RTFM代码检索层

Manual install (Cursor, Codex, Claude Desktop chat, other MCP clients)

For clients without Claude Code's plugin system :

pip install rtfm-ai
cd /path/to/your-project
rtfm init

Then point your MCP client at rtfm-serve (the entry exposed by the pip package). Optional extras via pip install rtfm-ai[embeddings,pdf].

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Quick start

Optional extras (semantic search, PDF parsing)

The core plugin is dependency-free. Heavier optional extras (embedding model, PDF parsers) install on demand into an isolated venv inside the plugin's data directory — no pollution of your system Python, no PEP 668 conflicts:

/rtfm:install-embeddings    # FastEmbed ONNX (~85 MB), semantic + hybrid search
/rtfm:install-pdf           # pdftext only (~50 MB), fast text extraction
/rtfm:install-pdf-full      # + marker-pdf + CPU-only torch (~1.5 GB), complex layouts

The pdf-full install uses PyTorch's CPU-only index (no CUDA, no GPU needed) to stay around 1.5 GB instead of 5 GB.

Restart Claude Code after install for the extras to be picked up.

CLI reference

```bash

Python API

```python from rtfm import Library

lib = Library("my_library.db")

Recommended — Claude Code plugin

In Claude Code (CLI or Desktop Code tab) :

/plugin marketplace add roomi-fields/claude-plugins
/plugin install rtfm@roomi-fields

RTFM is distributed via the roomi-fields/claude-plugins marketplace, which also ships notebooklm-mcp for citation-backed Q&A. To grab both at once:

/plugin install notebooklm@roomi-fields

That's it. The plugin auto-initializes each project on first use: - Creates .rtfm/library.db (one SQLite file) - Injects search instructions into CLAUDE.md - Pre-grants permission for the MCP tools (no prompt every search) - Indexes the project on the first prompt, re-indexes incrementally on every prompt

No pip install required. Pure Python, runs on Linux / macOS / Windows / WSL with Python 3.10+ already on PATH. The plugin bundles its own MCP server (no mcp SDK dep) and resolves python3 / python / py automatically.

Then say to Claude: "Find the authentication flow" — it uses rtfm_search instead of grepping.

NotebookLM integration

RTFM pairs naturally with notebooklm-mcp. NotebookLM caps you at 50 queries/day per notebook; RTFM removes that ceiling by indexing answers locally — ask once, retrieve forever, offline, in milliseconds.

notebooklm-mcp's /batch-to-vault endpoint writes citation-backed Q&A as {slug}.md (markdown with frontmatter) plus {slug}.json (structured nblm-answer-v1 sidecar). Both are guaranteed to coexist. Two integration paths, both ship today:

• Path A — Markdown (zero config): drop the vault into RTFM and rtfm sync. The default markdown parser slices each answer into question / answer / per-citation chunks automatically. No mapping, no schema, no code.
• Path B — JSON sidecar (typed metadata): drop a nblm-answer.yaml mapping into .rtfm/mappings/. Each .json answer file produces typed chunks with notebook_id, source_name, citation_marker queryable via SQL, plus cites edge candidates between answers and sources.

Use Path A unless you specifically need to filter or graph by structured citation fields.

Full NotebookLM recipe →

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Integration

• Native Claude Code plugin — /plugin install rtfm@roomi-fields/rtfm, auto-init per project
• Pure-Python MCP server — 0 external deps, no mcp SDK / pydantic / native binaries
• Cross-platform — Linux, macOS, Windows, WSL (only requires Python ≥ 3.10 on PATH)
• 13 MCP tools — search, context, expand, graph, history, sync, tags, ...
• Manual install fallback — pip install rtfm-ai for Cursor, Codex, Claude Desktop chat, any other MCP client
• CLI + Python API — scriptable for pipelines
• Non-invasive — doesn't touch your code, doesn't replace your editor

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Two levels of JSON integration

For JSON-based formats specifically, RTFM offers a second extensibility path that doesn't need any Python:

See JSON schema mappings for the full reference, and RTFM × NotebookLM for a concrete recipe.

How it compares

RTFM is the only open-source option that indexes multi-domain content with structural parsing, a code-level knowledge graph, and unlimited per-file history. That's the niche.

Different from MemPalace specifically: MemPalace is an entity-level memory for conversations (who/project/decision triples in SQLite, plus verbatim chunks in ChromaDB). RTFM is a retrieval layer for artefacts — parsed by format, linked at the file level, versioned over time. The two are stackable, not competing.

For a deeper breakdown of the design choices behind any RAG (chunking, retrieval, augmentation, integration, freshness, storage), see RAG Fundamentals — the 6 axes →

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