raglocal 0.2.5

Local-first, provider-agnostic RAG toolkit

raglocal

NOTE – Any legacy ragkit or rag-local configuration has been removed. Use the new raglocal settings in ~/.config/raglocal/config.toml.

Local-first RAG (Retrieval-Augmented Generation) toolkit designed as an MCP server. Index your code, journals, books, and docs once — query them from Claude, OpenCode, or any MCP-capable AI tool with minimal tokens per session.

• Zero daemons — embedded LanceDB, no Docker, no Qdrant
• Local embeddings — three size profiles, no API key required
• Two MCP tools — search returns ranked 250-char snippets; expand fetches full text on demand
• Heterogeneous content — code, markdown, PDFs, EPUB, DOCX, JSON/YAML, journal entries
• Portable corpora — bundle and share an indexed corpus as a single zip file

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Installation

pip install raglocal[local-embed]   # recommended — includes sentence-transformers

Optional extras:

pip install raglocal[local-embed,books]    # adds EPUB/MOBI/PDF book parsing
pip install raglocal[local-embed,office]   # adds DOCX/ODT support

Requires Python 3.12+.

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Quick start

rag init                        # interactive wizard — picks embedder profile, sets paths
rag index ~/journals/ ~/code/   # index your content (incremental, re-run any time)
rag stats                       # verify chunks were indexed
rag mcp                         # start the MCP server

Then point your AI tool at the server (see MCP setup below).

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Embedder profiles

Chosen once during rag init. To switch profiles later, edit ~/.config/raglocal/config.toml and run rag reindex — raglocal detects the mismatch via the on-disk manifest and refuses to mix incompatible vectors. See DESIGN.md §11 Hot-swap for details.

Profile	Model	Dim	Disk	RAM	Backend
lean	all-MiniLM-L6-v2	384	90 MB	400 MB	sentence-transformers
balanced	nomic-embed-text-v1.5	768	270 MB	1 GB	sentence-transformers
powerful	bge-m3	1024	2.3 GB	4 GB	Ollama (must be running)

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Indexing content

# Index a directory (all supported file types, recursive)
rag index ~/notes/

# Index multiple paths
rag index ~/code/myproject ~/journals ~/books/

# Target a specific collection
rag index ~/books/ --collection books

# Check what was indexed
rag stats

Indexing is incremental — files are skipped if unchanged since last run (mtime + content hash).

Supported file types

Category	Extensions
Code	.py .js .ts .go .rs .java .c .cpp .rb .swift and more
Docs	.md .txt .pdf .json .yaml
Office	.docx .odt (requires raglocal[office])
Books	.epub .mobi (requires raglocal[books])
Journal	.md / .txt with ISO date in filename or frontmatter
Sessions	.json (OpenCode/Claude conversation exports)
Web	.html .htm

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MCP setup

OpenCode

Add to ~/.config/opencode/config.json:

{
  "mcp": {
    "raglocal": {
      "command": "rag",
      "args": ["mcp"],
      "type": "stdio"
    }
  }
}

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "raglocal": {
      "command": "rag",
      "args": ["mcp"]
    }
  }
}

Restart your AI tool after editing. The search and expand tools will appear automatically.

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MCP tools

search

Semantic + keyword hybrid search. Returns up to top_k ranked snippets (≤250 chars each) with chunk IDs for use with expand.

{
  "query": "rust ownership model",
  "top_k": 10,
  "filter": {
    "collection": "code",
    "type": "code",
    "language": "rust",
    "since": "2025-01-01"
  }
}

Response includes hits array — each hit has id, snippet, score, source, collection.

expand

Fetch full text of specific chunks by ID. Use on hits from search that look worth reading in full.

{
  "ids": ["abc-123", "def-456"],
  "before": 1,
  "after": 1
}

before/after include neighbouring chunks for context. Response includes full text per chunk.

Note: chunk ID resolution was broken prior to v0.2.0 and is now fixed.

Token cost

Item	Tokens (approx)
Tool schemas	~400
Per search hit	~60
10-hit search	~1,000
expand (1 chunk)	~300–800

A typical session costs ~1,200–2,000 tokens for retrieval, vs. ~4,000+ for full-chunk RAG.

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CLI reference

rag init                    Interactive setup wizard
rag index <path...>         Index files/directories into the store
rag reindex [--collection]  Rebuild index after switching embedder profile
rag stats                   Show chunk counts per collection
rag mcp                     Start the MCP server
rag debug "<query>"         Debug search — print results to stderr
rag export <output.zip>     Bundle corpus for sharing
rag import <input.zip>      Import a shared corpus
rag models                  List configured model aliases
rag collections             List collections and chunk counts
rag doctor                  Health check (config, store, Ollama, model)

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Configuration

rag init writes ~/.config/raglocal/config.toml. See config.toml.example for all options.

Key sections:

[store]
backend = "lance"
path = "~/.local/share/raglocal/lance"

[embedder]
profile = "balanced"       # lean | balanced | powerful

[collections.docs]
chunk_size = 800
chunk_overlap = 100
chunk_strategy = "token"   # token | sentence | code-aware | chapter

[collections.code]
chunk_size = 500
chunk_overlap = 50
chunk_strategy = "code-aware"

---

Portable corpora

Share an indexed corpus without requiring re-indexing on the recipient's machine:

# Export
rag export ~/my-corpus.zip

# Recipient imports
rag import ~/my-corpus.zip
# or: unzip and run the self-contained bootstrap script
bash run.sh

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Development

git clone https://github.com/gpid007/raglocal
cd raglocal
uv sync --all-extras --dev
uv run pytest
uv run rag --help

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Further reading

• Hot-swap embedder — switching profiles on an existing index
• OpenCode integration guide
• Troubleshooting
• Architecture & design