docex 2.9.9

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

DocEX

DocEX is a lean document storage, metadata, vector indexing, and transport library for Python. LLM and RAG orchestration are intentionally kept out of the core package and provided as copy-adapt examples under examples/.

Current Version: 3.0.0 - This release focuses DocEX core on storage, metadata, transport, and provider-neutral vector indexing. See docs/MIGRATION_3_0.md for migration notes.

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
• 📊 Vector indexing & semantic search - Bring your own embedding function
• ☁️ 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

Base Installation (Lightweight)

Install the lightweight base package from PyPI:

pip install docex

This installs only the core dependencies needed for basic document management with SQLite. Perfect for getting started or when you don't need advanced features.

Optional Dependencies

DocEX uses optional dependency groups to keep the base installation lightweight. Install only what you need:

# PostgreSQL database support
pip install docex[postgres]

# Vector indexing and semantic search
pip install docex[vector]

# Amazon S3 storage backend
pip install docex[storage-s3]

# HTTP transport method
pip install docex[transport-http]

# SFTP transport method
pip install docex[transport-sftp]

# PDF text extraction
pip install docex[pdf]

# Word document text extraction
pip install docex[docx]

# All optional features
pip install docex[all]

# Development dependencies (testing, linting, etc.)
pip install docex[dev]

Combine multiple features:

pip install docex[postgres,vector]
pip install docex[all,dev]  # All features + dev tools

Development Installation (Editable)

For development, install in editable mode:

# Lightweight editable install
pip install -e .

# Editable with all features
pip install -e ".[all,dev]"

This allows you to edit the source code and see changes immediately without reinstalling.

What's Included in Base Installation?

The base installation includes:

• ✅ SQLite database support
• ✅ Filesystem storage
• ✅ Local transport
• ✅ Core document management
• ✅ Metadata management
• ✅ Basic CLI commands

Not included (install via optional dependencies):

• ❌ PostgreSQL support → docex[postgres]
• ❌ Vector indexing/semantic search → docex[vector]
• ❌ S3 storage → docex[storage-s3]
• ❌ HTTP/SFTP transport → docex[transport-http] / docex[transport-sftp]
• ❌ PDF/DOCX processing → docex[pdf] / docex[docx]

See Dependency Optimization Guide for detailed information.

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 — access fields as attributes, e.g. record.name, record.status
details = doc.get_details()
print(details.name, details.status, details.created_at)

hello_file.unlink()

These methods return typed BasketRecord and DocumentRecord instances instead of plain dictionaries.

Security and Multi-Tenancy

DocEX 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/RAG Integration Examples

DocEX core does not ship LLM adapters, prompt templates, RAG services, or domain-specific extractors. Reference implementations live in:

• examples/integrations/ for OpenAI, Anthropic, and local LLM adapter examples.
• examples/patterns/ for RAG, knowledge base, advanced chunking, and invoice extraction patterns.

Vector Indexing and Semantic Search

DocEX includes vector indexing and semantic search capabilities (requires docex[vector]). You provide the embedding function:

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

def embed(text: str) -> list[float]:
    # Replace with your provider/model of choice.
    return [float(len(text) % 10), 1.0, 0.0]

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

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

# Create vector indexing processor
vector_processor = VectorIndexingProcessor(
    embedding_fn=embed,
    vector_db_type='memory'  # Use 'pgvector' for production
)

# Index document
await vector_processor.process(document)

# Perform semantic search
search_service = SemanticSearchService(
    doc_ex=docEX,
    embedding_fn=embed,
    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
• Dependency Optimization Guide - Lightweight installation and optional dependencies
• 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