docex 2.6.0

A robust, lightweight, and developer friendly document management and transport system for Python

DocEX

DocEX is a robust, extensible document management and transport system for Python. It supports multiple storage backends, metadata management, and operation tracking, with a unified API for local, SFTP, HTTP, and other protocols. Version 2.2.0 introduces database-level multi-tenancy, enhanced security with UserContext, and LLM-powered document processing capabilities.

Features

• 📁 Document storage and metadata management
• 🔄 Transport layer with pluggable protocols (local, SFTP, HTTP, etc.)
• 🛣️ Configurable transport routes and routing rules
• 📝 Operation and audit tracking
• 🧩 Extensible architecture for new protocols and workflows
• 🤖 LLM adapter integration - Process documents with OpenAI and other LLM providers
• 📋 Prompt management - YAML-based prompt templates with Jinja2 support
• 🔍 Structured data extraction - Extract structured data from documents using LLMs
• 📊 Vector indexing & semantic search - Generate embeddings and perform similarity search
• 🔎 RAG support - Build retrieval-augmented generation applications
• ☁️ S3 storage support - Store documents in Amazon S3
• 🏢 Multi-tenancy support - Database-level isolation for secure multi-tenant deployments
• 🔐 Enhanced security - UserContext for audit logging and tenant routing

Installation

Install from PyPI:

pip install docex

Optional Dependencies

For PDF processing features:

pip install pdfminer.six

For Word document processing features:

pip install python-docx

For LLM features (included by default in 2.2.0+):

• openai>=1.0.0 - OpenAI API integration
• jinja2>=3.1.0 - Prompt templating

Quick Start

Before using DocEX in your code, you must initialize the system using the CLI:

# Run this once to set up configuration and database
$ docex init

Then you can use the Python API (minimal example):

from docex import DocEX
from pathlib import Path

# Create DocEX instance (will check initialization internally)
docEX = DocEX()

# Create a basket
basket = docEX.create_basket('mybasket')

# Create a simple text file
hello_file = Path('hello.txt')
hello_file.write_text('Hello scos.ai!')

# Add the document to the basket
doc = basket.add(str(hello_file))

# Print document details
print(doc.get_details())

hello_file.unlink()

Security and Multi-Tenancy

DocEX 2.2.0+ includes enhanced security features and multi-tenancy support:

from docex import DocEX
from docex.context import UserContext

# Create UserContext for audit logging and multi-tenancy
user_context = UserContext(
    user_id="alice",
    user_email="[e-mail]",
    tenant_id="tenant1",  # For multi-tenant applications
    roles=["admin"]
)

# Initialize DocEX with UserContext (enables audit logging)
docEX = DocEX(user_context=user_context)

# All operations are logged with user context
basket = docEX.create_basket("invoices")

Multi-Tenancy Models:

• Database-Level Isolation (Model B) - Each tenant has separate database/schema (✅ Implemented in 2.2.0)
• Row-Level Isolation (Model A) - Shared database with tenant_id columns (Proposed)

See Multi-Tenancy Guide and Security Best Practices for details.

LLM-Powered Document Processing

DocEX 2.2.0+ includes LLM adapters for intelligent document processing:

from docex import DocEX
from docex.processors.llm import OpenAIAdapter
import asyncio
import os

# Initialize DocEX
docEX = DocEX()

# Create a basket
basket = docEX.create_basket('my_basket')

# Add a document
document = basket.add('invoice.pdf', metadata={'biz_doc_type': 'invoice'})

# Create LLM adapter
adapter = OpenAIAdapter({
    'api_key': os.getenv('OPENAI_API_KEY'),
    'model': 'gpt-4o',
    'prompt_name': 'invoice_extraction',  # Uses prompts from docex/prompts/
    'generate_summary': True,
    'generate_embedding': True
})

# Process document with LLM
result = await adapter.process(document)

if result.success:
    # Access extracted data
    metadata = document.get_metadata_dict()
    print(f"Invoice Number: {metadata.get('invoice_number')}")
    print(f"Total Amount: {metadata.get('total_amount')}")
    print(f"Summary: {metadata.get('llm_summary')}")

Available Prompts:

• invoice_extraction - Extract invoice data (number, amounts, dates, line items)
• product_extraction - Extract product information
• document_summary - Generate document summaries
• generic_extraction - Generic structured data extraction

Custom Prompts: Create your own prompt files in YAML format in docex/prompts/:

name: my_custom_prompt
description: Custom extraction prompt
version: 1.0

system_prompt: |
  You are an expert data extraction system.
  Extract the following information...

user_prompt_template: |
  Please extract data from this text:
  
  {{ text }}

Vector Indexing and Semantic Search

DocEX 2.2.0+ includes vector indexing and semantic search capabilities:

from docex import DocEX
from docex.processors.llm import OpenAIAdapter
from docex.processors.vector import VectorIndexingProcessor, SemanticSearchService
import asyncio

# Initialize DocEX
docEX = DocEX()
basket = docEX.create_basket('my_basket')

# Add and index documents
document = basket.add('document.pdf')

# Create vector indexing processor
llm_adapter = OpenAIAdapter({
    'api_key': os.getenv('OPENAI_API_KEY'),
    'model': 'gpt-4o'
})

vector_processor = VectorIndexingProcessor({
    'llm_adapter': llm_adapter,
    'vector_db_type': 'memory'  # Use 'pgvector' for production
})

# Index document
await vector_processor.process(document)

# Perform semantic search
search_service = SemanticSearchService(
    doc_ex=docEX,
    llm_adapter=llm_adapter,
    vector_db_type='memory',
    vector_db_config={'vectors': vector_processor.vector_db['vectors']}
)

results = await search_service.search(
    query="What is machine learning?",
    top_k=5
)

for result in results:
    print(f"{result.document.name}: {result.similarity_score:.4f}")

Vector Database Options:

• Memory - For testing/development (no setup required)
• pgvector - PostgreSQL extension (recommended for production, handles up to 100M vectors)

See Vector Search Guide for detailed documentation.

Additional examples can be found in the examples/ folder.

Configuration

Configure routes and storage in default_config.yaml:

transport_config:
  routes:
    - name: local_backup
      purpose: backup
      protocol: local
      config:
        type: local
        name: local_backup_transport
        base_path: /path/to/backup
        create_dirs: true
      can_upload: true
      can_download: true
      enabled: true
  default_route: local_backup

Documentation

• Developer Guide
• Design Document
• Multi-Tenancy Guide
• Security Best Practices
• LLM Adapter Implementation
• LLM Adapter Proposal
• Vector Search Guide
• API Reference

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT License