LlamaIndex

LlamaIndex is an open-source data framework for building AI agents and RAG (Retrieval-Augmented Generation) pipelines. RAG is the technique of feeding an LLM your own private data, so it can answer questions based on documents, databases, and APIs it was never trained on. LlamaIndex handles the full pipeline: loading your data, structuring it into searchable indexes, retrieving the right pieces at query time, and passing them to the LLM as context. It is used by teams at Salesforce, AWS, and other large engineering organisations to build production-grade AI systems on top of internal knowledge bases.

How LlamaIndex works

LlamaIndex sits between your data and your LLM. When a user asks a question, the framework finds the most relevant chunks of your data, attaches them to the prompt, and sends the combined input to the model. Here is how the pipeline breaks down:

• Data connectors (loaders): Ingest data from over 160 formats and sources, including PDFs, SQL databases, APIs, spreadsheets, and cloud storage. These are available through LlamaHub, the community registry of integrations.
• Nodes: After loading, LlamaIndex splits documents into smaller chunks called nodes. Each node carries the text plus metadata about where it came from.
• Indexes: Nodes are converted into vector embeddings, which are numerical representations that capture meaning. The index lets the system find nodes that are semantically close to a query, not just keyword-matched.
• Query engine: Accepts a natural language question, converts it to an embedding, searches the index for matching nodes, and returns the retrieved context alongside the LLM’s answer.
• Agents: LLM-powered components that decide which tools or query engines to use, in what order, to complete a multi-step task. They follow a reasoning loop (ReAct or function-calling) rather than executing a fixed script.
• Workflows: Event-driven orchestration layer for sequencing agents, query engines, and LLM calls into complex, stateful pipelines.

What you can build with LlamaIndex

• Internal knowledge base Q&A: Point LlamaIndex at your company’s documentation, Notion pages, or Confluence wiki, and build a query engine that answers employee questions with citations from the actual source material.
• Agentic research assistant: Build an agent that can search across multiple indexed document sets, summarise findings, follow-up on gaps, and produce a structured report from a single user prompt.
• Customer support bot: Index your product documentation and support history, then use a chat engine to handle multi-turn conversations where the bot maintains context across messages.
• Multimodal report generator: Use LlamaParse to extract text and tables from complex PDFs, feed the structured output into an agent, and have it write a formatted report automatically.
• Coding assistant: Wire up a code interpreter tool to an agent that can read your codebase through an index, write functions, test them, and iterate on errors.

Productivity agent over Google Workspace: Use the GSuite integration to build an agent that reads email, calendar events, and Drive documents to help plan, summarise, and draft on your behalf.