Update app.py

app.py

@@ -69,8 +69,8 @@ async def lifespan(app: FastAPI):
     
         # Load models into the shared dictionary
         ml_models["embedder"] = HuggingFaceEmbeddings(
-                                                      model_name="BAAI/bge-large-en-v1.5", #better but lil slower
-                                                      # model_name="BAAI/bge-base-en-v1.5", #better but lil slower
+                                                      # model_name="BAAI/bge-large-en-v1.5", #better but lil slower
+                                                      model_name="BAAI/bge-base-en-v1.5", #better but lil slower
                                                       # model_name="intfloat/e5-large-v2", #lil faster  but  dont know response is slow 
                                                       encode_kwargs={
                                                           "batch_size": 64,
@@ -172,14 +172,14 @@ async def run_hackrx(req: RunRequest):
     # dense_retriever = Chroma.from_documents(documents=chunks, embedding=ml_models["embedder"]).as_retriever()
     ensemble_retriever = EnsembleRetriever(retrievers=[keyword_retriever, dense_retriever], weights=[0.4, 0.6],search_kwargs={"k": 10})
     ### to make it faster we are now using our built reranker thats why commenting the code below
-    # compression_retriever = ContextualCompressionRetriever(
-    #     base_retriever=ensemble_retriever, base_compressor=ml_models["reranker_compressor"]
-    # )
+    compression_retriever = ContextualCompressionRetriever(
+        base_retriever=ensemble_retriever, base_compressor=ml_models["reranker_compressor"]
+    )
 
     
     # Define the RAG chain using pre-loaded components
     hybrid_rag_chain = (
-        {"context": itemgetter("full_query") | ensemble_retriever, "full_query": itemgetter("full_query")}
+        {"context": itemgetter("full_query") | compression_retriever, "full_query": itemgetter("full_query")}
         | ml_models["prompt_template"]
         | ml_models["llm"]
     )