docs: Add 4 comprehensive vector database examples (Weaviate, Chroma, FAISS, Qdrant)
Created complete working examples for all 4 vector databases with RAG adaptors: Weaviate Example: - Comprehensive README with hybrid search guide - 3 Python scripts (generate, upload, query) - Sample outputs and query results - Covers hybrid search, filtering, schema design Chroma Example: - Simple, local-first approach - In-memory and persistent storage options - Semantic search and metadata filtering - Comparison with Weaviate FAISS Example: - Facebook AI Similarity Search integration - OpenAI embeddings generation - Index building and persistence - Performance-focused for scale Qdrant Example: - Advanced filtering capabilities - Production-ready features - Complex query patterns - Rust-based performance Each example includes: - Detailed README with setup and troubleshooting - requirements.txt with dependencies - 3 working Python scripts - Sample outputs directory Total files: 20 (4 examples × 5 files each) Documentation: 4 comprehensive READMEs (~800 lines total) Phase 2 of optional enhancements complete. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
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88
examples/weaviate-example/1_generate_skill.py
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88
examples/weaviate-example/1_generate_skill.py
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#!/usr/bin/env python3
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"""
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Step 1: Generate Skill for Weaviate
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This script:
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1. Scrapes React documentation (limited to 20 pages for demo)
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2. Packages the skill in Weaviate format
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3. Saves to output/react-weaviate.json
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Usage:
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python 1_generate_skill.py
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"""
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import subprocess
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import sys
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from pathlib import Path
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def main():
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print("=" * 60)
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print("Step 1: Generating Skill for Weaviate")
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print("=" * 60)
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# Check if skill-seekers is installed
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try:
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result = subprocess.run(
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["skill-seekers", "--version"],
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capture_output=True,
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text=True
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)
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print(f"\n✅ skill-seekers found: {result.stdout.strip()}")
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except FileNotFoundError:
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print("\n❌ skill-seekers not found!")
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print("Install it with: pip install skill-seekers")
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sys.exit(1)
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# Step 1: Scrape React docs (small sample for demo)
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print("\n📥 Step 1/2: Scraping React documentation (20 pages)...")
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print("This may take 1-2 minutes...\n")
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scrape_result = subprocess.run(
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[
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"skill-seekers", "scrape",
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"--config", "configs/react.json",
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"--max-pages", "20",
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],
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capture_output=True,
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text=True
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)
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if scrape_result.returncode != 0:
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print(f"❌ Scraping failed:\n{scrape_result.stderr}")
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sys.exit(1)
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print("✅ Scraping completed!")
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# Step 2: Package for Weaviate
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print("\n📦 Step 2/2: Packaging for Weaviate...\n")
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package_result = subprocess.run(
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[
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"skill-seekers", "package",
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"output/react",
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"--target", "weaviate",
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],
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capture_output=True,
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text=True
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)
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if package_result.returncode != 0:
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print(f"❌ Packaging failed:\n{package_result.stderr}")
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sys.exit(1)
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# Show the output
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print(package_result.stdout)
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# Check if output file exists
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output_file = Path("output/react-weaviate.json")
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if output_file.exists():
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size_kb = output_file.stat().st_size / 1024
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print(f"📄 File size: {size_kb:.1f} KB")
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print(f"📂 Location: {output_file.absolute()}")
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print("\n✅ Ready for upload! Next step: python 2_upload_to_weaviate.py")
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else:
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print("❌ Output file not found!")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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185
examples/weaviate-example/2_upload_to_weaviate.py
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185
examples/weaviate-example/2_upload_to_weaviate.py
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#!/usr/bin/env python3
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"""
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Step 2: Upload to Weaviate
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This script:
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1. Connects to Weaviate instance (local or cloud)
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2. Creates the schema (class + properties)
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3. Batch uploads all objects
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4. Verifies the upload
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Usage:
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# Local Docker
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python 2_upload_to_weaviate.py
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# Weaviate Cloud
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python 2_upload_to_weaviate.py --url https://your-cluster.weaviate.network --api-key YOUR_KEY
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# Reset existing data
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python 2_upload_to_weaviate.py --reset
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"""
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import argparse
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import json
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import sys
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from pathlib import Path
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try:
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import weaviate
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from weaviate.auth import AuthApiKey
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except ImportError:
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print("❌ weaviate-client not installed!")
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print("Install it with: pip install weaviate-client")
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sys.exit(1)
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def connect_to_weaviate(url: str, api_key: str = None):
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"""Connect to Weaviate instance."""
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print(f"\n🔗 Connecting to Weaviate at {url}...")
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try:
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if api_key:
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# Weaviate Cloud with authentication
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auth_config = AuthApiKey(api_key)
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client = weaviate.Client(
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url=url,
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auth_client_secret=auth_config
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)
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else:
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# Local Docker without authentication
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client = weaviate.Client(url=url)
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# Check if ready
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if client.is_ready():
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print("✅ Weaviate is ready!\n")
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return client
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else:
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print("❌ Weaviate is not ready")
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sys.exit(1)
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except Exception as e:
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print(f"❌ Connection failed: {e}")
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print("\n💡 Tips:")
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print(" - For local: Ensure Docker is running (docker ps | grep weaviate)")
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print(" - For cloud: Check your URL and API key")
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sys.exit(1)
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def load_skill_data(filepath: str = "output/react-weaviate.json"):
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"""Load the Weaviate-format skill JSON."""
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path = Path(filepath)
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if not path.exists():
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print(f"❌ Skill file not found: {filepath}")
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print("Run '1_generate_skill.py' first!")
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sys.exit(1)
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with open(path) as f:
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return json.load(f)
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def create_schema(client, schema: dict):
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"""Create Weaviate schema (class + properties)."""
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class_name = schema["class"]
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print(f"📊 Creating schema: {class_name}")
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# Check if class already exists
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existing_schema = client.schema.get()
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class_exists = any(c["class"] == class_name for c in existing_schema.get("classes", []))
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if class_exists:
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print(f"⚠️ Class '{class_name}' already exists")
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response = input("Delete and recreate? [y/N]: ")
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if response.lower() == "y":
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client.schema.delete_class(class_name)
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print(f"🗑️ Deleted existing class")
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else:
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print("Skipping schema creation")
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return
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# Create the class
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client.schema.create_class(schema)
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print("✅ Schema created successfully!\n")
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def upload_objects(client, class_name: str, objects: list):
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"""Batch upload objects to Weaviate."""
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total = len(objects)
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batch_size = 100
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print(f"📤 Uploading {total} objects in batches...")
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with client.batch as batch:
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batch.batch_size = batch_size
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for i, obj in enumerate(objects):
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# Add object to batch
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batch.add_data_object(
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data_object=obj["properties"],
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class_name=class_name,
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uuid=obj["id"]
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)
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# Print progress
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if (i + 1) % batch_size == 0:
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batch_num = (i + 1) // batch_size
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print(f"✅ Batch {batch_num} uploaded ({i + 1}/{total} objects)")
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# Final batch
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final_count = total % batch_size
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if final_count > 0:
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batch_num = (total // batch_size) + 1
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print(f"✅ Batch {batch_num} uploaded ({final_count} objects)")
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print(f"\n✅ Successfully uploaded {total} documents to Weaviate")
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def verify_upload(client, class_name: str):
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"""Verify objects were uploaded correctly."""
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result = client.query.aggregate(class_name).with_meta_count().do()
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count = result["data"]["Aggregate"][class_name][0]["meta"]["count"]
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print(f"🔍 Class '{class_name}' now contains {count} objects")
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def main():
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parser = argparse.ArgumentParser(description="Upload skill to Weaviate")
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parser.add_argument(
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"--url",
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default="http://localhost:8080",
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help="Weaviate URL (default: http://localhost:8080)"
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)
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parser.add_argument(
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"--api-key",
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help="Weaviate API key (for cloud instances)"
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)
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parser.add_argument(
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"--file",
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default="output/react-weaviate.json",
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help="Path to Weaviate JSON file"
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)
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parser.add_argument(
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"--reset",
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action="store_true",
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help="Delete existing class before uploading"
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)
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args = parser.parse_args()
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print("=" * 60)
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print("Step 2: Upload to Weaviate")
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print("=" * 60)
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# Connect to Weaviate
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client = connect_to_weaviate(args.url, args.api_key)
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# Load skill data
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data = load_skill_data(args.file)
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# Create schema
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create_schema(client, data["schema"])
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# Upload objects
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upload_objects(client, data["class_name"], data["objects"])
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# Verify
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verify_upload(client, data["class_name"])
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print("\n✅ Upload complete! Next step: python 3_query_example.py")
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if __name__ == "__main__":
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main()
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281
examples/weaviate-example/3_query_example.py
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281
examples/weaviate-example/3_query_example.py
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#!/usr/bin/env python3
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"""
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Step 3: Query Weaviate
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This script demonstrates various query patterns with Weaviate:
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1. Hybrid search (keyword + vector)
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2. Metadata filtering
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3. Limit and pagination
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Usage:
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# Local Docker
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python 3_query_example.py
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# Weaviate Cloud
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python 3_query_example.py --url https://your-cluster.weaviate.network --api-key YOUR_KEY
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"""
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import argparse
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import sys
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try:
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import weaviate
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from weaviate.auth import AuthApiKey
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from rich.console import Console
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from rich.table import Table
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from rich.panel import Panel
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except ImportError:
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print("❌ Missing dependencies!")
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print("Install with: pip install weaviate-client rich")
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sys.exit(1)
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console = Console()
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def connect_to_weaviate(url: str, api_key: str = None):
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"""Connect to Weaviate instance."""
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try:
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if api_key:
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auth_config = AuthApiKey(api_key)
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client = weaviate.Client(url=url, auth_client_secret=auth_config)
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else:
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client = weaviate.Client(url=url)
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if client.is_ready():
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return client
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else:
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console.print("[red]❌ Weaviate is not ready[/red]")
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sys.exit(1)
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except Exception as e:
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console.print(f"[red]❌ Connection failed: {e}[/red]")
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sys.exit(1)
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def hybrid_search_example(client, class_name: str = "React"):
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"""Example 1: Hybrid Search (keyword + vector)."""
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console.print("\n" + "=" * 60)
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console.print("[bold cyan]Example 1: Hybrid Search[/bold cyan]")
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console.print("=" * 60)
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query = "How do I use React hooks?"
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alpha = 0.5 # 50% keyword, 50% vector
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console.print(f"\n[yellow]Query:[/yellow] {query}")
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console.print(f"[yellow]Alpha:[/yellow] {alpha} (0=keyword only, 1=vector only)")
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try:
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result = (
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client.query.get(class_name, ["content", "source", "category", "file"])
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.with_hybrid(query=query, alpha=alpha)
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.with_limit(3)
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.do()
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)
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objects = result["data"]["Get"][class_name]
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if not objects:
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console.print("[red]No results found[/red]")
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return
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# Create results table
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table = Table(show_header=True, header_style="bold magenta")
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table.add_column("#", style="dim", width=3)
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table.add_column("Category", style="cyan")
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table.add_column("File", style="green")
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table.add_column("Content Preview", style="white")
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for i, obj in enumerate(objects, 1):
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content_preview = obj["content"][:100] + "..." if len(obj["content"]) > 100 else obj["content"]
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table.add_row(
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str(i),
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obj["category"],
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obj["file"],
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content_preview
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)
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console.print(table)
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except Exception as e:
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console.print(f"[red]Query failed: {e}[/red]")
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def keyword_only_search(client, class_name: str = "React"):
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"""Example 2: Keyword-Only Search (alpha=0)."""
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console.print("\n" + "=" * 60)
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console.print("[bold cyan]Example 2: Keyword-Only Search[/bold cyan]")
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console.print("=" * 60)
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query = "useState Hook"
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alpha = 0 # Pure keyword search
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console.print(f"\n[yellow]Query:[/yellow] {query}")
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console.print(f"[yellow]Alpha:[/yellow] {alpha} (pure keyword/BM25)")
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try:
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result = (
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client.query.get(class_name, ["content", "category", "file"])
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.with_hybrid(query=query, alpha=alpha)
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.with_limit(3)
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.do()
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)
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objects = result["data"]["Get"][class_name]
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for i, obj in enumerate(objects, 1):
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panel = Panel(
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f"[cyan]Category:[/cyan] {obj['category']}\n"
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f"[cyan]File:[/cyan] {obj['file']}\n\n"
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f"[white]{obj['content'][:200]}...[/white]",
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title=f"Result {i}",
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border_style="green"
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)
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console.print(panel)
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except Exception as e:
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console.print(f"[red]Query failed: {e}[/red]")
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def filtered_search(client, class_name: str = "React"):
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"""Example 3: Search with Metadata Filter."""
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console.print("\n" + "=" * 60)
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console.print("[bold cyan]Example 3: Filtered Search[/bold cyan]")
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console.print("=" * 60)
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query = "component"
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category_filter = "api"
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console.print(f"\n[yellow]Query:[/yellow] {query}")
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console.print(f"[yellow]Filter:[/yellow] category = '{category_filter}'")
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try:
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result = (
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client.query.get(class_name, ["content", "category", "file"])
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.with_hybrid(query=query, alpha=0.5)
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.with_where({
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"path": ["category"],
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"operator": "Equal",
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"valueText": category_filter
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})
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.with_limit(5)
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.do()
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)
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objects = result["data"]["Get"][class_name]
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if not objects:
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console.print("[red]No results found[/red]")
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return
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console.print(f"\n[green]Found {len(objects)} results in '{category_filter}' category:[/green]\n")
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for i, obj in enumerate(objects, 1):
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console.print(f"[bold]{i}. {obj['file']}[/bold]")
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console.print(f" {obj['content'][:150]}...\n")
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except Exception as e:
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console.print(f"[red]Query failed: {e}[/red]")
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def semantic_search(client, class_name: str = "React"):
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"""Example 4: Pure Semantic Search (alpha=1)."""
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console.print("\n" + "=" * 60)
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console.print("[bold cyan]Example 4: Semantic Search[/bold cyan]")
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console.print("=" * 60)
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query = "managing application state" # Conceptual query
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alpha = 1 # Pure vector/semantic search
|
||||
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||||
console.print(f"\n[yellow]Query:[/yellow] {query}")
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console.print(f"[yellow]Alpha:[/yellow] {alpha} (pure semantic/vector)")
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try:
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result = (
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client.query.get(class_name, ["content", "category", "file"])
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||||
.with_hybrid(query=query, alpha=alpha)
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.with_limit(3)
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||||
.do()
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||||
)
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||||
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objects = result["data"]["Get"][class_name]
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||||
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||||
for i, obj in enumerate(objects, 1):
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console.print(f"\n[bold green]Result {i}:[/bold green]")
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||||
console.print(f"[cyan]Category:[/cyan] {obj['category']}")
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console.print(f"[cyan]File:[/cyan] {obj['file']}")
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||||
console.print(f"[white]{obj['content'][:200]}...[/white]")
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||||
|
||||
except Exception as e:
|
||||
console.print(f"[red]Query failed: {e}[/red]")
|
||||
|
||||
def get_statistics(client, class_name: str = "React"):
|
||||
"""Show database statistics."""
|
||||
console.print("\n" + "=" * 60)
|
||||
console.print("[bold cyan]Database Statistics[/bold cyan]")
|
||||
console.print("=" * 60)
|
||||
|
||||
try:
|
||||
# Total count
|
||||
result = client.query.aggregate(class_name).with_meta_count().do()
|
||||
total_count = result["data"]["Aggregate"][class_name][0]["meta"]["count"]
|
||||
|
||||
console.print(f"\n[green]Total objects:[/green] {total_count}")
|
||||
|
||||
# Count by category
|
||||
result = (
|
||||
client.query.aggregate(class_name)
|
||||
.with_group_by_filter(["category"])
|
||||
.with_meta_count()
|
||||
.do()
|
||||
)
|
||||
|
||||
groups = result["data"]["Aggregate"][class_name]
|
||||
|
||||
console.print(f"\n[green]Objects by category:[/green]")
|
||||
for group in groups:
|
||||
category = group["groupedBy"]["value"]
|
||||
count = group["meta"]["count"]
|
||||
console.print(f" • {category}: {count}")
|
||||
|
||||
except Exception as e:
|
||||
console.print(f"[red]Statistics failed: {e}[/red]")
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Query Weaviate examples")
|
||||
parser.add_argument(
|
||||
"--url",
|
||||
default="http://localhost:8080",
|
||||
help="Weaviate URL (default: http://localhost:8080)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--api-key",
|
||||
help="Weaviate API key (for cloud instances)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--class",
|
||||
dest="class_name",
|
||||
default="React",
|
||||
help="Class name to query (default: React)"
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
console.print("[bold green]Weaviate Query Examples[/bold green]")
|
||||
console.print(f"[dim]Connected to: {args.url}[/dim]")
|
||||
|
||||
# Connect
|
||||
client = connect_to_weaviate(args.url, args.api_key)
|
||||
|
||||
# Get statistics
|
||||
get_statistics(client, args.class_name)
|
||||
|
||||
# Run examples
|
||||
hybrid_search_example(client, args.class_name)
|
||||
keyword_only_search(client, args.class_name)
|
||||
filtered_search(client, args.class_name)
|
||||
semantic_search(client, args.class_name)
|
||||
|
||||
console.print("\n[bold green]✅ All examples completed![/bold green]")
|
||||
console.print("\n[cyan]💡 Tips:[/cyan]")
|
||||
console.print(" • Adjust 'alpha' to balance keyword vs semantic search")
|
||||
console.print(" • Use filters to narrow results by metadata")
|
||||
console.print(" • Combine multiple filters with 'And'/'Or' operators")
|
||||
console.print(" • See README.md for more customization options")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
339
examples/weaviate-example/README.md
Normal file
339
examples/weaviate-example/README.md
Normal file
@@ -0,0 +1,339 @@
|
||||
# Weaviate Vector Database Example
|
||||
|
||||
This example demonstrates how to use Skill Seekers with Weaviate, a powerful vector database with hybrid search capabilities (keyword + semantic).
|
||||
|
||||
## What You'll Learn
|
||||
|
||||
- How to generate skills in Weaviate format
|
||||
- How to create a Weaviate schema and upload data
|
||||
- How to perform hybrid searches (keyword + vector)
|
||||
- How to filter by metadata categories
|
||||
|
||||
## Prerequisites
|
||||
|
||||
### 1. Weaviate Instance
|
||||
|
||||
**Option A: Weaviate Cloud (Recommended for production)**
|
||||
- Sign up at https://console.weaviate.cloud/
|
||||
- Create a free sandbox cluster
|
||||
- Get your cluster URL and API key
|
||||
|
||||
**Option B: Local Docker (Recommended for development)**
|
||||
```bash
|
||||
docker run -d \
|
||||
--name weaviate \
|
||||
-p 8080:8080 \
|
||||
-e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
|
||||
-e PERSISTENCE_DATA_PATH=/var/lib/weaviate \
|
||||
semitechnologies/weaviate:latest
|
||||
```
|
||||
|
||||
### 2. Python Dependencies
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## Step-by-Step Guide
|
||||
|
||||
### Step 1: Generate Skill from Documentation
|
||||
|
||||
First, we'll scrape React documentation and package it for Weaviate:
|
||||
|
||||
```bash
|
||||
python 1_generate_skill.py
|
||||
```
|
||||
|
||||
This script will:
|
||||
1. Scrape React docs (limited to 20 pages for demo)
|
||||
2. Package the skill in Weaviate format (JSON with schema + objects)
|
||||
3. Save to `sample_output/react-weaviate.json`
|
||||
|
||||
**Expected Output:**
|
||||
```
|
||||
✅ Weaviate data packaged successfully!
|
||||
📦 Output: output/react-weaviate.json
|
||||
📊 Total objects: 21
|
||||
📂 Categories: overview (1), guides (8), api (12)
|
||||
```
|
||||
|
||||
**What's in the JSON?**
|
||||
```json
|
||||
{
|
||||
"schema": {
|
||||
"class": "React",
|
||||
"description": "React documentation skill",
|
||||
"properties": [
|
||||
{"name": "content", "dataType": ["text"]},
|
||||
{"name": "source", "dataType": ["text"]},
|
||||
{"name": "category", "dataType": ["text"]},
|
||||
...
|
||||
]
|
||||
},
|
||||
"objects": [
|
||||
{
|
||||
"id": "uuid-here",
|
||||
"properties": {
|
||||
"content": "React is a JavaScript library...",
|
||||
"source": "react",
|
||||
"category": "overview",
|
||||
...
|
||||
}
|
||||
}
|
||||
],
|
||||
"class_name": "React"
|
||||
}
|
||||
```
|
||||
|
||||
### Step 2: Upload to Weaviate
|
||||
|
||||
Now we'll create the schema and upload all objects to Weaviate:
|
||||
|
||||
```bash
|
||||
python 2_upload_to_weaviate.py
|
||||
```
|
||||
|
||||
**For local Docker:**
|
||||
```bash
|
||||
python 2_upload_to_weaviate.py --url http://localhost:8080
|
||||
```
|
||||
|
||||
**For Weaviate Cloud:**
|
||||
```bash
|
||||
python 2_upload_to_weaviate.py \
|
||||
--url https://your-cluster.weaviate.network \
|
||||
--api-key YOUR_API_KEY
|
||||
```
|
||||
|
||||
This script will:
|
||||
1. Connect to your Weaviate instance
|
||||
2. Create the schema (class + properties)
|
||||
3. Batch upload all objects
|
||||
4. Verify the upload was successful
|
||||
|
||||
**Expected Output:**
|
||||
```
|
||||
🔗 Connecting to Weaviate at http://localhost:8080...
|
||||
✅ Weaviate is ready!
|
||||
|
||||
📊 Creating schema: React
|
||||
✅ Schema created successfully!
|
||||
|
||||
📤 Uploading 21 objects in batches...
|
||||
✅ Batch 1/1 uploaded (21 objects)
|
||||
|
||||
✅ Successfully uploaded 21 documents to Weaviate
|
||||
🔍 Class 'React' now contains 21 objects
|
||||
```
|
||||
|
||||
### Step 3: Query and Search
|
||||
|
||||
Now the fun part - querying your knowledge base!
|
||||
|
||||
```bash
|
||||
python 3_query_example.py
|
||||
```
|
||||
|
||||
**For local Docker:**
|
||||
```bash
|
||||
python 3_query_example.py --url http://localhost:8080
|
||||
```
|
||||
|
||||
**For Weaviate Cloud:**
|
||||
```bash
|
||||
python 3_query_example.py \
|
||||
--url https://your-cluster.weaviate.network \
|
||||
--api-key YOUR_API_KEY
|
||||
```
|
||||
|
||||
This script demonstrates:
|
||||
1. **Keyword Search**: Traditional text search
|
||||
2. **Hybrid Search**: Combines keyword + vector similarity
|
||||
3. **Metadata Filtering**: Filter by category
|
||||
4. **Limit and Offset**: Pagination
|
||||
|
||||
**Example Queries:**
|
||||
|
||||
**Query 1: Hybrid Search**
|
||||
```
|
||||
Query: "How do I use React hooks?"
|
||||
Alpha: 0.5 (50% keyword, 50% vector)
|
||||
|
||||
Results:
|
||||
1. Category: api
|
||||
Snippet: Hooks are functions that let you "hook into" React state and lifecycle...
|
||||
|
||||
2. Category: guides
|
||||
Snippet: To use a Hook, you need to call it at the top level of your component...
|
||||
```
|
||||
|
||||
**Query 2: Filter by Category**
|
||||
```
|
||||
Query: API reference
|
||||
Category: api
|
||||
|
||||
Results:
|
||||
1. useState Hook - Manage component state
|
||||
2. useEffect Hook - Perform side effects
|
||||
3. useContext Hook - Access context values
|
||||
```
|
||||
|
||||
## Understanding Weaviate Features
|
||||
|
||||
### Hybrid Search (`alpha` parameter)
|
||||
|
||||
Weaviate's killer feature is hybrid search, which combines:
|
||||
- **Keyword Search (BM25)**: Traditional text matching
|
||||
- **Vector Search (ANN)**: Semantic similarity
|
||||
|
||||
Control the balance with `alpha`:
|
||||
- `alpha=0`: Pure keyword search (BM25 only)
|
||||
- `alpha=0.5`: Balanced (default - recommended)
|
||||
- `alpha=1`: Pure vector search (semantic only)
|
||||
|
||||
**When to use what:**
|
||||
- **Exact terms** (API names, error messages): `alpha=0` to `alpha=0.3`
|
||||
- **Concepts** (how to do X, why does Y): `alpha=0.7` to `alpha=1`
|
||||
- **General queries**: `alpha=0.5` (balanced)
|
||||
|
||||
### Metadata Filtering
|
||||
|
||||
Filter results by any property:
|
||||
```python
|
||||
.with_where({
|
||||
"path": ["category"],
|
||||
"operator": "Equal",
|
||||
"valueText": "api"
|
||||
})
|
||||
```
|
||||
|
||||
Supported operators:
|
||||
- `Equal`, `NotEqual`
|
||||
- `GreaterThan`, `LessThan`
|
||||
- `And`, `Or`, `Not`
|
||||
|
||||
### Schema Design
|
||||
|
||||
Our schema includes:
|
||||
- **content**: The actual documentation text (vectorized)
|
||||
- **source**: Skill name (e.g., "react")
|
||||
- **category**: Document category (e.g., "api", "guides")
|
||||
- **file**: Source file name
|
||||
- **type**: Document type ("overview" or "reference")
|
||||
- **version**: Skill version
|
||||
|
||||
## Customization
|
||||
|
||||
### Generate Your Own Skill
|
||||
|
||||
Want to use a different documentation source? Easy:
|
||||
|
||||
```python
|
||||
# 1_generate_skill.py (modify line 10)
|
||||
"--config", "configs/vue.json", # Change to your config
|
||||
```
|
||||
|
||||
Or scrape from scratch:
|
||||
```bash
|
||||
skill-seekers scrape --config configs/your_framework.json
|
||||
skill-seekers package output/your_framework --target weaviate
|
||||
```
|
||||
|
||||
### Adjust Search Parameters
|
||||
|
||||
In `3_query_example.py`, modify:
|
||||
```python
|
||||
# Adjust hybrid search balance
|
||||
alpha=0.7 # More semantic, less keyword
|
||||
|
||||
# Adjust result count
|
||||
.with_limit(10) # Get more results
|
||||
|
||||
# Add more filters
|
||||
.with_where({
|
||||
"operator": "And",
|
||||
"operands": [
|
||||
{"path": ["category"], "operator": "Equal", "valueText": "api"},
|
||||
{"path": ["type"], "operator": "Equal", "valueText": "reference"}
|
||||
]
|
||||
})
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Connection Refused
|
||||
```
|
||||
Error: Connection refused to http://localhost:8080
|
||||
```
|
||||
|
||||
**Solution:** Ensure Weaviate is running:
|
||||
```bash
|
||||
docker ps | grep weaviate
|
||||
# If not running, start it:
|
||||
docker start weaviate
|
||||
```
|
||||
|
||||
### Schema Already Exists
|
||||
```
|
||||
Error: Class 'React' already exists
|
||||
```
|
||||
|
||||
**Solution:** Delete the existing class:
|
||||
```bash
|
||||
# In Python or using Weaviate API
|
||||
client.schema.delete_class("React")
|
||||
```
|
||||
|
||||
Or use the example's built-in reset:
|
||||
```bash
|
||||
python 2_upload_to_weaviate.py --reset
|
||||
```
|
||||
|
||||
### Empty Results
|
||||
```
|
||||
Query returned 0 results
|
||||
```
|
||||
|
||||
**Possible causes:**
|
||||
1. **No embeddings**: Weaviate needs a vectorizer configured (we use default)
|
||||
2. **Wrong class name**: Check the class name matches
|
||||
3. **Data not uploaded**: Verify with `client.query.aggregate("React").with_meta_count().do()`
|
||||
|
||||
**Solution:** Check object count:
|
||||
```python
|
||||
result = client.query.aggregate("React").with_meta_count().do()
|
||||
print(result) # Should show {"data": {"Aggregate": {"React": [{"meta": {"count": 21}}]}}}
|
||||
```
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. **Try other skills**: Generate skills for your favorite frameworks
|
||||
2. **Production deployment**: Use Weaviate Cloud for scalability
|
||||
3. **Add custom vectorizers**: Use OpenAI, Cohere, or local models
|
||||
4. **Build RAG apps**: Integrate with LangChain or LlamaIndex
|
||||
|
||||
## Resources
|
||||
|
||||
- **Weaviate Docs**: https://weaviate.io/developers/weaviate
|
||||
- **Hybrid Search**: https://weaviate.io/developers/weaviate/search/hybrid
|
||||
- **Python Client**: https://weaviate.io/developers/weaviate/client-libraries/python
|
||||
- **Skill Seekers Docs**: https://github.com/yourusername/skill-seekers
|
||||
|
||||
## File Structure
|
||||
|
||||
```
|
||||
weaviate-example/
|
||||
├── README.md # This file
|
||||
├── requirements.txt # Python dependencies
|
||||
├── 1_generate_skill.py # Generate Weaviate-format skill
|
||||
├── 2_upload_to_weaviate.py # Upload to Weaviate instance
|
||||
├── 3_query_example.py # Query demonstrations
|
||||
└── sample_output/ # Example outputs
|
||||
├── react-weaviate.json # Generated skill (21 objects)
|
||||
└── query_results.txt # Sample query results
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
**Last Updated:** February 2026
|
||||
**Tested With:** Weaviate v1.25.0, Python 3.10+, skill-seekers v2.10.0
|
||||
10
examples/weaviate-example/requirements.txt
Normal file
10
examples/weaviate-example/requirements.txt
Normal file
@@ -0,0 +1,10 @@
|
||||
# Weaviate Example Dependencies
|
||||
|
||||
# Skill Seekers (main package)
|
||||
skill-seekers>=2.10.0
|
||||
|
||||
# Weaviate Python client
|
||||
weaviate-client>=4.0.0
|
||||
|
||||
# For pretty output
|
||||
rich>=13.0.0
|
||||
117
examples/weaviate-example/sample_output/query_results.txt
Normal file
117
examples/weaviate-example/sample_output/query_results.txt
Normal file
@@ -0,0 +1,117 @@
|
||||
# Sample Query Results from Weaviate
|
||||
|
||||
## Database Statistics
|
||||
Total objects: 21
|
||||
|
||||
Objects by category:
|
||||
• overview: 1
|
||||
• guides: 8
|
||||
• api: 12
|
||||
|
||||
====================================================================================
|
||||
## Example 1: Hybrid Search
|
||||
|
||||
Query: How do I use React hooks?
|
||||
Alpha: 0.5 (50% keyword, 50% vector)
|
||||
|
||||
┌───┬──────────┬─────────────────────┬────────────────────────────────────────────────┐
|
||||
│ # │ Category │ File │ Content Preview │
|
||||
├───┼──────────┼─────────────────────┼────────────────────────────────────────────────┤
|
||||
│ 1 │ api │ hooks_reference.md │ Hooks are functions that let you "hook into" │
|
||||
│ │ │ │ React state and lifecycle features from function│
|
||||
│ │ │ │ components... │
|
||||
├───┼──────────┼─────────────────────┼────────────────────────────────────────────────┤
|
||||
│ 2 │ guides │ using_hooks.md │ To use a Hook, you need to call it at the top │
|
||||
│ │ │ │ level of your component... │
|
||||
├───┼──────────┼─────────────────────┼────────────────────────────────────────────────┤
|
||||
│ 3 │ api │ usestate.md │ useState is a Hook that lets you add state to │
|
||||
│ │ │ │ function components... │
|
||||
└───┴──────────┴─────────────────────┴────────────────────────────────────────────────┘
|
||||
|
||||
====================================================================================
|
||||
## Example 2: Keyword-Only Search
|
||||
|
||||
Query: useState Hook
|
||||
Alpha: 0 (pure keyword/BM25)
|
||||
|
||||
╭─ Result 1 ──────────────────────────────────────────────────────────────────╮
|
||||
│ Category: api │
|
||||
│ File: usestate.md │
|
||||
│ │
|
||||
│ useState is a Hook that lets you add state to function components. Call it │
|
||||
│ at the top level of your component to declare a state variable... │
|
||||
╰──────────────────────────────────────────────────────────────────────────────╯
|
||||
|
||||
╭─ Result 2 ──────────────────────────────────────────────────────────────────╮
|
||||
│ Category: api │
|
||||
│ File: hooks_reference.md │
|
||||
│ │
|
||||
│ This page describes the APIs for the built-in Hooks in React. useState is │
|
||||
│ the most commonly used Hook. It allows you to add state to function │
|
||||
│ components... │
|
||||
╰──────────────────────────────────────────────────────────────────────────────╯
|
||||
|
||||
====================================================================================
|
||||
## Example 3: Filtered Search
|
||||
|
||||
Query: component
|
||||
Filter: category = 'api'
|
||||
|
||||
Found 5 results in 'api' category:
|
||||
|
||||
1. usestate.md
|
||||
useState is a Hook that lets you add state to function components. Call it
|
||||
at the top level of your component to declare a state variable...
|
||||
|
||||
2. useeffect.md
|
||||
useEffect is a Hook for performing side effects in function components.
|
||||
It runs after render and can access props and state...
|
||||
|
||||
3. usecontext.md
|
||||
useContext is a Hook that lets you subscribe to React context without
|
||||
introducing nesting in your component tree...
|
||||
|
||||
4. usereducer.md
|
||||
useReducer is an alternative to useState. It's useful for managing complex
|
||||
state logic that involves multiple sub-values...
|
||||
|
||||
5. hooks_reference.md
|
||||
This page describes the APIs for the built-in Hooks in React. Hooks let
|
||||
you use different React features from your components...
|
||||
|
||||
====================================================================================
|
||||
## Example 4: Semantic Search
|
||||
|
||||
Query: managing application state
|
||||
Alpha: 1 (pure semantic/vector)
|
||||
|
||||
Result 1:
|
||||
Category: api
|
||||
File: usestate.md
|
||||
useState is a Hook that lets you add state to function components. Call it
|
||||
at the top level of your component to declare a state variable. The state
|
||||
will be preserved between re-renders...
|
||||
|
||||
Result 2:
|
||||
Category: api
|
||||
File: usereducer.md
|
||||
useReducer is an alternative to useState. It's useful for managing complex
|
||||
state logic that involves multiple sub-values or when the next state depends
|
||||
on the previous one...
|
||||
|
||||
Result 3:
|
||||
Category: guides
|
||||
File: state_and_lifecycle.md
|
||||
State is similar to props, but it is private and fully controlled by the
|
||||
component. You can convert a function component to a class component by
|
||||
adding state management...
|
||||
|
||||
====================================================================================
|
||||
|
||||
✅ All examples completed!
|
||||
|
||||
💡 Tips:
|
||||
• Adjust 'alpha' to balance keyword vs semantic search
|
||||
• Use filters to narrow results by metadata
|
||||
• Combine multiple filters with 'And'/'Or' operators
|
||||
• See README.md for more customization options
|
||||
Reference in New Issue
Block a user