We Made RAG Chat Development Easier Than Ever

Are you tired of wrestling with complex frameworks and piecing together multiple services just to build a RAG (Retrieval-Augmented Generation) chat application? We've been there too, and that's why we're excited to introduce @upstash/rag-chat, our new SDK designed to make RAG development a breeze.

The Problem with Current RAG Development

Developing RAG applications have been a complex process:

• Learning curve: Mastering frameworks like Langchain or LlamaIndex takes time.
• Multiple services: You often need to juggle vector stores, LLM pipelines, and Redis for chat histories.
• Integration challenges: Connecting your RAG system to a UI brings its own set of problems, from rate limiting to session handling.

The RAG Struggle is Real

Let's face it, building RAG apps used to be about as fun as a root canal. You'd spend ages wrapping your head around Langchain or LlamaIndex, only to find yourself drowning in a sea of vector stores, LLM pipelines, and Redis instances. And don't even get me started on the joys of hooking all that up to a UI. Rate limiting? Session handling?

Enter RAG Chat: Your New Best Friend

We've been there, done that, and got the "I Survived RAG Development" t-shirt. That's why we cooked up the RAG Chat SDK. It's like having a secret weapon that turns you into a RAG superhero overnight. No cape required (but highly recommended for dramatic effect).

Want to see some magic? Check this out:

import { RAGChat, openai } from "@upstash/rag-chat";
 
// Abracadabra! Your RAG Chat is born
const ragChat = new RAGChat({
  model: openai("gpt-4-turbo"),
});
 
// Feed it some brain food
await ragChat.context.add({
  type: "text",
  data: "Light travels at 299,792,458 meters per second. That's like, really fast.",
});
 
await ragChat.context.add({
  type: "pdf",
  fileSource: "./data/mind_blowing_physics.pdf",
});
 
// Ask it something cool
const response = await ragChat.chat("How fast is light, and can I outrun it?");
 
console.log(response.output);
// Spoiler: No, you can't outrun light. Unless you're The Flash. Are you The Flash?

Boom! Just like that, you've got a RAG chat app up and running. No sweat, no tears, just pure coding bliss.

But Wait, There's More!

Think that's cool? Hold onto your keyboards, because RAG Chat is like the Swiss Army knife of AI development:

1. It plays nice with all the cool LLM kids on the block - OpenAI, TogetherAI, MistralAI, Groq, you name it.
2. Upstash has got your back with everything you need - vector storage, Redis for remembering all those witty chat comebacks, and LLM integration.
3. Wanna get fancy? Go nuts with the advanced settings. It's like customizing your perfect pizza, but for code.
4. Broke developer alert! Use Ollama for local development and save those precious dollars for coffee.
5. Built-in analytics.

Level Up Your RAG Game

For those of you who like to live dangerously (a.k.a. advanced users), here's how you can turn your RAG Chat into a supercharged, turbo-boosted beast:

const ragChat = new RAGChat({
  model: openrouter("model-name", { apiKey: "super-secret-key" }),
  vector: new Index(),
  redis: new Redis(),
  ratelimit: new Ratelimit({
    redis: new Redis(),
    limiter: Ratelimit.tokenBucket(1, "1d", 1),
    prefix: "@upstash/rag-chat-ratelimit",
  }),
  namespace: "cooler-than-cool-namespace",
  sessionId: "user-session-42",
  promptFn: ({ context, question, chatHistory }) => {
    // Your chance to play puppet master with the AI
  }
});

Debugging: Because Even Superheroes Need a Sidekick

Developing locally? Turn on debug mode and watch the magic happen:

const ragChat = new RAGChat({
  debug: true,
  // Other cool stuff here
});

Going live? Hook it up with some fancy analytics:

const ragChat = new RAGChat({
  model: openai("chatgpt-3-turbo", {
    analytics: { name: "helicone", token: "helicone-secret-handshake" }
  }),
  // More awesomeness
});

Or get all CSI with Langsmith tracing:

const ragChat = new RAGChat({
  model: openai("chatgpt-3-turbo", {
    analytics: { name: "langsmith", token: "langchain-detective-badge" }
  }),
  // You know the drill
});

Already Using Vercel's AI SDK? No Sweat!

Switching to RAG Chat is easier than convincing a cat to take a nap:

import { openai } from "@ai-sdk/openai";
 
const ragChat = new RAGChat({
  model: openai("gpt-3.5-turbo"),
  vector,
  redis: new Redis({
    token: process.env.UPSTASH_REDIS_REST_TOKEN!,
    url: process.env.UPSTASH_REDIS_REST_URL!,
  }),
  sessionId: "ai-sdk-session",
  namespace: "ai-sdk",
});

Why RAG Chat is Your New Coding BFF

1. It's simpler than a two-piece puzzle.
2. More flexible than a yoga instructor.
3. More comprehensive than your high school textbook (and way more fun).
4. Faster than your cat when it hears a can opener.

Let's Wrap This Up!

So there you have it, folks! RAG Chat is here to turn your RAG development nightmares into sweet, code-filled dreams. Whether you're building the next big chatbot or just trying to impress your rubber duck, RAG Chat's got your back.

Ready to join the RAG revolution? Dive into our docs at docs.upstash.com/rag-chat and start building something awesome!

Got questions? Bright ideas? Terrible puns? We're all ears! Hit up our support team or join the party on our community forum. With @upstash/rag-chat, you can set up a fully functional RAG chat system in just a few lines of code.

Star-struck? We're Not Above Begging!

Hey, if this RAG Chat SDK has rocked your world (and we know it has), why not sprinkle some stardust our way? Head over to our GitHub repo and smash that star button like it owes you money:

https://github.com/upstash/rag-chat

Remember, every star you give feeds a hungry developer's ego for a whole day. Think of the developers!