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Feature Overview and Creation

Chat is used to create dataset-based question-answering applications. After configuring the datasets, large language model, system prompt, and retrieval parameters for a Chat, users can ask questions based on dataset content in the chat interface.

Chat supports dataset retrieval, citation display, keyword analysis, multi-turn conversation optimization, cross-language search, web search, and other capabilities.

After creating a Chat, configure its datasets, model, system prompt, and retrieval parameters for the actual business scenario before using it to answer questions. You can modify these settings later.

Create a Chat

  1. Select Chat in the top navigation bar.

  2. Click Create chat.

    Open Chat from the navigation bar

  3. Enter a name for the Chat.

  4. Confirm the creation.

    Enter a name for the Chat

  5. Click the gear icon on the right to open Chat setting. Configure Model, Opening greeting, and Datasets. Expand Advanced settings for shared options and the separate Naive and Agentic settings groups.

    Open the newly created Chat conversation page

    Configure the newly created Chat

  6. Save the setting, then open the chat window to test and use the Chat.

You do not need to complete every setting when creating a Chat. You can return to the corresponding Chat from the Chat list and update its configuration later.

Basic Information

You can set the following basic information for a Chat:

  • Name: The display name used to identify the Chat in the Chat list, published pages, or embedded scenarios. Use a clear name that reflects its business scope or purpose.
  • Avatar: The image displayed for the Chat. Select a brand, product, or general-purpose icon according to the scenario.
  • Description: A brief description of the Chat's purpose, service scope, intended audience, or dataset coverage.

Basic Chat information

Configure a Chat

Complete the main Chat settings according to the actual scenario:

  • Dataset: Select the dataset the Chat can retrieve from. When answering a question, the system searches for relevant content in the associated dataset.
  • Model: Select the large language model used to generate answers.
  • Naive settings: Under Advanced settings > Naive, configure System prompt, Similarity threshold, Keyword similarity weight, Top N, and other retrieval options. Keyword similarity weight is the full-text weight; the vector weight is 1 minus this value.
  • Shared options: Under Advanced settings, configure Show citations, Text to speech, and Web search provider.
  • Agentic settings: Under Advanced settings > Agentic, configure Decision model and Agentic template.

These groups configure the two modes. Select Naive or Agentic beside the message box when asking a question. See Chat Configuration for the complete settings and web search prerequisites.

Save the setting and test it in the chat window.

Configure a Chat

Start with the default parameters for a basic test. Then gradually adjust the retrieval parameters and system prompt based on the quality of the actual answers.

Modify an Existing Chat

You can modify an existing Chat at any time. Find the Chat in the Chat list, open Chat setting, update its datasets, model, system prompt, or retrieval parameters, and save the changes.

After changing the configuration, rerun tests with representative questions to confirm that the new settings meet expectations.