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>

examples/faiss-example/2_build_faiss_index.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+"""Build FAISS index with OpenAI embeddings"""
+import json, sys, os
+import numpy as np
+from pathlib import Path
+
+try:
+    import faiss
+    from openai import OpenAI
+    from rich.console import Console
+except ImportError:
+    print("❌ Missing dependencies! Run: pip install -r requirements.txt")
+    sys.exit(1)
+
+console = Console()
+
+# Check API key
+api_key = os.getenv("OPENAI_API_KEY")
+if not api_key:
+    console.print("[red]❌ OPENAI_API_KEY not set![/red]")
+    console.print("Set it with: export OPENAI_API_KEY=sk-...")
+    sys.exit(1)
+
+# Load data
+console.print("📥 Loading skill data...")
+with open("output/flask-faiss.json") as f:
+    data = json.load(f)
+
+documents = data["documents"]
+metadatas = data["metadatas"]
+ids = data["ids"]
+
+console.print(f"✅ Loaded {len(documents)} documents")
+
+# Generate embeddings
+console.print("\n🔄 Generating embeddings (this may take 30-60 seconds)...")
+console.print(f"   Cost: ~$0.001 for {len(documents)} documents")
+
+client = OpenAI(api_key=api_key)
+embeddings = []
+
+for i, doc in enumerate(documents):
+    response = client.embeddings.create(
+        model="text-embedding-ada-002",
+        input=doc[:8000]  # Truncate to max length
+    )
+    embeddings.append(response.data[0].embedding)
+
+    if (i + 1) % 5 == 0:
+        console.print(f"   Progress: {i+1}/{len(documents)}")
+
+console.print("✅ Embeddings generated!")
+
+# Build FAISS index
+console.print("\n🏗️  Building FAISS index...")
+dimension = len(embeddings[0])  # 1536 for ada-002
+vectors = np.array(embeddings).astype('float32')
+
+# Create index (L2 distance)
+index = faiss.IndexFlatL2(dimension)
+index.add(vectors)
+
+# Save everything
+faiss.write_index(index, "flask.index")
+with open("flask_metadata.json", "w") as f:
+    json.dump({"documents": documents, "metadatas": metadatas, "ids": ids}, f)
+
+console.print(f"✅ Index saved: flask.index")
+console.print(f"✅ Metadata saved: flask_metadata.json")
+console.print(f"\n💡 Total vectors: {index.ntotal}")
+console.print(f"💡 Dimension: {dimension}")
+console.print("\n➡️  Next: python 3_query_example.py")