013e0dc3f0
- 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>
23 lines
706 B
Python
23 lines
706 B
Python
from sentence_transformers import SentenceTransformer
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import faiss
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import numpy as np
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# Load your embedding model once
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embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
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# Load FAISS index from disk (or create it on startup)
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faiss_index = faiss.read_index("dr_faiss.index")
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# Map of vector index to DR IDs
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dr_id_map = {} # Load from persistent storage
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def vector_search(query: str, top_k=3) -> list:
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vec = embedding_model.encode([query], convert_to_numpy=True)
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D, I = faiss_index.search(vec, top_k)
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results = []
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for dist, idx in zip(D[0], I[0]):
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dr_id = dr_id_map.get(idx)
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if dr_id:
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results.append({"id": dr_id, "distance": dist})
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return results
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