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import logging
import sys
import nest_asyncio
nest_asyncio.apply()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index import (
    LLMPredictor,
    GPTVectorStoreIndex, 

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# https://www.mongodb.com/docs/atlas/atlas-search/knn-beta/
import pymongo
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import AzureOpenAI
import os
from rake_nltk import Rake
from langchain.agents import initialize_agent, Tool

MONGO_URI = "mongodb+srv://....mongodb.net/?retryWrites=true&w=majority&tls=true"
MONGODB_DATABASE = "demo_db"

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# https://www.mongodb.com/docs/atlas/atlas-search/knn-beta/
import pymongo
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import AzureOpenAI
import os
import glob
import time
from multiprocessing import Pool
from tqdm import tqdm
from rake_nltk import Rake

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# Inspired by: https://github.com/gkamradt/langchain-tutorials/blob/main/data_generation/5%20Levels%20Of%20Summarization%20-%20Novice%20To%20Expert.ipynb
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import AzureOpenAI
import numpy as np
from multiprocessing import Pool
from tqdm import tqdm
from langchain.chains.summarize import load_summarize_chain
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import MongoDBAtlasVectorSearch

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# inspired by: https://github.com/hollaugo/chatgpt-slack-app/blob/main/app.py
import json
import pymongo
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import AzureOpenAI
import os
import requests
from slack_bolt import App
from slack_bolt.adapter.socket_mode import SocketModeHandler
from dotenv import load_dotenv

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import requests  
def start_conversation(): 
    url = "https://api.mendable.ai/v0/newConversation"  
    
    payload = {  
        "api_key": "API_KEY_GOES_HERE"  
    }  
    
    headers = {  
        "Content-Type": "application/json"  

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from pymongo import MongoClient
import logging
from langchain.document_loaders import PlaywrightURLLoader
from langchain.vectorstores import MongoDBAtlasVectorSearch
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.embeddings import GPT4AllEmbeddings
gpt4all_embd = GPT4AllEmbeddings()


LOGGER = logging.getLogger(__name__)  

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import openai
from actionweaver.llms.openai.chat import OpenAIChatCompletion
from actionweaver import action, RequireNext
openai.api_key = "sk-test" #os.getenv("OPENAI_API_KEY")
from pymongo import MongoClient  

connection_string="mongodb+srv://test:[email protected]/?retryWrites=true&w=majority"
mongodb_client = MongoClient(connection_string) 

@action(name="query_airbnb",orch_expr=RequireNext(['query_airbnb']))

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import itertools
import openai
from actionweaver.llms.openai.chat import OpenAIChatCompletion
from actionweaver import action, RequireNext,SelectOne
openai.api_key = "sk-" #os.getenv("OPENAI_API_KEY")
from pymongo import MongoClient  
import json
connection_string="mongodb+srv://abc123:[email protected]/?retryWrites=true&w=majority"
mongodb_client = MongoClient(connection_string) 

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from council.agents import Agent
from council.chains import Chain
from council.llm import AzureLLM
from council.skills import LLMSkill
from council.agents import Agent
from council.filters import BasicFilter
from council.controllers import LLMController
from council.evaluators import LLMEvaluator

import os