feat(search): recherche semantique - embeddings vectoriels + hybride RRF (#70)

backend/requirements-semantic.txt

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+# ObsiGate — Optional dependencies for semantic search (#70)
+#
+# These are NOT required: the semantic search engine degrades gracefully to a
+# dependency-free hashing embedder and a pure-Python cosine store when they are
+# absent. Install this file to enable the full local model + fast vector index:
+#
+#   pip install -r backend/requirements-semantic.txt
+#
+# NOTE: sentence-transformers pulls in PyTorch (large download). If you only
+# want the vector acceleration, install numpy + faiss-cpu and configure an
+# external embedding endpoint instead (OBSIGATE_EMBEDDING_*).
+
+# Local embedding model (all-MiniLM-L6-v2, ~80 MB, CPU)
+sentence-transformers>=2.2.0
+
+# Vector storage / similarity search
+numpy>=1.24.0
+faiss-cpu>=1.7.4