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https://github.com/arc53/DocsGPT
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Merge pull request #1002 from ManishMadan2882/main
Better Error handling on /stream endpoint
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commit
76ed8f0ba2
@ -9,13 +9,11 @@ import traceback
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from pymongo import MongoClient
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from bson.objectid import ObjectId
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from application.core.settings import settings
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from application.llm.llm_creator import LLMCreator
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from application.retriever.retriever_creator import RetrieverCreator
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from application.error import bad_request
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logger = logging.getLogger(__name__)
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mongo = MongoClient(settings.MONGO_URI)
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@ -75,8 +73,10 @@ def run_async_chain(chain, question, chat_history):
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def get_data_from_api_key(api_key):
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data = api_key_collection.find_one({"key": api_key})
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# # Raise custom exception if the API key is not found
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if data is None:
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return bad_request(401, "Invalid API key")
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raise Exception("Invalid API Key, please generate new key", 401)
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return data
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@ -128,10 +128,10 @@ def save_conversation(conversation_id, question, response, source_log_docs, llm)
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"content": "Summarise following conversation in no more than 3 "
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"words, respond ONLY with the summary, use the same "
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"language as the system \n\nUser: "
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+ question
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+ "\n\n"
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+ "AI: "
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+ response,
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+question
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+"\n\n"
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+"AI: "
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+response,
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},
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{
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"role": "user",
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@ -173,33 +173,39 @@ def get_prompt(prompt_id):
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def complete_stream(question, retriever, conversation_id, user_api_key):
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response_full = ""
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source_log_docs = []
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answer = retriever.gen()
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for line in answer:
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if "answer" in line:
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response_full += str(line["answer"])
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data = json.dumps(line)
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yield f"data: {data}\n\n"
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elif "source" in line:
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source_log_docs.append(line["source"])
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llm = LLMCreator.create_llm(
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settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
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)
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conversation_id = save_conversation(
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conversation_id, question, response_full, source_log_docs, llm
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)
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# send data.type = "end" to indicate that the stream has ended as json
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data = json.dumps({"type": "id", "id": str(conversation_id)})
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yield f"data: {data}\n\n"
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data = json.dumps({"type": "end"})
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yield f"data: {data}\n\n"
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try:
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response_full = ""
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source_log_docs = []
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answer = retriever.gen()
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for line in answer:
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if "answer" in line:
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response_full += str(line["answer"])
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data = json.dumps(line)
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yield f"data: {data}\n\n"
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elif "source" in line:
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source_log_docs.append(line["source"])
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llm = LLMCreator.create_llm(
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settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
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)
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conversation_id = save_conversation(
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conversation_id, question, response_full, source_log_docs, llm
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)
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# send data.type = "end" to indicate that the stream has ended as json
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data = json.dumps({"type": "id", "id": str(conversation_id)})
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yield f"data: {data}\n\n"
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data = json.dumps({"type": "end"})
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yield f"data: {data}\n\n"
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except Exception as e:
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data = json.dumps({"type": "error","error":"Please try again later. We apologize for any inconvenience.",
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"error_exception": str(e)})
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yield f"data: {data}\n\n"
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return
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@answer.route("/stream", methods=["POST"])
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def stream():
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try:
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data = request.get_json()
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# get parameter from url question
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question = data["question"]
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@ -273,7 +279,29 @@ def stream():
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),
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mimetype="text/event-stream",
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)
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except ValueError:
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message = "Malformed request body"
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return Response(
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error_stream_generate(message),
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status=400,
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mimetype="text/event-stream",
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)
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except Exception as e:
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print("err",str(e))
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message = e.args[0]
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status_code = 400
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# # Custom exceptions with two arguments, index 1 as status code
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if(len(e.args) >= 2):
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status_code = e.args[1]
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return Response(
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error_stream_generate(message),
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status=status_code,
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mimetype="text/event-stream",
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)
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def error_stream_generate(err_response):
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data = json.dumps({"type": "error", "error":err_response})
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yield f"data: {data}\n\n"
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@answer.route("/api/answer", methods=["POST"])
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def api_answer():
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@ -257,8 +257,8 @@ def combined_json():
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}
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]
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# structure: name, language, version, description, fullName, date, docLink
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# append data from vectors_collection
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for index in vectors_collection.find({"user": user}):
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# append data from vectors_collection in sorted order in descending order of date
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for index in vectors_collection.find({"user": user}).sort("date", -1):
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data.append(
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{
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"name": index["name"],
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@ -68,6 +68,15 @@ export const fetchAnswer = createAsyncThunk<Answer, { question: string }>(
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query: { conversationId: data.id },
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}),
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);
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} else if (data.type === 'error') {
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// set status to 'failed'
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dispatch(conversationSlice.actions.setStatus('failed'));
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dispatch(
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conversationSlice.actions.raiseError({
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index: state.conversation.queries.length - 1,
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message: data.error,
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}),
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);
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} else {
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const result = data.answer;
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dispatch(
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@ -191,6 +200,13 @@ export const conversationSlice = createSlice({
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setStatus(state, action: PayloadAction<Status>) {
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state.status = action.payload;
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},
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raiseError(
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state,
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action: PayloadAction<{ index: number; message: string }>,
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) {
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const { index, message } = action.payload;
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state.queries[index].error = message;
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},
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},
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extraReducers(builder) {
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builder
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@ -204,7 +220,7 @@ export const conversationSlice = createSlice({
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}
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state.status = 'failed';
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state.queries[state.queries.length - 1].error =
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'Something went wrong. Please try again later.';
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'Something went wrong. Please check your internet connection.';
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});
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},
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});
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