Upload chain.py

rag_chain/chain.py

@@ -121,17 +121,16 @@ def get_rag_chain(model_name: str = "gpt-4", temperature: float = 0.2) -> tuple[
         k=15)
     practitioners_db_sparse_retriever = sparse_retriever_client.get_sparse_retriever()
 
-    # TODO Test the ensemble retriever for hyprid search (dense retriever seems to work better)
-    # Using only the filtered sparse retriever
+    # Ensemble retriever for hyprid search (dense retriever seems to work better but the dense retriever is good for acronyms like RMT)
     practitioners_ensemble_retriever = EnsembleRetriever(
         retrievers=[practitioners_db_dense_retriever,
-                    practitioners_db_sparse_retriever], weights=[0.1, 0.9]
+                    practitioners_db_sparse_retriever], weights=[0.2, 0.8]
     )
 
     # Compression retriever for practitioners db
-    # TODO Test the filtered ensemble retriever *** Using only the sparse retriever ***
+    # TODO
     practitioners_db_compression_retriever = compression_retriever_setup(
-        practitioners_db_sparse_retriever,
+        practitioners_ensemble_retriever,
         embeddings_model="text-embedding-ada-002",
         similarity_threshold=0.74
     )