import asyncio import os from flask import Blueprint, request, Response import json import datetime import logging import traceback from pymongo import MongoClient from bson.objectid import ObjectId from transformers import GPT2TokenizerFast from application.core.settings import settings from application.vectorstore.vector_creator import VectorCreator from application.llm.llm_creator import LLMCreator from application.error import bad_request logger = logging.getLogger(__name__) mongo = MongoClient(settings.MONGO_URI) db = mongo["docsgpt"] conversations_collection = db["conversations"] vectors_collection = db["vectors"] answer = Blueprint('answer', __name__) if settings.LLM_NAME == "gpt4": gpt_model = 'gpt-4' elif settings.LLM_NAME == "anthropic": gpt_model = 'claude-2' else: gpt_model = 'gpt-3.5-turbo' # load the prompts current_dir = os.path.dirname(os.path.dirname(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 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 def is_azure_configured(): return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME def complete_stream(question, docsearch, chat_history, api_key, conversation_id): llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key) docs = docsearch.search(question, k=2) if settings.LLM_NAME == "llama.cpp": docs = [docs[0]] # join all page_content together with a newline docs_together = "\n".join([doc.page_content for doc in docs]) p_chat_combine = chat_combine_template.replace("{summaries}", docs_together) messages_combine = [{"role": "system", "content": p_chat_combine}] source_log_docs = [] for doc in docs: if doc.metadata: data = json.dumps({"type": "source", "doc": doc.page_content, "metadata": doc.metadata}) source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content}) else: data = json.dumps({"type": "source", "doc": doc.page_content}) source_log_docs.append({"title": doc.page_content, "text": doc.page_content}) yield f"data:{data}\n\n" if len(chat_history) > 1: tokens_current_history = 0 # count tokens in history chat_history.reverse() for i in chat_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({"role": "user", "content": i["prompt"]}) messages_combine.append({"role": "system", "content": i["response"]}) messages_combine.append({"role": "user", "content": question}) response_full = "" completion = llm.gen_stream(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME, messages=messages_combine) for line in completion: data = json.dumps({"answer": str(line)}) response_full += str(line) yield f"data: {data}\n\n" # save conversation to database if conversation_id is not None: conversations_collection.update_one( {"_id": ObjectId(conversation_id)}, {"$push": {"queries": {"prompt": question, "response": response_full, "sources": source_log_docs}}}, ) else: # create new conversation # generate summary messages_summary = [{"role": "assistant", "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\n" + "AI: " + response_full}, {"role": "user", "content": "Summarise following conversation in no more than 3 words, " "respond ONLY with the summary, use the same language as the " "system"}] completion = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME, messages=messages_summary, max_tokens=30) conversation_id = conversations_collection.insert_one( {"user": "local", "date": datetime.datetime.utcnow(), "name": completion, "queries": [{"prompt": question, "response": response_full, "sources": source_log_docs}]} ).inserted_id # send data.type = "end" to indicate that the stream has ended as json data = json.dumps({"type": "id", "id": str(conversation_id)}) yield f"data: {data}\n\n" data = json.dumps({"type": "end"}) yield f"data: {data}\n\n" @answer.route("/stream", methods=["POST"]) def stream(): data = request.get_json() # get parameter from url question question = data["question"] history = data["history"] # history to json object from string history = json.loads(history) conversation_id = data["conversation_id"] # check if active_docs is set 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 if "active_docs" in data: vectorstore = get_vectorstore({"active_docs": data["active_docs"]}) else: vectorstore = "" docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, embeddings_key) return Response( complete_stream(question, docsearch, chat_history=history, api_key=api_key, conversation_id=conversation_id), mimetype="text/event-stream" ) @answer.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 = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, embeddings_key) llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key) docs = docsearch.search(question, k=2) # join all page_content together with a newline docs_together = "\n".join([doc.page_content for doc in docs]) p_chat_combine = chat_combine_template.replace("{summaries}", docs_together) messages_combine = [{"role": "system", "content": p_chat_combine}] source_log_docs = [] for doc in docs: if doc.metadata: source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content}) else: source_log_docs.append({"title": doc.page_content, "text": doc.page_content}) # join all page_content together with a newline if len(history) > 1: 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({"role": "user", "content": i["prompt"]}) messages_combine.append({"role": "system", "content": i["response"]}) messages_combine.append({"role": "user", "content": question}) completion = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME, messages=messages_combine) result = {"answer": completion, "sources": source_log_docs} logger.debug(result) # 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 = [ {"role": "assistant", "content": "Summarise following conversation in no more than 3 words, " "respond ONLY with the summary, use the same language as the system \n\n" "User: " + question + "\n\n" + "AI: " + result["answer"]}, {"role": "user", "content": "Summarise following conversation in no more than 3 words, " "respond ONLY with the summary, use the same language as the system"} ] completion = llm.gen( model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME, messages=messages_summary, max_tokens=30 ) conversation_id = conversations_collection.insert_one( {"user": "local", "date": datetime.datetime.utcnow(), "name": completion, "queries": [{"prompt": question, "response": result["answer"], "sources": source_log_docs}]} ).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))