GraphAcademy

Gen-AI - Hands-on Workshop

Knowledge Graphs, Unstructured Data, and Vectors

LLMs, RAG, Python, and LangChain

Agents

Agents

Agents wrap a model and give it access to a set of tools. These tools may access additional data sources, APIs, or functionality. The model is used to determine which of the tools to use to complete a task.

The agent you will create can chat about movies and search YouTube for movie trailers. You will use the YouTubeSearchTool to search YouTube for movie trailers.

Movie trailer agent

Open the 2-llm-rag-python-langchain\chat_agent.py file and review the program, before running it.

python
chat_agent.py
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You should be able to identify the following:

  1. A chat model is being used to have a conversation about movies

  2. The prompt which sets the context for the LLM and the input variables

  3. That memory is used to store the conversation history in a Neo4j database

What’s new

The following code is new:

  1. A tool is created using the chain:

    python
    Unresolved directive in lesson.adoc - include::{repository-raw}/main/2-llm-rag-python-langchain/solutions/chat_agent.py[tag=tools]

What’s new

  1. An agent is created that uses the tool:

    python
    Unresolved directive in lesson.adoc - include::{repository-raw}/main/2-llm-rag-python-langchain/solutions/chat_agent.py[tag=agent]

What’s new

  1. The agent is wrapped in a RunnableWithMessageHistory chain that allows it to interact with the memory:

    python
    Unresolved directive in lesson.adoc - include::{repository-raw}/main/2-llm-rag-python-langchain/solutions/chat_agent.py[tag=chat_agent]

Tools

Tools are interfaces that an agent can interact with. The name and description help the LLM select which tool to use when presented with a question.

Agents support multiple tools, so you pass them to the agent as a list (tools).

Run the agent and ask it movie related questions.

The agent uses a prompt hosted on the LangChain hub.

The agent will still run successfully but you will receive a LangSmithMissingAPIKeyWarning warning if you don’t have a LangSmith API key.

You can Create a LangSmith Personal Access Token API Key and assign it to the LANGSMITH_API_KEY environment variable to remove the warning.

Learn more about agents

The following code creates the agent:

python
Unresolved directive in lesson.adoc - include::{repository-raw}/main/2-llm-rag-python-langchain/solutions/chat_agent.py[tag=agent]

There are different types of agents that you can create. This example creates a ReAct - Reasoning and Acting) agent type.

An agent requires a prompt. You could create a prompt, but in this example, the program pulls a pre-existing prompt from the Langsmith Hub.

The hwcase17/react-chat prompt instructs the model to provide an answer using the tools available in a specific format.

The create_react_agent function creates the agent and expects the following parameters:

  • The llm that will manage the interactions and decide which tool to use

  • The tools that the agent can use

  • The prompt that the agent will use

The AgentExecutor class runs the agent. It expects the following parameters:

  • The agent to run

  • The tools that the agent can use

You may find the following additional parameters useful when initializing an agent:

  • max_iterations - the maximum number of iterations to run the LLM for. This is useful in preventing the LLM from running for too long or entering an infinite loop.

  • verbose - if True the agent will print out the LLM output and the tool output.

  • handle_parsing_errors - if True the agent will handle parsing errors and return a message to the user.

python
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    max_iterations=3,
    verbose=True,
    handle_parse_errors=True
)

Multiple tools

A key advantage of using an agent is that they can use multiple tools.

You can extend this example to allow it to search YouTube for movie trailers by adding the YouTubeSearchTool to the tools list.

YouTube Search

Import the YouTubeSearchTool and create a new tool.

python
Unresolved directive in lesson.adoc - include::{repository-raw}/main/2-llm-rag-python-langchain/solutions/chat_agent_trailer.py[tag=import-youtube]

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The YouTubeSearchTool tool expects a search term and the number of results passed as a comma-separated string.

The agent may pass queries containing commas, so create a function to strip the commas from the query and pass the query to the YouTubeSearchTool.

python
Unresolved directive in lesson.adoc - include::{repository-raw}/main/2-llm-rag-python-langchain/solutions/chat_agent_trailer.py[tag=trailer-search]

YouTube Search Tool

Add the call_trailer_search function to the tools list.

python
Unresolved directive in lesson.adoc - include::{repository-raw}/main/2-llm-rag-python-langchain/solutions/chat_agent_trailer.py[tag=tools]
Click here to reveal the complete program
python
Unresolved directive in lesson.adoc - include::{repository-raw}/main/2-llm-rag-python-langchain/solutions/chat_agent_trailer.py[tag=**]

What tool to use?

The model will use the name and description for each tool to decide which tool to use.

When prompted to find a movie trailer, the model should use the YouTubeSearchTool tool.

[user] Find the movie trailer for the Matrix.
[agent] Here are the movie trailers for "The Matrix":
The Matrix - Official Trailer #1 -
    https://www.youtube.com/watch?v=vKQi3bBA1y8&pp=ygUKVGhlIE1hdHJpeA%3D%3D
The Matrix - Official Trailer #2 -
    https://www.youtube.com/watch?v=xrYg_qKX-aI&pp=ygUKVGhlIE1hdHJpeA%3D%3D

However, when asked about movies, genres or plots, the model will use the chat_chain tool.

[user] Find a movie about the meaning of life
[agent] Certainly! One movie that explores the meaning of life is "The Tree of Life" ...

Agent memory

As the agent also uses the conversation memory, you can refer back to the previous questions, such as finding a trailer for a movie it has recommended:

[user] Can you find the trailer
[agent] Here are two links to the trailer for "The Tree of Life":
Link 1 - https://www.youtube.com/watch?v=RrAz1YLh8nY&pp=ygUQVGhlIFRyZWUgb2YgTGlmZQ%3D%3D
Link 2 - https://www.youtube.com/watch?v=cv-dH5gHi1c&pp=ygUQVGhlIFRyZWUgb2YgTGlmZQ%3D%3D

Agents and tools allow you to create more adaptable and flexible models to perform multiple tasks.

Lesson Summary

You learned about agents and how they use multiple tools to perform tasks.

Next you will learn about retrievers.

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