716 lines
24 KiB
Rust
716 lines
24 KiB
Rust
use super::access_token::{get_access_token, get_access_token_account_id};
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use super::oauth::{self, OAuthProvider};
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use super::openai_oauth::OpenAIOAuthProvider;
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use super::*;
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use crate::utils::strip_think_tag;
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use anyhow::{Context, Result, bail};
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use reqwest::{Client as ReqwestClient, RequestBuilder};
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use serde::Deserialize;
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use serde_json::{Value, json};
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const API_BASE: &str = "https://api.openai.com/v1";
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const CODEX_API_ENDPOINT: &str = "https://chatgpt.com/backend-api/codex/responses";
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#[derive(Debug, Clone, Deserialize, Default)]
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pub struct OpenAIConfig {
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pub name: Option<String>,
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pub api_key: Option<String>,
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pub api_base: Option<String>,
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pub organization_id: Option<String>,
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pub auth: Option<String>,
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#[serde(default)]
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pub models: Vec<ModelData>,
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pub patch: Option<RequestPatch>,
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pub extra: Option<ExtraConfig>,
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}
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impl OpenAIClient {
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config_get_fn!(api_key, get_api_key);
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config_get_fn!(api_base, get_api_base);
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create_oauth_supported_client_config!();
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}
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#[async_trait::async_trait]
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impl Client for OpenAIClient {
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client_common_fns!();
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fn supports_oauth(&self) -> bool {
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self.config.auth.as_deref() == Some("oauth")
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}
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async fn chat_completions_inner(
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&self,
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client: &ReqwestClient,
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data: ChatCompletionsData,
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) -> Result<ChatCompletionsOutput> {
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let uses_codex =
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self.config.auth.as_deref() == Some("oauth") && self.get_api_base().is_err();
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let request_data = prepare_chat_completions(self, client, data).await?;
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let builder = self.request_builder(client, request_data);
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if uses_codex {
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openai_responses_chat_completions(builder, self.model()).await
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} else {
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openai_chat_completions(builder, self.model()).await
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}
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}
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async fn chat_completions_streaming_inner(
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&self,
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client: &ReqwestClient,
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handler: &mut SseHandler,
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data: ChatCompletionsData,
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) -> Result<()> {
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let uses_codex =
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self.config.auth.as_deref() == Some("oauth") && self.get_api_base().is_err();
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let request_data = prepare_chat_completions(self, client, data).await?;
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let builder = self.request_builder(client, request_data);
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if uses_codex {
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openai_responses_streaming(builder, handler).await
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} else {
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openai_chat_completions_streaming(builder, handler, self.model()).await
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}
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}
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async fn embeddings_inner(
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&self,
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client: &ReqwestClient,
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data: &EmbeddingsData,
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) -> Result<EmbeddingsOutput> {
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let request_data = prepare_embeddings(self, client, data).await?;
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let builder = self.request_builder(client, request_data);
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openai_embeddings(builder, self.model()).await
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}
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async fn rerank_inner(
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&self,
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client: &ReqwestClient,
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data: &RerankData,
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) -> Result<RerankOutput> {
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let request_data = noop_prepare_rerank(self, data)?;
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let builder = self.request_builder(client, request_data);
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noop_rerank(builder, self.model()).await
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}
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}
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async fn prepare_chat_completions(
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self_: &OpenAIClient,
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client: &ReqwestClient,
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data: ChatCompletionsData,
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) -> Result<RequestData> {
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let uses_oauth = self_.config.auth.as_deref() == Some("oauth");
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let has_custom_base = self_.get_api_base().is_ok();
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let uses_codex = uses_oauth && !has_custom_base;
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let url = if uses_codex {
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CODEX_API_ENDPOINT.to_string()
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} else {
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let api_base = self_
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.get_api_base()
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.unwrap_or_else(|_| API_BASE.to_string());
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format!("{}/chat/completions", api_base.trim_end_matches('/'))
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};
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let body = if uses_codex {
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openai_build_responses_body(data, &self_.model)
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} else {
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openai_build_chat_completions_body(data, &self_.model)
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};
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let mut request_data = RequestData::new(url, body);
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if uses_oauth {
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let provider = OpenAIOAuthProvider;
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let ready = oauth::prepare_oauth_access_token(client, &provider, self_.name()).await?;
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if !ready {
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bail!(
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"OAuth configured but no tokens found for '{}'. Run: 'coyote --authenticate {}' or '.authenticate' in the REPL",
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self_.name(),
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self_.name()
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);
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}
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let token = get_access_token(self_.name())?;
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request_data.bearer_auth(token);
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if let Some(account_id) = get_access_token_account_id(self_.name()) {
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request_data.header("ChatGPT-Account-Id", account_id);
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}
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for (key, value) in provider.extra_request_headers() {
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request_data.header(key, value);
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}
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} else if let Ok(api_key) = self_.get_api_key() {
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request_data.bearer_auth(api_key);
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} else {
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bail!(
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"No authentication configured for '{}'. Set `api_key` or use `auth: oauth` with `coyote --authenticate {}`.",
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self_.name(),
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self_.name()
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);
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}
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if let Some(organization_id) = &self_.config.organization_id {
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request_data.header("OpenAI-Organization", organization_id);
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}
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Ok(request_data)
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}
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async fn prepare_embeddings(
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self_: &OpenAIClient,
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client: &ReqwestClient,
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data: &EmbeddingsData,
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) -> Result<RequestData> {
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let api_base = self_
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.get_api_base()
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.unwrap_or_else(|_| API_BASE.to_string());
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let url = format!("{api_base}/embeddings");
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let body = openai_build_embeddings_body(data, &self_.model);
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let mut request_data = RequestData::new(url, body);
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if self_.config.auth.as_deref() == Some("oauth") {
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let provider = OpenAIOAuthProvider;
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let ready = oauth::prepare_oauth_access_token(client, &provider, self_.name()).await?;
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if !ready {
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bail!(
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"OAuth configured but no tokens found for '{}'. Run: 'coyote --authenticate {}' or '.authenticate' in the REPL",
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self_.name(),
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self_.name()
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);
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}
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let token = get_access_token(self_.name())?;
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request_data.bearer_auth(token);
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} else if let Ok(api_key) = self_.get_api_key() {
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request_data.bearer_auth(api_key);
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} else {
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bail!(
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"No authentication configured for '{}'. Set `api_key` or use `auth: oauth` with `coyote --authenticate {}`.",
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self_.name(),
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self_.name()
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);
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}
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if let Some(organization_id) = &self_.config.organization_id {
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request_data.header("OpenAI-Organization", organization_id);
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}
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Ok(request_data)
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}
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pub async fn openai_chat_completions(
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builder: RequestBuilder,
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_model: &Model,
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) -> Result<ChatCompletionsOutput> {
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let res = builder.send().await?;
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let status = res.status();
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let data: Value = res.json().await?;
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if !status.is_success() {
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catch_error(&data, status.as_u16())?;
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}
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debug!("non-stream-data: {data}");
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openai_extract_chat_completions(&data)
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}
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pub async fn openai_chat_completions_streaming(
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builder: RequestBuilder,
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handler: &mut SseHandler,
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_model: &Model,
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) -> Result<()> {
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let mut call_id = String::new();
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let mut function_name = String::new();
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let mut function_arguments = String::new();
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let mut function_id = String::new();
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let mut reasoning_state = 0;
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let handle = |message: SseMessage| -> Result<bool> {
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if message.data == "[DONE]" {
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if !function_name.is_empty() {
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if function_arguments.is_empty() {
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function_arguments = String::from("{}");
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}
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let arguments: Value = function_arguments.parse().with_context(|| {
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format!(
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"Tool call '{function_name}' has non-JSON arguments '{function_arguments}'"
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)
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})?;
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handler.tool_call(ToolCall::new(
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function_name.clone(),
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arguments,
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normalize_function_id(&function_id),
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))?;
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}
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return Ok(true);
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}
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let data: Value = serde_json::from_str(&message.data)?;
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debug!("stream-data: {data}");
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if let Some(text) = data["choices"][0]["delta"]["content"]
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.as_str()
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.filter(|v| !v.is_empty())
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{
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if reasoning_state == 1 {
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handler.text("\n</think>\n\n")?;
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reasoning_state = 0;
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}
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handler.text(text)?;
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} else if let Some(text) = data["choices"][0]["delta"]["reasoning_content"]
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.as_str()
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.or_else(|| data["choices"][0]["delta"]["reasoning"].as_str())
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.filter(|v| !v.is_empty())
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{
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if reasoning_state == 0 {
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handler.text("<think>\n")?;
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reasoning_state = 1;
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}
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handler.text(text)?;
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}
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if let (Some(function), index, id) = (
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data["choices"][0]["delta"]["tool_calls"][0]["function"].as_object(),
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data["choices"][0]["delta"]["tool_calls"][0]["index"].as_u64(),
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data["choices"][0]["delta"]["tool_calls"][0]["id"]
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.as_str()
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.filter(|v| !v.is_empty()),
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) {
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if reasoning_state == 1 {
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handler.text("\n</think>\n\n")?;
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reasoning_state = 0;
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}
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let maybe_call_id = format!("{}/{}", id.unwrap_or_default(), index.unwrap_or_default());
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if maybe_call_id != call_id && maybe_call_id.len() >= call_id.len() {
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if !function_name.is_empty() {
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if function_arguments.is_empty() {
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function_arguments = String::from("{}");
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}
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let arguments: Value = function_arguments.parse().with_context(|| {
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format!("Tool call '{function_name}' has non-JSON arguments '{function_arguments}'")
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})?;
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handler.tool_call(ToolCall::new(
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function_name.clone(),
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arguments,
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normalize_function_id(&function_id),
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))?;
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}
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function_name.clear();
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function_arguments.clear();
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function_id.clear();
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call_id = maybe_call_id;
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}
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if let Some(name) = function.get("name").and_then(|v| v.as_str()) {
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if name.starts_with(&function_name) {
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function_name = name.to_string();
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} else {
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function_name.push_str(name);
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}
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}
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if let Some(arguments) = function.get("arguments").and_then(|v| v.as_str()) {
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function_arguments.push_str(arguments);
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}
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if let Some(id) = id {
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function_id = id.to_string();
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}
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}
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Ok(false)
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};
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sse_stream(builder, handle).await
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}
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pub async fn openai_embeddings(
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builder: RequestBuilder,
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_model: &Model,
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) -> Result<EmbeddingsOutput> {
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let res = builder.send().await?;
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let status = res.status();
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let data: Value = res.json().await?;
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if !status.is_success() {
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catch_error(&data, status.as_u16())?;
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}
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let res_body: EmbeddingsResBody =
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serde_json::from_value(data).context("Invalid embeddings data")?;
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let output = res_body.data.into_iter().map(|v| v.embedding).collect();
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Ok(output)
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}
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#[derive(Deserialize)]
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struct EmbeddingsResBody {
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data: Vec<EmbeddingsResBodyEmbedding>,
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}
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#[derive(Deserialize)]
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struct EmbeddingsResBodyEmbedding {
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embedding: Vec<f32>,
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}
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pub fn openai_build_chat_completions_body(data: ChatCompletionsData, model: &Model) -> Value {
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let ChatCompletionsData {
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messages,
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temperature,
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top_p,
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functions,
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stream,
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} = data;
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let messages_len = messages.len();
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let messages: Vec<Value> = messages
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.into_iter()
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.enumerate()
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.flat_map(|(i, message)| {
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let Message { role, content } = message;
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match content {
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MessageContent::ToolCalls(MessageContentToolCalls {
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tool_results,
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text: _,
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sequence,
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}) => {
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if !sequence {
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let tool_calls: Vec<_> = tool_results
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.iter()
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.map(|tool_result| {
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json!({
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"id": tool_result.call.id,
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"type": "function",
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"function": {
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"name": tool_result.call.name,
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"arguments": tool_result.call.arguments.to_string(),
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},
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})
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})
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.collect();
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let mut messages = vec![
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json!({ "role": MessageRole::Assistant, "tool_calls": tool_calls }),
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];
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for tool_result in tool_results {
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messages.push(json!({
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"role": "tool",
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"content": tool_result.output.to_string(),
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"tool_call_id": tool_result.call.id,
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}));
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}
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messages
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} else {
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tool_results.into_iter().flat_map(|tool_result| {
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vec![
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json!({
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"role": MessageRole::Assistant,
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"tool_calls": [
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{
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"id": tool_result.call.id,
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"type": "function",
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"function": {
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"name": tool_result.call.name,
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"arguments": tool_result.call.arguments.to_string(),
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},
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}
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]
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}),
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json!({
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"role": "tool",
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"content": tool_result.output.to_string(),
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"tool_call_id": tool_result.call.id,
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})
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]
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}).collect()
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}
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}
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MessageContent::Text(text) if role.is_assistant() && i != messages_len - 1 => {
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vec![json!({ "role": role, "content": strip_think_tag(&text) }
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)]
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}
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_ => vec![json!({ "role": role, "content": content })],
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}
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})
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.collect();
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let mut body = json!({
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"model": &model.real_name(),
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"messages": messages,
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});
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if let Some(v) = model.max_tokens_param() {
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if model
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.patch()
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.and_then(|v| v.get("body").and_then(|v| v.get("max_tokens")))
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== Some(&Value::Null)
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{
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body["max_completion_tokens"] = v.into();
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} else {
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body["max_tokens"] = v.into();
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}
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}
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if let Some(v) = temperature {
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body["temperature"] = v.into();
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}
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if let Some(v) = top_p {
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body["top_p"] = v.into();
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}
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if stream {
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body["stream"] = true.into();
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}
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if let Some(functions) = functions {
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body["tools"] = functions
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.iter()
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.map(|v| {
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json!({
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"type": "function",
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"function": v,
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})
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})
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.collect();
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}
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body
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}
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pub fn openai_build_embeddings_body(data: &EmbeddingsData, model: &Model) -> Value {
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json!({
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"input": data.texts,
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"model": model.real_name()
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})
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}
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pub fn openai_extract_chat_completions(data: &Value) -> Result<ChatCompletionsOutput> {
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let text = data["choices"][0]["message"]["content"]
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.as_str()
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.unwrap_or_default();
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let reasoning = data["choices"][0]["message"]["reasoning_content"]
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.as_str()
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.or_else(|| data["choices"][0]["message"]["reasoning"].as_str())
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.unwrap_or_default()
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.trim();
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let mut tool_calls = vec![];
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if let Some(calls) = data["choices"][0]["message"]["tool_calls"].as_array() {
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for call in calls {
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if let (Some(name), Some(arguments), Some(id)) = (
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call["function"]["name"].as_str(),
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call["function"]["arguments"].as_str(),
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call["id"].as_str(),
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) {
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let arguments: Value = arguments.parse().with_context(|| {
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format!("Tool call '{name}' has non-JSON arguments '{arguments}'")
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})?;
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tool_calls.push(ToolCall::new(
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name.to_string(),
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arguments,
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Some(id.to_string()),
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));
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}
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}
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};
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|
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if text.is_empty() && tool_calls.is_empty() {
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bail!("Invalid response data: {data}");
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}
|
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let text = if !reasoning.is_empty() {
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format!("<think>\n{reasoning}\n</think>\n\n{text}")
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} else {
|
|
text.to_string()
|
|
};
|
|
let output = ChatCompletionsOutput { text, tool_calls };
|
|
Ok(output)
|
|
}
|
|
|
|
fn normalize_function_id(value: &str) -> Option<String> {
|
|
if value.is_empty() {
|
|
None
|
|
} else {
|
|
Some(value.to_string())
|
|
}
|
|
}
|
|
|
|
pub fn openai_build_responses_body(data: ChatCompletionsData, model: &Model) -> Value {
|
|
let ChatCompletionsData {
|
|
messages,
|
|
temperature,
|
|
top_p,
|
|
functions,
|
|
stream,
|
|
} = data;
|
|
|
|
let messages_len = messages.len();
|
|
let input: Vec<Value> = messages
|
|
.into_iter()
|
|
.enumerate()
|
|
.flat_map(|(i, message)| {
|
|
let Message { role, content } = message;
|
|
match content {
|
|
MessageContent::ToolCalls(MessageContentToolCalls {
|
|
tool_results,
|
|
text: _,
|
|
sequence: _,
|
|
}) => tool_results
|
|
.into_iter()
|
|
.flat_map(|tool_result| {
|
|
vec![
|
|
json!({
|
|
"type": "function_call",
|
|
"call_id": tool_result.call.id,
|
|
"name": tool_result.call.name,
|
|
"arguments": tool_result.call.arguments.to_string(),
|
|
}),
|
|
json!({
|
|
"type": "function_call_output",
|
|
"call_id": tool_result.call.id,
|
|
"output": tool_result.output.to_string(),
|
|
}),
|
|
]
|
|
})
|
|
.collect(),
|
|
MessageContent::Text(text) if role.is_assistant() && i != messages_len - 1 => {
|
|
vec![json!({ "role": role, "content": strip_think_tag(&text) })]
|
|
}
|
|
_ => vec![json!({ "role": role, "content": content })],
|
|
}
|
|
})
|
|
.collect();
|
|
|
|
let mut body = json!({
|
|
"model": &model.real_name(),
|
|
"input": input,
|
|
"store": false,
|
|
});
|
|
|
|
if let Some(v) = model.max_tokens_param() {
|
|
body["max_output_tokens"] = v.into();
|
|
}
|
|
if let Some(v) = temperature {
|
|
body["temperature"] = v.into();
|
|
}
|
|
if let Some(v) = top_p {
|
|
body["top_p"] = v.into();
|
|
}
|
|
if stream {
|
|
body["stream"] = true.into();
|
|
}
|
|
if let Some(functions) = functions {
|
|
body["tools"] = functions
|
|
.iter()
|
|
.map(|v| {
|
|
let mut tool = serde_json::to_value(v).unwrap_or_default();
|
|
tool["type"] = "function".into();
|
|
tool
|
|
})
|
|
.collect();
|
|
}
|
|
body
|
|
}
|
|
|
|
pub async fn openai_responses_chat_completions(
|
|
builder: RequestBuilder,
|
|
_model: &Model,
|
|
) -> Result<ChatCompletionsOutput> {
|
|
let res = builder.send().await?;
|
|
let status = res.status();
|
|
let data: Value = res.json().await?;
|
|
|
|
if !status.is_success() {
|
|
catch_error(&data, status.as_u16())?;
|
|
}
|
|
|
|
debug!("non-stream-data: {data}");
|
|
openai_extract_responses(&data)
|
|
}
|
|
|
|
pub fn openai_extract_responses(data: &Value) -> Result<ChatCompletionsOutput> {
|
|
let mut text = String::new();
|
|
let mut tool_calls = vec![];
|
|
|
|
if let Some(output) = data["output"].as_array() {
|
|
for item in output {
|
|
match item["type"].as_str() {
|
|
Some("message") => {
|
|
if let Some(content) = item["content"].as_array() {
|
|
for part in content {
|
|
if part["type"].as_str() == Some("output_text")
|
|
&& let Some(t) = part["text"].as_str()
|
|
{
|
|
text.push_str(t);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
Some("function_call") => {
|
|
if let (Some(name), Some(arguments_str), Some(call_id)) = (
|
|
item["name"].as_str(),
|
|
item["arguments"].as_str(),
|
|
item["call_id"].as_str(),
|
|
) {
|
|
let arguments: Value = arguments_str.parse().with_context(|| {
|
|
format!("Tool call '{name}' has non-JSON arguments '{arguments_str}'")
|
|
})?;
|
|
tool_calls.push(ToolCall::new(
|
|
name.to_string(),
|
|
arguments,
|
|
Some(call_id.to_string()),
|
|
));
|
|
}
|
|
}
|
|
_ => {}
|
|
}
|
|
}
|
|
}
|
|
|
|
if text.is_empty() && tool_calls.is_empty() {
|
|
bail!("Invalid response data: {data}");
|
|
}
|
|
Ok(ChatCompletionsOutput { text, tool_calls })
|
|
}
|
|
|
|
pub async fn openai_responses_streaming(
|
|
builder: RequestBuilder,
|
|
handler: &mut SseHandler,
|
|
) -> Result<()> {
|
|
let handle = |message: SseMessage| -> Result<bool> {
|
|
if message.data == "[DONE]" {
|
|
return Ok(true);
|
|
}
|
|
let data: Value = serde_json::from_str(&message.data)?;
|
|
debug!("stream-data: {data}");
|
|
|
|
match data["type"].as_str() {
|
|
Some("response.output_text.delta") => {
|
|
if let Some(delta) = data["delta"].as_str().filter(|v| !v.is_empty()) {
|
|
handler.text(delta)?;
|
|
}
|
|
}
|
|
Some("response.output_item.done") => {
|
|
let item = &data["item"];
|
|
if item["type"].as_str() == Some("function_call")
|
|
&& let (Some(name), Some(arguments_str), Some(call_id)) = (
|
|
item["name"].as_str(),
|
|
item["arguments"].as_str(),
|
|
item["call_id"].as_str(),
|
|
)
|
|
{
|
|
let arguments: Value = arguments_str.parse().with_context(|| {
|
|
format!("Tool call '{name}' has non-JSON arguments '{arguments_str}'")
|
|
})?;
|
|
handler.tool_call(ToolCall::new(
|
|
name.to_string(),
|
|
arguments,
|
|
Some(call_id.to_string()),
|
|
))?;
|
|
}
|
|
}
|
|
Some("response.completed") => {
|
|
return Ok(true);
|
|
}
|
|
_ => {}
|
|
}
|
|
Ok(false)
|
|
};
|
|
|
|
sse_stream(builder, handle).await
|
|
}
|