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LlamaIndex AI technology page Top Builders

Explore the top contributors showcasing the highest number of LlamaIndex AI technology page app submissions within our community.

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:

AI Tutorials

LlamaIndex Libraries

A curated list of libraries and technologies to help you build great projects with LlamaIndex.

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LlamaIndex AI technology page Hackathon projects

Discover innovative solutions crafted with LlamaIndex AI technology page, developed by our community members during our engaging hackathons.

PANDORA

PANDORA

Starting from an existing core infrastructure capability, the network planning problem can be viewed as a resource allocation at the distribution layer. This allocation of resources is not uniform, i.e. different user profiles and use cases are considered, each associated with a quality of service. In the case of public networks, the highest priority is assigned to government institutions; guaranteeing government services is key for a city in order to improve its socioeconomic sustainability. We created an AI Tool supported by genetic algorithms together with LLM to optimize latency and bandwidth levels following the recommendations given by different standardization organizations such as ITU, IEEE and 3GPP.

AkamiCare

AkamiCare

AkamiCare is an innovative solution that combines the power of a chatbot and an intuitive dashboard to streamline access to critical healthcare information. Designed with Hawaii's unique healthcare challenges in mind, it enables medical staff to effortlessly retrieve patient records, hospital database entries, and healthcare guidelines. The chatbot offers real-time, conversational interaction, answering queries from CSV databases and PDFs about healthcare conditions. Meanwhile, the dashboard provides a centralized view of essential metrics, ensuring quick and efficient decision-making. AkamiCare is more than just a tool—it’s a solution built to empower Hawaii’s healthcare professionals, reduce inefficiencies, and improve patient outcomes.

GreenField Verdant

GreenField Verdant

Una herramienta imprescindible para los agricultores que desean maximizar el potencial de sus tierras y proteger sus cultivos. Esta app combina tecnología avanzada y facilidad de uso para ofrecer un dashboard interactivo que centraliza toda la información clave de las áreas guardadas. Los agricultores pueden acceder a predicciones detalladas sobre inundaciones, sequías, incendios y la probabilidad de éxito de sus cultivos. Además, la app permite guardar plantas individuales y diagnosticar posibles enfermedades mediante un modelo de predicción. Para garantizar que esta información sea fácil de entender y aplicar, integra un asistente inteligente que responde preguntas y guía a los usuarios en cada paso. Adicionalmente, incluye mapas con datos actualizados de la NASA para identificar focos de calor, incendios y sequías, permitiendo una gestión proactiva y eficiente de las áreas de cultivo. Esta app no solo protege los cultivos, sino que también mejora la toma de decisiones con herramientas innovadoras que impulsan la productividad agrícola.

iHelp

iHelp

iHelp is a fast, intuitive, and easy-to-use platform designed to help businesses quickly create and deploy AI-powered customer support assistants. By leveraging IBM Watson's cutting-edge conversational AI, iHelp seamlessly transforms your website into a smart, automated assistant that can efficiently handle customer inquiries, provide support, and automate routine tasks. With iHelp, businesses can generate a fully functional assistant in just a few simple steps, eliminating the need for complex coding or technical expertise. Whether you're looking to automate FAQs, assist customers with order tracking, or provide detailed product information, iHelp empowers your assistant to engage with customers 24/7, ensuring that your business is always accessible. This not only reduces the reliance on human agents but also significantly improves customer service efficiency and response times. Designed for businesses of all sizes, iHelp streamlines support operations, enhances customer satisfaction, and enables a seamless user experience—all while saving time and costs. From small businesses looking to automate basic support to larger enterprises needing a robust, scalable solution, iHelp is the simplest and most effective way to launch a Watson-powered assistant and enhance your customer experience in just minutes.

Synth Dev

Synth Dev

## Problem 1. AI coding assistants (Copilot, Cursor, Aider.chat) accelerate software development. 2. People typically code not by reading documentation but by asking Llama, ChatGPT, Claude, or other LLMs. 3. LLMs struggle to understand documentation as it requires reasoning. 4. New projects or updated documentation often get overshadowed by legacy code. ## Solution - To help LLMs comprehend new documentation, we need to generate a large number of usage examples. ## How we do it 1. Download the documentation from the URL and clean it by removing menus, headers, footers, tables of contents, and other boilerplate. 2. Analyze the documentation to extract main ideas, tools, use cases, and target audiences. 3. Brainstorm relevant use cases. 4. Refine each use case. 5. Conduct a human review of the code. 6. Organize the validated use cases into a dataset or RAG system. ## Tools we used https://github.com/kirilligum/synth-dev - **Restack**: To run, debug, log, and restart all steps of the pipeline. - **TogetherAI**: For LLM API and example usage. See: https://github.com/kirilligum/synth-dev/blob/main/streamlit_fastapi_togetherai_llama/src/functions/function.py - **Llama**: We used Llama 3.2 3b, breaking the pipeline into smaller steps to leverage a more cost-effective model. Scientific research shows that creating more data with smaller models is more efficient than using larger models. See: https://github.com/kirilligum/synth-dev/blob/main/streamlit_fastapi_togetherai_llama/src/functions/function.py - **LlamaIndex**: For LLM calls, prototyping, initial web crawling, and RAG. See: https://github.com/kirilligum/synth-dev/blob/main/streamlit_fastapi_togetherai_llama/src/functions/function.py

Rag Custom Pipiline Generator

Rag Custom Pipiline Generator

Creating a custom RAG (Retrieve and Generate) pipeline without any coding is now accessible to users and beginners through intuitive no-code platforms. Start by selecting a user-friendly tool like Microsoft Power Automate, Zapier, or specialized AI platforms such as LangChain’s no-code solutions. These platforms offer drag-and-drop interfaces where you can easily configure data sources for retrieval, such as databases, APIs, or cloud storage. Next, integrate AI models for the generation phase by selecting from pre-built templates or connecting to services like OpenAI. Customize the workflow by setting parameters, defining triggers, and establishing how retrieved information is used to generate responses. Additionally, leverage built-in analytics and testing features to refine your pipeline. No-code RAG pipelines empower users to efficiently build robust, tailored solutions for tasks like customer support, content creation, and data analysis without needing programming expertise. This approach accelerates deployment, reduces errors, and allows for easy adjustments, making advanced AI capabilities accessible to everyone.