Summarizes the input text.


oci generative-ai-inference summarize-text-result summarize-text [OPTIONS]

Required Parameters

--compartment-id, -c [text]

The OCID of compartment that the user is authorized to use to call into the Generative AI service.

--input [text]

The input string to be summarized.

--serving-mode [complex type]

This is a complex type whose value must be valid JSON. The value can be provided as a string on the command line or passed in as a file using the file://path/to/file syntax.

The --generate-param-json-input option can be used to generate an example of the JSON which must be provided. We recommend storing this example in a file, modifying it as needed and then passing it back in via the file:// syntax.

Optional Parameters

--additional-command [text]

A free-form instruction for modifying how the summaries get generated. Should complete the sentence “Generate a summary _”. For example, “focusing on the next steps” or “written by Yoda”.

--extractiveness [text]

Controls how close to the original text the summary is. High extractiveness summaries will lean towards reusing sentences verbatim, while low extractiveness summaries will tend to paraphrase more.

Accepted values are:

--format [text]

Indicates the style in which the summary will be delivered - in a free form paragraph or in bullet points. If “AUTO” is selected, the best option will be picked based on the input text.

Accepted values are:

--from-json [text]

Provide input to this command as a JSON document from a file using the file://path-to/file syntax.

The --generate-full-command-json-input option can be used to generate a sample json file to be used with this command option. The key names are pre-populated and match the command option names (converted to camelCase format, e.g. compartment-id –> compartmentId), while the values of the keys need to be populated by the user before using the sample file as an input to this command. For any command option that accepts multiple values, the value of the key can be a JSON array.

Options can still be provided on the command line. If an option exists in both the JSON document and the command line then the command line specified value will be used.

For examples on usage of this option, please see our “using CLI with advanced JSON options” link:

--is-echo [boolean]

Whether or not to include the original inputs in the response.

--length [text]

Indicates the approximate length of the summary. If “AUTO” is selected, the best option will be picked based on the input text.

Accepted values are:

--temperature [text]

A number that sets the randomness of the generated output. Lower temperatures mean less random generations.

Use lower numbers for tasks with a correct answer such as question answering or summarizing. High temperatures can generate hallucinations or factually incorrect information. Start with temperatures lower than 1.0, and increase the temperature for more creative outputs, as you regenerate the prompts to refine the outputs.

Example using required parameter

Copy and paste the following example into a JSON file, replacing the example parameters with your own.

    oci generative-ai-inference summarize-text-result summarize-text --generate-param-json-input serving-mode > serving-mode.json

Copy the following CLI commands into a file named Run the command by typing “bash” and replacing the example parameters with your own.

Please note this sample will only work in the POSIX-compliant bash-like shell. You need to set up the OCI configuration and appropriate security policies before trying the examples.

    export compartment_id=<substitute-value-of-compartment_id> #
    export input=<substitute-value-of-input> #

    oci generative-ai-inference summarize-text-result summarize-text --compartment-id $compartment_id --input $input --serving-mode file://serving-mode.json

Other Examples


Send an inference request for text summarization


 oci generative-ai-inference summarize-text-result summarize-text --compartment-id $COMPARTMENT_ID --serving-mode "{\"servingType\": \"ON_DEMAND\", \"modelId\": \"$SERVING_MODEL_ID\"}" --input "$INPUT" --read-timeout 240 --region "$REGION"


 "data": {
      "id": "91a266f1-107d-4803-b8a8-4576b8c0a360",
      "input": null,
      "model-id": "cohere.command",
      "model-version": "15.6",
      "summary": "Quantum dots are nanometer-sized semiconductor particles with unique optical and electronic properties arising from quantum mechanical effects that differ from those of bulk materials. When UV-lit, electrons in quantum dots can be excited to higher energy levels. These excited electrons can then release this energy as light, an effect known as photoluminescence. The color of the emitted light depends on the energy gap between the conduction and valence bands or between the discrete energy levels in the quantum dots. As a result of these properties, quantum dots have several applications in nanotechnology and materials science, including electronics, quantum computing, healthcare, and solar cells."