jw-my-rag 0.1.1

OCR Vector Database - Document parsing, semantic segmentation, and vector search

OCR Vector DB

A document processing and semantic search system that parses documents (PDFs, Markdown, plain text), creates semantic embeddings, and stores them in PostgreSQL with pgvector for similarity search.

Features

• Multi-format parsing: PDF (with OCR fallback), Markdown, plain text
• Semantic segmentation: Intelligent grouping of text, code, and images
• Multi-view embeddings: Separate embeddings for text, code, images, tables
• Parent-child hierarchy: Context-aware retrieval with parent documents
• RAG support: LLM-powered question answering over your documents
• PostgreSQL + pgvector: Scalable vector storage with HNSW indexing

Installation

From PyPI

pip install ocr-vector-db

Using uv (recommended)

uv add ocr-vector-db

Prerequisites

• Python 3.12+
• PostgreSQL with pgvector extension
• Google API key (for Gemini embeddings/LLM) or Voyage API key

Database Setup

Start PostgreSQL with pgvector using Docker:

docker run -d \
  --name pgvector \
  -e POSTGRES_USER=langchain \
  -e POSTGRES_PASSWORD=langchain \
  -e POSTGRES_DB=vectordb \
  -p 5432:5432 \
  pgvector/pgvector:pg16

Or use docker-compose (if provided in the repository).

Quick Start

1. Configure Environment

Create a .env file:

# Required
PG_CONN=postgresql+psycopg://langchain:langchain@localhost:5432/vectordb
COLLECTION_NAME=my_documents
GOOGLE_API_KEY=your-api-key-here
EMBEDDING_PROVIDER=gemini

2. Ingest Documents

# Ingest PDF files
myrag ingest documents/*.pdf

# Ingest with dry-run (parse only)
myrag ingest report.pdf --dry-run

3. Search

# Direct search
myrag search "vector database optimization"

# Search with filters
myrag search "async function" --view code --language javascript

# JSON output
myrag search "machine learning" --json

4. RAG (Question Answering)

# Ask a question
myrag rag "What is the main topic of this document?"

# With sources
myrag rag "How does the authentication work?" --sources

5. Interactive REPL

# Start search REPL
myrag

# Start RAG REPL
myrag --rag

CLI Commands

myrag (default)

Start the interactive REPL for search or RAG queries.

myrag              # Search mode
myrag --rag        # RAG mode (LLM-powered)
myrag --view code  # Default filter

myrag search

Run a single search query.

myrag search "query" [options]

Options:
  --view {text,code,image,caption,table,figure}  Filter by content type
  --language LANG       Filter by programming language
  --top-k N, -k N       Number of results (default: 5)
  --no-context          Disable parent context expansion
  --json                Output JSON format
  --verbose, -v         Enable verbose logging

myrag ingest

Ingest documents into the vector database.

myrag ingest FILE [FILE ...] [options]

Options:
  --dry-run      Parse only, no database writes
  --no-cache     Disable OCR cache (re-process all pages)

myrag rag

Ask a question using RAG (Retrieval-Augmented Generation).

myrag rag "question" [options]

Options:
  --view {text,code,image,caption,table,figure}  Filter by content type
  --language LANG       Filter by programming language
  --top-k N, -k N       Number of context documents (default: 5)
  --sources             Show source documents in response
  --verbose, -v         Enable verbose logging

myrag quality

Inspect data quality and statistics.

myrag quality

Output includes:

• Document/concept/fragment/embedding counts
• View distribution
• Orphan entity check

Configuration

All settings are configured via environment variables. See .env.example for the complete list.

Key Settings

Variable	Description	Default
PG_CONN	PostgreSQL connection string	(required)
COLLECTION_NAME	Vector store collection name	(required)
EMBEDDING_PROVIDER	gemini or voyage	voyage
GOOGLE_API_KEY	Google API key for Gemini	-
VOYAGE_API_KEY	Voyage AI API key	-
EMBEDDING_DIM	Embedding dimension	768
PARENT_MODE	Grouping mode: unit, page, section, page_section	unit
ENABLE_IMAGE_OCR	Enable Gemini Vision OCR for PDFs	true

Architecture

Document -> Concept -> Fragment -> Embedding

• Document: Source file (PDF, Markdown, text)
• Concept: Semantic unit (related paragraphs, code blocks)
• Fragment: Individual piece of content (text paragraph, code block, image)
• Embedding: Vector representation for similarity search

Multi-View Strategy

Documents are segmented into distinct views:

• text: Natural language paragraphs
• code: Code blocks with language detection
• image: Image references with alt text
• caption: Figure/table captions
• table, figure: Structured content

Each view can be filtered during search for targeted retrieval.

Development

Install from Source (uv)

git clone https://github.com/ocr-vector-db/ocr-vector-db.git
cd ocr-vector-db

# Install with uv (recommended)
uv sync --all-extras

# Or with pip
pip install -e ".[dev,test]"

Build Package

# Using uv
uv build

# Or using pip
python -m build

Run CLI (development)

# Using uv
uv run myrag --help
uv run myrag search "query"
uv run myrag ingest docs/*.pdf

# Or after pip install -e .
myrag --help

Run Tests

uv run pytest
# or
pytest

Sync Lock File

uv lock

License

MIT License - see LICENSE for details.