WIP

app.py

@@ -140,79 +140,121 @@ async def chat(query,history,audience,sources,reports):
         "transform_query":("🔄️ Thinking step by step to answer the question",True),
         "retrieve_documents":("🔄️ Searching in the knowledge base",False),
     }
-
+    
+    used_documents = []
     try:
         async for event in result:
-            if event["event"] == "on_chat_model_stream" and event["name"] != "transform_query": # if streaming answer
-                if start_streaming == False:
-                    start_streaming = True
-                    # history[-1] = (query,"")
-                    history.append(ChatMessage(role="assistant", content = ""))
-                if type(history[-1].metadata) != dict :
-                    # print("metadata : ", history[-1].metadata)
-                    # history.append(ChatMessage(role="assistant", content = ""))
-
+            if "langgraph_node" in event["metadata"]:
+                node = event["metadata"]["langgraph_node"]
+
+                if event["event"] == "on_chain_end" and event["name"] == "retrieve_documents" :# when documents are retrieved
+                    try:
+                        docs = event["data"]["output"]["documents"]
+                        docs_html = []
+                        for i, d in enumerate(docs, 1):
+                            docs_html.append(make_html_source(d, i))
+                        
+                        used_documents = used_documents + [d.metadata["name"] for d in docs]
+                        # already_used_sources =
+                        history[-1].content = "Adding source :\n" + "\n".join(np.unique(used_documents))
+                            
+                        docs_html = "".join(docs_html)
+                        
+                    except Exception as e:
+                        print(f"Error getting documents: {e}")
+                        print(event)
+ 
+                elif event["name"] in steps_display.keys() and event["event"] == "on_chain_start": #display steps
+                    event_description,display_output = steps_display[node]
+                    if not hasattr(history[-1], 'metadata') or history[-1].metadata["title"] != event_description: # if a new step begins
+                        history.append(ChatMessage(role="assistant", content = "", metadata={'title' :event_description}))
+ 
+                elif event["name"] != "transform_query" and event["event"] == "on_chat_model_stream" and node in ["answer_rag", "answer_search"]:# if streaming answer
+                    if start_streaming == False:
+                        start_streaming = True
+                        history.append(ChatMessage(role="assistant", content = ""))
                     last_message_content = history[-1].content
-                    last_message_content += event["data"]["chunk"].content
-                    last_message_content = parse_output_llm_with_sources(last_message_content)
-                    history[-1] = ChatMessage(role="assistant", content = last_message_content)
-                # new_token = event["data"]["chunk"].content
-                # # time.sleep(0.01)
-                # previous_answer = history[-1][1]
-                # previous_answer = previous_answer if previous_answer is not None else ""
-                # answer_yet = previous_answer + new_token
-                # answer_yet = parse_output_llm_with_sources(answer_yet)
-                # history[-1] = (query,answer_yet)
+                    new_message_content = last_message_content + event["data"]["chunk"].content
+                    new_message_content = parse_output_llm_with_sources(new_message_content)
+                    history[-1] = ChatMessage(role="assistant", content = new_message_content)
 
+                if event["name"] == "transform_query" and event["event"] =="on_chain_end":
+                    if hasattr(history[-1],"content"):
+                        history[-1].content += "Decompose question : \n\n" + "\n".join([q["question"] for q in event["data"]["output"]["remaining_questions"]])
             
-            elif event["name"] == "retrieve_documents" and event["event"] == "on_chain_end": # when documents are retrieved
-                try:
-                    docs = event["data"]["output"]["documents"]
-                    docs_html = []
-                    for i, d in enumerate(docs, 1):
-                        docs_html.append(make_html_source(d, i))
-                    docs_html = "".join(docs_html)
-                except Exception as e:
-                    print(f"Error getting documents: {e}")
-                    print(event)
-
-            # elif event["name"] == "retrieve_documents" and event["event"] == "on_chain_start":
-            #     print(event)
-            #     questions = event["data"]["input"]["questions"]
-            #     questions = "\n".join([f"{i+1}. {q['question']} ({q['source']})" for i,q in enumerate(questions)])
-            #     answer_yet = "🔄️ Searching in the knowledge base\n{questions}"
-            #     history[-1] = (query,answer_yet)
-
-            # TODO  append step de tool avec les questions qui sont utilisées pour la recherche
-            for event_name,(event_description,display_output) in steps_display.items(): # display steps
-                if event["name"] == event_name:
-                    if event["event"] == "on_chain_start":
-                        # answer_yet = f"<p><span class='loader'></span>{event_description}</p>"
-                        # answer_yet = make_toolbox(event_description, "", checked = False)
-                        answer_yet = event_description
-                        # answer_yet = ChatMessage(role="assistant", content = "processing", metadata={'title' :event_description})
 
-                        history.append(ChatMessage(role="assistant", content = "", metadata={'title' :event_description}))
-                        # history[-1] = (query,answer_yet)
-                    # elif event["event"] == "on_chain_end":
-                    #     answer_yet = ""
-                    #     history[-1] = (query,answer_yet)
-                        # if display_output:
-                        #     print(event["data"]["output"])
-
-            # if op['path'] == path_reformulation: # reforulated question
-            #     try:
-            #         output_language = op['value']["language"] # str
-            #         output_query = op["value"]["question"]
-            #     except Exception as e:
-            #         raise gr.Error(f"ClimateQ&A Error: {e} - The error has been noted, try another question and if the error remains, you can contact us :)")
+
+
+            # if event["event"] == "on_chat_model_stream" and event["name"] != "transform_query": # if streaming answer
+            #     if start_streaming == False:
+            #         start_streaming = True
+            #         # history[-1] = (query,"")
+            #         history.append(ChatMessage(role="assistant", content = ""))
+            #     if type(history[-1].metadata) != dict :
+            #         # print("metadata : ", history[-1].metadata)
+            #         # history.append(ChatMessage(role="assistant", content = ""))
+
+            #         last_message_content = history[-1].content
+            #         last_message_content += event["data"]["chunk"].content
+            #         last_message_content = parse_output_llm_with_sources(last_message_content)
+            #         history[-1] = ChatMessage(role="assistant", content = last_message_content)
+            #     # new_token = event["data"]["chunk"].content
+            #     # # time.sleep(0.01)
+            #     # previous_answer = history[-1][1]
+            #     # previous_answer = previous_answer if previous_answer is not None else ""
+            #     # answer_yet = previous_answer + new_token
+            #     # answer_yet = parse_output_llm_with_sources(answer_yet)
+            #     # history[-1] = (query,answer_yet)
+
             
-            # if op["path"] == path_keywords:
+            # elif event["name"] == "retrieve_documents" and event["event"] == "on_chain_end": # when documents are retrieved
             #     try:
-            #         output_keywords = op['value']["keywords"] # str
-            #         output_keywords = " AND ".join(output_keywords)
+            #         docs = event["data"]["output"]["documents"]
+            #         docs_html = []
+            #         for i, d in enumerate(docs, 1):
+            #             docs_html.append(make_html_source(d, i))
+            #         docs_html = "".join(docs_html)
             #     except Exception as e:
-            #         pass
+            #         print(f"Error getting documents: {e}")
+            #         print(event)
+
+            # # elif event["name"] == "retrieve_documents" and event["event"] == "on_chain_start":
+            # #     print(event)
+            # #     questions = event["data"]["input"]["questions"]
+            # #     questions = "\n".join([f"{i+1}. {q['question']} ({q['source']})" for i,q in enumerate(questions)])
+            # #     answer_yet = "🔄️ Searching in the knowledge base\n{questions}"
+            # #     history[-1] = (query,answer_yet)
+
+            # # TODO  append step de tool avec les questions qui sont utilisées pour la recherche
+            # for event_name,(event_description,display_output) in steps_display.items(): # display steps
+            #     if event["name"] == event_name:
+            #         if event["event"] == "on_chain_start":
+            #             # answer_yet = f"<p><span class='loader'></span>{event_description}</p>"
+            #             # answer_yet = make_toolbox(event_description, "", checked = False)
+            #             answer_yet = event_description
+            #             # answer_yet = ChatMessage(role="assistant", content = "processing", metadata={'title' :event_description})
+
+            #             history.append(ChatMessage(role="assistant", content = "", metadata={'title' :event_description}))
+            #             # history[-1] = (query,answer_yet)
+            #         # elif event["event"] == "on_chain_end":
+            #         #     answer_yet = ""
+            #         #     history[-1] = (query,answer_yet)
+            #             # if display_output:
+            #             #     print(event["data"]["output"])
+
+            # # if op['path'] == path_reformulation: # reforulated question
+            # #     try:
+            # #         output_language = op['value']["language"] # str
+            # #         output_query = op["value"]["question"]
+            # #     except Exception as e:
+            # #         raise gr.Error(f"ClimateQ&A Error: {e} - The error has been noted, try another question and if the error remains, you can contact us :)")
+            
+            # # if op["path"] == path_keywords:
+            # #     try:
+            # #         output_keywords = op['value']["keywords"] # str
+            # #         output_keywords = " AND ".join(output_keywords)
+            # #     except Exception as e:
+            # #         pass
 
 
 
@@ -220,6 +262,7 @@ async def chat(query,history,audience,sources,reports):
             yield history,docs_html,output_query,output_language,gallery,output_query,output_keywords
 
     except Exception as e:
+        print(event, "has failed")
         raise gr.Error(f"{e}")
 
 

style.css

@@ -426,7 +426,7 @@ span.chatbot > p > img{
   
   .loader {
     border: 1px solid #d0d0d0 !important; /* Light grey background */
-    border-top: 1px solid #3498db !important; /* Blue color */
+    border-top: 1px solid #db3434 !important; /* Blue color */
     border-right: 1px solid #3498db !important; /* Blue color */
     border-radius: 50%;
     width: 20px;
@@ -464,4 +464,4 @@ span.chatbot > p > img{
   
   .score-orange{
     color:red !important;
-  }
+  }