Note
Recommendation from the Upstash Team
The RAG Chat SDK is designed to facilitate easier and faster prototype creation. For real projects, use Vercel AI SDK, Langchain, and LlamaIndex.
Note
This project is a Community Project.
The project is maintained and supported by the community. Upstash may contribute but does not officially support or assume responsibility for it.
The @upstash/rag-chat package makes it easy to develop retrieval-augmented generation (RAG) chat applications with minimal setup and configuration.
Features:
- Next.js compatibility with streaming support
- Ingest entire websites, PDFs, and more out of the box
- Built-in vector store for your knowledge base
- (Optional) built-in Redis compatibility for fast chat history management
- (Optional) built-in rate limiting
- (Optional) disableRag option to use it as LLM + chat history
- (Optional) Analytics via Helicone, Langsmith, and Cloudflare AI Gateway
Install the package using your preferred package manager:
pnpm add @upstash/rag-chat
bun add @upstash/rag-chat
npm i @upstash/rag-chat- Set up your environment variables:
UPSTASH_VECTOR_REST_URL="XXXXX"
UPSTASH_VECTOR_REST_TOKEN="XXXXX"
# if you use OpenAI compatible models
OPENAI_API_KEY="XXXXX"
# or if you use Upstash hosted models
QSTASH_TOKEN="XXXXX"
# Optional: For Redis-based chat history (default is in-memory)
UPSTASH_REDIS_REST_URL="XXXXX"
UPSTASH_REDIS_REST_TOKEN="XXXXX"- Initialize and use RAGChat:
import { RAGChat } from "@upstash/rag-chat";
const ragChat = new RAGChat();
const response = await ragChat.chat("Tell me about machine learning");
console.log(response);import { RAGChat, openai } from "@upstash/rag-chat";
export const ragChat = new RAGChat({
model: openai("gpt-4-turbo"),
});
await ragChat.context.add({
type: "text",
data: "The speed of light is approximately 299,792,458 meters per second.",
});
await ragChat.context.add({
type: "pdf",
fileSource: "./data/physics_basics.pdf",
});
const response = await ragChat.chat("What is the speed of light?");
console.log(response.output);
