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/qdrant-example/3_query_example.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+"""Query Qdrant (demonstrates filtering without vectors)"""
+import argparse
+
+try:
+    from qdrant_client import QdrantClient
+    from qdrant_client.models import Filter, FieldCondition, MatchValue
+    from rich.console import Console
+    from rich.table import Table
+except ImportError:
+    print("❌ Run: pip install qdrant-client rich")
+    exit(1)
+
+console = Console()
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--url", default="http://localhost:6333")
+args = parser.parse_args()
+
+console.print("[bold green]Qdrant Query Examples[/bold green]")
+console.print(f"[dim]Connected to: {args.url}[/dim]\n")
+
+# Connect
+client = QdrantClient(url=args.url)
+collection_name = "django"
+
+# Example 1: Scroll (get all) with filter
+console.print("[bold cyan]Example 1: Filter by Category[/bold cyan]\n")
+
+result = client.scroll(
+    collection_name=collection_name,
+    scroll_filter=Filter(
+        must=[
+            FieldCondition(
+                key="category",
+                match=MatchValue(value="api")
+            )
+        ]
+    ),
+    limit=5
+)
+
+points = result[0]
+table = Table(show_header=True, header_style="bold magenta")
+table.add_column("ID")
+table.add_column("Category")
+table.add_column("File")
+table.add_column("Content Preview")
+
+for point in points:
+    preview = point.payload["content"][:60] + "..."
+    table.add_row(
+        str(point.id)[:8] + "...",
+        point.payload["category"],
+        point.payload["file"],
+        preview
+    )
+
+console.print(table)
+
+# Example 2: Complex filter (AND condition)
+console.print("\n[bold cyan]Example 2: Complex Filter (AND)[/bold cyan]\n")
+
+result = client.scroll(
+    collection_name=collection_name,
+    scroll_filter=Filter(
+        must=[
+            FieldCondition(key="category", match=MatchValue(value="guides")),
+            FieldCondition(key="type", match=MatchValue(value="reference"))
+        ]
+    ),
+    limit=3
+)
+
+console.print(f"[green]Found {len(result[0])} points matching both conditions:[/green]\n")
+
+for i, point in enumerate(result[0], 1):
+    console.print(f"[bold]{i}. {point.payload['file']}[/bold]")
+    console.print(f"   {point.payload['content'][:100]}...\n")
+
+console.print("✅ Query examples completed!")
+console.print("\n[yellow]💡 Note:[/yellow] For vector search, add embeddings to points!")