mirror of
https://github.com/arc53/DocsGPT.git
synced 2025-11-29 00:23:17 +00:00
fixes to make it work
This commit is contained in:
Alex
@@ -1,20 +1,44 @@
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import asyncio
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import os
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from flask import Blueprint, request, jsonify, Response
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import requests
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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 celery.result import AsyncResult
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from langchain.chat_models import AzureChatOpenAI
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from pymongo import MongoClient
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from bson.objectid import ObjectId
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from werkzeug.utils import secure_filename
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import http.client
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from transformers import GPT2TokenizerFast
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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 application.app import (logger, count_tokens, chat_combine_template, gpt_model,
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api_key_set, embeddings_key_set, get_docsearch, get_vectorstore)
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from application.core.settings import settings
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from application.llm.openai import OpenAILLM
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from application.core.settings import settings
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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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@@ -22,28 +46,118 @@ conversations_collection = db["conversations"]
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vectors_collection = db["vectors"]
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answer = Blueprint('answer', __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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# load the prompts
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current_dir = os.path.dirname(os.path.dirname(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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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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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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def complete_stream(question, docsearch, chat_history, api_key, conversation_id):
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# openai.api_key = api_key
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if is_azure_configured():
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# logger.debug("in Azure")
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# openai.api_type = "azure"
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# openai.api_version = settings.OPENAI_API_VERSION
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# openai.api_base = settings.OPENAI_API_BASE
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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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llm = OpenAILLM(api_key=api_key)
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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 = OpenAILLM(api_key=api_key)
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# llm = ChatOpenAI(openai_api_key=api_key)
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docs = docsearch.similarity_search(question, k=2)
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# join all page_content together with a newline
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docs_together = "\n".join([doc.page_content for doc in docs])
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@@ -71,33 +185,20 @@ def complete_stream(question, docsearch, chat_history, api_key, conversation_id)
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messages_combine.append({"role": "user", "content": i["prompt"]})
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messages_combine.append({"role": "system", "content": i["response"]})
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messages_combine.append({"role": "user", "content": question})
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# completion = openai.ChatCompletion.create(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
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# messages=messages_combine, stream=True, max_tokens=500, temperature=0)
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import sys
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print(api_key)
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reponse_full = ""
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# for line in completion:
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# if "content" in line["choices"][0]["delta"]:
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# # check if the delta contains content
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# data = json.dumps({"answer": str(line["choices"][0]["delta"]["content"])})
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# reponse_full += str(line["choices"][0]["delta"]["content"])
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# yield f"data: {data}\n\n"
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# reponse_full = ""
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print(llm)
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response_full = ""
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completion = llm.gen_stream(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
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messages=messages_combine)
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messages=messages_combine)
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for line in completion:
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data = json.dumps({"answer": str(line)})
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reponse_full += str(line)
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response_full += str(line)
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yield f"data: {data}\n\n"
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# save conversation to database
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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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{"$push": {"queries": {"prompt": question, "response": reponse_full, "sources": source_log_docs}}},
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{"$push": {"queries": {"prompt": question, "response": response_full, "sources": source_log_docs}}},
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)
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else:
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@@ -107,19 +208,18 @@ def complete_stream(question, docsearch, chat_history, api_key, conversation_id)
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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\n" +
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"AI: " +
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reponse_full},
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response_full},
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{"role": "user", "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.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
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messages=messages_combine, max_tokens=30)
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messages=messages_summary, max_tokens=30)
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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["choices"][0]["message"]["content"],
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"queries": [{"prompt": question, "response": reponse_full, "sources": source_log_docs}]}
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"name": completion,
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"queries": [{"prompt": question, "response": response_full, "sources": source_log_docs}]}
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).inserted_id
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# send data.type = "end" to indicate that the stream has ended as json
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@@ -160,4 +260,165 @@ def stream():
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complete_stream(question, docsearch,
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chat_history=history, api_key=api_key,
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conversation_id=conversation_id), mimetype="text/event-stream"
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)
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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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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']}]}
|
||||
).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:
|
||||
# print whole traceback
|
||||
traceback.print_exc()
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||||
print(str(e))
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||||
return bad_request(500, str(e))
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||||
|
||||
@@ -1,69 +1,18 @@
|
||||
import asyncio
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||||
import datetime
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import platform
|
||||
import traceback
|
||||
|
||||
|
||||
import dotenv
|
||||
import openai
|
||||
import requests
|
||||
from celery import Celery
|
||||
from celery.result import AsyncResult
|
||||
from flask import Flask, request, send_from_directory, jsonify, Response, redirect
|
||||
from langchain import FAISS
|
||||
from langchain import VectorDBQA, Cohere, OpenAI
|
||||
from langchain.chains import LLMChain, ConversationalRetrievalChain
|
||||
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
|
||||
from langchain.chains.question_answering import load_qa_chain
|
||||
from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
|
||||
from langchain.embeddings import (
|
||||
OpenAIEmbeddings,
|
||||
HuggingFaceHubEmbeddings,
|
||||
CohereEmbeddings,
|
||||
HuggingFaceInstructEmbeddings,
|
||||
)
|
||||
from langchain.prompts import PromptTemplate
|
||||
from langchain.prompts.chat import (
|
||||
ChatPromptTemplate,
|
||||
SystemMessagePromptTemplate,
|
||||
HumanMessagePromptTemplate,
|
||||
AIMessagePromptTemplate,
|
||||
)
|
||||
from langchain.schema import HumanMessage, AIMessage
|
||||
from flask import Flask, request, redirect
|
||||
|
||||
from pymongo import MongoClient
|
||||
from werkzeug.utils import secure_filename
|
||||
|
||||
from application.core.settings import settings
|
||||
from application.error import bad_request
|
||||
from application.worker import ingest_worker
|
||||
from bson.objectid import ObjectId
|
||||
from application.api.user.routes import user
|
||||
from application.api.answer.routes import answer
|
||||
from transformers import GPT2TokenizerFast
|
||||
|
||||
# os.environ["LANGCHAIN_HANDLER"] = "langchain"
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
if settings.LLM_NAME == "gpt4":
|
||||
gpt_model = 'gpt-4'
|
||||
else:
|
||||
gpt_model = 'gpt-3.5-turbo'
|
||||
|
||||
if settings.SELF_HOSTED_MODEL:
|
||||
from langchain.llms import HuggingFacePipeline
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
|
||||
|
||||
model_id = settings.LLM_NAME # hf model id (Arc53/docsgpt-7b-falcon, Arc53/docsgpt-14b)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id)
|
||||
pipe = pipeline(
|
||||
"text-generation", model=model,
|
||||
tokenizer=tokenizer, max_new_tokens=2000,
|
||||
device_map="auto", eos_token_id=tokenizer.eos_token_id
|
||||
)
|
||||
hf = HuggingFacePipeline(pipeline=pipe)
|
||||
|
||||
# Redirect PosixPath to WindowsPath on Windows
|
||||
|
||||
@@ -76,25 +25,7 @@ if platform.system() == "Windows":
|
||||
# loading the .env file
|
||||
dotenv.load_dotenv()
|
||||
|
||||
# load the prompts
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
with open(os.path.join(current_dir, "prompts", "combine_prompt.txt"), "r") as f:
|
||||
template = f.read()
|
||||
|
||||
with open(os.path.join(current_dir, "prompts", "combine_prompt_hist.txt"), "r") as f:
|
||||
template_hist = f.read()
|
||||
|
||||
with open(os.path.join(current_dir, "prompts", "question_prompt.txt"), "r") as f:
|
||||
template_quest = f.read()
|
||||
|
||||
with open(os.path.join(current_dir, "prompts", "chat_combine_prompt.txt"), "r") as f:
|
||||
chat_combine_template = f.read()
|
||||
|
||||
with open(os.path.join(current_dir, "prompts", "chat_reduce_prompt.txt"), "r") as f:
|
||||
chat_reduce_template = f.read()
|
||||
|
||||
api_key_set = settings.API_KEY is not None
|
||||
embeddings_key_set = settings.EMBEDDINGS_KEY is not None
|
||||
|
||||
app = Flask(__name__)
|
||||
app.register_blueprint(user)
|
||||
@@ -105,65 +36,9 @@ app.config["CELERY_RESULT_BACKEND"] = settings.CELERY_RESULT_BACKEND
|
||||
app.config["MONGO_URI"] = settings.MONGO_URI
|
||||
celery = Celery()
|
||||
celery.config_from_object("application.celeryconfig")
|
||||
mongo = MongoClient(app.config["MONGO_URI"])
|
||||
db = mongo["docsgpt"]
|
||||
vectors_collection = db["vectors"]
|
||||
conversations_collection = db["conversations"]
|
||||
|
||||
|
||||
async def async_generate(chain, question, chat_history):
|
||||
result = await chain.arun({"question": question, "chat_history": chat_history})
|
||||
return result
|
||||
|
||||
def count_tokens(string):
|
||||
|
||||
tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
|
||||
return len(tokenizer(string)['input_ids'])
|
||||
|
||||
def run_async_chain(chain, question, chat_history):
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
result = {}
|
||||
try:
|
||||
answer = loop.run_until_complete(async_generate(chain, question, chat_history))
|
||||
finally:
|
||||
loop.close()
|
||||
result["answer"] = answer
|
||||
return result
|
||||
|
||||
|
||||
def get_vectorstore(data):
|
||||
if "active_docs" in data:
|
||||
if data["active_docs"].split("/")[0] == "local":
|
||||
if data["active_docs"].split("/")[1] == "default":
|
||||
vectorstore = ""
|
||||
else:
|
||||
vectorstore = "indexes/" + data["active_docs"]
|
||||
else:
|
||||
vectorstore = "vectors/" + data["active_docs"]
|
||||
if data["active_docs"] == "default":
|
||||
vectorstore = ""
|
||||
else:
|
||||
vectorstore = ""
|
||||
vectorstore = os.path.join("application", vectorstore)
|
||||
return vectorstore
|
||||
|
||||
|
||||
def get_docsearch(vectorstore, embeddings_key):
|
||||
if settings.EMBEDDINGS_NAME == "openai_text-embedding-ada-002":
|
||||
if is_azure_configured():
|
||||
os.environ["OPENAI_API_TYPE"] = "azure"
|
||||
openai_embeddings = OpenAIEmbeddings(model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME)
|
||||
else:
|
||||
openai_embeddings = OpenAIEmbeddings(openai_api_key=embeddings_key)
|
||||
docsearch = FAISS.load_local(vectorstore, openai_embeddings)
|
||||
elif settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
|
||||
docsearch = FAISS.load_local(vectorstore, HuggingFaceHubEmbeddings())
|
||||
elif settings.EMBEDDINGS_NAME == "huggingface_hkunlp/instructor-large":
|
||||
docsearch = FAISS.load_local(vectorstore, HuggingFaceInstructEmbeddings())
|
||||
elif settings.EMBEDDINGS_NAME == "cohere_medium":
|
||||
docsearch = FAISS.load_local(vectorstore, CohereEmbeddings(cohere_api_key=embeddings_key))
|
||||
return docsearch
|
||||
|
||||
|
||||
@celery.task(bind=True)
|
||||
@@ -188,170 +63,8 @@ def home():
|
||||
|
||||
|
||||
|
||||
def is_azure_configured():
|
||||
return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
|
||||
|
||||
|
||||
@app.route("/api/answer", methods=["POST"])
|
||||
def api_answer():
|
||||
data = request.get_json()
|
||||
question = data["question"]
|
||||
history = data["history"]
|
||||
if "conversation_id" not in data:
|
||||
conversation_id = None
|
||||
else:
|
||||
conversation_id = data["conversation_id"]
|
||||
print("-" * 5)
|
||||
if not api_key_set:
|
||||
api_key = data["api_key"]
|
||||
else:
|
||||
api_key = settings.API_KEY
|
||||
if not embeddings_key_set:
|
||||
embeddings_key = data["embeddings_key"]
|
||||
else:
|
||||
embeddings_key = settings.EMBEDDINGS_KEY
|
||||
|
||||
# use try and except to check for exception
|
||||
try:
|
||||
# check if the vectorstore is set
|
||||
vectorstore = get_vectorstore(data)
|
||||
# loading the index and the store and the prompt template
|
||||
# Note if you have used other embeddings than OpenAI, you need to change the embeddings
|
||||
docsearch = get_docsearch(vectorstore, embeddings_key)
|
||||
|
||||
q_prompt = PromptTemplate(
|
||||
input_variables=["context", "question"], template=template_quest, template_format="jinja2"
|
||||
)
|
||||
if settings.LLM_NAME == "openai_chat":
|
||||
if is_azure_configured():
|
||||
logger.debug("in Azure")
|
||||
llm = AzureChatOpenAI(
|
||||
openai_api_key=api_key,
|
||||
openai_api_base=settings.OPENAI_API_BASE,
|
||||
openai_api_version=settings.OPENAI_API_VERSION,
|
||||
deployment_name=settings.AZURE_DEPLOYMENT_NAME,
|
||||
)
|
||||
else:
|
||||
logger.debug("plain OpenAI")
|
||||
llm = ChatOpenAI(openai_api_key=api_key, model_name=gpt_model) # optional parameter: model_name="gpt-4"
|
||||
messages_combine = [SystemMessagePromptTemplate.from_template(chat_combine_template)]
|
||||
if history:
|
||||
tokens_current_history = 0
|
||||
# count tokens in history
|
||||
history.reverse()
|
||||
for i in history:
|
||||
if "prompt" in i and "response" in i:
|
||||
tokens_batch = count_tokens(i["prompt"]) + count_tokens(i["response"])
|
||||
if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
|
||||
tokens_current_history += tokens_batch
|
||||
messages_combine.append(HumanMessagePromptTemplate.from_template(i["prompt"]))
|
||||
messages_combine.append(AIMessagePromptTemplate.from_template(i["response"]))
|
||||
messages_combine.append(HumanMessagePromptTemplate.from_template("{question}"))
|
||||
p_chat_combine = ChatPromptTemplate.from_messages(messages_combine)
|
||||
elif settings.LLM_NAME == "openai":
|
||||
llm = OpenAI(openai_api_key=api_key, temperature=0)
|
||||
elif settings.SELF_HOSTED_MODEL:
|
||||
llm = hf
|
||||
elif settings.LLM_NAME == "cohere":
|
||||
llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
|
||||
else:
|
||||
raise ValueError("unknown LLM model")
|
||||
|
||||
if settings.LLM_NAME == "openai_chat":
|
||||
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
|
||||
doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
|
||||
chain = ConversationalRetrievalChain(
|
||||
retriever=docsearch.as_retriever(k=2),
|
||||
question_generator=question_generator,
|
||||
combine_docs_chain=doc_chain,
|
||||
)
|
||||
chat_history = []
|
||||
# result = chain({"question": question, "chat_history": chat_history})
|
||||
# generate async with async generate method
|
||||
result = run_async_chain(chain, question, chat_history)
|
||||
elif settings.SELF_HOSTED_MODEL:
|
||||
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
|
||||
doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
|
||||
chain = ConversationalRetrievalChain(
|
||||
retriever=docsearch.as_retriever(k=2),
|
||||
question_generator=question_generator,
|
||||
combine_docs_chain=doc_chain,
|
||||
)
|
||||
chat_history = []
|
||||
# result = chain({"question": question, "chat_history": chat_history})
|
||||
# generate async with async generate method
|
||||
result = run_async_chain(chain, question, chat_history)
|
||||
|
||||
else:
|
||||
qa_chain = load_qa_chain(
|
||||
llm=llm, chain_type="map_reduce", combine_prompt=chat_combine_template, question_prompt=q_prompt
|
||||
)
|
||||
chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=3)
|
||||
result = chain({"query": question})
|
||||
|
||||
print(result)
|
||||
|
||||
# some formatting for the frontend
|
||||
if "result" in result:
|
||||
result["answer"] = result["result"]
|
||||
result["answer"] = result["answer"].replace("\\n", "\n")
|
||||
try:
|
||||
result["answer"] = result["answer"].split("SOURCES:")[0]
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
sources = docsearch.similarity_search(question, k=2)
|
||||
sources_doc = []
|
||||
for doc in sources:
|
||||
if doc.metadata:
|
||||
sources_doc.append({'title': doc.metadata['title'], 'text': doc.page_content})
|
||||
else:
|
||||
sources_doc.append({'title': doc.page_content, 'text': doc.page_content})
|
||||
result['sources'] = sources_doc
|
||||
|
||||
# generate conversationId
|
||||
if conversation_id is not None:
|
||||
conversations_collection.update_one(
|
||||
{"_id": ObjectId(conversation_id)},
|
||||
{"$push": {"queries": {"prompt": question,
|
||||
"response": result["answer"], "sources": result['sources']}}},
|
||||
)
|
||||
|
||||
else:
|
||||
# create new conversation
|
||||
# generate summary
|
||||
messages_summary = [AIMessage(content="Summarise following conversation in no more than 3 " +
|
||||
"words, respond ONLY with the summary, use the same " +
|
||||
"language as the system \n\nUser: " + question + "\n\nAI: " +
|
||||
result["answer"]),
|
||||
HumanMessage(content="Summarise following conversation in no more than 3 words, " +
|
||||
"respond ONLY with the summary, use the same language as the " +
|
||||
"system")]
|
||||
|
||||
# completion = openai.ChatCompletion.create(model='gpt-3.5-turbo', engine=settings.AZURE_DEPLOYMENT_NAME,
|
||||
# messages=messages_summary, max_tokens=30, temperature=0)
|
||||
completion = llm.predict_messages(messages_summary)
|
||||
conversation_id = conversations_collection.insert_one(
|
||||
{"user": "local",
|
||||
"date": datetime.datetime.utcnow(),
|
||||
"name": completion.content,
|
||||
"queries": [{"prompt": question, "response": result["answer"], "sources": result['sources']}]}
|
||||
).inserted_id
|
||||
|
||||
result["conversation_id"] = str(conversation_id)
|
||||
|
||||
# mock result
|
||||
# result = {
|
||||
# "answer": "The answer is 42",
|
||||
# "sources": ["https://en.wikipedia.org/wiki/42_(number)", "https://en.wikipedia.org/wiki/42_(number)"]
|
||||
# }
|
||||
return result
|
||||
except Exception as e:
|
||||
# print whole traceback
|
||||
traceback.print_exc()
|
||||
print(str(e))
|
||||
return bad_request(500, str(e))
|
||||
|
||||
|
||||
# handling CORS
|
||||
@app.after_request
|
||||
|
||||
@@ -15,12 +15,18 @@ class OpenAILLM(BaseLLM):
|
||||
openai.api_key = self.api_key
|
||||
return openai
|
||||
|
||||
def gen(self, *args, **kwargs):
|
||||
# This is just a stub. In the real implementation, you'd hit the OpenAI API or any other service.
|
||||
return "Non-streaming response from OpenAI."
|
||||
def gen(self, model, engine, messages, stream=False, **kwargs):
|
||||
response = openai.ChatCompletion.create(
|
||||
model=model,
|
||||
engine=engine,
|
||||
messages=messages,
|
||||
stream=stream,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
return response["choices"][0]["message"]["content"]
|
||||
|
||||
def gen_stream(self, model, engine, messages, stream=True, **kwargs):
|
||||
# openai = self._get_openai() # Get the openai module with the API key set
|
||||
response = openai.ChatCompletion.create(
|
||||
model=model,
|
||||
engine=engine,
|
||||
@@ -32,3 +38,19 @@ class OpenAILLM(BaseLLM):
|
||||
for line in response:
|
||||
if "content" in line["choices"][0]["delta"]:
|
||||
yield line["choices"][0]["delta"]["content"]
|
||||
|
||||
|
||||
class AzureOpenAILLM(OpenAILLM):
|
||||
|
||||
def __init__(self, openai_api_key, openai_api_base, openai_api_version, deployment_name):
|
||||
super().__init__(openai_api_key)
|
||||
self.api_base = openai_api_base
|
||||
self.api_version = openai_api_version
|
||||
self.deployment_name = deployment_name
|
||||
|
||||
def _get_openai(self):
|
||||
openai = super()._get_openai()
|
||||
openai.api_base = self.api_base
|
||||
openai.api_version = self.api_version
|
||||
openai.api_type = "azure"
|
||||
return openai
|
||||
|
||||
Reference in New Issue
Block a user