rig_core/providers/gemini/

completion.rs

// ================================================================
//! Google Gemini Completion Integration
//! From [Gemini API Reference](https://ai.google.dev/api/generate-content)
// ================================================================
/// `gemini-3.1-flash-lite-preview` completion model
pub const GEMINI_3_1_FLASH_LITE_PREVIEW: &str = "gemini-3.1-flash-lite-preview";
/// `gemini-3-flash-preview` completion model
pub const GEMINI_3_FLASH_PREVIEW: &str = "gemini-3-flash-preview";
/// `gemini-2.5-pro-preview-06-05` completion model
pub const GEMINI_2_5_PRO_PREVIEW_06_05: &str = "gemini-2.5-pro-preview-06-05";
/// `gemini-2.5-pro-preview-05-06` completion model
pub const GEMINI_2_5_PRO_PREVIEW_05_06: &str = "gemini-2.5-pro-preview-05-06";
/// `gemini-2.5-pro-preview-03-25` completion model
pub const GEMINI_2_5_PRO_PREVIEW_03_25: &str = "gemini-2.5-pro-preview-03-25";
/// `gemini-2.5-flash-preview-04-17` completion model
pub const GEMINI_2_5_FLASH_PREVIEW_04_17: &str = "gemini-2.5-flash-preview-04-17";
/// `gemini-2.5-pro-exp-03-25` experimental completion model
pub const GEMINI_2_5_PRO_EXP_03_25: &str = "gemini-2.5-pro-exp-03-25";
/// `gemini-2.5-flash` completion model
pub const GEMINI_2_5_FLASH: &str = "gemini-2.5-flash";
/// `gemini-2.5-flash-image` image generation model, commonly referred to as Nano Banana.
#[cfg(feature = "image")]
#[cfg_attr(docsrs, doc(cfg(feature = "image")))]
pub const GEMINI_2_5_FLASH_IMAGE: &str = "gemini-2.5-flash-image";
/// `gemini-2.0-flash-lite` completion model
pub const GEMINI_2_0_FLASH_LITE: &str = "gemini-2.0-flash-lite";
/// `gemini-2.0-flash` completion model
pub const GEMINI_2_0_FLASH: &str = "gemini-2.0-flash";

use self::gemini_api_types::tool_parameters_to_schema;
use crate::http_client::HttpClientExt;
use crate::message::{self, MimeType, Reasoning};

use crate::providers::gemini::completion::gemini_api_types::{
    AdditionalParameters, FunctionCallingMode, ToolConfig,
};
use crate::providers::gemini::streaming::StreamingCompletionResponse;
use crate::telemetry::SpanCombinator;
use crate::{
    OneOrMany,
    completion::{self, CompletionError, CompletionRequest, GetTokenUsage},
};
use gemini_api_types::{
    Content, FinishReason, FunctionDeclaration, GenerateContentRequest, GenerateContentResponse,
    GenerationConfig, Part, PartKind, Role, Tool,
};
use serde_json::{Map, Value};
use std::convert::TryFrom;
use tracing::{Level, enabled, info_span};
use tracing_futures::Instrument;

use super::Client;

// =================================================================
// Rig Implementation Types
// =================================================================

#[derive(Clone, Debug)]
pub struct CompletionModel<T = reqwest::Client> {
    pub(crate) client: Client<T>,
    pub model: String,
}

impl<T> CompletionModel<T> {
    pub fn new(client: Client<T>, model: impl Into<String>) -> Self {
        Self {
            client,
            model: model.into(),
        }
    }

    pub fn with_model(client: Client<T>, model: &str) -> Self {
        Self {
            client,
            model: model.into(),
        }
    }
}

impl<T> completion::CompletionModel for CompletionModel<T>
where
    T: HttpClientExt + Clone + 'static,
{
    type Response = GenerateContentResponse;
    type StreamingResponse = StreamingCompletionResponse;
    type Client = super::Client<T>;

    fn make(client: &Self::Client, model: impl Into<String>) -> Self {
        Self::new(client.clone(), model)
    }

    async fn completion(
        &self,
        completion_request: CompletionRequest,
    ) -> Result<completion::CompletionResponse<GenerateContentResponse>, CompletionError> {
        let request_model = resolve_request_model(&self.model, &completion_request);
        let span = if tracing::Span::current().is_disabled() {
            info_span!(
                target: "rig::completions",
                "generate_content",
                gen_ai.operation.name = "generate_content",
                gen_ai.provider.name = "gcp.gemini",
                gen_ai.request.model = &request_model,
                gen_ai.system_instructions = &completion_request.preamble,
                gen_ai.response.id = tracing::field::Empty,
                gen_ai.response.model = tracing::field::Empty,
                gen_ai.usage.output_tokens = tracing::field::Empty,
                gen_ai.usage.input_tokens = tracing::field::Empty,
                gen_ai.usage.cache_read.input_tokens = tracing::field::Empty,
                gen_ai.usage.cache_creation.input_tokens = tracing::field::Empty,
                gen_ai.usage.tool_use_prompt_tokens = tracing::field::Empty,
                gen_ai.usage.reasoning_tokens = tracing::field::Empty,
            )
        } else {
            tracing::Span::current()
        };

        let request = create_request_body(completion_request)?;

        if enabled!(Level::TRACE) {
            tracing::trace!(
                target: "rig::completions",
                "Gemini completion request: {}",
                serde_json::to_string_pretty(&request)?
            );
        }

        let body = serde_json::to_vec(&request)?;

        let path = completion_endpoint(&request_model);

        let request = self
            .client
            .post(path.as_str())?
            .body(body)
            .map_err(|e| CompletionError::HttpError(e.into()))?;

        async move {
            let response = self.client.send::<_, Vec<u8>>(request).await?;

            if response.status().is_success() {
                let response_body = response
                    .into_body()
                    .await
                    .map_err(CompletionError::HttpError)?;

                let response_text = String::from_utf8_lossy(&response_body).to_string();

                let response: GenerateContentResponse = serde_json::from_slice(&response_body)
                    .map_err(|err| {
                        tracing::error!(
                            error = %err,
                            body = %response_text,
                            "Failed to deserialize Gemini completion response"
                        );
                        CompletionError::JsonError(err)
                    })?;

                let span = tracing::Span::current();
                span.record_response_metadata(&response);
                span.record_token_usage(&response.usage_metadata);

                if enabled!(Level::TRACE) {
                    tracing::trace!(
                        target: "rig::completions",
                        "Gemini completion response: {}",
                        serde_json::to_string_pretty(&response)?
                    );
                }

                response.try_into()
            } else {
                let text = String::from_utf8_lossy(
                    &response
                        .into_body()
                        .await
                        .map_err(CompletionError::HttpError)?,
                )
                .into();

                Err(CompletionError::ProviderError(text))
            }
        }
        .instrument(span)
        .await
    }

    async fn stream(
        &self,
        request: CompletionRequest,
    ) -> Result<
        crate::streaming::StreamingCompletionResponse<Self::StreamingResponse>,
        CompletionError,
    > {
        CompletionModel::stream(self, request).await
    }
}

pub(crate) fn create_request_body(
    completion_request: CompletionRequest,
) -> Result<GenerateContentRequest, CompletionError> {
    let chat_history = completion_request.chat_history_with_documents();

    let CompletionRequest {
        model: _,
        preamble,
        chat_history: _,
        documents: _,
        tools: function_tools,
        temperature,
        max_tokens,
        tool_choice,
        mut additional_params,
        output_schema,
    } = completion_request;

    let mut full_history = Vec::new();
    full_history.extend(chat_history);
    let (history_system, full_history) = split_system_messages_from_history(full_history);

    let mut additional_params_payload = additional_params
        .take()
        .unwrap_or_else(|| Value::Object(Map::new()));
    let mut additional_tools =
        extract_tools_from_additional_params(&mut additional_params_payload)?;

    let AdditionalParameters {
        mut generation_config,
        additional_params,
    } = serde_json::from_value::<AdditionalParameters>(additional_params_payload)?;

    // Apply output_schema to generation_config, creating one if needed
    if let Some(schema) = output_schema {
        let cfg = generation_config.get_or_insert_with(GenerationConfig::default);
        cfg.response_mime_type = Some("application/json".to_string());
        cfg.response_json_schema = Some(schema.to_value());
    }

    generation_config = generation_config.map(|mut cfg| {
        if let Some(temp) = temperature {
            cfg.temperature = Some(temp);
        };

        if let Some(max_tokens) = max_tokens {
            cfg.max_output_tokens = Some(max_tokens);
        };

        cfg
    });

    let mut system_parts: Vec<Part> = Vec::new();
    if let Some(preamble) = preamble.filter(|preamble| !preamble.is_empty()) {
        system_parts.push(preamble.into());
    }
    for content in history_system {
        if !content.is_empty() {
            system_parts.push(content.into());
        }
    }
    let system_instruction = if system_parts.is_empty() {
        None
    } else {
        Some(Content {
            parts: system_parts,
            role: Some(Role::Model),
        })
    };

    let mut tools = if function_tools.is_empty() {
        Vec::new()
    } else {
        vec![serde_json::to_value(Tool::try_from(function_tools)?)?]
    };
    tools.append(&mut additional_tools);
    let tools = if tools.is_empty() { None } else { Some(tools) };

    let tool_config = if let Some(cfg) = tool_choice {
        Some(ToolConfig {
            function_calling_config: Some(FunctionCallingMode::try_from(cfg)?),
        })
    } else {
        None
    };

    let request = GenerateContentRequest {
        contents: full_history
            .into_iter()
            .map(|msg| {
                msg.try_into()
                    .map_err(|e| CompletionError::RequestError(Box::new(e)))
            })
            .collect::<Result<Vec<_>, _>>()?,
        generation_config,
        safety_settings: None,
        tools,
        tool_config,
        system_instruction,
        additional_params,
    };

    Ok(request)
}

fn split_system_messages_from_history(
    history: Vec<completion::Message>,
) -> (Vec<String>, Vec<completion::Message>) {
    let mut system = Vec::new();
    let mut remaining = Vec::new();

    for message in history {
        match message {
            completion::Message::System { content } => system.push(content),
            other => remaining.push(other),
        }
    }

    (system, remaining)
}

fn extract_tools_from_additional_params(
    additional_params: &mut Value,
) -> Result<Vec<Value>, CompletionError> {
    if let Some(map) = additional_params.as_object_mut()
        && let Some(raw_tools) = map.remove("tools")
    {
        return serde_json::from_value::<Vec<Value>>(raw_tools).map_err(|err| {
            CompletionError::RequestError(
                format!("Invalid Gemini `additional_params.tools` payload: {err}").into(),
            )
        });
    }

    Ok(Vec::new())
}

pub(crate) fn resolve_request_model(
    default_model: &str,
    completion_request: &CompletionRequest,
) -> String {
    completion_request
        .model
        .clone()
        .unwrap_or_else(|| default_model.to_string())
}

pub(crate) fn completion_endpoint(model: &str) -> String {
    format!("/v1beta/models/{model}:generateContent")
}

pub(crate) fn streaming_endpoint(model: &str) -> String {
    format!("/v1beta/models/{model}:streamGenerateContent")
}

impl TryFrom<completion::ToolDefinition> for Tool {
    type Error = CompletionError;

    fn try_from(tool: completion::ToolDefinition) -> Result<Self, Self::Error> {
        let parameters = tool_parameters_to_schema(tool.parameters)?;

        Ok(Self {
            function_declarations: vec![FunctionDeclaration {
                name: tool.name,
                description: tool.description,
                parameters,
            }],
            code_execution: None,
        })
    }
}

impl TryFrom<Vec<completion::ToolDefinition>> for Tool {
    type Error = CompletionError;

    fn try_from(tools: Vec<completion::ToolDefinition>) -> Result<Self, Self::Error> {
        let mut function_declarations = Vec::new();

        for tool in tools {
            let parameters = tool_parameters_to_schema(tool.parameters).map_err(|e| {
                CompletionError::ProviderError(format!(
                    "Tool '{}' could not be converted to a schema: {:?}",
                    tool.name, e,
                ))
            })?;

            function_declarations.push(FunctionDeclaration {
                name: tool.name,
                description: tool.description,
                parameters,
            });
        }

        Ok(Self {
            function_declarations,
            code_execution: None,
        })
    }
}

pub(crate) fn function_call_finish_reason_error(
    reason: &FinishReason,
    finish_message: Option<&str>,
) -> Option<CompletionError> {
    match reason {
        FinishReason::MalformedFunctionCall
        | FinishReason::UnexpectedToolCall
        | FinishReason::MissingThoughtSignature
        | FinishReason::TooManyToolCalls
        | FinishReason::MalformedResponse => {
            let message = finish_message.unwrap_or("no finish message provided");
            Some(CompletionError::ResponseError(format!(
                "Gemini stopped with finish_reason={reason:?}: {message}"
            )))
        }
        _ => None,
    }
}

impl TryFrom<GenerateContentResponse> for completion::CompletionResponse<GenerateContentResponse> {
    type Error = CompletionError;

    fn try_from(response: GenerateContentResponse) -> Result<Self, Self::Error> {
        let candidate = response.candidates.first().ok_or_else(|| {
            CompletionError::ResponseError("No response candidates in response".into())
        })?;

        if let Some(reason) = candidate.finish_reason.as_ref()
            && let Some(err) =
                function_call_finish_reason_error(reason, candidate.finish_message.as_deref())
        {
            return Err(err);
        }

        let content = candidate
            .content
            .as_ref()
            .ok_or_else(|| {
                let reason = candidate
                    .finish_reason
                    .as_ref()
                    .map(|r| format!("finish_reason={r:?}"))
                    .unwrap_or_else(|| "finish_reason=<unknown>".to_string());
                let message = candidate
                    .finish_message
                    .as_deref()
                    .unwrap_or("no finish message provided");
                CompletionError::ResponseError(format!(
                    "Gemini candidate missing content ({reason}, finish_message={message})"
                ))
            })?
            .parts
            .iter()
            .map(
                |Part {
                     thought,
                     thought_signature,
                     part,
                     ..
                 }| {
                    Ok(match part {
                        PartKind::Text(text) => {
                            if let Some(thought) = thought
                                && *thought
                            {
                                completion::AssistantContent::Reasoning(
                                    Reasoning::new_with_signature(text, thought_signature.clone()),
                                )
                            } else {
                                completion::AssistantContent::text(text)
                            }
                        }
                        PartKind::InlineData(inline_data) => {
                            let mime_type =
                                message::MediaType::from_mime_type(&inline_data.mime_type);

                            match mime_type {
                                Some(message::MediaType::Image(media_type)) => {
                                    message::AssistantContent::image_base64(
                                        &inline_data.data,
                                        Some(media_type),
                                        Some(message::ImageDetail::default()),
                                    )
                                }
                                _ => {
                                    return Err(CompletionError::ResponseError(format!(
                                        "Unsupported media type {mime_type:?}"
                                    )));
                                }
                            }
                        }
                        PartKind::FunctionCall(function_call) => {
                            completion::AssistantContent::ToolCall(
                                message::ToolCall::new(
                                    function_call.name.clone(),
                                    message::ToolFunction::new(
                                        function_call.name.clone(),
                                        function_call.args.clone(),
                                    ),
                                )
                                .with_signature(thought_signature.clone()),
                            )
                        }
                        _ => {
                            return Err(CompletionError::ResponseError(
                                "Response did not contain a message or tool call".into(),
                            ));
                        }
                    })
                },
            )
            .collect::<Result<Vec<_>, _>>()?;

        let choice = OneOrMany::many(content).map_err(|_| {
            CompletionError::ResponseError(
                "Response contained no message or tool call (empty)".to_owned(),
            )
        })?;

        let usage = response
            .usage_metadata
            .as_ref()
            .map(GetTokenUsage::token_usage)
            .unwrap_or_default();

        Ok(completion::CompletionResponse {
            choice,
            usage,
            raw_response: response,
            message_id: None,
        })
    }
}

pub mod gemini_api_types {
    use crate::telemetry::ProviderResponseExt;
    use std::{collections::HashMap, convert::Infallible, str::FromStr};

    // =================================================================
    // Gemini API Types
    // =================================================================
    use serde::{Deserialize, Serialize};
    use serde_json::{Value, json};

    use crate::completion::GetTokenUsage;
    use crate::message::{DocumentSourceKind, ImageMediaType, MessageError, MimeType};
    use crate::{
        completion::CompletionError,
        message::{self},
        providers::gemini::gemini_api_types::{CodeExecutionResult, ExecutableCode},
    };

    #[derive(Debug, Deserialize, Serialize, Default)]
    #[serde(rename_all = "camelCase")]
    pub struct AdditionalParameters {
        /// Change your Gemini request configuration.
        pub generation_config: Option<GenerationConfig>,
        /// Any additional parameters that you want.
        #[serde(flatten, skip_serializing_if = "Option::is_none")]
        pub additional_params: Option<serde_json::Value>,
    }

    impl AdditionalParameters {
        pub fn with_config(mut self, cfg: GenerationConfig) -> Self {
            self.generation_config = Some(cfg);
            self
        }

        pub fn with_params(mut self, params: serde_json::Value) -> Self {
            self.additional_params = Some(params);
            self
        }
    }

    /// Response from the model supporting multiple candidate responses.
    /// Safety ratings and content filtering are reported for both prompt in GenerateContentResponse.prompt_feedback
    /// and for each candidate in finishReason and in safetyRatings.
    /// The API:
    ///     - Returns either all requested candidates or none of them
    ///     - Returns no candidates at all only if there was something wrong with the prompt (check promptFeedback)
    ///     - Reports feedback on each candidate in finishReason and safetyRatings.
    #[derive(Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct GenerateContentResponse {
        pub response_id: String,
        /// Candidate responses from the model.
        pub candidates: Vec<ContentCandidate>,
        /// Returns the prompt's feedback related to the content filters.
        pub prompt_feedback: Option<PromptFeedback>,
        /// Output only. Metadata on the generation requests' token usage.
        pub usage_metadata: Option<UsageMetadata>,
        pub model_version: Option<String>,
    }

    impl ProviderResponseExt for GenerateContentResponse {
        type OutputMessage = ContentCandidate;
        type Usage = UsageMetadata;

        fn get_response_id(&self) -> Option<String> {
            Some(self.response_id.clone())
        }

        fn get_response_model_name(&self) -> Option<String> {
            self.model_version.clone()
        }

        fn get_output_messages(&self) -> Vec<Self::OutputMessage> {
            self.candidates.clone()
        }

        fn get_text_response(&self) -> Option<String> {
            let str = self
                .candidates
                .iter()
                .filter_map(|x| {
                    let content = x.content.as_ref()?;
                    if content.role.as_ref().is_none_or(|y| y != &Role::Model) {
                        return None;
                    }

                    let res = content
                        .parts
                        .iter()
                        .filter_map(|part| {
                            if let PartKind::Text(ref str) = part.part {
                                Some(str.to_owned())
                            } else {
                                None
                            }
                        })
                        .collect::<Vec<String>>()
                        .join("\n");

                    Some(res)
                })
                .collect::<Vec<String>>()
                .join("\n");

            if str.is_empty() { None } else { Some(str) }
        }

        fn get_usage(&self) -> Option<Self::Usage> {
            self.usage_metadata.clone()
        }
    }

    /// A response candidate generated from the model.
    #[derive(Clone, Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct ContentCandidate {
        /// Output only. Generated content returned from the model.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub content: Option<Content>,
        /// Optional. Output only. The reason why the model stopped generating tokens.
        /// If empty, the model has not stopped generating tokens.
        pub finish_reason: Option<FinishReason>,
        /// List of ratings for the safety of a response candidate.
        /// There is at most one rating per category.
        pub safety_ratings: Option<Vec<SafetyRating>>,
        /// Output only. Citation information for model-generated candidate.
        /// This field may be populated with recitation information for any text included in the content.
        /// These are passages that are "recited" from copyrighted material in the foundational LLM's training data.
        pub citation_metadata: Option<CitationMetadata>,
        /// Output only. Token count for this candidate.
        pub token_count: Option<i32>,
        /// Output only.
        pub avg_logprobs: Option<f64>,
        /// Output only. Log-likelihood scores for the response tokens and top tokens
        pub logprobs_result: Option<LogprobsResult>,
        /// Output only. Index of the candidate in the list of response candidates.
        pub index: Option<i32>,
        /// Output only. Additional information about why the model stopped generating tokens.
        pub finish_message: Option<String>,
    }

    #[derive(Clone, Debug, Deserialize, Serialize)]
    pub struct Content {
        /// Ordered Parts that constitute a single message. Parts may have different MIME types.
        #[serde(default)]
        pub parts: Vec<Part>,
        /// The producer of the content. Must be either 'user' or 'model'.
        /// Useful to set for multi-turn conversations, otherwise can be left blank or unset.
        pub role: Option<Role>,
    }

    impl TryFrom<message::Message> for Content {
        type Error = message::MessageError;

        fn try_from(msg: message::Message) -> Result<Self, Self::Error> {
            Ok(match msg {
                message::Message::System { content } => Content {
                    parts: vec![content.into()],
                    role: Some(Role::User),
                },
                message::Message::User { content } => Content {
                    parts: content
                        .into_iter()
                        .map(|c| c.try_into())
                        .collect::<Result<Vec<_>, _>>()?,
                    role: Some(Role::User),
                },
                message::Message::Assistant { content, .. } => Content {
                    role: Some(Role::Model),
                    parts: content
                        .into_iter()
                        .map(|content| content.try_into())
                        .collect::<Result<Vec<_>, _>>()?,
                },
            })
        }
    }

    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    #[serde(rename_all = "lowercase")]
    pub enum Role {
        User,
        Model,
    }

    #[derive(Debug, Default, Deserialize, Serialize, Clone, PartialEq)]
    #[serde(rename_all = "camelCase")]
    pub struct Part {
        /// whether or not the part is a reasoning/thinking text or not
        #[serde(skip_serializing_if = "Option::is_none")]
        pub thought: Option<bool>,
        /// an opaque sig for the thought so it can be reused - is a base64 string
        #[serde(skip_serializing_if = "Option::is_none")]
        pub thought_signature: Option<String>,
        #[serde(flatten)]
        pub part: PartKind,
        #[serde(flatten, skip_serializing_if = "Option::is_none")]
        pub additional_params: Option<Value>,
    }

    /// A datatype containing media that is part of a multi-part [Content] message.
    /// A Part consists of data which has an associated datatype. A Part can only contain one of the accepted types in Part.data.
    /// A Part must have a fixed IANA MIME type identifying the type and subtype of the media if the inlineData field is filled with raw bytes.
    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    #[serde(rename_all = "camelCase")]
    pub enum PartKind {
        Text(String),
        InlineData(Blob),
        FunctionCall(FunctionCall),
        FunctionResponse(FunctionResponse),
        FileData(FileData),
        ExecutableCode(ExecutableCode),
        CodeExecutionResult(CodeExecutionResult),
    }

    // This default instance is primarily so we can easily fill in the optional fields of `Part`
    // So this instance for `PartKind` (and the allocation it would cause) should be optimized away
    impl Default for PartKind {
        fn default() -> Self {
            Self::Text(String::new())
        }
    }

    impl From<String> for Part {
        fn from(text: String) -> Self {
            Self {
                thought: Some(false),
                thought_signature: None,
                part: PartKind::Text(text),
                additional_params: None,
            }
        }
    }

    impl From<&str> for Part {
        fn from(text: &str) -> Self {
            Self::from(text.to_string())
        }
    }

    impl FromStr for Part {
        type Err = Infallible;

        fn from_str(s: &str) -> Result<Self, Self::Err> {
            Ok(s.into())
        }
    }

    impl TryFrom<(ImageMediaType, DocumentSourceKind)> for PartKind {
        type Error = message::MessageError;
        fn try_from(
            (mime_type, doc_src): (ImageMediaType, DocumentSourceKind),
        ) -> Result<Self, Self::Error> {
            let mime_type = mime_type.to_mime_type().to_string();
            let part = match doc_src {
                DocumentSourceKind::Url(url) => PartKind::FileData(FileData {
                    mime_type: Some(mime_type),
                    file_uri: url,
                }),
                DocumentSourceKind::Base64(data) | DocumentSourceKind::String(data) => {
                    PartKind::InlineData(Blob { mime_type, data })
                }
                DocumentSourceKind::Raw(_) => {
                    return Err(message::MessageError::ConversionError(
                        "Raw files not supported, encode as base64 first".into(),
                    ));
                }
                DocumentSourceKind::FileId(_) => {
                    return Err(message::MessageError::ConversionError(
                        "Provider file IDs are not supported for Gemini image inputs".into(),
                    ));
                }
                DocumentSourceKind::Unknown => {
                    return Err(message::MessageError::ConversionError(
                        "Can't convert an unknown document source".to_string(),
                    ));
                }
            };

            Ok(part)
        }
    }

    impl TryFrom<message::UserContent> for Part {
        type Error = message::MessageError;

        fn try_from(content: message::UserContent) -> Result<Self, Self::Error> {
            match content {
                message::UserContent::Text(message::Text { text, .. }) => Ok(Part {
                    thought: Some(false),
                    thought_signature: None,
                    part: PartKind::Text(text),
                    additional_params: None,
                }),
                message::UserContent::ToolResult(message::ToolResult { id, content, .. }) => {
                    let mut response_json: Option<serde_json::Value> = None;
                    let mut parts: Vec<FunctionResponsePart> = Vec::new();

                    for item in content.iter() {
                        match item {
                            message::ToolResultContent::Text(text) => {
                                let result: serde_json::Value =
                                    serde_json::from_str(&text.text).unwrap_or_else(|error| {
                                        tracing::trace!(
                                            ?error,
                                            "Tool result is not a valid JSON, treat it as normal string"
                                        );
                                        json!(&text.text)
                                    });

                                response_json = Some(match response_json {
                                    Some(mut existing) => {
                                        if let serde_json::Value::Object(ref mut map) = existing {
                                            map.insert("text".to_string(), result);
                                        }
                                        existing
                                    }
                                    None => json!({ "result": result }),
                                });
                            }
                            message::ToolResultContent::Image(image) => {
                                let part = match &image.data {
                                    DocumentSourceKind::Base64(b64) => {
                                        let mime_type = image
                                            .media_type
                                            .as_ref()
                                            .ok_or(message::MessageError::ConversionError(
                                                "Image media type is required for Gemini tool results".to_string(),
                                            ))?
                                            .to_mime_type();

                                        FunctionResponsePart {
                                            inline_data: Some(FunctionResponseInlineData {
                                                mime_type: mime_type.to_string(),
                                                data: b64.clone(),
                                                display_name: None,
                                            }),
                                            file_data: None,
                                        }
                                    }
                                    DocumentSourceKind::Url(url) => {
                                        let mime_type = image
                                            .media_type
                                            .as_ref()
                                            .map(|mt| mt.to_mime_type().to_string());

                                        FunctionResponsePart {
                                            inline_data: None,
                                            file_data: Some(FileData {
                                                mime_type,
                                                file_uri: url.clone(),
                                            }),
                                        }
                                    }
                                    _ => {
                                        return Err(message::MessageError::ConversionError(
                                            "Unsupported image source kind for tool results"
                                                .to_string(),
                                        ));
                                    }
                                };
                                parts.push(part);
                            }
                        }
                    }

                    Ok(Part {
                        thought: Some(false),
                        thought_signature: None,
                        part: PartKind::FunctionResponse(FunctionResponse {
                            name: id,
                            response: response_json,
                            parts: if parts.is_empty() { None } else { Some(parts) },
                        }),
                        additional_params: None,
                    })
                }
                message::UserContent::Image(message::Image {
                    data, media_type, ..
                }) => match media_type {
                    Some(media_type) => match media_type {
                        message::ImageMediaType::JPEG
                        | message::ImageMediaType::PNG
                        | message::ImageMediaType::WEBP
                        | message::ImageMediaType::HEIC
                        | message::ImageMediaType::HEIF => {
                            let part = PartKind::try_from((media_type, data))?;
                            Ok(Part {
                                thought: Some(false),
                                thought_signature: None,
                                part,
                                additional_params: None,
                            })
                        }
                        _ => Err(message::MessageError::ConversionError(format!(
                            "Unsupported image media type {media_type:?}"
                        ))),
                    },
                    None => Err(message::MessageError::ConversionError(
                        "Media type for image is required for Gemini".to_string(),
                    )),
                },
                message::UserContent::Document(message::Document {
                    data, media_type, ..
                }) => {
                    let Some(media_type) = media_type else {
                        return Err(MessageError::ConversionError(
                            "A mime type is required for document inputs to Gemini".to_string(),
                        ));
                    };

                    // For text-like documents (RAG context), convert inline content to plain text.
                    // URL-backed files should stay as file_data references so Gemini can fetch them.
                    if matches!(
                        media_type,
                        message::DocumentMediaType::TXT
                            | message::DocumentMediaType::RTF
                            | message::DocumentMediaType::HTML
                            | message::DocumentMediaType::CSS
                            | message::DocumentMediaType::MARKDOWN
                            | message::DocumentMediaType::CSV
                            | message::DocumentMediaType::XML
                            | message::DocumentMediaType::Javascript
                            | message::DocumentMediaType::Python
                    ) {
                        use base64::Engine;
                        let part = match data {
                            DocumentSourceKind::String(text) => PartKind::Text(text),
                            DocumentSourceKind::Base64(data) => {
                                // Decode base64 text payloads.
                                let text = String::from_utf8(
                                    base64::engine::general_purpose::STANDARD
                                        .decode(&data)
                                        .map_err(|e| {
                                            MessageError::ConversionError(format!(
                                                "Failed to decode base64: {e}"
                                            ))
                                        })?,
                                )
                                .map_err(|e| {
                                    MessageError::ConversionError(format!(
                                        "Invalid UTF-8 in document: {e}"
                                    ))
                                })?;
                                PartKind::Text(text)
                            }
                            DocumentSourceKind::Url(file_uri) => PartKind::FileData(FileData {
                                mime_type: Some(media_type.to_mime_type().to_string()),
                                file_uri,
                            }),
                            DocumentSourceKind::Raw(_) => {
                                return Err(MessageError::ConversionError(
                                    "Raw files not supported, encode as base64 first".to_string(),
                                ));
                            }
                            DocumentSourceKind::FileId(_) => {
                                return Err(MessageError::ConversionError(
                                    "Provider file IDs are not supported for Gemini documents"
                                        .to_string(),
                                ));
                            }
                            DocumentSourceKind::Unknown => {
                                return Err(MessageError::ConversionError(
                                    "Document has no body".to_string(),
                                ));
                            }
                        };

                        Ok(Part {
                            thought: Some(false),
                            part,
                            ..Default::default()
                        })
                    } else if !media_type.is_code() {
                        let mime_type = media_type.to_mime_type().to_string();

                        let part = match data {
                            DocumentSourceKind::Url(file_uri) => PartKind::FileData(FileData {
                                mime_type: Some(mime_type),
                                file_uri,
                            }),
                            DocumentSourceKind::Base64(data) | DocumentSourceKind::String(data) => {
                                PartKind::InlineData(Blob { mime_type, data })
                            }
                            DocumentSourceKind::Raw(_) => {
                                return Err(message::MessageError::ConversionError(
                                    "Raw files not supported, encode as base64 first".into(),
                                ));
                            }
                            _ => {
                                return Err(message::MessageError::ConversionError(
                                    "Document has no body".to_string(),
                                ));
                            }
                        };

                        Ok(Part {
                            thought: Some(false),
                            part,
                            ..Default::default()
                        })
                    } else {
                        Err(message::MessageError::ConversionError(format!(
                            "Unsupported document media type {media_type:?}"
                        )))
                    }
                }

                message::UserContent::Audio(message::Audio {
                    data, media_type, ..
                }) => {
                    let Some(media_type) = media_type else {
                        return Err(MessageError::ConversionError(
                            "A mime type is required for audio inputs to Gemini".to_string(),
                        ));
                    };

                    let mime_type = media_type.to_mime_type().to_string();

                    let part = match data {
                        DocumentSourceKind::Base64(data) => {
                            PartKind::InlineData(Blob { data, mime_type })
                        }

                        DocumentSourceKind::Url(file_uri) => PartKind::FileData(FileData {
                            mime_type: Some(mime_type),
                            file_uri,
                        }),
                        DocumentSourceKind::String(_) => {
                            return Err(message::MessageError::ConversionError(
                                "Strings cannot be used as audio files!".into(),
                            ));
                        }
                        DocumentSourceKind::Raw(_) => {
                            return Err(message::MessageError::ConversionError(
                                "Raw files not supported, encode as base64 first".into(),
                            ));
                        }
                        DocumentSourceKind::FileId(_) => {
                            return Err(message::MessageError::ConversionError(
                                "Provider file IDs are not supported for Gemini audio inputs"
                                    .into(),
                            ));
                        }
                        DocumentSourceKind::Unknown => {
                            return Err(message::MessageError::ConversionError(
                                "Content has no body".to_string(),
                            ));
                        }
                    };

                    Ok(Part {
                        thought: Some(false),
                        part,
                        ..Default::default()
                    })
                }
                message::UserContent::Video(message::Video {
                    data,
                    media_type,
                    additional_params,
                    ..
                }) => {
                    let mime_type = media_type.map(|media_ty| media_ty.to_mime_type().to_string());

                    let part = match data {
                        DocumentSourceKind::Url(file_uri) => {
                            if file_uri.starts_with("https://www.youtube.com") {
                                PartKind::FileData(FileData {
                                    mime_type,
                                    file_uri,
                                })
                            } else {
                                if mime_type.is_none() {
                                    return Err(MessageError::ConversionError(
                                        "A mime type is required for non-Youtube video file inputs to Gemini"
                                            .to_string(),
                                    ));
                                }

                                PartKind::FileData(FileData {
                                    mime_type,
                                    file_uri,
                                })
                            }
                        }
                        DocumentSourceKind::Base64(data) => {
                            let Some(mime_type) = mime_type else {
                                return Err(MessageError::ConversionError(
                                    "A media type is expected for base64 encoded strings"
                                        .to_string(),
                                ));
                            };
                            PartKind::InlineData(Blob { mime_type, data })
                        }
                        DocumentSourceKind::String(_) => {
                            return Err(message::MessageError::ConversionError(
                                "Strings cannot be used as audio files!".into(),
                            ));
                        }
                        DocumentSourceKind::Raw(_) => {
                            return Err(message::MessageError::ConversionError(
                                "Raw file data not supported, encode as base64 first".into(),
                            ));
                        }
                        DocumentSourceKind::FileId(_) => {
                            return Err(message::MessageError::ConversionError(
                                "Provider file IDs are not supported for Gemini video inputs"
                                    .into(),
                            ));
                        }
                        DocumentSourceKind::Unknown => {
                            return Err(message::MessageError::ConversionError(
                                "Media type for video is required for Gemini".to_string(),
                            ));
                        }
                    };

                    Ok(Part {
                        thought: Some(false),
                        thought_signature: None,
                        part,
                        additional_params,
                    })
                }
            }
        }
    }

    impl TryFrom<message::AssistantContent> for Part {
        type Error = message::MessageError;

        fn try_from(content: message::AssistantContent) -> Result<Self, Self::Error> {
            match content {
                message::AssistantContent::Text(message::Text { text, .. }) => Ok(text.into()),
                message::AssistantContent::Image(message::Image {
                    data, media_type, ..
                }) => match media_type {
                    Some(media_type) => match media_type {
                        message::ImageMediaType::JPEG
                        | message::ImageMediaType::PNG
                        | message::ImageMediaType::WEBP
                        | message::ImageMediaType::HEIC
                        | message::ImageMediaType::HEIF => {
                            let part = PartKind::try_from((media_type, data))?;
                            Ok(Part {
                                thought: Some(false),
                                thought_signature: None,
                                part,
                                additional_params: None,
                            })
                        }
                        _ => Err(message::MessageError::ConversionError(format!(
                            "Unsupported image media type {media_type:?}"
                        ))),
                    },
                    None => Err(message::MessageError::ConversionError(
                        "Media type for image is required for Gemini".to_string(),
                    )),
                },
                message::AssistantContent::ToolCall(tool_call) => Ok(tool_call.into()),
                message::AssistantContent::Reasoning(reasoning) => Ok(Part {
                    thought: Some(true),
                    thought_signature: reasoning.first_signature().map(str::to_owned),
                    part: PartKind::Text(reasoning.display_text()),
                    additional_params: None,
                }),
            }
        }
    }

    impl From<message::ToolCall> for Part {
        fn from(tool_call: message::ToolCall) -> Self {
            Self {
                thought: Some(false),
                thought_signature: tool_call.signature,
                part: PartKind::FunctionCall(FunctionCall {
                    name: tool_call.function.name,
                    args: tool_call.function.arguments,
                }),
                additional_params: None,
            }
        }
    }

    /// Raw media bytes.
    /// Text should not be sent as raw bytes, use the 'text' field.
    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    #[serde(rename_all = "camelCase")]
    pub struct Blob {
        /// The IANA standard MIME type of the source data. Examples: - image/png - image/jpeg
        /// If an unsupported MIME type is provided, an error will be returned.
        pub mime_type: String,
        /// Raw bytes for media formats. A base64-encoded string.
        pub data: String,
    }

    /// A predicted FunctionCall returned from the model that contains a string representing the
    /// FunctionDeclaration.name with the arguments and their values.
    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    pub struct FunctionCall {
        /// Required. The name of the function to call. Must be a-z, A-Z, 0-9, or contain underscores
        /// and dashes, with a maximum length of 63.
        pub name: String,
        /// Optional. The function parameters and values in JSON object format.
        pub args: serde_json::Value,
    }

    impl From<message::ToolCall> for FunctionCall {
        fn from(tool_call: message::ToolCall) -> Self {
            Self {
                name: tool_call.function.name,
                args: tool_call.function.arguments,
            }
        }
    }

    /// The result output from a FunctionCall that contains a string representing the FunctionDeclaration.name
    /// and a structured JSON object containing any output from the function is used as context to the model.
    /// This should contain the result of aFunctionCall made based on model prediction.
    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    pub struct FunctionResponse {
        /// The name of the function to call. Must be a-z, A-Z, 0-9, or contain underscores and dashes,
        /// with a maximum length of 63.
        pub name: String,
        /// The function response in JSON object format.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub response: Option<serde_json::Value>,
        /// Multimodal parts for the function response (e.g., images).
        #[serde(skip_serializing_if = "Option::is_none")]
        pub parts: Option<Vec<FunctionResponsePart>>,
    }

    /// A part of a multimodal function response.
    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    #[serde(rename_all = "camelCase")]
    pub struct FunctionResponsePart {
        /// Inline data containing base64-encoded media content.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub inline_data: Option<FunctionResponseInlineData>,
        /// File data containing a URI reference.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub file_data: Option<FileData>,
    }

    /// Inline data for function response parts.
    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    #[serde(rename_all = "camelCase")]
    pub struct FunctionResponseInlineData {
        /// The IANA standard MIME type of the source data.
        pub mime_type: String,
        /// Raw bytes for media formats. A base64-encoded string.
        pub data: String,
        /// Optional display name for the content.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub display_name: Option<String>,
    }

    /// URI based data.
    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    #[serde(rename_all = "camelCase")]
    pub struct FileData {
        /// Optional. The IANA standard MIME type of the source data.
        pub mime_type: Option<String>,
        /// Required. URI.
        pub file_uri: String,
    }

    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    pub struct SafetyRating {
        pub category: HarmCategory,
        pub probability: HarmProbability,
    }

    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    #[serde(rename_all = "SCREAMING_SNAKE_CASE")]
    pub enum HarmProbability {
        HarmProbabilityUnspecified,
        Negligible,
        Low,
        Medium,
        High,
    }

    #[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
    #[serde(rename_all = "SCREAMING_SNAKE_CASE")]
    pub enum HarmCategory {
        HarmCategoryUnspecified,
        HarmCategoryDerogatory,
        HarmCategoryToxicity,
        HarmCategoryViolence,
        HarmCategorySexually,
        HarmCategoryMedical,
        HarmCategoryDangerous,
        HarmCategoryHarassment,
        HarmCategoryHateSpeech,
        HarmCategorySexuallyExplicit,
        HarmCategoryDangerousContent,
        HarmCategoryCivicIntegrity,
    }

    #[derive(Debug, Deserialize, Clone, Default, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct UsageMetadata {
        #[serde(default)]
        pub prompt_token_count: i32,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub cached_content_token_count: Option<i32>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub candidates_token_count: Option<i32>,
        pub total_token_count: i32,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub thoughts_token_count: Option<i32>,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        pub prompt_tokens_details: Option<Vec<ModalityTokenCount>>,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        pub cache_tokens_details: Option<Vec<ModalityTokenCount>>,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        pub candidates_tokens_details: Option<Vec<ModalityTokenCount>>,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        pub tool_use_prompt_token_count: Option<i32>,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        pub tool_use_prompt_tokens_details: Option<Vec<ModalityTokenCount>>,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        pub traffic_type: Option<TrafficType>,
    }

    #[derive(Clone, Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct ModalityTokenCount {
        pub modality: Modality,
        pub token_count: i32,
    }

    #[derive(Clone, Debug, Deserialize, Serialize)]
    #[serde(rename_all = "SCREAMING_SNAKE_CASE")]
    pub enum Modality {
        ModalityUnspecified,
        Text,
        Image,
        Video,
        Audio,
        Document,
    }

    #[derive(Clone, Debug, Deserialize, Serialize)]
    #[serde(rename_all = "SCREAMING_SNAKE_CASE")]
    pub enum TrafficType {
        TrafficTypeUnspecified,
        OnDemand,
        ProvisionedThroughput,
    }

    impl std::fmt::Display for UsageMetadata {
        fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
            write!(
                f,
                "Prompt token count: {}\nCached content token count: {}\nCandidates token count: {}\nTotal token count: {}",
                self.prompt_token_count,
                match self.cached_content_token_count {
                    Some(count) => count.to_string(),
                    None => "n/a".to_string(),
                },
                match self.candidates_token_count {
                    Some(count) => count.to_string(),
                    None => "n/a".to_string(),
                },
                self.total_token_count
            )
        }
    }

    impl GetTokenUsage for UsageMetadata {
        fn token_usage(&self) -> crate::completion::Usage {
            let mut usage = crate::completion::Usage::new();

            usage.input_tokens = self.prompt_token_count as u64;
            usage.output_tokens = self.candidates_token_count.unwrap_or_default() as u64;
            usage.cached_input_tokens = self.cached_content_token_count.unwrap_or_default() as u64;
            usage.reasoning_tokens = self.thoughts_token_count.unwrap_or_default() as u64;
            usage.tool_use_prompt_tokens =
                self.tool_use_prompt_token_count.unwrap_or_default() as u64;
            usage.total_tokens = self.total_token_count as u64;

            usage
        }
    }

    /// A set of the feedback metadata the prompt specified in [GenerateContentRequest.contents](GenerateContentRequest).
    #[derive(Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct PromptFeedback {
        /// Optional. If set, the prompt was blocked and no candidates are returned. Rephrase the prompt.
        pub block_reason: Option<BlockReason>,
        /// Ratings for safety of the prompt. There is at most one rating per category.
        pub safety_ratings: Option<Vec<SafetyRating>>,
    }

    /// Reason why a prompt was blocked by the model
    #[derive(Debug, Deserialize, Serialize)]
    #[serde(rename_all = "SCREAMING_SNAKE_CASE")]
    pub enum BlockReason {
        /// Default value. This value is unused.
        BlockReasonUnspecified,
        /// Prompt was blocked due to safety reasons. Inspect safetyRatings to understand which safety category blocked it.
        Safety,
        /// Prompt was blocked due to unknown reasons.
        Other,
        /// Prompt was blocked due to the terms which are included from the terminology blocklist.
        Blocklist,
        /// Prompt was blocked due to prohibited content.
        ProhibitedContent,
    }

    #[derive(Clone, Debug, Deserialize, Serialize)]
    #[serde(rename_all = "SCREAMING_SNAKE_CASE")]
    pub enum FinishReason {
        /// Default value. This value is unused.
        FinishReasonUnspecified,
        /// Natural stop point of the model or provided stop sequence.
        Stop,
        /// The maximum number of tokens as specified in the request was reached.
        MaxTokens,
        /// The response candidate content was flagged for safety reasons.
        Safety,
        /// The response candidate content was flagged for recitation reasons.
        Recitation,
        /// The response candidate content was flagged for using an unsupported language.
        Language,
        /// Unknown reason.
        Other,
        /// Token generation stopped because the content contains forbidden terms.
        Blocklist,
        /// Token generation stopped for potentially containing prohibited content.
        ProhibitedContent,
        /// Token generation stopped because the content potentially contains Sensitive Personally Identifiable Information (SPII).
        Spii,
        /// The function call generated by the model is invalid.
        MalformedFunctionCall,
        /// The model emitted a tool call that was not expected by the request.
        UnexpectedToolCall,
        /// The response omitted a thought signature required for a tool-calling turn.
        MissingThoughtSignature,
        /// The model emitted more tool calls than the provider allows for the request.
        TooManyToolCalls,
        /// The provider could not parse the generated response into a valid protocol shape.
        MalformedResponse,
    }

    #[derive(Clone, Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct CitationMetadata {
        pub citation_sources: Vec<CitationSource>,
    }

    #[derive(Clone, Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct CitationSource {
        #[serde(skip_serializing_if = "Option::is_none")]
        pub uri: Option<String>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub start_index: Option<i32>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub end_index: Option<i32>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub license: Option<String>,
    }

    #[derive(Clone, Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct LogprobsResult {
        pub top_candidate: Vec<TopCandidate>,
        pub chosen_candidate: Vec<LogProbCandidate>,
    }

    #[derive(Clone, Debug, Deserialize, Serialize)]
    pub struct TopCandidate {
        pub candidates: Vec<LogProbCandidate>,
    }

    #[derive(Clone, Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct LogProbCandidate {
        pub token: String,
        pub token_id: String,
        pub log_probability: f64,
    }

    /// Gemini API Configuration options for model generation and outputs. Not all parameters are
    /// configurable for every model. From [Gemini API Reference](https://ai.google.dev/api/generate-content#generationconfig)
    /// ### Rig Note:
    /// Can be used to construct a typesafe `additional_params` in rig_core::[AgentBuilder](crate::agent::AgentBuilder).
    #[derive(Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct GenerationConfig {
        /// The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop
        /// at the first appearance of a stop_sequence. The stop sequence will not be included as part of the response.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub stop_sequences: Option<Vec<String>>,
        /// MIME type of the generated candidate text. Supported MIME types are:
        ///     - text/plain:  (default) Text output
        ///     - application/json: JSON response in the response candidates.
        ///     - text/x.enum: ENUM as a string response in the response candidates.
        /// Refer to the docs for a list of all supported text MIME types
        #[serde(skip_serializing_if = "Option::is_none")]
        pub response_mime_type: Option<String>,
        /// Output schema of the generated candidate text. Schemas must be a subset of the OpenAPI schema and can be
        /// objects, primitives or arrays. If set, a compatible responseMimeType must also  be set. Compatible MIME
        /// types: application/json: Schema for JSON response. Refer to the JSON text generation guide for more details.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub response_schema: Option<Schema>,
        /// Optional. The output schema of the generated response.
        /// This is an alternative to responseSchema that accepts a standard JSON Schema.
        /// If this is set, responseSchema must be omitted.
        /// Compatible MIME type: application/json.
        /// Supported properties: $id, $defs, $ref, type, properties, etc.
        #[serde(
            skip_serializing_if = "Option::is_none",
            rename = "_responseJsonSchema"
        )]
        pub _response_json_schema: Option<Value>,
        /// Internal or alternative representation for `response_json_schema`.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub response_json_schema: Option<Value>,
        /// Number of generated responses to return. Currently, this value can only be set to 1. If
        /// unset, this will default to 1.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub candidate_count: Option<i32>,
        /// The maximum number of tokens to include in a response candidate. Note: The default value varies by model, see
        /// the Model.output_token_limit attribute of the Model returned from the getModel function.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub max_output_tokens: Option<u64>,
        /// Controls the randomness of the output. Note: The default value varies by model, see the Model.temperature
        /// attribute of the Model returned from the getModel function. Values can range from [0.0, 2.0].
        #[serde(skip_serializing_if = "Option::is_none")]
        pub temperature: Option<f64>,
        /// The maximum cumulative probability of tokens to consider when sampling. The model uses combined Top-k and
        /// Top-p (nucleus) sampling. Tokens are sorted based on their assigned probabilities so that only the most
        /// likely tokens are considered. Top-k sampling directly limits the maximum number of tokens to consider, while
        /// Nucleus sampling limits the number of tokens based on the cumulative probability. Note: The default value
        /// varies by Model and is specified by theModel.top_p attribute returned from the getModel function. An empty
        /// topK attribute indicates that the model doesn't apply top-k sampling and doesn't allow setting topK on requests.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub top_p: Option<f64>,
        /// The maximum number of tokens to consider when sampling. Gemini models use Top-p (nucleus) sampling or a
        /// combination of Top-k and nucleus sampling. Top-k sampling considers the set of topK most probable tokens.
        /// Models running with nucleus sampling don't allow topK setting. Note: The default value varies by Model and is
        /// specified by theModel.top_p attribute returned from the getModel function. An empty topK attribute indicates
        /// that the model doesn't apply top-k sampling and doesn't allow setting topK on requests.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub top_k: Option<i32>,
        /// Presence penalty applied to the next token's logprobs if the token has already been seen in the response.
        /// This penalty is binary on/off and not dependent on the number of times the token is used (after the first).
        /// Use frequencyPenalty for a penalty that increases with each use. A positive penalty will discourage the use
        /// of tokens that have already been used in the response, increasing the vocabulary. A negative penalty will
        /// encourage the use of tokens that have already been used in the response, decreasing the vocabulary.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub presence_penalty: Option<f64>,
        /// Frequency penalty applied to the next token's logprobs, multiplied by the number of times each token has been
        /// seen in the response so far. A positive penalty will discourage the use of tokens that have already been
        /// used, proportional to the number of times the token has been used: The more a token is used, the more
        /// difficult it is for the  model to use that token again increasing the vocabulary of responses. Caution: A
        /// negative penalty will encourage the model to reuse tokens proportional to the number of times the token has
        /// been used. Small negative values will reduce the vocabulary of a response. Larger negative values will cause
        /// the model to  repeating a common token until it hits the maxOutputTokens limit: "...the the the the the...".
        #[serde(skip_serializing_if = "Option::is_none")]
        pub frequency_penalty: Option<f64>,
        /// If true, export the logprobs results in response.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub response_logprobs: Option<bool>,
        /// Only valid if responseLogprobs=True. This sets the number of top logprobs to return at each decoding step in
        /// [Candidate.logprobs_result].
        #[serde(skip_serializing_if = "Option::is_none")]
        pub logprobs: Option<i32>,
        /// Configuration for thinking/reasoning.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub thinking_config: Option<ThinkingConfig>,
        /// Response modalities requested from models that support multimodal output.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub response_modalities: Option<Vec<ResponseModality>>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub image_config: Option<ImageConfig>,
    }

    impl Default for GenerationConfig {
        fn default() -> Self {
            Self {
                temperature: Some(1.0),
                max_output_tokens: Some(4096),
                stop_sequences: None,
                response_mime_type: None,
                response_schema: None,
                _response_json_schema: None,
                response_json_schema: None,
                candidate_count: None,
                top_p: None,
                top_k: None,
                presence_penalty: None,
                frequency_penalty: None,
                response_logprobs: None,
                logprobs: None,
                thinking_config: None,
                response_modalities: None,
                image_config: None,
            }
        }
    }

    /// Response modalities supported by Gemini multimodal output models.
    #[derive(Clone, Debug, Deserialize, Serialize, PartialEq)]
    #[serde(rename_all = "SCREAMING_SNAKE_CASE")]
    pub enum ResponseModality {
        Text,
        Image,
        Audio,
    }

    /// Thinking depth level for Gemini 3 models.
    #[derive(Clone, Debug, Deserialize, Serialize, PartialEq)]
    #[serde(rename_all = "snake_case")]
    pub enum ThinkingLevel {
        Minimal,
        Low,
        Medium,
        High,
    }

    /// Configuration for the model's thinking/reasoning process.
    /// Note: `thinking_budget` (Gemini 2.5) and `thinking_level` (Gemini 3) are mutually exclusive
    /// and cannot be set in the same request.
    #[derive(Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct ThinkingConfig {
        /// Token budget for thinking. Used by Gemini 2.5 models. Range: 0 to 32768.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub thinking_budget: Option<u32>,
        /// Thinking depth level. Used by Gemini 3 models.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub thinking_level: Option<ThinkingLevel>,
        /// When true, includes summarized versions of the model's reasoning in the response.
        #[serde(skip_serializing_if = "Option::is_none")]
        pub include_thoughts: Option<bool>,
    }

    #[derive(Debug, Deserialize, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct ImageConfig {
        #[serde(skip_serializing_if = "Option::is_none")]
        pub aspect_ratio: Option<String>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub image_size: Option<String>,
    }

    /// The Schema object allows the definition of input and output data types. These types can be objects, but also
    /// primitives and arrays. Represents a select subset of an OpenAPI 3.0 schema object.
    /// From [Gemini API Reference](https://ai.google.dev/api/caching#Schema)
    #[derive(Debug, Deserialize, Serialize, Clone)]
    pub struct Schema {
        pub r#type: String,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub format: Option<String>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub description: Option<String>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub nullable: Option<bool>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub r#enum: Option<Vec<String>>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub max_items: Option<i32>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub min_items: Option<i32>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub properties: Option<HashMap<String, Schema>>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub required: Option<Vec<String>>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub items: Option<Box<Schema>>,
    }

    /// Converts Rig tool parameters into Gemini's schema representation.
    ///
    /// Gemini does not need a `parameters` object for no-argument tools, and it
    /// does not support JSON Schema references, so this helper keeps those
    /// conventions centralized for all Gemini transports.
    pub fn tool_parameters_to_schema(parameters: Value) -> Result<Option<Schema>, CompletionError> {
        if parameters.is_null() || parameters == json!({"type": "object", "properties": {}}) {
            Ok(None)
        } else {
            parameters.try_into().map(Some)
        }
    }

    /// Flattens a JSON schema by resolving all `$ref` references inline.
    /// It takes a JSON schema that may contain `$ref` references to definitions
    /// in `$defs` or `definitions` sections and returns a new schema with all references
    /// resolved and inlined. This is necessary for APIs like Gemini that don't support
    /// schema references.
    pub fn flatten_schema(mut schema: Value) -> Result<Value, CompletionError> {
        // extracting $defs if they exist
        let defs = if let Some(obj) = schema.as_object() {
            obj.get("$defs").or_else(|| obj.get("definitions")).cloned()
        } else {
            None
        };

        let Some(defs_value) = defs else {
            return Ok(schema);
        };

        let Some(defs_obj) = defs_value.as_object() else {
            return Err(CompletionError::ResponseError(
                "$defs must be an object".into(),
            ));
        };

        resolve_refs(&mut schema, defs_obj)?;

        // removing $defs from the final schema because we have inlined everything
        if let Some(obj) = schema.as_object_mut() {
            obj.remove("$defs");
            obj.remove("definitions");
        }

        Ok(schema)
    }

    /// Recursively resolves all `$ref` references in a JSON value by
    /// replacing them with their definitions.
    fn resolve_refs(
        value: &mut Value,
        defs: &serde_json::Map<String, Value>,
    ) -> Result<(), CompletionError> {
        match value {
            Value::Object(obj) => {
                if let Some(ref_value) = obj.get("$ref")
                    && let Some(ref_str) = ref_value.as_str()
                {
                    // "#/$defs/Person" -> "Person"
                    let def_name = parse_ref_path(ref_str)?;

                    let def = defs.get(&def_name).ok_or_else(|| {
                        CompletionError::ResponseError(format!("Reference not found: {}", ref_str))
                    })?;

                    let mut resolved = def.clone();
                    resolve_refs(&mut resolved, defs)?;
                    *value = resolved;
                    return Ok(());
                }

                for (_, v) in obj.iter_mut() {
                    resolve_refs(v, defs)?;
                }
            }
            Value::Array(arr) => {
                for item in arr.iter_mut() {
                    resolve_refs(item, defs)?;
                }
            }
            _ => {}
        }

        Ok(())
    }

    /// Parses a JSON Schema `$ref` path to extract the definition name.
    ///
    /// JSON Schema references use URI fragment syntax to point to definitions within
    /// the same document. This function extracts the definition name from common
    /// reference patterns used in JSON Schema.
    fn parse_ref_path(ref_str: &str) -> Result<String, CompletionError> {
        if let Some(fragment) = ref_str.strip_prefix('#') {
            if let Some(name) = fragment.strip_prefix("/$defs/") {
                Ok(name.to_string())
            } else if let Some(name) = fragment.strip_prefix("/definitions/") {
                Ok(name.to_string())
            } else {
                Err(CompletionError::ResponseError(format!(
                    "Unsupported reference format: {}",
                    ref_str
                )))
            }
        } else {
            Err(CompletionError::ResponseError(format!(
                "Only fragment references (#/...) are supported: {}",
                ref_str
            )))
        }
    }

    /// Helper function to extract the type string from a JSON value.
    /// Handles both direct string types and array types.
    fn extract_type(type_value: &Value) -> Option<String> {
        if let Some(t) = type_value.as_str() {
            return Some(t.to_string());
        }

        type_value.as_array().and_then(|arr| {
            arr.iter()
                .filter_map(|v| v.as_str())
                .find(|t| *t != "null")
                .or_else(|| arr.iter().find_map(|v| v.as_str()))
                .map(str::to_owned)
        })
    }

    fn schema_is_null(obj: &serde_json::Map<String, Value>) -> bool {
        obj.get("type")
            .and_then(extract_type)
            .as_deref()
            .is_some_and(|t| t == "null")
    }

    fn schema_is_nullable(obj: &serde_json::Map<String, Value>) -> bool {
        obj.get("nullable")
            .and_then(|v| v.as_bool())
            .unwrap_or(false)
            || obj
                .get("type")
                .and_then(|v| v.as_array())
                .is_some_and(|arr| arr.iter().any(|v| v.as_str() == Some("null")))
            || ["anyOf", "oneOf", "allOf"].iter().any(|key| {
                obj.get(*key).and_then(|v| v.as_array()).is_some_and(|arr| {
                    arr.iter()
                        .filter_map(|schema| schema.as_object())
                        .any(schema_is_null)
                })
            })
    }

    /// Helper function to extract type from anyOf, oneOf, or allOf schemas.
    /// Returns the type of the first non-null schema found.
    fn extract_type_from_composition(composition: &Value) -> Option<String> {
        composition.as_array().and_then(|arr| {
            arr.iter().find_map(|schema| {
                let obj = schema.as_object()?;
                if schema_is_null(obj) {
                    return None;
                }

                obj.get("type").and_then(extract_type).or_else(|| {
                    if obj.contains_key("properties") {
                        Some("object".to_string())
                    } else if obj.contains_key("enum") {
                        // Enum schemas without explicit type are string-backed
                        Some("string".to_string())
                    } else {
                        None
                    }
                })
            })
        })
    }

    /// Helper function to extract the first non-null schema from anyOf, oneOf, or allOf.
    /// Returns the schema object that should be used for properties, required, etc.
    fn extract_schema_from_composition(
        composition: &Value,
    ) -> Option<serde_json::Map<String, Value>> {
        composition.as_array().and_then(|arr| {
            arr.iter().find_map(|schema| {
                let obj = schema.as_object()?;
                if schema_is_null(obj) {
                    None
                } else {
                    Some(obj.clone())
                }
            })
        })
    }

    fn extract_schema_from_composition_obj(
        obj: &serde_json::Map<String, Value>,
    ) -> Option<serde_json::Map<String, Value>> {
        obj.get("anyOf")
            .and_then(extract_schema_from_composition)
            .or_else(|| obj.get("oneOf").and_then(extract_schema_from_composition))
            .or_else(|| obj.get("allOf").and_then(extract_schema_from_composition))
    }

    /// Helper function to infer the type of a schema object.
    /// Checks for explicit type, then anyOf/oneOf/allOf, then infers from properties.
    fn infer_type(obj: &serde_json::Map<String, Value>) -> String {
        // First, try direct type field
        if let Some(type_val) = obj.get("type")
            && let Some(type_str) = extract_type(type_val)
        {
            return type_str;
        }

        // Then try anyOf, oneOf, allOf (in that order)
        if let Some(any_of) = obj.get("anyOf")
            && let Some(type_str) = extract_type_from_composition(any_of)
        {
            return type_str;
        }

        if let Some(one_of) = obj.get("oneOf")
            && let Some(type_str) = extract_type_from_composition(one_of)
        {
            return type_str;
        }

        if let Some(all_of) = obj.get("allOf")
            && let Some(type_str) = extract_type_from_composition(all_of)
        {
            return type_str;
        }

        // Finally, infer object type if properties are present
        if obj.contains_key("properties") {
            "object".to_string()
        } else if obj.contains_key("enum") {
            "string".to_string()
        } else {
            String::new()
        }
    }

    impl TryFrom<Value> for Schema {
        type Error = CompletionError;

        fn try_from(value: Value) -> Result<Self, Self::Error> {
            let flattened_val = flatten_schema(value)?;
            if let Some(obj) = flattened_val.as_object() {
                // Determine which object to use for extracting properties and required fields.
                // If this object has anyOf/oneOf/allOf, we need to extract properties from the composition.
                let composition_source = extract_schema_from_composition_obj(obj);
                let props_source = if obj.get("properties").is_none() {
                    composition_source.clone().unwrap_or(obj.clone())
                } else {
                    obj.clone()
                };

                let schema_type = infer_type(obj);
                let items = obj
                    .get("items")
                    .or_else(|| props_source.get("items"))
                    .and_then(|v| v.clone().try_into().ok())
                    .map(Box::new);

                // Gemini requires `items` on array-typed schemas; default to
                // string items when the source schema omits it.
                let items = if schema_type == "array" && items.is_none() {
                    Some(Box::new(Schema {
                        r#type: "string".to_string(),
                        format: None,
                        description: None,
                        nullable: None,
                        r#enum: None,
                        max_items: None,
                        min_items: None,
                        properties: None,
                        required: None,
                        items: None,
                    }))
                } else {
                    items
                };

                Ok(Schema {
                    r#type: schema_type,
                    format: obj
                        .get("format")
                        .or_else(|| props_source.get("format"))
                        .and_then(|v| v.as_str())
                        .map(String::from),
                    description: obj
                        .get("description")
                        .or_else(|| props_source.get("description"))
                        .and_then(|v| v.as_str())
                        .map(String::from),
                    nullable: if schema_is_nullable(obj)
                        || composition_source.as_ref().is_some_and(schema_is_nullable)
                    {
                        Some(true)
                    } else {
                        None
                    },
                    r#enum: obj
                        .get("enum")
                        .or_else(|| props_source.get("enum"))
                        .and_then(|v| v.as_array())
                        .map(|arr| {
                            arr.iter()
                                .filter_map(|v| v.as_str().map(String::from))
                                .collect()
                        }),
                    max_items: obj
                        .get("maxItems")
                        .and_then(|v| v.as_i64())
                        .map(|v| v as i32),
                    min_items: obj
                        .get("minItems")
                        .and_then(|v| v.as_i64())
                        .map(|v| v as i32),
                    properties: props_source
                        .get("properties")
                        .and_then(|v| v.as_object())
                        .map(|map| {
                            map.iter()
                                .filter_map(|(k, v)| {
                                    v.clone().try_into().ok().map(|schema| (k.clone(), schema))
                                })
                                .collect()
                        }),
                    required: props_source
                        .get("required")
                        .and_then(|v| v.as_array())
                        .map(|arr| {
                            arr.iter()
                                .filter_map(|v| v.as_str().map(String::from))
                                .collect()
                        }),
                    items,
                })
            } else {
                Err(CompletionError::ResponseError(
                    "Expected a JSON object for Schema".into(),
                ))
            }
        }
    }

    #[derive(Debug, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct GenerateContentRequest {
        pub contents: Vec<Content>,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub tools: Option<Vec<Value>>,
        pub tool_config: Option<ToolConfig>,
        /// Optional. Configuration options for model generation and outputs.
        pub generation_config: Option<GenerationConfig>,
        /// Optional. A list of unique SafetySetting instances for blocking unsafe content. This will be enforced on the
        /// [GenerateContentRequest.contents] and [GenerateContentResponse.candidates]. There should not be more than one
        /// setting for each SafetyCategory type. The API will block any contents and responses that fail to meet the
        /// thresholds set by these settings. This list overrides the default settings for each SafetyCategory specified
        /// in the safetySettings. If there is no SafetySetting for a given SafetyCategory provided in the list, the API
        /// will use the default safety setting for that category. Harm categories:
        ///     - HARM_CATEGORY_HATE_SPEECH,
        ///     - HARM_CATEGORY_SEXUALLY_EXPLICIT
        ///     - HARM_CATEGORY_DANGEROUS_CONTENT
        ///     - HARM_CATEGORY_HARASSMENT
        /// are supported.
        /// Refer to the guide for detailed information on available safety settings. Also refer to the Safety guidance
        /// to learn how to incorporate safety considerations in your AI applications.
        pub safety_settings: Option<Vec<SafetySetting>>,
        /// Optional. Developer set system instruction(s). Currently, text only.
        /// From [Gemini API Reference](https://ai.google.dev/gemini-api/docs/system-instructions?lang=rest)
        pub system_instruction: Option<Content>,
        // cachedContent: Optional<String>
        /// Additional parameters.
        #[serde(flatten, skip_serializing_if = "Option::is_none")]
        pub additional_params: Option<serde_json::Value>,
    }

    #[derive(Debug, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct Tool {
        pub function_declarations: Vec<FunctionDeclaration>,
        pub code_execution: Option<CodeExecution>,
    }

    #[derive(Debug, Serialize, Clone)]
    #[serde(rename_all = "camelCase")]
    pub struct FunctionDeclaration {
        pub name: String,
        pub description: String,
        #[serde(skip_serializing_if = "Option::is_none")]
        pub parameters: Option<Schema>,
    }

    #[derive(Debug, Serialize, Deserialize)]
    #[serde(rename_all = "camelCase")]
    pub struct ToolConfig {
        pub function_calling_config: Option<FunctionCallingMode>,
    }

    #[derive(Debug, Serialize, Deserialize, Default)]
    #[serde(tag = "mode", rename_all = "UPPERCASE")]
    pub enum FunctionCallingMode {
        #[default]
        Auto,
        None,
        Any {
            #[serde(skip_serializing_if = "Option::is_none")]
            allowed_function_names: Option<Vec<String>>,
        },
    }

    impl TryFrom<message::ToolChoice> for FunctionCallingMode {
        type Error = CompletionError;
        fn try_from(value: message::ToolChoice) -> Result<Self, Self::Error> {
            let res = match value {
                message::ToolChoice::Auto => Self::Auto,
                message::ToolChoice::None => Self::None,
                message::ToolChoice::Required => Self::Any {
                    allowed_function_names: None,
                },
                message::ToolChoice::Specific { function_names } => Self::Any {
                    allowed_function_names: Some(function_names),
                },
            };

            Ok(res)
        }
    }

    #[derive(Debug, Serialize)]
    pub struct CodeExecution {}

    #[derive(Debug, Serialize)]
    #[serde(rename_all = "camelCase")]
    pub struct SafetySetting {
        pub category: HarmCategory,
        pub threshold: HarmBlockThreshold,
    }

    #[derive(Debug, Serialize)]
    #[serde(rename_all = "SCREAMING_SNAKE_CASE")]
    pub enum HarmBlockThreshold {
        HarmBlockThresholdUnspecified,
        BlockLowAndAbove,
        BlockMediumAndAbove,
        BlockOnlyHigh,
        BlockNone,
        Off,
    }
}

#[cfg(test)]
mod tests {
    use crate::{
        message,
        providers::gemini::completion::gemini_api_types::{
            ContentCandidate, FinishReason, FunctionCall, Schema, UsageMetadata, flatten_schema,
            tool_parameters_to_schema,
        },
    };

    use super::*;
    use serde_json::json;

    #[test]
    fn test_resolve_request_model_uses_override() {
        let request = CompletionRequest {
            model: Some("gemini-2.5-flash".to_string()),
            preamble: None,
            chat_history: crate::OneOrMany::one("Hello".into()),
            documents: vec![],
            tools: vec![],
            temperature: None,
            max_tokens: None,
            tool_choice: None,
            additional_params: None,
            output_schema: None,
        };

        let request_model = resolve_request_model("gemini-2.0-flash", &request);
        assert_eq!(request_model, "gemini-2.5-flash");
        assert_eq!(
            completion_endpoint(&request_model),
            "/v1beta/models/gemini-2.5-flash:generateContent"
        );
        assert_eq!(
            streaming_endpoint(&request_model),
            "/v1beta/models/gemini-2.5-flash:streamGenerateContent"
        );
    }

    #[test]
    fn test_resolve_request_model_uses_default_when_unset() {
        let request = CompletionRequest {
            model: None,
            preamble: None,
            chat_history: crate::OneOrMany::one("Hello".into()),
            documents: vec![],
            tools: vec![],
            temperature: None,
            max_tokens: None,
            tool_choice: None,
            additional_params: None,
            output_schema: None,
        };

        assert_eq!(
            resolve_request_model("gemini-2.0-flash", &request),
            "gemini-2.0-flash"
        );
    }

    #[test]
    fn test_deserialize_message_user() {
        let raw_message = r#"{
            "parts": [
                {"text": "Hello, world!"},
                {"inlineData": {"mimeType": "image/png", "data": "base64encodeddata"}},
                {"functionCall": {"name": "test_function", "args": {"arg1": "value1"}}},
                {"functionResponse": {"name": "test_function", "response": {"result": "success"}}},
                {"fileData": {"mimeType": "application/pdf", "fileUri": "http://example.com/file.pdf"}},
                {"executableCode": {"code": "print('Hello, world!')", "language": "PYTHON"}},
                {"codeExecutionResult": {"output": "Hello, world!", "outcome": "OUTCOME_OK"}}
            ],
            "role": "user"
        }"#;

        let content: Content = {
            let jd = &mut serde_json::Deserializer::from_str(raw_message);
            serde_path_to_error::deserialize(jd).unwrap_or_else(|err| {
                panic!("Deserialization error at {}: {}", err.path(), err);
            })
        };
        assert_eq!(content.role, Some(Role::User));
        assert_eq!(content.parts.len(), 7);

        let parts: Vec<Part> = content.parts.into_iter().collect();

        if let Part {
            part: PartKind::Text(text),
            ..
        } = &parts[0]
        {
            assert_eq!(text, "Hello, world!");
        } else {
            panic!("Expected text part");
        }

        if let Part {
            part: PartKind::InlineData(inline_data),
            ..
        } = &parts[1]
        {
            assert_eq!(inline_data.mime_type, "image/png");
            assert_eq!(inline_data.data, "base64encodeddata");
        } else {
            panic!("Expected inline data part");
        }

        if let Part {
            part: PartKind::FunctionCall(function_call),
            ..
        } = &parts[2]
        {
            assert_eq!(function_call.name, "test_function");
            assert_eq!(
                function_call.args.as_object().unwrap().get("arg1").unwrap(),
                "value1"
            );
        } else {
            panic!("Expected function call part");
        }

        if let Part {
            part: PartKind::FunctionResponse(function_response),
            ..
        } = &parts[3]
        {
            assert_eq!(function_response.name, "test_function");
            assert_eq!(
                function_response
                    .response
                    .as_ref()
                    .unwrap()
                    .get("result")
                    .unwrap(),
                "success"
            );
        } else {
            panic!("Expected function response part");
        }

        if let Part {
            part: PartKind::FileData(file_data),
            ..
        } = &parts[4]
        {
            assert_eq!(file_data.mime_type.as_ref().unwrap(), "application/pdf");
            assert_eq!(file_data.file_uri, "http://example.com/file.pdf");
        } else {
            panic!("Expected file data part");
        }

        if let Part {
            part: PartKind::ExecutableCode(executable_code),
            ..
        } = &parts[5]
        {
            assert_eq!(executable_code.code, "print('Hello, world!')");
        } else {
            panic!("Expected executable code part");
        }

        if let Part {
            part: PartKind::CodeExecutionResult(code_execution_result),
            ..
        } = &parts[6]
        {
            assert_eq!(
                code_execution_result.clone().output.unwrap(),
                "Hello, world!"
            );
        } else {
            panic!("Expected code execution result part");
        }
    }

    #[test]
    fn test_deserialize_message_model() {
        let json_data = json!({
            "parts": [{"text": "Hello, user!"}],
            "role": "model"
        });

        let content: Content = serde_json::from_value(json_data).unwrap();
        assert_eq!(content.role, Some(Role::Model));
        assert_eq!(content.parts.len(), 1);
        if let Some(Part {
            part: PartKind::Text(text),
            ..
        }) = content.parts.first()
        {
            assert_eq!(text, "Hello, user!");
        } else {
            panic!("Expected text part");
        }
    }

    #[test]
    fn test_message_conversion_user() {
        let msg = message::Message::user("Hello, world!");
        let content: Content = msg.try_into().unwrap();
        assert_eq!(content.role, Some(Role::User));
        assert_eq!(content.parts.len(), 1);
        if let Some(Part {
            part: PartKind::Text(text),
            ..
        }) = &content.parts.first()
        {
            assert_eq!(text, "Hello, world!");
        } else {
            panic!("Expected text part");
        }
    }

    #[test]
    fn test_message_conversion_model() {
        let msg = message::Message::assistant("Hello, user!");

        let content: Content = msg.try_into().unwrap();
        assert_eq!(content.role, Some(Role::Model));
        assert_eq!(content.parts.len(), 1);
        if let Some(Part {
            part: PartKind::Text(text),
            ..
        }) = &content.parts.first()
        {
            assert_eq!(text, "Hello, user!");
        } else {
            panic!("Expected text part");
        }
    }

    #[test]
    fn test_thought_signature_is_preserved_from_response_reasoning_part() {
        let response = GenerateContentResponse {
            response_id: "resp_1".to_string(),
            candidates: vec![ContentCandidate {
                content: Some(Content {
                    parts: vec![Part {
                        thought: Some(true),
                        thought_signature: Some("thought_sig_123".to_string()),
                        part: PartKind::Text("thinking text".to_string()),
                        additional_params: None,
                    }],
                    role: Some(Role::Model),
                }),
                finish_reason: Some(FinishReason::Stop),
                safety_ratings: None,
                citation_metadata: None,
                token_count: None,
                avg_logprobs: None,
                logprobs_result: None,
                index: Some(0),
                finish_message: None,
            }],
            prompt_feedback: None,
            usage_metadata: None,
            model_version: None,
        };

        let converted: crate::completion::CompletionResponse<GenerateContentResponse> =
            response.try_into().expect("convert response");
        let first = converted.choice.first();
        assert!(matches!(
            first,
            message::AssistantContent::Reasoning(message::Reasoning { content, .. })
                if matches!(
                    content.first(),
                    Some(message::ReasoningContent::Text {
                        text,
                        signature: Some(signature)
                    }) if text == "thinking text" && signature == "thought_sig_123"
                )
        ));
    }

    #[test]
    fn test_tool_protocol_finish_reason_returns_response_error() {
        for (reason, finish_message) in [
            (
                FinishReason::MalformedFunctionCall,
                "malformed function call: default_api",
            ),
            (
                FinishReason::UnexpectedToolCall,
                "unexpected tool call: default_api",
            ),
            (
                FinishReason::MissingThoughtSignature,
                "missing thought signature for tool call",
            ),
            (
                FinishReason::TooManyToolCalls,
                "too many tool calls in response",
            ),
            (
                FinishReason::MalformedResponse,
                "malformed response from provider",
            ),
        ] {
            let reason_name = format!("{reason:?}");
            let response = GenerateContentResponse {
                response_id: "resp_tool_protocol_error".to_string(),
                candidates: vec![ContentCandidate {
                    content: Some(Content {
                        parts: vec![Part {
                            thought: None,
                            thought_signature: None,
                            part: PartKind::FunctionCall(FunctionCall {
                                name: "default_api".to_string(),
                                args: json!({"x": 1}),
                            }),
                            additional_params: None,
                        }],
                        role: Some(Role::Model),
                    }),
                    finish_reason: Some(reason),
                    safety_ratings: None,
                    citation_metadata: None,
                    token_count: None,
                    avg_logprobs: None,
                    logprobs_result: None,
                    index: Some(0),
                    finish_message: Some(finish_message.to_string()),
                }],
                prompt_feedback: None,
                usage_metadata: None,
                model_version: None,
            };

            let err = crate::completion::CompletionResponse::<GenerateContentResponse>::try_from(
                response,
            )
            .expect_err("tool protocol finish reason should fail");

            assert!(matches!(
                err,
                CompletionError::ResponseError(message)
                    if message.contains(&reason_name)
                        && message.contains(finish_message)
            ));
        }
    }

    #[test]
    fn test_completion_response_usage_preserves_cached_and_reasoning_tokens() {
        let response = GenerateContentResponse {
            response_id: "resp_1".to_string(),
            candidates: vec![ContentCandidate {
                content: Some(Content {
                    parts: vec![Part {
                        thought: None,
                        thought_signature: None,
                        part: PartKind::Text("answer".to_string()),
                        additional_params: None,
                    }],
                    role: Some(Role::Model),
                }),
                finish_reason: Some(FinishReason::Stop),
                safety_ratings: None,
                citation_metadata: None,
                token_count: None,
                avg_logprobs: None,
                logprobs_result: None,
                index: Some(0),
                finish_message: None,
            }],
            prompt_feedback: None,
            usage_metadata: Some(UsageMetadata {
                prompt_token_count: 40,
                cached_content_token_count: Some(20),
                candidates_token_count: Some(30),
                total_token_count: 100,
                thoughts_token_count: Some(10),
                prompt_tokens_details: None,
                cache_tokens_details: None,
                candidates_tokens_details: None,
                tool_use_prompt_token_count: Some(12),
                tool_use_prompt_tokens_details: None,
                traffic_type: None,
            }),
            model_version: Some("gemini-2.0-flash-001".to_string()),
        };

        let converted: crate::completion::CompletionResponse<GenerateContentResponse> =
            response.try_into().expect("convert response");

        assert_eq!(converted.usage.input_tokens, 40);
        assert_eq!(converted.usage.cached_input_tokens, 20);
        assert_eq!(converted.usage.output_tokens, 30);
        assert_eq!(converted.usage.reasoning_tokens, 10);
        assert_eq!(converted.usage.tool_use_prompt_tokens, 12);
        assert_eq!(converted.usage.total_tokens, 100);
    }

    #[test]
    fn test_reasoning_signature_is_emitted_in_gemini_part() {
        let msg = message::Message::Assistant {
            id: None,
            content: OneOrMany::one(message::AssistantContent::Reasoning(
                message::Reasoning::new_with_signature(
                    "structured thought",
                    Some("reuse_sig_456".to_string()),
                ),
            )),
        };

        let converted: Content = msg.try_into().expect("convert message");
        let first = converted.parts.first().expect("reasoning part");
        assert_eq!(first.thought, Some(true));
        assert_eq!(first.thought_signature.as_deref(), Some("reuse_sig_456"));
        assert!(matches!(
            &first.part,
            PartKind::Text(text) if text == "structured thought"
        ));
    }

    #[test]
    fn test_message_conversion_tool_call() {
        let tool_call = message::ToolCall {
            id: "test_tool".to_string(),
            call_id: None,
            function: message::ToolFunction {
                name: "test_function".to_string(),
                arguments: json!({"arg1": "value1"}),
            },
            signature: None,
            additional_params: None,
        };

        let msg = message::Message::Assistant {
            id: None,
            content: OneOrMany::one(message::AssistantContent::ToolCall(tool_call)),
        };

        let content: Content = msg.try_into().unwrap();
        assert_eq!(content.role, Some(Role::Model));
        assert_eq!(content.parts.len(), 1);
        if let Some(Part {
            part: PartKind::FunctionCall(function_call),
            ..
        }) = content.parts.first()
        {
            assert_eq!(function_call.name, "test_function");
            assert_eq!(
                function_call.args.as_object().unwrap().get("arg1").unwrap(),
                "value1"
            );
        } else {
            panic!("Expected function call part");
        }
    }

    #[test]
    fn test_vec_schema_conversion() {
        let schema_with_ref = json!({
            "type": "array",
            "items": {
                "$ref": "#/$defs/Person"
            },
            "$defs": {
                "Person": {
                    "type": "object",
                    "properties": {
                        "first_name": {
                            "type": ["string", "null"],
                            "description": "The person's first name, if provided (null otherwise)"
                        },
                        "last_name": {
                            "type": ["string", "null"],
                            "description": "The person's last name, if provided (null otherwise)"
                        },
                        "job": {
                            "type": ["string", "null"],
                            "description": "The person's job, if provided (null otherwise)"
                        }
                    },
                    "required": []
                }
            }
        });

        let result: Result<Schema, _> = schema_with_ref.try_into();

        match result {
            Ok(schema) => {
                assert_eq!(schema.r#type, "array");

                if let Some(items) = schema.items {
                    println!("item types: {}", items.r#type);

                    assert_ne!(items.r#type, "", "Items type should not be empty string!");
                    assert_eq!(items.r#type, "object", "Items should be object type");
                } else {
                    panic!("Schema should have items field for array type");
                }
            }
            Err(e) => println!("Schema conversion failed: {:?}", e),
        }
    }

    #[test]
    fn test_object_schema() {
        let simple_schema = json!({
            "type": "object",
            "properties": {
                "name": {
                    "type": "string"
                }
            }
        });

        let schema: Schema = simple_schema.try_into().unwrap();
        assert_eq!(schema.r#type, "object");
        assert!(schema.properties.is_some());
    }

    #[test]
    fn test_array_with_inline_items() {
        let inline_schema = json!({
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "name": {
                        "type": "string"
                    }
                }
            }
        });

        let schema: Schema = inline_schema.try_into().unwrap();
        assert_eq!(schema.r#type, "array");

        if let Some(items) = schema.items {
            assert_eq!(items.r#type, "object");
            assert!(items.properties.is_some());
        } else {
            panic!("Schema should have items field");
        }
    }
    #[test]
    fn test_flattened_schema() {
        let ref_schema = json!({
            "type": "array",
            "items": {
                "$ref": "#/$defs/Person"
            },
            "$defs": {
                "Person": {
                    "type": "object",
                    "properties": {
                        "name": { "type": "string" }
                    }
                }
            }
        });

        let flattened = flatten_schema(ref_schema).unwrap();
        let schema: Schema = flattened.try_into().unwrap();

        assert_eq!(schema.r#type, "array");

        if let Some(items) = schema.items {
            println!("Flattened items type: '{}'", items.r#type);

            assert_eq!(items.r#type, "object");
            assert!(items.properties.is_some());
        }
    }

    #[test]
    fn test_array_without_items_gets_default() {
        let schema_json = json!({
            "type": "object",
            "properties": {
                "service_ids": {
                    "type": "array",
                    "description": "A list of service IDs"
                }
            }
        });

        let schema: Schema = schema_json.try_into().unwrap();
        let props = schema.properties.unwrap();
        let service_ids = props.get("service_ids").unwrap();
        assert_eq!(service_ids.r#type, "array");
        let items = service_ids
            .items
            .as_ref()
            .expect("array schema missing items should get a default");
        assert_eq!(items.r#type, "string");
    }

    #[test]
    fn test_tool_parameters_to_schema_maps_no_arg_tool_to_none() {
        let schema = tool_parameters_to_schema(json!({"type": "object", "properties": {}}))
            .expect("schema conversion");

        assert!(schema.is_none());
    }

    #[test]
    fn test_tool_parameters_to_schema_resolves_defs_ref() {
        let schema_json = json!({
            "type": "object",
            "properties": {
                "destination": { "$ref": "#/$defs/Destination" }
            },
            "required": ["destination"],
            "$defs": {
                "Destination": {
                    "type": "object",
                    "properties": {
                        "city": { "type": "string" }
                    },
                    "required": ["city"]
                }
            }
        });

        let schema = tool_parameters_to_schema(schema_json)
            .expect("schema conversion")
            .expect("schema");
        let props = schema.properties.expect("properties");
        let destination = props.get("destination").expect("destination prop");

        assert_eq!(destination.r#type, "object");
        assert_eq!(destination.required, Some(vec!["city".to_string()]));
    }

    #[test]
    fn test_tool_parameters_to_schema_handles_nullable_type_arrays() {
        let schema_json = json!({
            "type": "object",
            "properties": {
                "nickname": { "type": ["null", "string"] }
            }
        });

        let schema = tool_parameters_to_schema(schema_json)
            .expect("schema conversion")
            .expect("schema");
        let props = schema.properties.expect("properties");
        let nickname = props.get("nickname").expect("nickname prop");

        assert_eq!(nickname.r#type, "string");
        assert_eq!(nickname.nullable, Some(true));
    }

    #[test]
    fn test_txt_document_conversion_to_text_part() {
        // Test that TXT documents are converted to plain text parts, not inline data
        use crate::message::{DocumentMediaType, UserContent};

        let doc = UserContent::document(
            "Note: test.md\nPath: /test.md\nContent: Hello World!",
            Some(DocumentMediaType::TXT),
        );

        let content: Content = message::Message::User {
            content: crate::OneOrMany::one(doc),
        }
        .try_into()
        .unwrap();

        if let Part {
            part: PartKind::Text(text),
            ..
        } = &content.parts[0]
        {
            assert!(text.contains("Note: test.md"));
            assert!(text.contains("Hello World!"));
        } else {
            panic!(
                "Expected text part for TXT document, got: {:?}",
                content.parts[0]
            );
        }
    }

    #[test]
    fn test_tool_result_with_image_content() {
        // Test that a ToolResult with image content converts correctly to Gemini's Part format
        use crate::OneOrMany;
        use crate::message::{
            DocumentSourceKind, Image, ImageMediaType, ToolResult, ToolResultContent,
        };

        // Create a tool result with both text and image content
        let tool_result = ToolResult {
            id: "test_tool".to_string(),
            call_id: None,
            content: OneOrMany::many(vec![
                ToolResultContent::Text(message::Text::new(r#"{"status": "success"}"#.to_string())),
                ToolResultContent::Image(Image {
                    data: DocumentSourceKind::Base64("iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg==".to_string()),
                    media_type: Some(ImageMediaType::PNG),
                    detail: None,
                    additional_params: None,
                }),
            ]).expect("Should create OneOrMany with multiple items"),
        };

        let user_content = message::UserContent::ToolResult(tool_result);
        let msg = message::Message::User {
            content: OneOrMany::one(user_content),
        };

        // Convert to Gemini Content
        let content: Content = msg.try_into().expect("Should convert to Gemini Content");
        assert_eq!(content.role, Some(Role::User));
        assert_eq!(content.parts.len(), 1);

        // Verify the part is a FunctionResponse with both response and parts
        if let Some(Part {
            part: PartKind::FunctionResponse(function_response),
            ..
        }) = content.parts.first()
        {
            assert_eq!(function_response.name, "test_tool");

            // Check that response JSON is present
            assert!(function_response.response.is_some());
            let response = function_response.response.as_ref().unwrap();
            assert!(response.get("result").is_some());

            // Check that parts with image data are present
            assert!(function_response.parts.is_some());
            let parts = function_response.parts.as_ref().unwrap();
            assert_eq!(parts.len(), 1);

            let image_part = &parts[0];
            assert!(image_part.inline_data.is_some());
            let inline_data = image_part.inline_data.as_ref().unwrap();
            assert_eq!(inline_data.mime_type, "image/png");
            assert!(!inline_data.data.is_empty());
        } else {
            panic!("Expected FunctionResponse part");
        }
    }

    #[test]
    fn test_markdown_document_conversion_to_text_part() {
        // Test that MARKDOWN documents are converted to plain text parts
        use crate::message::{DocumentMediaType, UserContent};

        let doc = UserContent::document(
            "# Heading\n\n* List item",
            Some(DocumentMediaType::MARKDOWN),
        );

        let content: Content = message::Message::User {
            content: crate::OneOrMany::one(doc),
        }
        .try_into()
        .unwrap();

        if let Part {
            part: PartKind::Text(text),
            ..
        } = &content.parts[0]
        {
            assert_eq!(text, "# Heading\n\n* List item");
        } else {
            panic!(
                "Expected text part for MARKDOWN document, got: {:?}",
                content.parts[0]
            );
        }
    }

    #[test]
    fn test_markdown_url_document_conversion_to_file_data_part() {
        // URL-backed MARKDOWN documents should be represented as file_data.
        use crate::message::{DocumentMediaType, DocumentSourceKind, UserContent};

        let doc = UserContent::Document(message::Document {
            data: DocumentSourceKind::Url(
                "https://generativelanguage.googleapis.com/v1beta/files/test-markdown".to_string(),
            ),
            media_type: Some(DocumentMediaType::MARKDOWN),
            additional_params: None,
        });

        let content: Content = message::Message::User {
            content: crate::OneOrMany::one(doc),
        }
        .try_into()
        .unwrap();

        if let Part {
            part: PartKind::FileData(file_data),
            ..
        } = &content.parts[0]
        {
            assert_eq!(
                file_data.file_uri,
                "https://generativelanguage.googleapis.com/v1beta/files/test-markdown"
            );
            assert_eq!(file_data.mime_type.as_deref(), Some("text/markdown"));
        } else {
            panic!(
                "Expected file_data part for URL MARKDOWN document, got: {:?}",
                content.parts[0]
            );
        }
    }

    #[test]
    fn test_tool_result_with_url_image() {
        // Test that a ToolResult with a URL-based image converts to file_data
        use crate::OneOrMany;
        use crate::message::{
            DocumentSourceKind, Image, ImageMediaType, ToolResult, ToolResultContent,
        };

        let tool_result = ToolResult {
            id: "screenshot_tool".to_string(),
            call_id: None,
            content: OneOrMany::one(ToolResultContent::Image(Image {
                data: DocumentSourceKind::Url("https://example.com/image.png".to_string()),
                media_type: Some(ImageMediaType::PNG),
                detail: None,
                additional_params: None,
            })),
        };

        let user_content = message::UserContent::ToolResult(tool_result);
        let msg = message::Message::User {
            content: OneOrMany::one(user_content),
        };

        let content: Content = msg.try_into().expect("Should convert to Gemini Content");
        assert_eq!(content.role, Some(Role::User));
        assert_eq!(content.parts.len(), 1);

        if let Some(Part {
            part: PartKind::FunctionResponse(function_response),
            ..
        }) = content.parts.first()
        {
            assert_eq!(function_response.name, "screenshot_tool");

            // URL images should have parts with file_data
            assert!(function_response.parts.is_some());
            let parts = function_response.parts.as_ref().unwrap();
            assert_eq!(parts.len(), 1);

            let image_part = &parts[0];
            assert!(image_part.file_data.is_some());
            let file_data = image_part.file_data.as_ref().unwrap();
            assert_eq!(file_data.file_uri, "https://example.com/image.png");
            assert_eq!(file_data.mime_type.as_ref().unwrap(), "image/png");
        } else {
            panic!("Expected FunctionResponse part");
        }
    }

    #[test]
    fn test_create_request_body_with_documents() {
        // Test that documents are injected into chat history
        use crate::OneOrMany;
        use crate::completion::request::{CompletionRequest, Document};
        use crate::message::Message;

        let documents = vec![
            Document {
                id: "doc1".to_string(),
                text: "Note: first.md\nContent: First note".to_string(),
                additional_props: std::collections::HashMap::new(),
            },
            Document {
                id: "doc2".to_string(),
                text: "Note: second.md\nContent: Second note".to_string(),
                additional_props: std::collections::HashMap::new(),
            },
        ];

        let documents_message = CompletionRequest {
            preamble: None,
            chat_history: OneOrMany::one(Message::user("placeholder")),
            documents,
            tools: vec![],
            temperature: None,
            model: None,
            output_schema: None,
            max_tokens: None,
            tool_choice: None,
            additional_params: None,
        }
        .normalized_documents()
        .unwrap();

        let completion_request = CompletionRequest {
            preamble: Some("You are a helpful assistant".to_string()),
            chat_history: OneOrMany::many(vec![
                documents_message,
                Message::user("What are my notes about?"),
            ])
            .unwrap(),
            documents: vec![],
            tools: vec![],
            temperature: None,
            model: None,
            output_schema: None,
            max_tokens: None,
            tool_choice: None,
            additional_params: None,
        };

        let request = create_request_body(completion_request).unwrap();

        // Should have 2 contents: 1 for documents, 1 for user message
        assert_eq!(
            request.contents.len(),
            2,
            "Expected 2 contents (documents + user message)"
        );

        // First content should be documents with role User
        assert_eq!(request.contents[0].role, Some(Role::User));
        assert_eq!(
            request.contents[0].parts.len(),
            2,
            "Expected 2 document parts"
        );

        // Check that documents are text parts
        for part in &request.contents[0].parts {
            if let Part {
                part: PartKind::Text(text),
                ..
            } = part
            {
                assert!(
                    text.contains("Note:") && text.contains("Content:"),
                    "Document should contain note metadata"
                );
            } else {
                panic!("Document parts should be text, not {:?}", part);
            }
        }

        // Second content should be the user message
        assert_eq!(request.contents[1].role, Some(Role::User));
        if let Part {
            part: PartKind::Text(text),
            ..
        } = &request.contents[1].parts[0]
        {
            assert_eq!(text, "What are my notes about?");
        } else {
            panic!("Expected user message to be text");
        }
    }

    #[test]
    fn test_create_request_body_without_documents() {
        // Test backward compatibility: requests without documents work as before
        use crate::OneOrMany;
        use crate::completion::request::CompletionRequest;
        use crate::message::Message;

        let completion_request = CompletionRequest {
            preamble: Some("You are a helpful assistant".to_string()),
            chat_history: OneOrMany::one(Message::user("Hello")),
            documents: vec![], // No documents
            tools: vec![],
            temperature: None,
            max_tokens: None,
            tool_choice: None,
            model: None,
            output_schema: None,
            additional_params: None,
        };

        let request = create_request_body(completion_request).unwrap();

        // Should have only 1 content (the user message)
        assert_eq!(request.contents.len(), 1, "Expected only user message");
        assert_eq!(request.contents[0].role, Some(Role::User));

        if let Part {
            part: PartKind::Text(text),
            ..
        } = &request.contents[0].parts[0]
        {
            assert_eq!(text, "Hello");
        } else {
            panic!("Expected user message to be text");
        }
    }

    #[test]
    fn test_from_tool_output_parses_image_json() {
        // Test the ToolResultContent::from_tool_output helper with image JSON
        use crate::message::{DocumentSourceKind, ToolResultContent};

        // Test simple image JSON format
        let image_json = r#"{"type": "image", "data": "base64data==", "mimeType": "image/jpeg"}"#;
        let result = ToolResultContent::from_tool_output(image_json);

        assert_eq!(result.len(), 1);
        if let ToolResultContent::Image(img) = result.first() {
            assert!(matches!(img.data, DocumentSourceKind::Base64(_)));
            if let DocumentSourceKind::Base64(data) = &img.data {
                assert_eq!(data, "base64data==");
            }
            assert_eq!(img.media_type, Some(crate::message::ImageMediaType::JPEG));
        } else {
            panic!("Expected Image content");
        }
    }

    #[test]
    fn test_from_tool_output_parses_hybrid_json() {
        // Test the ToolResultContent::from_tool_output helper with hybrid response/parts format
        use crate::message::{DocumentSourceKind, ToolResultContent};

        let hybrid_json = r#"{
            "response": {"status": "ok", "count": 42},
            "parts": [
                {"type": "image", "data": "imgdata1==", "mimeType": "image/png"},
                {"type": "image", "data": "https://example.com/img.jpg", "mimeType": "image/jpeg"}
            ]
        }"#;

        let result = ToolResultContent::from_tool_output(hybrid_json);

        // Should have 3 items: 1 text (response) + 2 images (parts)
        assert_eq!(result.len(), 3);

        let items: Vec<_> = result.iter().collect();

        // First should be text with the response JSON
        if let ToolResultContent::Text(text) = &items[0] {
            assert!(text.text.contains("status"));
            assert!(text.text.contains("ok"));
        } else {
            panic!("Expected Text content first");
        }

        // Second should be base64 image
        if let ToolResultContent::Image(img) = &items[1] {
            assert!(matches!(img.data, DocumentSourceKind::Base64(_)));
        } else {
            panic!("Expected Image content second");
        }

        // Third should be URL image
        if let ToolResultContent::Image(img) = &items[2] {
            assert!(matches!(img.data, DocumentSourceKind::Url(_)));
        } else {
            panic!("Expected Image content third");
        }
    }

    /// E2E test that verifies Gemini can process tool results containing images.
    /// This test creates an agent with a tool that returns an image, invokes it,
    /// and verifies that Gemini can interpret the image in the tool result.
    #[tokio::test]
    #[ignore = "requires GEMINI_API_KEY environment variable"]
    async fn test_gemini_agent_with_image_tool_result_e2e() -> anyhow::Result<()> {
        use crate::completion::Prompt;
        use crate::prelude::*;
        use crate::providers::gemini;
        use crate::test_utils::MockImageGeneratorTool;

        let client = gemini::Client::from_env()?;

        let agent = client
            .agent("gemini-3-flash-preview")
            .preamble("You are a helpful assistant. When asked about images, use the generate_test_image tool to create one, then describe what you see in the image.")
            .tool(MockImageGeneratorTool)
            .build();

        // This prompt should trigger the tool, which returns an image that Gemini should process
        let response_text = agent
            .prompt("Please generate a test image and tell me what color the pixel is.")
            .await?;
        println!("Response: {response_text}");
        // Gemini should have been able to see the image and potentially describe its color
        anyhow::ensure!(!response_text.is_empty(), "Response should not be empty");

        Ok(())
    }
}