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+dependencies = [ + "proc-macro2", + "quote", + "syn", +] diff --git a/extensions/anthropic/Cargo.toml b/extensions/anthropic/Cargo.toml new file mode 100644 index 0000000000000000000000000000000000000000..25dfe72b0e92ca10a2537be2d5f94173a2921acf --- /dev/null +++ b/extensions/anthropic/Cargo.toml @@ -0,0 +1,17 @@ +[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" diff --git a/extensions/anthropic/extension.toml b/extensions/anthropic/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..c37b8aca34f6cfcf2d70715eb24383077e207366 --- /dev/null +++ b/extensions/anthropic/extension.toml @@ -0,0 +1,13 @@ +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" \ No newline at end of file diff --git a/extensions/anthropic/icons/anthropic.svg b/extensions/anthropic/icons/anthropic.svg new file mode 100644 index 0000000000000000000000000000000000000000..75c1a7e0014e7dd7bbd573d44fe6854e0c0760a3 --- /dev/null +++ b/extensions/anthropic/icons/anthropic.svg @@ -0,0 +1,11 @@ + + + + + + + + + + + diff --git a/extensions/anthropic/src/anthropic.rs b/extensions/anthropic/src/anthropic.rs new file mode 100644 index 0000000000000000000000000000000000000000..26d364cf90acbc9692e12c926f7dbb1f3ce4fd09 --- /dev/null +++ b/extensions/anthropic/src/anthropic.rs @@ -0,0 +1,803 @@ +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>, + next_stream_id: Mutex, +} + +struct StreamState { + response_stream: Option, + buffer: String, + started: bool, + current_tool_use: Option, + stop_reason: Option, + pending_signature: Option, +} + +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, + #[serde(skip_serializing_if = "Option::is_none")] + system: Option, + #[serde(skip_serializing_if = "Option::is_none")] + thinking: Option, + #[serde(skip_serializing_if = "Vec::is_empty")] + tools: Vec, + #[serde(skip_serializing_if = "Option::is_none")] + tool_choice: Option, + #[serde(skip_serializing_if = "Vec::is_empty")] + stop_sequences: Vec, + #[serde(skip_serializing_if = "Option::is_none")] + temperature: Option, + stream: bool, +} + +#[derive(Serialize)] +struct AnthropicThinking { + #[serde(rename = "type")] + thinking_type: String, + #[serde(skip_serializing_if = "Option::is_none")] + budget_tokens: Option, +} + +#[derive(Serialize)] +struct AnthropicMessage { + role: String, + content: Vec, +} + +#[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, +} + +#[derive(Deserialize, Debug, Default)] +struct AnthropicUsage { + #[serde(default)] + input_tokens: Option, + #[serde(default)] + output_tokens: Option, + #[serde(default)] + cache_creation_input_tokens: Option, + #[serde(default)] + cache_read_input_tokens: Option, +} + +#[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 { + let model_def = + get_model_definition(model_id).ok_or_else(|| format!("Unknown model: {}", model_id))?; + + let mut messages: Vec = 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 = 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 = 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 = 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 { + 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 { + vec![LlmProviderInfo { + id: "anthropic".into(), + name: "Anthropic".into(), + icon: Some("icons/anthropic.svg".into()), + }] + } + + fn llm_provider_models(&self, _provider_id: &str) -> Result, 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 { + 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 { + 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, 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); diff --git a/extensions/copilot_chat/Cargo.lock b/extensions/copilot_chat/Cargo.lock new file mode 100644 index 0000000000000000000000000000000000000000..1ba4a97d7291c7661f68c87d5c5dde7d292dada8 --- /dev/null +++ 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b/extensions/copilot_chat/Cargo.toml new file mode 100644 index 0000000000000000000000000000000000000000..6daae73229776d50cb05223d65e5c921629041c9 --- /dev/null +++ b/extensions/copilot_chat/Cargo.toml @@ -0,0 +1,17 @@ +[package] +name = "copilot_chat" +version = "0.1.0" +edition = "2021" +publish = false +license = "Apache-2.0" + +[workspace] + +[lib] +path = "src/copilot_chat.rs" +crate-type = ["cdylib"] + +[dependencies] +zed_extension_api = { path = "../../crates/extension_api" } +serde = { version = "1.0", features = ["derive"] } +serde_json = "1.0" diff --git a/extensions/copilot_chat/extension.toml b/extensions/copilot_chat/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..9afa188462fcb632fe1e162964997d957c567fbf --- /dev/null +++ b/extensions/copilot_chat/extension.toml @@ -0,0 +1,13 @@ +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" \ No newline at end of file diff --git a/extensions/copilot_chat/icons/copilot.svg b/extensions/copilot_chat/icons/copilot.svg new file mode 100644 index 0000000000000000000000000000000000000000..2584cd631006c10ea9535408657fd881f0748249 --- /dev/null +++ b/extensions/copilot_chat/icons/copilot.svg @@ -0,0 +1,9 @@ + + + + + + + + + diff --git a/extensions/copilot_chat/src/copilot_chat.rs b/extensions/copilot_chat/src/copilot_chat.rs new file mode 100644 index 0000000000000000000000000000000000000000..2c64d430689f94fce36c2060728e8cd7bb326f23 --- /dev/null +++ b/extensions/copilot_chat/src/copilot_chat.rs @@ -0,0 +1,696 @@ +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>, + next_stream_id: Mutex, +} + +struct StreamState { + response_stream: Option, + buffer: String, + started: bool, + tool_calls: HashMap, + 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, + 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, + #[serde(skip_serializing_if = "Option::is_none")] + max_tokens: Option, + #[serde(skip_serializing_if = "Vec::is_empty")] + tools: Vec, + #[serde(skip_serializing_if = "Option::is_none")] + tool_choice: Option, + #[serde(skip_serializing_if = "Vec::is_empty")] + stop: Vec, + #[serde(skip_serializing_if = "Option::is_none")] + temperature: Option, + stream: bool, + #[serde(skip_serializing_if = "Option::is_none")] + stream_options: Option, +} + +#[derive(Serialize)] +struct StreamOptions { + include_usage: bool, +} + +#[derive(Serialize)] +struct OpenAiMessage { + role: String, + #[serde(skip_serializing_if = "Option::is_none")] + content: Option, + #[serde(skip_serializing_if = "Option::is_none")] + tool_calls: Option>, + #[serde(skip_serializing_if = "Option::is_none")] + tool_call_id: Option, +} + +#[derive(Serialize, Clone)] +#[serde(untagged)] +enum OpenAiContent { + Text(String), + Parts(Vec), +} + +#[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, + #[serde(default)] + usage: Option, +} + +#[derive(Deserialize, Debug)] +struct OpenAiStreamChoice { + delta: OpenAiDelta, + finish_reason: Option, +} + +#[derive(Deserialize, Debug, Default)] +struct OpenAiDelta { + #[serde(default)] + content: Option, + #[serde(default)] + tool_calls: Option>, +} + +#[derive(Deserialize, Debug)] +struct OpenAiToolCallDelta { + index: usize, + #[serde(default)] + id: Option, + #[serde(default)] + function: Option, +} + +#[derive(Deserialize, Debug, Default)] +struct OpenAiFunctionDelta { + #[serde(default)] + name: Option, + #[serde(default)] + arguments: Option, +} + +#[derive(Deserialize, Debug)] +struct OpenAiUsage { + prompt_tokens: u64, + completion_tokens: u64, +} + +fn convert_request( + model_id: &str, + request: &LlmCompletionRequest, +) -> Result { + let mut messages: Vec = 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 = Vec::new(); + let mut tool_result_messages: Vec = 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 = 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 = 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 { + 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 { + 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, 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 { + 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 { + 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, 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: {}", 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0000000000000000000000000000000000000000..1b1cb382a7835d79adf93af225b19ced9a5f551c --- /dev/null +++ b/extensions/google-ai/extension.toml @@ -0,0 +1,13 @@ +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" \ No newline at end of file diff --git a/extensions/google-ai/icons/google-ai.svg b/extensions/google-ai/icons/google-ai.svg new file mode 100644 index 0000000000000000000000000000000000000000..bdde44ed2475313f0dfd418a496f372ca61db22d --- /dev/null +++ b/extensions/google-ai/icons/google-ai.svg @@ -0,0 +1,3 @@ + + + diff --git a/extensions/google-ai/src/google_ai.rs b/extensions/google-ai/src/google_ai.rs new file mode 100644 index 0000000000000000000000000000000000000000..61baca80b19d9d0b0dd6f53f913400e9f6069c4f --- /dev/null +++ b/extensions/google-ai/src/google_ai.rs @@ -0,0 +1,840 @@ +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>, + next_stream_id: Mutex, +} + +struct StreamState { + response_stream: Option, + buffer: String, + started: bool, + stop_reason: Option, + wants_tool_use: bool, +} + +struct ModelDefinition { + real_id: &'static str, + display_name: &'static str, + max_tokens: u64, + max_output_tokens: Option, + 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, + #[serde(skip_serializing_if = "Option::is_none")] + system_instruction: Option, + #[serde(skip_serializing_if = "Option::is_none")] + generation_config: Option, + #[serde(skip_serializing_if = "Option::is_none")] + tools: Option>, + #[serde(skip_serializing_if = "Option::is_none")] + tool_config: Option, +} + +#[derive(Serialize)] +#[serde(rename_all = "camelCase")] +struct GoogleSystemInstruction { + parts: Vec, +} + +#[derive(Serialize, Deserialize, Debug, Clone)] +#[serde(rename_all = "camelCase")] +struct GoogleContent { + parts: Vec, + #[serde(skip_serializing_if = "Option::is_none")] + role: Option, +} + +#[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, +} + +#[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, + #[serde(skip_serializing_if = "Option::is_none")] + stop_sequences: Option>, + #[serde(skip_serializing_if = "Option::is_none")] + max_output_tokens: Option, + #[serde(skip_serializing_if = "Option::is_none")] + temperature: Option, + #[serde(skip_serializing_if = "Option::is_none")] + thinking_config: Option, +} + +#[derive(Serialize)] +#[serde(rename_all = "camelCase")] +struct GoogleThinkingConfig { + thinking_budget: u32, +} + +#[derive(Serialize)] +#[serde(rename_all = "camelCase")] +struct GoogleTool { + function_declarations: Vec, +} + +#[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>, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +struct GoogleStreamResponse { + #[serde(default)] + candidates: Vec, + #[serde(default)] + usage_metadata: Option, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +struct GoogleCandidate { + #[serde(default)] + content: Option, + #[serde(default)] + finish_reason: Option, +} + +#[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 = Vec::new(); + let mut system_parts: Vec = 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 = 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 = 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> = if request.tools.is_empty() { + None + } else { + let declarations: Vec = 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 { + 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 { + 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, 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 { + 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 { + 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, 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. 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b/extensions/open_router/Cargo.toml new file mode 100644 index 0000000000000000000000000000000000000000..47a87b5d52571a19c09e044e3cdf8dd9d0e571a2 --- /dev/null +++ b/extensions/open_router/Cargo.toml @@ -0,0 +1,17 @@ +[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" diff --git a/extensions/open_router/extension.toml b/extensions/open_router/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..d321b3d9620c23ca8206af247468162d8f875d0f --- /dev/null +++ b/extensions/open_router/extension.toml @@ -0,0 +1,13 @@ +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" \ No newline at end of file diff --git a/extensions/open_router/icons/open-router.svg b/extensions/open_router/icons/open-router.svg new file mode 100644 index 0000000000000000000000000000000000000000..b6f5164e0b385f26e7b22a12253d18200dbff24e --- /dev/null +++ b/extensions/open_router/icons/open-router.svg @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/extensions/open_router/src/open_router.rs b/extensions/open_router/src/open_router.rs new file mode 100644 index 0000000000000000000000000000000000000000..8d8b143cd70a3c7bf3cadc1e4dd0d3042f38d580 --- /dev/null +++ b/extensions/open_router/src/open_router.rs @@ -0,0 +1,830 @@ +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>, + next_stream_id: Mutex, +} + +struct StreamState { + response_stream: Option, + buffer: String, + started: bool, + tool_calls: HashMap, + 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, + 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, + #[serde(skip_serializing_if = "Option::is_none")] + max_tokens: Option, + #[serde(skip_serializing_if = "Vec::is_empty")] + tools: Vec, + #[serde(skip_serializing_if = "Option::is_none")] + tool_choice: Option, + #[serde(skip_serializing_if = "Vec::is_empty")] + stop: Vec, + #[serde(skip_serializing_if = "Option::is_none")] + temperature: Option, + stream: bool, +} + +#[derive(Serialize)] +struct OpenRouterMessage { + role: String, + #[serde(skip_serializing_if = "Option::is_none")] + content: Option, + #[serde(skip_serializing_if = "Option::is_none")] + tool_calls: Option>, + #[serde(skip_serializing_if = "Option::is_none")] + tool_call_id: Option, +} + +#[derive(Serialize, Clone)] +#[serde(untagged)] +enum OpenRouterContent { + Text(String), + Parts(Vec), +} + +#[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, + #[serde(default)] + usage: Option, +} + +#[derive(Deserialize, Debug)] +struct OpenRouterStreamChoice { + delta: OpenRouterDelta, + finish_reason: Option, +} + +#[derive(Deserialize, Debug, Default)] +struct OpenRouterDelta { + #[serde(default)] + content: Option, + #[serde(default)] + tool_calls: Option>, +} + +#[derive(Deserialize, Debug)] +struct OpenRouterToolCallDelta { + index: usize, + #[serde(default)] + id: Option, + #[serde(default)] + function: Option, +} + +#[derive(Deserialize, Debug, Default)] +struct OpenRouterFunctionDelta { + #[serde(default)] + name: Option, + #[serde(default)] + arguments: Option, +} + +#[derive(Deserialize, Debug)] +struct OpenRouterUsage { + prompt_tokens: u64, + completion_tokens: u64, +} + +fn convert_request( + model_id: &str, + request: &LlmCompletionRequest, +) -> Result { + let mut messages: Vec = 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 = Vec::new(); + let mut tool_result_messages: Vec = 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 = 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 = 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 { + 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 { + 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, 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 { + 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 { + 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, 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)); + } + } 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0000000000000000000000000000000000000000..94788688716f1d8a6534684b0cb39febf98585ef --- /dev/null +++ b/extensions/openai/extension.toml @@ -0,0 +1,13 @@ +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" \ No newline at end of file diff --git a/extensions/openai/icons/openai.svg b/extensions/openai/icons/openai.svg new file mode 100644 index 0000000000000000000000000000000000000000..e45ac315a011853a9e9343171659b7623017fb31 --- /dev/null +++ b/extensions/openai/icons/openai.svg @@ -0,0 +1,3 @@ + + + diff --git a/extensions/openai/src/openai.rs b/extensions/openai/src/openai.rs new file mode 100644 index 0000000000000000000000000000000000000000..40a99352abd5da1145b0a0ec6641247e363677b8 --- /dev/null +++ b/extensions/openai/src/openai.rs @@ -0,0 +1,727 @@ +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>, + next_stream_id: Mutex, +} + +struct StreamState { + response_stream: Option, + buffer: String, + started: bool, + tool_calls: HashMap, + 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, + 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, + #[serde(skip_serializing_if = "Option::is_none")] + tools: Option>, + #[serde(skip_serializing_if = "Option::is_none")] + tool_choice: Option, + #[serde(skip_serializing_if = "Option::is_none")] + temperature: Option, + #[serde(skip_serializing_if = "Option::is_none")] + max_tokens: Option, + #[serde(skip_serializing_if = "Vec::is_empty")] + stop: Vec, + stream: bool, + stream_options: Option, +} + +#[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 }, + #[serde(rename = "assistant")] + Assistant { + #[serde(skip_serializing_if = "Option::is_none")] + content: Option, + #[serde(skip_serializing_if = "Option::is_none")] + tool_calls: Option>, + }, + #[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, + #[serde(default)] + usage: Option, +} + +#[derive(Deserialize, Debug)] +struct OpenAiChoice { + delta: OpenAiDelta, + finish_reason: Option, +} + +#[derive(Deserialize, Debug, Default)] +struct OpenAiDelta { + #[serde(default)] + content: Option, + #[serde(default)] + tool_calls: Option>, +} + +#[derive(Deserialize, Debug)] +struct OpenAiToolCallDelta { + index: usize, + #[serde(default)] + id: Option, + #[serde(default)] + function: Option, +} + +#[derive(Deserialize, Debug)] +struct OpenAiFunctionDelta { + #[serde(default)] + name: Option, + #[serde(default)] + arguments: Option, +} + +#[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 { + 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::>() + .join("\n"); + if !text.is_empty() { + messages.push(OpenAiMessage::System { content: text }); + } + } + LlmMessageRole::User => { + let parts: Vec = 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 = None; + let mut tool_calls: Vec = 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> = 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 { + 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 { + vec![LlmProviderInfo { + id: "openai".into(), + name: "OpenAI".into(), + icon: Some("icons/openai.svg".into()), + }] + } + + fn llm_provider_models(&self, _provider_id: &str) -> Result, 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 { + 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 { + 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, 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 = 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);