.upsert(
    collection_name="react-docs",
    points=data
)

Query

from qdrant_client.models import Filter, FieldCondition, Match

# Search with filter
results = client.search(
    collection_name="react-docs",
    query_vector=embedding,
    query_filter=Filter(
        must=[FieldCondition(
            key="category",
            match=Match(value="api")
        )]
    ),
    limit=5
)

Features

• ✅ Rust engine - High performance
• ✅ Advanced filtering - Complex queries
• ✅ Payload indexing - Fast metadata search
• ✅ Self-hosted or cloud - Flexible deployment