Memory

the-librarian

Build and search lightweight quantized document indexes with TurboVec. Use when you need to create searchable indexes from documents for RAG applications with minimal memory footprint, or when you need semantic search on resource-constrained hardware. Triggers on phrases like "build a document index", "search my documents", "create a RAG system", "quantized vector search", "lightweight RAG", "document search on raspberry pi", "semantic search without FAISS".

What it does

The skill document

The Librarian

Lightweight document search with TurboVec quantization. Build semantic search indexes that run on minimal hardware.

Author: RandTrad Consulting — Document Intelligence for SMEs License: MIT — Free for personal and commercial use with attribution Version: 1.2.0 Security: See Security section for safe usage guidelines and vulnerability history

What It Does

Builds quantized vector indexes from Markdown/text documents
Supports hybrid search (vector + BM25 keyword matching)
Optional Flashrank reranking for improved accuracy
Chunk expansion for surrounding context
8-16x smaller indexes than FAISS

When to Use

Use Case	Choose The Librarian
Resource-constrained hardware	✅ Runs on Raspberry Pi, 512MB RAM
Personal knowledge base	✅ Zero infrastructure
Embedded/offline deployment	✅ No cloud, no database
100K+ documents on limited hardware	✅ Fits where FAISS doesn't
Medical/legal records	❌ Use FAISS instead
Maximum accuracy required	❌ Use FAISS + Flashrank

Accuracy: ~97-98% of FAISS for 4-bit quantization. Top results may occasionally swap ranking.

Quick Start

Prerequisites

# Install BLAS library (required for TurboVec)
sudo apt install libblas3

# Create venv and install dependencies
cd /path/to/the-librarian
python3 -m venv venv
source venv/bin/activate
pip install turbovec numpy requests rank-bm25 flashrank

Build an Index

# Using the wrapper (recommended)
./scripts/librarian build /path/to/documents/ index/my_library

# With options
./scripts/librarian build /path/to/docs/ index/my_library --bits 3 --chunk-size 800

# Direct Python
LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libblas.so.3 \
  python scripts/build_index.py --input /path/to/docs/ --output index/my_library

Search

# Pure vector search
./scripts/librarian search "habit formation" index/my_library

# Hybrid (vector + BM25)
./scripts/librarian search "habit formation" index/my_library --hybrid

# Hybrid + rerank (best accuracy)
./scripts/librarian search "habit formation" index/my_library --hybrid --rerank

# With context expansion
./scripts/librarian search "habit formation" index/my_library --hybrid --rerank --expand 1

# JSON output
./scripts/librarian search "habit formation" index/my_library --json

Search Modes

Mode	Time	Accuracy	Use Case
Vector only	~130ms	Good	Semantic concepts, synonyms
Hybrid	~140ms	Better	Combines semantic + exact keywords
Hybrid + rerank	~320ms	Best	Maximum precision

Bit Width Options

Bits	Compression	Accuracy	Use Case
4-bit	8x	~97-98%	Default, best balance
3-bit	10.7x	~95-96%	Tight memory
2-bit	16x	~93-95%	Extreme compression

File Structure

the-librarian/
├── SKILL.md
├── scripts/
│   ├── librarian           # Wrapper script (handles LD_PRELOAD)
│   ├── build_index.py      # Build quantized index
│   └── search.py           # Search with hybrid + rerank
└── references/
    └── quantization.md     # How TurboVec compression works

Index Files

After building, you'll have:

index/my_library/
├── library.qindex      # TurboVec quantized index
├── chunks.json         # Document chunks with metadata
├── bm25_index.json     # BM25 keyword index (JSON — safe, no code execution risk)
└── stats.json          # Build statistics

Security: BM25 index stored as JSON (not pickle) to prevent arbitrary code execution from untrusted index files. Legacy bm25_index.pkl files are detected but NOT loaded — rebuild indexes to migrate.

Security

Safe Usage Guidelines

Only load indexes you built yourself or received from a trusted source
Always use a local Ollama instance (localhost or 127.0.0.1) for sensitive documents — document text is sent to the embedding API over HTTP
Never share index directories — they contain full document text in chunks.json
If you receive an index from someone else, ensure it uses bm25_index.json (not .pkl)
The --api flag can point to any HTTP endpoint — verify the host before running
If using a remote embedding API, be aware that document text and queries are transmitted over the network

Vulnerability History

v1.1.0 (July 2026):

Critical fix: pickle.load on bm25_index.pkl removed. Pickle files can execute arbitrary code when loaded. BM25 index now stored as JSON. Legacy pickle files are detected but refused to load.
High fix: Embedding API URL now checked against localhost. If a non-local API endpoint is configured, a privacy warning is displayed before any data is transmitted.
Medium fix: SKILL.md now documents that document text and search queries are transmitted to the Ollama HTTP API for embedding generation.

v1.2.0 (July 2026):

Removed all private infrastructure details (local file paths, API endpoints, credentials paths, phone numbers, cron job IDs) from published skill. Skill is now generic and safe for public distribution.

Reporting Security Issues

Report vulnerabilities to randtradbusiness@gmail.com. Security fixes will be published on ClawHub.

Accuracy Guidance

For critical applications (medical, legal, financial):

Use FAISS instead. The ~2-3% ranking variance in TurboVec is acceptable for personal knowledge bases, parts catalogs, and general document search, but not for applications where missing a result has consequences.

For personal/team use:

TurboVec is ideal. The accuracy difference is negligible for most queries, and the size savings enable deployment on hardware that couldn't run FAISS at all.

Performance Comparison

Metric	FAISS	TurboVec 4-bit
Cold query	~150-165ms	~150-165ms
Warm query	~35-40ms	~130-135ms
Pure search	~10-12ms	~10-15ms
Index size	100%	~7-12%
RAM required	High	Low

Note: Both spend ~120-140ms generating embeddings via Ollama. The search difference is minimal.

References

references/quantization.md - Technical details on how TurboVec compression works

Author

RandTrad Consulting — Document Intelligence consultancy for SMEs

Website: https://www.randtradconsulting.com
Contact: randtradbusiness@gmail.com
Services: AI Training, EU AI Act Compliance, Document RAG Systems

Built by Enda Rochford — RandTrad Consulting

License

MIT License — Free for personal and commercial use with attribution.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files, to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, subject to the following condition:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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