logiguard fork: GPUI xdg-activation keyboard-focus serial fix

Single-commit orphan branch: full zed-industries/zed @ 8c74db0 source tree
with a 3-file patch applied (no upstream history).

Patch (crates/gpui_linux/src/linux/wayland/):
  - serial.rs: add SerialKind::KeyboardEnter
  - client.rs: store wl_keyboard.enter serial; add latest_serial_of()
  - window.rs: activate() uses keyboard-enter serial (Mutter focus gate)

Mutter honors window activation only when the token carries the keyboard-
focus serial from wl_keyboard.enter; GPUI used a stale mouse-press serial.
See docs/tray-window-focus-wayland.md in logiguard.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

crates/google_ai/Cargo.toml

@@ -0,0 +1,26 @@
+[package]
+name = "google_ai"
+version = "0.1.0"
+edition.workspace = true
+publish.workspace = true
+license = "GPL-3.0-or-later"
+
+[lints]
+workspace = true
+
+[lib]
+path = "src/google_ai.rs"
+
+[features]
+schemars = ["dep:schemars"]
+
+[dependencies]
+anyhow.workspace = true
+futures.workspace = true
+http_client.workspace = true
+language_model_core.workspace = true
+log.workspace = true
+schemars = { workspace = true, optional = true }
+serde.workspace = true
+serde_json.workspace = true
+strum.workspace = true

crates/google_ai/LICENSE-GPL

@@ -0,0 +1 @@
+../../LICENSE-GPL

crates/google_ai/src/completion.rs

@@ -0,0 +1,470 @@
+use anyhow::Result;
+use futures::{Stream, StreamExt};
+use language_model_core::{
+    LanguageModelCompletionError, LanguageModelCompletionEvent, LanguageModelRequest,
+    LanguageModelToolChoice, LanguageModelToolUse, LanguageModelToolUseId, MessageContent, Role,
+    StopReason, TokenUsage,
+};
+use std::pin::Pin;
+use std::sync::Arc;
+use std::sync::atomic::{self, AtomicU64};
+
+use crate::{
+    Content, FunctionCallingConfig, FunctionCallingMode, FunctionDeclaration,
+    GenerateContentResponse, GenerationConfig, GenerativeContentBlob, GoogleModelMode,
+    InlineDataPart, ModelName, Part, SystemInstruction, TextPart, ThinkingConfig, ToolConfig,
+    UsageMetadata,
+};
+
+pub fn into_google(
+    mut request: LanguageModelRequest,
+    model_id: String,
+    mode: GoogleModelMode,
+) -> crate::GenerateContentRequest {
+    fn map_content(content: Vec<MessageContent>) -> Vec<Part> {
+        content
+            .into_iter()
+            .flat_map(|content| match content {
+                MessageContent::Text(text) => {
+                    if !text.is_empty() {
+                        vec![Part::TextPart(TextPart { text })]
+                    } else {
+                        vec![]
+                    }
+                }
+                MessageContent::Thinking {
+                    text: _,
+                    signature: Some(signature),
+                } => {
+                    if !signature.is_empty() {
+                        vec![Part::ThoughtPart(crate::ThoughtPart {
+                            thought: true,
+                            thought_signature: signature,
+                        })]
+                    } else {
+                        vec![]
+                    }
+                }
+                MessageContent::Thinking { .. } => {
+                    vec![]
+                }
+                MessageContent::RedactedThinking(_) => vec![],
+                MessageContent::Image(image) => {
+                    vec![Part::InlineDataPart(InlineDataPart {
+                        inline_data: GenerativeContentBlob {
+                            mime_type: "image/png".to_string(),
+                            data: image.source.to_string(),
+                        },
+                    })]
+                }
+                MessageContent::ToolUse(tool_use) => {
+                    // Normalize empty string signatures to None
+                    let thought_signature = tool_use.thought_signature.filter(|s| !s.is_empty());
+
+                    vec![Part::FunctionCallPart(crate::FunctionCallPart {
+                        function_call: crate::FunctionCall {
+                            name: tool_use.name.to_string(),
+                            args: tool_use.input,
+                        },
+                        thought_signature,
+                    })]
+                }
+                MessageContent::ToolResult(tool_result) => {
+                    let mut text_output = String::new();
+                    let mut images: Vec<InlineDataPart> = Vec::new();
+                    for part in tool_result.content {
+                        match part {
+                            language_model_core::LanguageModelToolResultContent::Text(text) => {
+                                text_output.push_str(&text);
+                            }
+                            language_model_core::LanguageModelToolResultContent::Image(image) => {
+                                images.push(InlineDataPart {
+                                    inline_data: GenerativeContentBlob {
+                                        mime_type: "image/png".to_string(),
+                                        data: image.source.to_string(),
+                                    },
+                                });
+                            }
+                        }
+                    }
+                    let output = if text_output.is_empty() && !images.is_empty() {
+                        "Tool responded with an image".to_string()
+                    } else {
+                        text_output
+                    };
+                    let mut parts = vec![Part::FunctionResponsePart(crate::FunctionResponsePart {
+                        function_response: crate::FunctionResponse {
+                            name: tool_result.tool_name.to_string(),
+                            // The API expects a valid JSON object
+                            response: serde_json::json!({
+                                "output": output
+                            }),
+                        },
+                    })];
+                    parts.extend(images.into_iter().map(Part::InlineDataPart));
+                    parts
+                }
+            })
+            .collect()
+    }
+
+    let system_instructions = if request
+        .messages
+        .first()
+        .is_some_and(|msg| matches!(msg.role, Role::System))
+    {
+        let message = request.messages.remove(0);
+        Some(SystemInstruction {
+            parts: map_content(message.content),
+        })
+    } else {
+        None
+    };
+
+    crate::GenerateContentRequest {
+        model: ModelName { model_id },
+        system_instruction: system_instructions,
+        contents: request
+            .messages
+            .into_iter()
+            .filter_map(|message| {
+                let parts = map_content(message.content);
+                if parts.is_empty() {
+                    None
+                } else {
+                    Some(Content {
+                        parts,
+                        role: match message.role {
+                            Role::User => crate::Role::User,
+                            Role::Assistant => crate::Role::Model,
+                            Role::System => crate::Role::User, // Google AI doesn't have a system role
+                        },
+                    })
+                }
+            })
+            .collect(),
+        generation_config: Some(GenerationConfig {
+            candidate_count: Some(1),
+            stop_sequences: Some(request.stop),
+            max_output_tokens: None,
+            temperature: request.temperature.map(|t| t as f64).or(Some(1.0)),
+            thinking_config: match (request.thinking_allowed, mode) {
+                (true, GoogleModelMode::Thinking { budget_tokens }) => {
+                    budget_tokens.map(|thinking_budget| ThinkingConfig { thinking_budget })
+                }
+                _ => None,
+            },
+            top_p: None,
+            top_k: None,
+        }),
+        safety_settings: None,
+        tools: (!request.tools.is_empty()).then(|| {
+            vec![crate::Tool {
+                function_declarations: request
+                    .tools
+                    .into_iter()
+                    .map(|tool| FunctionDeclaration {
+                        name: tool.name,
+                        description: tool.description,
+                        parameters: tool.input_schema,
+                    })
+                    .collect(),
+            }]
+        }),
+        tool_config: request.tool_choice.map(|choice| ToolConfig {
+            function_calling_config: FunctionCallingConfig {
+                mode: match choice {
+                    LanguageModelToolChoice::Auto => FunctionCallingMode::Auto,
+                    LanguageModelToolChoice::Any => FunctionCallingMode::Any,
+                    LanguageModelToolChoice::None => FunctionCallingMode::None,
+                },
+                allowed_function_names: None,
+            },
+        }),
+    }
+}
+
+pub struct GoogleEventMapper {
+    usage: UsageMetadata,
+    stop_reason: StopReason,
+}
+
+impl GoogleEventMapper {
+    pub fn new() -> Self {
+        Self {
+            usage: UsageMetadata::default(),
+            stop_reason: StopReason::EndTurn,
+        }
+    }
+
+    pub fn map_stream(
+        mut self,
+        events: Pin<Box<dyn Send + Stream<Item = Result<GenerateContentResponse>>>>,
+    ) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
+    {
+        events
+            .map(Some)
+            .chain(futures::stream::once(async { None }))
+            .flat_map(move |event| {
+                futures::stream::iter(match event {
+                    Some(Ok(event)) => self.map_event(event),
+                    Some(Err(error)) => {
+                        vec![Err(LanguageModelCompletionError::from(error))]
+                    }
+                    None => vec![Ok(LanguageModelCompletionEvent::Stop(self.stop_reason))],
+                })
+            })
+    }
+
+    pub fn map_event(
+        &mut self,
+        event: GenerateContentResponse,
+    ) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
+        static TOOL_CALL_COUNTER: AtomicU64 = AtomicU64::new(0);
+
+        let mut events: Vec<_> = Vec::new();
+        let mut wants_to_use_tool = false;
+        if let Some(usage_metadata) = event.usage_metadata {
+            update_usage(&mut self.usage, &usage_metadata);
+            events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(
+                convert_usage(&self.usage),
+            )))
+        }
+
+        if let Some(prompt_feedback) = event.prompt_feedback
+            && let Some(block_reason) = prompt_feedback.block_reason.as_deref()
+        {
+            self.stop_reason = match block_reason {
+                "SAFETY" | "OTHER" | "BLOCKLIST" | "PROHIBITED_CONTENT" | "IMAGE_SAFETY" => {
+                    StopReason::Refusal
+                }
+                _ => {
+                    log::error!("Unexpected Google block_reason: {block_reason}");
+                    StopReason::Refusal
+                }
+            };
+            events.push(Ok(LanguageModelCompletionEvent::Stop(self.stop_reason)));
+
+            return events;
+        }
+
+        if let Some(candidates) = event.candidates {
+            for candidate in candidates {
+                if let Some(finish_reason) = candidate.finish_reason.as_deref() {
+                    self.stop_reason = match finish_reason {
+                        "STOP" => StopReason::EndTurn,
+                        "MAX_TOKENS" => StopReason::MaxTokens,
+                        _ => {
+                            log::error!("Unexpected google finish_reason: {finish_reason}");
+                            StopReason::EndTurn
+                        }
+                    };
+                }
+                candidate
+                    .content
+                    .parts
+                    .into_iter()
+                    .for_each(|part| match part {
+                        Part::TextPart(text_part) => {
+                            events.push(Ok(LanguageModelCompletionEvent::Text(text_part.text)))
+                        }
+                        Part::InlineDataPart(_) => {}
+                        Part::FunctionCallPart(function_call_part) => {
+                            wants_to_use_tool = true;
+                            let name: Arc<str> = function_call_part.function_call.name.into();
+                            let next_tool_id =
+                                TOOL_CALL_COUNTER.fetch_add(1, atomic::Ordering::SeqCst);
+                            let id: LanguageModelToolUseId =
+                                format!("{}-{}", name, next_tool_id).into();
+
+                            // Normalize empty string signatures to None
+                            let thought_signature = function_call_part
+                                .thought_signature
+                                .filter(|s| !s.is_empty());
+
+                            events.push(Ok(LanguageModelCompletionEvent::ToolUse(
+                                LanguageModelToolUse {
+                                    id,
+                                    name,
+                                    is_input_complete: true,
+                                    raw_input: function_call_part.function_call.args.to_string(),
+                                    input: function_call_part.function_call.args,
+                                    thought_signature,
+                                },
+                            )));
+                        }
+                        Part::FunctionResponsePart(_) => {}
+                        Part::ThoughtPart(part) => {
+                            events.push(Ok(LanguageModelCompletionEvent::Thinking {
+                                text: "(Encrypted thought)".to_string(), // TODO: Can we populate this from thought summaries?
+                                signature: Some(part.thought_signature),
+                            }));
+                        }
+                    });
+            }
+        }
+
+        // Even when Gemini wants to use a Tool, the API
+        // responds with `finish_reason: STOP`
+        if wants_to_use_tool {
+            self.stop_reason = StopReason::ToolUse;
+            events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
+        }
+        events
+    }
+}
+
+fn update_usage(usage: &mut UsageMetadata, new: &UsageMetadata) {
+    if let Some(prompt_token_count) = new.prompt_token_count {
+        usage.prompt_token_count = Some(prompt_token_count);
+    }
+    if let Some(cached_content_token_count) = new.cached_content_token_count {
+        usage.cached_content_token_count = Some(cached_content_token_count);
+    }
+    if let Some(candidates_token_count) = new.candidates_token_count {
+        usage.candidates_token_count = Some(candidates_token_count);
+    }
+    if let Some(tool_use_prompt_token_count) = new.tool_use_prompt_token_count {
+        usage.tool_use_prompt_token_count = Some(tool_use_prompt_token_count);
+    }
+    if let Some(thoughts_token_count) = new.thoughts_token_count {
+        usage.thoughts_token_count = Some(thoughts_token_count);
+    }
+    if let Some(total_token_count) = new.total_token_count {
+        usage.total_token_count = Some(total_token_count);
+    }
+}
+
+fn convert_usage(usage: &UsageMetadata) -> TokenUsage {
+    let prompt_tokens = usage.prompt_token_count.unwrap_or(0);
+    let cached_tokens = usage.cached_content_token_count.unwrap_or(0);
+    let input_tokens = prompt_tokens - cached_tokens;
+    let output_tokens = usage.candidates_token_count.unwrap_or(0);
+
+    TokenUsage {
+        input_tokens,
+        output_tokens,
+        cache_read_input_tokens: cached_tokens,
+        cache_creation_input_tokens: 0,
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::{
+        Content, FunctionCall, FunctionCallPart, GenerateContentCandidate, GenerateContentResponse,
+        Part, Role as GoogleRole,
+    };
+    use serde_json::json;
+
+    #[test]
+    fn test_function_call_with_signature_creates_tool_use_with_signature() {
+        let mut mapper = GoogleEventMapper::new();
+
+        let response = GenerateContentResponse {
+            candidates: Some(vec![GenerateContentCandidate {
+                index: Some(0),
+                content: Content {
+                    parts: vec![Part::FunctionCallPart(FunctionCallPart {
+                        function_call: FunctionCall {
+                            name: "test_function".to_string(),
+                            args: json!({"arg": "value"}),
+                        },
+                        thought_signature: Some("test_signature_123".to_string()),
+                    })],
+                    role: GoogleRole::Model,
+                },
+                finish_reason: None,
+                finish_message: None,
+                safety_ratings: None,
+                citation_metadata: None,
+            }]),
+            prompt_feedback: None,
+            usage_metadata: None,
+        };
+
+        let events = mapper.map_event(response);
+        assert_eq!(events.len(), 2);
+
+        if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[0] {
+            assert_eq!(tool_use.name.as_ref(), "test_function");
+            assert_eq!(
+                tool_use.thought_signature.as_deref(),
+                Some("test_signature_123")
+            );
+        } else {
+            panic!("Expected ToolUse event");
+        }
+    }
+
+    #[test]
+    fn test_function_call_without_signature_has_none() {
+        let mut mapper = GoogleEventMapper::new();
+
+        let response = GenerateContentResponse {
+            candidates: Some(vec![GenerateContentCandidate {
+                index: Some(0),
+                content: Content {
+                    parts: vec![Part::FunctionCallPart(FunctionCallPart {
+                        function_call: FunctionCall {
+                            name: "test_function".to_string(),
+                            args: json!({"arg": "value"}),
+                        },
+                        thought_signature: None,
+                    })],
+                    role: GoogleRole::Model,
+                },
+                finish_reason: None,
+                finish_message: None,
+                safety_ratings: None,
+                citation_metadata: None,
+            }]),
+            prompt_feedback: None,
+            usage_metadata: None,
+        };
+
+        let events = mapper.map_event(response);
+        assert_eq!(events.len(), 2);
+
+        if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[0] {
+            assert!(tool_use.thought_signature.is_none());
+        } else {
+            panic!("Expected ToolUse event");
+        }
+    }
+
+    #[test]
+    fn test_empty_string_signature_normalized_to_none() {
+        let mut mapper = GoogleEventMapper::new();
+
+        let response = GenerateContentResponse {
+            candidates: Some(vec![GenerateContentCandidate {
+                index: Some(0),
+                content: Content {
+                    parts: vec![Part::FunctionCallPart(FunctionCallPart {
+                        function_call: FunctionCall {
+                            name: "test_function".to_string(),
+                            args: json!({"arg": "value"}),
+                        },
+                        thought_signature: Some("".to_string()),
+                    })],
+                    role: GoogleRole::Model,
+                },
+                finish_reason: None,
+                finish_message: None,
+                safety_ratings: None,
+                citation_metadata: None,
+            }]),
+            prompt_feedback: None,
+            usage_metadata: None,
+        };
+
+        let events = mapper.map_event(response);
+        if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[0] {
+            assert!(tool_use.thought_signature.is_none());
+        } else {
+            panic!("Expected ToolUse event");
+        }
+    }
+}

crates/google_ai/src/google_ai.rs

@@ -0,0 +1,688 @@
+use std::mem;
+
+use anyhow::{Result, anyhow, bail};
+use futures::{AsyncBufReadExt, AsyncReadExt, StreamExt, io::BufReader, stream::BoxStream};
+use http_client::{AsyncBody, HttpClient, Method, Request as HttpRequest};
+pub use language_model_core::ModelMode as GoogleModelMode;
+use serde::{Deserialize, Deserializer, Serialize, Serializer};
+pub mod completion;
+
+pub const API_URL: &str = "https://generativelanguage.googleapis.com";
+
+pub async fn stream_generate_content(
+    client: &dyn HttpClient,
+    api_url: &str,
+    api_key: &str,
+    mut request: GenerateContentRequest,
+) -> Result<BoxStream<'static, Result<GenerateContentResponse>>> {
+    let api_key = api_key.trim();
+    validate_generate_content_request(&request)?;
+
+    // The `model` field is emptied as it is provided as a path parameter.
+    let model_id = mem::take(&mut request.model.model_id);
+
+    let uri =
+        format!("{api_url}/v1beta/models/{model_id}:streamGenerateContent?alt=sse&key={api_key}",);
+
+    let request_builder = HttpRequest::builder()
+        .method(Method::POST)
+        .uri(uri)
+        .header("Content-Type", "application/json");
+
+    let request = request_builder.body(AsyncBody::from(serde_json::to_string(&request)?))?;
+    let mut response = client.send(request).await?;
+    if response.status().is_success() {
+        let reader = BufReader::new(response.into_body());
+        Ok(reader
+            .lines()
+            .filter_map(|line| async move {
+                match line {
+                    Ok(line) => {
+                        if let Some(line) = line.strip_prefix("data: ") {
+                            match serde_json::from_str(line) {
+                                Ok(response) => Some(Ok(response)),
+                                Err(error) => Some(Err(anyhow!(format!(
+                                    "Error parsing JSON: {error:?}\n{line:?}"
+                                )))),
+                            }
+                        } else {
+                            None
+                        }
+                    }
+                    Err(error) => Some(Err(anyhow!(error))),
+                }
+            })
+            .boxed())
+    } else {
+        let mut text = String::new();
+        response.body_mut().read_to_string(&mut text).await?;
+        Err(anyhow!(
+            "error during streamGenerateContent, status code: {:?}, body: {}",
+            response.status(),
+            text
+        ))
+    }
+}
+
+pub fn validate_generate_content_request(request: &GenerateContentRequest) -> Result<()> {
+    if request.model.is_empty() {
+        bail!("Model must be specified");
+    }
+
+    if request.contents.is_empty() {
+        bail!("Request must contain at least one content item");
+    }
+
+    if let Some(user_content) = request
+        .contents
+        .iter()
+        .find(|content| content.role == Role::User)
+        && user_content.parts.is_empty()
+    {
+        bail!("User content must contain at least one part");
+    }
+
+    Ok(())
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+pub enum Task {
+    #[serde(rename = "generateContent")]
+    GenerateContent,
+    #[serde(rename = "streamGenerateContent")]
+    StreamGenerateContent,
+    #[serde(rename = "embedContent")]
+    EmbedContent,
+    #[serde(rename = "batchEmbedContents")]
+    BatchEmbedContents,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct GenerateContentRequest {
+    #[serde(default, skip_serializing_if = "ModelName::is_empty")]
+    pub model: ModelName,
+    pub contents: Vec<Content>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub system_instruction: Option<SystemInstruction>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub generation_config: Option<GenerationConfig>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub safety_settings: Option<Vec<SafetySetting>>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tools: Option<Vec<Tool>>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_config: Option<ToolConfig>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct GenerateContentResponse {
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub candidates: Option<Vec<GenerateContentCandidate>>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub prompt_feedback: Option<PromptFeedback>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub usage_metadata: Option<UsageMetadata>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct GenerateContentCandidate {
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub index: Option<usize>,
+    pub content: Content,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub finish_reason: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub finish_message: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub safety_ratings: Option<Vec<SafetyRating>>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub citation_metadata: Option<CitationMetadata>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct Content {
+    #[serde(default)]
+    pub parts: Vec<Part>,
+    pub role: Role,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct SystemInstruction {
+    pub parts: Vec<Part>,
+}
+
+#[derive(Debug, PartialEq, Deserialize, Serialize)]
+#[serde(rename_all = "camelCase")]
+pub enum Role {
+    User,
+    Model,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(untagged)]
+pub enum Part {
+    TextPart(TextPart),
+    InlineDataPart(InlineDataPart),
+    FunctionCallPart(FunctionCallPart),
+    FunctionResponsePart(FunctionResponsePart),
+    ThoughtPart(ThoughtPart),
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct TextPart {
+    pub text: String,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct InlineDataPart {
+    pub inline_data: GenerativeContentBlob,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct GenerativeContentBlob {
+    pub mime_type: String,
+    pub data: String,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct FunctionCallPart {
+    pub function_call: FunctionCall,
+    /// Thought signature returned by the model for function calls.
+    /// Only present on the first function call in parallel call scenarios.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub thought_signature: Option<String>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct FunctionResponsePart {
+    pub function_response: FunctionResponse,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct ThoughtPart {
+    pub thought: bool,
+    pub thought_signature: String,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct CitationSource {
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub start_index: Option<usize>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub end_index: Option<usize>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub uri: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub license: Option<String>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct CitationMetadata {
+    pub citation_sources: Vec<CitationSource>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct PromptFeedback {
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub block_reason: Option<String>,
+    pub safety_ratings: Option<Vec<SafetyRating>>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub block_reason_message: Option<String>,
+}
+
+#[derive(Debug, Serialize, Deserialize, Default)]
+#[serde(rename_all = "camelCase")]
+pub struct UsageMetadata {
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub prompt_token_count: Option<u64>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub cached_content_token_count: Option<u64>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub candidates_token_count: Option<u64>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_use_prompt_token_count: Option<u64>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub thoughts_token_count: Option<u64>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub total_token_count: Option<u64>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct ThinkingConfig {
+    pub thinking_budget: u32,
+}
+
+#[derive(Debug, Deserialize, Serialize)]
+#[serde(rename_all = "camelCase")]
+pub struct GenerationConfig {
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub candidate_count: Option<usize>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub stop_sequences: Option<Vec<String>>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub max_output_tokens: Option<usize>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub temperature: Option<f64>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub top_p: Option<f64>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub top_k: Option<usize>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub thinking_config: Option<ThinkingConfig>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct SafetySetting {
+    pub category: HarmCategory,
+    pub threshold: HarmBlockThreshold,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+pub enum HarmCategory {
+    #[serde(rename = "HARM_CATEGORY_UNSPECIFIED")]
+    Unspecified,
+    #[serde(rename = "HARM_CATEGORY_DEROGATORY")]
+    Derogatory,
+    #[serde(rename = "HARM_CATEGORY_TOXICITY")]
+    Toxicity,
+    #[serde(rename = "HARM_CATEGORY_VIOLENCE")]
+    Violence,
+    #[serde(rename = "HARM_CATEGORY_SEXUAL")]
+    Sexual,
+    #[serde(rename = "HARM_CATEGORY_MEDICAL")]
+    Medical,
+    #[serde(rename = "HARM_CATEGORY_DANGEROUS")]
+    Dangerous,
+    #[serde(rename = "HARM_CATEGORY_HARASSMENT")]
+    Harassment,
+    #[serde(rename = "HARM_CATEGORY_HATE_SPEECH")]
+    HateSpeech,
+    #[serde(rename = "HARM_CATEGORY_SEXUALLY_EXPLICIT")]
+    SexuallyExplicit,
+    #[serde(rename = "HARM_CATEGORY_DANGEROUS_CONTENT")]
+    DangerousContent,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "SCREAMING_SNAKE_CASE")]
+pub enum HarmBlockThreshold {
+    #[serde(rename = "HARM_BLOCK_THRESHOLD_UNSPECIFIED")]
+    Unspecified,
+    BlockLowAndAbove,
+    BlockMediumAndAbove,
+    BlockOnlyHigh,
+    BlockNone,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "SCREAMING_SNAKE_CASE")]
+pub enum HarmProbability {
+    #[serde(rename = "HARM_PROBABILITY_UNSPECIFIED")]
+    Unspecified,
+    Negligible,
+    Low,
+    Medium,
+    High,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct SafetyRating {
+    pub category: HarmCategory,
+    pub probability: HarmProbability,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+pub struct FunctionCall {
+    pub name: String,
+    pub args: serde_json::Value,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+pub struct FunctionResponse {
+    pub name: String,
+    pub response: serde_json::Value,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct Tool {
+    pub function_declarations: Vec<FunctionDeclaration>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct ToolConfig {
+    pub function_calling_config: FunctionCallingConfig,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct FunctionCallingConfig {
+    pub mode: FunctionCallingMode,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub allowed_function_names: Option<Vec<String>>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "lowercase")]
+pub enum FunctionCallingMode {
+    Auto,
+    Any,
+    None,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+pub struct FunctionDeclaration {
+    pub name: String,
+    pub description: String,
+    pub parameters: serde_json::Value,
+}
+
+#[derive(Debug, Default)]
+pub struct ModelName {
+    pub model_id: String,
+}
+
+impl ModelName {
+    pub fn is_empty(&self) -> bool {
+        self.model_id.is_empty()
+    }
+}
+
+const MODEL_NAME_PREFIX: &str = "models/";
+
+impl Serialize for ModelName {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        serializer.serialize_str(&format!("{MODEL_NAME_PREFIX}{}", &self.model_id))
+    }
+}
+
+impl<'de> Deserialize<'de> for ModelName {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let string = String::deserialize(deserializer)?;
+        if let Some(id) = string.strip_prefix(MODEL_NAME_PREFIX) {
+            Ok(Self {
+                model_id: id.to_string(),
+            })
+        } else {
+            Err(serde::de::Error::custom(format!(
+                "Expected model name to begin with {}, got: {}",
+                MODEL_NAME_PREFIX, string
+            )))
+        }
+    }
+}
+
+#[cfg_attr(feature = "schemars", derive(schemars::JsonSchema))]
+#[derive(Clone, Default, Debug, Deserialize, Serialize, PartialEq, Eq, strum::EnumIter)]
+pub enum Model {
+    #[serde(
+        rename = "gemini-2.5-flash-lite",
+        alias = "gemini-2.5-flash-lite-preview-06-17",
+        alias = "gemini-2.0-flash-lite-preview"
+    )]
+    Gemini25FlashLite,
+    #[serde(
+        rename = "gemini-2.5-flash",
+        alias = "gemini-2.0-flash-thinking-exp",
+        alias = "gemini-2.5-flash-preview-04-17",
+        alias = "gemini-2.5-flash-preview-05-20",
+        alias = "gemini-2.5-flash-preview-latest",
+        alias = "gemini-2.0-flash"
+    )]
+    #[default]
+    Gemini25Flash,
+    #[serde(
+        rename = "gemini-2.5-pro",
+        alias = "gemini-2.0-pro-exp",
+        alias = "gemini-2.5-pro-preview-latest",
+        alias = "gemini-2.5-pro-exp-03-25",
+        alias = "gemini-2.5-pro-preview-03-25",
+        alias = "gemini-2.5-pro-preview-05-06",
+        alias = "gemini-2.5-pro-preview-06-05"
+    )]
+    Gemini25Pro,
+    #[serde(rename = "gemini-3.1-flash-lite")]
+    Gemini31FlashLite,
+    #[serde(rename = "gemini-3-flash-preview")]
+    Gemini3Flash,
+    #[serde(rename = "gemini-3.1-pro-preview", alias = "gemini-3-pro-preview")]
+    Gemini31Pro,
+    #[serde(rename = "custom")]
+    Custom {
+        name: String,
+        /// The name displayed in the UI, such as in the agent panel model dropdown menu.
+        display_name: Option<String>,
+        max_tokens: u64,
+        #[serde(default)]
+        mode: GoogleModelMode,
+    },
+}
+
+impl Model {
+    pub fn default_fast() -> Self {
+        Self::Gemini31FlashLite
+    }
+
+    pub fn id(&self) -> &str {
+        match self {
+            Self::Gemini25FlashLite => "gemini-2.5-flash-lite",
+            Self::Gemini25Flash => "gemini-2.5-flash",
+            Self::Gemini25Pro => "gemini-2.5-pro",
+            Self::Gemini31FlashLite => "gemini-3.1-flash-lite",
+            Self::Gemini3Flash => "gemini-3-flash-preview",
+            Self::Gemini31Pro => "gemini-3.1-pro-preview",
+            Self::Custom { name, .. } => name,
+        }
+    }
+    pub fn request_id(&self) -> &str {
+        match self {
+            Self::Gemini25FlashLite => "gemini-2.5-flash-lite",
+            Self::Gemini25Flash => "gemini-2.5-flash",
+            Self::Gemini25Pro => "gemini-2.5-pro",
+            Self::Gemini31FlashLite => "gemini-3.1-flash-lite",
+            Self::Gemini3Flash => "gemini-3-flash-preview",
+            Self::Gemini31Pro => "gemini-3.1-pro-preview",
+            Self::Custom { name, .. } => name,
+        }
+    }
+
+    pub fn display_name(&self) -> &str {
+        match self {
+            Self::Gemini25FlashLite => "Gemini 2.5 Flash-Lite",
+            Self::Gemini25Flash => "Gemini 2.5 Flash",
+            Self::Gemini25Pro => "Gemini 2.5 Pro",
+            Self::Gemini31FlashLite => "Gemini 3.1 Flash Lite",
+            Self::Gemini3Flash => "Gemini 3 Flash",
+            Self::Gemini31Pro => "Gemini 3.1 Pro",
+            Self::Custom {
+                name, display_name, ..
+            } => display_name.as_ref().unwrap_or(name),
+        }
+    }
+
+    pub fn max_token_count(&self) -> u64 {
+        match self {
+            Self::Gemini25FlashLite
+            | Self::Gemini25Flash
+            | Self::Gemini25Pro
+            | Self::Gemini31FlashLite
+            | Self::Gemini3Flash
+            | Self::Gemini31Pro => 1_048_576,
+            Self::Custom { max_tokens, .. } => *max_tokens,
+        }
+    }
+
+    pub fn max_output_tokens(&self) -> Option<u64> {
+        match self {
+            Model::Gemini25FlashLite
+            | Model::Gemini25Flash
+            | Model::Gemini25Pro
+            | Model::Gemini31FlashLite
+            | Model::Gemini3Flash
+            | Model::Gemini31Pro => Some(65_536),
+            Model::Custom { .. } => None,
+        }
+    }
+
+    pub fn supports_tools(&self) -> bool {
+        true
+    }
+
+    pub fn supports_images(&self) -> bool {
+        true
+    }
+
+    pub fn mode(&self) -> GoogleModelMode {
+        match self {
+            Self::Gemini25FlashLite | Self::Gemini25Flash | Self::Gemini25Pro => {
+                GoogleModelMode::Thinking {
+                    // By default these models are set to "auto", so we preserve that behavior
+                    // but indicate they are capable of thinking mode
+                    budget_tokens: None,
+                }
+            }
+            Self::Gemini3Flash => GoogleModelMode::Default,
+            Self::Gemini31FlashLite => GoogleModelMode::Default,
+            Self::Gemini31Pro => GoogleModelMode::Thinking {
+                budget_tokens: None,
+            },
+            Self::Custom { mode, .. } => *mode,
+        }
+    }
+}
+
+impl std::fmt::Display for Model {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "{}", self.id())
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use serde_json::json;
+
+    #[test]
+    fn test_function_call_part_with_signature_serializes_correctly() {
+        let part = FunctionCallPart {
+            function_call: FunctionCall {
+                name: "test_function".to_string(),
+                args: json!({"arg": "value"}),
+            },
+            thought_signature: Some("test_signature".to_string()),
+        };
+
+        let serialized = serde_json::to_value(&part).unwrap();
+
+        assert_eq!(serialized["functionCall"]["name"], "test_function");
+        assert_eq!(serialized["functionCall"]["args"]["arg"], "value");
+        assert_eq!(serialized["thoughtSignature"], "test_signature");
+    }
+
+    #[test]
+    fn test_function_call_part_without_signature_omits_field() {
+        let part = FunctionCallPart {
+            function_call: FunctionCall {
+                name: "test_function".to_string(),
+                args: json!({"arg": "value"}),
+            },
+            thought_signature: None,
+        };
+
+        let serialized = serde_json::to_value(&part).unwrap();
+
+        assert_eq!(serialized["functionCall"]["name"], "test_function");
+        assert_eq!(serialized["functionCall"]["args"]["arg"], "value");
+        // thoughtSignature field should not be present when None
+        assert!(serialized.get("thoughtSignature").is_none());
+    }
+
+    #[test]
+    fn test_function_call_part_deserializes_with_signature() {
+        let json = json!({
+            "functionCall": {
+                "name": "test_function",
+                "args": {"arg": "value"}
+            },
+            "thoughtSignature": "test_signature"
+        });
+
+        let part: FunctionCallPart = serde_json::from_value(json).unwrap();
+
+        assert_eq!(part.function_call.name, "test_function");
+        assert_eq!(part.thought_signature, Some("test_signature".to_string()));
+    }
+
+    #[test]
+    fn test_function_call_part_deserializes_without_signature() {
+        let json = json!({
+            "functionCall": {
+                "name": "test_function",
+                "args": {"arg": "value"}
+            }
+        });
+
+        let part: FunctionCallPart = serde_json::from_value(json).unwrap();
+
+        assert_eq!(part.function_call.name, "test_function");
+        assert_eq!(part.thought_signature, None);
+    }
+
+    #[test]
+    fn test_function_call_part_round_trip() {
+        let original = FunctionCallPart {
+            function_call: FunctionCall {
+                name: "test_function".to_string(),
+                args: json!({"arg": "value", "nested": {"key": "val"}}),
+            },
+            thought_signature: Some("round_trip_signature".to_string()),
+        };
+
+        let serialized = serde_json::to_value(&original).unwrap();
+        let deserialized: FunctionCallPart = serde_json::from_value(serialized).unwrap();
+
+        assert_eq!(deserialized.function_call.name, original.function_call.name);
+        assert_eq!(deserialized.function_call.args, original.function_call.args);
+        assert_eq!(deserialized.thought_signature, original.thought_signature);
+    }
+
+    #[test]
+    fn test_function_call_part_with_empty_signature_serializes() {
+        let part = FunctionCallPart {
+            function_call: FunctionCall {
+                name: "test_function".to_string(),
+                args: json!({"arg": "value"}),
+            },
+            thought_signature: Some("".to_string()),
+        };
+
+        let serialized = serde_json::to_value(&part).unwrap();
+
+        // Empty string should still be serialized (normalization happens at a higher level)
+        assert_eq!(serialized["thoughtSignature"], "");
+    }
+}