RageshAntony

import openai
from colorama import Fore
from langchain import PromptTemplate
from langchain.chains import ConversationalRetrievalChain, RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.schema import SystemMessage
from langchain.text_splitter import RecursiveCharacterTextSplitter

RageshAntony

import json

import openai
import pymysql
from colorama import Fore
from tabulate import tabulate


def dictfetchall(cursor, fet_rows):
    """Returns all rows from a cursor as a list of dicts"""

RageshAntony

{"nodes":[{"width":300,"height":526,"id":"chatOpenAI_0","position":{"x":429.55643898332966,"y":-202.71994150214903},"type":"customNode","data":{"id":"chatOpenAI_0","label":"ChatOpenAI","name":"chatOpenAI","type":"ChatOpenAI","baseClasses":["ChatOpenAI","BaseChatModel","BaseLanguageModel","BaseLangChain","Serializable"],"category":"Chat Models","description":"Wrapper around OpenAI large language models that use the Chat endpoint","inputParams":[{"label":"OpenAI Api Key","name":"openAIApiKey","type":"password","id":"chatOpenAI_0-input-openAIApiKey-password"},{"label":"Model Name","name":"modelName","type":"options","options":[{"label":"gpt-4","name":"gpt-4"},{"label":"gpt-4-0314","name":"gpt-4-0314"},{"label":"gpt-4-32k-0314","name":"gpt-4-32k-0314"},{"label":"gpt-4-0613","name":"gpt-4-0613"},{"label":"gpt-3.5-turbo","name":"gpt-3.5-turbo"},{"label":"gpt-3.5-turbo-0301","name":"gpt-3.5-turbo-0301"},{"label":"gpt-3.5-turbo-0613","name":"gpt-3.5-turbo-0613"}],"default":"gpt-3.5-turbo","optional":true,"id":"chatOp

RageshAntony

Sales Executive Documentation for TechSamurai:-

Products by TechSamurai:
1. FLIXSamurai
2. HouseTalk
3. SweetSangeet

RageshAntony

from colorama import Fore

from langchain import SQLDatabase, LLMMathChain
from langchain.agents import initialize_agent, AgentType, create_sql_agent, AgentExecutor
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.chains import ConversationChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, MessagesPlaceholder, \
    HumanMessagePromptTemplate

RageshAntony

from colorama import Fore
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma

RageshAntony

import json

from colorama import Fore
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.conversational_retrieval.base import BaseConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.prompts import PromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter

RageshAntony

const readline = require('readline');
const fs = require('fs');
const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout
});

const filename = "chat_export.txt";
let datetime_str = "";

RageshAntony

import json 
import requests
from types import SimpleNamespace

movieSchema = {
    "id": "<unique id of the web series",
    "plot": "<plot of the Web Series>",
    "maturity": "<Random Int  value from (13+,16+,18+)>",
    "release": "<Date when the Series released. in YYYY-MM-DD format>",
    "seasons": 1,

RageshAntony

package com.example.bmiapp

class BMIcalculator {

    fun calculateBMI(heightCMS: Float, weightKGS: Float ): String {

        val heightMtr = heightCMS / 100.0
        val bmiScore = weightKGS / (heightMtr * heightMtr)

        return getBMIStatus(bmiScore)