Как добавить память в load_qa_chain или как реализовать ConversationalRetrievalChain с помощью специального приглашения с несколькими входами

Question

Я пытаюсь предоставить специальную подсказку для вопросов и ответов в langchain. Мне не удалось сделать это с помощью ConversationalRetrievalChain, поскольку он не позволял вводить несколько пользовательских входных данных в пользовательском приглашении. Следовательно, я использовал load_qa_chain, но с load_qa_chain я не могу использовать память.

Как добавить память в load_qa_chain или как реализовать ConversationalRetrivalChain с помощью специального приглашения с несколькими входами.

      import openai
import numpy as np
import pandas as pd
import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA, ConversationalRetrievalChain,RetrievalQAWithSourcesChain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.chains.question_answering import load_qa_chain
from langchain.document_loaders import UnstructuredFileLoader
from langchain.prompts import PromptTemplate

from langchain.document_loaders import UnstructuredExcelLoader
loader = UnstructuredFileLoader("../document.pdf", mode="elements")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
#embeddings = OpenAIEmbeddings()
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vectorDB = Chroma.from_documents(texts,embeddings)


prompt_template = "You are a Chat customer support agent.\
        Address the customer as Dear Mr. or Miss. depending on customer's gender followed by Customer's First Name.\
        Use the following pieces of context to answer the question at the end.\
        If you don't know the answer, just say that you don't know, don't try to make up an answer.\
        Below are the details of the customer:\
        Customer's Name : {Customer_Name} \
        Customer's Resident State: {Customer_State}\
        Customer's Gender: {Customer_Gender}\
        {context}\
        Question: {question}\
        Answer: "

import json
 
# Opening JSON file
with open('Customer_profile.json', 'r') as openfile:
# Reading from json file
    json_object = json.load(openfile)
 
cName=json_object['Customer_Name']
cState=json_object['Customer_State']
cGen=json_object['Customer_Gender']

PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "question","Customer_Name","Customer_State","Customer_Gender"]
)

chain_type_kwargs = {"prompt": PROMPT}

from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer',return_messages=True)

#qa = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), retriever=vectorDB.as_retriever(),chain_type="stuff", memory=memory,return_source_documents=True,chain_type_kwargs=chain_type_kwargs)
#qa = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), retriever=vectorDB.as_retriever(),chain_type="stuff", memory=memory,return_source_documents=True)
#qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), retriever=vectorDB.as_retriever(),chain_type="stuff", memory=memory,return_source_documents=True,chain_type_kwargs=chain_type_kwargs)
#qa = load_qa_with_sources_chain(OpenAI(temperature=0),chain_type="stuff",prompt=PROMPT)
qa = load_qa_chain(OpenAI(temperature=0.1),chain_type="stuff",prompt=PROMPT)

import langchain
langchain.debug=False
query="How's the weather in my place?"
docs = vectorDB.similarity_search(query)



#vectordbkwargs = {"search_distance": 0.9}
#result=qa({"input_documents": docs,"question": query,'Customer_Gender':'Male','Customer_State':'Madhya Pradesh','Customer_Name':'Bob'})
result=qa({"input_documents": docs,"question": query,'Customer_Gender':'Male','Customer_State':'Madhya Pradesh','Customer_Name':'Bob'})
#result=qa({"question": query})
print(result['output_text'])

Профиль клиента.JSON

      {
    "Customer_Name": "Bob",
    "Customer_State": "NY",
    "Customer_Gender": "Male"
}

Answer 1

На основе моего пользовательского PDF-файла вы можете использовать следующую логику: вы можете обратиться к моей записной книжке для получения более подробной информации.

      # doc string prompt
#
prompt_template = """You are a Chat customer support agent.\
        Address the customer as Dear Mr. or Miss. depending on customer's gender followed by Customer's First Name.\
        Use the following pieces of context to answer the question at the end.\
        If you don't know the answer, just say that you don't know, don't try to make up an answer.\
        Below are the details of the customer:\
        Customer's Name : {Customer_Name} \
        Customer's Resident State: {Customer_State}\
        Customer's Gender: {Customer_Gender}\
        {context}\
        Question: {question}\
        Answer: """
PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "question","Customer_Name","Customer_State","Customer_Gender"]
)
from langchain.chains.question_answering import load_qa_chain

memory = ConversationBufferMemory(memory_key="chat_history", input_key="question")
chain = load_qa_chain(
    OpenAI(temperature=0), chain_type="stuff", memory=memory, prompt=PROMPT
)


query="why frog sent letter to Todd??"
docs=db.similarity_search(query=query)

# building the dictionary for chain

chain_input={
    "input_documents": docs,
    "context":"This is contextless",
    "question":query,
    "Customer_Name":"Bob",
    "Customer_State":"NY",
    "Customer_Gender":"Male"
}

result=chain(chain_input, return_only_outputs=True)

выход:

       Dear Mr. Bob, Frog sent a letter to Toad because he wanted to show Toad that he was glad to be his best friend. He wrote in the letter, "Dear Toad, I am glad that you are my best friend. Your best friend, Frog."'

если вы планируете вести историю чата, можете использоватьmemory.