Chat With Your Documents: Built-in Knowledge Base (RAG)

# Chat With Your Documents: Built-in Knowledge Base (RAG)

## Summary

Add a built-in knowledge base: drag documents (PDF, DOCX, TXT, MD, CSV) into a workspace and have agents retrieve from them automatically when answering. This is the single most-requested capability in local AI apps today, and QwenPaw has no equivalent.

## Component(s) Affected

- [x] Core / Backend (app, agents, config, providers, utils, local_models)
- [x] Console (frontend web UI)
- [ ] Channels (DingTalk, Feishu, QQ, Discord, iMessage, etc.)
- [ ] Skills
- [ ] CLI
- [x] Documentation (website)
- [ ] Tests
- [ ] CI/CD
- [ ] Scripts / Deploy

## Problem / Motivation

Agents can only work from their conversation history and whatever tools fetch at runtime. There is no way to give an agent a private corpus — product catalogs, manuals, policies, invoices — and have it ground its answers in those documents. Every business user needs this: "answer customer questions from OUR catalog" is the first thing people try to build. Today they must bolt on an external RAG stack by hand, which defeats QwenPaw's plug-and-play appeal.

The ReMe memory layer already ships in-process and even has an (unused) embedding config — the foundation is half-built.

## Proposed Solution

- Per-agent (or shared) "Knowledge" tab in the console: drag-and-drop files, see chunk/index status.
- Ingestion pipeline: parse → chunk → embed → store. Ship a zero-config default (embedded vector store such as LanceDB/sqlite-vec) so it works out of the box, with optional connectors (pgvector, Qdrant, Milvus) for power users.
- Embeddings via the existing provider system (an Ollama/OpenAI-compatible embedding model like bge-m3), reusing `reme_light_memory_config.embedding_model_config` which already exists in agent.json.
- Retrieval automatically injected into agent context (top-k with citation of source file), plus a `search_knowledge` tool the agent can call explicitly.
- Works across all channels — a Telegram or WhatsApp agent grounded in the company's documents is a killer combination no other app offers.

## Alternatives Considered

- External RAG via MCP servers: works but requires users to install, run, and maintain a separate stack, and every retrieval costs an MCP round-trip. Built-in retrieval is faster, cheaper in tokens, and zero-setup.
- Stuffing documents into the system prompt: blows the context window and cost.

## Additional Context

The existing ReMe integration (BM25 + file store, `embedding backfill skipped: reason=embedding_disabled` in logs) suggests much of the plumbing exists and is one embedding backend away from document RAG.

## Willing to Contribute

- [ ] I am willing to open a PR for this feature (after discussion).