use super::*;
use crate::utils::strip_think_tag;
use anyhow::{bail, Context, Result};
use reqwest::RequestBuilder;
use serde::Deserialize;
use serde_json::{json, Value};
const API_BASE: &str = "https://api.openai.com/v1";
#[derive(Debug, Clone, Deserialize, Default)]
pub struct OpenAIConfig {
pub name: Option<String>,
pub api_key: Option<String>,
pub api_base: Option<String>,
pub organization_id: Option<String>,
#[serde(default)]
pub models: Vec<ModelData>,
pub patch: Option<RequestPatch>,
pub extra: Option<ExtraConfig>,
}
impl OpenAIClient {
config_get_fn!(api_key, get_api_key);
config_get_fn!(api_base, get_api_base);
pub const PROMPTS: [PromptAction<'static>; 1] = [("api_key", "API Key", None)];
}
impl_client_trait!(
OpenAIClient,
(
prepare_chat_completions,
openai_chat_completions,
openai_chat_completions_streaming
),
(prepare_embeddings, openai_embeddings),
(noop_prepare_rerank, noop_rerank),
);
fn prepare_chat_completions(
self_: &OpenAIClient,
data: ChatCompletionsData,
) -> Result<RequestData> {
let api_key = self_.get_api_key()?;
let api_base = self_
.get_api_base()
.unwrap_or_else(|_| API_BASE.to_string());
let url = format!("{}/chat/completions", api_base.trim_end_matches('/'));
let body = openai_build_chat_completions_body(data, &self_.model);
let mut request_data = RequestData::new(url, body);
request_data.bearer_auth(api_key);
if let Some(organization_id) = &self_.config.organization_id {
request_data.header("OpenAI-Organization", organization_id);
}
Ok(request_data)
}
fn prepare_embeddings(self_: &OpenAIClient, data: &EmbeddingsData) -> Result<RequestData> {
let api_key = self_.get_api_key()?;
let api_base = self_
.get_api_base()
.unwrap_or_else(|_| API_BASE.to_string());
let url = format!("{api_base}/embeddings");
let body = openai_build_embeddings_body(data, &self_.model);
let mut request_data = RequestData::new(url, body);
request_data.bearer_auth(api_key);
if let Some(organization_id) = &self_.config.organization_id {
request_data.header("OpenAI-Organization", organization_id);
}
Ok(request_data)
}
pub async fn openai_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_chat_completions(&data)
}
pub async fn openai_chat_completions_streaming(
builder: RequestBuilder,
handler: &mut SseHandler,
_model: &Model,
) -> Result<()> {
let mut call_id = String::new();
let mut function_name = String::new();
let mut function_arguments = String::new();
let mut function_id = String::new();
let mut reasoning_state = 0;
let handle = |message: SseMmessage| -> Result<bool> {
if message.data == "[DONE]" {
if !function_name.is_empty() {
if function_arguments.is_empty() {
function_arguments = String::from("{}");
}
let arguments: Value = function_arguments.parse().with_context(|| {
format!("Tool call '{function_name}' have non-JSON arguments '{function_arguments}'")
})?;
handler.tool_call(ToolCall::new(
function_name.clone(),
arguments,
normalize_function_id(&function_id),
))?;
}
return Ok(true);
}
let data: Value = serde_json::from_str(&message.data)?;
debug!("stream-data: {data}");
if let Some(text) = data["choices"][0]["delta"]["content"]
.as_str()
.filter(|v| !v.is_empty())
{
if reasoning_state == 1 {
handler.text("\n</think>\n\n")?;
reasoning_state = 0;
}
handler.text(text)?;
} else if let Some(text) = data["choices"][0]["delta"]["reasoning_content"]
.as_str()
.or_else(|| data["choices"][0]["delta"]["reasoning"].as_str())
.filter(|v| !v.is_empty())
{
if reasoning_state == 0 {
handler.text("<think>\n")?;
reasoning_state = 1;
}
handler.text(text)?;
}
if let (Some(function), index, id) = (
data["choices"][0]["delta"]["tool_calls"][0]["function"].as_object(),
data["choices"][0]["delta"]["tool_calls"][0]["index"].as_u64(),
data["choices"][0]["delta"]["tool_calls"][0]["id"]
.as_str()
.filter(|v| !v.is_empty()),
) {
if reasoning_state == 1 {
handler.text("\n</think>\n\n")?;
reasoning_state = 0;
}
let maybe_call_id = format!("{}/{}", id.unwrap_or_default(), index.unwrap_or_default());
if maybe_call_id != call_id && maybe_call_id.len() >= call_id.len() {
if !function_name.is_empty() {
if function_arguments.is_empty() {
function_arguments = String::from("{}");
}
let arguments: Value = function_arguments.parse().with_context(|| {
format!("Tool call '{function_name}' have non-JSON arguments '{function_arguments}'")
})?;
handler.tool_call(ToolCall::new(
function_name.clone(),
arguments,
normalize_function_id(&function_id),
))?;
}
function_name.clear();
function_arguments.clear();
function_id.clear();
call_id = maybe_call_id;
}
if let Some(name) = function.get("name").and_then(|v| v.as_str()) {
if name.starts_with(&function_name) {
function_name = name.to_string();
} else {
function_name.push_str(name);
}
}
if let Some(arguments) = function.get("arguments").and_then(|v| v.as_str()) {
function_arguments.push_str(arguments);
}
if let Some(id) = id {
function_id = id.to_string();
}
}
Ok(false)
};
sse_stream(builder, handle).await
}
pub async fn openai_embeddings(
builder: RequestBuilder,
_model: &Model,
) -> Result<EmbeddingsOutput> {
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())?;
}
let res_body: EmbeddingsResBody =
serde_json::from_value(data).context("Invalid embeddings data")?;
let output = res_body.data.into_iter().map(|v| v.embedding).collect();
Ok(output)
}
#[derive(Deserialize)]
struct EmbeddingsResBody {
data: Vec<EmbeddingsResBodyEmbedding>,
}
#[derive(Deserialize)]
struct EmbeddingsResBodyEmbedding {
embedding: Vec<f32>,
}
pub fn openai_build_chat_completions_body(data: ChatCompletionsData, model: &Model) -> Value {
let ChatCompletionsData {
messages,
temperature,
top_p,
functions,
stream,
} = data;
let messages_len = messages.len();
let messages: Vec<Value> = messages
.into_iter()
.enumerate()
.flat_map(|(i, message)| {
let Message { role, content } = message;
match content {
MessageContent::ToolCalls(MessageContentToolCalls {
tool_results,
text: _,
sequence,
}) => {
if !sequence {
let tool_calls: Vec<_> = tool_results.iter().map(|tool_result| {
json!({
"id": tool_result.call.id,
"type": "function",
"function": {
"name": tool_result.call.name,
"arguments": tool_result.call.arguments.to_string(),
},
})
}).collect();
let mut messages = vec![
json!({ "role": MessageRole::Assistant, "tool_calls": tool_calls })
];
for tool_result in tool_results {
messages.push(
json!({
"role": "tool",
"content": tool_result.output.to_string(),
"tool_call_id": tool_result.call.id,
})
);
}
messages
} else {
tool_results.into_iter().flat_map(|tool_result| {
vec![
json!({
"role": MessageRole::Assistant,
"tool_calls": [
{
"id": tool_result.call.id,
"type": "function",
"function": {
"name": tool_result.call.name,
"arguments": tool_result.call.arguments.to_string(),
},
}
]
}),
json!({
"role": "tool",
"content": tool_result.output.to_string(),
"tool_call_id": tool_result.call.id,
})
]
}).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(),
"messages": messages,
});
if let Some(v) = model.max_tokens_param() {
if model
.patch()
.and_then(|v| v.get("body").and_then(|v| v.get("max_tokens")))
== Some(&Value::Null)
{
body["max_completion_tokens"] = v.into();
} else {
body["max_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| {
json!({
"type": "function",
"function": v,
})
})
.collect();
}
body
}
pub fn openai_build_embeddings_body(data: &EmbeddingsData, model: &Model) -> Value {
json!({
"input": data.texts,
"model": model.real_name()
})
}
pub fn openai_extract_chat_completions(data: &Value) -> Result<ChatCompletionsOutput> {
let text = data["choices"][0]["message"]["content"]
.as_str()
.unwrap_or_default();
let reasoning = data["choices"][0]["message"]["reasoning_content"]
.as_str()
.or_else(|| data["choices"][0]["message"]["reasoning"].as_str())
.unwrap_or_default()
.trim();
let mut tool_calls = vec![];
if let Some(calls) = data["choices"][0]["message"]["tool_calls"].as_array() {
for call in calls {
if let (Some(name), Some(arguments), Some(id)) = (
call["function"]["name"].as_str(),
call["function"]["arguments"].as_str(),
call["id"].as_str(),
) {
let arguments: Value = arguments.parse().with_context(|| {
format!("Tool call '{name}' have non-JSON arguments '{arguments}'")
})?;
tool_calls.push(ToolCall::new(
name.to_string(),
arguments,
Some(id.to_string()),
));
}
}
};
if text.is_empty() && tool_calls.is_empty() {
bail!("Invalid response data: {data}");
}
let text = if !reasoning.is_empty() {
format!("<think>\n{reasoning}\n</think>\n\n{text}")
} else {
text.to_string()
};
let output = ChatCompletionsOutput {
text,
tool_calls,
id: data["id"].as_str().map(|v| v.to_string()),
input_tokens: data["usage"]["prompt_tokens"].as_u64(),
output_tokens: data["usage"]["completion_tokens"].as_u64(),
};
Ok(output)
}
fn normalize_function_id(value: &str) -> Option<String> {
if value.is_empty() {
None
} else {
Some(value.to_string())
}
}