LiteRAG

Local retrieval skill for large documentation corpora using independent SQLite knowledge libraries with keyword plus vector hybrid search. Use when searching Blender manuals, API references, SDK docs, framework docs, product docs, blog/article archives, exported markdown doc sets, or any other large external documentation that should not live in OpenClaw's main memory index. Also use when indexing, reindexing, debugging retrieval quality, checking index compatibility/status, or inspecting LiteRAG sqlite metadata. Usage: /literag search <library> <query> | /literag inspect <library> <path> [--start N --end N] | /literag index <library> | /literag status <library> | /literag meta <library> | /literag benchmark <library> --query ...

Use this skill when the target corpus is too large or too noisy for main agent memory.

Install

Packaged dependency install:

bash

python3 -m pip install -r {baseDir}/requirements.txt

Layout

• Config + databases live under <workspace>/.literag/
• Main config: <workspace>/.literag/knowledge-libs.json
• Default workspace resolution order: OPENCLAW_WORKSPACE → WORKSPACE → walk upward from the current path until the OpenClaw workspace sentinel files are found
• Core scripts live under skills/literag/scripts/
• Skill bin entrypoint: skills/literag/bin/literag
• Workspace convenience wrappers live at scripts/literag-query.py, scripts/literag-index.py, scripts/literag-status.py, scripts/literag-meta.py, and scripts/lq

Rules

• Keep personal/work memory in OpenClaw builtin memory
• Keep large external corpora in LiteRAG, not memory_search
• Treat each knowledge base as an independent library with its own SQLite
• Search first, inspect second
• Prefer grouped document hits over raw chunk spam
• Prefer source-relative paths when citing files back to the user
• Use local OpenAI-compatible embeddings by default unless explicitly changed in config

Read these files when needed

• Always read <workspace>/.literag/knowledge-libs.json when targeting a library or changing config
• Read references/usage.md when you need command examples, output schema, or the intended search → inspect workflow
• Read references/configuration.md when adding libraries, source roots, excludes, chunking overrides, or ranking overrides
• Read references/agent-prompts.md when another agent / ACP harness needs a ready-made LiteRAG prompt template
• Read references/optimization-playbook.md when a specific library needs retrieval-quality tuning, ranking cleanup, or indexing-throughput tuning
• Read scripts under skills/literag/scripts/ only when editing behavior or diagnosing bugs

Slash / user-invocable usage

When invoked as /literag ..., parse the remaining argument string as a subcommand.

Supported forms:

• /literag search <library> <query>
• /literag inspect <library> <path> [--start N --end N]
• /literag index <library> [--limit-files N] [--embedding-batch-size N]
• /literag index-all [--limit-files N] [--embedding-batch-size N]
• /literag status <library>
• /literag meta <library>
• /literag benchmark <library> --query ...

If the user gives a natural-language request instead of a strict subcommand, translate it to the nearest supported operation instead of being pedantic.

Supported commands

• index_library.py — index one library
• index_all.py — index all configured libraries
• search_library.py — grouped hybrid/fts/vector retrieval
• inspect_result.py — expand a hit by file path + chunk range
• status_library.py — show index health / compatibility / counts
• meta_library.py — dump raw sqlite meta records
• benchmark_library.py — benchmark hybrid/fts/vector latency + hit shape across fixed query sets
• bin/literag — packaged CLI entrypoint for search / inspect / index / status / meta / benchmark
• scripts/literag-query.py — query/search/inspect wrapper
• scripts/literag-index.py — index wrapper for one library or all libraries
• scripts/literag-status.py — status wrapper
• scripts/literag-meta.py — meta wrapper
• scripts/literag-benchmark.py — benchmark wrapper
• scripts/lq — tiny shell alias for literag-query.py

Operating workflow

1. Read <workspace>/.literag/knowledge-libs.json
2. Resolve the target library
3. Run search_library.py for grouped retrieval
4. If needed, run inspect_result.py on the top hit or chosen range
5. For quick operator use, prefer scripts/literag-query.py or scripts/lq
6. Use scripts/literag-index.py when you need a short indexing entrypoint
7. Use scripts/literag-status.py before debugging weird retrieval or after config changes
8. Use scripts/literag-meta.py when you need the raw stored metadata
9. Use scripts/literag-benchmark.py or skills/literag/scripts/benchmark_library.py when you need repeatable retrieval latency / hit-shape comparisons
10. Keep LiteRAG separate from builtin memory unless the user explicitly wants a durable summary copied into workspace memory

Current intent

Use LiteRAG for:

• Blender manual + Blender Python reference
• Future blog/article/site knowledge bases
• Any large external docs where hybrid retrieval is needed without polluting builtin memory