Llama Index: Streamlining Generative AI Applications with Agents and RAG

Unlocking the Power of Generative AI: Revolutionizing Agent Development and Unstructured Data Processing with Llama Index.

  • * Lori, VP of Developer Relations at Llama Index, will discuss Llama Index, agents, RAG, and relevant design patterns.
  • * Llama Index is a framework for building generative AI applications, particularly good at building agents.
  • * Llama Index has a service called Llama Parse to parse complicated document formats like PDFs, Word, PowerPoints, etc.
  • * Llama Cloud is an enterprise service that costs money, available as SaaS or on-premise private cloud.
  • * Llama Hub is a website with a registry of open-source software adapters for various services and LLMs.
  • * Llama Index helps users build applications faster by skipping boilerplate, using best practices, and getting to production quickly.
  • * The framework is particularly good at retrieval augmented generation (RAG) and agents.
  • * An agent is a piece of semi-autonomous software that uses tools to achieve a goal without explicitly specifying steps.
  • * Agents are powerful for handling unstructured data, messy inputs, and turning large bodies of text into smaller ones.
  • * Llama Index's RAG feature helps by embedding all data as vectors in a vector search, making it cheaper and faster to send less data to the LLM.
  • * Agents improve RAG performance both in terms of speed and accuracy.
  • * Anthropic's design patterns for building effective agents include chaining, routing, parallelization, orchestrator workers, and evaluator optimizers.
  • * Chaining is using one LLM's output as another's input, easily implemented with Llama Index workflows.
  • * Routing involves creating multiple LLM-based tools to solve problems in different ways and giving the LLM decision-making power.
  • * Parallelization has two flavors: sectioning (acting on the same input differently) and voting (taking the same query and giving it to multiple LLMs).
  • * Orchestrator workers use an LLM to split complex tasks into simpler questions, answered in parallel.
  • * Evaluator optimizers, or self-reflection, involve using the LLM to decide if it has done a good job and provide feedback.
  • * Llama Index makes it easy to combine these patterns for arbitrarily complex workflows.
  • * Tools are Python functions wrapped in a step wrapper, allowing the creation of multi-agent systems.
  • * Users can learn more about building agents and workflows with the provided notebook tutorial.

Source: AI Engineer via YouTube

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