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