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https://github.com/arc53/DocsGPT.git
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533 lines
17 KiB
Python
533 lines
17 KiB
Python
import asyncio
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import os
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import sys
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from flask import Blueprint, request, Response, current_app
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import json
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import datetime
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import logging
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import traceback
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from pymongo import MongoClient
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from bson.objectid import ObjectId
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from bson.dbref import DBRef
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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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db = mongo["docsgpt"]
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conversations_collection = db["conversations"]
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sources_collection = db["sources"]
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prompts_collection = db["prompts"]
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api_key_collection = db["api_keys"]
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user_logs_collection = db["user_logs"]
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answer = Blueprint("answer", __name__)
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gpt_model = ""
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# to have some kind of default behaviour
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if settings.LLM_NAME == "openai":
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gpt_model = "gpt-3.5-turbo"
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elif settings.LLM_NAME == "anthropic":
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gpt_model = "claude-2"
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if settings.MODEL_NAME: # in case there is particular model name configured
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gpt_model = settings.MODEL_NAME
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# load the prompts
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current_dir = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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with open(os.path.join(current_dir, "prompts", "chat_combine_default.txt"), "r") as f:
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chat_combine_template = f.read()
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with open(os.path.join(current_dir, "prompts", "chat_reduce_prompt.txt"), "r") as f:
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chat_reduce_template = f.read()
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with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r") as f:
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chat_combine_creative = f.read()
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with open(os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r") as f:
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chat_combine_strict = f.read()
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api_key_set = settings.API_KEY is not None
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embeddings_key_set = settings.EMBEDDINGS_KEY is not None
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async def async_generate(chain, question, chat_history):
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result = await chain.arun({"question": question, "chat_history": chat_history})
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return result
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def run_async_chain(chain, question, chat_history):
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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result = {}
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try:
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answer = loop.run_until_complete(async_generate(chain, question, chat_history))
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finally:
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loop.close()
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result["answer"] = answer
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return result
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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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raise Exception("Invalid API Key, please generate new key", 401)
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if "retriever" not in data:
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data["retriever"] = None
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if "source" in data and isinstance(data["source"], DBRef):
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source_doc = db.dereference(data["source"])
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data["source"] = str(source_doc["_id"])
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if "retriever" in source_doc:
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data["retriever"] = source_doc["retriever"]
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else:
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data["source"] = {}
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return data
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def get_retriever(source_id: str):
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doc = sources_collection.find_one({"_id": ObjectId(source_id)})
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if doc is None:
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raise Exception("Source document does not exist", 404)
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retriever_name = None if "retriever" not in doc else doc["retriever"]
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return retriever_name
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def is_azure_configured():
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return (
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settings.OPENAI_API_BASE
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and settings.OPENAI_API_VERSION
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and settings.AZURE_DEPLOYMENT_NAME
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)
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def save_conversation(conversation_id, question, response, source_log_docs, llm):
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if conversation_id is not None and conversation_id != "None":
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conversations_collection.update_one(
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{"_id": ObjectId(conversation_id)},
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{
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"$push": {
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"queries": {
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"prompt": question,
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"response": response,
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"sources": source_log_docs,
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}
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}
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},
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)
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else:
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# create new conversation
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# generate summary
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messages_summary = [
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{
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"role": "assistant",
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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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},
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{
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"role": "user",
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"content": "Summarise following conversation in no more than 3 words, "
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"respond ONLY with the summary, use the same language as the "
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"system",
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},
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]
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completion = llm.gen(model=gpt_model, messages=messages_summary, max_tokens=30)
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conversation_id = conversations_collection.insert_one(
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{
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"user": "local",
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"date": datetime.datetime.utcnow(),
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"name": completion,
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"queries": [
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{
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"prompt": question,
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"response": response,
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"sources": source_log_docs,
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}
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],
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}
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).inserted_id
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return conversation_id
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def get_prompt(prompt_id):
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if prompt_id == "default":
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prompt = chat_combine_template
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elif prompt_id == "creative":
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prompt = chat_combine_creative
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elif prompt_id == "strict":
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prompt = chat_combine_strict
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else:
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prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
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return prompt
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def complete_stream(
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question, retriever, conversation_id, user_api_key, isNoneDoc=False
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):
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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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sources = retriever.search()
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for source in sources:
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if("text" in source):
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source["text"] = source["text"][:100].strip()+"..."
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if(len(sources) > 0):
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data = json.dumps({"type":"source","source":sources})
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yield f"data: {data}\n\n"
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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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if isNoneDoc:
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for doc in source_log_docs:
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doc["source"] = "None"
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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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if user_api_key is None:
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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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retriever_params = retriever.get_params()
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user_logs_collection.insert_one(
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{
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"action": "stream_answer",
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"level": "info",
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"user": "local",
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"api_key": user_api_key,
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"question": question,
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"response": response_full,
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"sources": source_log_docs,
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"retriever_params": retriever_params,
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"timestamp": datetime.datetime.now(datetime.timezone.utc),
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}
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)
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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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print("\033[91merr", str(e), file=sys.stderr)
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data = json.dumps(
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{
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"type": "error",
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"error": "Please try again later. We apologize for any inconvenience.",
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"error_exception": str(e),
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}
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)
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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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question = data["question"]
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if "history" not in data:
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history = []
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else:
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history = data["history"]
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history = json.loads(history)
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if "conversation_id" not in data:
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conversation_id = None
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else:
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conversation_id = data["conversation_id"]
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if "prompt_id" in data:
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prompt_id = data["prompt_id"]
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else:
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prompt_id = "default"
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if "selectedDocs" in data and data["selectedDocs"] is None:
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chunks = 0
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elif "chunks" in data:
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chunks = int(data["chunks"])
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else:
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chunks = 2
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if "token_limit" in data:
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token_limit = data["token_limit"]
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else:
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token_limit = settings.DEFAULT_MAX_HISTORY
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## retriever can be "brave_search, duckduck_search or classic"
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retriever_name = data["retriever"] if "retriever" in data else "classic"
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# check if active_docs or api_key is set
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if "api_key" in data:
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data_key = get_data_from_api_key(data["api_key"])
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chunks = int(data_key["chunks"])
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prompt_id = data_key["prompt_id"]
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source = {"active_docs": data_key["source"]}
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retriever_name = data_key["retriever"] or retriever_name
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user_api_key = data["api_key"]
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elif "active_docs" in data:
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source = {"active_docs": data["active_docs"]}
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retriever_name = get_retriever(data["active_docs"]) or retriever_name
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user_api_key = None
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else:
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source = {}
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user_api_key = None
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current_app.logger.info(
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f"/stream - request_data: {data}, source: {source}",
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extra={"data": json.dumps({"request_data": data, "source": source})},
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)
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prompt = get_prompt(prompt_id)
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retriever = RetrieverCreator.create_retriever(
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retriever_name,
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question=question,
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source=source,
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chat_history=history,
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prompt=prompt,
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chunks=chunks,
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token_limit=token_limit,
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gpt_model=gpt_model,
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user_api_key=user_api_key,
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)
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return Response(
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complete_stream(
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question=question,
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retriever=retriever,
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conversation_id=conversation_id,
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user_api_key=user_api_key,
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isNoneDoc=data.get("isNoneDoc"),
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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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print("\033[91merr", str(message), file=sys.stderr)
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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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current_app.logger.error(
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f"/stream - error: {str(e)} - traceback: {traceback.format_exc()}",
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extra={"error": str(e), "traceback": traceback.format_exc()},
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)
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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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data = request.get_json()
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question = data["question"]
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if "history" not in data:
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history = []
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else:
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history = data["history"]
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if "conversation_id" not in data:
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conversation_id = None
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else:
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conversation_id = data["conversation_id"]
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print("-" * 5)
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if "prompt_id" in data:
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prompt_id = data["prompt_id"]
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else:
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prompt_id = "default"
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if "chunks" in data:
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chunks = int(data["chunks"])
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else:
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chunks = 2
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if "token_limit" in data:
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token_limit = data["token_limit"]
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else:
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token_limit = settings.DEFAULT_MAX_HISTORY
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## retriever can be brave_search, duckduck_search or classic
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retriever_name = data["retriever"] if "retriever" in data else "classic"
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# use try and except to check for exception
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try:
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# check if the vectorstore is set
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if "api_key" in data:
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data_key = get_data_from_api_key(data["api_key"])
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chunks = int(data_key["chunks"])
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prompt_id = data_key["prompt_id"]
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source = {"active_docs": data_key["source"]}
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retriever_name = data_key["retriever"] or retriever_name
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user_api_key = data["api_key"]
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elif "active_docs" in data:
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source = {"active_docs": data["active_docs"]}
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retriever_name = get_retriever(data["active_docs"]) or retriever_name
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user_api_key = None
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else:
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source = {}
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user_api_key = None
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prompt = get_prompt(prompt_id)
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current_app.logger.info(
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f"/api/answer - request_data: {data}, source: {source}",
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extra={"data": json.dumps({"request_data": data, "source": source})},
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)
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retriever = RetrieverCreator.create_retriever(
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retriever_name,
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question=question,
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source=source,
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chat_history=history,
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prompt=prompt,
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chunks=chunks,
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token_limit=token_limit,
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gpt_model=gpt_model,
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user_api_key=user_api_key,
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)
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source_log_docs = []
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response_full = ""
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for line in retriever.gen():
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if "source" in line:
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source_log_docs.append(line["source"])
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elif "answer" in line:
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response_full += line["answer"]
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if data.get("isNoneDoc"):
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for doc in source_log_docs:
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doc["source"] = "None"
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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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result = {"answer": response_full, "sources": source_log_docs}
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result["conversation_id"] = str(
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save_conversation(
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conversation_id, question, response_full, source_log_docs, llm
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)
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)
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retriever_params = retriever.get_params()
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user_logs_collection.insert_one(
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{
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"action": "api_answer",
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"level": "info",
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"user": "local",
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"api_key": user_api_key,
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"question": question,
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"response": response_full,
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"sources": source_log_docs,
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"retriever_params": retriever_params,
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"timestamp": datetime.datetime.now(datetime.timezone.utc),
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}
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)
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return result
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except Exception as e:
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current_app.logger.error(
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f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
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extra={"error": str(e), "traceback": traceback.format_exc()},
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)
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return bad_request(500, str(e))
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@answer.route("/api/search", methods=["POST"])
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def api_search():
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data = request.get_json()
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question = data["question"]
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if "chunks" in data:
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chunks = int(data["chunks"])
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else:
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chunks = 2
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if "api_key" in data:
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data_key = get_data_from_api_key(data["api_key"])
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chunks = int(data_key["chunks"])
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source = {"active_docs":data_key["source"]}
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user_api_key = data["api_key"]
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elif "active_docs" in data:
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source = {"active_docs": data["active_docs"]}
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user_api_key = None
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else:
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source = {}
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user_api_key = None
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if "retriever" in data:
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retriever_name = data["retriever"]
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else:
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retriever_name = "classic"
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if "token_limit" in data:
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token_limit = data["token_limit"]
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else:
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token_limit = settings.DEFAULT_MAX_HISTORY
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current_app.logger.info(
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f"/api/answer - request_data: {data}, source: {source}",
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extra={"data": json.dumps({"request_data": data, "source": source})},
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)
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retriever = RetrieverCreator.create_retriever(
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retriever_name,
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question=question,
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source=source,
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chat_history=[],
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prompt="default",
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chunks=chunks,
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token_limit=token_limit,
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gpt_model=gpt_model,
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user_api_key=user_api_key,
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)
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docs = retriever.search()
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retriever_params = retriever.get_params()
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user_logs_collection.insert_one(
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{
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"action": "api_search",
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"level": "info",
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"user": "local",
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"api_key": user_api_key,
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"question": question,
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"sources": docs,
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"retriever_params": retriever_params,
|
|
"timestamp": datetime.datetime.now(datetime.timezone.utc),
|
|
}
|
|
)
|
|
|
|
if data.get("isNoneDoc"):
|
|
for doc in docs:
|
|
doc["source"] = "None"
|
|
|
|
return docs
|