feat: Add FAISS similarity search adaptor (Task #12)

🎯 What's New

- FAISS adaptor for efficient similarity search
- JSON-based metadata management (secure & portable)
- Comprehensive usage examples with 3 index types
- Supports dynamic document addition and filtered search

📦 Implementation Details

FAISS (Facebook AI Similarity Search) is a library for efficient similarity
search but requires separate metadata management. Unlike Weaviate/Chroma,
FAISS doesn't have built-in metadata support, so we store it separately as JSON.

**Key Components:**
- src/skill_seekers/cli/adaptors/faiss_helpers.py (399 lines)
  - FAISSHelpers class inheriting from SkillAdaptor
  - _generate_id(): Deterministic ID from content hash (MD5)
  - format_skill_md(): Converts docs to FAISS-compatible JSON
  - package(): Creates JSON with documents, metadatas, ids, config
  - upload(): Provides comprehensive example code (370 lines)

**Output Format:**
{
  "documents": ["doc1", "doc2", ...],
  "metadatas": [{"source": "...", "category": "..."}, ...],
  "ids": ["hash1", "hash2", ...],
  "config": {
    "index_type": "IndexFlatL2",
    "dimension": 1536,
    "metric": "L2"
  }
}

**Security Consideration:**
- Uses JSON instead of pickle for metadata storage
- Avoids arbitrary code execution risk
- More portable and human-readable

**Example Code Includes:**
1. Loading JSON data and generating embeddings (OpenAI ada-002)
2. Creating FAISS index with 3 options:
   - IndexFlatL2 (exact search, <1M vectors)
   - IndexIVFFlat (fast approximate, >100k vectors)
   - IndexHNSWFlat (graph-based, very fast)
3. Saving index + JSON metadata separately
4. Search with metadata filtering (post-processing)
5. Loading saved index for reuse
6. Adding new documents dynamically

🔧 Files Changed

- src/skill_seekers/cli/adaptors/__init__.py
  - Added FAISSHelpers import
  - Registered 'faiss' in ADAPTORS dict

- src/skill_seekers/cli/package_skill.py
  - Added 'faiss' to --target choices

- src/skill_seekers/cli/main.py
  - Added 'faiss' to unified CLI --target choices

✅ Testing

- Tested with ansible skill: skill-seekers-package output/ansible --target faiss
- Verified JSON structure with jq
- Output: ansible-faiss.json (9.7 KB, 1 document)
- Package size: 9,717 bytes (9.5 KB)

📊 Week 2 Progress: 3/9 tasks complete

Task #12 Complete ✅
- Weaviate (Task #10) ✅
- Chroma (Task #11) ✅
- FAISS (Task #12) ✅ ← Just completed

Next: Task #13 (Qdrant adaptor)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

src/skill_seekers/cli/package_skill.py

@@ -155,7 +155,7 @@ Examples:
 
     parser.add_argument(
         "--target",
-        choices=["claude", "gemini", "openai", "markdown", "langchain", "llama-index", "weaviate", "chroma"],
+        choices=["claude", "gemini", "openai", "markdown", "langchain", "llama-index", "weaviate", "chroma", "faiss"],
         default="claude",
         help="Target LLM platform (default: claude)",
     )