Cdaprod

Cdaprod

Cdaprod

Cdaprod

#!/usr/local/bin/python

import logging
import weaviate
import langchain
from langchain.document_loaders import AirtableLoader
from langchain.vectorstores.weaviate import Weaviate
from langchain.llms import OpenAI
from langchain.chains import ChatVectorDBChain
from llama_index.langchain_helpers.agents import IndexToolConfig, LlamaIndexTool, create_llama_chat_agent

Cdaprod

from functools import wraps
import logging
from supabase_py import create_client
from weaviate import Client, AuthClientSecret
import os

# BaseTool class
class BaseTool:
    def execute(self, input_data: str) -> str:
        pass

Cdaprod

from flask import Flask, jsonify
import logging

from airtable import Airtable
from twitter_api import TwitterAPI  # This is a placeholder, replace it with the actual Twitter API library you are using
from linkedin_api import LinkedinAPI  # This is a placeholder, replace it with the actual Linkedin API library you are using
from facebook_api import FacebookAPI  # This is a placeholder, replace it with the actual Facebook API library you are using

app = Flask(__name__)

Cdaprod

import requests
import openai
from pydantic import BaseModel, Field
from langchain.tools import tool

AIRTABLE_BASE_ID = "your_airtable_base_id"
AIRTABLE_API_KEY = "your_airtable_api_key"
TABLE = "your_airtable_table"
MODEL_NAME = "gpt-3.5-turbo-0613"

Cdaprod

from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool
from typing import Optional, Type
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)

class TableToWeaviateClassTool(BaseTool):

Cdaprod

Cdaprod