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.


LlamaIndex AI technology page Hackathon projects

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

EthiSecureAI

EthiSecureAI

This project leverages Falcon LLMs for compliance, risk management, and real-time monitoring of LLM chatbots. Implementation Azure CosmosDB: Stores user info and violations. Multithreading in Flask: Enhances scalability. LRU Cache: Reduces latency for similar queries. Falcon LLM-powered Keyword Extractor: Creates a Rule Knowledge Graph (KG) from admin-uploaded PDFs, serving as an alternative to fine-tuning. Azure Blob Storage & Neo4j: Stores admin’s PDFs and the Knowledge Graph. Semantic Router: Retrieves relevant subgraphs and identifies violations. User Authentication: Clerk integrated with CosmosDB. Deployment: Frontend on Vercel, backend on PythonAnywhere. Objective Periodically updated Rules Knowledge Graph. Display rule violations, reasons, and priority levels. Support for multi-modal (image+text) query checks. Assign Risk scores based on violations. Analyze user behavior patterns. Methodology Flask Server: Multithreaded for handling requests and database queries. AI Pipeline: Rule Registration: Extracts and decomposes rules from PDFs, forms a knowledge graph, integrates external sources, and allows admin modifications. Query Processing: Degenerates user queries, retrieves relevant subgraphs, checks for rule violations, and reasons for violations. Risk Scoring Risk scores based on EU AI Act categories: Critical, High, Medium, Minimal. Analytics Admin panel displays rule violation frequency, custom timeframe violations, and user-wise analytics using Chart.js. Scope Compliance Assurance: Adherence to policies and regulations. Risk Management: Identifies and mitigates risks. Real-time Monitoring: Ensures transparency and accountability.

Voithos

Voithos

Developed a system to process student queries and classify them into one or more categories: scheduler, wellness, diet, and note maker. Based on the classification, the system generates a tailored prompt for each category. If a query falls into multiple categories, the system creates separate prompts for each relevant category and sends them to the respective agents. Each agent processes its prompt and returns an output. The system then aggregates these outputs into a single, comprehensive response, ensuring the student receives all necessary information and tasks are completed efficiently in one go. Agent Processing πŸ“… Scheduler Agent Handles calendar-related queries. Interacts directly with the Google Calendar API to manage events and schedules. Focuses on efficient API interactions without using RAG (Retrieval-Augmented Generation). πŸ““ Note Maker Agent Manages study notes and creates study materials. Uses RAG for generating quizzes, flashcards, and mindmaps ensuring contextually relevant content. Interacts with a dedicated note storage system for basic note management. πŸ‹οΈβ€β™‚οΈ Fitness Agent This agent assists in developing personalized fitness plans tailored to your specific goals. By completing a straightforward questionnaire, users can receive expert guidance in planning, deciding, and adhering to their customized fitness regimen. Potential future integration with external fitness tracking APIs. 😊 Mood Agent This Agent provides comprehensive emotional support and mood management, offering empathetic responses, and personalized coping strategies. Users can log their current moods, and receive tailored recommendations such as breathing exercises, mindfulness practices, and physical activities. By analyzing mood data, the Mood Agent helps users understand their emotional health and offers simple actionable advice to instantly uplift their moods and help maintain a balanced and positive mindset that in turn, boosts productivity!