reuse_prediction_agent_and_get_prediction

This tool provides prediction input data to a previously created Chronulus BinaryPredictor agent and returns the consensus a prediction from a panel of experts along with their individual estimates and text explanations. The agent also returns the alpha and beta parameters for a Beta distribution that allows you to estimate the confidence interval of its consensus probability estimate.

When to use this tool:

Use this tool to request a prediction from a Chronulus prediction agent that you have already created and when your input data model is unchanged
Use this tool to request a probability estimate from an existing prediction agent in a situation when there is a binary outcome
This tool is specifically made to estimate the probability of an event occurring and not occurring and does not require historical data

How to use this tool:

First, make sure you have a session_id for the prediction use case.
Next, think about the features / characteristics most suitable for producing the requested prediction and then create an input_data_model that corresponds to the input_data you will provide for the thing or event being predicted.
Remember to pass all relevant information to Chronulus including text and images provided by the user.
If a user gives you files about a thing you are forecasting or predicting, you should pass these as inputs to the agent using one of the following types:
- ImageFromFile
- List[ImageFromFile]
- TextFromFile
- List[TextFromFile]
- PdfFromFile
- List[PdfFromFile]
If you have a large amount of text (over 500 words) to pass to the agent, you should use the Text or List[Text] field types
Finally, provide the number of experts to consult. The minimum and default number is 2, but users may request up to 30 30 opinions in situations where reproducibility and risk sensitively is of the utmost importance. In most cases, 2 to 5 experts is sufficient.

How to use this tool:

First, make sure you have an agent_id for the prediction agent. The agent is already attached to the correct session. So you do not need to provide a session_id.
Next, reference the input data model that you previously used with the agent and create new input data for the item being predicted that aligns with the previously specified input data model
Remember to pass all relevant information to Chronulus including text and images provided by the user.
If a user gives you files about a thing you are forecasting or predicting, you should pass these as inputs to the agent using one of the following types:
- ImageFromFile
- List[ImageFromFile]
- TextFromFile
- List[TextFromFile]
- PdfFromFile
- List[PdfFromFile]
If you have a large amount of text (over 500 words) to pass to the agent, you should use the Text or List[Text] field types
Finally, provide the number of experts to consult. The minimum and default number is 2, but users may request up to 30 30 opinions in situations where reproducibility and risk sensitively is of the utmost importance. In most cases, 2 to 5 experts is sufficient.

Name	Required	Description
`agent_id`	Yes	The agent_id for the forecasting or prediction use case and previously defined input_data_model
`input_data`	Yes	The forecast inputs that you will pass to the chronulus agent to make the prediction. The keys of the dict should correspond to the InputField name you provided in input_fields.
`num_experts`	Yes	The number of experts to consult when forming consensus

{
  "properties": {
    "agent_id": {
      "description": "The agent_id for the forecasting or prediction use case and previously defined input_data_model",
      "title": "Agent Id",
      "type": "string"
    },
    "input_data": {
      "additionalProperties": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "object"
          },
          {
            "items": {
              "type": "object"
            },
            "type": "array"
          }
        ]
      },
      "description": "The forecast inputs that you will pass to the chronulus agent to make the prediction. The keys of the dict should correspond to the InputField name you provided in input_fields.",
      "title": "Input Data",
      "type": "object"
    },
    "num_experts": {
      "description": "The number of experts to consult when forming consensus",
      "title": "Num Experts",
      "type": "integer"
    }
  },
  "required": [
    "agent_id",
    "input_data",
    "num_experts"
  ],
  "title": "reuse_prediction_agent_and_get_predictionArguments",
  "type": "object"
}

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