LangSmith

LangSmith is a powerful development platform for LLM applications that provides valuable insights, debugging tools, and performance monitoring. Integrating LangSmith with RAGChat can significantly enhance your development workflow and application quality.

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Install RAG Chat SDK

Initialize the project and install the required packages:

npm init es6
npm install dotenv
npm install @upstash/rag-chat

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Setup Upstash Redis

Create a Redis database using Upstash Console or Upstash CLI and copy the UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN into your .env file.

UPSTASH_REDIS_REST_URL=<YOUR_URL>
UPSTASH_REDIS_REST_TOKEN=<YOUR_TOKEN>

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Setup Upstash Vector

Create a Vector index using Upstash Console or Upstash CLI and copy the UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN into your .env file.

UPSTASH_VECTOR_REST_URL=<YOUR_URL>
UPSTASH_VECTOR_REST_TOKEN=<YOUR_TOKEN>

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Setup QStash LLM

Navigate to QStash Console and copy the QSTASH_TOKEN into your .env file.

QSTASH_TOKEN=<YOUR_TOKEN>

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Setup LangSmith

Create a LangSmith account and get an API key from LangSmith -> Settings -> API Keys. Set your LangSmith API key as an environment variable:

LANGCHAIN_API_KEY=<YOUR_API_KEY>

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Setup the Project

Initialize RAGChat with LangSmith analytics:

import { RAGChat, upstash } from "@upstash/rag-chat";
import "dotenv/config";

const ragChat = new RAGChat({
  model: upstash("meta-llama/Meta-Llama-3-8B-Instruct", {
    apiKey: process.env.QSTASH_TOKEN,
    analytics: { name: "langsmith", token: process.env.LANGCHAIN_API_KEY! },
  }),
});

Add context to the RAG Chat:

await ragChat.context.add("The speed of light is approximately 299,792,458 meters per second.");

Chat with the RAG Chat:

const response = await ragChat.chat("What is the speed of light?");
console.log(response);

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Run

Run the project:

npx tsx index.ts

Go to the LangSmith Dashboard and navigate to Projects to view your analytics.