Create semantic/faiss_index.py

semantic/faiss_index.py

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+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+
+MODEL = SentenceTransformer("all-MiniLM-L6-v2")
+
+class SemanticIndex:
+    def __init__(self):
+        self.index = None
+        self.texts = []
+
+    def build(self, texts):
+        self.texts = texts
+        embeddings = MODEL.encode(texts, convert_to_numpy=True)
+        dim = embeddings.shape[1]
+        self.index = faiss.IndexFlatL2(dim)
+        self.index.add(embeddings)
+
+    def search(self, query, k=10):
+        if not self.index:
+            return []
+
+        q_emb = MODEL.encode([query], convert_to_numpy=True)
+        distances, indices = self.index.search(q_emb, k)
+
+        return [self.texts[i] for i in indices[0] if i < len(self.texts)]