Skip to main content

Production-ready RAG infrastructure for multilingual applications

Project description

Maktaba

CI PyPI version Python 3.10+ License: MIT

The library for building libraries - By NuhaTech

From the Arabic word for library, Maktaba is a modern RAG infrastructure for building intelligent knowledge systems in any language.

Features

  • 🔌 Provider-agnostic: Works with OpenAI, Cohere, Azure, and more
  • 🚀 Production-ready: Built for scale with async-first design
  • 🧩 Modular: Use only what you need
  • 🌍 Multilingual: Optimized for Arabic and international languages
  • 📊 Type-safe: Full type hints and Pydantic validation
  • 🧪 Well-tested: Comprehensive test coverage

Installation

Using UV (Recommended)

# Install UV if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Add maktaba to your project
uv add maktaba

# With OpenAI + Qdrant
uv add "maktaba[openai,qdrant]"

# With all providers
uv add "maktaba[all]"

Using pip

# Basic installation
pip install maktaba

# With OpenAI + Qdrant
pip install "maktaba[openai,qdrant]"

# With all providers
pip install "maktaba[all]"

Quick Start

from maktaba.pipeline import QueryPipeline
from maktaba.embedding import OpenAIEmbedder
from maktaba.storage import QdrantStore
from maktaba.reranking import CohereReranker

# Create pipeline
pipeline = QueryPipeline(
    embedder=OpenAIEmbedder(api_key="..."),
    vector_store=QdrantStore(url="http://localhost:6333", collection_name="docs"),
    reranker=CohereReranker(api_key="...")
)

# Search with automatic reranking and citation formatting
result = await pipeline.search(
    query="What is Tawhid?",
    top_k=10,
    rerank=True
)

# Use in your LLM prompt
print(result["formatted_context"])  # [1]: content... [2]: content...
print(result["citations"])          # [{id: 1, source: "...", score: 0.95}, ...]

Development

Running Checks Before Push

Before pushing to the remote repository, run all quality checks:

Linux/Mac/Git Bash:

./scripts/check.sh

Windows CMD:

scripts\check.bat

This will run:

  • Ruff linting
  • MyPy type checking
  • Pytest tests

All checks must pass before pushing.

Documentation

  • Overview: docs/Overview.md
  • Quickstart: docs/Quickstart.md
  • Pipelines: docs/Pipelines.md
  • Providers: docs/Providers.md
  • Examples: docs/Examples.md
  • Troubleshooting: docs/Troubleshooting.md

Website (coming soon): maktaba.nuhatech.com

License

MIT License - see LICENSE

About NuhaTech

Built by NuhaTech - creators of Kutub and Muqabia.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

maktaba-0.1.3.tar.gz (446.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

maktaba-0.1.3-py3-none-any.whl (35.5 kB view details)

Uploaded Python 3

File details

Details for the file maktaba-0.1.3.tar.gz.

File metadata

  • Download URL: maktaba-0.1.3.tar.gz
  • Upload date:
  • Size: 446.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for maktaba-0.1.3.tar.gz
Algorithm Hash digest
SHA256 5fb442f1395f191a02fd726225b739b71d153d7f66245b2993420415845315d6
MD5 a68956b2fe9e2cbce0c6f0ba83fae687
BLAKE2b-256 5005ec2cdf4c5dc58658e097f054b0d9d280ab73c981b6fecc1d37c5c8c07985

See more details on using hashes here.

Provenance

The following attestation bundles were made for maktaba-0.1.3.tar.gz:

Publisher: publish.yml on nuhatech/maktaba

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file maktaba-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: maktaba-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 35.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for maktaba-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 8985af1ad6160a87d36fe361cd1f1530d948abbe5b928b24b5e3d69cff50e010
MD5 f5ba75d974282122c4258ba84da23a51
BLAKE2b-256 a20f0b9512fd531dccfb49f1ff8e75d76fe99e7033a6ba5d4942948448ef0bb3

See more details on using hashes here.

Provenance

The following attestation bundles were made for maktaba-0.1.3-py3-none-any.whl:

Publisher: publish.yml on nuhatech/maktaba

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page