weaviate-query-agent

Weaviate Query Agent Skill

This skill helps you search and retrieve data from your local Weaviate collections using semantic vector search, keyword search, filters, and RAG capabilities.

Important Note

This skill is designed for LOCAL Weaviate instances only. Ensure you have Weaviate running locally in Docker before using this skill.

Purpose

Query your local Weaviate collections intelligently to find relevant information, perform Q&A, and analyze your data.

When to Use This Skill

• User wants to search for information in a collection
• User asks questions that need semantic search
• User needs to filter results by specific criteria
• User wants to use RAG (Retrieval Augmented Generation) for Q&A
• User asks about finding similar items
• User needs to combine vector search with filters

Prerequisites Check

Claude should verify these prerequisites before proceeding:

1. ✅ weaviate-local-setup completed - Python environment and dependencies installed
2. ✅ weaviate-connection completed - Successfully connected to Weaviate
3. ✅ weaviate-data-ingestion used - Collection has data to query
4. ✅ Docker container running - Weaviate is accessible at localhost:8080

If any prerequisites are missing, Claude should:

• Load the required prerequisite skill first
• Guide the user through the setup
• Then return to this skill

Prerequisites

• Local Weaviate running in Docker (see weaviate-local-setup skill)
• Active Weaviate connection (use weaviate-connection skill first)
• Collection with data (use weaviate-data-ingestion skill to add data)
• Python weaviate-client library installed

Query Types

1. Semantic Search (Vector Search)

Find objects semantically similar to your query:

import weaviate

# Assuming client is already connected
collection = client.collections.get("Articles")

# Search by meaning
response = collection.query.near_text(
    query="artificial intelligence and machine learning",
    limit=5
)

# Display results
for obj in response.objects:
    print(f"Title: {obj.properties['title']}")
    print(f"Content: {obj.properties['content'][:200]}...")
    print(f"Score: {obj.metadata.score}\n")