LlamaIndex is an open-source data orchestration framework designed to help developers build large language model (LLM) applications. It specializes in Retrieval-Augmented Generation (RAG), enabling LLMs to access and use private, domain-specific, or up-to-date data for more accurate and context-aware responses.

What is Retrieval-Augmented Generation (RAG)?

RAG is a technique that combines information retrieval with language generation. LlamaIndex retrieves relevant data from your documents or databases and uses it to ground the LLM’s responses, reducing hallucinations and improving accuracy.

What types of data can LlamaIndex work with?

LlamaIndex supports unstructured data like text files, PDFs, Notion, Slack, and web pages; structured data such as SQL databases and CSV files; complex documents including PDFs with tables, charts, and images; and multiple data sources, allowing for combining and routing queries across different formats and locations.

How does LlamaIndex handle unstructured documents?

LlamaIndex can ingest unstructured documents, parse them (using tools like LlamaParse), and index the content for semantic search and summarization. It supports advanced features like multi-document queries and routing across heterogeneous data sources.

Can LlamaIndex query structured data?

Yes. LlamaIndex can perform text-to-SQL and text-to-Pandas operations, allowing you to ask natural language questions over SQL databases and CSV files.

What are the main use cases for LlamaIndex?

The main use cases for LlamaIndex include building document agents for customer support such as FAQs, manuals, and policies; creating chatbots and query engines for domain-specific knowledge; developing AI agents with web data retrieval capabilities; and supporting multi-step, temporal, and multi-document queries.

How does LlamaIndex support multi-document queries?

LlamaIndex can generate a query plan to answer questions that require information from multiple documents. It can break down complex queries into sub-questions, retrieve answers from different sources, and synthesize the final response.

Can LlamaIndex route queries across different data sources?

Yes. LlamaIndex can route queries to the most appropriate data source based on the question, using a router query engine that selects the best sub-index or tool for the task.

What is the workflow for building a LlamaIndex application?

The workflow for building a LlamaIndex application involves several steps: first, ingest data from various sources; second, process and chunk the data; third, index the data using embedding models; fourth, retrieve relevant information via semantic search; and finally, generate responses using an LLM, grounded in the retrieved context.

LlamaIndex is open-source and free to use. However, some advanced features or integrations (like LlamaCloud or LlamaParse) may have associated costs or require credits.

How does LlamaIndex integrate with other frameworks?

LlamaIndex integrates seamlessly with popular frameworks like LangChain, Flask, Docker, and ChatGPT, making it easy to build and deploy AI agents and applications.

What are the key features for production AI systems?

Key features for production AI systems include data connectors for various sources such as APIs, PDFs, Word documents, SQL databases, and web pages; advanced indexing and retrieval techniques; support for real-time web scraping and structured data extraction; and tools for building AI agents with data retrieval capabilities.

How does LlamaIndex handle temporal queries?

LlamaIndex can answer questions that require an understanding of time by utilizing temporal relationships between data nodes and filtering outdated context.

Where can I find tutorials and guides?

Official tutorials, guides, and cookbooks are available in the LlamaIndex documentation and community resources.

LlamaIndex

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LlamaIndex lets you build smart AI tools that read, understand, and act on your company's complex documents. It uses your own data to make AI more accurate, helping it make better decisions and automate tasks across various industries.

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Overview Features Use Cases FAQ Integrations Pricing

About LlamaIndex

Who It's For

This tool is for developers and AI teams building smart AI applications. It helps create AI agents that understand and use all kinds of company documents. From finance to healthcare, it connects AI with your specific business information.

What You Get

You get powerful tools to read and understand complex documents, including tables, images, or handwriting. It helps AI agents extract key information, find insights, and take actions. This makes your AI applications reliable, precise, and saves time. You can also build multi-step AI processes.

How It Works

The tool first takes your documents and uses advanced parsing to understand all content. It then organizes this data so AI models quickly find and use relevant information. You then create AI agents that combine these steps to answer questions, automate tasks, and make smarter decisions using your unique company data.

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Features & Capabilities

📄 Advanced Document Processing

LlamaParse

Provides industry-leading parsing for over 90 unstructured file types, including complex layouts, images, and handwritten notes.

LlamaExtract

Transforms unstructured content into structured insights using schema-based, LLM-powered extraction agents with citations and confidence scores.

Enterprise-Grade Indexing

Offers a robust chunking and embedding pipeline designed for precision and relevance in retrieval-augmented generation (RAG).

⚙️ AI Workflow Orchestration

Multi-Step Workflow Automation

Enables easy chaining, looping, and parallel paths to orchestrate complex AI processes and document pipelines.

Asynchronous Processing

Built with an async-first design for high-speed workflows that integrate seamlessly with modern Python applications like FastAPI.

Event-Driven Architecture

Supports stateful workflows that can be launched, paused, and resumed based on events for precise control.

🛠️ Developer Framework & Integration

Modular Agent Building Blocks

Offers core components like state, memory, and human-in-the-loop review to rapidly build GenAI applications.

Developer-First SDKs

Provides fully-featured Python and Typescript SDKs for easy embedding into existing technology stacks.

Broad Third-Party Integration

Includes pre-built connectors for various LLMs, data sources, and vector databases to integrate anywhere.

Use Cases

Building Intelligent Customer Support & Internal Knowledge Bases

Businesses struggle to provide quick, accurate answers from their vast documentation, leading to frustrated customers and inefficient support teams. LlamaIndex allows developers to build AI agents and chatbots that leverage RAG to instantly retrieve precise information from FAQs, manuals, and internal documents, significantly boosting agent accuracy and user satisfaction.

Enterprise OperationsFor: Customer Support Teams, Internal Knowledge Management, Developers

Automating Financial Research and Due Diligence

Financial institutions face challenges rapidly processing and extracting accurate insights from highly complex documents like reports, filings, and invoices. LlamaIndex, using LlamaParse and LlamaExtract, automates the high-accuracy parsing and structured data extraction from these documents, enabling financial analysts to conduct research and due diligence significantly faster and with greater precision.

Financial ServicesFor: Financial Analysts, Investment Managers, Data Scientists

Accelerating Document Processing in Regulated Industries

Industries like Insurance and Healthcare are overwhelmed by complex, often handwritten, and unstructured documents such as claims, medical records, and policy documents, hindering efficient operations. LlamaIndex offers robust parsing and extraction capabilities to transform this data into structured insights, automating critical processes like underwriting, claims processing, and administrative workflows with enhanced accuracy.

Regulated Industries (Insurance, Healthcare)For: Operations Managers, Compliance Officers, Data Entry Specialists

Accelerating Production-Ready GenAI Applications and Custom Agents

AI development teams face significant hurdles in building and deploying scalable, accurate Generative AI applications that integrate with their unique enterprise data and complex workflows. LlamaIndex offers a comprehensive, developer-first framework with modular components, robust parsing, and an orchestration engine to drastically reduce time-to-production for custom RAG applications and intelligent agents.

Software Development, Enterprise AIFor: AI Developers, Machine Learning Engineers, Solution Architects

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