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@@ -1,17 +0,0 @@
-[package]
-name = "anthropic"
-version = "0.1.0"
-edition = "2021"
-publish = false
-license = "Apache-2.0"
-
-[workspace]
-
-[lib]
-path = "src/anthropic.rs"
-crate-type = ["cdylib"]
-
-[dependencies]
-zed_extension_api = { path = "../../crates/extension_api" }
-serde = { version = "1.0", features = ["derive"] }
-serde_json = "1.0"
@@ -1,13 +0,0 @@
-id = "anthropic"
-name = "Anthropic"
-description = "Anthropic Claude LLM provider for Zed."
-version = "0.1.0"
-schema_version = 1
-authors = ["Zed Team"]
-repository = "https://github.com/zed-industries/zed"
-
-[language_model_providers.anthropic]
-name = "Anthropic"
-
-[language_model_providers.anthropic.auth]
-env_var = "ANTHROPIC_API_KEY"
@@ -1,11 +0,0 @@
-<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
-<g clip-path="url(#clip0_1896_18)">
-<path d="M11.094 3.09999H8.952L12.858 12.9H15L11.094 3.09999Z" fill="black"/>
-<path d="M4.906 3.09999L1 12.9H3.184L3.98284 10.842H8.06915L8.868 12.9H11.052L7.146 3.09999H4.906ZM4.68928 9.02199L6.026 5.57799L7.3627 9.02199H4.68928Z" fill="black"/>
-</g>
-<defs>
-<clipPath id="clip0_1896_18">
-<rect width="14" height="9.8" fill="white" transform="translate(1 3.09999)"/>
-</clipPath>
-</defs>
-</svg>
@@ -1,803 +0,0 @@
-use std::collections::HashMap;
-use std::sync::Mutex;
-
-use serde::{Deserialize, Serialize};
-use zed_extension_api::http_client::{HttpMethod, HttpRequest, HttpResponseStream, RedirectPolicy};
-use zed_extension_api::{self as zed, *};
-
-struct AnthropicProvider {
- streams: Mutex<HashMap<String, StreamState>>,
- next_stream_id: Mutex<u64>,
-}
-
-struct StreamState {
- response_stream: Option<HttpResponseStream>,
- buffer: String,
- started: bool,
- current_tool_use: Option<ToolUseState>,
- stop_reason: Option<LlmStopReason>,
- pending_signature: Option<String>,
-}
-
-struct ToolUseState {
- id: String,
- name: String,
- input_json: String,
-}
-
-struct ModelDefinition {
- real_id: &'static str,
- display_name: &'static str,
- max_tokens: u64,
- max_output_tokens: u64,
- supports_images: bool,
- supports_thinking: bool,
- is_default: bool,
- is_default_fast: bool,
-}
-
-const MODELS: &[ModelDefinition] = &[
- ModelDefinition {
- real_id: "claude-opus-4-5-20251101",
- display_name: "Claude Opus 4.5",
- max_tokens: 200_000,
- max_output_tokens: 8_192,
- supports_images: true,
- supports_thinking: false,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "claude-opus-4-5-20251101",
- display_name: "Claude Opus 4.5 Thinking",
- max_tokens: 200_000,
- max_output_tokens: 8_192,
- supports_images: true,
- supports_thinking: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "claude-sonnet-4-5-20250929",
- display_name: "Claude Sonnet 4.5",
- max_tokens: 200_000,
- max_output_tokens: 8_192,
- supports_images: true,
- supports_thinking: false,
- is_default: true,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "claude-sonnet-4-5-20250929",
- display_name: "Claude Sonnet 4.5 Thinking",
- max_tokens: 200_000,
- max_output_tokens: 8_192,
- supports_images: true,
- supports_thinking: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "claude-sonnet-4-20250514",
- display_name: "Claude Sonnet 4",
- max_tokens: 200_000,
- max_output_tokens: 8_192,
- supports_images: true,
- supports_thinking: false,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "claude-sonnet-4-20250514",
- display_name: "Claude Sonnet 4 Thinking",
- max_tokens: 200_000,
- max_output_tokens: 8_192,
- supports_images: true,
- supports_thinking: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "claude-haiku-4-5-20251001",
- display_name: "Claude Haiku 4.5",
- max_tokens: 200_000,
- max_output_tokens: 64_000,
- supports_images: true,
- supports_thinking: false,
- is_default: false,
- is_default_fast: true,
- },
- ModelDefinition {
- real_id: "claude-haiku-4-5-20251001",
- display_name: "Claude Haiku 4.5 Thinking",
- max_tokens: 200_000,
- max_output_tokens: 64_000,
- supports_images: true,
- supports_thinking: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "claude-3-5-sonnet-latest",
- display_name: "Claude 3.5 Sonnet",
- max_tokens: 200_000,
- max_output_tokens: 8_192,
- supports_images: true,
- supports_thinking: false,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "claude-3-5-haiku-latest",
- display_name: "Claude 3.5 Haiku",
- max_tokens: 200_000,
- max_output_tokens: 8_192,
- supports_images: true,
- supports_thinking: false,
- is_default: false,
- is_default_fast: false,
- },
-];
-
-fn get_model_definition(display_name: &str) -> Option<&'static ModelDefinition> {
- MODELS.iter().find(|m| m.display_name == display_name)
-}
-
-// Anthropic API Request Types
-
-#[derive(Serialize)]
-struct AnthropicRequest {
- model: String,
- max_tokens: u64,
- messages: Vec<AnthropicMessage>,
- #[serde(skip_serializing_if = "Option::is_none")]
- system: Option<String>,
- #[serde(skip_serializing_if = "Option::is_none")]
- thinking: Option<AnthropicThinking>,
- #[serde(skip_serializing_if = "Vec::is_empty")]
- tools: Vec<AnthropicTool>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tool_choice: Option<AnthropicToolChoice>,
- #[serde(skip_serializing_if = "Vec::is_empty")]
- stop_sequences: Vec<String>,
- #[serde(skip_serializing_if = "Option::is_none")]
- temperature: Option<f32>,
- stream: bool,
-}
-
-#[derive(Serialize)]
-struct AnthropicThinking {
- #[serde(rename = "type")]
- thinking_type: String,
- #[serde(skip_serializing_if = "Option::is_none")]
- budget_tokens: Option<u32>,
-}
-
-#[derive(Serialize)]
-struct AnthropicMessage {
- role: String,
- content: Vec<AnthropicContent>,
-}
-
-#[derive(Serialize, Clone)]
-#[serde(tag = "type")]
-enum AnthropicContent {
- #[serde(rename = "text")]
- Text { text: String },
- #[serde(rename = "thinking")]
- Thinking { thinking: String, signature: String },
- #[serde(rename = "redacted_thinking")]
- RedactedThinking { data: String },
- #[serde(rename = "image")]
- Image { source: AnthropicImageSource },
- #[serde(rename = "tool_use")]
- ToolUse {
- id: String,
- name: String,
- input: serde_json::Value,
- },
- #[serde(rename = "tool_result")]
- ToolResult {
- tool_use_id: String,
- is_error: bool,
- content: String,
- },
-}
-
-#[derive(Serialize, Clone)]
-struct AnthropicImageSource {
- #[serde(rename = "type")]
- source_type: String,
- media_type: String,
- data: String,
-}
-
-#[derive(Serialize)]
-struct AnthropicTool {
- name: String,
- description: String,
- input_schema: serde_json::Value,
-}
-
-#[derive(Serialize)]
-#[serde(tag = "type", rename_all = "lowercase")]
-enum AnthropicToolChoice {
- Auto,
- Any,
- None,
-}
-
-// Anthropic API Response Types
-
-#[derive(Deserialize, Debug)]
-#[serde(tag = "type")]
-#[allow(dead_code)]
-enum AnthropicEvent {
- #[serde(rename = "message_start")]
- MessageStart { message: AnthropicMessageResponse },
- #[serde(rename = "content_block_start")]
- ContentBlockStart {
- index: usize,
- content_block: AnthropicContentBlock,
- },
- #[serde(rename = "content_block_delta")]
- ContentBlockDelta { index: usize, delta: AnthropicDelta },
- #[serde(rename = "content_block_stop")]
- ContentBlockStop { index: usize },
- #[serde(rename = "message_delta")]
- MessageDelta {
- delta: AnthropicMessageDelta,
- usage: AnthropicUsage,
- },
- #[serde(rename = "message_stop")]
- MessageStop,
- #[serde(rename = "ping")]
- Ping,
- #[serde(rename = "error")]
- Error { error: AnthropicApiError },
-}
-
-#[derive(Deserialize, Debug)]
-struct AnthropicMessageResponse {
- #[allow(dead_code)]
- id: String,
- #[allow(dead_code)]
- role: String,
- #[serde(default)]
- usage: AnthropicUsage,
-}
-
-#[derive(Deserialize, Debug)]
-#[serde(tag = "type")]
-enum AnthropicContentBlock {
- #[serde(rename = "text")]
- Text { text: String },
- #[serde(rename = "thinking")]
- Thinking { thinking: String },
- #[serde(rename = "redacted_thinking")]
- RedactedThinking { data: String },
- #[serde(rename = "tool_use")]
- ToolUse { id: String, name: String },
-}
-
-#[derive(Deserialize, Debug)]
-#[serde(tag = "type")]
-enum AnthropicDelta {
- #[serde(rename = "text_delta")]
- TextDelta { text: String },
- #[serde(rename = "thinking_delta")]
- ThinkingDelta { thinking: String },
- #[serde(rename = "signature_delta")]
- SignatureDelta { signature: String },
- #[serde(rename = "input_json_delta")]
- InputJsonDelta { partial_json: String },
-}
-
-#[derive(Deserialize, Debug)]
-struct AnthropicMessageDelta {
- stop_reason: Option<String>,
-}
-
-#[derive(Deserialize, Debug, Default)]
-struct AnthropicUsage {
- #[serde(default)]
- input_tokens: Option<u64>,
- #[serde(default)]
- output_tokens: Option<u64>,
- #[serde(default)]
- cache_creation_input_tokens: Option<u64>,
- #[serde(default)]
- cache_read_input_tokens: Option<u64>,
-}
-
-#[derive(Deserialize, Debug)]
-struct AnthropicApiError {
- #[serde(rename = "type")]
- #[allow(dead_code)]
- error_type: String,
- message: String,
-}
-
-fn convert_request(
- model_id: &str,
- request: &LlmCompletionRequest,
-) -> Result<AnthropicRequest, String> {
- let model_def =
- get_model_definition(model_id).ok_or_else(|| format!("Unknown model: {}", model_id))?;
-
- let mut messages: Vec<AnthropicMessage> = Vec::new();
- let mut system_message = String::new();
-
- for msg in &request.messages {
- match msg.role {
- LlmMessageRole::System => {
- for content in &msg.content {
- if let LlmMessageContent::Text(text) = content {
- if !system_message.is_empty() {
- system_message.push('\n');
- }
- system_message.push_str(text);
- }
- }
- }
- LlmMessageRole::User => {
- let mut contents: Vec<AnthropicContent> = Vec::new();
-
- for content in &msg.content {
- match content {
- LlmMessageContent::Text(text) => {
- if !text.is_empty() {
- contents.push(AnthropicContent::Text { text: text.clone() });
- }
- }
- LlmMessageContent::Image(img) => {
- contents.push(AnthropicContent::Image {
- source: AnthropicImageSource {
- source_type: "base64".to_string(),
- media_type: "image/png".to_string(),
- data: img.source.clone(),
- },
- });
- }
- LlmMessageContent::ToolResult(result) => {
- let content_text = match &result.content {
- LlmToolResultContent::Text(t) => t.clone(),
- LlmToolResultContent::Image(_) => "[Image]".to_string(),
- };
- contents.push(AnthropicContent::ToolResult {
- tool_use_id: result.tool_use_id.clone(),
- is_error: result.is_error,
- content: content_text,
- });
- }
- _ => {}
- }
- }
-
- if !contents.is_empty() {
- messages.push(AnthropicMessage {
- role: "user".to_string(),
- content: contents,
- });
- }
- }
- LlmMessageRole::Assistant => {
- let mut contents: Vec<AnthropicContent> = Vec::new();
-
- for content in &msg.content {
- match content {
- LlmMessageContent::Text(text) => {
- if !text.is_empty() {
- contents.push(AnthropicContent::Text { text: text.clone() });
- }
- }
- LlmMessageContent::ToolUse(tool_use) => {
- let input: serde_json::Value =
- serde_json::from_str(&tool_use.input).unwrap_or_default();
- contents.push(AnthropicContent::ToolUse {
- id: tool_use.id.clone(),
- name: tool_use.name.clone(),
- input,
- });
- }
- LlmMessageContent::Thinking(thinking) => {
- if !thinking.text.is_empty() {
- contents.push(AnthropicContent::Thinking {
- thinking: thinking.text.clone(),
- signature: thinking.signature.clone().unwrap_or_default(),
- });
- }
- }
- LlmMessageContent::RedactedThinking(data) => {
- if !data.is_empty() {
- contents.push(AnthropicContent::RedactedThinking {
- data: data.clone(),
- });
- }
- }
- _ => {}
- }
- }
-
- if !contents.is_empty() {
- messages.push(AnthropicMessage {
- role: "assistant".to_string(),
- content: contents,
- });
- }
- }
- }
- }
-
- let tools: Vec<AnthropicTool> = request
- .tools
- .iter()
- .map(|t| AnthropicTool {
- name: t.name.clone(),
- description: t.description.clone(),
- input_schema: serde_json::from_str(&t.input_schema)
- .unwrap_or(serde_json::Value::Object(Default::default())),
- })
- .collect();
-
- let tool_choice = request.tool_choice.as_ref().map(|tc| match tc {
- LlmToolChoice::Auto => AnthropicToolChoice::Auto,
- LlmToolChoice::Any => AnthropicToolChoice::Any,
- LlmToolChoice::None => AnthropicToolChoice::None,
- });
-
- let thinking = if model_def.supports_thinking && request.thinking_allowed {
- Some(AnthropicThinking {
- thinking_type: "enabled".to_string(),
- budget_tokens: Some(4096),
- })
- } else {
- None
- };
-
- Ok(AnthropicRequest {
- model: model_def.real_id.to_string(),
- max_tokens: model_def.max_output_tokens,
- messages,
- system: if system_message.is_empty() {
- None
- } else {
- Some(system_message)
- },
- thinking,
- tools,
- tool_choice,
- stop_sequences: request.stop_sequences.clone(),
- temperature: request.temperature,
- stream: true,
- })
-}
-
-fn parse_sse_line(line: &str) -> Option<AnthropicEvent> {
- let data = line.strip_prefix("data: ")?;
- serde_json::from_str(data).ok()
-}
-
-impl zed::Extension for AnthropicProvider {
- fn new() -> Self {
- Self {
- streams: Mutex::new(HashMap::new()),
- next_stream_id: Mutex::new(0),
- }
- }
-
- fn llm_providers(&self) -> Vec<LlmProviderInfo> {
- vec![LlmProviderInfo {
- id: "anthropic".into(),
- name: "Anthropic".into(),
- icon: Some("icons/anthropic.svg".into()),
- }]
- }
-
- fn llm_provider_models(&self, _provider_id: &str) -> Result<Vec<LlmModelInfo>, String> {
- Ok(MODELS
- .iter()
- .map(|m| LlmModelInfo {
- id: m.display_name.to_string(),
- name: m.display_name.to_string(),
- max_token_count: m.max_tokens,
- max_output_tokens: Some(m.max_output_tokens),
- capabilities: LlmModelCapabilities {
- supports_images: m.supports_images,
- supports_tools: true,
- supports_tool_choice_auto: true,
- supports_tool_choice_any: true,
- supports_tool_choice_none: true,
- supports_thinking: m.supports_thinking,
- tool_input_format: LlmToolInputFormat::JsonSchema,
- },
- is_default: m.is_default,
- is_default_fast: m.is_default_fast,
- })
- .collect())
- }
-
- fn llm_provider_is_authenticated(&self, _provider_id: &str) -> bool {
- llm_get_credential("anthropic").is_some()
- }
-
- fn llm_provider_settings_markdown(&self, _provider_id: &str) -> Option<String> {
- Some(
- r#"# Anthropic Setup
-
-Welcome to **Anthropic**! This extension provides access to Claude models.
-
-## Configuration
-
-Enter your Anthropic API key below. You can get your API key at [console.anthropic.com](https://console.anthropic.com/).
-
-## Available Models
-
-| Display Name | Real Model | Context | Output |
-|--------------|------------|---------|--------|
-| Claude Opus 4.5 | claude-opus-4-5 | 200K | 8K |
-| Claude Opus 4.5 Thinking | claude-opus-4-5 | 200K | 8K |
-| Claude Sonnet 4.5 | claude-sonnet-4-5 | 200K | 8K |
-| Claude Sonnet 4.5 Thinking | claude-sonnet-4-5 | 200K | 8K |
-| Claude Sonnet 4 | claude-sonnet-4 | 200K | 8K |
-| Claude Sonnet 4 Thinking | claude-sonnet-4 | 200K | 8K |
-| Claude Haiku 4.5 | claude-haiku-4-5 | 200K | 64K |
-| Claude Haiku 4.5 Thinking | claude-haiku-4-5 | 200K | 64K |
-| Claude 3.5 Sonnet | claude-3-5-sonnet | 200K | 8K |
-| Claude 3.5 Haiku | claude-3-5-haiku | 200K | 8K |
-
-## Features
-
-- ✅ Full streaming support
-- ✅ Tool/function calling
-- ✅ Vision (image inputs)
-- ✅ Extended thinking support
-- ✅ All Claude models
-
-## Pricing
-
-Uses your Anthropic API credits. See [Anthropic pricing](https://www.anthropic.com/pricing) for details.
-"#
- .to_string(),
- )
- }
-
- fn llm_provider_authenticate(&mut self, _provider_id: &str) -> Result<(), String> {
- let provided = llm_request_credential(
- "anthropic",
- LlmCredentialType::ApiKey,
- "Anthropic API Key",
- "sk-ant-...",
- )?;
- if provided {
- Ok(())
- } else {
- Err("Authentication cancelled".to_string())
- }
- }
-
- fn llm_provider_reset_credentials(&mut self, _provider_id: &str) -> Result<(), String> {
- llm_delete_credential("anthropic")
- }
-
- fn llm_stream_completion_start(
- &mut self,
- _provider_id: &str,
- model_id: &str,
- request: &LlmCompletionRequest,
- ) -> Result<String, String> {
- let api_key = llm_get_credential("anthropic").ok_or_else(|| {
- "No API key configured. Please add your Anthropic API key in settings.".to_string()
- })?;
-
- let anthropic_request = convert_request(model_id, request)?;
-
- let body = serde_json::to_vec(&anthropic_request)
- .map_err(|e| format!("Failed to serialize request: {}", e))?;
-
- let http_request = HttpRequest {
- method: HttpMethod::Post,
- url: "https://api.anthropic.com/v1/messages".to_string(),
- headers: vec![
- ("Content-Type".to_string(), "application/json".to_string()),
- ("x-api-key".to_string(), api_key),
- ("anthropic-version".to_string(), "2023-06-01".to_string()),
- ],
- body: Some(body),
- redirect_policy: RedirectPolicy::FollowAll,
- };
-
- let response_stream = http_request
- .fetch_stream()
- .map_err(|e| format!("HTTP request failed: {}", e))?;
-
- let stream_id = {
- let mut id_counter = self.next_stream_id.lock().unwrap();
- let id = format!("anthropic-stream-{}", *id_counter);
- *id_counter += 1;
- id
- };
-
- self.streams.lock().unwrap().insert(
- stream_id.clone(),
- StreamState {
- response_stream: Some(response_stream),
- buffer: String::new(),
- started: false,
- current_tool_use: None,
- stop_reason: None,
- pending_signature: None,
- },
- );
-
- Ok(stream_id)
- }
-
- fn llm_stream_completion_next(
- &mut self,
- stream_id: &str,
- ) -> Result<Option<LlmCompletionEvent>, String> {
- let mut streams = self.streams.lock().unwrap();
- let state = streams
- .get_mut(stream_id)
- .ok_or_else(|| format!("Unknown stream: {}", stream_id))?;
-
- if !state.started {
- state.started = true;
- return Ok(Some(LlmCompletionEvent::Started));
- }
-
- let response_stream = state
- .response_stream
- .as_mut()
- .ok_or_else(|| "Stream already closed".to_string())?;
-
- loop {
- 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 line.trim().is_empty() || line.starts_with("event:") {
- continue;
- }
-
- if let Some(event) = parse_sse_line(&line) {
- match event {
- AnthropicEvent::MessageStart { message } => {
- if let (Some(input), Some(output)) =
- (message.usage.input_tokens, message.usage.output_tokens)
- {
- return Ok(Some(LlmCompletionEvent::Usage(LlmTokenUsage {
- input_tokens: input,
- output_tokens: output,
- cache_creation_input_tokens: message
- .usage
- .cache_creation_input_tokens,
- cache_read_input_tokens: message.usage.cache_read_input_tokens,
- })));
- }
- }
- AnthropicEvent::ContentBlockStart { content_block, .. } => {
- match content_block {
- AnthropicContentBlock::Text { text } => {
- if !text.is_empty() {
- return Ok(Some(LlmCompletionEvent::Text(text)));
- }
- }
- AnthropicContentBlock::Thinking { thinking } => {
- return Ok(Some(LlmCompletionEvent::Thinking(
- LlmThinkingContent {
- text: thinking,
- signature: None,
- },
- )));
- }
- AnthropicContentBlock::RedactedThinking { data } => {
- return Ok(Some(LlmCompletionEvent::RedactedThinking(data)));
- }
- AnthropicContentBlock::ToolUse { id, name } => {
- state.current_tool_use = Some(ToolUseState {
- id,
- name,
- input_json: String::new(),
- });
- }
- }
- }
- AnthropicEvent::ContentBlockDelta { delta, .. } => match delta {
- AnthropicDelta::TextDelta { text } => {
- if !text.is_empty() {
- return Ok(Some(LlmCompletionEvent::Text(text)));
- }
- }
- AnthropicDelta::ThinkingDelta { thinking } => {
- return Ok(Some(LlmCompletionEvent::Thinking(
- LlmThinkingContent {
- text: thinking,
- signature: None,
- },
- )));
- }
- AnthropicDelta::SignatureDelta { signature } => {
- state.pending_signature = Some(signature.clone());
- return Ok(Some(LlmCompletionEvent::Thinking(
- LlmThinkingContent {
- text: String::new(),
- signature: Some(signature),
- },
- )));
- }
- AnthropicDelta::InputJsonDelta { partial_json } => {
- if let Some(ref mut tool_use) = state.current_tool_use {
- tool_use.input_json.push_str(&partial_json);
- }
- }
- },
- AnthropicEvent::ContentBlockStop { .. } => {
- if let Some(tool_use) = state.current_tool_use.take() {
- return Ok(Some(LlmCompletionEvent::ToolUse(LlmToolUse {
- id: tool_use.id,
- name: tool_use.name,
- input: tool_use.input_json,
- thought_signature: state.pending_signature.take(),
- })));
- }
- }
- AnthropicEvent::MessageDelta { delta, usage } => {
- if let Some(reason) = delta.stop_reason {
- state.stop_reason = Some(match reason.as_str() {
- "end_turn" => LlmStopReason::EndTurn,
- "max_tokens" => LlmStopReason::MaxTokens,
- "tool_use" => LlmStopReason::ToolUse,
- _ => LlmStopReason::EndTurn,
- });
- }
- if let Some(output) = usage.output_tokens {
- return Ok(Some(LlmCompletionEvent::Usage(LlmTokenUsage {
- input_tokens: usage.input_tokens.unwrap_or(0),
- output_tokens: output,
- cache_creation_input_tokens: usage.cache_creation_input_tokens,
- cache_read_input_tokens: usage.cache_read_input_tokens,
- })));
- }
- }
- AnthropicEvent::MessageStop => {
- if let Some(stop_reason) = state.stop_reason.take() {
- return Ok(Some(LlmCompletionEvent::Stop(stop_reason)));
- }
- return Ok(Some(LlmCompletionEvent::Stop(LlmStopReason::EndTurn)));
- }
- AnthropicEvent::Ping => {}
- AnthropicEvent::Error { error } => {
- return Err(format!("API error: {}", error.message));
- }
- }
- }
-
- continue;
- }
-
- match response_stream.next_chunk() {
- Ok(Some(chunk)) => {
- let text = String::from_utf8_lossy(&chunk);
- state.buffer.push_str(&text);
- }
- Ok(None) => {
- if let Some(stop_reason) = state.stop_reason.take() {
- return Ok(Some(LlmCompletionEvent::Stop(stop_reason)));
- }
- return Ok(None);
- }
- Err(e) => {
- return Err(format!("Stream error: {}", e));
- }
- }
- }
- }
-
- fn llm_stream_completion_close(&mut self, stream_id: &str) {
- self.streams.lock().unwrap().remove(stream_id);
- }
-}
-
-zed::register_extension!(AnthropicProvider);
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-edition = "2021"
-publish = false
-license = "Apache-2.0"
-
-[workspace]
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-[lib]
-path = "src/copilot_chat.rs"
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-
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-zed_extension_api = { path = "../../crates/extension_api" }
-serde = { version = "1.0", features = ["derive"] }
-serde_json = "1.0"
@@ -1,13 +0,0 @@
-id = "copilot_chat"
-name = "Copilot Chat"
-description = "GitHub Copilot Chat LLM provider for Zed."
-version = "0.1.0"
-schema_version = 1
-authors = ["Zed Team"]
-repository = "https://github.com/zed-industries/zed"
-
-[language_model_providers.copilot_chat]
-name = "Copilot Chat"
-
-[language_model_providers.copilot_chat.auth]
-env_var = "GH_COPILOT_TOKEN"
@@ -1,9 +0,0 @@
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-use std::collections::HashMap;
-use std::sync::Mutex;
-
-use serde::{Deserialize, Serialize};
-use zed_extension_api::http_client::{HttpMethod, HttpRequest, HttpResponseStream, RedirectPolicy};
-use zed_extension_api::{self as zed, *};
-
-struct CopilotChatProvider {
- streams: Mutex<HashMap<String, StreamState>>,
- next_stream_id: Mutex<u64>,
-}
-
-struct StreamState {
- response_stream: Option<HttpResponseStream>,
- buffer: String,
- started: bool,
- tool_calls: HashMap<usize, AccumulatedToolCall>,
- tool_calls_emitted: bool,
-}
-
-#[derive(Clone, Default)]
-struct AccumulatedToolCall {
- id: String,
- name: String,
- arguments: String,
-}
-
-struct ModelDefinition {
- id: &'static str,
- display_name: &'static str,
- max_tokens: u64,
- max_output_tokens: Option<u64>,
- supports_images: bool,
- is_default: bool,
- is_default_fast: bool,
-}
-
-const MODELS: &[ModelDefinition] = &[
- ModelDefinition {
- id: "gpt-4o",
- display_name: "GPT-4o",
- max_tokens: 128_000,
- max_output_tokens: Some(16_384),
- supports_images: true,
- is_default: true,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "gpt-4o-mini",
- display_name: "GPT-4o Mini",
- max_tokens: 128_000,
- max_output_tokens: Some(16_384),
- supports_images: true,
- is_default: false,
- is_default_fast: true,
- },
- ModelDefinition {
- id: "gpt-4.1",
- display_name: "GPT-4.1",
- max_tokens: 1_000_000,
- max_output_tokens: Some(32_768),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "o1",
- display_name: "o1",
- max_tokens: 200_000,
- max_output_tokens: Some(100_000),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "o3-mini",
- display_name: "o3-mini",
- max_tokens: 200_000,
- max_output_tokens: Some(100_000),
- supports_images: false,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "claude-3.5-sonnet",
- display_name: "Claude 3.5 Sonnet",
- max_tokens: 200_000,
- max_output_tokens: Some(8_192),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "claude-3.7-sonnet",
- display_name: "Claude 3.7 Sonnet",
- max_tokens: 200_000,
- max_output_tokens: Some(8_192),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "gemini-2.0-flash-001",
- display_name: "Gemini 2.0 Flash",
- max_tokens: 1_000_000,
- max_output_tokens: Some(8_192),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
-];
-
-fn get_model_definition(model_id: &str) -> Option<&'static ModelDefinition> {
- MODELS.iter().find(|m| m.id == model_id)
-}
-
-#[derive(Serialize)]
-struct OpenAiRequest {
- model: String,
- messages: Vec<OpenAiMessage>,
- #[serde(skip_serializing_if = "Option::is_none")]
- max_tokens: Option<u64>,
- #[serde(skip_serializing_if = "Vec::is_empty")]
- tools: Vec<OpenAiTool>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tool_choice: Option<String>,
- #[serde(skip_serializing_if = "Vec::is_empty")]
- stop: Vec<String>,
- #[serde(skip_serializing_if = "Option::is_none")]
- temperature: Option<f32>,
- stream: bool,
- #[serde(skip_serializing_if = "Option::is_none")]
- stream_options: Option<StreamOptions>,
-}
-
-#[derive(Serialize)]
-struct StreamOptions {
- include_usage: bool,
-}
-
-#[derive(Serialize)]
-struct OpenAiMessage {
- role: String,
- #[serde(skip_serializing_if = "Option::is_none")]
- content: Option<OpenAiContent>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tool_calls: Option<Vec<OpenAiToolCall>>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tool_call_id: Option<String>,
-}
-
-#[derive(Serialize, Clone)]
-#[serde(untagged)]
-enum OpenAiContent {
- Text(String),
- Parts(Vec<OpenAiContentPart>),
-}
-
-#[derive(Serialize, Clone)]
-#[serde(tag = "type")]
-enum OpenAiContentPart {
- #[serde(rename = "text")]
- Text { text: String },
- #[serde(rename = "image_url")]
- ImageUrl { image_url: ImageUrl },
-}
-
-#[derive(Serialize, Clone)]
-struct ImageUrl {
- url: String,
-}
-
-#[derive(Serialize, Clone)]
-struct OpenAiToolCall {
- id: String,
- #[serde(rename = "type")]
- call_type: String,
- function: OpenAiFunctionCall,
-}
-
-#[derive(Serialize, Clone)]
-struct OpenAiFunctionCall {
- name: String,
- arguments: String,
-}
-
-#[derive(Serialize)]
-struct OpenAiTool {
- #[serde(rename = "type")]
- tool_type: String,
- function: OpenAiFunctionDef,
-}
-
-#[derive(Serialize)]
-struct OpenAiFunctionDef {
- name: String,
- description: String,
- parameters: serde_json::Value,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenAiStreamResponse {
- choices: Vec<OpenAiStreamChoice>,
- #[serde(default)]
- usage: Option<OpenAiUsage>,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenAiStreamChoice {
- delta: OpenAiDelta,
- finish_reason: Option<String>,
-}
-
-#[derive(Deserialize, Debug, Default)]
-struct OpenAiDelta {
- #[serde(default)]
- content: Option<String>,
- #[serde(default)]
- tool_calls: Option<Vec<OpenAiToolCallDelta>>,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenAiToolCallDelta {
- index: usize,
- #[serde(default)]
- id: Option<String>,
- #[serde(default)]
- function: Option<OpenAiFunctionDelta>,
-}
-
-#[derive(Deserialize, Debug, Default)]
-struct OpenAiFunctionDelta {
- #[serde(default)]
- name: Option<String>,
- #[serde(default)]
- arguments: Option<String>,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenAiUsage {
- prompt_tokens: u64,
- completion_tokens: u64,
-}
-
-fn convert_request(
- model_id: &str,
- request: &LlmCompletionRequest,
-) -> Result<OpenAiRequest, String> {
- let mut messages: Vec<OpenAiMessage> = Vec::new();
-
- for msg in &request.messages {
- match msg.role {
- LlmMessageRole::System => {
- let mut text_content = String::new();
- for content in &msg.content {
- if let LlmMessageContent::Text(text) = content {
- if !text_content.is_empty() {
- text_content.push('\n');
- }
- text_content.push_str(text);
- }
- }
- if !text_content.is_empty() {
- messages.push(OpenAiMessage {
- role: "system".to_string(),
- content: Some(OpenAiContent::Text(text_content)),
- tool_calls: None,
- tool_call_id: None,
- });
- }
- }
- LlmMessageRole::User => {
- let mut parts: Vec<OpenAiContentPart> = Vec::new();
- let mut tool_result_messages: Vec<OpenAiMessage> = Vec::new();
-
- for content in &msg.content {
- match content {
- LlmMessageContent::Text(text) => {
- if !text.is_empty() {
- parts.push(OpenAiContentPart::Text { text: text.clone() });
- }
- }
- LlmMessageContent::Image(img) => {
- let data_url = format!("data:image/png;base64,{}", img.source);
- parts.push(OpenAiContentPart::ImageUrl {
- image_url: ImageUrl { url: data_url },
- });
- }
- LlmMessageContent::ToolResult(result) => {
- let content_text = match &result.content {
- LlmToolResultContent::Text(t) => t.clone(),
- LlmToolResultContent::Image(_) => "[Image]".to_string(),
- };
- tool_result_messages.push(OpenAiMessage {
- role: "tool".to_string(),
- content: Some(OpenAiContent::Text(content_text)),
- tool_calls: None,
- tool_call_id: Some(result.tool_use_id.clone()),
- });
- }
- _ => {}
- }
- }
-
- if !parts.is_empty() {
- let content = if parts.len() == 1 {
- if let OpenAiContentPart::Text { text } = &parts[0] {
- OpenAiContent::Text(text.clone())
- } else {
- OpenAiContent::Parts(parts)
- }
- } else {
- OpenAiContent::Parts(parts)
- };
-
- messages.push(OpenAiMessage {
- role: "user".to_string(),
- content: Some(content),
- tool_calls: None,
- tool_call_id: None,
- });
- }
-
- messages.extend(tool_result_messages);
- }
- LlmMessageRole::Assistant => {
- let mut text_content = String::new();
- let mut tool_calls: Vec<OpenAiToolCall> = Vec::new();
-
- for content in &msg.content {
- match content {
- LlmMessageContent::Text(text) => {
- if !text.is_empty() {
- if !text_content.is_empty() {
- text_content.push('\n');
- }
- text_content.push_str(text);
- }
- }
- LlmMessageContent::ToolUse(tool_use) => {
- tool_calls.push(OpenAiToolCall {
- id: tool_use.id.clone(),
- call_type: "function".to_string(),
- function: OpenAiFunctionCall {
- name: tool_use.name.clone(),
- arguments: tool_use.input.clone(),
- },
- });
- }
- _ => {}
- }
- }
-
- messages.push(OpenAiMessage {
- role: "assistant".to_string(),
- content: if text_content.is_empty() {
- None
- } else {
- Some(OpenAiContent::Text(text_content))
- },
- tool_calls: if tool_calls.is_empty() {
- None
- } else {
- Some(tool_calls)
- },
- tool_call_id: None,
- });
- }
- }
- }
-
- let tools: Vec<OpenAiTool> = request
- .tools
- .iter()
- .map(|t| OpenAiTool {
- tool_type: "function".to_string(),
- function: OpenAiFunctionDef {
- name: t.name.clone(),
- description: t.description.clone(),
- parameters: serde_json::from_str(&t.input_schema)
- .unwrap_or(serde_json::Value::Object(Default::default())),
- },
- })
- .collect();
-
- let tool_choice = request.tool_choice.as_ref().map(|tc| match tc {
- LlmToolChoice::Auto => "auto".to_string(),
- LlmToolChoice::Any => "required".to_string(),
- LlmToolChoice::None => "none".to_string(),
- });
-
- let model_def = get_model_definition(model_id);
- let max_tokens = request
- .max_tokens
- .or(model_def.and_then(|m| m.max_output_tokens));
-
- Ok(OpenAiRequest {
- model: model_id.to_string(),
- messages,
- max_tokens,
- tools,
- tool_choice,
- stop: request.stop_sequences.clone(),
- temperature: request.temperature,
- stream: true,
- stream_options: Some(StreamOptions {
- include_usage: true,
- }),
- })
-}
-
-fn parse_sse_line(line: &str) -> Option<OpenAiStreamResponse> {
- let data = line.strip_prefix("data: ")?;
- if data.trim() == "[DONE]" {
- return None;
- }
- serde_json::from_str(data).ok()
-}
-
-impl zed::Extension for CopilotChatProvider {
- fn new() -> Self {
- Self {
- streams: Mutex::new(HashMap::new()),
- next_stream_id: Mutex::new(0),
- }
- }
-
- fn llm_providers(&self) -> Vec<LlmProviderInfo> {
- vec![LlmProviderInfo {
- id: "copilot_chat".into(),
- name: "Copilot Chat".into(),
- icon: Some("icons/copilot.svg".into()),
- }]
- }
-
- fn llm_provider_models(&self, _provider_id: &str) -> Result<Vec<LlmModelInfo>, String> {
- Ok(MODELS
- .iter()
- .map(|m| LlmModelInfo {
- id: m.id.to_string(),
- name: m.display_name.to_string(),
- max_token_count: m.max_tokens,
- max_output_tokens: m.max_output_tokens,
- capabilities: LlmModelCapabilities {
- supports_images: m.supports_images,
- 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::JsonSchema,
- },
- is_default: m.is_default,
- is_default_fast: m.is_default_fast,
- })
- .collect())
- }
-
- fn llm_provider_is_authenticated(&self, _provider_id: &str) -> bool {
- llm_get_credential("copilot_chat").is_some()
- }
-
- fn llm_provider_settings_markdown(&self, _provider_id: &str) -> Option<String> {
- Some(
- r#"# Copilot Chat Setup
-
-Welcome to **Copilot Chat**! This extension provides access to GitHub Copilot's chat models.
-
-## Configuration
-
-Enter your GitHub Copilot token below. You need an active GitHub Copilot subscription.
-
-To get your token:
-1. Ensure you have a GitHub Copilot subscription
-2. Generate a token from your GitHub Copilot settings
-
-## Available Models
-
-| Model | Context | Output |
-|-------|---------|--------|
-| GPT-4o | 128K | 16K |
-| GPT-4o Mini | 128K | 16K |
-| GPT-4.1 | 1M | 32K |
-| o1 | 200K | 100K |
-| o3-mini | 200K | 100K |
-| Claude 3.5 Sonnet | 200K | 8K |
-| Claude 3.7 Sonnet | 200K | 8K |
-| Gemini 2.0 Flash | 1M | 8K |
-
-## Features
-
-- ✅ Full streaming support
-- ✅ Tool/function calling
-- ✅ Vision (image inputs)
-- ✅ Multiple model providers via Copilot
-
-## Note
-
-This extension requires an active GitHub Copilot subscription.
-"#
- .to_string(),
- )
- }
-
- fn llm_provider_authenticate(&mut self, _provider_id: &str) -> Result<(), String> {
- let provided = llm_request_credential(
- "copilot_chat",
- LlmCredentialType::ApiKey,
- "GitHub Copilot Token",
- "ghu_...",
- )?;
- if provided {
- Ok(())
- } else {
- Err("Authentication cancelled".to_string())
- }
- }
-
- fn llm_provider_reset_credentials(&mut self, _provider_id: &str) -> Result<(), String> {
- llm_delete_credential("copilot_chat")
- }
-
- fn llm_stream_completion_start(
- &mut self,
- _provider_id: &str,
- model_id: &str,
- request: &LlmCompletionRequest,
- ) -> Result<String, String> {
- let api_key = llm_get_credential("copilot_chat").ok_or_else(|| {
- "No token configured. Please add your GitHub Copilot token in settings.".to_string()
- })?;
-
- let openai_request = convert_request(model_id, request)?;
-
- let body = serde_json::to_vec(&openai_request)
- .map_err(|e| format!("Failed to serialize request: {}", e))?;
-
- let http_request = HttpRequest {
- method: HttpMethod::Post,
- url: "https://api.githubcopilot.com/chat/completions".to_string(),
- headers: vec![
- ("Content-Type".to_string(), "application/json".to_string()),
- ("Authorization".to_string(), format!("Bearer {}", api_key)),
- (
- "Copilot-Integration-Id".to_string(),
- "vscode-chat".to_string(),
- ),
- ("Editor-Version".to_string(), "Zed/1.0.0".to_string()),
- ],
- body: Some(body),
- redirect_policy: RedirectPolicy::FollowAll,
- };
-
- let response_stream = http_request
- .fetch_stream()
- .map_err(|e| format!("HTTP request failed: {}", e))?;
-
- let stream_id = {
- let mut id_counter = self.next_stream_id.lock().unwrap();
- let id = format!("copilot-stream-{}", *id_counter);
- *id_counter += 1;
- id
- };
-
- self.streams.lock().unwrap().insert(
- stream_id.clone(),
- StreamState {
- response_stream: Some(response_stream),
- buffer: String::new(),
- started: false,
- tool_calls: HashMap::new(),
- tool_calls_emitted: false,
- },
- );
-
- Ok(stream_id)
- }
-
- fn llm_stream_completion_next(
- &mut self,
- stream_id: &str,
- ) -> Result<Option<LlmCompletionEvent>, String> {
- let mut streams = self.streams.lock().unwrap();
- let state = streams
- .get_mut(stream_id)
- .ok_or_else(|| format!("Unknown stream: {}", stream_id))?;
-
- if !state.started {
- state.started = true;
- return Ok(Some(LlmCompletionEvent::Started));
- }
-
- let response_stream = state
- .response_stream
- .as_mut()
- .ok_or_else(|| "Stream already closed".to_string())?;
-
- loop {
- 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 line.trim().is_empty() {
- continue;
- }
-
- if let Some(response) = parse_sse_line(&line) {
- if let Some(choice) = response.choices.first() {
- if let Some(content) = &choice.delta.content {
- if !content.is_empty() {
- return Ok(Some(LlmCompletionEvent::Text(content.clone())));
- }
- }
-
- if let Some(tool_calls) = &choice.delta.tool_calls {
- for tc in tool_calls {
- let entry = state
- .tool_calls
- .entry(tc.index)
- .or_insert_with(AccumulatedToolCall::default);
-
- if let Some(id) = &tc.id {
- entry.id = id.clone();
- }
- if let Some(func) = &tc.function {
- if let Some(name) = &func.name {
- entry.name = name.clone();
- }
- if let Some(args) = &func.arguments {
- entry.arguments.push_str(args);
- }
- }
- }
- }
-
- if let Some(finish_reason) = &choice.finish_reason {
- if !state.tool_calls.is_empty() && !state.tool_calls_emitted {
- state.tool_calls_emitted = true;
- let mut tool_calls: Vec<_> = state.tool_calls.drain().collect();
- tool_calls.sort_by_key(|(idx, _)| *idx);
-
- if let Some((_, tc)) = tool_calls.into_iter().next() {
- return Ok(Some(LlmCompletionEvent::ToolUse(LlmToolUse {
- id: tc.id,
- name: tc.name,
- input: tc.arguments,
- thought_signature: None,
- })));
- }
- }
-
- let stop_reason = match finish_reason.as_str() {
- "stop" => LlmStopReason::EndTurn,
- "length" => LlmStopReason::MaxTokens,
- "tool_calls" => LlmStopReason::ToolUse,
- "content_filter" => LlmStopReason::Refusal,
- _ => LlmStopReason::EndTurn,
- };
- return Ok(Some(LlmCompletionEvent::Stop(stop_reason)));
- }
- }
-
- if let Some(usage) = response.usage {
- return Ok(Some(LlmCompletionEvent::Usage(LlmTokenUsage {
- input_tokens: usage.prompt_tokens,
- output_tokens: usage.completion_tokens,
- cache_creation_input_tokens: None,
- cache_read_input_tokens: None,
- })));
- }
- }
-
- continue;
- }
-
- match response_stream.next_chunk() {
- Ok(Some(chunk)) => {
- let text = String::from_utf8_lossy(&chunk);
- state.buffer.push_str(&text);
- }
- Ok(None) => {
- return Ok(None);
- }
- Err(e) => {
- return Err(format!("Stream error: {}", e));
- }
- }
- }
- }
-
- fn llm_stream_completion_close(&mut self, stream_id: &str) {
- self.streams.lock().unwrap().remove(stream_id);
- }
-}
-
-zed::register_extension!(CopilotChatProvider);
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- "yoke",
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-dependencies = [
- "proc-macro2",
- "quote",
- "syn",
-]
@@ -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"
@@ -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-ai]
-name = "Google AI"
-
-[language_model_providers.google-ai.auth]
-env_var = "GEMINI_API_KEY"
@@ -1,3 +0,0 @@
-<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
-<path d="M7.44 12.27C7.81333 13.1217 8 14.0317 8 15C8 14.0317 8.18083 13.1217 8.5425 12.27C8.91583 11.4183 9.4175 10.6775 10.0475 10.0475C10.6775 9.4175 11.4183 8.92167 12.27 8.56C13.1217 8.18667 14.0317 8 15 8C14.0317 8 13.1217 7.81917 12.27 7.4575C11.4411 7.1001 10.6871 6.5895 10.0475 5.9525C9.4105 5.31293 8.8999 4.55891 8.5425 3.73C8.18083 2.87833 8 1.96833 8 1C8 1.96833 7.81333 2.87833 7.44 3.73C7.07833 4.58167 6.5825 5.3225 5.9525 5.9525C5.31293 6.5895 4.55891 7.1001 3.73 7.4575C2.87833 7.81917 1.96833 8 1 8C1.96833 8 2.87833 8.18667 3.73 8.56C4.58167 8.92167 5.3225 9.4175 5.9525 10.0475C6.5825 10.6775 7.07833 11.4183 7.44 12.27Z" fill="black"/>
-</svg>
@@ -1,840 +0,0 @@
-use std::collections::HashMap;
-use std::sync::atomic::{AtomicU64, Ordering};
-use std::sync::Mutex;
-
-use serde::{Deserialize, Serialize};
-use zed_extension_api::http_client::{HttpMethod, HttpRequest, HttpResponseStream, RedirectPolicy};
-use zed_extension_api::{self as zed, *};
-
-static TOOL_CALL_COUNTER: AtomicU64 = AtomicU64::new(0);
-
-struct GoogleAiProvider {
- streams: Mutex<HashMap<String, StreamState>>,
- next_stream_id: Mutex<u64>,
-}
-
-struct StreamState {
- response_stream: Option<HttpResponseStream>,
- buffer: String,
- started: bool,
- stop_reason: Option<LlmStopReason>,
- wants_tool_use: bool,
-}
-
-struct ModelDefinition {
- real_id: &'static str,
- display_name: &'static str,
- max_tokens: u64,
- max_output_tokens: Option<u64>,
- supports_images: bool,
- supports_thinking: bool,
- is_default: bool,
- is_default_fast: bool,
-}
-
-const MODELS: &[ModelDefinition] = &[
- ModelDefinition {
- real_id: "gemini-2.5-flash-lite",
- display_name: "Gemini 2.5 Flash-Lite",
- max_tokens: 1_048_576,
- max_output_tokens: Some(65_536),
- supports_images: true,
- supports_thinking: true,
- is_default: false,
- is_default_fast: true,
- },
- ModelDefinition {
- real_id: "gemini-2.5-flash",
- display_name: "Gemini 2.5 Flash",
- max_tokens: 1_048_576,
- max_output_tokens: Some(65_536),
- supports_images: true,
- supports_thinking: true,
- is_default: true,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "gemini-2.5-pro",
- display_name: "Gemini 2.5 Pro",
- max_tokens: 1_048_576,
- max_output_tokens: Some(65_536),
- supports_images: true,
- supports_thinking: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "gemini-3-pro-preview",
- display_name: "Gemini 3 Pro",
- max_tokens: 1_048_576,
- max_output_tokens: Some(65_536),
- supports_images: true,
- supports_thinking: true,
- is_default: false,
- is_default_fast: false,
- },
-];
-
-fn get_real_model_id(display_name: &str) -> Option<&'static str> {
- MODELS
- .iter()
- .find(|m| m.display_name == display_name)
- .map(|m| m.real_id)
-}
-
-fn get_model_supports_thinking(display_name: &str) -> bool {
- MODELS
- .iter()
- .find(|m| m.display_name == display_name)
- .map(|m| m.supports_thinking)
- .unwrap_or(false)
-}
-
-/// Adapts a JSON schema to be compatible with Google's API subset.
-/// Google only supports a specific subset of JSON Schema fields.
-/// See: https://ai.google.dev/api/caching#Schema
-fn adapt_schema_for_google(json: &mut serde_json::Value) {
- adapt_schema_for_google_impl(json, true);
-}
-
-fn adapt_schema_for_google_impl(json: &mut serde_json::Value, is_schema: bool) {
- if let serde_json::Value::Object(obj) = json {
- // Google's Schema only supports these fields:
- // type, format, title, description, nullable, enum, maxItems, minItems,
- // properties, required, minProperties, maxProperties, minLength, maxLength,
- // pattern, example, anyOf, propertyOrdering, default, items, minimum, maximum
- const ALLOWED_KEYS: &[&str] = &[
- "type",
- "format",
- "title",
- "description",
- "nullable",
- "enum",
- "maxItems",
- "minItems",
- "properties",
- "required",
- "minProperties",
- "maxProperties",
- "minLength",
- "maxLength",
- "pattern",
- "example",
- "anyOf",
- "propertyOrdering",
- "default",
- "items",
- "minimum",
- "maximum",
- ];
-
- // Convert oneOf to anyOf before filtering keys
- if let Some(one_of) = obj.remove("oneOf") {
- obj.insert("anyOf".to_string(), one_of);
- }
-
- // If type is an array (e.g., ["string", "null"]), take just the first type
- if let Some(type_field) = obj.get_mut("type") {
- if let serde_json::Value::Array(types) = type_field {
- if let Some(first_type) = types.first().cloned() {
- *type_field = first_type;
- }
- }
- }
-
- // Only filter keys if this is a schema object, not a properties map
- if is_schema {
- obj.retain(|key, _| ALLOWED_KEYS.contains(&key.as_str()));
- }
-
- // Recursively process nested values
- // "properties" contains a map of property names -> schemas
- // "items" and "anyOf" contain schemas directly
- for (key, value) in obj.iter_mut() {
- if key == "properties" {
- // properties is a map of property_name -> schema
- if let serde_json::Value::Object(props) = value {
- for (_, prop_schema) in props.iter_mut() {
- adapt_schema_for_google_impl(prop_schema, true);
- }
- }
- } else if key == "items" {
- // items is a schema
- adapt_schema_for_google_impl(value, true);
- } else if key == "anyOf" {
- // anyOf is an array of schemas
- if let serde_json::Value::Array(arr) = value {
- for item in arr.iter_mut() {
- adapt_schema_for_google_impl(item, true);
- }
- }
- }
- }
- } else if let serde_json::Value::Array(arr) = json {
- for item in arr.iter_mut() {
- adapt_schema_for_google_impl(item, true);
- }
- }
-}
-
-#[derive(Serialize)]
-#[serde(rename_all = "camelCase")]
-struct GoogleRequest {
- contents: Vec<GoogleContent>,
- #[serde(skip_serializing_if = "Option::is_none")]
- system_instruction: Option<GoogleSystemInstruction>,
- #[serde(skip_serializing_if = "Option::is_none")]
- generation_config: Option<GoogleGenerationConfig>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tools: Option<Vec<GoogleTool>>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tool_config: Option<GoogleToolConfig>,
-}
-
-#[derive(Serialize)]
-#[serde(rename_all = "camelCase")]
-struct GoogleSystemInstruction {
- parts: Vec<GooglePart>,
-}
-
-#[derive(Serialize, Deserialize, Debug, Clone)]
-#[serde(rename_all = "camelCase")]
-struct GoogleContent {
- parts: Vec<GooglePart>,
- #[serde(skip_serializing_if = "Option::is_none")]
- role: Option<String>,
-}
-
-#[derive(Serialize, Deserialize, Debug, Clone)]
-#[serde(untagged)]
-enum GooglePart {
- Text(GoogleTextPart),
- InlineData(GoogleInlineDataPart),
- FunctionCall(GoogleFunctionCallPart),
- FunctionResponse(GoogleFunctionResponsePart),
- Thought(GoogleThoughtPart),
-}
-
-#[derive(Serialize, Deserialize, Debug, Clone)]
-#[serde(rename_all = "camelCase")]
-struct GoogleTextPart {
- text: String,
-}
-
-#[derive(Serialize, Deserialize, Debug, Clone)]
-#[serde(rename_all = "camelCase")]
-struct GoogleInlineDataPart {
- inline_data: GoogleBlob,
-}
-
-#[derive(Serialize, Deserialize, Debug, Clone)]
-#[serde(rename_all = "camelCase")]
-struct GoogleBlob {
- mime_type: String,
- data: String,
-}
-
-#[derive(Serialize, Deserialize, Debug, Clone)]
-#[serde(rename_all = "camelCase")]
-struct GoogleFunctionCallPart {
- function_call: GoogleFunctionCall,
- #[serde(skip_serializing_if = "Option::is_none")]
- thought_signature: Option<String>,
-}
-
-#[derive(Serialize, Deserialize, Debug, Clone)]
-#[serde(rename_all = "camelCase")]
-struct GoogleFunctionCall {
- name: String,
- args: serde_json::Value,
-}
-
-#[derive(Serialize, Deserialize, Debug, Clone)]
-#[serde(rename_all = "camelCase")]
-struct GoogleFunctionResponsePart {
- function_response: GoogleFunctionResponse,
-}
-
-#[derive(Serialize, Deserialize, Debug, Clone)]
-#[serde(rename_all = "camelCase")]
-struct GoogleFunctionResponse {
- name: String,
- response: serde_json::Value,
-}
-
-#[derive(Serialize, Deserialize, Debug, Clone)]
-#[serde(rename_all = "camelCase")]
-struct GoogleThoughtPart {
- thought: bool,
- thought_signature: String,
-}
-
-#[derive(Serialize)]
-#[serde(rename_all = "camelCase")]
-struct GoogleGenerationConfig {
- #[serde(skip_serializing_if = "Option::is_none")]
- candidate_count: Option<usize>,
- #[serde(skip_serializing_if = "Option::is_none")]
- stop_sequences: Option<Vec<String>>,
- #[serde(skip_serializing_if = "Option::is_none")]
- max_output_tokens: Option<usize>,
- #[serde(skip_serializing_if = "Option::is_none")]
- temperature: Option<f64>,
- #[serde(skip_serializing_if = "Option::is_none")]
- thinking_config: Option<GoogleThinkingConfig>,
-}
-
-#[derive(Serialize)]
-#[serde(rename_all = "camelCase")]
-struct GoogleThinkingConfig {
- thinking_budget: u32,
-}
-
-#[derive(Serialize)]
-#[serde(rename_all = "camelCase")]
-struct GoogleTool {
- function_declarations: Vec<GoogleFunctionDeclaration>,
-}
-
-#[derive(Serialize)]
-#[serde(rename_all = "camelCase")]
-struct GoogleFunctionDeclaration {
- name: String,
- description: String,
- parameters: serde_json::Value,
-}
-
-#[derive(Serialize)]
-#[serde(rename_all = "camelCase")]
-struct GoogleToolConfig {
- function_calling_config: GoogleFunctionCallingConfig,
-}
-
-#[derive(Serialize)]
-#[serde(rename_all = "camelCase")]
-struct GoogleFunctionCallingConfig {
- mode: String,
- #[serde(skip_serializing_if = "Option::is_none")]
- allowed_function_names: Option<Vec<String>>,
-}
-
-#[derive(Deserialize, Debug)]
-#[serde(rename_all = "camelCase")]
-struct GoogleStreamResponse {
- #[serde(default)]
- candidates: Vec<GoogleCandidate>,
- #[serde(default)]
- usage_metadata: Option<GoogleUsageMetadata>,
-}
-
-#[derive(Deserialize, Debug)]
-#[serde(rename_all = "camelCase")]
-struct GoogleCandidate {
- #[serde(default)]
- content: Option<GoogleContent>,
- #[serde(default)]
- finish_reason: Option<String>,
-}
-
-#[derive(Deserialize, Debug)]
-#[serde(rename_all = "camelCase")]
-struct GoogleUsageMetadata {
- #[serde(default)]
- prompt_token_count: u64,
- #[serde(default)]
- candidates_token_count: u64,
-}
-
-fn convert_request(
- model_id: &str,
- request: &LlmCompletionRequest,
-) -> Result<(GoogleRequest, String), String> {
- let real_model_id =
- get_real_model_id(model_id).ok_or_else(|| format!("Unknown model: {}", model_id))?;
-
- let supports_thinking = get_model_supports_thinking(model_id);
-
- let mut contents: Vec<GoogleContent> = Vec::new();
- let mut system_parts: Vec<GooglePart> = Vec::new();
-
- for msg in &request.messages {
- match msg.role {
- LlmMessageRole::System => {
- for content in &msg.content {
- if let LlmMessageContent::Text(text) = content {
- if !text.is_empty() {
- system_parts
- .push(GooglePart::Text(GoogleTextPart { text: text.clone() }));
- }
- }
- }
- }
- LlmMessageRole::User => {
- let mut parts: Vec<GooglePart> = Vec::new();
-
- for content in &msg.content {
- match content {
- LlmMessageContent::Text(text) => {
- if !text.is_empty() {
- parts.push(GooglePart::Text(GoogleTextPart { text: text.clone() }));
- }
- }
- LlmMessageContent::Image(img) => {
- parts.push(GooglePart::InlineData(GoogleInlineDataPart {
- inline_data: GoogleBlob {
- mime_type: "image/png".to_string(),
- data: img.source.clone(),
- },
- }));
- }
- LlmMessageContent::ToolResult(result) => {
- let response_value = match &result.content {
- LlmToolResultContent::Text(t) => {
- serde_json::json!({ "output": t })
- }
- LlmToolResultContent::Image(_) => {
- serde_json::json!({ "output": "Tool responded with an image" })
- }
- };
- parts.push(GooglePart::FunctionResponse(GoogleFunctionResponsePart {
- function_response: GoogleFunctionResponse {
- name: result.tool_name.clone(),
- response: response_value,
- },
- }));
- }
- _ => {}
- }
- }
-
- if !parts.is_empty() {
- contents.push(GoogleContent {
- parts,
- role: Some("user".to_string()),
- });
- }
- }
- LlmMessageRole::Assistant => {
- let mut parts: Vec<GooglePart> = Vec::new();
-
- for content in &msg.content {
- match content {
- LlmMessageContent::Text(text) => {
- if !text.is_empty() {
- parts.push(GooglePart::Text(GoogleTextPart { text: text.clone() }));
- }
- }
- LlmMessageContent::ToolUse(tool_use) => {
- let thought_signature =
- tool_use.thought_signature.clone().filter(|s| !s.is_empty());
-
- let args: serde_json::Value =
- serde_json::from_str(&tool_use.input).unwrap_or_default();
-
- parts.push(GooglePart::FunctionCall(GoogleFunctionCallPart {
- function_call: GoogleFunctionCall {
- name: tool_use.name.clone(),
- args,
- },
- thought_signature,
- }));
- }
- LlmMessageContent::Thinking(thinking) => {
- if let Some(ref signature) = thinking.signature {
- if !signature.is_empty() {
- parts.push(GooglePart::Thought(GoogleThoughtPart {
- thought: true,
- thought_signature: signature.clone(),
- }));
- }
- }
- }
- _ => {}
- }
- }
-
- if !parts.is_empty() {
- contents.push(GoogleContent {
- parts,
- role: Some("model".to_string()),
- });
- }
- }
- }
- }
-
- let system_instruction = if system_parts.is_empty() {
- None
- } else {
- Some(GoogleSystemInstruction {
- parts: system_parts,
- })
- };
-
- let tools: Option<Vec<GoogleTool>> = if request.tools.is_empty() {
- None
- } else {
- let declarations: Vec<GoogleFunctionDeclaration> = request
- .tools
- .iter()
- .map(|t| {
- let mut parameters: serde_json::Value = serde_json::from_str(&t.input_schema)
- .unwrap_or(serde_json::Value::Object(Default::default()));
- adapt_schema_for_google(&mut parameters);
- GoogleFunctionDeclaration {
- name: t.name.clone(),
- description: t.description.clone(),
- parameters,
- }
- })
- .collect();
- Some(vec![GoogleTool {
- function_declarations: declarations,
- }])
- };
-
- let tool_config = request.tool_choice.as_ref().map(|tc| {
- let mode = match tc {
- LlmToolChoice::Auto => "AUTO",
- LlmToolChoice::Any => "ANY",
- LlmToolChoice::None => "NONE",
- };
- GoogleToolConfig {
- function_calling_config: GoogleFunctionCallingConfig {
- mode: mode.to_string(),
- allowed_function_names: None,
- },
- }
- });
-
- let thinking_config = if supports_thinking && request.thinking_allowed {
- Some(GoogleThinkingConfig {
- thinking_budget: 8192,
- })
- } else {
- None
- };
-
- let generation_config = Some(GoogleGenerationConfig {
- candidate_count: Some(1),
- stop_sequences: if request.stop_sequences.is_empty() {
- None
- } else {
- Some(request.stop_sequences.clone())
- },
- max_output_tokens: None,
- temperature: request.temperature.map(|t| t as f64).or(Some(1.0)),
- thinking_config,
- });
-
- Ok((
- GoogleRequest {
- contents,
- system_instruction,
- generation_config,
- tools,
- tool_config,
- },
- real_model_id.to_string(),
- ))
-}
-
-fn parse_stream_line(line: &str) -> Option<GoogleStreamResponse> {
- let trimmed = line.trim();
- if trimmed.is_empty() || trimmed == "[" || trimmed == "]" || trimmed == "," {
- return None;
- }
-
- let json_str = trimmed.strip_prefix("data: ").unwrap_or(trimmed);
- let json_str = json_str.trim_start_matches(',').trim();
-
- if json_str.is_empty() {
- return None;
- }
-
- serde_json::from_str(json_str).ok()
-}
-
-impl zed::Extension for GoogleAiProvider {
- fn new() -> Self {
- Self {
- streams: Mutex::new(HashMap::new()),
- next_stream_id: Mutex::new(0),
- }
- }
-
- fn llm_providers(&self) -> Vec<LlmProviderInfo> {
- vec![LlmProviderInfo {
- id: "google-ai".into(),
- name: "Google AI".into(),
- icon: Some("icons/google-ai.svg".into()),
- }]
- }
-
- fn llm_provider_models(&self, _provider_id: &str) -> Result<Vec<LlmModelInfo>, String> {
- Ok(MODELS
- .iter()
- .map(|m| LlmModelInfo {
- id: m.display_name.to_string(),
- name: m.display_name.to_string(),
- max_token_count: m.max_tokens,
- max_output_tokens: m.max_output_tokens,
- capabilities: LlmModelCapabilities {
- supports_images: m.supports_images,
- supports_tools: true,
- supports_tool_choice_auto: true,
- supports_tool_choice_any: true,
- supports_tool_choice_none: true,
- supports_thinking: m.supports_thinking,
- tool_input_format: LlmToolInputFormat::JsonSchema,
- },
- is_default: m.is_default,
- is_default_fast: m.is_default_fast,
- })
- .collect())
- }
-
- fn llm_provider_is_authenticated(&self, _provider_id: &str) -> bool {
- llm_get_credential("google-ai").is_some()
- }
-
- fn llm_provider_settings_markdown(&self, _provider_id: &str) -> Option<String> {
- Some(
- r#"# Google AI Setup
-
-Welcome to **Google AI**! This extension provides access to Google Gemini models.
-
-## Configuration
-
-Enter your Google AI API key below. You can get your API key at [aistudio.google.com/apikey](https://aistudio.google.com/apikey).
-
-## Available Models
-
-| Display Name | Real Model | Context | Output |
-|--------------|------------|---------|--------|
-| Gemini 2.5 Flash-Lite | gemini-2.5-flash-lite | 1M | 65K |
-| Gemini 2.5 Flash | gemini-2.5-flash | 1M | 65K |
-| Gemini 2.5 Pro | gemini-2.5-pro | 1M | 65K |
-| Gemini 3 Pro | gemini-3-pro-preview | 1M | 65K |
-
-## Features
-
-- ✅ Full streaming support
-- ✅ Tool/function calling with thought signatures
-- ✅ Vision (image inputs)
-- ✅ Extended thinking support
-- ✅ All Gemini models
-
-## Pricing
-
-Uses your Google AI API credits. See [Google AI pricing](https://ai.google.dev/pricing) for details.
-"#
- .to_string(),
- )
- }
-
- fn llm_provider_authenticate(&mut self, _provider_id: &str) -> Result<(), String> {
- let provided = llm_request_credential(
- "google-ai",
- LlmCredentialType::ApiKey,
- "Google AI API Key",
- "AIza...",
- )?;
- if provided {
- Ok(())
- } else {
- Err("Authentication cancelled".to_string())
- }
- }
-
- fn llm_provider_reset_credentials(&mut self, _provider_id: &str) -> Result<(), String> {
- llm_delete_credential("google-ai")
- }
-
- fn llm_stream_completion_start(
- &mut self,
- _provider_id: &str,
- model_id: &str,
- request: &LlmCompletionRequest,
- ) -> Result<String, String> {
- let api_key = llm_get_credential("google-ai").ok_or_else(|| {
- "No API key configured. Please add your Google AI API key in settings.".to_string()
- })?;
-
- let (google_request, real_model_id) = convert_request(model_id, request)?;
-
- let body = serde_json::to_vec(&google_request)
- .map_err(|e| format!("Failed to serialize request: {}", e))?;
-
- let url = format!(
- "https://generativelanguage.googleapis.com/v1beta/models/{}:streamGenerateContent?alt=sse&key={}",
- real_model_id, api_key
- );
-
- let http_request = HttpRequest {
- method: HttpMethod::Post,
- url,
- headers: vec![("Content-Type".to_string(), "application/json".to_string())],
- body: Some(body),
- redirect_policy: RedirectPolicy::FollowAll,
- };
-
- let response_stream = http_request
- .fetch_stream()
- .map_err(|e| format!("HTTP request failed: {}", e))?;
-
- let stream_id = {
- let mut id_counter = self.next_stream_id.lock().unwrap();
- let id = format!("google-ai-stream-{}", *id_counter);
- *id_counter += 1;
- id
- };
-
- self.streams.lock().unwrap().insert(
- stream_id.clone(),
- StreamState {
- response_stream: Some(response_stream),
- buffer: String::new(),
- started: false,
- stop_reason: None,
- wants_tool_use: false,
- },
- );
-
- Ok(stream_id)
- }
-
- fn llm_stream_completion_next(
- &mut self,
- stream_id: &str,
- ) -> Result<Option<LlmCompletionEvent>, String> {
- let mut streams = self.streams.lock().unwrap();
- let state = streams
- .get_mut(stream_id)
- .ok_or_else(|| format!("Unknown stream: {}", stream_id))?;
-
- if !state.started {
- state.started = true;
- return Ok(Some(LlmCompletionEvent::Started));
- }
-
- let response_stream = state
- .response_stream
- .as_mut()
- .ok_or_else(|| "Stream already closed".to_string())?;
-
- loop {
- 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(response) = parse_stream_line(&line) {
- for candidate in response.candidates {
- if let Some(finish_reason) = &candidate.finish_reason {
- state.stop_reason = Some(match finish_reason.as_str() {
- "STOP" => {
- if state.wants_tool_use {
- LlmStopReason::ToolUse
- } else {
- LlmStopReason::EndTurn
- }
- }
- "MAX_TOKENS" => LlmStopReason::MaxTokens,
- "SAFETY" => LlmStopReason::Refusal,
- _ => LlmStopReason::EndTurn,
- });
- }
-
- if let Some(content) = candidate.content {
- for part in content.parts {
- match part {
- GooglePart::Text(text_part) => {
- if !text_part.text.is_empty() {
- return Ok(Some(LlmCompletionEvent::Text(
- text_part.text,
- )));
- }
- }
- GooglePart::FunctionCall(fc_part) => {
- state.wants_tool_use = true;
- let next_tool_id =
- TOOL_CALL_COUNTER.fetch_add(1, Ordering::SeqCst);
- let id = format!(
- "{}-{}",
- fc_part.function_call.name, next_tool_id
- );
-
- let thought_signature =
- fc_part.thought_signature.filter(|s| !s.is_empty());
-
- return Ok(Some(LlmCompletionEvent::ToolUse(LlmToolUse {
- id,
- name: fc_part.function_call.name,
- input: fc_part.function_call.args.to_string(),
- thought_signature,
- })));
- }
- GooglePart::Thought(thought_part) => {
- return Ok(Some(LlmCompletionEvent::Thinking(
- LlmThinkingContent {
- text: "(Encrypted thought)".to_string(),
- signature: Some(thought_part.thought_signature),
- },
- )));
- }
- _ => {}
- }
- }
- }
- }
-
- if let Some(usage) = response.usage_metadata {
- return Ok(Some(LlmCompletionEvent::Usage(LlmTokenUsage {
- input_tokens: usage.prompt_token_count,
- output_tokens: usage.candidates_token_count,
- cache_creation_input_tokens: None,
- cache_read_input_tokens: None,
- })));
- }
- }
-
- continue;
- }
-
- match response_stream.next_chunk() {
- Ok(Some(chunk)) => {
- let text = String::from_utf8_lossy(&chunk);
- state.buffer.push_str(&text);
- }
- Ok(None) => {
- // Stream ended - check if we have a stop reason
- if let Some(stop_reason) = state.stop_reason.take() {
- return Ok(Some(LlmCompletionEvent::Stop(stop_reason)));
- }
-
- // No stop reason - this is unexpected. Check if buffer contains error info
- let mut error_msg = String::from("Stream ended unexpectedly.");
-
- // Try to parse remaining buffer as potential error response
- if !state.buffer.is_empty() {
- error_msg.push_str(&format!(
- "\nRemaining buffer: {}",
- &state.buffer[..state.buffer.len().min(1000)]
- ));
- }
-
- return Err(error_msg);
- }
- Err(e) => {
- return Err(format!("Stream error: {}", e));
- }
- }
- }
- }
-
- fn llm_stream_completion_close(&mut self, stream_id: &str) {
- self.streams.lock().unwrap().remove(stream_id);
- }
-}
-
-zed::register_extension!(GoogleAiProvider);
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-[package]
-name = "open_router"
-version = "0.1.0"
-edition = "2021"
-publish = false
-license = "Apache-2.0"
-
-[workspace]
-
-[lib]
-path = "src/open_router.rs"
-crate-type = ["cdylib"]
-
-[dependencies]
-zed_extension_api = { path = "../../crates/extension_api" }
-serde = { version = "1.0", features = ["derive"] }
-serde_json = "1.0"
@@ -1,13 +0,0 @@
-id = "open_router"
-name = "OpenRouter"
-description = "OpenRouter LLM provider - access multiple AI models through a unified API."
-version = "0.1.0"
-schema_version = 1
-authors = ["Zed Team"]
-repository = "https://github.com/zed-industries/zed"
-
-[language_model_providers.open_router]
-name = "OpenRouter"
-
-[language_model_providers.open_router.auth]
-env_var = "OPENROUTER_API_KEY"
@@ -1,8 +0,0 @@
-<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
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-<path d="M14.4277 10.7809L10.8085 8.6915V12.8703L14.4277 10.7809Z" fill="black" stroke="black"/>
-</svg>
@@ -1,830 +0,0 @@
-use std::collections::HashMap;
-use std::sync::Mutex;
-
-use serde::{Deserialize, Serialize};
-use zed_extension_api::http_client::{HttpMethod, HttpRequest, HttpResponseStream, RedirectPolicy};
-use zed_extension_api::{self as zed, *};
-
-struct OpenRouterProvider {
- streams: Mutex<HashMap<String, StreamState>>,
- next_stream_id: Mutex<u64>,
-}
-
-struct StreamState {
- response_stream: Option<HttpResponseStream>,
- buffer: String,
- started: bool,
- tool_calls: HashMap<usize, AccumulatedToolCall>,
- tool_calls_emitted: bool,
-}
-
-#[derive(Clone, Default)]
-struct AccumulatedToolCall {
- id: String,
- name: String,
- arguments: String,
-}
-
-struct ModelDefinition {
- id: &'static str,
- display_name: &'static str,
- max_tokens: u64,
- max_output_tokens: Option<u64>,
- supports_images: bool,
- supports_tools: bool,
- is_default: bool,
- is_default_fast: bool,
-}
-
-const MODELS: &[ModelDefinition] = &[
- // Anthropic Models
- ModelDefinition {
- id: "anthropic/claude-sonnet-4",
- display_name: "Claude Sonnet 4",
- max_tokens: 200_000,
- max_output_tokens: Some(8_192),
- supports_images: true,
- supports_tools: true,
- is_default: true,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "anthropic/claude-opus-4",
- display_name: "Claude Opus 4",
- max_tokens: 200_000,
- max_output_tokens: Some(8_192),
- supports_images: true,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "anthropic/claude-haiku-4",
- display_name: "Claude Haiku 4",
- max_tokens: 200_000,
- max_output_tokens: Some(8_192),
- supports_images: true,
- supports_tools: true,
- is_default: false,
- is_default_fast: true,
- },
- ModelDefinition {
- id: "anthropic/claude-3.5-sonnet",
- display_name: "Claude 3.5 Sonnet",
- max_tokens: 200_000,
- max_output_tokens: Some(8_192),
- supports_images: true,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- // OpenAI Models
- ModelDefinition {
- id: "openai/gpt-4o",
- display_name: "GPT-4o",
- max_tokens: 128_000,
- max_output_tokens: Some(16_384),
- supports_images: true,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "openai/gpt-4o-mini",
- display_name: "GPT-4o Mini",
- max_tokens: 128_000,
- max_output_tokens: Some(16_384),
- supports_images: true,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "openai/o1",
- display_name: "o1",
- max_tokens: 200_000,
- max_output_tokens: Some(100_000),
- supports_images: true,
- supports_tools: false,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "openai/o3-mini",
- display_name: "o3-mini",
- max_tokens: 200_000,
- max_output_tokens: Some(100_000),
- supports_images: false,
- supports_tools: false,
- is_default: false,
- is_default_fast: false,
- },
- // Google Models
- ModelDefinition {
- id: "google/gemini-2.0-flash-001",
- display_name: "Gemini 2.0 Flash",
- max_tokens: 1_000_000,
- max_output_tokens: Some(8_192),
- supports_images: true,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "google/gemini-2.5-pro-preview",
- display_name: "Gemini 2.5 Pro",
- max_tokens: 1_000_000,
- max_output_tokens: Some(8_192),
- supports_images: true,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- // Meta Models
- ModelDefinition {
- id: "meta-llama/llama-3.3-70b-instruct",
- display_name: "Llama 3.3 70B",
- max_tokens: 128_000,
- max_output_tokens: Some(4_096),
- supports_images: false,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "meta-llama/llama-4-maverick",
- display_name: "Llama 4 Maverick",
- max_tokens: 128_000,
- max_output_tokens: Some(4_096),
- supports_images: true,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- // Mistral Models
- ModelDefinition {
- id: "mistralai/mistral-large-2411",
- display_name: "Mistral Large",
- max_tokens: 128_000,
- max_output_tokens: Some(4_096),
- supports_images: false,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "mistralai/codestral-latest",
- display_name: "Codestral",
- max_tokens: 32_000,
- max_output_tokens: Some(4_096),
- supports_images: false,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- // DeepSeek Models
- ModelDefinition {
- id: "deepseek/deepseek-chat-v3-0324",
- display_name: "DeepSeek V3",
- max_tokens: 64_000,
- max_output_tokens: Some(8_192),
- supports_images: false,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- id: "deepseek/deepseek-r1",
- display_name: "DeepSeek R1",
- max_tokens: 64_000,
- max_output_tokens: Some(8_192),
- supports_images: false,
- supports_tools: false,
- is_default: false,
- is_default_fast: false,
- },
- // Qwen Models
- ModelDefinition {
- id: "qwen/qwen3-235b-a22b",
- display_name: "Qwen 3 235B",
- max_tokens: 40_000,
- max_output_tokens: Some(8_192),
- supports_images: false,
- supports_tools: true,
- is_default: false,
- is_default_fast: false,
- },
-];
-
-fn get_model_definition(model_id: &str) -> Option<&'static ModelDefinition> {
- MODELS.iter().find(|m| m.id == model_id)
-}
-
-#[derive(Serialize)]
-struct OpenRouterRequest {
- model: String,
- messages: Vec<OpenRouterMessage>,
- #[serde(skip_serializing_if = "Option::is_none")]
- max_tokens: Option<u64>,
- #[serde(skip_serializing_if = "Vec::is_empty")]
- tools: Vec<OpenRouterTool>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tool_choice: Option<String>,
- #[serde(skip_serializing_if = "Vec::is_empty")]
- stop: Vec<String>,
- #[serde(skip_serializing_if = "Option::is_none")]
- temperature: Option<f32>,
- stream: bool,
-}
-
-#[derive(Serialize)]
-struct OpenRouterMessage {
- role: String,
- #[serde(skip_serializing_if = "Option::is_none")]
- content: Option<OpenRouterContent>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tool_calls: Option<Vec<OpenRouterToolCall>>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tool_call_id: Option<String>,
-}
-
-#[derive(Serialize, Clone)]
-#[serde(untagged)]
-enum OpenRouterContent {
- Text(String),
- Parts(Vec<OpenRouterContentPart>),
-}
-
-#[derive(Serialize, Clone)]
-#[serde(tag = "type")]
-enum OpenRouterContentPart {
- #[serde(rename = "text")]
- Text { text: String },
- #[serde(rename = "image_url")]
- ImageUrl { image_url: ImageUrl },
-}
-
-#[derive(Serialize, Clone)]
-struct ImageUrl {
- url: String,
-}
-
-#[derive(Serialize, Clone)]
-struct OpenRouterToolCall {
- id: String,
- #[serde(rename = "type")]
- call_type: String,
- function: OpenRouterFunctionCall,
-}
-
-#[derive(Serialize, Clone)]
-struct OpenRouterFunctionCall {
- name: String,
- arguments: String,
-}
-
-#[derive(Serialize)]
-struct OpenRouterTool {
- #[serde(rename = "type")]
- tool_type: String,
- function: OpenRouterFunctionDef,
-}
-
-#[derive(Serialize)]
-struct OpenRouterFunctionDef {
- name: String,
- description: String,
- parameters: serde_json::Value,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenRouterStreamResponse {
- choices: Vec<OpenRouterStreamChoice>,
- #[serde(default)]
- usage: Option<OpenRouterUsage>,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenRouterStreamChoice {
- delta: OpenRouterDelta,
- finish_reason: Option<String>,
-}
-
-#[derive(Deserialize, Debug, Default)]
-struct OpenRouterDelta {
- #[serde(default)]
- content: Option<String>,
- #[serde(default)]
- tool_calls: Option<Vec<OpenRouterToolCallDelta>>,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenRouterToolCallDelta {
- index: usize,
- #[serde(default)]
- id: Option<String>,
- #[serde(default)]
- function: Option<OpenRouterFunctionDelta>,
-}
-
-#[derive(Deserialize, Debug, Default)]
-struct OpenRouterFunctionDelta {
- #[serde(default)]
- name: Option<String>,
- #[serde(default)]
- arguments: Option<String>,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenRouterUsage {
- prompt_tokens: u64,
- completion_tokens: u64,
-}
-
-fn convert_request(
- model_id: &str,
- request: &LlmCompletionRequest,
-) -> Result<OpenRouterRequest, String> {
- let mut messages: Vec<OpenRouterMessage> = Vec::new();
-
- for msg in &request.messages {
- match msg.role {
- LlmMessageRole::System => {
- let mut text_content = String::new();
- for content in &msg.content {
- if let LlmMessageContent::Text(text) = content {
- if !text_content.is_empty() {
- text_content.push('\n');
- }
- text_content.push_str(text);
- }
- }
- if !text_content.is_empty() {
- messages.push(OpenRouterMessage {
- role: "system".to_string(),
- content: Some(OpenRouterContent::Text(text_content)),
- tool_calls: None,
- tool_call_id: None,
- });
- }
- }
- LlmMessageRole::User => {
- let mut parts: Vec<OpenRouterContentPart> = Vec::new();
- let mut tool_result_messages: Vec<OpenRouterMessage> = Vec::new();
-
- for content in &msg.content {
- match content {
- LlmMessageContent::Text(text) => {
- if !text.is_empty() {
- parts.push(OpenRouterContentPart::Text { text: text.clone() });
- }
- }
- LlmMessageContent::Image(img) => {
- let data_url = format!("data:image/png;base64,{}", img.source);
- parts.push(OpenRouterContentPart::ImageUrl {
- image_url: ImageUrl { url: data_url },
- });
- }
- LlmMessageContent::ToolResult(result) => {
- let content_text = match &result.content {
- LlmToolResultContent::Text(t) => t.clone(),
- LlmToolResultContent::Image(_) => "[Image]".to_string(),
- };
- tool_result_messages.push(OpenRouterMessage {
- role: "tool".to_string(),
- content: Some(OpenRouterContent::Text(content_text)),
- tool_calls: None,
- tool_call_id: Some(result.tool_use_id.clone()),
- });
- }
- _ => {}
- }
- }
-
- if !parts.is_empty() {
- let content = if parts.len() == 1 {
- if let OpenRouterContentPart::Text { text } = &parts[0] {
- OpenRouterContent::Text(text.clone())
- } else {
- OpenRouterContent::Parts(parts)
- }
- } else {
- OpenRouterContent::Parts(parts)
- };
-
- messages.push(OpenRouterMessage {
- role: "user".to_string(),
- content: Some(content),
- tool_calls: None,
- tool_call_id: None,
- });
- }
-
- messages.extend(tool_result_messages);
- }
- LlmMessageRole::Assistant => {
- let mut text_content = String::new();
- let mut tool_calls: Vec<OpenRouterToolCall> = Vec::new();
-
- for content in &msg.content {
- match content {
- LlmMessageContent::Text(text) => {
- if !text.is_empty() {
- if !text_content.is_empty() {
- text_content.push('\n');
- }
- text_content.push_str(text);
- }
- }
- LlmMessageContent::ToolUse(tool_use) => {
- tool_calls.push(OpenRouterToolCall {
- id: tool_use.id.clone(),
- call_type: "function".to_string(),
- function: OpenRouterFunctionCall {
- name: tool_use.name.clone(),
- arguments: tool_use.input.clone(),
- },
- });
- }
- _ => {}
- }
- }
-
- messages.push(OpenRouterMessage {
- role: "assistant".to_string(),
- content: if text_content.is_empty() {
- None
- } else {
- Some(OpenRouterContent::Text(text_content))
- },
- tool_calls: if tool_calls.is_empty() {
- None
- } else {
- Some(tool_calls)
- },
- tool_call_id: None,
- });
- }
- }
- }
-
- let model_def = get_model_definition(model_id);
- let supports_tools = model_def.map(|m| m.supports_tools).unwrap_or(true);
-
- let tools: Vec<OpenRouterTool> = if supports_tools {
- request
- .tools
- .iter()
- .map(|t| OpenRouterTool {
- tool_type: "function".to_string(),
- function: OpenRouterFunctionDef {
- name: t.name.clone(),
- description: t.description.clone(),
- parameters: serde_json::from_str(&t.input_schema)
- .unwrap_or(serde_json::Value::Object(Default::default())),
- },
- })
- .collect()
- } else {
- Vec::new()
- };
-
- let tool_choice = if supports_tools {
- request.tool_choice.as_ref().map(|tc| match tc {
- LlmToolChoice::Auto => "auto".to_string(),
- LlmToolChoice::Any => "required".to_string(),
- LlmToolChoice::None => "none".to_string(),
- })
- } else {
- None
- };
-
- let max_tokens = request
- .max_tokens
- .or(model_def.and_then(|m| m.max_output_tokens));
-
- Ok(OpenRouterRequest {
- model: model_id.to_string(),
- messages,
- max_tokens,
- tools,
- tool_choice,
- stop: request.stop_sequences.clone(),
- temperature: request.temperature,
- stream: true,
- })
-}
-
-fn parse_sse_line(line: &str) -> Option<OpenRouterStreamResponse> {
- let data = line.strip_prefix("data: ")?;
- if data.trim() == "[DONE]" {
- return None;
- }
- serde_json::from_str(data).ok()
-}
-
-impl zed::Extension for OpenRouterProvider {
- fn new() -> Self {
- Self {
- streams: Mutex::new(HashMap::new()),
- next_stream_id: Mutex::new(0),
- }
- }
-
- fn llm_providers(&self) -> Vec<LlmProviderInfo> {
- vec![LlmProviderInfo {
- id: "open_router".into(),
- name: "OpenRouter".into(),
- icon: Some("icons/open-router.svg".into()),
- }]
- }
-
- fn llm_provider_models(&self, _provider_id: &str) -> Result<Vec<LlmModelInfo>, String> {
- Ok(MODELS
- .iter()
- .map(|m| LlmModelInfo {
- id: m.id.to_string(),
- name: m.display_name.to_string(),
- max_token_count: m.max_tokens,
- max_output_tokens: m.max_output_tokens,
- capabilities: LlmModelCapabilities {
- supports_images: m.supports_images,
- supports_tools: m.supports_tools,
- supports_tool_choice_auto: m.supports_tools,
- supports_tool_choice_any: m.supports_tools,
- supports_tool_choice_none: m.supports_tools,
- supports_thinking: false,
- tool_input_format: LlmToolInputFormat::JsonSchema,
- },
- is_default: m.is_default,
- is_default_fast: m.is_default_fast,
- })
- .collect())
- }
-
- fn llm_provider_is_authenticated(&self, _provider_id: &str) -> bool {
- llm_get_credential("open_router").is_some()
- }
-
- fn llm_provider_settings_markdown(&self, _provider_id: &str) -> Option<String> {
- Some(
- r#"# OpenRouter Setup
-
-Welcome to **OpenRouter**! Access multiple AI models through a single API.
-
-## Configuration
-
-Enter your OpenRouter API key below. Get your API key at [openrouter.ai/keys](https://openrouter.ai/keys).
-
-## Available Models
-
-### Anthropic
-| Model | Context | Output |
-|-------|---------|--------|
-| Claude Sonnet 4 | 200K | 8K |
-| Claude Opus 4 | 200K | 8K |
-| Claude Haiku 4 | 200K | 8K |
-| Claude 3.5 Sonnet | 200K | 8K |
-
-### OpenAI
-| Model | Context | Output |
-|-------|---------|--------|
-| GPT-4o | 128K | 16K |
-| GPT-4o Mini | 128K | 16K |
-| o1 | 200K | 100K |
-| o3-mini | 200K | 100K |
-
-### Google
-| Model | Context | Output |
-|-------|---------|--------|
-| Gemini 2.0 Flash | 1M | 8K |
-| Gemini 2.5 Pro | 1M | 8K |
-
-### Meta
-| Model | Context | Output |
-|-------|---------|--------|
-| Llama 3.3 70B | 128K | 4K |
-| Llama 4 Maverick | 128K | 4K |
-
-### Mistral
-| Model | Context | Output |
-|-------|---------|--------|
-| Mistral Large | 128K | 4K |
-| Codestral | 32K | 4K |
-
-### DeepSeek
-| Model | Context | Output |
-|-------|---------|--------|
-| DeepSeek V3 | 64K | 8K |
-| DeepSeek R1 | 64K | 8K |
-
-### Qwen
-| Model | Context | Output |
-|-------|---------|--------|
-| Qwen 3 235B | 40K | 8K |
-
-## Features
-
-- ✅ Full streaming support
-- ✅ Tool/function calling (model dependent)
-- ✅ Vision (model dependent)
-- ✅ Access to 200+ models
-- ✅ Unified billing
-
-## Pricing
-
-Pay-per-use based on model. See [openrouter.ai/models](https://openrouter.ai/models) for pricing.
-"#
- .to_string(),
- )
- }
-
- fn llm_provider_authenticate(&mut self, _provider_id: &str) -> Result<(), String> {
- let provided = llm_request_credential(
- "open_router",
- LlmCredentialType::ApiKey,
- "OpenRouter API Key",
- "sk-or-v1-...",
- )?;
- if provided {
- Ok(())
- } else {
- Err("Authentication cancelled".to_string())
- }
- }
-
- fn llm_provider_reset_credentials(&mut self, _provider_id: &str) -> Result<(), String> {
- llm_delete_credential("open_router")
- }
-
- fn llm_stream_completion_start(
- &mut self,
- _provider_id: &str,
- model_id: &str,
- request: &LlmCompletionRequest,
- ) -> Result<String, String> {
- let api_key = llm_get_credential("open_router").ok_or_else(|| {
- "No API key configured. Please add your OpenRouter API key in settings.".to_string()
- })?;
-
- let openrouter_request = convert_request(model_id, request)?;
-
- let body = serde_json::to_vec(&openrouter_request)
- .map_err(|e| format!("Failed to serialize request: {}", e))?;
-
- let http_request = HttpRequest {
- method: HttpMethod::Post,
- url: "https://openrouter.ai/api/v1/chat/completions".to_string(),
- headers: vec![
- ("Content-Type".to_string(), "application/json".to_string()),
- ("Authorization".to_string(), format!("Bearer {}", api_key)),
- ("HTTP-Referer".to_string(), "https://zed.dev".to_string()),
- ("X-Title".to_string(), "Zed Editor".to_string()),
- ],
- body: Some(body),
- redirect_policy: RedirectPolicy::FollowAll,
- };
-
- let response_stream = http_request
- .fetch_stream()
- .map_err(|e| format!("HTTP request failed: {}", e))?;
-
- let stream_id = {
- let mut id_counter = self.next_stream_id.lock().unwrap();
- let id = format!("openrouter-stream-{}", *id_counter);
- *id_counter += 1;
- id
- };
-
- self.streams.lock().unwrap().insert(
- stream_id.clone(),
- StreamState {
- response_stream: Some(response_stream),
- buffer: String::new(),
- started: false,
- tool_calls: HashMap::new(),
- tool_calls_emitted: false,
- },
- );
-
- Ok(stream_id)
- }
-
- fn llm_stream_completion_next(
- &mut self,
- stream_id: &str,
- ) -> Result<Option<LlmCompletionEvent>, String> {
- let mut streams = self.streams.lock().unwrap();
- let state = streams
- .get_mut(stream_id)
- .ok_or_else(|| format!("Unknown stream: {}", stream_id))?;
-
- if !state.started {
- state.started = true;
- return Ok(Some(LlmCompletionEvent::Started));
- }
-
- let response_stream = state
- .response_stream
- .as_mut()
- .ok_or_else(|| "Stream already closed".to_string())?;
-
- loop {
- 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 line.trim().is_empty() {
- continue;
- }
-
- if let Some(response) = parse_sse_line(&line) {
- if let Some(choice) = response.choices.first() {
- if let Some(content) = &choice.delta.content {
- if !content.is_empty() {
- return Ok(Some(LlmCompletionEvent::Text(content.clone())));
- }
- }
-
- if let Some(tool_calls) = &choice.delta.tool_calls {
- for tc in tool_calls {
- let entry = state
- .tool_calls
- .entry(tc.index)
- .or_insert_with(AccumulatedToolCall::default);
-
- if let Some(id) = &tc.id {
- entry.id = id.clone();
- }
- if let Some(func) = &tc.function {
- if let Some(name) = &func.name {
- entry.name = name.clone();
- }
- if let Some(args) = &func.arguments {
- entry.arguments.push_str(args);
- }
- }
- }
- }
-
- if let Some(finish_reason) = &choice.finish_reason {
- if !state.tool_calls.is_empty() && !state.tool_calls_emitted {
- state.tool_calls_emitted = true;
- let mut tool_calls: Vec<_> = state.tool_calls.drain().collect();
- tool_calls.sort_by_key(|(idx, _)| *idx);
-
- if let Some((_, tc)) = tool_calls.into_iter().next() {
- return Ok(Some(LlmCompletionEvent::ToolUse(LlmToolUse {
- id: tc.id,
- name: tc.name,
- input: tc.arguments,
- thought_signature: None,
- })));
- }
- }
-
- let stop_reason = match finish_reason.as_str() {
- "stop" => LlmStopReason::EndTurn,
- "length" => LlmStopReason::MaxTokens,
- "tool_calls" => LlmStopReason::ToolUse,
- "content_filter" => LlmStopReason::Refusal,
- _ => LlmStopReason::EndTurn,
- };
- return Ok(Some(LlmCompletionEvent::Stop(stop_reason)));
- }
- }
-
- if let Some(usage) = response.usage {
- return Ok(Some(LlmCompletionEvent::Usage(LlmTokenUsage {
- input_tokens: usage.prompt_tokens,
- output_tokens: usage.completion_tokens,
- cache_creation_input_tokens: None,
- cache_read_input_tokens: None,
- })));
- }
- }
-
- continue;
- }
-
- match response_stream.next_chunk() {
- Ok(Some(chunk)) => {
- let text = String::from_utf8_lossy(&chunk);
- state.buffer.push_str(&text);
- }
- Ok(None) => {
- return Ok(None);
- }
- Err(e) => {
- return Err(format!("Stream error: {}", e));
- }
- }
- }
- }
-
- fn llm_stream_completion_close(&mut self, stream_id: &str) {
- self.streams.lock().unwrap().remove(stream_id);
- }
-}
-
-zed::register_extension!(OpenRouterProvider);
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@@ -1,17 +0,0 @@
-[package]
-name = "openai"
-version = "0.1.0"
-edition = "2021"
-publish = false
-license = "Apache-2.0"
-
-[workspace]
-
-[lib]
-path = "src/openai.rs"
-crate-type = ["cdylib"]
-
-[dependencies]
-zed_extension_api = { path = "../../crates/extension_api" }
-serde = { version = "1.0", features = ["derive"] }
-serde_json = "1.0"
@@ -1,13 +0,0 @@
-id = "openai"
-name = "OpenAI"
-description = "OpenAI GPT LLM provider for Zed."
-version = "0.1.0"
-schema_version = 1
-authors = ["Zed Team"]
-repository = "https://github.com/zed-industries/zed"
-
-[language_model_providers.openai]
-name = "OpenAI"
-
-[language_model_providers.openai.auth]
-env_var = "OPENAI_API_KEY"
@@ -1,3 +0,0 @@
-<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
@@ -1,727 +0,0 @@
-use std::collections::HashMap;
-use std::sync::Mutex;
-
-use serde::{Deserialize, Serialize};
-use zed_extension_api::http_client::{HttpMethod, HttpRequest, HttpResponseStream, RedirectPolicy};
-use zed_extension_api::{self as zed, *};
-
-struct OpenAiProvider {
- streams: Mutex<HashMap<String, StreamState>>,
- next_stream_id: Mutex<u64>,
-}
-
-struct StreamState {
- response_stream: Option<HttpResponseStream>,
- buffer: String,
- started: bool,
- tool_calls: HashMap<usize, AccumulatedToolCall>,
- tool_calls_emitted: bool,
-}
-
-#[derive(Clone, Default)]
-struct AccumulatedToolCall {
- id: String,
- name: String,
- arguments: String,
-}
-
-struct ModelDefinition {
- real_id: &'static str,
- display_name: &'static str,
- max_tokens: u64,
- max_output_tokens: Option<u64>,
- supports_images: bool,
- is_default: bool,
- is_default_fast: bool,
-}
-
-const MODELS: &[ModelDefinition] = &[
- ModelDefinition {
- real_id: "gpt-4o",
- display_name: "GPT-4o",
- max_tokens: 128_000,
- max_output_tokens: Some(16_384),
- supports_images: true,
- is_default: true,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "gpt-4o-mini",
- display_name: "GPT-4o-mini",
- max_tokens: 128_000,
- max_output_tokens: Some(16_384),
- supports_images: true,
- is_default: false,
- is_default_fast: true,
- },
- ModelDefinition {
- real_id: "gpt-4.1",
- display_name: "GPT-4.1",
- max_tokens: 1_047_576,
- max_output_tokens: Some(32_768),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "gpt-4.1-mini",
- display_name: "GPT-4.1-mini",
- max_tokens: 1_047_576,
- max_output_tokens: Some(32_768),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "gpt-4.1-nano",
- display_name: "GPT-4.1-nano",
- max_tokens: 1_047_576,
- max_output_tokens: Some(32_768),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "gpt-5",
- display_name: "GPT-5",
- max_tokens: 272_000,
- max_output_tokens: Some(32_768),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "gpt-5-mini",
- display_name: "GPT-5-mini",
- max_tokens: 272_000,
- max_output_tokens: Some(32_768),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "o1",
- display_name: "o1",
- max_tokens: 200_000,
- max_output_tokens: Some(100_000),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "o3",
- display_name: "o3",
- max_tokens: 200_000,
- max_output_tokens: Some(100_000),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "o3-mini",
- display_name: "o3-mini",
- max_tokens: 200_000,
- max_output_tokens: Some(100_000),
- supports_images: false,
- is_default: false,
- is_default_fast: false,
- },
- ModelDefinition {
- real_id: "o4-mini",
- display_name: "o4-mini",
- max_tokens: 200_000,
- max_output_tokens: Some(100_000),
- supports_images: true,
- is_default: false,
- is_default_fast: false,
- },
-];
-
-fn get_real_model_id(display_name: &str) -> Option<&'static str> {
- MODELS
- .iter()
- .find(|m| m.display_name == display_name)
- .map(|m| m.real_id)
-}
-
-#[derive(Serialize)]
-struct OpenAiRequest {
- model: String,
- messages: Vec<OpenAiMessage>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tools: Option<Vec<OpenAiTool>>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tool_choice: Option<String>,
- #[serde(skip_serializing_if = "Option::is_none")]
- temperature: Option<f32>,
- #[serde(skip_serializing_if = "Option::is_none")]
- max_tokens: Option<u64>,
- #[serde(skip_serializing_if = "Vec::is_empty")]
- stop: Vec<String>,
- stream: bool,
- stream_options: Option<StreamOptions>,
-}
-
-#[derive(Serialize)]
-struct StreamOptions {
- include_usage: bool,
-}
-
-#[derive(Serialize)]
-#[serde(tag = "role")]
-enum OpenAiMessage {
- #[serde(rename = "system")]
- System { content: String },
- #[serde(rename = "user")]
- User { content: Vec<OpenAiContentPart> },
- #[serde(rename = "assistant")]
- Assistant {
- #[serde(skip_serializing_if = "Option::is_none")]
- content: Option<String>,
- #[serde(skip_serializing_if = "Option::is_none")]
- tool_calls: Option<Vec<OpenAiToolCall>>,
- },
- #[serde(rename = "tool")]
- Tool {
- tool_call_id: String,
- content: String,
- },
-}
-
-#[derive(Serialize)]
-#[serde(tag = "type")]
-enum OpenAiContentPart {
- #[serde(rename = "text")]
- Text { text: String },
- #[serde(rename = "image_url")]
- ImageUrl { image_url: ImageUrl },
-}
-
-#[derive(Serialize)]
-struct ImageUrl {
- url: String,
-}
-
-#[derive(Serialize, Deserialize, Clone)]
-struct OpenAiToolCall {
- id: String,
- #[serde(rename = "type")]
- call_type: String,
- function: OpenAiFunctionCall,
-}
-
-#[derive(Serialize, Deserialize, Clone)]
-struct OpenAiFunctionCall {
- name: String,
- arguments: String,
-}
-
-#[derive(Serialize)]
-struct OpenAiTool {
- #[serde(rename = "type")]
- tool_type: String,
- function: OpenAiFunctionDef,
-}
-
-#[derive(Serialize)]
-struct OpenAiFunctionDef {
- name: String,
- description: String,
- parameters: serde_json::Value,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenAiStreamEvent {
- choices: Vec<OpenAiChoice>,
- #[serde(default)]
- usage: Option<OpenAiUsage>,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenAiChoice {
- delta: OpenAiDelta,
- finish_reason: Option<String>,
-}
-
-#[derive(Deserialize, Debug, Default)]
-struct OpenAiDelta {
- #[serde(default)]
- content: Option<String>,
- #[serde(default)]
- tool_calls: Option<Vec<OpenAiToolCallDelta>>,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenAiToolCallDelta {
- index: usize,
- #[serde(default)]
- id: Option<String>,
- #[serde(default)]
- function: Option<OpenAiFunctionDelta>,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenAiFunctionDelta {
- #[serde(default)]
- name: Option<String>,
- #[serde(default)]
- arguments: Option<String>,
-}
-
-#[derive(Deserialize, Debug)]
-struct OpenAiUsage {
- prompt_tokens: u64,
- completion_tokens: u64,
-}
-
-#[allow(dead_code)]
-#[derive(Deserialize, Debug)]
-struct OpenAiError {
- error: OpenAiErrorDetail,
-}
-
-#[allow(dead_code)]
-#[derive(Deserialize, Debug)]
-struct OpenAiErrorDetail {
- message: String,
-}
-
-fn convert_request(
- model_id: &str,
- request: &LlmCompletionRequest,
-) -> Result<OpenAiRequest, String> {
- let real_model_id =
- get_real_model_id(model_id).ok_or_else(|| format!("Unknown model: {}", model_id))?;
-
- let mut messages = Vec::new();
-
- for msg in &request.messages {
- match msg.role {
- LlmMessageRole::System => {
- let text: String = msg
- .content
- .iter()
- .filter_map(|c| match c {
- LlmMessageContent::Text(t) => Some(t.as_str()),
- _ => None,
- })
- .collect::<Vec<_>>()
- .join("\n");
- if !text.is_empty() {
- messages.push(OpenAiMessage::System { content: text });
- }
- }
- LlmMessageRole::User => {
- let parts: Vec<OpenAiContentPart> = msg
- .content
- .iter()
- .filter_map(|c| match c {
- LlmMessageContent::Text(t) => {
- Some(OpenAiContentPart::Text { text: t.clone() })
- }
- LlmMessageContent::Image(img) => Some(OpenAiContentPart::ImageUrl {
- image_url: ImageUrl {
- url: format!("data:image/png;base64,{}", img.source),
- },
- }),
- LlmMessageContent::ToolResult(_) => None,
- _ => None,
- })
- .collect();
-
- for content in &msg.content {
- if let LlmMessageContent::ToolResult(result) = content {
- let content_text = match &result.content {
- LlmToolResultContent::Text(t) => t.clone(),
- LlmToolResultContent::Image(_) => "[Image]".to_string(),
- };
- messages.push(OpenAiMessage::Tool {
- tool_call_id: result.tool_use_id.clone(),
- content: content_text,
- });
- }
- }
-
- if !parts.is_empty() {
- messages.push(OpenAiMessage::User { content: parts });
- }
- }
- LlmMessageRole::Assistant => {
- let mut content_text: Option<String> = None;
- let mut tool_calls: Vec<OpenAiToolCall> = Vec::new();
-
- for c in &msg.content {
- match c {
- LlmMessageContent::Text(t) => {
- content_text = Some(t.clone());
- }
- LlmMessageContent::ToolUse(tool_use) => {
- tool_calls.push(OpenAiToolCall {
- id: tool_use.id.clone(),
- call_type: "function".to_string(),
- function: OpenAiFunctionCall {
- name: tool_use.name.clone(),
- arguments: tool_use.input.clone(),
- },
- });
- }
- _ => {}
- }
- }
-
- messages.push(OpenAiMessage::Assistant {
- content: content_text,
- tool_calls: if tool_calls.is_empty() {
- None
- } else {
- Some(tool_calls)
- },
- });
- }
- }
- }
-
- let tools: Option<Vec<OpenAiTool>> = if request.tools.is_empty() {
- None
- } else {
- Some(
- request
- .tools
- .iter()
- .map(|t| OpenAiTool {
- tool_type: "function".to_string(),
- function: OpenAiFunctionDef {
- name: t.name.clone(),
- description: t.description.clone(),
- parameters: serde_json::from_str(&t.input_schema)
- .unwrap_or(serde_json::Value::Object(Default::default())),
- },
- })
- .collect(),
- )
- };
-
- let tool_choice = request.tool_choice.as_ref().map(|tc| match tc {
- LlmToolChoice::Auto => "auto".to_string(),
- LlmToolChoice::Any => "required".to_string(),
- LlmToolChoice::None => "none".to_string(),
- });
-
- Ok(OpenAiRequest {
- model: real_model_id.to_string(),
- messages,
- tools,
- tool_choice,
- temperature: request.temperature,
- max_tokens: request.max_tokens,
- stop: request.stop_sequences.clone(),
- stream: true,
- stream_options: Some(StreamOptions {
- include_usage: true,
- }),
- })
-}
-
-fn parse_sse_line(line: &str) -> Option<OpenAiStreamEvent> {
- if let Some(data) = line.strip_prefix("data: ") {
- if data == "[DONE]" {
- return None;
- }
- serde_json::from_str(data).ok()
- } else {
- None
- }
-}
-
-impl zed::Extension for OpenAiProvider {
- fn new() -> Self {
- Self {
- streams: Mutex::new(HashMap::new()),
- next_stream_id: Mutex::new(0),
- }
- }
-
- fn llm_providers(&self) -> Vec<LlmProviderInfo> {
- vec![LlmProviderInfo {
- id: "openai".into(),
- name: "OpenAI".into(),
- icon: Some("icons/openai.svg".into()),
- }]
- }
-
- fn llm_provider_models(&self, _provider_id: &str) -> Result<Vec<LlmModelInfo>, String> {
- Ok(MODELS
- .iter()
- .map(|m| LlmModelInfo {
- id: m.display_name.to_string(),
- name: m.display_name.to_string(),
- max_token_count: m.max_tokens,
- max_output_tokens: m.max_output_tokens,
- capabilities: LlmModelCapabilities {
- supports_images: m.supports_images,
- 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::JsonSchema,
- },
- is_default: m.is_default,
- is_default_fast: m.is_default_fast,
- })
- .collect())
- }
-
- fn llm_provider_is_authenticated(&self, _provider_id: &str) -> bool {
- llm_get_credential("openai").is_some()
- }
-
- fn llm_provider_settings_markdown(&self, _provider_id: &str) -> Option<String> {
- Some(
- r#"# OpenAI Setup
-
-Welcome to **OpenAI**! This extension provides access to OpenAI GPT models.
-
-## Configuration
-
-Enter your OpenAI API key below. You can find your API key at [platform.openai.com/api-keys](https://platform.openai.com/api-keys).
-
-## Available Models
-
-| Display Name | Real Model | Context | Output |
-|--------------|------------|---------|--------|
-| GPT-4o | gpt-4o | 128K | 16K |
-| GPT-4o-mini | gpt-4o-mini | 128K | 16K |
-| GPT-4.1 | gpt-4.1 | 1M | 32K |
-| GPT-4.1-mini | gpt-4.1-mini | 1M | 32K |
-| GPT-5 | gpt-5 | 272K | 32K |
-| GPT-5-mini | gpt-5-mini | 272K | 32K |
-| o1 | o1 | 200K | 100K |
-| o3 | o3 | 200K | 100K |
-| o3-mini | o3-mini | 200K | 100K |
-
-## Features
-
-- ✅ Full streaming support
-- ✅ Tool/function calling
-- ✅ Vision (image inputs)
-- ✅ All OpenAI models
-
-## Pricing
-
-Uses your OpenAI API credits. See [OpenAI pricing](https://openai.com/pricing) for details.
-"#
- .to_string(),
- )
- }
-
- fn llm_provider_authenticate(&mut self, _provider_id: &str) -> Result<(), String> {
- let provided = llm_request_credential(
- "openai",
- LlmCredentialType::ApiKey,
- "OpenAI API Key",
- "sk-...",
- )?;
- if provided {
- Ok(())
- } else {
- Err("Authentication cancelled".to_string())
- }
- }
-
- fn llm_provider_reset_credentials(&mut self, _provider_id: &str) -> Result<(), String> {
- llm_delete_credential("openai")
- }
-
- fn llm_stream_completion_start(
- &mut self,
- _provider_id: &str,
- model_id: &str,
- request: &LlmCompletionRequest,
- ) -> Result<String, String> {
- let api_key = llm_get_credential("openai").ok_or_else(|| {
- "No API key configured. Please add your OpenAI API key in settings.".to_string()
- })?;
-
- let openai_request = convert_request(model_id, request)?;
-
- let body = serde_json::to_vec(&openai_request)
- .map_err(|e| format!("Failed to serialize request: {}", e))?;
-
- let http_request = HttpRequest {
- method: HttpMethod::Post,
- url: "https://api.openai.com/v1/chat/completions".to_string(),
- headers: vec![
- ("Content-Type".to_string(), "application/json".to_string()),
- ("Authorization".to_string(), format!("Bearer {}", api_key)),
- ],
- body: Some(body),
- redirect_policy: RedirectPolicy::FollowAll,
- };
-
- let response_stream = http_request
- .fetch_stream()
- .map_err(|e| format!("HTTP request failed: {}", e))?;
-
- let stream_id = {
- let mut id_counter = self.next_stream_id.lock().unwrap();
- let id = format!("openai-stream-{}", *id_counter);
- *id_counter += 1;
- id
- };
-
- self.streams.lock().unwrap().insert(
- stream_id.clone(),
- StreamState {
- response_stream: Some(response_stream),
- buffer: String::new(),
- started: false,
- tool_calls: HashMap::new(),
- tool_calls_emitted: false,
- },
- );
-
- Ok(stream_id)
- }
-
- fn llm_stream_completion_next(
- &mut self,
- stream_id: &str,
- ) -> Result<Option<LlmCompletionEvent>, String> {
- let mut streams = self.streams.lock().unwrap();
- let state = streams
- .get_mut(stream_id)
- .ok_or_else(|| format!("Unknown stream: {}", stream_id))?;
-
- if !state.started {
- state.started = true;
- return Ok(Some(LlmCompletionEvent::Started));
- }
-
- let response_stream = state
- .response_stream
- .as_mut()
- .ok_or_else(|| "Stream already closed".to_string())?;
-
- loop {
- if let Some(newline_pos) = state.buffer.find('\n') {
- let line = state.buffer[..newline_pos].trim().to_string();
- state.buffer = state.buffer[newline_pos + 1..].to_string();
-
- if line.is_empty() {
- continue;
- }
-
- if let Some(event) = parse_sse_line(&line) {
- if let Some(choice) = event.choices.first() {
- if let Some(tool_calls) = &choice.delta.tool_calls {
- for tc in tool_calls {
- let entry = state.tool_calls.entry(tc.index).or_default();
-
- if let Some(id) = &tc.id {
- entry.id = id.clone();
- }
-
- if let Some(func) = &tc.function {
- if let Some(name) = &func.name {
- entry.name = name.clone();
- }
- if let Some(args) = &func.arguments {
- entry.arguments.push_str(args);
- }
- }
- }
- }
-
- if let Some(reason) = &choice.finish_reason {
- if reason == "tool_calls" && !state.tool_calls_emitted {
- state.tool_calls_emitted = true;
- if let Some((&index, _)) = state.tool_calls.iter().next() {
- if let Some(tool_call) = state.tool_calls.remove(&index) {
- return Ok(Some(LlmCompletionEvent::ToolUse(LlmToolUse {
- id: tool_call.id,
- name: tool_call.name,
- input: tool_call.arguments,
- thought_signature: None,
- })));
- }
- }
- }
-
- let stop_reason = match reason.as_str() {
- "stop" => LlmStopReason::EndTurn,
- "length" => LlmStopReason::MaxTokens,
- "tool_calls" => LlmStopReason::ToolUse,
- "content_filter" => LlmStopReason::Refusal,
- _ => LlmStopReason::EndTurn,
- };
-
- if let Some(usage) = event.usage {
- return Ok(Some(LlmCompletionEvent::Usage(LlmTokenUsage {
- input_tokens: usage.prompt_tokens,
- output_tokens: usage.completion_tokens,
- cache_creation_input_tokens: None,
- cache_read_input_tokens: None,
- })));
- }
-
- return Ok(Some(LlmCompletionEvent::Stop(stop_reason)));
- }
-
- if let Some(content) = &choice.delta.content {
- if !content.is_empty() {
- return Ok(Some(LlmCompletionEvent::Text(content.clone())));
- }
- }
- }
-
- if event.choices.is_empty() {
- if let Some(usage) = event.usage {
- return Ok(Some(LlmCompletionEvent::Usage(LlmTokenUsage {
- input_tokens: usage.prompt_tokens,
- output_tokens: usage.completion_tokens,
- cache_creation_input_tokens: None,
- cache_read_input_tokens: None,
- })));
- }
- }
- }
-
- continue;
- }
-
- match response_stream.next_chunk() {
- Ok(Some(chunk)) => {
- let text = String::from_utf8_lossy(&chunk);
- state.buffer.push_str(&text);
- }
- Ok(None) => {
- if !state.tool_calls.is_empty() && !state.tool_calls_emitted {
- state.tool_calls_emitted = true;
- let keys: Vec<usize> = state.tool_calls.keys().copied().collect();
- if let Some(&key) = keys.first() {
- if let Some(tool_call) = state.tool_calls.remove(&key) {
- return Ok(Some(LlmCompletionEvent::ToolUse(LlmToolUse {
- id: tool_call.id,
- name: tool_call.name,
- input: tool_call.arguments,
- thought_signature: None,
- })));
- }
- }
- }
- return Ok(None);
- }
- Err(e) => {
- return Err(format!("Stream error: {}", e));
- }
- }
- }
- }
-
- fn llm_stream_completion_close(&mut self, stream_id: &str) {
- self.streams.lock().unwrap().remove(stream_id);
- }
-}
-
-zed::register_extension!(OpenAiProvider);