Generative Search

Generative Search utilizes large language models to transform query results into engaging narratives. This approach processes natural language to generate human-like outputs, suitable for enhancing user engagement with data-driven content.

For instance, if your data includes various historical events, users can simply enter a general prompt like “important events in the 20th century” and receive a narrative summarizing the events in an engaging manner. This method is beneficial for creating narrative content, resonating with human readers, and summarizing complex data into key insights.

Usage

From One

This method generates a result for each object based on a single prompt. You can reference object properties within the prompt using curly braces to dynamically insert values.

Syntax:

From Many

This method generates a result by applying a single prompt to a group of objects. For each object returned by the query, the specified properties are extracted and combined into an array. This array, along with the prompt, will be used to generate the result.

Syntax:

Options

Advanced Usage

Using Messages Instead of Prompts

In addition to using a single prompt, you can leverage an array of messages to define interactions. This method allows for richer context and more dynamic input-output control. Each message can have the following properties:

• content: (string) The actual content of the message.
• type: (text | image) The type of content. Defaults to text.
• role: (system | user | assistant) Defines the role of the message. The system role sets the behavior or instructions for the assistant, while user is the input from the user, and assistant is the response from the AI. Defaults to “user”.

This method provides a way to handle multi-turn conversations or context-aware responses. Below is an example of how you might structure your messages:

messages: [
  {
    "role": "system",
    "content": "Answer user questions based on this video. {video}"
  },
  {
    "content": "What's this video about?"
  }
]

Using Images as Input

Generative Search supports image inputs through messages. To use images as input:

• The message type must be set to "image".
• The message content must include one of the following:
  • A valid image URL (e.g., HTTPS URL) or a base64-encoded image.
  • A JSONPath pointing to one or more image URLs.
• When using HTTPS URLs, ensure the image size is less than 5MB, as larger images are not supported.

This allows you to provide images as part of the context for generating a result. The model will process the image(s) along with any accompanying instructions.

Using vars

The vars option allows you to transform query results into JSON-serializable data, which can then be referenced in the messages’ content. This feature enables dynamic data injection into the conversation.

Each var has the following properties:

• name: (string) The name of the variable. It can be referenced in the message content by using either "{varName}" or "$..varName".
• formatter: (string) Currently, the only supported formatter is jq.
• expression: (string) The jq expression used to transform the query result into the desired structure.

Note: The vars are calculated sequentially, meaning the output of one var can be used in the next. You can reference the var in the expression by using $varName. You can also reference the original query result by using $root.

Using Cross-References in vars

When using cross-references (relations) in vars or within message content, you need to fetch at least one property from the referenced object. This ensures the related object data is accessible during query execution.

Supported Models