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RAG & Knowledge Systems

RAG Chat SDK

RAG Chat SDK is a TypeScript package for building retrieval-augmented chat applications. It ingests text, PDFs and websites into a knowledge context, uses a built-in vector store for retrieval, and sends questions to a configured language model.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

RAG Chat SDK — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a TypeScript starting point for a prototype that retrieves from a supplied knowledge base before generating chat responses.

Who it is for

  • Developers prototyping RAG chat applications in TypeScript.
  • Teams adding knowledge-based question answering to a Next.js or other documented web application.

Documented uses

  • Create a chat that answers questions using supplied text or PDF documents.
  • Add website content as a source for a knowledge-grounded chat.
  • Add streaming RAG chat to a Next.js application.
  • Use Redis-backed conversation history instead of the default in-memory history.
Before you choose

Requirements & limits

  • The package includes a vector store and connects to Upstash Vector through REST credentials.
  • The README lists integrations for several model providers, including OpenAI, Anthropic, Groq, Mistral AI, Ollama, OpenRouter and Together AI.
  • Conversation history defaults to in-memory; Redis-backed history is optional and requires Redis REST credentials.
  • The repository describes the project as community-maintained and intended to speed up prototyping, not as an officially supported Upstash project.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up a basic RAG Chat SDK question flow

Install the package, configure the documented service credentials and add a text or PDF context before asking a question.

Before you start

  • A TypeScript application and a package manager such as npm, pnpm or Bun.
  • Upstash Vector REST URL and token.
  • An OpenAI-compatible API key, or a QStash token for Upstash hosted models.
  • A PDF file if using the PDF context example.

Steps

  1. Install @upstash/rag-chat with your package manager.

  2. Set UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN, plus the relevant model credential.

  3. Import RAGChat, configure a model if needed, and add text or a PDF with ragChat.context.add.

  4. Call ragChat.chat with a question and read the response output.

Expected outcome

A basic application flow that supplies text or PDF context and requests a model response to a question.

Guide limitations

  • The example demonstrates text and a local PDF; website ingestion is listed as supported, but its implementation steps are not detailed here.
  • The README positions the SDK for prototypes and recommends other frameworks for real projects.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
RAG Chat SDKCreate a chat that answers questions using supplied text or PDF documents.Server
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
12 October 2026
Original website title
upstash/rag-chat · Official homepage title · retained in its source language
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