context_length_exceeded | This model's maximum context length is 16385 tokens. However, your messages resulted in 59678 tokens. Please reduce the length of the messages | invalid_request_error

[AshokJangidBDApp]

const dotenv = require('dotenv');
const { DataSource } = require("typeorm");
const { SqlDatabase } = require("langchain/sql_db");
const { ChatOpenAI } = require("@langchain/openai");
const { RunnablePassthrough, RunnableSequence } = require("@langchain/core/runnables");
const { PromptTemplate } = require("@langchain/core/prompts");
const { StringOutputParser } = require("@langchain/core/output_parsers");
dotenv.config();
async function main() {
    try {
      console.log("Ai chat testing...");
      // Set up database connection
      const datasource = new DataSource({
        type: "mysql",
        host: process.env.DB_HOST,
        port: process.env.DB_PORT,
        username: process.env.DB_USERNAME,
        password: process.env.DB_PASSWORD,
        database: process.env.DB_DATABASE,
        dialect: process.env.DB_DIALECT,
      });
      const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource });
      // Prepare OpenAI model
      const model = new ChatOpenAI({
        openAIApiKey: "sk-888******************************************6BY",
      });
      // Define prompt templates
      const prompt = PromptTemplate.fromTemplate(`Based on the table schema below, write a SQL query that would answer the user's question:
  {schema}
  Question: {question}
  SQL Query:`);
      const finalResponsePrompt = PromptTemplate.fromTemplate(`Based on the table schema below, question, sql query, and sql response, write a natural language response:
  {schema}
  Question: {question}
  SQL Query: {query}
  SQL Response: {response}`);
      // Define runnable chains
      const sqlQueryGeneratorChain = RunnableSequence.from([
        RunnablePassthrough.assign({
          schema: async () => db.getTableInfo(),
        }),
        prompt,
        model.bind({ stop: ["\nSQLResult:"] }),
        new StringOutputParser(),
      ]);
      // console.log("sqlQueryGeneratorChain =====================>", finalResponsePrompt);
      const fullChain = RunnableSequence.from([
        RunnablePassthrough.assign({
          query: sqlQueryGeneratorChain,
        }),
        {
          schema: async () => db.getTableInfo(),
          question: (input) => input.question,
          query: (input) => input.query,
          response: (input) => db.run(input.query),
        },
        finalResponsePrompt,
        model,
      ]);
      console.log("fullChain =====================>", fullChain);
     // Define function to split messages
function splitMessage(message, maxLength) {
    const segments = [];
    for (let i = 0; i < message.length; i += maxLength) {
      segments.push(message.slice(i, i + maxLength));
    }
    return segments;
  }
  // Inside your main function
  const question = "lowest service plan?";
  const segments = splitMessage(question, 50); // Adjust maximum length as needed
  const finalResponses = [];
// Define a function to optimize messages by reducing token count
function optimizeMessage(message) {
    // Example: Removing stop words
    const stopWords = ["and", "the", "of", "to", "in", "a", "is", "that", "it", "on", "for", "with", "as", "at", "by", "this", "from", "are", "you", "or", "we", "an", "be", "your", "not", "have", "but", "which", "they", "will", "can"];
    const words = message.split(" ");
    const optimizedWords = words.filter(word => !stopWords.includes(word.toLowerCase()));
    const optimizedMessage = optimizedWords.join(" ");
    // Return the optimized message
    return optimizedMessage;
}
// Set up runnable chains and invoke the model with optimized messages
const optimizedSegments = segments.map(segment => optimizeMessage(segment));
for (const segment of optimizedSegments) {
    const finalResponse = await fullChain.invoke({ question: segment });
    finalResponses.push(finalResponse);
} 
      // Output final responses
      console.log("Final responses:", finalResponses);
    } catch (error) {
      console.error('Error:', error);
    }
  } 
  // Call the main function
  main();
  
  
  
![image](https://github.com/Significant-Gravitas/AutoGPT/assets/133368241/95c6eaca-f79c-4a32-bf04-2cf4dbc8c761)

[AshokJangidBDApp]

[AshokJangidBDApp]

import dotenv from 'dotenv';
dotenv.config();

import { DataSource } from "typeorm";
import { SqlDatabase } from "langchain/sql_db";
import { ChatOpenAI } from "@langchain/openai";
import {
  RunnablePassthrough,
  RunnableSequence,
} from "@langchain/core/runnables";
import { PromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

const datasource = new DataSource({
  type: "mysql",
  host: process.env.DB_HOST,
  port: process.env.DB_PORT,
  username: process.env.DB_USERNAME,
  password: process.env.DB_PASSWORD,
  database: process.env.DB_NAME,
  dialect: process.env.DB_DIALECT,
});

const db = await SqlDatabase.fromDataSourceParams({
  appDataSource: datasource,
});

const prompt = PromptTemplate.fromTemplate(`Based on the table schema below, write a SQL query that would answer the user's question:
{schema}

Question: {question}
SQL Query:`);

const model = new ChatOpenAI({
  openAIApiKey: "sk-XbcwjbY***********lbkFJ3nClZsOpGaSZUgPMK6BY",
});

const sqlQueryGeneratorChain = RunnableSequence.from([
  RunnablePassthrough.assign({
    schema: async () => db.getTableInfo(),
  }),
  prompt,
  model.bind({ stop: ["\nSQLResult:"] }),
  new StringOutputParser(),
]);

const finalResponsePrompt = PromptTemplate.fromTemplate(`Based on the table schema below, question, sql query, and sql response, write a natural language response:
{schema}

Question: {question}
SQL Query: {query}
SQL Response: {response}`);

const fullChain = RunnableSequence.from([
  RunnablePassthrough.assign({
    query: sqlQueryGeneratorChain,
  }),
  {
    schema: async () => db.getTableInfo(),
    question: (input) => input.question,
    query: (input) => input.query,
    response: (input) => db.run(input.query),
  },
  finalResponsePrompt,
  model,
]);

const finalResponse1 = await fullChain.invoke({
  question: ["How many employee present current?"],
});
const finalResponse = await fullChain.invoke({
  question: ["can you give names of present employees?"],
});

console.log(finalResponse, finalResponse1);




**It working fine

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[github-actions]

This issue was closed automatically because it has been stale for 10 days with no activity.