Visualize LangChain Interactions with LangChain Visualizer

Are you ready to elevate your experience with LangChain? With the power of the LangChain Visualizer, you can visually inspect how your workflows interact with language models using a stunning UI. This article will guide you through the installation, configuration, and usage of the LangChain Visualizer, with helpful troubleshooting tips along the way.

Getting Started with LangChain Visualizer

To unleash the potential of the LangChain Visualizer, follow these steps:

Step 1: Installation

First, you’ll need to install the library. If you’re on a Linux-based distribution, make sure to install the required dependencies as well:

pip install langchain-visualizer

apt install -y libyaml-dev

Step 2: Import the Visualizer

Next, import the visualizer in your Python entrypoint file:

import langchain_visualizer

Step 3: Visualize Your Workflow

Now it’s time to write an async function that will visualize your workflow. Use the following code snippet to do just that:

async def search_agent_demo():

    return agent.run(Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?)

langchain_visualizer.visualize(search_agent_demo)

Understanding the Code through Analogy

Think of your code like directing a theater play:

• **Actor (agent)**: Represents the performer who responds to queries.
• **Script (prompt)**: The text you provide that guides the actor’s performance.
• **Audience (you)**: Observing how the actor interprets the script in real-time.
• **Stage (visualization)**: The platform where the action unfolds, complete with lighting and props to indicate what’s happening (like coloring for template variables).

Just as a director can see how well the actors perform on stage, you will be able to view how well your workflow functions through the visualizer.

Running a Demonstration

If you’re curious about the demo depicted in the screenshot, follow these steps:

pip install langchain-visualizer google-search-results openai

Make sure to set up your OpenAI API keys or SERP API keys. If not, you can replay recorded interactions by cloning the mentioned repository and running:

OPENAI_API_KEY=dummy python tests/agents/test_langchain_getting_started.py

Using Jupyter Notebook for Visualization

Great news! Jupyter notebooks are now supported! Adapt the import statement to:

from langchain_visualizer.jupyter import visualize

For inspiration, check out the demo notebook to explore how it functions in Jupyter.

Visualizing Document Embeddings

If you want to visualize documents being chunked for embeddings, make sure to call visualize_embeddings before your main chain:

from langchain_visualizer import visualize, visualize_embeddings

async def run_chain():

    ...

visualize_embeddings()

visualize(run_chain)

Troubleshooting Tips

If you’re encountering issues with the visualizer or any part of the process, here are some troubleshooting ideas:

• Ensure all library dependencies are installed correctly.
• Double-check your API keys to confirm they’re set up properly.
• Refer to the documentation at your own pace to clarify any conceptual confusion or installation errors.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.