Update app.py

app.py

@@ -115,13 +115,13 @@ def rag_chain(llm, prompt, db):
     completion = rag_chain({"query": prompt})
     return completion
 
-def wandb_trace(rag_option, prompt, prompt_template, result, completion, chain_name):
+def wandb_trace(rag_option, prompt, prompt_template, result, completion, chain_name, status_msg):
     wandb.init(project = "openai-llm-rag")
     trace = Trace(
         name = chain_name,
         kind = "chain",
-        #status_code = status,
-        #status_message = status_message,
+        status_code = "TODO",
+        status_message = status_msg,
         metadata={
             "chunk_overlap": config["chunk_overlap"],
             "chunk_size": config["chunk_size"],
@@ -129,8 +129,8 @@ def wandb_trace(rag_option, prompt, prompt_template, result, completion, chain_n
             "model": config["model"],
             "temperature": config["temperature"],
         },
-        #start_time_ms = start_time_ms,
-        #end_time_ms = end_time_ms,
+        start_time_ms = 123,
+        end_time_ms = 456,
         inputs = {"rag_option": rag_option, "prompt": prompt, "prompt_template": prompt_template},
         outputs = {"result": str(result), "completion": str(completion)},
     )
@@ -144,6 +144,8 @@ def invoke(openai_api_key, rag_option, prompt):
         raise gr.Error("Retrieval Augmented Generation is required.")
     if (prompt == ""):
         raise gr.Error("Prompt is required.")
+    completion = ""
+    status_msg = ""
     try:
         llm = ChatOpenAI(model_name = config["model"], 
                          openai_api_key = openai_api_key, 
@@ -155,7 +157,7 @@ def invoke(openai_api_key, rag_option, prompt):
             completion = rag_chain(llm, prompt, db)
             result = completion["result"]
             prompt_template = rag_template
-            chain_name = type(RetrievalQA)
+            chain_name = RetrievalQA.__class__.__name__
         elif (rag_option == "MongoDB"):
             #splits = document_loading_splitting()
             #document_storage_mongodb(splits)
@@ -163,17 +165,17 @@ def invoke(openai_api_key, rag_option, prompt):
             completion = rag_chain(llm, prompt, db)
             result = completion["result"]
             prompt_template = rag_template
-            chain_name = type(RetrievalQA)
+            chain_name = RetrievalQA.__class__.__name__
         else:
             result = llm_chain(llm, prompt)
             completion = result
             prompt_template = llm_template
-            chain_name = type(LLMChain)
+            chain_name = LLMChain.__class__.__name__
     except Exception as e:
-        completion = e
+        status_msg = e
         raise gr.Error(e)
     finally:
-        wandb_trace(rag_option, prompt, prompt_template, result, completion, chain_name)
+        wandb_trace(rag_option, prompt, prompt_template, result, completion, chain_name, status_msg)
     return result
 
 description = """<strong>Overview:</strong> Context-aware multimodal reasoning application using a <strong>large language model (LLM)</strong> with