Understanding Dash Agents: A LangChain Case Study
Tags: langchain, codegen, commanddash, llm
Published: 2024-06-12 18:23:51

In software development, integrating APIs and SDKs is crucial. Yet, the process can be tedious and time-consuming. Documentation, syntax complexities, and debugging can quickly become time-consuming roadblocks. Thankfully, this is where Dash Agents comes in to ease these headaches.

This blog post explores the capabilities of Dash Agents. It shows how they simplify the integration of APIs and SDKs for tools and frameworks like LangChain.

## What is LangChain?

LangChain is a framework that allows users to integrate LLMs into their projects. It provides a comprehensive toolkit for:

- **Model Handling**: It supports various LLMs and allows for easy integration of custom models, offering prompt templates and engineering tools.

- **Data Processing and Pipelines**: LangChain provides tools for data preprocessing and augmentation, along with the ability to create custom pipelines with a modular design.

- **Deployment and Monitoring**: It supports cloud deployment and scaling, offers evaluation metrics and real-time monitoring, and ensures security and compliance measures are in place.

You can learn more about the LangChain framework on their [Official site](https://www.langchain.com).

## Introducing LangChain Agent

Imagine having an intelligent assistant in your IDE. It's always there to help you integrate any LangChain-based code into your project. That's precisely what the LangChain Dash Agent offers.

Why You'll Love LangChain Agent:

- **Extensive API Knowledge**: It is shared with vast knowledge of LangChain framework documentation and sample codes, allowing it to provide nearly accurate code and insights.

- **Contextual Awareness**: It is designed to respond to queries with the understanding of your codebase and project context. Thus, capable of providing better assistance and solutions.

- **Saving Time During Coding**:  It generates the required code for you and if required debugs the code for you, saving you a lot of time. Thus, forget the days of tedious manual API docs searching and then coding only to realise that you need to fix a bug by searching multiple sources.

## Install LangChain Agent

Let's start by setting up your agent. If you've already LangChain installed please continue to the next section.

**1. Install CommandDash Extension**

Head over to the VS Code Marketplace and search for "CommandDash." Install the extension.

![Fig. 1 — Install CommandDash](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/k1fzi6oggi66n9d1k45b.png)

**2. Sign in to CommandDash**

Open the extension once installed. You'll need to sign in and provide your [Gemini key](https://aistudio.google.com/app/apikey).

**3. Install LangChain Agent**

Then, go to the CommandDash Marketplace within your Extension.

![Fig. 2 - Open CommandDash Marketplace Page](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/xa2dojblwdc62xe4pm8h.png)

Search for "LangChain" and install the agent.

![Fig. 3 — Install LangChain Agent](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/nannm0od1ougihztji80.png)

Next, it's time to put the installed LangChain agent to use.

## Getting Started with LangChain Agent

Finally, it is time to put the agent to work with a compelling use case: building a creative text generator in Python. 

This program takes the starting phrases of the poem as input. Then, it uses an LLM to extract the user's writing style and generate the remaining part of the poem.

Start by activating the LangChain agent. In your CommandDash extension, type `@` in the bottom text field and select "LangChain" from the dropdown:

![Fig. 3 — Activate LangChain Agent](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/jbug7vofze2f604kbihp.gif)

This activates the agent within your current workspace.

For demo purposes, we will attempt to use OpenAI's latest model - "GPT-4o". Type the following query into the CommandDash text field:

> "Generate the Python code to create an application to generate poem with the user-supplied initial phrases using OpenAI gpt-4o model"

The agent will analyze your request and will respond something like:

```python
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.messages import HumanMessage, SystemMessage

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

def generate_poem(initial_phrases, model="gpt-4o"):
    """Generates a poem using OpenAI's GPT-4o model.

    Args:
        initial_phrases (list): A list of initial phrases to start the poem.
        model (str, optional): The OpenAI model to use. Defaults to "gpt-4o".

    Returns:
        str: The generated poem.
    """

    llm = ChatOpenAI(model=model)

    prompt_template = ChatPromptTemplate.from_messages(
        [
            SystemMessage(content="You are a helpful and creative poet. Generate a poem using the following initial phrases as inspiration: "),
            HumanMessage(content=" ".join(initial_phrases))
        ]
    )

    messages = prompt_template.format_messages()
    response = llm.invoke(messages)

    return response.content

if __name__ == "__main__":
    initial_phrases = input("Enter your initial phrases separated by commas: ").split(",")
    poem = generate_poem(initial_phrases)
    print("Generated Poem:\n\n", poem)

Explanation:
Some explanation about the code
...
```

In our case, we haven't asked for any API integration request but rather an app itself. Thus, the agent supplied us with the whole end-to-end Python code.

Copy the code to a blank Python file and read the generated code. Right now, you can also update the code as needed. For instance, the current generated code is missing the `import os` statement. So, I will manually add the missing import.

> **Tip**: At times, if you ever feel like the solution isn't straightforward, or you want to explore the problem more deeply or find a quick fix. Feel free to continue conversing with the agent.

Finally, add your [OpenAI API](https://platform.openai.com/docs/quickstart/account-setup) key and run the app by executing the updated code:

![Fig. 4 — Run Python App](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/p6pfzqe2pvssheyswn2n.gif)

## Closing Thoughts

LangChain agent is not all that CommandDash has to offer. We have more agents for various APIs and SDKs, such as [Firebase Genkit](https://commanddash.hashnode.dev/how-to-use-firebase-genkit-in-your-nodejs-applications?source=more_articles_bottom_blogs), Pandas AI, Vercel AI, and more. 

Additionally, we also allow developers like you to create public agents for your teams and other developers. If  interested, do visit the [Dash Agents Docs](https://www.commanddash.io/docs/introduction).

That concludes this blog post. Now it's time to try it out yourself and materialize your LangChain use case to code instantly with CommandDash. We always love to hear what you build!