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import json
from langchain_core.prompts import MessagesPlaceholder
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.messages import AIMessage, HumanMessage
from langchain.agents.format_scratchpad.openai_tools import (
format_to_openai_tool_messages,
)
from langchain import PromptTemplate, LLMChain
import os
from dotenv import load_dotenv
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
from langchain.chains import ConversationChain
from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
from pymongo import MongoClient
from langchain_openai import ChatOpenAI
load_dotenv()
default_json = {}
class FormFillingBot:
def __init__(self):
self.llm = ChatOpenAI(model="gpt-3.5-turbo", openai_api_key=os.getenv("OPENAI_API_KEY"), temperature=0)
def get_current_form_json(self,uuid):
CONNECTION_STRING = "mongodb+srv://dharasolanki:8apF57ZCeX16fKny@cluster0.egh8f3k.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"
# Connect to the MongoDB client
client = MongoClient(CONNECTION_STRING)
# Select the database and collection
db = client['benefitboost']
collection = db['currentFormJson']
# The UUID to search for
uuid_to_find = uuid
# Find the document with the specified UUID
document =collection.find_one({"uuid": uuid_to_find})
if document:
currentformjson = document.get('currentformjson', None)
if currentformjson:
print("Current Form JSON:", currentformjson)
else:
print("The 'currentformjson' field is not found in the document.")
else:
print(f"No document found with UUID: {uuid_to_find}")
return currentformjson
def form_filling(self, form_data,default_json):
"Use this tool for form filling task"
friendly_prompt = PromptTemplate(
input_variables=["user_input", "default_json"],
template="""You are a form-filling expert that takes the data from user input and finds the related field in {default_json} and returns the json provided with the details of the user. If you do not encounter fields of form then give response as you are a question-answering bot. If you do not find the details put it blank, do not try to fill every detail if it is not present in user_input. Here are the details entered by the user: {user_input}"""
)
friendly_chain = LLMChain(llm=self.llm, prompt=friendly_prompt)
response = friendly_chain.invoke({"user_input": form_data, "default_json": default_json})
return response["text"] |