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Conversational Agent

Conversational Agent Node

Set Up the Conversational Agent

Prerequisites:

Context:

Unlike standard large language models (LLMs), which provide general-purpose models for performing language-based tasks, conversational agents are more sophisticated as they are designed specifically for managing conversations effectively.

You can use Akilibot conversational agent to create a comprehensive and interactive conversation experience.

Steps:

  1. Access the Chatflows menu.

    1. Open your browser and go to https://akilibot.dev.
    2. In Akilibot, click Chatflows.

  2. Create a new chatflow:

    1. Click Add New.
    2. Enter a name for your chatflow and click Save.

  3. Add a conversational agent node:

    1. Click Add Node.
    2. Search for the conversational agent.
    3. Drag and drop the conversational agent node into your chatflow workspace.

  4. Add a SearchAPI node. This node enables the agent to fetch data from Google search results:

    1. Click Add Node.
    2. Search for the SearchAPI node. It displays in the Tools section of the search results.
    3. Drag and drop the SearchAPI node into your chatflow workspace.
    4. Create a free SearchAPI account and retrieve your SearchAPI API key. The SearchAPI node needs this key to authenticate and perform search queries.
    5. On the SearchAPI node, click Connect Credentials > Create New.
    6. Enter a name for your credentials e.g. SearchAPI Credentials, copy and paste your SearchAPI API key into the SearchAPI API Key field and click Add.
    7. Connect the SearchAPI node to the Conversational Agent node by drawing a line from the Output section of the SearchAPI node to the Allowed Tools Inputs section of the Conversational Agent node.
    8. Click Save Chatflow to save your progress.

  5. Add a ChatOllama chat model node. This node enables the agent to use Ollama language models to generate responses:

    1. Click Add Node.
    2. Search for the ChatOllama node. It displays in the Chat Models section of the search results.
    3. Drag and drop the ChatOllama node into your chatflow workspace.
    4. On the Model Name field, enter the model you’d like to use. We recommend llama3.2.
    5. On the Temperature field, set a temperature value between 0 and 1. The temperature parameter controls the randomness of the model's responses. Low temperature produces deterministic and focused responses. High temperature produces creative and varied responses. We recommend a temperature value of 0.5.
    6. Connect the ChatOllama node to the Conversational Agent node by drawing a line from the Output section of the ChatOllama node to the Chat Model Inputs section of the Conversational Agent node.
    7. Click Save Chatflow to save your progress.

  6. Add a Redis chat memory node. This node enables the agent to remember previous interactions and store them in the chat history, enhancing the overall user experience:

    1. Click Add Node.
    2. Search for the Redis-Backed Chat Memory node. It displays in the Memory section of the search results.
    3. Drag and drop the Redis-Backed Chat Memory node into your chatflow workspace.
    4. On the Redis-Backed Chat Memory node, click Connect Credentials > Create New.
    5. Enter either your Redis API username and password or your Redis credential name and URL and click Add.
    6. Connect the Redis-Backed Chat Memory node to the Conversational Agent node by drawing a line from the Output section of the Redis node to the Memory Inputs section of the Conversational Agent node.
    7. Click Save Chatflow to save your progress.

Result:

By following these steps, you will have successfully created a conversational agent that you can chat with and ask questions.

Next Steps:

Click the chat icon to interact with your newly created conversational agent.

For additional information and troubleshooting, refer to the Redis documentation for the LangChain conversational agent.