LlamaIndex.TS

A framework for building LLM applications

CommonProductProgrammingLLMNatural Language Processing

LlamaIndex.TS is a framework designed for building applications based on large language models (LLMs). It focuses on helping users ingest, structure, and access private or domain-specific data. This framework provides a natural language interface to connect humans with inferred data, enabling developers to enhance their software capabilities through LLMs without needing to become experts in machine learning or natural language processing. LlamaIndex.TS supports popular runtime environments such as Node.js, Vercel Edge Functions, and Deno.

Structured Data Extraction: Convert complex
unstructured
and semi-structured data into a unified
programmatically accessible format.
Retrieval-Augmented Generation (RAG): Answer queries across internal data by providing current
semantically relevant context
including Q&A systems and chatbots.
Autonomous Agents: Build software that can intelligently select and utilize tools to accomplish tasks in an interactive
unsupervised manner.
Modular Architecture: LlamaIndex.TS adopts a modular design

\The target audience is AI engineers
specifically developers who build software in any field that can be enhanced by LLM capabilities. LlamaIndex.TS is particularly suitable for those looking to quickly build and deploy LLM applications without delving into the complexities of machine learning and natural language processing.\

Build an internal Q&A system using LlamaIndex.TS to integrate the company knowledge base for instant and accurate responses.
Develop a chatbot that connects to the enterprise database through LlamaIndex.TS to automate customer consultation services.
Create an automated data analysis tool that utilizes the autonomous agent feature of LlamaIndex.TS to intelligently select analytical tools and generate reports.

1. Visit the official documentation of LlamaIndex.TS to understand the installation and configuration guidelines.
2. Use the npm command `npm install llamaindex` to install LlamaIndex.TS.
3. Follow the getting started tutorial in the documentation to build your first RAG application.
4. Explore the modular architecture of LlamaIndex.TS to learn how to customize and extend various modules.
5. Engage with the LlamaIndex community through Twitter and Discord for support and experience sharing.

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LlamaIndex.TS Visit Over Time

Monthly Visits

14096

Bounce Rate

52.03%

Page per Visit

3.8

Visit Duration

00:06:17

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