Upstash RAG Chat Integration

Integrate Helicone with Upstash RAG Chat to enhance your retrieval-augmented generation applications with comprehensive observability and analytics.

Introduction

Upstash RAG Chat is a TypeScript toolkit for building powerful retrieval-augmented generation (RAG) applications. This integration with Helicone allows you to leverage advanced observability tools for your RAG Chat applications, providing deeper insights into your LLM operations.

Key Features

• Framework Compatibility: Seamlessly integrates with Next.js, Svelte, Nuxt.js, and Solid.js.
• Diverse Data Ingestion: Ingest websites by URL, text files, CSVs, PDFs, and more out of the box.
• Real-time Streaming: Stream AI-generated content in real time.
• Built-in Vector Store: Efficiently manage your knowledge base.
• Optional Redis Integration: Store chat messages for persistent conversations.
• Flexible Chat Sessions: Support for maintaining context across interactions.
• Customizable Prompts: Fully customizable prompts for tailored experiences.
• Pure LLM Chat Mode: Option to use without RAG for traditional LLM interactions.

Integration Steps

1

Create a Helicone account + Generate an API Key

Log into Helicone or create an account. Once you have an account, you can generate an API key.

Make sure to generate a write only API key.

2

Install RAG Chat

Install the RAG Chat package using your preferred package manager:

npm i @upstash/rag-chat
# or
pnpm add @upstash/rag-chat
# or
yarn add @upstash/rag-chat

3

Configure RAG Chat with Helicone

Initialize RAG Chat with Helicone integration:

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

const ragChat = new RAGChat({
  model: openai("gpt-3.5-turbo", {
    apiKey: process.env.OPENAI_API_KEY!,
    analytics: { name: "helicone", token: process.env.HELICONE_API_KEY },
  }),
});

4

Use RAG Chat in Your Application

Documented in the Upstash RAG Chat documentation.

5

View Your Logs

🎉 You’re all set! View your logs at Helicone.

By integrating Upstash RAG Chat with Helicone, you can build advanced RAG applications with comprehensive observability, allowing you to optimize your LLM operations and deliver more efficient, context-aware AI interactions.

For more details on RAG Chat features and usage, visit the Upstash RAG Chat documentation.