mirror of
https://github.com/arc53/DocsGPT.git
synced 2025-11-29 16:43:16 +00:00
650 lines
23 KiB
Python
650 lines
23 KiB
Python
import asyncio
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import datetime
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import json
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import os
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import traceback
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import logging
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from bson.dbref import DBRef
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from bson.objectid import ObjectId
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from flask import Blueprint, make_response, request, Response
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from flask_restx import fields, Namespace, Resource
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from application.core.mongo_db import MongoDB
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from application.core.settings import settings
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from application.error import bad_request
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from application.extensions import api
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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.utils import check_required_fields, limit_chat_history
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logger = logging.getLogger(__name__)
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mongo = MongoDB.get_client()
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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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answer_ns = Namespace("answer", description="Answer related operations", path="/")
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api.add_namespace(answer_ns)
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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-4o-mini"
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elif settings.LLM_NAME == "anthropic":
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gpt_model = "claude-2"
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elif settings.LLM_NAME == "groq":
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gpt_model = "llama3-8b-8192"
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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,index=None):
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if conversation_id is not None and index is not None:
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conversations_collection.update_one(
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{"_id": ObjectId(conversation_id), f"queries.{index}": {"$exists": True}},
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{
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"$set": {
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f"queries.{index}.prompt": question,
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f"queries.{index}.response": response,
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f"queries.{index}.sources": source_log_docs,
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}
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}
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)
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##remove following queries from the array
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conversations_collection.update_one(
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{"_id": ObjectId(conversation_id), f"queries.{index}": {"$exists": True}},
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{
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"$push":{
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"queries":{
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"$each":[],
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"$slice":index+1
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}
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}
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}
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)
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elif 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",
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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 \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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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,index=None
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):
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try:
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import sys
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try:
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logger.info(f"Stream question, inside complete_stream: {question}")
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except Exception as e:
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print(f"Error in logging: {str(e)}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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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,index
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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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logger.error(f"Error in stream: {str(e)}")
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logger.error(traceback.format_exc())
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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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}
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)
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yield f"data: {data}\n\n"
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return
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@answer_ns.route("/stream")
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class Stream(Resource):
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stream_model = api.model(
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"StreamModel",
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{
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"question": fields.String(
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required=True, description="Question to be asked"
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),
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"history": fields.List(
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fields.String, required=False, description="Chat history"
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),
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"conversation_id": fields.String(
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required=False, description="Conversation ID"
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),
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"prompt_id": fields.String(
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required=False, default="default", description="Prompt ID"
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),
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"chunks": fields.Integer(
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required=False, default=2, description="Number of chunks"
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),
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"token_limit": fields.Integer(required=False, description="Token limit"),
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"retriever": fields.String(required=False, description="Retriever type"),
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"api_key": fields.String(required=False, description="API key"),
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"active_docs": fields.String(
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required=False, description="Active documents"
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),
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"isNoneDoc": fields.Boolean(
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required=False, description="Flag indicating if no document is used"
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),
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"index":fields.Integer(
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required=False, description="The position where query is to be updated"
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),
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},
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)
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@api.expect(stream_model)
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@api.doc(description="Stream a response based on the question and retriever")
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def post(self):
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data = request.get_json()
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required_fields = ["question"]
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if "index" in data:
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required_fields = ["question","conversation_id"]
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missing_fields = check_required_fields(data, required_fields)
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if missing_fields:
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return missing_fields
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try:
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question = data["question"]
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history = limit_chat_history(json.loads(data.get("history", [])), gpt_model=gpt_model)
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conversation_id = data.get("conversation_id")
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prompt_id = data.get("prompt_id", "default")
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index=data.get("index",None)
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chunks = int(data.get("chunks", 2))
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token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
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retriever_name = data.get("retriever", "classic")
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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.get("chunks", 2))
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prompt_id = data_key.get("prompt_id", "default")
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source = {"active_docs": data_key.get("source")}
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retriever_name = data_key.get("retriever", 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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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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if "isNoneDoc" in data and data["isNoneDoc"] is True:
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chunks = 0
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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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index=index,
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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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logger.error(f"/stream - error: {message}")
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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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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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status_code = 400
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return Response(
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error_stream_generate('Unknown error occurred'),
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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_ns.route("/api/answer")
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class Answer(Resource):
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answer_model = api.model(
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"AnswerModel",
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{
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"question": fields.String(
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required=True, description="The question to answer"
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),
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"history": fields.List(
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fields.String, required=False, description="Conversation history"
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),
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"conversation_id": fields.String(
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required=False, description="Conversation ID"
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),
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"prompt_id": fields.String(
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required=False, default="default", description="Prompt ID"
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),
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"chunks": fields.Integer(
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required=False, default=2, description="Number of chunks"
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),
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"token_limit": fields.Integer(required=False, description="Token limit"),
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"retriever": fields.String(required=False, description="Retriever type"),
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"api_key": fields.String(required=False, description="API key"),
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"active_docs": fields.String(
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required=False, description="Active documents"
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),
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"isNoneDoc": fields.Boolean(
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required=False, description="Flag indicating if no document is used"
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),
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},
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)
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@api.expect(answer_model)
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@api.doc(description="Provide an answer based on the question and retriever")
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def post(self):
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data = request.get_json()
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required_fields = ["question"]
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missing_fields = check_required_fields(data, required_fields)
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if missing_fields:
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return missing_fields
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try:
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question = data["question"]
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history = limit_chat_history(json.loads(data.get("history", [])), gpt_model=gpt_model)
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conversation_id = data.get("conversation_id")
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prompt_id = data.get("prompt_id", "default")
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chunks = int(data.get("chunks", 2))
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token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
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retriever_name = data.get("retriever", "classic")
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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.get("chunks", 2))
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prompt_id = data_key.get("prompt_id", "default")
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source = {"active_docs": data_key.get("source")}
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retriever_name = data_key.get("retriever", 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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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
|
|
)
|
|
|
|
result = {"answer": response_full, "sources": source_log_docs}
|
|
result["conversation_id"] = str(
|
|
save_conversation(
|
|
conversation_id, question, response_full, source_log_docs, llm
|
|
)
|
|
)
|
|
retriever_params = retriever.get_params()
|
|
user_logs_collection.insert_one(
|
|
{
|
|
"action": "api_answer",
|
|
"level": "info",
|
|
"user": "local",
|
|
"api_key": user_api_key,
|
|
"question": question,
|
|
"response": response_full,
|
|
"sources": source_log_docs,
|
|
"retriever_params": retriever_params,
|
|
"timestamp": datetime.datetime.now(datetime.timezone.utc),
|
|
}
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(
|
|
f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
|
|
extra={"error": str(e), "traceback": traceback.format_exc()},
|
|
)
|
|
return bad_request(500, str(e))
|
|
|
|
return make_response(result, 200)
|
|
|
|
|
|
@answer_ns.route("/api/search")
|
|
class Search(Resource):
|
|
search_model = api.model(
|
|
"SearchModel",
|
|
{
|
|
"question": fields.String(
|
|
required=True, description="The question to search"
|
|
),
|
|
"chunks": fields.Integer(
|
|
required=False, default=2, description="Number of chunks"
|
|
),
|
|
"api_key": fields.String(
|
|
required=False, description="API key for authentication"
|
|
),
|
|
"active_docs": fields.String(
|
|
required=False, description="Active documents for retrieval"
|
|
),
|
|
"retriever": fields.String(required=False, description="Retriever type"),
|
|
"token_limit": fields.Integer(
|
|
required=False, description="Limit for tokens"
|
|
),
|
|
"isNoneDoc": fields.Boolean(
|
|
required=False, description="Flag indicating if no document is used"
|
|
),
|
|
},
|
|
)
|
|
|
|
@api.expect(search_model)
|
|
@api.doc(
|
|
description="Search for relevant documents based on the question and retriever"
|
|
)
|
|
def post(self):
|
|
data = request.get_json()
|
|
required_fields = ["question"]
|
|
missing_fields = check_required_fields(data, required_fields)
|
|
if missing_fields:
|
|
return missing_fields
|
|
|
|
try:
|
|
question = data["question"]
|
|
chunks = int(data.get("chunks", 2))
|
|
token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
|
|
retriever_name = data.get("retriever", "classic")
|
|
|
|
if "api_key" in data:
|
|
data_key = get_data_from_api_key(data["api_key"])
|
|
chunks = int(data_key.get("chunks", 2))
|
|
source = {"active_docs": data_key.get("source")}
|
|
user_api_key = data["api_key"]
|
|
elif "active_docs" in data:
|
|
source = {"active_docs": data["active_docs"]}
|
|
user_api_key = None
|
|
else:
|
|
source = {}
|
|
user_api_key = None
|
|
|
|
logger.info(
|
|
f"/api/answer - request_data: {data}, source: {source}",
|
|
extra={"data": json.dumps({"request_data": data, "source": source})},
|
|
)
|
|
|
|
retriever = RetrieverCreator.create_retriever(
|
|
retriever_name,
|
|
question=question,
|
|
source=source,
|
|
chat_history=[],
|
|
prompt="default",
|
|
chunks=chunks,
|
|
token_limit=token_limit,
|
|
gpt_model=gpt_model,
|
|
user_api_key=user_api_key,
|
|
)
|
|
|
|
docs = retriever.search()
|
|
retriever_params = retriever.get_params()
|
|
|
|
user_logs_collection.insert_one(
|
|
{
|
|
"action": "api_search",
|
|
"level": "info",
|
|
"user": "local",
|
|
"api_key": user_api_key,
|
|
"question": question,
|
|
"sources": docs,
|
|
"retriever_params": retriever_params,
|
|
"timestamp": datetime.datetime.now(datetime.timezone.utc),
|
|
}
|
|
)
|
|
|
|
if data.get("isNoneDoc"):
|
|
for doc in docs:
|
|
doc["source"] = "None"
|
|
|
|
except Exception as e:
|
|
logger.error(
|
|
f"/api/search - error: {str(e)} - traceback: {traceback.format_exc()}",
|
|
extra={"error": str(e), "traceback": traceback.format_exc()},
|
|
)
|
|
return bad_request(500, str(e))
|
|
|
|
return make_response(docs, 200)
|