maktaba 0.1.9

Production-ready RAG infrastructure for multilingual applications

Maktaba

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
• 🔍 Deep research: Built-in iterative planning for long-form reports

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}, ...]

Deep Research Pipeline

Learn how to customise the default prompts via maktaba_templates.md.

from maktaba.pipeline import create_deep_research_pipeline
from maktaba.embedding import OpenAIEmbedder
from maktaba.storage import QdrantStore
from maktaba.llm import OpenAILLM

pipeline = create_deep_research_pipeline(
    embedder=OpenAIEmbedder(api_key="..."),
    store=QdrantStore(url="http://localhost:6333", collection_name="docs"),
    llm=OpenAILLM(api_key="...", model="gpt-4o-mini"),
)

result = await pipeline.run_research("Impacts of lunar dust on spacecraft design")

chunks = [chunk async for chunk in result.stream]
print("".join(chunks))       # Final long-form report
print(result.queries_used)   # Queries issued during research
print(result.source_indices) # 1-based indices of retained sources

For a full walkthrough (configuration knobs, streaming, stage overrides), see docs/DeepResearch.md and examples/deep_research_pipeline.py.

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.