Create semantic.py

semantic.py

@@ -1,18 +1,32 @@
-import faiss
-import numpy as np
-from sentence_transformers import SentenceTransformer
-from typing import List, Dict
+FAISS_AVAILABLE = False
 
-model = SentenceTransformer("all-MiniLM-L6-v2")
+try:
+    import faiss
+    from sentence_transformers import SentenceTransformer
+    FAISS_AVAILABLE = True
+except Exception:
+    FAISS_AVAILABLE = False
 
-def build_faiss_index(docs: List[Dict]):
-    texts = [d["content"] for d in docs]
-    embeddings = model.encode(texts)
-    index = faiss.IndexFlatL2(embeddings.shape[1])
-    index.add(np.array(embeddings))
-    return index, embeddings
+class SemanticIndex:
+    def __init__(self):
+        if not FAISS_AVAILABLE:
+            raise RuntimeError("FAISS unavailable")
 
-def semantic_search(query: str, docs: List[Dict], index):
-    q_emb = model.encode([query])
-    D, I = index.search(np.array(q_emb), k=5)
-    return [docs[i] for i in I[0]]
+        self.model = SentenceTransformer("all-MiniLM-L6-v2")
+        self.index = None
+        self.texts = []
+
+    def build(self, texts):
+        embeddings = self.model.encode(texts)
+        dim = embeddings.shape[1]
+        self.index = faiss.IndexFlatL2(dim)
+        self.index.add(embeddings)
+        self.texts = texts
+
+    def search(self, query, k=5):
+        if not self.index:
+            return []
+
+        q = self.model.encode([query])
+        _, idxs = self.index.search(q, k)
+        return [self.texts[i] for i in idxs[0]]