codex/codex-rs/core/src/client_common_tests.rs

use codex_api::OpenAiVerbosity;
use codex_api::ResponsesApiRequest;
use codex_api::TextControls;
use codex_api::create_text_param_for_request;
use codex_protocol::config_types::ServiceTier;
use codex_protocol::models::FunctionCallOutputPayload;
use codex_protocol::models::ImageDetail;
use pretty_assertions::assert_eq;

use super::*;

fn prompt_with_image_outputs() -> Prompt {
    Prompt {
        input: vec![
            ResponseItem::Message {
                id: None,
                role: "user".to_string(),
                content: vec![ContentItem::InputImage {
                    image_url: "https://example.com/image.png".to_string(),
                    detail: Some(ImageDetail::Original),
                }],
                phase: None,
            },
            ResponseItem::FunctionCallOutput {
                call_id: "function-call".to_string(),
                output: FunctionCallOutputPayload::from_content_items(vec![
                    FunctionCallOutputContentItem::InputImage {
                        image_url: "data:image/png;base64,function".to_string(),
                        detail: Some(ImageDetail::High),
                    },
                ]),
            },
            ResponseItem::CustomToolCallOutput {
                call_id: "custom-call".to_string(),
                name: None,
                output: FunctionCallOutputPayload::from_content_items(vec![
                    FunctionCallOutputContentItem::InputImage {
                        image_url: "data:image/png;base64,custom".to_string(),
                        detail: Some(ImageDetail::Auto),
                    },
                ]),
            },
        ],
        ..Default::default()
    }
}

#[test]
fn responses_lite_request_copies_strip_image_details() {
    let prompt = prompt_with_image_outputs();
    let original = prompt.input.clone();

    let stripped = prompt.get_formatted_input_for_request(/*use_responses_lite*/ true);

    assert_eq!(
        stripped,
        vec![
            ResponseItem::Message {
                id: None,
                role: "user".to_string(),
                content: vec![ContentItem::InputImage {
                    image_url: "https://example.com/image.png".to_string(),
                    detail: None,
                }],
                phase: None,
            },
            ResponseItem::FunctionCallOutput {
                call_id: "function-call".to_string(),
                output: FunctionCallOutputPayload::from_content_items(vec![
                    FunctionCallOutputContentItem::InputImage {
                        image_url: "data:image/png;base64,function".to_string(),
                        detail: None,
                    },
                ]),
            },
            ResponseItem::CustomToolCallOutput {
                call_id: "custom-call".to_string(),
                name: None,
                output: FunctionCallOutputPayload::from_content_items(vec![
                    FunctionCallOutputContentItem::InputImage {
                        image_url: "data:image/png;base64,custom".to_string(),
                        detail: None,
                    },
                ]),
            },
        ]
    );
    assert_eq!(prompt.input, original);
    assert_eq!(
        prompt.get_formatted_input_for_request(/*use_responses_lite*/ false),
        original
    );
}

#[test]
fn serializes_text_verbosity_when_set() {
    let input: Vec<ResponseItem> = vec![];
    let tools: Vec<serde_json::Value> = vec![];
    let req = ResponsesApiRequest {
        model: "gpt-5.4".to_string(),
        instructions: "i".to_string(),
        input,
        tools,
        tool_choice: "auto".to_string(),
        parallel_tool_calls: true,
        reasoning: None,
        store: false,
        stream: true,
        include: vec![],
        prompt_cache_key: None,
        service_tier: None,
        text: Some(TextControls {
            verbosity: Some(OpenAiVerbosity::Low),
            format: None,
        }),
        client_metadata: None,
    };

    let v = serde_json::to_value(&req).expect("json");
    assert_eq!(
        v.get("text")
            .and_then(|t| t.get("verbosity"))
            .and_then(|s| s.as_str()),
        Some("low")
    );
}

#[test]
fn serializes_text_schema_with_strict_format() {
    let input: Vec<ResponseItem> = vec![];
    let tools: Vec<serde_json::Value> = vec![];
    let schema = serde_json::json!({
        "type": "object",
        "properties": {
            "answer": {"type": "string"}
        },
        "required": ["answer"],
    });
    let text_controls = create_text_param_for_request(
        /*verbosity*/ None,
        &Some(schema.clone()),
        /*output_schema_strict*/ true,
    )
    .expect("text controls");

    let req = ResponsesApiRequest {
        model: "gpt-5.4".to_string(),
        instructions: "i".to_string(),
        input,
        tools,
        tool_choice: "auto".to_string(),
        parallel_tool_calls: true,
        reasoning: None,
        store: false,
        stream: true,
        include: vec![],
        prompt_cache_key: None,
        service_tier: None,
        text: Some(text_controls),
        client_metadata: None,
    };

    let v = serde_json::to_value(&req).expect("json");
    let text = v.get("text").expect("text field");
    assert!(text.get("verbosity").is_none());
    let format = text.get("format").expect("format field");

    assert_eq!(
        format.get("name"),
        Some(&serde_json::Value::String("codex_output_schema".into()))
    );
    assert_eq!(
        format.get("type"),
        Some(&serde_json::Value::String("json_schema".into()))
    );
    assert_eq!(format.get("strict"), Some(&serde_json::Value::Bool(true)));
    assert_eq!(format.get("schema"), Some(&schema));
}

#[test]
fn serializes_text_schema_with_non_strict_format() {
    let schema = serde_json::json!({
        "type": "object",
        "properties": {
            "answer": {"type": "string"},
            "rationale": {"type": "string"}
        },
        "required": ["answer"],
        "additionalProperties": false
    });
    let text_controls = create_text_param_for_request(
        /*verbosity*/ None,
        &Some(schema.clone()),
        /*output_schema_strict*/ false,
    )
    .expect("text controls");

    let format = text_controls.format.expect("format field");
    assert!(!format.strict);
    assert_eq!(format.schema, schema);
}

#[test]
fn omits_text_when_not_set() {
    let input: Vec<ResponseItem> = vec![];
    let tools: Vec<serde_json::Value> = vec![];
    let req = ResponsesApiRequest {
        model: "gpt-5.4".to_string(),
        instructions: "i".to_string(),
        input,
        tools,
        tool_choice: "auto".to_string(),
        parallel_tool_calls: true,
        reasoning: None,
        store: false,
        stream: true,
        include: vec![],
        prompt_cache_key: None,
        service_tier: None,
        text: None,
        client_metadata: None,
    };

    let v = serde_json::to_value(&req).expect("json");
    assert!(v.get("text").is_none());
}

#[test]
fn serializes_flex_service_tier_when_set() {
    let req = ResponsesApiRequest {
        model: "gpt-5.4".to_string(),
        instructions: "i".to_string(),
        input: vec![],
        tools: vec![],
        tool_choice: "auto".to_string(),
        parallel_tool_calls: true,
        reasoning: None,
        store: false,
        stream: true,
        include: vec![],
        prompt_cache_key: None,
        service_tier: Some(ServiceTier::Flex.to_string()),
        text: None,
        client_metadata: None,
    };

    let v = serde_json::to_value(&req).expect("json");
    assert_eq!(
        v.get("service_tier").and_then(|tier| tier.as_str()),
        Some("flex")
    );
}