Welcome to Docsy

Docsy is a headless RAG architecture built specifically for TypeScript developers. It provides the core infrastructure to add AI-powered semantic search and chat directly to your application, without the overhead of Python frameworks or the lock-in of enterprise SaaS platforms.

If you have built a SaaS product or an impressive portfolio and already have a README.md or a few scattered markdown files in your repository, Docsy turns them into an intelligent knowledge base in minutes.

The Problem

You have built something great. You have already written the necessary explanations—they live in your README.md and a handful of .md files directly inside your GitHub repository.

But right now, your users have to leave your application, navigate to your GitHub repo, and dig through fragmented markdown files just to find answers.

You want to give your users an intelligent chatbot to answer their questions instantly on your landing page, but you do not want the overhead of building, styling, and maintaining a dedicated /docs website.

Building a custom documentation chatbot for this exact use case currently forces you into terrible compromises. It usually requires:

• Over-engineering: Spending weeks learning complex machine learning frameworks for a targeted problem.
• Vendor Lock-in: Paying for expensive, inflexible no-code dashboards that ruin your seamless UI.
• Context-Switching: Forcing you to write Python backends when your entire stack is TypeScript.
• Infrastructure Bloat: Provisioning complex vector databases just to parse a few static markdown files.

Every existing solution forces you to choose between building a bloated documentation site you don’t actually want, or using the wrong programming language for your stack.

The Vision

We are building the developer-first RAG layer that:

• Installs via package manager in under 60 seconds.
• Configures entirely with TypeScript, ensuring full type safety and autocomplete.
• Integrates seamlessly with your existing stack (Next.js, Vercel AI SDK).
• Allows you to swap LLMs, embedding models, and vector stores without rewriting core logic.
• Stays out of your way and lets you own the UI and the infrastructure.

Who This Is For

Docsy is engineered for developers who:

• Build SaaS or portfolio projects and want an AI chatbot without maintaining a full /docs routing system.
• Maintain open-source projects with markdown docs scattered across repositories.
• Prefer writing code in a modern editor over clicking through SaaS dashboards.
• Need to fetch content directly from their GitHub repositories to power semantic Q&A.

Capabilities

Available Today

• GitHub Ingestion: Intelligent filtering and parsing of repository markdown files.
• Markdown-Aware Chunking: Preserves code blocks, headers, and document structure.
• Pluggable Embeddings: Out-of-the-box support for Gemini and OpenAI.
• Vector Storage: Native integration with Qdrant.
• Semantic Retrieval: Fast vector search with streaming responses.
• Vercel AI SDK: Native useChat compatibility for modern React apps.
• CLI Tooling: Ingest github files from your cli.

In Development

• Live website scraping for deployed documentation sites.
• Query optimization and reranking pipelines.
• Advanced RAG patterns (Sequential, Self-RAG, Multi-query).
• Extended vector database support (Pinecone, Supabase, Upstash).

What Docsy Is Not

• Not a SaaS platform: You hold the API keys. You own the vector database.
• Not a UI library: We provide the retrieval hooks; you build the interface.
• Not enterprise software: No mandatory sales calls, no bloated compliance dashboards.
• Not a generic LLM wrapper: Highly opinionated and optimized specifically for documentation workflows.

Get Started

Add intelligent search to your application today.

Built for developers who value clean code and intelligent search.