Weaviate

Enterprise vector search with Weaviate—GraphQL interface and modular AI.

Quick Start

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

Setup

pip install weaviate-client>=4.0.0

Python Example (v4 API)

import weaviate
import json

# Connect to local Weaviate
client = weaviate.connect_to_local()

# Load data
with open("output/react-weaviate.json") as f:
    data = json.load(f)

# Create collection if it doesn't exist
from weaviate.classes.config import Configure, Property, DataType

if not client.collections.exists("ReactDoc"):
    client.collections.create(
        name="ReactDoc",
        vectorizer_config=Configure.Vectorizer.none(),  # We'll provide vectors
        properties=[
            Property(name="content", data_type=DataType.TEXT),
            Property(name="category", data_type=DataType.TEXT),
            Property(name="source", data_type=DataType.TEXT),
        ]
    )

# Get collection
collection = client.collections.get("ReactDoc")

# Import data with embeddings
with collection.batch.dynamic() as batch:
    for item in data:
        batch.add_object(
            properties={
                "content": item["content"],
                "category": item.get("category", ""),
                "source": item.get("source", "")
            },
            vector=item["embedding"]
        )

client.close()

Query with v4 API

import weaviate

client = weaviate.connect_to_local()
collection = client.collections.get("ReactDoc")

# Vector search
response = collection.query.near_text(
    query="React Hooks",
    limit=3,
    return_properties=["content", "category", "source"]
)

for obj in response.objects:
    print(f"Content: {obj.properties['content'][:200]}...")
    print(f"Category: {obj.properties['category']}")
    print("---")

client.close()
# Combine vector and keyword search
response = collection.query.hybrid(
    query="React Hooks useState",
    alpha=0.5,  # Balance between vector and keyword
    limit=5,
    return_properties=["content", "category"]
)

Features

  • ✅ GraphQL interface - Flexible queries
  • ✅ Modular AI - Choose your vectorizer
  • ✅ Multi-tenancy - Enterprise security
  • ✅ Real-time - Instant updates
  • ✅ Hybrid search - Vector + keyword

Weaviate Cloud

For production, use Weaviate Cloud:

import weaviate

client = weaviate.connect_to_weaviate_cloud(
    cluster_url="https://your-cluster.weaviate.cloud",
    auth_credentials=weaviate.auth.AuthApiKey("your-api-key")
)

Next Steps