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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.

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.

AlphaBeam

AlphaBeam

AlphaBeam is a multilingual platform for Conversational Business intelligence, that redefines data interaction by seamlessly helping business users to explore their data without having to rely on their tech teams every time. While traditional BI tools have empowered data exploration through dashboards and reports, they often cater to a specialized audience, requiring technical expertise and leaving out crucial stakeholders. This results in missed insights, delayed decisions, and a limited understanding of their data's true potential. To address this shortcoming in fostering a truly interactive and user-centric experience for non-technical users, AlphaBeam seamlessly blends conversational capabilities with advanced analytics, creating a symbiotic relationship between business users and their data. It also fosters business interaction in local African languages, to capture cultural contexts. At a glance, AlphaBeam is a data-agnostic solution to ingest data from different sources, a semantic layer which translates raw data into business vocabularies and user queries into precise metrics and visualisations, a conversational interface for ad-hoc analysis and AI-powered insights through Llama 3.2 in 50+ African languages, and a visualisation layer which transforms the retrieved data into compelling dashboards. These capabilities empower users to carry out the following: - Conversational Inquiries: Business users can ask questions about already existing dashboards in English or 50+ African languages, just as they would converse with a colleague. They could dig deeper into the data behind the visualisations, gaining a comprehensive understanding of the data. - Comprehensive Metric Exploration: Engage in a conversational dialogue to uncover insights about any metric or data point. - Decision Making at the Speed of Data: Through conversational querying, AlphaBeam empowers users to make informed decisions very quickly based on readily available insights.

LinguaLink

LinguaLink

Lingualink is a digital health app that bridges language gaps in emergency medical settings, enhancing response times and care quality for non-English-speaking patients. Designed to transcribe low-resource languages into English, Lingualink allows triage nurses to quickly assess patient conditions who do not speak English efficiently, ensuring critical symptoms are accurately conveyed. In the US, 8.6% of the population faces language barriers in the ER, leading to potential treatment delays. Even a one-minute reduction in response time can save 10,000 lives annually, highlighting the urgent need for accessible translation. Lingualink operates with real-time transcription tailored to medical contexts, delivering precise, clinically informed translations for underrepresented languages in medical emergency settings. Cost-effective at $3,000 per month, Lingualink provides hospitals an affordable alternative to on-site or third-party translators. With a Total Addressable Market (TAM) of $2.64 billion, a Serviceable Available Market (SAM) of $1.32 billion, and a Serviceable Obtainable Market (SOM) of $132 million, our SaaS model targets US hospitals facing significant language barriers. Our competitive advantage includes rapid response, accuracy, and data-driven translations verified by medical research, making Lingualink a vital tool for ER staff. By reducing communication barriers, Lingualink enhances ER experiences for non-English speakers, helping to lower healthcare costs, accelerate treatment, and potentially save thousands of lives annually. Our app leverages a fine-tuned model trained on low-resource languages, enabling seamless, adaptive communication with patients in their native languages. This approach breaks down language barriers between medical staff and patients, ensuring that patients can comfortably discuss their health concerns in their preferred language, thereby improving the quality and accessibility of healthcare

ServeIQ

ServeIQ

ServeIQ: Technical Overview Purpose: ServeIQ is a multilingual, AI-powered platform designed to provide real-time public resources, emergency information, and enterprise-level data querying capabilities. Real-Time Public Service Navigation: Dynamic queries for public resources (e.g., tax filing, healthcare, emergency contacts). Real-time updates for emergencies (e.g., shelters, disaster alerts). Enterprise Integration: Private data querying for enterprises via LlamaIndex. Secure integration for accessing internal resources. Advanced Query Processing: Context-aware, intelligent query understanding using Llama 3.2 via Together AI. Combines static and dynamic data for precise responses.