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- "spdx",
- "url",
- "wasm-encoder",
- "wasmparser",
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diff --git a/extensions/google-ai/Cargo.toml b/extensions/google-ai/Cargo.toml
deleted file mode 100644
index f6de35d40669382e65d6acb1ee5041453ff92dbd..0000000000000000000000000000000000000000
--- a/extensions/google-ai/Cargo.toml
+++ /dev/null
@@ -1,17 +0,0 @@
-[package]
-name = "google-ai"
-version = "0.1.0"
-edition = "2021"
-publish = false
-license = "Apache-2.0"
-
-[workspace]
-
-[lib]
-path = "src/google_ai.rs"
-crate-type = ["cdylib"]
-
-[dependencies]
-zed_extension_api = { path = "../../crates/extension_api" }
-serde = { version = "1.0", features = ["derive"] }
-serde_json = "1.0"
diff --git a/extensions/google-ai/LICENSE-APACHE b/extensions/google-ai/LICENSE-APACHE
deleted file mode 120000
index 1cd601d0a3affae83854be02a0afdec3b7a9ec4d..0000000000000000000000000000000000000000
--- a/extensions/google-ai/LICENSE-APACHE
+++ /dev/null
@@ -1 +0,0 @@
-../../LICENSE-APACHE
\ No newline at end of file
diff --git a/extensions/google-ai/extension.toml b/extensions/google-ai/extension.toml
deleted file mode 100644
index 9b3a554ab8c9862e26b9618112133d11a194e43c..0000000000000000000000000000000000000000
--- a/extensions/google-ai/extension.toml
+++ /dev/null
@@ -1,13 +0,0 @@
-id = "google-ai"
-name = "Google AI"
-description = "Google Gemini LLM provider for Zed."
-version = "0.1.0"
-schema_version = 1
-authors = ["Zed Team"]
-repository = "https://github.com/zed-industries/zed"
-
-[language_model_providers.google]
-name = "Google AI"
-
-[language_model_providers.google.auth]
-env_vars = ["GEMINI_API_KEY", "GOOGLE_AI_API_KEY"]
diff --git a/extensions/google-ai/icons/google-ai.svg b/extensions/google-ai/icons/google-ai.svg
deleted file mode 100644
index bdde44ed2475313f0dfd418a496f372ca61db22d..0000000000000000000000000000000000000000
--- a/extensions/google-ai/icons/google-ai.svg
+++ /dev/null
@@ -1,3 +0,0 @@
-
diff --git a/extensions/google-ai/src/google_ai.rs b/extensions/google-ai/src/google_ai.rs
deleted file mode 100644
index b8198694284030eabdd55ab6742c69d7754ae876..0000000000000000000000000000000000000000
--- a/extensions/google-ai/src/google_ai.rs
+++ /dev/null
@@ -1,1128 +0,0 @@
-use std::collections::HashMap;
-use std::sync::atomic::{AtomicU64, Ordering};
-
-static TOOL_CALL_COUNTER: AtomicU64 = AtomicU64::new(0);
-
-use serde::{Deserialize, Deserializer, Serialize, Serializer};
-use zed_extension_api::{
- self as zed, http_client::HttpMethod, http_client::HttpRequest,
- llm_get_env_var, llm_get_provider_settings,
- LlmCacheConfiguration, LlmCompletionEvent, LlmCompletionRequest, LlmCustomModelConfig,
- LlmMessageContent, LlmMessageRole, LlmModelCapabilities, LlmModelInfo, LlmProviderInfo,
- LlmStopReason, LlmThinkingContent, LlmTokenUsage, LlmToolInputFormat, LlmToolUse,
-};
-
-pub const DEFAULT_API_URL: &str = "https://generativelanguage.googleapis.com";
-
-fn get_api_url() -> String {
- llm_get_provider_settings(PROVIDER_ID)
- .and_then(|s| s.api_url)
- .unwrap_or_else(|| DEFAULT_API_URL.to_string())
-}
-
-fn get_custom_models() -> Vec {
- llm_get_provider_settings(PROVIDER_ID)
- .map(|s| s.available_models)
- .unwrap_or_default()
-}
-
-fn stream_generate_content(
- model_id: &str,
- request: &LlmCompletionRequest,
- streams: &mut HashMap,
- next_stream_id: &mut u64,
-) -> Result {
- let api_key = get_api_key().ok_or_else(|| "API key not configured".to_string())?;
-
- let generate_content_request = build_generate_content_request(model_id, request)?;
- validate_generate_content_request(&generate_content_request)?;
-
- let api_url = get_api_url();
- let uri = format!(
- "{}/v1beta/models/{}:streamGenerateContent?alt=sse&key={}",
- api_url, model_id, api_key
- );
-
- let body = serde_json::to_vec(&generate_content_request)
- .map_err(|e| format!("Failed to serialize request: {}", e))?;
-
- let http_request = HttpRequest::builder()
- .method(HttpMethod::Post)
- .url(&uri)
- .header("Content-Type", "application/json")
- .body(body)
- .build()?;
-
- let response_stream = http_request.fetch_stream()?;
-
- let stream_id = format!("stream-{}", *next_stream_id);
- *next_stream_id += 1;
-
- streams.insert(
- stream_id.clone(),
- StreamState {
- response_stream,
- buffer: String::new(),
- usage: None,
- pending_events: Vec::new(),
- wants_to_use_tool: false,
- },
- );
-
- Ok(stream_id)
-}
-
-fn count_tokens(model_id: &str, request: &LlmCompletionRequest) -> Result {
- let api_key = get_api_key().ok_or_else(|| "API key not configured".to_string())?;
-
- let generate_content_request = build_generate_content_request(model_id, request)?;
- validate_generate_content_request(&generate_content_request)?;
- let count_request = CountTokensRequest {
- generate_content_request,
- };
-
- let api_url = get_api_url();
- let uri = format!(
- "{}/v1beta/models/{}:countTokens?key={}",
- api_url, model_id, api_key
- );
-
- let body = serde_json::to_vec(&count_request)
- .map_err(|e| format!("Failed to serialize request: {}", e))?;
-
- let http_request = HttpRequest::builder()
- .method(HttpMethod::Post)
- .url(&uri)
- .header("Content-Type", "application/json")
- .body(body)
- .build()?;
-
- let response = http_request.fetch()?;
- let response_body: CountTokensResponse = serde_json::from_slice(&response.body)
- .map_err(|e| format!("Failed to parse response: {}", e))?;
-
- Ok(response_body.total_tokens)
-}
-
-fn validate_generate_content_request(request: &GenerateContentRequest) -> Result<(), String> {
- if request.model.is_empty() {
- return Err("Model must be specified".to_string());
- }
-
- if request.contents.is_empty() {
- return Err("Request must contain at least one content item".to_string());
- }
-
- if let Some(user_content) = request
- .contents
- .iter()
- .find(|content| content.role == Role::User)
- {
- if user_content.parts.is_empty() {
- return Err("User content must contain at least one part".to_string());
- }
- }
-
- Ok(())
-}
-
-// Extension implementation
-
-const PROVIDER_ID: &str = "google";
-const PROVIDER_NAME: &str = "Google AI";
-
-struct GoogleAiExtension {
- streams: HashMap,
- next_stream_id: u64,
-}
-
-struct StreamState {
- response_stream: zed::http_client::HttpResponseStream,
- buffer: String,
- usage: Option,
- pending_events: Vec,
- wants_to_use_tool: bool,
-}
-
-impl zed::Extension for GoogleAiExtension {
- fn new() -> Self {
- Self {
- streams: HashMap::new(),
- next_stream_id: 0,
- }
- }
-
- fn llm_providers(&self) -> Vec {
- vec![LlmProviderInfo {
- id: PROVIDER_ID.to_string(),
- name: PROVIDER_NAME.to_string(),
- icon: Some("icons/google-ai.svg".to_string()),
- }]
- }
-
- fn llm_provider_models(&self, provider_id: &str) -> Result, String> {
- if provider_id != PROVIDER_ID {
- return Err(format!("Unknown provider: {}", provider_id));
- }
- Ok(get_models())
- }
-
- fn llm_provider_settings_markdown(&self, provider_id: &str) -> Option {
- if provider_id != PROVIDER_ID {
- return None;
- }
-
- Some(
- r#"## Google AI Setup
-
-To use Google AI models in Zed, you need a Gemini API key.
-
-1. Go to [Google AI Studio](https://aistudio.google.com/apikey)
-2. Create or select a project
-3. Generate an API key
-4. Set the `GEMINI_API_KEY` or `GOOGLE_AI_API_KEY` environment variable
-
-You can set this in your shell profile or use a `.envrc` file with [direnv](https://direnv.net/).
-"#
- .to_string(),
- )
- }
-
- fn llm_provider_is_authenticated(&self, provider_id: &str) -> bool {
- if provider_id != PROVIDER_ID {
- return false;
- }
- get_api_key().is_some()
- }
-
- fn llm_provider_reset_credentials(&mut self, provider_id: &str) -> Result<(), String> {
- if provider_id != PROVIDER_ID {
- return Err(format!("Unknown provider: {}", provider_id));
- }
- Ok(())
- }
-
- fn llm_count_tokens(
- &self,
- provider_id: &str,
- model_id: &str,
- request: &LlmCompletionRequest,
- ) -> Result {
- if provider_id != PROVIDER_ID {
- return Err(format!("Unknown provider: {}", provider_id));
- }
- count_tokens(model_id, request)
- }
-
- fn llm_stream_completion_start(
- &mut self,
- provider_id: &str,
- model_id: &str,
- request: &LlmCompletionRequest,
- ) -> Result {
- if provider_id != PROVIDER_ID {
- return Err(format!("Unknown provider: {}", provider_id));
- }
- stream_generate_content(model_id, request, &mut self.streams, &mut self.next_stream_id)
- }
-
- fn llm_stream_completion_next(
- &mut self,
- stream_id: &str,
- ) -> Result, String> {
- stream_generate_content_next(stream_id, &mut self.streams)
- }
-
- fn llm_stream_completion_close(&mut self, stream_id: &str) {
- self.streams.remove(stream_id);
- }
-
- fn llm_cache_configuration(
- &self,
- provider_id: &str,
- _model_id: &str,
- ) -> Option {
- if provider_id != PROVIDER_ID {
- return None;
- }
-
- Some(LlmCacheConfiguration {
- max_cache_anchors: 1,
- should_cache_tool_definitions: false,
- min_total_token_count: 32768,
- })
- }
-}
-
-zed::register_extension!(GoogleAiExtension);
-
-// Helper functions
-
-fn get_api_key() -> Option {
- llm_get_env_var("GEMINI_API_KEY").or_else(|| llm_get_env_var("GOOGLE_AI_API_KEY"))
-}
-
-fn get_default_models() -> Vec {
- vec![
- LlmModelInfo {
- id: "gemini-2.5-flash-lite".to_string(),
- name: "Gemini 2.5 Flash-Lite".to_string(),
- max_token_count: 1_048_576,
- max_output_tokens: Some(65_536),
- capabilities: LlmModelCapabilities {
- supports_images: true,
- supports_tools: true,
- supports_tool_choice_auto: true,
- supports_tool_choice_any: true,
- supports_tool_choice_none: true,
- supports_thinking: true,
- tool_input_format: LlmToolInputFormat::JsonSchemaSubset,
- },
- is_default: false,
- is_default_fast: true,
- },
- LlmModelInfo {
- id: "gemini-2.5-flash".to_string(),
- name: "Gemini 2.5 Flash".to_string(),
- max_token_count: 1_048_576,
- max_output_tokens: Some(65_536),
- capabilities: LlmModelCapabilities {
- supports_images: true,
- supports_tools: true,
- supports_tool_choice_auto: true,
- supports_tool_choice_any: true,
- supports_tool_choice_none: true,
- supports_thinking: true,
- tool_input_format: LlmToolInputFormat::JsonSchemaSubset,
- },
- is_default: true,
- is_default_fast: false,
- },
- LlmModelInfo {
- id: "gemini-2.5-pro".to_string(),
- name: "Gemini 2.5 Pro".to_string(),
- max_token_count: 1_048_576,
- max_output_tokens: Some(65_536),
- capabilities: LlmModelCapabilities {
- supports_images: true,
- supports_tools: true,
- supports_tool_choice_auto: true,
- supports_tool_choice_any: true,
- supports_tool_choice_none: true,
- supports_thinking: true,
- tool_input_format: LlmToolInputFormat::JsonSchemaSubset,
- },
- is_default: false,
- is_default_fast: false,
- },
- LlmModelInfo {
- id: "gemini-3-pro-preview".to_string(),
- name: "Gemini 3 Pro".to_string(),
- max_token_count: 1_048_576,
- max_output_tokens: Some(65_536),
- capabilities: LlmModelCapabilities {
- supports_images: true,
- supports_tools: true,
- supports_tool_choice_auto: true,
- supports_tool_choice_any: true,
- supports_tool_choice_none: true,
- supports_thinking: true,
- tool_input_format: LlmToolInputFormat::JsonSchemaSubset,
- },
- is_default: false,
- is_default_fast: false,
- },
- LlmModelInfo {
- id: "gemini-3-flash-preview".to_string(),
- name: "Gemini 3 Flash".to_string(),
- max_token_count: 1_048_576,
- max_output_tokens: Some(65_536),
- capabilities: LlmModelCapabilities {
- supports_images: true,
- supports_tools: true,
- supports_tool_choice_auto: true,
- supports_tool_choice_any: true,
- supports_tool_choice_none: true,
- supports_thinking: false,
- tool_input_format: LlmToolInputFormat::JsonSchemaSubset,
- },
- is_default: false,
- is_default_fast: false,
- },
- ]
-}
-
-/// Model aliases for backward compatibility with old model names.
-/// Maps old names to canonical model IDs.
-fn get_model_aliases() -> Vec<(&'static str, &'static str)> {
- vec![
- // Gemini 2.5 Flash-Lite aliases
- ("gemini-2.5-flash-lite-preview-06-17", "gemini-2.5-flash-lite"),
- ("gemini-2.0-flash-lite-preview", "gemini-2.5-flash-lite"),
- // Gemini 2.5 Flash aliases
- ("gemini-2.0-flash-thinking-exp", "gemini-2.5-flash"),
- ("gemini-2.5-flash-preview-04-17", "gemini-2.5-flash"),
- ("gemini-2.5-flash-preview-05-20", "gemini-2.5-flash"),
- ("gemini-2.5-flash-preview-latest", "gemini-2.5-flash"),
- ("gemini-2.0-flash", "gemini-2.5-flash"),
- // Gemini 2.5 Pro aliases
- ("gemini-2.0-pro-exp", "gemini-2.5-pro"),
- ("gemini-2.5-pro-preview-latest", "gemini-2.5-pro"),
- ("gemini-2.5-pro-exp-03-25", "gemini-2.5-pro"),
- ("gemini-2.5-pro-preview-03-25", "gemini-2.5-pro"),
- ("gemini-2.5-pro-preview-05-06", "gemini-2.5-pro"),
- ("gemini-2.5-pro-preview-06-05", "gemini-2.5-pro"),
- ]
-}
-
-fn get_models() -> Vec {
- let mut models: HashMap = HashMap::new();
-
- // Add default models
- for model in get_default_models() {
- models.insert(model.id.clone(), model);
- }
-
- // Add aliases as separate model entries (pointing to the same underlying model)
- for (alias, canonical_id) in get_model_aliases() {
- if let Some(canonical_model) = models.get(canonical_id) {
- let mut alias_model = canonical_model.clone();
- alias_model.id = alias.to_string();
- alias_model.is_default = false;
- alias_model.is_default_fast = false;
- models.insert(alias.to_string(), alias_model);
- }
- }
-
- // Add/override with custom models from settings
- for custom_model in get_custom_models() {
- let model = LlmModelInfo {
- id: custom_model.name.clone(),
- name: custom_model.display_name.unwrap_or(custom_model.name.clone()),
- max_token_count: custom_model.max_tokens,
- max_output_tokens: custom_model.max_output_tokens,
- capabilities: LlmModelCapabilities {
- supports_images: true,
- supports_tools: true,
- supports_tool_choice_auto: true,
- supports_tool_choice_any: true,
- supports_tool_choice_none: true,
- supports_thinking: custom_model.thinking_budget.is_some(),
- tool_input_format: LlmToolInputFormat::JsonSchemaSubset,
- },
- is_default: false,
- is_default_fast: false,
- };
- models.insert(custom_model.name, model);
- }
-
- models.into_values().collect()
-}
-
-/// Get the thinking budget for a specific model from custom settings.
-fn get_model_thinking_budget(model_id: &str) -> Option {
- get_custom_models()
- .into_iter()
- .find(|m| m.name == model_id)
- .and_then(|m| m.thinking_budget)
-}
-
-fn stream_generate_content_next(
- stream_id: &str,
- streams: &mut HashMap,
-) -> Result, String> {
- let state = streams
- .get_mut(stream_id)
- .ok_or_else(|| format!("Unknown stream: {}", stream_id))?;
-
- loop {
- // Return any pending events first
- if let Some(event) = state.pending_events.pop() {
- return Ok(Some(event));
- }
-
- if let Some(newline_pos) = state.buffer.find('\n') {
- let line = state.buffer[..newline_pos].to_string();
- state.buffer = state.buffer[newline_pos + 1..].to_string();
-
- if let Some(data) = line.strip_prefix("data: ") {
- if data.trim().is_empty() {
- continue;
- }
-
- let response: GenerateContentResponse = match serde_json::from_str(data) {
- Ok(response) => response,
- Err(parse_error) => {
- // Try to parse as an API error response
- if let Ok(api_error) = serde_json::from_str::(data) {
- let error_msg = api_error
- .error
- .message
- .unwrap_or_else(|| "Unknown API error".to_string());
- let status = api_error.error.status.unwrap_or_default();
- let code = api_error.error.code.unwrap_or(0);
- return Err(format!(
- "Google AI API error ({}): {} [status: {}]",
- code, error_msg, status
- ));
- }
- // If it's not an error response, return the parse error
- return Err(format!(
- "Failed to parse SSE data: {} - {}",
- parse_error, data
- ));
- }
- };
-
- // Handle prompt feedback (blocked prompts)
- if let Some(ref prompt_feedback) = response.prompt_feedback {
- if let Some(ref block_reason) = prompt_feedback.block_reason {
- let _stop_reason = match block_reason.as_str() {
- "SAFETY" | "OTHER" | "BLOCKLIST" | "PROHIBITED_CONTENT"
- | "IMAGE_SAFETY" => LlmStopReason::Refusal,
- _ => LlmStopReason::Refusal,
- };
- return Ok(Some(LlmCompletionEvent::Stop(LlmStopReason::Refusal)));
- }
- }
-
- // Send usage updates immediately when received
- if let Some(ref usage) = response.usage_metadata {
- let cached_tokens = usage.cached_content_token_count.unwrap_or(0);
- let prompt_tokens = usage.prompt_token_count.unwrap_or(0);
- let input_tokens = prompt_tokens.saturating_sub(cached_tokens);
- state.pending_events.push(LlmCompletionEvent::Usage(LlmTokenUsage {
- input_tokens,
- output_tokens: usage.candidates_token_count.unwrap_or(0),
- cache_creation_input_tokens: None,
- cache_read_input_tokens: Some(cached_tokens).filter(|&c| c > 0),
- }));
- state.usage = Some(usage.clone());
- }
-
- if let Some(candidates) = response.candidates {
- for candidate in candidates {
- for part in candidate.content.parts {
- match part {
- Part::TextPart(text_part) => {
- return Ok(Some(LlmCompletionEvent::Text(text_part.text)));
- }
- Part::ThoughtPart(thought_part) => {
- return Ok(Some(LlmCompletionEvent::Thinking(
- LlmThinkingContent {
- text: "(Encrypted thought)".to_string(),
- signature: Some(thought_part.thought_signature),
- },
- )));
- }
- Part::FunctionCallPart(fc_part) => {
- state.wants_to_use_tool = true;
- // Normalize empty string signatures to None
- let thought_signature =
- fc_part.thought_signature.filter(|s| !s.is_empty());
- // Generate unique tool use ID like hardcoded implementation
- let next_tool_id = TOOL_CALL_COUNTER.fetch_add(1, Ordering::SeqCst);
- let tool_use_id = format!("{}-{}", fc_part.function_call.name, next_tool_id);
- return Ok(Some(LlmCompletionEvent::ToolUse(LlmToolUse {
- id: tool_use_id,
- name: fc_part.function_call.name,
- input: serde_json::to_string(&fc_part.function_call.args)
- .unwrap_or_default(),
- is_input_complete: true,
- thought_signature,
- })));
- }
- _ => {}
- }
- }
-
- if let Some(finish_reason) = candidate.finish_reason {
- // Even when Gemini wants to use a Tool, the API
- // responds with `finish_reason: STOP`, so we check
- // wants_to_use_tool to override
- let stop_reason = if state.wants_to_use_tool {
- LlmStopReason::ToolUse
- } else {
- match finish_reason.as_str() {
- "STOP" => LlmStopReason::EndTurn,
- "MAX_TOKENS" => LlmStopReason::MaxTokens,
- "TOOL_USE" | "FUNCTION_CALL" => LlmStopReason::ToolUse,
- "SAFETY" | "RECITATION" | "OTHER" => LlmStopReason::Refusal,
- _ => LlmStopReason::EndTurn,
- }
- };
-
- return Ok(Some(LlmCompletionEvent::Stop(stop_reason)));
- }
- }
- }
- }
-
- continue;
- }
-
- // Check if the buffer contains a non-SSE error response (no "data: " prefix)
- // This can happen when Google returns an immediate error without streaming
- if !state.buffer.is_empty()
- && !state.buffer.contains("data: ")
- && state.buffer.contains("\"error\"")
- {
- // Try to parse the entire buffer as an error response
- if let Ok(api_error) = serde_json::from_str::(&state.buffer) {
- let error_msg = api_error
- .error
- .message
- .unwrap_or_else(|| "Unknown API error".to_string());
- let status = api_error.error.status.unwrap_or_default();
- let code = api_error.error.code.unwrap_or(0);
- streams.remove(stream_id);
- return Err(format!(
- "Google AI API error ({}): {} [status: {}]",
- code, error_msg, status
- ));
- }
- }
-
- match state.response_stream.next_chunk() {
- Ok(Some(chunk)) => {
- let chunk_str = String::from_utf8_lossy(&chunk);
- state.buffer.push_str(&chunk_str);
- }
- Ok(None) => {
- streams.remove(stream_id);
- return Ok(None);
- }
- Err(e) => {
- streams.remove(stream_id);
- return Err(e);
- }
- }
- }
-}
-
-fn build_generate_content_request(
- model_id: &str,
- request: &LlmCompletionRequest,
-) -> Result {
- let mut contents: Vec = Vec::new();
- let mut system_instruction: Option = None;
-
- for message in &request.messages {
- match message.role {
- LlmMessageRole::System => {
- let parts = convert_content_to_parts(&message.content)?;
- system_instruction = Some(SystemInstruction { parts });
- }
- LlmMessageRole::User | LlmMessageRole::Assistant => {
- let role = match message.role {
- LlmMessageRole::User => Role::User,
- LlmMessageRole::Assistant => Role::Model,
- _ => continue,
- };
- let parts = convert_content_to_parts(&message.content)?;
- contents.push(Content { parts, role });
- }
- }
- }
-
- let tools = if !request.tools.is_empty() {
- Some(vec![Tool {
- function_declarations: request
- .tools
- .iter()
- .map(|t| FunctionDeclaration {
- name: t.name.clone(),
- description: t.description.clone(),
- parameters: serde_json::from_str(&t.input_schema).unwrap_or_default(),
- })
- .collect(),
- }])
- } else {
- None
- };
-
- let tool_config = request.tool_choice.as_ref().map(|choice| {
- let mode = match choice {
- zed::LlmToolChoice::Auto => FunctionCallingMode::Auto,
- zed::LlmToolChoice::Any => FunctionCallingMode::Any,
- zed::LlmToolChoice::None => FunctionCallingMode::None,
- };
- ToolConfig {
- function_calling_config: FunctionCallingConfig {
- mode,
- allowed_function_names: None,
- },
- }
- });
-
- let generation_config = Some(GenerationConfig {
- candidate_count: Some(1),
- stop_sequences: if request.stop_sequences.is_empty() {
- None
- } else {
- Some(request.stop_sequences.clone())
- },
- max_output_tokens: request.max_tokens.map(|t| t as usize),
- temperature: request.temperature.map(|t| t as f64).or(Some(1.0)),
- top_p: None,
- top_k: None,
- thinking_config: if request.thinking_allowed {
- // Check if this model has a custom thinking budget configured
- get_model_thinking_budget(model_id).map(|thinking_budget| ThinkingConfig {
- thinking_budget,
- })
- } else {
- None
- },
- });
-
- Ok(GenerateContentRequest {
- model: ModelName {
- model_id: model_id.to_string(),
- },
- contents,
- system_instruction,
- generation_config,
- safety_settings: None,
- tools,
- tool_config,
- })
-}
-
-fn convert_content_to_parts(content: &[LlmMessageContent]) -> Result, String> {
- let mut parts = Vec::new();
-
- for item in content {
- match item {
- LlmMessageContent::Text(text) => {
- parts.push(Part::TextPart(TextPart { text: text.clone() }));
- }
- LlmMessageContent::Image(image) => {
- parts.push(Part::InlineDataPart(InlineDataPart {
- inline_data: GenerativeContentBlob {
- mime_type: "image/png".to_string(),
- data: image.source.clone(),
- },
- }));
- }
- LlmMessageContent::ToolUse(tool_use) => {
- // Normalize empty string signatures to None
- let thought_signature = tool_use
- .thought_signature
- .clone()
- .filter(|s| !s.is_empty());
- parts.push(Part::FunctionCallPart(FunctionCallPart {
- function_call: FunctionCall {
- name: tool_use.name.clone(),
- args: serde_json::from_str(&tool_use.input).unwrap_or_default(),
- },
- thought_signature,
- }));
- }
- LlmMessageContent::ToolResult(tool_result) => {
- match &tool_result.content {
- zed::LlmToolResultContent::Text(text) => {
- parts.push(Part::FunctionResponsePart(FunctionResponsePart {
- function_response: FunctionResponse {
- name: tool_result.tool_name.clone(),
- response: serde_json::json!({ "output": text }),
- },
- }));
- }
- zed::LlmToolResultContent::Image(image) => {
- // Send both the function response and the image inline
- parts.push(Part::FunctionResponsePart(FunctionResponsePart {
- function_response: FunctionResponse {
- name: tool_result.tool_name.clone(),
- response: serde_json::json!({ "output": "Tool responded with an image" }),
- },
- }));
- parts.push(Part::InlineDataPart(InlineDataPart {
- inline_data: GenerativeContentBlob {
- mime_type: "image/png".to_string(),
- data: image.source.clone(),
- },
- }));
- }
- }
- }
- LlmMessageContent::Thinking(thinking) => {
- if let Some(signature) = &thinking.signature {
- parts.push(Part::ThoughtPart(ThoughtPart {
- thought: true,
- thought_signature: signature.clone(),
- }));
- }
- }
- LlmMessageContent::RedactedThinking(_) => {}
- }
- }
-
- Ok(parts)
-}
-
-// Data structures for Google AI API
-
-#[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,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub system_instruction: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub generation_config: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub safety_settings: Option>,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub tools: Option>,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub tool_config: Option,
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-#[serde(rename_all = "camelCase")]
-pub struct GenerateContentResponse {
- #[serde(skip_serializing_if = "Option::is_none")]
- pub candidates: Option>,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub prompt_feedback: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub usage_metadata: Option,
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-#[serde(rename_all = "camelCase")]
-pub struct GenerateContentCandidate {
- #[serde(skip_serializing_if = "Option::is_none")]
- pub index: Option,
- pub content: Content,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub finish_reason: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub finish_message: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub safety_ratings: Option>,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub citation_metadata: Option,
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-#[serde(rename_all = "camelCase")]
-pub struct Content {
- #[serde(default)]
- pub parts: Vec,
- pub role: Role,
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-#[serde(rename_all = "camelCase")]
-pub struct SystemInstruction {
- pub parts: Vec,
-}
-
-#[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,
-}
-
-#[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,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub end_index: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub uri: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub license: Option,
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-#[serde(rename_all = "camelCase")]
-pub struct CitationMetadata {
- pub citation_sources: Vec,
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-#[serde(rename_all = "camelCase")]
-pub struct PromptFeedback {
- #[serde(skip_serializing_if = "Option::is_none")]
- pub block_reason: Option,
- pub safety_ratings: Option>,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub block_reason_message: Option,
-}
-
-#[derive(Debug, Clone, Serialize, Deserialize, Default)]
-#[serde(rename_all = "camelCase")]
-pub struct UsageMetadata {
- #[serde(skip_serializing_if = "Option::is_none")]
- pub prompt_token_count: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub cached_content_token_count: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub candidates_token_count: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub tool_use_prompt_token_count: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub thoughts_token_count: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub total_token_count: Option,
-}
-
-#[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,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub stop_sequences: Option>,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub max_output_tokens: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub temperature: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub top_p: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub top_k: Option,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub thinking_config: Option,
-}
-
-#[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)]
-#[serde(rename_all = "camelCase")]
-pub struct CountTokensRequest {
- pub generate_content_request: GenerateContentRequest,
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-#[serde(rename_all = "camelCase")]
-pub struct CountTokensResponse {
- pub total_tokens: u64,
-}
-
-#[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,
-}
-
-#[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>,
-}
-
-#[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/";
-
-/// Google API error response structure
-#[derive(Debug, Deserialize)]
-pub struct ApiErrorResponse {
- pub error: ApiError,
-}
-
-#[derive(Debug, Deserialize)]
-pub struct ApiError {
- pub code: Option,
- pub message: Option,
- pub status: Option,
-}
-
-impl Serialize for ModelName {
- fn serialize(&self, serializer: S) -> Result
- where
- S: Serializer,
- {
- serializer.serialize_str(&format!("{MODEL_NAME_PREFIX}{}", &self.model_id))
- }
-}
-
-impl<'de> Deserialize<'de> for ModelName {
- fn deserialize(deserializer: D) -> Result
- 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
- )))
- }
- }
-}