knowledge-base-builder

name	knowledge-base-builder
description	Build searchable knowledge bases from document collections (PDFs, Word, text files). Use for creating technical libraries, standards repositories, research databases, or any large document collection requiring full-text search.
type	reference
version	1.1.0
last_updated	"2026-01-02T00:00:00.000Z"
category	data
related_skills	["semantic-search-setup","rag-system-builder"]
capabilities	[]
requires	[]
tags	[]

Knowledge Base Builder

Overview

This skill creates searchable knowledge bases from document collections using SQLite FTS5 full-text search indexing. It handles PDF extraction, text chunking, metadata cataloging, and search interface creation.

Quick Start

import sqlite3

conn = sqlite3.connect("knowledge.db", timeout=30)
cursor = conn.cursor()

# Create FTS5 search table
cursor.execute('''
    CREATE VIRTUAL TABLE IF NOT EXISTS search_index
    USING fts5(content, filename)
''')

# Add content
cursor.execute('INSERT INTO search_index VALUES (?, ?)',
               ("Sample document text...", "doc.pdf"))

# Search
cursor.execute("SELECT * FROM search_index WHERE search_index MATCH 'sample'")
print(cursor.fetchall())

When to Use

• Building searchable technical standards libraries
• Creating research paper databases
• Indexing corporate document repositories
• Setting up knowledge management systems
• Converting file-based document collections to queryable databases

Architecture

Document Collection
       |
       v
+------------------+
|  1. Inventory    |  Scan files, extract metadata
+--------+---------+
         v
+------------------+
|  2. Extract      |  PDF -> text, chunk by pages
+--------+---------+
         v
+------------------+
|  3. Index        |  SQLite FTS5 full-text search
+--------+---------+
         v
+------------------+
|  4. Search CLI   |  Query interface with filtering
+------------------+

Implementation Steps

See references/implementation.md for detailed code for each step (schema creation, document inventory, PDF extraction, search interface, CLI template, and usage examples).

Related Skills

• semantic-search-setup - Add vector embeddings for AI search
• rag-system-builder - Build AI Q&A on top of knowledge base
• pdf/text-extractor - Detailed PDF extraction options

Version History

• 1.1.0 (2026-01-02): Added Quick Start, Execution Checklist, Error Handling, Metrics sections; updated frontmatter with version, category, related_skills