weaviate-data-ingestion

Weaviate Data Ingestion Skill

This skill helps you upload data to your local Weaviate collections efficiently, handling everything from single objects to large batch imports.

Important Note

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

Purpose

Add data to your local Weaviate collections with automatic vectorization, proper error handling, and progress tracking.

When to Use This Skill

• User wants to add data to a collection
• User needs to upload documents, articles, or records
• User has images or multi-modal content to ingest
• User wants to import data from files (JSON, CSV, text)
• User asks about batch uploading or bulk data import

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-collection-manager used - Target collection exists
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)
• Existing collection (use weaviate-collection-manager skill to create)
• Python weaviate-client library installed

Operations

1. Add a Single Object

import weaviate
from weaviate.classes.data import DataObject

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

# Add one object
uuid = collection.data.insert(
    properties={
        "title": "Introduction to Vector Databases",
        "content": "Vector databases enable semantic search by storing embeddings...",
        "author": "John Doe",
        "publishDate": "2025-01-20T10:00:00Z"
    }
)

print(f"✅ Object created with UUID: {uuid}")