Reliable, fully local RAG agents with LLaMA3

Lance from L chain So Meta discusses the release of LLaMA3, a new language model with strong performance characteristics. He outlines a method for building reliable, locally running agents using LLaMA3, which can be executed on a personal laptop. Lance draws from three different RAG (Retrieval-Augmented Generation) papers to create a complex RAG flow that includes routing, retrieval, grading, and fallback to web search if necessary. He emphasizes the importance of an agent's planning, memory, and tool usage capabilities. The demonstration includes building a local index, grading retrieved documents for relevance, and generating responses with LLaMA3. The process is designed to be highly reliable and flexible, with a focus on local execution and the ability to incorporate self-correction mechanisms to check for hallucinations and relevance in the generated responses. The entire flow is tested and shown to run successfully on Lance's laptop, showcasing the practicality and potential of LLaMA3 for local, reliable agent development.

Takeaways

• 🚀 Llama 3, a new language model with 8 billion parameters, has been released and is claimed to perform well on various benchmarks.
• 💡 Lance from L chain So Meta discusses building reliable, local agents using Llama 3 that can run on a personal laptop.
• 🔍 The concept of routing questions to either a vector store or web search based on the content is introduced from the adaptive RAG paper.
• 📚 A fallback mechanism is implemented for retrieval from the vector store and grading of documents, followed by a web search if needed.
• 🔧 Self-correction is included to check for hallucinations and relevance in the generated responses, with a fallback to web search if issues are found.
• 💻 The demonstration of running a complex RAG flow reliably and locally on a Mac M2 with 32GB RAM is highlighted.
• 📝 The importance of an agent having planning, memory, and the ability to use tools is emphasized.
• 🔄 A control flow is designed to increase reliability by predetermining the agent's actions, reducing the need for real-time decision-making by the LLM.
• 🧩 The agent's functionality is broken down into nodes and edges, with nodes representing functions and edges determining the flow based on state.
• 🔬 Tracing is used to inspect the agent's operations in real-time, providing transparency into the agent's decision-making process.
• ⚙️ The agent is tested with a question related to current events, demonstrating the routing, retrieval, grading, and generation processes in action.

Q & A

What is the significance of LLaMA3's release according to Lance from L chain So Meta?

LLaMA3's release is significant because it offers strong performance characteristics, even outperforming mraw on several popular metrics or benchmarks, which is exciting and something Lance has been eagerly waiting for.

What is the primary goal of Lance's discussion on building reliable agents using LLaMA3?

The primary goal is to demonstrate how to construct reliable agents that can operate locally, specifically on a laptop, using LLaMA3's capabilities.

What are the three different RAG (Retrieval-Augmented Generation) papers that Lance refers to in the transcript?

The transcript does not provide specific names for the three RAG papers. However, it mentions that Lance is drawing ideas from these papers to create a complex RAG flow involving routing, fallback mechanisms, and self-correction.

What does Lance mean by 'routing' in the context of building agents?

In the context of building agents, 'routing' refers to the process of directing a question to either a vector store or a web search based on the content of the question. This decision is part of the adaptive RAG approach.

How does Lance propose to handle situations where the retrieved documents are not relevant to the question?

Lance proposes a fallback mechanism where if the retrieved documents are not relevant to the question, the system will perform a web search to find more appropriate information.

What is the role of the 'hallucination grader' in Lance's proposed system?

The 'hallucination grader' is responsible for checking the generations (output responses) for any inaccuracies or 'hallucinations', which are false or irrelevant information. If the output contains such issues, the system will fallback and perform a web search.

What is the benefit of using a control flow approach instead of a reactive agent approach?

The control flow approach increases reliability because it predetermines the steps the agent will take, reducing the chances of errors that can occur when an agent has to make decisions at every step in a reactive approach.

What is the significance of using a local language model (LLM) like LLaMA3 for building agents?

Using a local LLM like LLaMA3 allows the agent to run reliably and locally on a personal device, such as a laptop, without needing to rely on cloud-based services or external servers.

How does Lance ensure that the agent's actions are reliable and consistent across different tasks?

Lance ensures reliability and consistency by defining a control flow that the agent follows each time it runs, which includes predetermined steps and decision points based on the state of the system.

What is the purpose of the 'trace' that Lance refers to in the transcript?

The 'trace' allows Lance to inspect and monitor the internal workings of the agent in real-time. It provides a detailed log of each step the agent takes, which is useful for debugging and understanding the agent's decision-making process.

How does Lance's system handle questions related to current events that may not be present in the vector store?

The system uses a router that decides whether to use the vector store or fall back to a web search based on the relevance of the question to the topics in the vector store. For current events, it is expected that the router would choose web search.

Outlines

00:00

🚀 Introduction to Building Local Agents with LLaMa 3

Lance from L chain So Meta introduces the release of LLaMa 3, an AI model with strong performance characteristics. He expresses excitement about building reliable agents that can run locally on a laptop. Lance plans to leverage ideas from three research papers to create a complex retrieval-augmented generation (RAG) flow with routing, fallback mechanisms, and self-correction. He emphasizes the importance of an agent having planning, memory, and tool-using capabilities. The paragraph outlines the process of building a local agent using LLaMa 3, comparing reactive agents with those guided by a control flow for increased reliability.

05:03

📚 Constructing the Agent's Functional Components

The paragraph delves into the practical coding aspect of building the agent. Lance demonstrates the process of setting up a local language model (LLM), using LLaMa 3, and creating an index of web pages. He then discusses implementing a retrieval-grader function to assess the relevance of retrieved documents. The integration of LLaMa 3 for grading is showcased, highlighting the model's JSON output capability. Lance also covers the generation process using a custom RAG prompt and concludes with the setup of a web search tool and the definition of a graph to represent the agent's control flow.

10:05

🔍 Document Grading and Web Search Integration

This section focuses on the grading of documents for relevance and the conditional logic for web search integration. Lance outlines the process of filtering out irrelevant documents and triggering a web search if needed. He introduces the concept of a conditional edge in the graph, which determines the next step based on the state. The paragraph also details the construction of the graph, registering nodes, and setting the order of operations. The successful execution of the graph, including retrieval, grading, and generation, is tested and verified through live tracing.

15:06

🤖 Enhancing the Agent with Self-RAG and Routing

Lance enhances the agent by adding self-RAG components, which involve grading the generations for hallucinations and relevance. Two additional graders are introduced, and their integration into the graph is explained. The paragraph also discusses the setup of a router that decides whether to use the vector store or fallback to web search based on the question's content. The router's functionality is tested, and the control flow is updated to include routing as the entry point. The paragraph concludes with a demonstration of the agent's ability to handle a question related to current events, showcasing the successful routing to web search.

20:07

🎯 Conclusion and Encouragement to Experiment

In the concluding paragraph, Lance summarizes the capabilities of the built agent. He emphasizes that the agent, which incorporates routing, retrieval, grading, and generation, runs reliably and locally on his laptop using LLaMa 3. The use of control flow is highlighted as a key factor in the agent's reliability. Lance encourages others to experiment with the code, which he promises to make public, and invites comments for further discussion.

Keywords

💡LLaMA3

LLaMA3 refers to the latest release of a language model from the LLaMA (Large Language Model AI) series. It is a significant update that has been eagerly anticipated by the speaker, Lance, as it offers improved performance characteristics over its predecessors. In the context of the video, LLaMA3 is used to build reliable agents that can run locally on a laptop, showcasing its capabilities in natural language processing and machine learning.

💡Reliable Agents

Reliable agents are autonomous systems designed to perform tasks or make decisions on behalf of users. In the video, Lance discusses building such agents using LLaMA3 that can operate reliably even on local hardware like a laptop. These agents are capable of planning, memory retention, and tool usage, which are essential for executing complex tasks.

💡Vector Store

A vector store is a database that holds information in the form of vectors, which are mathematical representations of words or phrases in a multi-dimensional space. In the context of the video, the vector store is used for document retrieval, where relevant documents are fetched based on the content of a given question. This is a part of the RAG (Retrieval-Augmented Generation) flow discussed by Lance.

💡Web Search

Web search refers to the process of querying the internet to find relevant information. In the video, if a question posed to the agent is not found in the vector store, a web search is initiated as a fallback mechanism. This ensures that the agent can still provide useful responses even when the internal data does not contain the required information.

💡Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation is a technique that combines retrieval systems with generative models. It involves first retrieving relevant information from a database and then using that information to generate responses. In the video, Lance discusses implementing RAG with LLaMA3 to create a sophisticated flow for answering questions by retrieving and grading documents before generating a response.

💡Graph State

In the context of the video, a graph state refers to a method of persisting information across different steps of the agent's control flow. It acts as a form of memory that holds relevant data, such as documents and questions, which are used by the agent to perform its tasks. The graph state is crucial for maintaining consistency and context throughout the agent's operations.

💡Control Flow

Control flow is the order in which actions are executed by a program or system. In the video, Lance outlines a predefined control flow for the agent, which dictates the sequence of steps the agent takes when processing a question. This approach enhances reliability as it reduces the chances of the agent making incorrect decisions during operation.

💡React Framework

React is a popular JavaScript library for building user interfaces, but in the context of the video, it refers to a style of agent operation where the agent continuously makes decisions based on the current state. Lance contrasts this with the control flow approach, where the sequence of actions is predetermined, highlighting the increased reliability of the latter.

💡Hallucination

In the context of language models and AI, 'hallucination' refers to the generation of content that is not grounded in the provided information or facts. Lance discusses implementing a grader to check for hallucinations in the agent's responses, ensuring that the generated answers are relevant and supported by the retrieved documents.

💡LSmith Traces

LSmith traces are debugging tools that allow developers to inspect the internal workings of a program or system. In the video, Lance uses LSmith traces to monitor the agent's operations in real-time, providing insights into how the agent processes questions, retrieves documents, and generates responses.

💡Local Model

A local model refers to an AI model that runs on the user's local machine rather than relying on remote servers. Lance emphasizes the ability to run LLaMA3 as a local model on his laptop, which is significant for applications that require low latency or operate in environments with limited internet connectivity.

Highlights

Lance from L chain So Meta discusses the release of LLaMA3, a highly anticipated model with strong performance characteristics.

LLaMA3 outperforms mraw on popular metrics and benchmarks, indicating its potential for reliable agent construction.

A complex RAG (Retrieval-Augmented Generation) flow is proposed, combining ideas from three sophisticated RAG papers.

The adaptive routing from the RAG paper directs questions to either a vector store or web search based on content.

Introduction of a fallback mechanism for retrieval from the vector store and grading of documents.

Self-correction involves checking generations for hallucinations and relevance to the original question.

Lance demonstrates the implementation of a reliable and local agent on a Mac M2 with 32GB RAM.

The definition of an agent includes planning, memory, and tool usage capabilities.

Contrasting the React framework's flexibility with the reliability of a predefined control flow using Lang Graph.

Lance outlines the process of building a corrective RAG with components like retrieval, grading, and generation.

AMA (Ask Me Anything) integration allows for easy access to LLaMA3 for local model usage.

Use of GPD embeddings for local models and the importance of prompt format when using LLaMA3.

Building an index of web pages for document retrieval as part of the RAG flow.

Real-time inspection of the agent's inner workings through tools like Lang Smith.

The agent's control flow is defined by a graph where nodes represent functions and edges represent decisions.

Conditional edges allow for decision-making based on the state, such as whether to web search or generate an answer.

Incorporating self-correction with additional graders to check for hallucinations and question relevance.

Router functionality to decide between vector store retrieval and web search based on question content.

The entire RAG flow, including routing, retrieval, grading, and generation, runs reliably on a local machine.

Lance emphasizes the importance of control flows for reliable local agent operation and encourages experimentation with the provided code.