/bookstrap-ingest - Load Research Corpus

Load initial research materials into the Bookstrap database for use during writing.

Purpose

Ingest source documents into the database to build the research corpus. This command processes multiple file types (PDF, markdown, HTML, plain text), generates embeddings for semantic search, extracts entities (characters, locations, events, concepts), builds relationships between entities, and constructs the knowledge timeline.

Input Arguments

Accept one or more of the following:

• File paths: Individual files to ingest (e.g.,

  ./research/soe-training.pdf

  )
• Directories: Recursively process all files in a directory (e.g.,

  ./research/

  )
• URLs: Web pages to fetch and ingest (e.g.,

  https://example.com/article.html

  )

Multiple sources can be provided in a single invocation:

/bookstrap-ingest ./research/documents/ https://example.com/article.html ./notes.md

Supported File Types

.pdf

ingest-file.py

.md

.markdown

.html

.htm

.txt

Processing Workflow

For each source provided:

1. Source Collection

• Parse arguments to identify file paths, directories, and URLs
• If directory: recursively find all supported files
• If URL: fetch content using WebFetch
• Track all sources for batch processing

2. File Ingestion

For each file, execute the ingestion pipeline:

python scripts/ingest-file.py <file-path>

The

ingest-file.py

• File reading: Load content from supported formats
• Semantic chunking: Use LLM to identify natural breakpoints (paragraph boundaries, topic shifts, scene changes) rather than fixed token windows
• Embedding generation: Call configured embedding provider (Gemini, OpenAI, Ollama, LM Studio) via

  scripts/generate-embedding.py

• Entity extraction: Use LLM to extract characters, locations, events, concepts with context via

  scripts/extract-entities.py

• Database storage: Store source, chunks, embeddings, and entities in SurrealDB

3. Relationship Building

After entity extraction, automatically create graph relationships:

• Link sources to extracted concepts (

  source->supports->concept

  )
• Link events in chronological order (

  event->precedes->event

  ,

  event->follows->event

  )
• Link entities mentioned together (

  character->knows->character

  ,

  concept->related_to->concept

  )

4. Timeline Construction

Order events by:

• Extracted dates (if available)
• Sequence numbers from document structure
• Contextual ordering from content analysis

5. Metadata Recording

Store ingestion metadata for each source:

CREATE source SET
  title = $title,
  content = $content,
  embedding = $embedding_vector,
  url = $url,
  source_type = $source_type,  -- 'primary', 'secondary', 'web'
  reliability = $reliability,   -- 'high', 'medium', 'low'
  ingested_at = time::now(),
  ingested_during = 'bootstrap'
;

Statistics Reporting

After ingestion completes, report:

INGESTION COMPLETE
==================

SOURCES PROCESSED: 15 files, 3 URLs
  - PDF: 8 files
  - Markdown: 5 files
  - HTML (web): 3 URLs
  - Plain Text: 2 files

ENTITIES EXTRACTED: 247 total
  - Characters: 34
  - Locations: 52
  - Events: 123
  - Concepts: 38

RELATIONSHIPS CREATED: 412 edges
  - source->supports->concept: 156
  - event->precedes->event: 98
  - event->follows->event: 98
  - character->knows->character: 24
  - concept->related_to->concept: 36

EMBEDDINGS GENERATED: 347 vectors
  - Sources: 15
  - Chunks: 285
  - Entities: 47

TIMELINE ENTRIES: 123 events ordered chronologically

STORAGE
-------
Database: bookstrap.my_book
Namespace: bookstrap
Total size: 12.4 MB

Error Handling

Handle common ingestion errors gracefully:

Configuration

Ingestion behavior is controlled by

bookstrap.config.json

{
  "embeddings": {
    "provider": "gemini",
    "model": "text-embedding-004",
    "dimensions": 768
  },
  "extraction": {
    "provider": "llm",
    "chunking": {
      "strategy": "semantic",
      "max_tokens": 1024,
      "overlap": 128
    }
  },
  "surrealdb": {
    "host": "localhost",
    "port": 2665,
    "namespace": "bookstrap",
    "database": "my_book"
  }
}

Implementation Notes

• Batch processing: Process files sequentially to avoid overwhelming the embedding API
• Rate limiting: Respect embedding provider rate limits (configured in

  bookstrap.config.json

  )
• Idempotency: Re-ingesting the same file updates existing records rather than creating duplicates (match on title + content hash)
• Progress tracking: Log each file as it's processed for visibility during long ingestions
• Database connection: Verify SurrealDB is running before starting ingestion

Example Usage

# Ingest a single file
/bookstrap-ingest ./research/soe-training-manual.pdf

# Ingest an entire directory
/bookstrap-ingest ./research/primary-sources/

# Ingest multiple sources at once
/bookstrap-ingest ./research/ https://en.wikipedia.org/wiki/SOE https://example.com/lyon-resistance.html

# Ingest web content only
/bookstrap-ingest https://archive.org/details/soe-field-manual

Pre-requisites

Before running

/bookstrap-ingest

1. BRD created:

   /bookstrap-init

   must have been run to create the Book Requirements Document
2. SurrealDB running: Database must be accessible (started via

   docker-compose up -d

   or

   ./scripts/start-surreal.sh

   )
3. Schema initialized: Database schema must be loaded via

   ./scripts/init-schema.sh

4. API keys configured: Embedding provider API key must be set in environment variables (e.g.,

   GEMINI_API_KEY

   )

Related Commands

• /bookstrap-init

  - Create BRD and initialize database

• /bookstrap-plan-research

  - Identify knowledge gaps after ingestion

• /bookstrap-research

  - Fill gaps with targeted web research

• /bookstrap-status

  - View corpus statistics and coverage

Supporting Scripts

scripts/ingest-file.py

scripts/generate-embedding.py

scripts/extract-entities.py

scripts/chunk.py

See individual script documentation for configuration options and advanced usage.