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LlamaIndex: a Data Framework for LLM Applications

LlamaIndex is an open source data framework that allows you to connect custom data sources to large language models (LLMs) like GPT-4, Claude, Cohere LLMs or AI21 Studio. It provides tools for ingesting, indexing, and querying data to build powerful AI applications augmented by your own knowledge.

General
AuthorLlamaIndex
Repositoryhttps://github.com/jerryjliu/llama_index
TypeData framework for LLM applications

Key Features of LlamaIndex

  • Data Ingestion: Easily connect to existing data sources like APIs, documents, databases, etc. and ingest data in various formats.
  • Data Indexing: Store and structure ingested data for optimized retrieval and usage with LLMs. Integrate with vector stores and databases.
  • Query Interface: LlamaIndex provides a simple prompt-based interface to query your indexed data. Ask a question in natural language and get an LLM-powered response augmented with your data.
  • Flexible & Customizable: LlamaIndex is designed to be highly flexible. You can customize data connectors, indices, retrieval, and other components to fit your use case.

How to Get Started with LlamaIndex

LlamaIndex is open source and available on GitHub. Visit the repo to install the Python package, access documentation, guides, examples, and join the community:

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LlamaIndex Libraries

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ConnectSense - AI for South Asia

ConnectSense - AI for South Asia

ConnectSense: Bridging South Asia's Digital Divide ConnectSense addresses a critical challenge across South Asia, where over 900 million people in rural and remote communities lack reliable internet due to challenging geography, severe weather, limited budgets, and complex regulations. This digital exclusion impacts education, healthcare, and economic opportunities in the region's most vulnerable communities. Designed specifically for non-technical stakeholders like government officials, school administrators, and healthcare providers, ConnectSense is an AI-powered connectivity advisor that transforms complex telecommunications decisions into accessible guidance. The system evaluates geographical conditions, assesses appropriate technologies from fiber to satellite, optimizes budgets, and navigates country-specific regulations to deliver customized connectivity solutions in plain language. Built on a Python-based architecture using FastAPI, LlamaIndex, and FAISS vector database technology, ConnectSense processes region-specific connectivity knowledge through multiple AI models including Groq, and Gemini. Its Streamlit-powered interface offers an intuitive chat experience that maintains conversation history for iterative planning. ConnectSense enables real-world impact across diverse scenarios: helping Nepalese school principals identify satellite options within budget constraints, supporting Bangladeshi health officials in deploying weather-resistant networks for telemedicine, guiding Pakistani village councils through licensing requirements, and assisting Indian administrators with phased connectivity planning. By democratizing access to telecommunications expertise, ConnectSense empowers communities to build sustainable digital infrastructure and create pathways to opportunity in South Asia's most underserved regions.