Create retriever.py

src/retriever.py

@@ -0,0 +1,17 @@
+import pickle
+from langchain.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+
+DB_PATH = "data/faiss_index"
+METADATA_PATH = "data/metadata.pkl"
+
+cached_retriever = None
+
+def setup_retriever():
+    global cached_retriever
+    if cached_retriever is None:
+        db = FAISS.load_local(DB_PATH, HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5'))
+        with open(METADATA_PATH, "rb") as f:
+            chunked_docs = pickle.load(f)
+        cached_retriever = db.as_retriever(search_type="similarity", search_kwargs={'k': 4})
+    return cached_retriever