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>
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examples/faiss-example/1_generate_skill.py
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examples/faiss-example/1_generate_skill.py
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#!/usr/bin/env python3
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"""Generate skill for FAISS (same as other examples)"""
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import subprocess, sys
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from pathlib import Path
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print("=" * 60)
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print("Step 1: Generating Skill for FAISS")
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print("=" * 60)
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# Scrape
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subprocess.run([
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"skill-seekers", "scrape",
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"--config", "configs/flask.json",
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"--max-pages", "20"
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], check=True)
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# Package
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subprocess.run([
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"skill-seekers", "package",
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"output/flask",
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"--target", "faiss"
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], check=True)
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output = Path("output/flask-faiss.json")
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print(f"\n✅ Ready: {output} ({output.stat().st_size/1024:.1f} KB)")
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print("Next: python 2_build_faiss_index.py (requires OPENAI_API_KEY)")
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examples/faiss-example/2_build_faiss_index.py
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examples/faiss-example/2_build_faiss_index.py
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#!/usr/bin/env python3
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"""Build FAISS index with OpenAI embeddings"""
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import json, sys, os
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import numpy as np
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from pathlib import Path
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try:
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import faiss
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from openai import OpenAI
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from rich.console import Console
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except ImportError:
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print("❌ Missing dependencies! Run: pip install -r requirements.txt")
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sys.exit(1)
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console = Console()
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# Check API key
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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console.print("[red]❌ OPENAI_API_KEY not set![/red]")
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console.print("Set it with: export OPENAI_API_KEY=sk-...")
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sys.exit(1)
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# Load data
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console.print("📥 Loading skill data...")
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with open("output/flask-faiss.json") as f:
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data = json.load(f)
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documents = data["documents"]
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metadatas = data["metadatas"]
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ids = data["ids"]
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console.print(f"✅ Loaded {len(documents)} documents")
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# Generate embeddings
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console.print("\n🔄 Generating embeddings (this may take 30-60 seconds)...")
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console.print(f" Cost: ~$0.001 for {len(documents)} documents")
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client = OpenAI(api_key=api_key)
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embeddings = []
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for i, doc in enumerate(documents):
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response = client.embeddings.create(
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model="text-embedding-ada-002",
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input=doc[:8000] # Truncate to max length
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)
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embeddings.append(response.data[0].embedding)
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if (i + 1) % 5 == 0:
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console.print(f" Progress: {i+1}/{len(documents)}")
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console.print("✅ Embeddings generated!")
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# Build FAISS index
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console.print("\n🏗️ Building FAISS index...")
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dimension = len(embeddings[0]) # 1536 for ada-002
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vectors = np.array(embeddings).astype('float32')
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# Create index (L2 distance)
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index = faiss.IndexFlatL2(dimension)
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index.add(vectors)
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# Save everything
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faiss.write_index(index, "flask.index")
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with open("flask_metadata.json", "w") as f:
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json.dump({"documents": documents, "metadatas": metadatas, "ids": ids}, f)
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console.print(f"✅ Index saved: flask.index")
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console.print(f"✅ Metadata saved: flask_metadata.json")
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console.print(f"\n💡 Total vectors: {index.ntotal}")
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console.print(f"💡 Dimension: {dimension}")
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console.print("\n➡️ Next: python 3_query_example.py")
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examples/faiss-example/3_query_example.py
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examples/faiss-example/3_query_example.py
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#!/usr/bin/env python3
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"""Query FAISS index"""
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import json, sys, os
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import numpy as np
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try:
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import faiss
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from openai import OpenAI
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from rich.console import Console
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from rich.table import Table
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except ImportError:
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print("❌ Run: pip install -r requirements.txt")
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sys.exit(1)
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console = Console()
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# Load index and metadata
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console.print("📥 Loading FAISS index...")
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index = faiss.read_index("flask.index")
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with open("flask_metadata.json") as f:
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data = json.load(f)
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console.print(f"✅ Loaded {index.ntotal} vectors")
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# Initialize OpenAI
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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def search(query_text: str, k: int = 5):
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"""Search FAISS index"""
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console.print(f"\n[yellow]Query:[/yellow] {query_text}")
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# Generate query embedding
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response = client.embeddings.create(
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model="text-embedding-ada-002",
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input=query_text
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)
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query_vector = np.array([response.data[0].embedding]).astype('float32')
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# Search
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distances, indices = index.search(query_vector, k)
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# Display results
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table = Table(show_header=True, header_style="bold magenta")
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table.add_column("#", width=3)
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table.add_column("Distance", width=10)
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table.add_column("Category", width=12)
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table.add_column("Content Preview")
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for i, (dist, idx) in enumerate(zip(distances[0], indices[0]), 1):
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doc = data["documents"][idx]
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meta = data["metadatas"][idx]
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preview = doc[:80] + "..." if len(doc) > 80 else doc
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table.add_row(
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str(i),
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f"{dist:.2f}",
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meta.get("category", "N/A"),
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preview
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)
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console.print(table)
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console.print("[dim]💡 Distance: Lower = more similar[/dim]")
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# Example queries
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console.print("[bold green]FAISS Query Examples[/bold green]\n")
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search("How do I create a Flask route?", k=3)
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search("database models and ORM", k=3)
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search("authentication and security", k=3)
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console.print("\n✅ All examples completed!")
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95
examples/faiss-example/README.md
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examples/faiss-example/README.md
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# FAISS Vector Database Example
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Facebook AI Similarity Search (FAISS) is a library for efficient similarity search of dense vectors. Perfect for large-scale semantic search.
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## Quick Start
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```bash
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# 1. Install dependencies
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pip install -r requirements.txt
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# 2. Generate skill
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python 1_generate_skill.py
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# 3. Build FAISS index (requires OpenAI API key)
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export OPENAI_API_KEY=sk-...
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python 2_build_faiss_index.py
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# 4. Query the index
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python 3_query_example.py
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```
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## What's Different About FAISS?
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- **No database server**: Pure Python library
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- **Blazing fast**: Optimized C++ implementation
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- **Scales to billions**: Efficient for massive datasets
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- **Requires embeddings**: You must generate vectors (we use OpenAI)
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## Key Features
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### Generate Embeddings
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FAISS doesn't generate embeddings - you must provide them:
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```python
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from openai import OpenAI
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client = OpenAI()
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# Generate embedding
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response = client.embeddings.create(
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model="text-embedding-ada-002",
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input="Your text here"
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)
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embedding = response.data[0].embedding # 1536-dim vector
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```
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### Build Index
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```python
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import faiss
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import numpy as np
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# Create index (L2 distance)
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dimension = 1536 # OpenAI ada-002
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index = faiss.IndexFlatL2(dimension)
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# Add vectors
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vectors = np.array(embeddings).astype('float32')
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index.add(vectors)
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# Save to disk
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faiss.write_index(index, "skill.index")
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```
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### Search
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```python
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# Load index
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index = faiss.read_index("skill.index")
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# Query (returns distances + indices)
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distances, indices = index.search(query_vector, k=5)
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```
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## Cost Estimate
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OpenAI embeddings: ~$0.10 per 1M tokens
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- 20 documents (~10K tokens): < $0.001
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- 1000 documents (~500K tokens): ~$0.05
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## Files Structure
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- `1_generate_skill.py` - Package for FAISS
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- `2_build_faiss_index.py` - Generate embeddings & build index
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- `3_query_example.py` - Search queries
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## Resources
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- **FAISS GitHub**: https://github.com/facebookresearch/faiss
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- **FAISS Wiki**: https://github.com/facebookresearch/faiss/wiki
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- **OpenAI Embeddings**: https://platform.openai.com/docs/guides/embeddings
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---
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**Note**: FAISS is best for advanced users who need maximum performance at scale. For simpler use cases, try ChromaDB or Weaviate.
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6
examples/faiss-example/requirements.txt
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examples/faiss-example/requirements.txt
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# FAISS Example Dependencies
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skill-seekers>=2.10.0
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faiss-cpu>=1.7.4 # or faiss-gpu for GPU support
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openai>=1.0.0
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numpy>=1.24.0
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rich>=13.0.0
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