Update app.py

app.py

@@ -468,16 +468,48 @@ answer_prompt.invoke(
 )
 
 
+
+
+# Instantiate the retriever and the ConversationalRetrievalChain :
+
+retriever_Google = retrieval_blocks(
+    create_vectorstore=False,
+    LLM_service="Google",
+    vectorstore_name="Vit_All_Google_Embeddings",
+    retriever_type="Cohere_reranker",
+    base_retriever_search_type="similarity", base_retriever_k=12,
+    compression_retriever_k=16,
+    cohere_api_key=cohere_api_key,cohere_top_n=10,
+)
+
+"""
+chain_gemini,memory_gemini = custom_ConversationalRetrievalChain(
+    llm = instantiate_LLM(
+        LLM_provider="Google",api_key=google_api_key,temperature=0.5,model_name="gemini-pro"
+    ),
+    condense_question_llm = instantiate_LLM(
+        LLM_provider="Google",api_key=google_api_key,temperature=0.1,model_name="gemini-pro"),
+    retriever=retriever_Google,
+    language="english",
+    llm_provider="Google",
+    model_name="gemini-pro"
+)
+
+
+memory_gemini.clear()
+"""
+
+
 chain = ConversationalRetrievalChain.from_llm(
     condense_question_prompt=standalone_question_prompt,
     combine_docs_chain_kwargs={'prompt': answer_prompt},
     condense_question_llm=instantiate_LLM(
-        LLM_provider="Google",api_key=HF_key,temperature=0.1,
+        LLM_provider="Google",api_key=google_api_key,temperature=0.1,
         model_name="gemini-pro"),
     memory=create_memory("gemini-pro"),
-    retriever = BaseRetriever,
+    retriever = retriever_Google,  #base_retriever_HF
     llm=instantiate_LLM(
-        LLM_provider="Google",api_key=HF_key,temperature=0.5,
+        LLM_provider="Google",api_key=google_api_key,temperature=0.5,
         model_name="gemini-pro"),
     chain_type= "stuff",
     verbose= False,