LlamaIndex by LlamaIndex | AI Agents

Agent framework

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By LlamaIndex

LlamaIndex is an advanced AI agent framework designed to help developers and enterprises build sophisticated, context-augmented applications. By bridging the gap between large language models (LLMs) and diverse data sources, LlamaIndex enables the creation of production-ready AI applications that can effectively interact with complex enterprise data. This framework is particularly useful for organizations seeking to enhance decision-making processes, automate information retrieval, and develop custom knowledge assistants tailored to their specific needs.

Features

LlamaIndex offers a wide array of features that facilitate the development of context-augmented applications. These features encompass data integration, advanced generation techniques, customizable frameworks, and tools for managing the execution of AI agents.

Feature	Description
Data Integration and Management	Seamlessly integrates with various data sources, including APIs, databases, PDFs, and web pages, using built-in connectors for rapid data ingestion.
Advanced Retrieval-Augmented Generation (RAG) Techniques	Enhances LLM utility by indexing data for optimal retrieval, enabling natural language queries against private data without retraining models.
Flexible and Customizable Framework	Provides end-to-end tools for developing AI agents capable of information retrieval, data synthesis, and action execution over enterprise data.
Agent Runner and Agent Worker	Manages agent state and task execution, with customizable options for specific reasoning and data processing needs.
Production-Ready LLM Agent Deployment	Handles complex unstructured enterprise data, providing efficient data retrieval and storage solutions.
Open-Source with Active Developer Community	Encourages community contributions for continuous improvement and expansion of the framework.

Use cases

LlamaIndex is versatile and can be applied in various industries such as finance, manufacturing, and IT. Below are some specific examples of how LlamaIndex can be utilized:

AI Sales Assistant: NVIDIA developed an AI sales assistant using LlamaIndex, enhancing sales team productivity by providing seamless access to internal data. This application improves information retrieval accuracy and assists in tasks such as translation and email drafting.
Data-Augmented Applications: Organizations can build applications that retrieve answers from unstructured data sources like PDFs, PPTs, web pages, and images. This capability makes LlamaIndex suitable for a wide range of data-augmented applications.

How to get started

To begin using LlamaIndex, developers and enterprises can access the framework as an open-source solution. Interested individuals can visit the official GitHub repository for documentation, installation instructions, and to explore the active developer community. This provides an excellent opportunity to collaborate, contribute, and leverage the capabilities of LlamaIndex in building advanced AI applications.

LlamaIndex Pricing Overview

The pricing for LlamaIndex is variable and depends on the underlying Large Language Model (LLM) calls made during index building and querying.

LLM Costs

OpenAI GPT-3.5-turbo: $0.002 per 1,000 tokens

LlamaExtract Credits

Free Users: 1,000 credits per day
Paid Users: 7,000 credits per week
Additional Credits: $3 per 1,000 credits after the initial allocation

Parsing Costs

Normal Parsing: 1 credit per page ($3 / 1,000 pages)
GPT-4o Parsing: 10 credits per page ($30 / 1,000 pages)
Fast Mode Parsing: 1 credit per 3 pages (minimum 1 credit per document) ($1 / 1,000 pages)

For a detailed cost analysis, please refer to the official LlamaIndex pricing documentation.

Features

Feature

Description

Data Integration and Management

Seamlessly integrates with various data sources, including APIs, databases, PDFs, and web pages, using built-in connectors for rapid data ingestion.

Advanced Retrieval-Augmented Generation (RAG) Techniques

Enhances LLM utility by indexing data for optimal retrieval, enabling natural language queries against private data without retraining models.

Flexible and Customizable Framework

Provides end-to-end tools for developing AI agents capable of information retrieval, data synthesis, and action execution over enterprise data.

Agent Runner and Agent Worker

Manages agent state and task execution, with customizable options for specific reasoning and data processing needs.

Production-Ready LLM Agent Deployment

Handles complex unstructured enterprise data, providing efficient data retrieval and storage solutions.

Open-Source with Active Developer Community

Encourages community contributions for continuous improvement and expansion of the framework.

Use cases

LlamaIndex is versatile and can be applied in various industries such as finance, manufacturing, and IT. Below are some specific examples of how LlamaIndex can be utilized:

AI Sales Assistant: NVIDIA developed an AI sales assistant using LlamaIndex, enhancing sales team productivity by providing seamless access to internal data. This application improves information retrieval accuracy and assists in tasks such as translation and email drafting.

Data-Augmented Applications: Organizations can build applications that retrieve answers from unstructured data sources like PDFs, PPTs, web pages, and images. This capability makes LlamaIndex suitable for a wide range of data-augmented applications.

How to get started

LlamaIndex Pricing Overview

The pricing for LlamaIndex is variable and depends on the underlying Large Language Model (LLM) calls made during index building and querying.

LLM Costs

OpenAI GPT-3.5-turbo: $0.002 per 1,000 tokens

LlamaExtract Credits

Free Users: 1,000 credits per day

Paid Users: 7,000 credits per week

Additional Credits: $3 per 1,000 credits after the initial allocation

Parsing Costs

Normal Parsing: 1 credit per page ($3 / 1,000 pages)

GPT-4o Parsing: 10 credits per page ($30 / 1,000 pages)

Fast Mode Parsing: 1 credit per 3 pages (minimum 1 credit per document) ($1 / 1,000 pages)

For a detailed cost analysis, please refer to the official LlamaIndex pricing documentation.