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/3_query_example.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+"""Query FAISS index"""
+import json, sys, os
+import numpy as np
+
+try:
+    import faiss
+    from openai import OpenAI
+    from rich.console import Console
+    from rich.table import Table
+except ImportError:
+    print("❌ Run: pip install -r requirements.txt")
+    sys.exit(1)
+
+console = Console()
+
+# Load index and metadata
+console.print("📥 Loading FAISS index...")
+index = faiss.read_index("flask.index")
+
+with open("flask_metadata.json") as f:
+    data = json.load(f)
+
+console.print(f"✅ Loaded {index.ntotal} vectors")
+
+# Initialize OpenAI
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+def search(query_text: str, k: int = 5):
+    """Search FAISS index"""
+    console.print(f"\n[yellow]Query:[/yellow] {query_text}")
+
+    # Generate query embedding
+    response = client.embeddings.create(
+        model="text-embedding-ada-002",
+        input=query_text
+    )
+    query_vector = np.array([response.data[0].embedding]).astype('float32')
+
+    # Search
+    distances, indices = index.search(query_vector, k)
+
+    # Display results
+    table = Table(show_header=True, header_style="bold magenta")
+    table.add_column("#", width=3)
+    table.add_column("Distance", width=10)
+    table.add_column("Category", width=12)
+    table.add_column("Content Preview")
+
+    for i, (dist, idx) in enumerate(zip(distances[0], indices[0]), 1):
+        doc = data["documents"][idx]
+        meta = data["metadatas"][idx]
+        preview = doc[:80] + "..." if len(doc) > 80 else doc
+
+        table.add_row(
+            str(i),
+            f"{dist:.2f}",
+            meta.get("category", "N/A"),
+            preview
+        )
+
+    console.print(table)
+    console.print("[dim]💡 Distance: Lower = more similar[/dim]")
+
+# Example queries
+console.print("[bold green]FAISS Query Examples[/bold green]\n")
+
+search("How do I create a Flask route?", k=3)
+search("database models and ORM", k=3)
+search("authentication and security", k=3)
+
+console.print("\n✅ All examples completed!")