Initial commit - BUBBLE decision tracking system

- Added core BUBBLE architecture with decision envelopes and policy store
- Implemented bundle API with FastAPI skeleton and OpenAPI specification
- Added Go-based storage implementation with SQLite and RocksDB support
- Created integrations for peer sync, vector search, and N8N workflows
- Added comprehensive testing framework and documentation
- Implemented provenance walking and decision checking algorithms

🤖 Generated with [Claude Code](https://claude.ai/code)

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

src/integrations/27-vector-search-integration.py

@@ -0,0 +1,22 @@
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+
+# Load your embedding model once
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Load FAISS index from disk (or create it on startup)
+faiss_index = faiss.read_index("dr_faiss.index")
+
+# Map of vector index to DR IDs
+dr_id_map = {}  # Load from persistent storage
+
+def vector_search(query: str, top_k=3) -> list:
+    vec = embedding_model.encode([query], convert_to_numpy=True)
+    D, I = faiss_index.search(vec, top_k)
+    results = []
+    for dist, idx in zip(D[0], I[0]):
+        dr_id = dr_id_map.get(idx)
+        if dr_id:
+            results.append({"id": dr_id, "distance": dist})
+    return results