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390 lines
11 KiB
Python
390 lines
11 KiB
Python
import asyncio
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import os
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from flask import Blueprint, request, Response
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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 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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vectors_collection = db["vectors"]
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prompts_collection = db["prompts"]
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api_key_collection = db["api_keys"]
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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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if data is None:
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return bad_request(401, "Invalid API key")
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return data
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def get_vectorstore(data):
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if "active_docs" in data:
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if data["active_docs"].split("/")[0] == "default":
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vectorstore = ""
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elif data["active_docs"].split("/")[0] == "local":
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vectorstore = "indexes/" + data["active_docs"]
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else:
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vectorstore = "vectors/" + data["active_docs"]
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if data["active_docs"] == "default":
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vectorstore = ""
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else:
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vectorstore = ""
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vectorstore = os.path.join("application", vectorstore)
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return vectorstore
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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(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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@answer.route("/stream", methods=["POST"])
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def stream():
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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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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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prompt = get_prompt(prompt_id)
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# check if active_docs 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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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 (
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source["active_docs"].split("/")[0] == "default"
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or source["active_docs"].split("/")[0] == "local"
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):
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retriever_name = "classic"
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else:
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retriever_name = source["active_docs"]
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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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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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),
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mimetype="text/event-stream",
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)
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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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prompt = get_prompt(prompt_id)
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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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source = {"active_docs": data_key["source"]}
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user_api_key = data["api_key"]
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else:
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source = {data}
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user_api_key = None
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if (
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source["active_docs"].split("/")[0] == "default"
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or source["active_docs"].split("/")[0] == "local"
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):
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retriever_name = "classic"
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else:
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retriever_name = source["active_docs"]
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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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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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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"] = save_conversation(
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conversation_id, question, response_full, source_log_docs, llm
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)
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return result
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except Exception as e:
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# print whole traceback
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traceback.print_exc()
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print(str(e))
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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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# get parameter from url question
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question = data["question"]
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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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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 "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 (
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source["active_docs"].split("/")[0] == "default"
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or source["active_docs"].split("/")[0] == "local"
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):
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retriever_name = "classic"
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else:
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retriever_name = source["active_docs"]
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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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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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return docs
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