This repository provides example notebooks demonstrating how to build LLM applications using LlamaCloud for data pipeline management, orchestrated by LlamaIndex. It targets developers looking to integrate LLM data processing into their workflows, offering a practical guide to creating end-to-end pipelines with LlamaCloud as the backend.

How It Works

The project leverages LlamaIndex as the core orchestration framework for LLM applications. LlamaCloud serves as the backend for managing data pipelines, allowing users to define data sources, sinks, embeddings, and transformations. The notebooks guide users through initializing an index within LlamaCloud and then connecting to it from a Jupyter environment using the LlamaIndex library.

Quick Start & Requirements

• Install/Run:

  python3 -m venv .venv
  source .venv/bin/activate
  pip install -r requirements.txt
  jupyter lab
  

• Prerequisites: Python 3, Jupyter Lab. Requires an API key from LlamaCloud (https://cloud.llamaindex.ai/).
• Setup: Estimated setup time is minimal, primarily involving environment creation and dependency installation.

Highlighted Details

• Demonstrates end-to-end LLM application pipelines.
• Utilizes LlamaCloud for data pipeline management.
• Orchestrated using the LlamaIndex framework.
• Provides example Jupyter notebooks for practical implementation.

Maintenance & Community

No specific information on contributors, sponsorships, or community channels is provided in the README.

Licensing & Compatibility

The README does not specify a license.

Limitations & Caveats

The project is presented as a demo and cookbook, implying it may not be production-ready. Specific compatibility or production-readiness details are not elaborated upon.