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https://github.com/arc53/DocsGPT.git
synced 2025-11-29 16:43:16 +00:00
fixes to make it work
This commit is contained in:
Alex
@@ -1,69 +1,18 @@
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import asyncio
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import datetime
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import json
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import logging
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import os
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import platform
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import traceback
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import dotenv
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import openai
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import requests
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from celery import Celery
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from celery.result import AsyncResult
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from flask import Flask, request, send_from_directory, jsonify, Response, redirect
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from langchain import FAISS
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from langchain import VectorDBQA, Cohere, OpenAI
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from langchain.chains import LLMChain, ConversationalRetrievalChain
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from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
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from langchain.chains.question_answering import load_qa_chain
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from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
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from langchain.embeddings import (
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OpenAIEmbeddings,
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HuggingFaceHubEmbeddings,
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CohereEmbeddings,
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HuggingFaceInstructEmbeddings,
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)
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from langchain.prompts import PromptTemplate
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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SystemMessagePromptTemplate,
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HumanMessagePromptTemplate,
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AIMessagePromptTemplate,
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)
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from langchain.schema import HumanMessage, AIMessage
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from flask import Flask, request, redirect
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from pymongo import MongoClient
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from werkzeug.utils import secure_filename
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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.worker import ingest_worker
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from bson.objectid import ObjectId
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from application.api.user.routes import user
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from application.api.answer.routes import answer
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from transformers import GPT2TokenizerFast
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# os.environ["LANGCHAIN_HANDLER"] = "langchain"
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logger = logging.getLogger(__name__)
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if settings.LLM_NAME == "gpt4":
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gpt_model = 'gpt-4'
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else:
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gpt_model = 'gpt-3.5-turbo'
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if settings.SELF_HOSTED_MODEL:
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from langchain.llms import HuggingFacePipeline
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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model_id = settings.LLM_NAME # hf model id (Arc53/docsgpt-7b-falcon, Arc53/docsgpt-14b)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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pipe = pipeline(
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"text-generation", model=model,
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tokenizer=tokenizer, max_new_tokens=2000,
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device_map="auto", eos_token_id=tokenizer.eos_token_id
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)
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hf = HuggingFacePipeline(pipeline=pipe)
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# Redirect PosixPath to WindowsPath on Windows
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@@ -76,25 +25,7 @@ if platform.system() == "Windows":
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# loading the .env file
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dotenv.load_dotenv()
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# load the prompts
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current_dir = os.path.dirname(os.path.abspath(__file__))
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with open(os.path.join(current_dir, "prompts", "combine_prompt.txt"), "r") as f:
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template = f.read()
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with open(os.path.join(current_dir, "prompts", "combine_prompt_hist.txt"), "r") as f:
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template_hist = f.read()
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with open(os.path.join(current_dir, "prompts", "question_prompt.txt"), "r") as f:
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template_quest = f.read()
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with open(os.path.join(current_dir, "prompts", "chat_combine_prompt.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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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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app = Flask(__name__)
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app.register_blueprint(user)
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@@ -105,65 +36,9 @@ app.config["CELERY_RESULT_BACKEND"] = settings.CELERY_RESULT_BACKEND
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app.config["MONGO_URI"] = settings.MONGO_URI
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celery = Celery()
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celery.config_from_object("application.celeryconfig")
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mongo = MongoClient(app.config["MONGO_URI"])
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db = mongo["docsgpt"]
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vectors_collection = db["vectors"]
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conversations_collection = db["conversations"]
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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 count_tokens(string):
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tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
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return len(tokenizer(string)['input_ids'])
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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_vectorstore(data):
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if "active_docs" in data:
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if data["active_docs"].split("/")[0] == "local":
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if data["active_docs"].split("/")[1] == "default":
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vectorstore = ""
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else:
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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 get_docsearch(vectorstore, embeddings_key):
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if settings.EMBEDDINGS_NAME == "openai_text-embedding-ada-002":
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if is_azure_configured():
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os.environ["OPENAI_API_TYPE"] = "azure"
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openai_embeddings = OpenAIEmbeddings(model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME)
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else:
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openai_embeddings = OpenAIEmbeddings(openai_api_key=embeddings_key)
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docsearch = FAISS.load_local(vectorstore, openai_embeddings)
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elif settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
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docsearch = FAISS.load_local(vectorstore, HuggingFaceHubEmbeddings())
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elif settings.EMBEDDINGS_NAME == "huggingface_hkunlp/instructor-large":
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docsearch = FAISS.load_local(vectorstore, HuggingFaceInstructEmbeddings())
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elif settings.EMBEDDINGS_NAME == "cohere_medium":
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docsearch = FAISS.load_local(vectorstore, CohereEmbeddings(cohere_api_key=embeddings_key))
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return docsearch
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@celery.task(bind=True)
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@@ -188,170 +63,8 @@ def home():
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def is_azure_configured():
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return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
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@app.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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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 not api_key_set:
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api_key = data["api_key"]
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else:
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api_key = settings.API_KEY
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if not embeddings_key_set:
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embeddings_key = data["embeddings_key"]
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else:
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embeddings_key = settings.EMBEDDINGS_KEY
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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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vectorstore = get_vectorstore(data)
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# loading the index and the store and the prompt template
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# Note if you have used other embeddings than OpenAI, you need to change the embeddings
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docsearch = get_docsearch(vectorstore, embeddings_key)
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q_prompt = PromptTemplate(
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input_variables=["context", "question"], template=template_quest, template_format="jinja2"
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)
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if settings.LLM_NAME == "openai_chat":
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if is_azure_configured():
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logger.debug("in Azure")
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llm = AzureChatOpenAI(
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openai_api_key=api_key,
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openai_api_base=settings.OPENAI_API_BASE,
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openai_api_version=settings.OPENAI_API_VERSION,
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deployment_name=settings.AZURE_DEPLOYMENT_NAME,
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)
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else:
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logger.debug("plain OpenAI")
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llm = ChatOpenAI(openai_api_key=api_key, model_name=gpt_model) # optional parameter: model_name="gpt-4"
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messages_combine = [SystemMessagePromptTemplate.from_template(chat_combine_template)]
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if history:
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tokens_current_history = 0
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# count tokens in history
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history.reverse()
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for i in history:
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if "prompt" in i and "response" in i:
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tokens_batch = count_tokens(i["prompt"]) + count_tokens(i["response"])
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if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
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tokens_current_history += tokens_batch
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messages_combine.append(HumanMessagePromptTemplate.from_template(i["prompt"]))
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messages_combine.append(AIMessagePromptTemplate.from_template(i["response"]))
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messages_combine.append(HumanMessagePromptTemplate.from_template("{question}"))
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p_chat_combine = ChatPromptTemplate.from_messages(messages_combine)
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elif settings.LLM_NAME == "openai":
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llm = OpenAI(openai_api_key=api_key, temperature=0)
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elif settings.SELF_HOSTED_MODEL:
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llm = hf
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elif settings.LLM_NAME == "cohere":
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llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
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else:
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raise ValueError("unknown LLM model")
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if settings.LLM_NAME == "openai_chat":
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question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
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doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
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chain = ConversationalRetrievalChain(
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retriever=docsearch.as_retriever(k=2),
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question_generator=question_generator,
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combine_docs_chain=doc_chain,
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)
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chat_history = []
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# result = chain({"question": question, "chat_history": chat_history})
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# generate async with async generate method
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result = run_async_chain(chain, question, chat_history)
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elif settings.SELF_HOSTED_MODEL:
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question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
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doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
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chain = ConversationalRetrievalChain(
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retriever=docsearch.as_retriever(k=2),
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question_generator=question_generator,
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combine_docs_chain=doc_chain,
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)
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chat_history = []
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# result = chain({"question": question, "chat_history": chat_history})
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# generate async with async generate method
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result = run_async_chain(chain, question, chat_history)
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else:
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qa_chain = load_qa_chain(
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llm=llm, chain_type="map_reduce", combine_prompt=chat_combine_template, question_prompt=q_prompt
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)
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chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=3)
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result = chain({"query": question})
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print(result)
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# some formatting for the frontend
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if "result" in result:
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result["answer"] = result["result"]
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result["answer"] = result["answer"].replace("\\n", "\n")
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try:
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result["answer"] = result["answer"].split("SOURCES:")[0]
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except Exception:
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pass
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sources = docsearch.similarity_search(question, k=2)
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sources_doc = []
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for doc in sources:
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if doc.metadata:
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sources_doc.append({'title': doc.metadata['title'], 'text': doc.page_content})
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else:
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sources_doc.append({'title': doc.page_content, 'text': doc.page_content})
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result['sources'] = sources_doc
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# generate conversationId
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if conversation_id is not None:
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conversations_collection.update_one(
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{"_id": ObjectId(conversation_id)},
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{"$push": {"queries": {"prompt": question,
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"response": result["answer"], "sources": result['sources']}}},
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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 = [AIMessage(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: " + question + "\n\nAI: " +
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result["answer"]),
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HumanMessage(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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# completion = openai.ChatCompletion.create(model='gpt-3.5-turbo', engine=settings.AZURE_DEPLOYMENT_NAME,
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# messages=messages_summary, max_tokens=30, temperature=0)
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completion = llm.predict_messages(messages_summary)
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conversation_id = conversations_collection.insert_one(
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{"user": "local",
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"date": datetime.datetime.utcnow(),
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"name": completion.content,
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"queries": [{"prompt": question, "response": result["answer"], "sources": result['sources']}]}
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).inserted_id
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result["conversation_id"] = str(conversation_id)
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# mock result
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# result = {
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# "answer": "The answer is 42",
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# "sources": ["https://en.wikipedia.org/wiki/42_(number)", "https://en.wikipedia.org/wiki/42_(number)"]
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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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# handling CORS
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@app.after_request
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