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Alex
@@ -1,43 +1,24 @@
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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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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 celery.result import AsyncResult
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from pymongo import MongoClient
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from bson.objectid import ObjectId
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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.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.llm.openai import OpenAILLM, AzureOpenAILLM
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from application.vectorstore.faiss import FaissStore
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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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@@ -125,21 +106,21 @@ def get_vectorstore(data):
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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 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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@@ -148,7 +129,7 @@ def is_azure_configured():
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def complete_stream(question, docsearch, chat_history, api_key, conversation_id):
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if is_azure_configured():
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llm = AzureChatOpenAI(
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llm = AzureOpenAILLM(
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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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@@ -158,7 +139,7 @@ def complete_stream(question, docsearch, chat_history, api_key, conversation_id)
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logger.debug("plain OpenAI")
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llm = OpenAILLM(api_key=api_key)
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docs = docsearch.similarity_search(question, k=2)
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docs = docsearch.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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p_chat_combine = chat_combine_template.replace("{summaries}", docs_together)
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@@ -253,9 +234,8 @@ def stream():
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vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
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else:
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vectorstore = ""
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docsearch = get_docsearch(vectorstore, embeddings_key)
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docsearch = FaissStore(vectorstore, embeddings_key)
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# question = "Hi"
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return Response(
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complete_stream(question, docsearch,
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chat_history=history, api_key=api_key,
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@@ -288,97 +268,55 @@ def api_answer():
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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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docsearch = FaissStore(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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if is_azure_configured():
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llm = AzureOpenAILLM(
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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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raise ValueError("unknown LLM model")
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logger.debug("plain OpenAI")
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llm = OpenAILLM(api_key=api_key)
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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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docs = docsearch.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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p_chat_combine = chat_combine_template.replace("{summaries}", docs_together)
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messages_combine = [{"role": "system", "content": p_chat_combine}]
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source_log_docs = []
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for doc in docs:
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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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source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "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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source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
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# join all page_content together with a newline
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if len(history) > 1:
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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({"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 = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
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messages=messages_combine)
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result = {"answer": completion, "sources": source_log_docs}
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logger.debug(result)
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# generate conversationId
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if conversation_id is not None:
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@@ -391,22 +329,22 @@ def api_answer():
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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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messages_summary = [{"role": "assistant", "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\n" +
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"AI: " +
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result["answer"]},
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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.predict_messages(messages_summary)
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completion = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
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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.content,
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"queries": [{"prompt": question, "response": result["answer"], "sources": result['sources']}]}
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"date": datetime.datetime.utcnow(),
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"name": completion,
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"queries": [{"prompt": question, "response": result["answer"], "sources": source_log_docs}]}
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).inserted_id
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result["conversation_id"] = str(conversation_id)
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