Conversational Agent

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Node Details

  • Name: conversationalAgent
  • Type: AgentExecutor
  • Category: Agents
  • Version: 3.0

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Description

The Conversational Agent node creates an AI assistant capable of maintaining context over multiple interactions, using tools to gather information or perform actions, and providing coherent responses. It’s particularly suited for chat-based applications where context retention and task execution are important.

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Parameters

  1. Allowed Tools (Required)
    • Type: Tool[]
    • Description: A list of tools that the agent can use to perform tasks or gather information.
  2. Chat Model (Required)
    • Type: BaseChatModel
    • Description: The language model used for generating responses and making decisions.
  3. Memory (Required)
    • Type: BaseChatMemory
    • Description: The memory component used to store and retrieve conversation history.
  4. System Message (Optional)
    • Type: string
    • Default: A predefined message describing the assistant’s capabilities
    • Description: A system message to set the behavior and capabilities of the agent.
  5. Input Moderation (Optional)
    • Type: Moderation[]
    • Description: Moderation tools to detect and prevent harmful input.
  6. Max Iterations (Optional)
    • Type: number
    • Description: The maximum number of iterations the agent will perform for a single input.

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Input

A string containing the user’s message or query.

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Output

  • A string or object containing the agent’s response, which may include:
    • Text response to the user’s input
    • Source documents used (if any)
    • Tools used in the process of generating the response

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How It Works

  1. The agent is initialized with the provided tools, chat model, memory, and optional parameters.
  2. It receives a user input, which is first checked by any specified moderation tools.
  3. The agent then:
    • Retrieves relevant context from its memory
    • Analyzes the input and context to determine the next action
    • Uses tools if necessary to gather information or perform tasks
    • Generates a response using the chat model
  4. The response and any used tools or source documents are returned.
  5. The conversation history is updated in the memory for future context.

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Use Cases

  • Interactive chatbots and virtual assistants
  • Customer support systems
  • Educational tutoring systems
  • Task-oriented dialogue systems
  • Interactive documentation or FAQ systems

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Special Features

  • Vision Support: If the chat model supports vision capabilities, the agent can process and respond to image inputs.
  • Streaming: The agent supports streaming responses, allowing for real-time interaction.
  • Tool Integration: Can use a variety of tools to enhance its capabilities and perform actions.
  • Memory Management: Maintains conversation history for contextual understanding.
  • Moderation: Can implement input moderation to ensure safe interactions.

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Notes

  • The agent uses a sophisticated prompt structure to maintain consistent behavior.
  • It can handle multi-modal inputs if the underlying chat model supports it (e.g., text and images).
  • The system message can be customized to tailor the agent’s personality and capabilities.
  • The max iterations parameter can be used to control the depth of the agent’s problem-solving attempts.
The Conversational Agent node provides a powerful, flexible foundation for building interactive AI systems. Its ability to maintain context, use tools, and engage in multi-turn dialogues makes it suitable for a wide range of applications where natural, intelligent conversation is required. The integration of memory, tools, and optional features like vision support and moderation makes it a comprehensive solution for complex conversational AI tasks.