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/README.md

@@ -0,0 +1,82 @@
+# Qdrant Vector Database Example
+
+Qdrant is a vector similarity search engine with extended filtering support. Built in Rust for maximum performance.
+
+## Quick Start
+
+```bash
+# 1. Start Qdrant (Docker)
+docker run -p 6333:6333 qdrant/qdrant:latest
+
+# 2. Install dependencies
+pip install -r requirements.txt
+
+# 3. Generate and upload
+python 1_generate_skill.py
+python 2_upload_to_qdrant.py
+
+# 4. Query
+python 3_query_example.py
+```
+
+## What Makes Qdrant Special?
+
+- **Advanced Filtering**: Rich payload queries with AND/OR/NOT
+- **High Performance**: Rust-based, handles billions of vectors
+- **Production Ready**: Clustering, replication, persistence built-in
+- **Flexible Storage**: In-memory or on-disk, cloud or self-hosted
+
+## Key Features
+
+### Rich Payload Filtering
+
+```python
+# Complex filters
+collection.search(
+    query_vector=vector,
+    query_filter=models.Filter(
+        must=[
+            models.FieldCondition(
+                key="category",
+                match=models.MatchValue(value="api")
+            )
+        ],
+        should=[
+            models.FieldCondition(
+                key="type",
+                match=models.MatchValue(value="reference")
+            )
+        ]
+    ),
+    limit=5
+)
+```
+
+### Hybrid Search
+
+Combine vector similarity with payload filtering:
+- Filter first (fast): Narrow by metadata, then search
+- Search first: Find similar, then filter results
+
+### Production Features
+
+- **Snapshots**: Point-in-time backups
+- **Replication**: High availability
+- **Sharding**: Horizontal scaling
+- **Monitoring**: Prometheus metrics
+
+## Files
+
+- `1_generate_skill.py` - Package for Qdrant
+- `2_upload_to_qdrant.py` - Upload to Qdrant
+- `3_query_example.py` - Query examples
+
+## Resources
+
+- **Qdrant Docs**: https://qdrant.tech/documentation/
+- **API Reference**: https://qdrant.tech/documentation/quick-start/
+- **Cloud**: https://cloud.qdrant.io/
+
+---
+
+**Note**: Qdrant excels at production deployments with complex filtering needs. For simpler use cases, try ChromaDB.