Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-import langchain, openai, os, time, wandb
+import openai, os, time, wandb
 
 from langchain.chains import LLMChain, RetrievalQA
 from langchain.chat_models import ChatOpenAI
@@ -32,8 +32,6 @@ MONGODB_INDEX_NAME = "default"
 
 description = os.environ["DESCRIPTION"]
 
-#langchain.verbose = True
-
 config = {
     "chunk_overlap": 150,
     "chunk_size": 1500,
@@ -104,7 +102,7 @@ def document_retrieval_mongodb(llm, prompt):
     return db
 
 def llm_chain(llm, prompt):
-    llm_chain = LLMChain(llm = llm, prompt = LLM_CHAIN_PROMPT, verbose = True)
+    llm_chain = LLMChain(llm = llm, prompt = LLM_CHAIN_PROMPT, verbose = False)
     completion = llm_chain.run({"question": prompt})
     return completion, llm_chain
 
@@ -113,14 +111,14 @@ def rag_chain(llm, prompt, db):
                                             chain_type_kwargs = {"prompt": RAG_CHAIN_PROMPT}, 
                                             retriever = db.as_retriever(search_kwargs = {"k": config["k"]}), 
                                             return_source_documents = True,
-                                            verbose = True)
+                                            verbose = False)
     completion = rag_chain({"query": prompt})
     return completion, rag_chain
 
 def wandb_trace(rag_option, prompt, completion, chain, status_msg, start_time_ms, end_time_ms):
-    #print(chain.inputKey)
-    #print(chain.outputKey)
-    #print(chain.retriever)
+    print(chain.inputKey)
+    print(chain.outputKey)
+    print(chain.retriever)
     wandb.init(project = "openai-llm-rag")
     if (rag_option == "Off" or str(status_msg) != ""):
         result = completion