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Projects: A Knowledge Base for Your AI Chats

LLM Gateway Chat now has Projects: group related chats, upload files as a knowledge base, and get answers grounded in your own documents via RAG — with source citations, on any of 280+ models. Available to every Chat user.

A glossy 3D circuit board with a glowing folder holding documents at its center, representing a project knowledge base feeding AI chats

Every new chat starts from zero. You paste the same product spec for the third time this week, re-upload the same README, and re-explain the same context the model forgot the moment you closed the tab. The model is smart; your setup is amnesiac.

Today we're adding Projects to LLM Gateway Chat — a knowledge base and workspace for your chats. Create a project, upload your files once, and every chat inside it answers from those documents, with the source file cited in the reply. It's retrieval-augmented generation (RAG) without standing up a vector database, and it works with any of the 280+ models in the picker.

What's in a project

A project is three things:

  • A knowledge base. Upload text, markdown, code, CSV, JSON, or YAML files. Each file is indexed automatically and marked when it's ready.
  • Instructions. Per-project guidance that applies to every chat in it — "answer from the knowledge base and cite the file", "respond in French", "assume the reader is on the enterprise plan".
  • Its chats. Chats started in a project stay grouped there, so the research for one client, one codebase, or one launch doesn't scatter across your history.

Open a chat inside a project and a banner shows what context it's running with. Switch models mid-conversation like always — the knowledge base follows the project, not the model.

How the knowledge base answers

When you send a message in a project chat, LLM Gateway Chat embeds your question, pulls the most relevant passages from your files by similarity, and hands the model those passages plus your project instructions before it answers. The model responds from your documents and names the file it used.

Ask "what did we decide about retry behavior?" and instead of a confident guess, you get the actual decision — with (architecture-notes.md) at the end of the sentence.

Under the hood:

Step What happens
Upload Files are split into ~1,500-character passages along paragraph boundaries
Index Passages are embedded with text-embedding-3-small through the gateway itself
Ask Your question is embedded and the top-matching passages are retrieved per message
Answer The model gets instructions + passages in its system prompt and cites source files

Retrieval runs on every message, so follow-up questions pull fresh passages instead of reusing whatever the first question happened to surface.

No separate bill, no separate infrastructure

Indexing and retrieval run through the same gateway and the same balance as the chat itself. Embedding a 500 KB file costs fractions of a cent with text-embedding-3-small; there's no vector-database add-on, no per-seat knowledge fee, and no plan gate. Projects are available to every LLM Gateway Chat user today — on the Chat plan context or under a dashboard organization.

Current limits: 20 files per project, up to 500 KB of text per file. PDFs aren't supported yet — export to text or markdown first.

Get started in three steps

  1. Open chat.llmgateway.io/projects and create a project.
  2. Add files to its knowledge base and optionally write instructions.
  3. Hit New chat and ask a question your documents can answer.

Frequently Asked Questions

How is a project knowledge base different from attaching a file to a chat?

An attachment lives and dies with one conversation, and large files eat your context window on every message. A knowledge base is indexed once and shared by every chat in the project — only the passages relevant to each question are sent to the model, so a 500 KB handbook doesn't cost you 500 KB of context per message.

Which models work with project knowledge bases?

All of them. Retrieval happens before the model is called, so the grounded context works the same whether the chat runs GPT-5, Claude, Gemini, or any other model on the gateway — and you can switch models mid-project.

What file types can I upload?

Text-based formats: plain text, markdown, source code, CSV, JSON, YAML, XML, HTML, and logs. PDFs and other binary formats aren't supported yet.

Does RAG cost extra?

No. Embeddings are billed to the same credits as your chats at standard gateway rates, which for text-embedding-3-small works out to fractions of a cent per file.

Try it now: