Create vector_store.py

vector_store.py

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+import faiss
+import numpy as np
+from langchain.vectorstores import FAISS
+from langchain.schema import Document
+from utils.embeddings import embedding_model
+
+# Initialize FAISS index
+dim = 384  # MiniLM embedding size
+index = faiss.IndexFlatL2(dim)
+vector_db = FAISS(embedding_function=embedding_model, index=index)
+
+def add_document(text, doc_id):
+    """Add document embeddings to FAISS."""
+    doc = Document(page_content=text, metadata={"id": doc_id})
+    vector_db.add_documents([doc])
+
+def search(query, top_k=3):
+    """Search FAISS index for similar documents."""
+    results = vector_db.similarity_search(query, k=top_k)
+    return results