Reliable, fully local RAG agents with LLaMA3

How to Build Reliable Agents Using Llama 3 Locally on Your Laptop

1. Introduction to Llama 3:

   • Llama 3 was released recently and offers improved performance compared to previous versions.
   • The goal is to build reliable agents using Llama 3 that can run on your laptop.

2. Understanding Agent Building:

   • An agent should have planning capabilities to break down tasks into sub-goals.
   • It should have memory, such as chat history and a vector store.
   • Agents can be built using frameworks like React.

3. Building a React Agent:

   • The typical flow for a React agent involves selecting an action, observing the result, thinking, and choosing the next action.
   • The agent uses memory and tools like chat history and vector stores for decision-making.

4. Implementing Control Flow:

   • Control flow involves pre-defining decisions for the agent instead of making decisions at every step.
   • Use a graph state to persist information across the control flow, including documents and questions.

5. Tradeoffs:

   • React agents offer flexibility but may have lower reliability.
   • Control flow agents are more constrained but can be more reliable, especially with smaller LLMs.

6. Testing Components Individually:

   • Set up an index using a local Vector store for the RAG flow.
   • Implement a retrieval grader to retrieve and grade documents for relevance to a question.

7. Creating a Graph State:

   • Define nodes in the graph as functions that update the state with relevant information.
   • Implement edges to make decisions based on the state, guiding the agent through the control flow.

8. Adding Conditional Edges:

   • Add conditional edges to the graph based on the results of grading documents or generations.
   • Update the graph to route the agent based on the outcomes of the grading process.

9. Routing Questions:

   • Use a router to direct questions to appropriate sources like a Vector store or web search.
   • Update the graph to include routing decisions based on the content of the questions.

10. Final Testing and Evaluation:

    • Run the agent through the defined control flow to test routing, retrieval, grading, and decision-making processes.
    • Monitor the performance, latencies, and overall reliability of the agent running locally on your laptop.

By following these steps, you can successfully build reliable agents using Llama 3 locally on your laptop, leveraging control flows and graph-based decision-making to enhance the agent's performance and reliability.