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:

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LlamaIndex Libraries

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


LlamaIndex AI technology page Hackathon projects

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

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

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.