use super::*;
use crate::{
config::{Config, GlobalConfig, Input},
function::{eval_tool_calls, FunctionDeclaration, ToolCall, ToolResult},
render::render_stream,
utils::*,
};
use anyhow::{bail, Context, Result};
use fancy_regex::Regex;
use indexmap::IndexMap;
use inquire::{
list_option::ListOption, required, validator::Validation, MultiSelect, Select, Text,
};
use reqwest::{Client as ReqwestClient, RequestBuilder};
use serde::Deserialize;
use serde_json::{json, Value};
use std::sync::LazyLock;
use std::time::Duration;
use tokio::sync::mpsc::unbounded_channel;
const MODELS_YAML: &str = include_str!("../../models.yaml");
pub static ALL_PROVIDER_MODELS: LazyLock<Vec<ProviderModels>> = LazyLock::new(|| {
Config::loal_models_override()
.ok()
.unwrap_or_else(|| serde_yaml::from_str(MODELS_YAML).unwrap())
});
static EMBEDDING_MODEL_RE: LazyLock<Regex> = LazyLock::new(|| {
Regex::new(r"((^|/)(bge-|e5-|uae-|gte-|text-)|embed|multilingual|minilm)").unwrap()
});
static ESCAPE_SLASH_RE: LazyLock<Regex> = LazyLock::new(|| Regex::new(r"(?<!\\)/").unwrap());
#[async_trait::async_trait]
pub trait Client: Sync + Send {
fn global_config(&self) -> &GlobalConfig;
fn extra_config(&self) -> Option<&ExtraConfig>;
fn patch_config(&self) -> Option<&RequestPatch>;
fn name(&self) -> &str;
fn model(&self) -> &Model;
fn model_mut(&mut self) -> &mut Model;
fn build_client(&self) -> Result<ReqwestClient> {
let mut builder = ReqwestClient::builder();
let extra = self.extra_config();
let timeout = extra.and_then(|v| v.connect_timeout).unwrap_or(10);
if let Some(proxy) = extra.and_then(|v| v.proxy.as_deref()) {
builder = set_proxy(builder, proxy)?;
}
if let Some(user_agent) = self.global_config().read().user_agent.as_ref() {
builder = builder.user_agent(user_agent);
}
let client = builder
.connect_timeout(Duration::from_secs(timeout))
.build()
.with_context(|| "Failed to build client")?;
Ok(client)
}
async fn chat_completions(&self, input: Input) -> Result<ChatCompletionsOutput> {
if self.global_config().read().dry_run {
let content = input.echo_messages();
return Ok(ChatCompletionsOutput::new(&content));
}
let client = self.build_client()?;
let data = input.prepare_completion_data(self.model(), false)?;
self.chat_completions_inner(&client, data)
.await
.with_context(|| "Failed to call chat-completions api")
}
async fn chat_completions_streaming(
&self,
input: &Input,
handler: &mut SseHandler,
) -> Result<()> {
let abort_signal = handler.abort();
let input = input.clone();
tokio::select! {
ret = async {
if self.global_config().read().dry_run {
let content = input.echo_messages();
handler.text(&content)?;
return Ok(());
}
let client = self.build_client()?;
let data = input.prepare_completion_data(self.model(), true)?;
self.chat_completions_streaming_inner(&client, handler, data).await
} => {
handler.done();
ret.with_context(|| "Failed to call chat-completions api")
}
_ = wait_abort_signal(&abort_signal) => {
handler.done();
Ok(())
},
}
}
async fn embeddings(&self, data: &EmbeddingsData) -> Result<Vec<Vec<f32>>> {
let client = self.build_client()?;
self.embeddings_inner(&client, data)
.await
.context("Failed to call embeddings api")
}
async fn rerank(&self, data: &RerankData) -> Result<RerankOutput> {
let client = self.build_client()?;
self.rerank_inner(&client, data)
.await
.context("Failed to call rerank api")
}
async fn chat_completions_inner(
&self,
client: &ReqwestClient,
data: ChatCompletionsData,
) -> Result<ChatCompletionsOutput>;
async fn chat_completions_streaming_inner(
&self,
client: &ReqwestClient,
handler: &mut SseHandler,
data: ChatCompletionsData,
) -> Result<()>;
async fn embeddings_inner(
&self,
_client: &ReqwestClient,
_data: &EmbeddingsData,
) -> Result<EmbeddingsOutput> {
bail!("The client doesn't support embeddings api")
}
async fn rerank_inner(
&self,
_client: &ReqwestClient,
_data: &RerankData,
) -> Result<RerankOutput> {
bail!("The client doesn't support rerank api")
}
fn request_builder(
&self,
client: &reqwest::Client,
mut request_data: RequestData,
) -> RequestBuilder {
self.patch_request_data(&mut request_data);
request_data.into_builder(client)
}
fn patch_request_data(&self, request_data: &mut RequestData) {
let model_type = self.model().model_type();
if let Some(patch) = self.model().patch() {
request_data.apply_patch(patch.clone());
}
let patch_map = std::env::var(get_env_name(&format!(
"patch_{}_{}",
self.model().client_name(),
model_type.api_name(),
)))
.ok()
.and_then(|v| serde_json::from_str(&v).ok())
.or_else(|| {
self.patch_config()
.and_then(|v| model_type.extract_patch(v))
.cloned()
});
let patch_map = match patch_map {
Some(v) => v,
_ => return,
};
for (key, patch) in patch_map {
let key = ESCAPE_SLASH_RE.replace_all(&key, r"\/");
if let Ok(regex) = Regex::new(&format!("^({key})$")) {
if let Ok(true) = regex.is_match(self.model().name()) {
request_data.apply_patch(patch);
return;
}
}
}
}
}
impl Default for ClientConfig {
fn default() -> Self {
Self::OpenAIConfig(OpenAIConfig::default())
}
}
#[derive(Debug, Clone, Deserialize, Default)]
pub struct ExtraConfig {
pub proxy: Option<String>,
pub connect_timeout: Option<u64>,
}
#[derive(Debug, Clone, Deserialize, Default)]
pub struct RequestPatch {
pub chat_completions: Option<ApiPatch>,
pub embeddings: Option<ApiPatch>,
pub rerank: Option<ApiPatch>,
}
pub type ApiPatch = IndexMap<String, Value>;
pub struct RequestData {
pub url: String,
pub headers: IndexMap<String, String>,
pub body: Value,
}
impl RequestData {
pub fn new<T>(url: T, body: Value) -> Self
where
T: std::fmt::Display,
{
Self {
url: url.to_string(),
headers: Default::default(),
body,
}
}
pub fn bearer_auth<T>(&mut self, auth: T)
where
T: std::fmt::Display,
{
self.headers
.insert("authorization".into(), format!("Bearer {auth}"));
}
pub fn header<K, V>(&mut self, key: K, value: V)
where
K: std::fmt::Display,
V: std::fmt::Display,
{
self.headers.insert(key.to_string(), value.to_string());
}
pub fn into_builder(self, client: &ReqwestClient) -> RequestBuilder {
let RequestData { url, headers, body } = self;
debug!("Request {url} {body}");
let mut builder = client.post(url);
for (key, value) in headers {
builder = builder.header(key, value);
}
builder = builder.json(&body);
builder
}
pub fn apply_patch(&mut self, patch: Value) {
if let Some(patch_url) = patch["url"].as_str() {
self.url = patch_url.into();
}
if let Some(patch_body) = patch.get("body") {
json_patch::merge(&mut self.body, patch_body)
}
if let Some(patch_headers) = patch["headers"].as_object() {
for (key, value) in patch_headers {
if let Some(value) = value.as_str() {
self.header(key, value)
} else if value.is_null() {
self.headers.swap_remove(key);
}
}
}
}
}
#[derive(Debug)]
pub struct ChatCompletionsData {
pub messages: Vec<Message>,
pub temperature: Option<f64>,
pub top_p: Option<f64>,
pub functions: Option<Vec<FunctionDeclaration>>,
pub stream: bool,
}
#[derive(Debug, Clone, Default)]
pub struct ChatCompletionsOutput {
pub text: String,
pub tool_calls: Vec<ToolCall>,
pub id: Option<String>,
pub input_tokens: Option<u64>,
pub output_tokens: Option<u64>,
}
impl ChatCompletionsOutput {
pub fn new(text: &str) -> Self {
Self {
text: text.to_string(),
..Default::default()
}
}
}
#[derive(Debug)]
pub struct EmbeddingsData {
pub texts: Vec<String>,
pub query: bool,
}
impl EmbeddingsData {
pub fn new(texts: Vec<String>, query: bool) -> Self {
Self { texts, query }
}
}
pub type EmbeddingsOutput = Vec<Vec<f32>>;
#[derive(Debug)]
pub struct RerankData {
pub query: String,
pub documents: Vec<String>,
pub top_n: usize,
}
impl RerankData {
pub fn new(query: String, documents: Vec<String>, top_n: usize) -> Self {
Self {
query,
documents,
top_n,
}
}
}
pub type RerankOutput = Vec<RerankResult>;
#[derive(Debug, Deserialize)]
pub struct RerankResult {
pub index: usize,
pub relevance_score: f64,
}
pub type PromptAction<'a> = (&'a str, &'a str, Option<&'a str>);
pub async fn create_config(
prompts: &[PromptAction<'static>],
client: &str,
) -> Result<(String, Value)> {
let mut config = json!({
"type": client,
});
for (key, desc, help_message) in prompts {
let env_name = format!("{client}_{key}").to_ascii_uppercase();
let required = std::env::var(&env_name).is_err();
let value = prompt_input_string(desc, required, *help_message)?;
if !value.is_empty() {
config[key] = value.into();
}
}
let model = set_client_models_config(&mut config, client).await?;
let clients = json!(vec![config]);
Ok((model, clients))
}
pub async fn create_openai_compatible_client_config(
client: &str,
) -> Result<Option<(String, Value)>> {
let api_base = super::OPENAI_COMPATIBLE_PROVIDERS
.into_iter()
.find(|(name, _)| client == *name)
.map(|(_, api_base)| api_base)
.unwrap_or("http(s)://{API_ADDR}/v1");
let name = if client == OpenAICompatibleClient::NAME {
let value = prompt_input_string("Provider Name", true, None)?;
value.replace(' ', "-")
} else {
client.to_string()
};
let mut config = json!({
"type": OpenAICompatibleClient::NAME,
"name": &name,
});
let api_base = if api_base.contains('{') {
prompt_input_string("API Base", true, Some(&format!("e.g. {api_base}")))?
} else {
api_base.to_string()
};
config["api_base"] = api_base.into();
let api_key = prompt_input_string("API Key", false, None)?;
if !api_key.is_empty() {
config["api_key"] = api_key.into();
}
let model = set_client_models_config(&mut config, &name).await?;
let clients = json!(vec![config]);
Ok(Some((model, clients)))
}
pub async fn call_chat_completions(
input: &Input,
print: bool,
extract_code: bool,
client: &dyn Client,
abort_signal: AbortSignal,
) -> Result<(String, Vec<ToolResult>)> {
let ret = abortable_run_with_spinner(
client.chat_completions(input.clone()),
"Generating",
abort_signal,
)
.await;
match ret {
Ok(ret) => {
let ChatCompletionsOutput {
mut text,
tool_calls,
..
} = ret;
if !text.is_empty() {
if extract_code {
text = extract_code_block(&strip_think_tag(&text)).to_string();
}
if print {
client.global_config().read().print_markdown(&text)?;
}
}
Ok((text, eval_tool_calls(client.global_config(), tool_calls)?))
}
Err(err) => Err(err),
}
}
pub async fn call_chat_completions_streaming(
input: &Input,
client: &dyn Client,
abort_signal: AbortSignal,
) -> Result<(String, Vec<ToolResult>)> {
let (tx, rx) = unbounded_channel();
let mut handler = SseHandler::new(tx, abort_signal.clone());
let (send_ret, render_ret) = tokio::join!(
client.chat_completions_streaming(input, &mut handler),
render_stream(rx, client.global_config(), abort_signal.clone()),
);
if handler.abort().aborted() {
bail!("Aborted.");
}
render_ret?;
let (text, tool_calls) = handler.take();
match send_ret {
Ok(_) => {
if !text.is_empty() && !text.ends_with('\n') {
println!();
}
Ok((text, eval_tool_calls(client.global_config(), tool_calls)?))
}
Err(err) => {
if !text.is_empty() {
println!();
}
Err(err)
}
}
}
pub fn noop_prepare_embeddings<T>(_client: &T, _data: &EmbeddingsData) -> Result<RequestData> {
bail!("The client doesn't support embeddings api")
}
pub async fn noop_embeddings(_builder: RequestBuilder, _model: &Model) -> Result<EmbeddingsOutput> {
bail!("The client doesn't support embeddings api")
}
pub fn noop_prepare_rerank<T>(_client: &T, _data: &RerankData) -> Result<RequestData> {
bail!("The client doesn't support rerank api")
}
pub async fn noop_rerank(_builder: RequestBuilder, _model: &Model) -> Result<RerankOutput> {
bail!("The client doesn't support rerank api")
}
pub fn catch_error(data: &Value, status: u16) -> Result<()> {
if (200..300).contains(&status) {
return Ok(());
}
debug!("Invalid response, status: {status}, data: {data}");
if let Some(error) = data["error"].as_object() {
if let (Some(typ), Some(message)) = (
json_str_from_map(error, "type"),
json_str_from_map(error, "message"),
) {
bail!("{message} (type: {typ})");
} else if let (Some(typ), Some(message)) = (
json_str_from_map(error, "code"),
json_str_from_map(error, "message"),
) {
bail!("{message} (code: {typ})");
}
} else if let Some(error) = data["errors"][0].as_object() {
if let (Some(code), Some(message)) = (
error.get("code").and_then(|v| v.as_u64()),
json_str_from_map(error, "message"),
) {
bail!("{message} (status: {code})")
}
} else if let Some(error) = data[0]["error"].as_object() {
if let (Some(status), Some(message)) = (
json_str_from_map(error, "status"),
json_str_from_map(error, "message"),
) {
bail!("{message} (status: {status})")
}
} else if let (Some(detail), Some(status)) = (data["detail"].as_str(), data["status"].as_i64())
{
bail!("{detail} (status: {status})");
} else if let Some(error) = data["error"].as_str() {
bail!("{error}");
} else if let Some(message) = data["message"].as_str() {
bail!("{message}");
}
bail!("Invalid response data: {data} (status: {status})");
}
pub fn json_str_from_map<'a>(
map: &'a serde_json::Map<String, Value>,
field_name: &str,
) -> Option<&'a str> {
map.get(field_name).and_then(|v| v.as_str())
}
async fn set_client_models_config(client_config: &mut Value, client: &str) -> Result<String> {
if let Some(provider) = ALL_PROVIDER_MODELS.iter().find(|v| v.provider == client) {
let models: Vec<String> = provider
.models
.iter()
.filter(|v| v.model_type == "chat")
.map(|v| v.name.clone())
.collect();
let model_name = select_model(models)?;
return Ok(format!("{client}:{model_name}"));
}
let mut model_names = vec![];
if let (Some(true), Some(api_base), api_key) = (
client_config["type"]
.as_str()
.map(|v| v == OpenAICompatibleClient::NAME),
client_config["api_base"].as_str(),
client_config["api_key"]
.as_str()
.map(|v| v.to_string())
.or_else(|| {
let env_name = format!("{client}_api_key").to_ascii_uppercase();
std::env::var(&env_name).ok()
}),
) {
match abortable_run_with_spinner(
fetch_models(api_base, api_key.as_deref()),
"Fetching models",
create_abort_signal(),
)
.await
{
Ok(fetched_models) => {
model_names = MultiSelect::new("LLMs to include (required):", fetched_models)
.with_validator(|list: &[ListOption<&String>]| {
if list.is_empty() {
Ok(Validation::Invalid(
"At least one item must be selected".into(),
))
} else {
Ok(Validation::Valid)
}
})
.prompt()?;
}
Err(err) => {
eprintln!("✗ Fetch models failed: {err}");
}
}
}
if model_names.is_empty() {
model_names = prompt_input_string(
"LLMs to add",
true,
Some("Separated by commas, e.g. llama3.3,qwen2.5"),
)?
.split(',')
.filter_map(|v| {
let v = v.trim();
if v.is_empty() {
None
} else {
Some(v.to_string())
}
})
.collect::<Vec<_>>();
}
if model_names.is_empty() {
bail!("No models");
}
let models: Vec<Value> = model_names
.iter()
.map(|v| {
let l = v.to_lowercase();
if l.contains("rank") {
json!({
"name": v,
"type": "reranker",
})
} else if let Ok(true) = EMBEDDING_MODEL_RE.is_match(&l) {
json!({
"name": v,
"type": "embedding",
"default_chunk_size": 1000,
"max_batch_size": 100
})
} else if v.contains("vision") {
json!({
"name": v,
"supports_vision": true
})
} else {
json!({
"name": v,
})
}
})
.collect();
client_config["models"] = models.into();
let model_name = select_model(model_names)?;
Ok(format!("{client}:{model_name}"))
}
fn select_model(model_names: Vec<String>) -> Result<String> {
if model_names.is_empty() {
bail!("No models");
}
let model = if model_names.len() == 1 {
model_names[0].clone()
} else {
Select::new("Default Model (required):", model_names).prompt()?
};
Ok(model)
}
fn prompt_input_string(
desc: &str,
required: bool,
help_message: Option<&str>,
) -> anyhow::Result<String> {
let desc = if required {
format!("{desc} (required):")
} else {
format!("{desc} (optional):")
};
let mut text = Text::new(&desc);
if required {
text = text.with_validator(required!("This field is required"))
}
if let Some(help_message) = help_message {
text = text.with_help_message(help_message);
}
let text = text.prompt()?;
Ok(text)
}