558 lines
19 KiB
Rust
558 lines
19 KiB
Rust
use super::access_token::*;
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use super::claude::*;
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use super::openai::*;
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use super::*;
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use anyhow::{Context, Result, anyhow, bail};
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use chrono::{Duration, Utc};
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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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use std::{path::PathBuf, str::FromStr};
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#[derive(Debug, Clone, Deserialize, Default)]
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pub struct VertexAIConfig {
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pub name: Option<String>,
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pub project_id: Option<String>,
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pub location: Option<String>,
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pub adc_file: 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 VertexAIClient {
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config_get_fn!(project_id, get_project_id);
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config_get_fn!(location, get_location);
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create_client_config!([
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("project_id", "Project ID", None, false),
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("location", "Location", None, false),
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]);
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}
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#[async_trait::async_trait]
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impl Client for VertexAIClient {
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client_common_fns!();
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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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prepare_gcloud_access_token(client, self.name(), &self.config.adc_file).await?;
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let model = self.model();
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let model_category = ModelCategory::from_str(model.real_name())?;
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let request_data = prepare_chat_completions(self, data, &model_category)?;
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let builder = self.request_builder(client, request_data);
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match model_category {
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ModelCategory::Gemini => gemini_chat_completions(builder, model).await,
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ModelCategory::Claude => claude_chat_completions(builder, model).await,
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ModelCategory::Mistral => openai_chat_completions(builder, 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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prepare_gcloud_access_token(client, self.name(), &self.config.adc_file).await?;
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let model = self.model();
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let model_category = ModelCategory::from_str(model.real_name())?;
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let request_data = prepare_chat_completions(self, data, &model_category)?;
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let builder = self.request_builder(client, request_data);
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match model_category {
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ModelCategory::Gemini => {
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gemini_chat_completions_streaming(builder, handler, model).await
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}
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ModelCategory::Claude => {
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claude_chat_completions_streaming(builder, handler, model).await
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}
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ModelCategory::Mistral => {
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openai_chat_completions_streaming(builder, handler, model).await
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}
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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<Vec<Vec<f32>>> {
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prepare_gcloud_access_token(client, self.name(), &self.config.adc_file).await?;
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let request_data = prepare_embeddings(self, data)?;
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let builder = self.request_builder(client, request_data);
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embeddings(builder, self.model()).await
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}
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}
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fn prepare_chat_completions(
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self_: &VertexAIClient,
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data: ChatCompletionsData,
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model_category: &ModelCategory,
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) -> Result<RequestData> {
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let project_id = self_.get_project_id()?;
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let location = self_.get_location()?;
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let access_token = get_access_token(self_.name())?;
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let base_url = if location == "global" {
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format!(
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"https://aiplatform.googleapis.com/v1/projects/{project_id}/locations/global/publishers"
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)
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} else {
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format!(
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"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/publishers"
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)
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};
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let model_name = self_.model.real_name();
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let url = match model_category {
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ModelCategory::Gemini => {
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let func = match data.stream {
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true => "streamGenerateContent",
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false => "generateContent",
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};
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format!("{base_url}/google/models/{model_name}:{func}")
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}
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ModelCategory::Claude => {
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format!("{base_url}/anthropic/models/{model_name}:streamRawPredict")
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}
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ModelCategory::Mistral => {
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let func = match data.stream {
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true => "streamRawPredict",
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false => "rawPredict",
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};
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format!("{base_url}/mistralai/models/{model_name}:{func}")
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}
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};
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let body = match model_category {
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ModelCategory::Gemini => gemini_build_chat_completions_body(data, &self_.model)?,
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ModelCategory::Claude => {
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let mut body = claude_build_chat_completions_body(data, &self_.model)?;
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if let Some(body_obj) = body.as_object_mut() {
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body_obj.remove("model");
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}
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body["anthropic_version"] = "vertex-2023-10-16".into();
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body
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}
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ModelCategory::Mistral => {
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let mut body = openai_build_chat_completions_body(data, &self_.model);
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if let Some(body_obj) = body.as_object_mut() {
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body_obj["model"] = strip_model_version(self_.model.real_name()).into();
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}
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body
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}
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};
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let mut request_data = RequestData::new(url, body);
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request_data.bearer_auth(access_token);
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Ok(request_data)
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}
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fn prepare_embeddings(self_: &VertexAIClient, data: &EmbeddingsData) -> Result<RequestData> {
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let project_id = self_.get_project_id()?;
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let location = self_.get_location()?;
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let access_token = get_access_token(self_.name())?;
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let base_url = if location == "global" {
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format!(
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"https://aiplatform.googleapis.com/v1/projects/{project_id}/locations/global/publishers"
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)
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} else {
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format!(
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"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/publishers"
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)
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};
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let url = format!(
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"{base_url}/google/models/{}:predict",
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self_.model.real_name()
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);
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let instances: Vec<_> = data.texts.iter().map(|v| json!({"content": v})).collect();
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let body = json!({
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"instances": instances,
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});
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let mut request_data = RequestData::new(url, body);
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request_data.bearer_auth(access_token);
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Ok(request_data)
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}
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pub async fn gemini_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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gemini_extract_chat_completions_text(&data)
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}
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pub async fn gemini_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 res = builder.send().await?;
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let status = res.status();
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if !status.is_success() {
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let data: Value = res.json().await?;
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catch_error(&data, status.as_u16())?;
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} else {
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let handle = |value: &str| -> Result<()> {
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let data: Value = serde_json::from_str(value)?;
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debug!("stream-data: {data}");
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if let Some(parts) = data["candidates"][0]["content"]["parts"].as_array() {
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for (i, part) in parts.iter().enumerate() {
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if let Some(text) = part["text"].as_str() {
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if i > 0 {
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handler.text("\n\n")?;
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}
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handler.text(text)?;
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} else if let (Some(name), Some(args)) = (
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part["functionCall"]["name"].as_str(),
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part["functionCall"]["args"].as_object(),
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) {
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let thought_signature = part["thoughtSignature"]
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.as_str()
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.or_else(|| part["thought_signature"].as_str())
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.map(|s| s.to_string());
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handler.tool_call(
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ToolCall::new(name.to_string(), json!(args), None)
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.with_thought_signature(thought_signature),
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)?;
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}
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}
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} else if let Some("SAFETY") = data["promptFeedback"]["blockReason"]
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.as_str()
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.or_else(|| data["candidates"][0]["finishReason"].as_str())
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{
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bail!("Blocked due to safety")
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}
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Ok(())
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};
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json_stream(res.bytes_stream(), handle).await?;
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}
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Ok(())
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}
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async fn embeddings(builder: RequestBuilder, _model: &Model) -> 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
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.predictions
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.into_iter()
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.map(|v| v.embeddings.values)
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.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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predictions: Vec<EmbeddingsResBodyPrediction>,
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}
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#[derive(Deserialize)]
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struct EmbeddingsResBodyPrediction {
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embeddings: EmbeddingsResBodyPredictionEmbeddings,
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}
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#[derive(Deserialize)]
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struct EmbeddingsResBodyPredictionEmbeddings {
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values: Vec<f32>,
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}
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fn gemini_extract_chat_completions_text(data: &Value) -> Result<ChatCompletionsOutput> {
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let mut text_parts = vec![];
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let mut tool_calls = vec![];
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if let Some(parts) = data["candidates"][0]["content"]["parts"].as_array() {
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for part in parts {
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if let Some(text) = part["text"].as_str() {
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text_parts.push(text);
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}
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if let (Some(name), Some(args)) = (
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part["functionCall"]["name"].as_str(),
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part["functionCall"]["args"].as_object(),
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) {
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let thought_signature = part["thoughtSignature"]
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.as_str()
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.or_else(|| part["thought_signature"].as_str())
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.map(|s| s.to_string());
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tool_calls.push(
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ToolCall::new(name.to_string(), json!(args), None)
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.with_thought_signature(thought_signature),
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);
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}
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}
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}
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let text = text_parts.join("\n\n");
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if text.is_empty() && tool_calls.is_empty() {
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if let Some("SAFETY") = data["promptFeedback"]["blockReason"]
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.as_str()
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.or_else(|| data["candidates"][0]["finishReason"].as_str())
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{
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bail!("Blocked due to safety")
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} else {
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bail!("Invalid response data: {data}");
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}
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}
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let output = ChatCompletionsOutput { text, tool_calls };
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Ok(output)
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}
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pub fn gemini_build_chat_completions_body(
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data: ChatCompletionsData,
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model: &Model,
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) -> Result<Value> {
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let ChatCompletionsData {
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mut 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 system_message = extract_system_message(&mut messages);
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let mut network_image_urls = vec![];
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let contents: Vec<Value> = messages
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.into_iter()
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.flat_map(|message| {
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let Message { role, content } = message;
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let role = match role {
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MessageRole::User => "user",
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_ => "model",
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};
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match content {
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MessageContent::Text(text) => vec![json!({
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"role": role,
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"parts": [{ "text": text }]
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})],
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MessageContent::Array(list) => {
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let parts: Vec<Value> = list
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.into_iter()
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.map(|item| match item {
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MessageContentPart::Text { text } => json!({"text": text}),
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MessageContentPart::ImageUrl { image_url: ImageUrl { url } } => {
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if let Some((mime_type, data)) = url.strip_prefix("data:").and_then(|v| v.split_once(";base64,")) {
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json!({ "inline_data": { "mime_type": mime_type, "data": data } })
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} else {
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network_image_urls.push(url.clone());
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json!({ "url": url })
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}
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},
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})
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.collect();
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vec![json!({ "role": role, "parts": parts })]
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},
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MessageContent::ToolCalls(MessageContentToolCalls { tool_results, .. }) => {
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let model_parts: Vec<Value> = tool_results.iter().map(|tool_result| {
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let mut part = json!({
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"functionCall": {
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"name": tool_result.call.name,
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"args": tool_result.call.arguments,
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}
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});
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if let Some(sig) = &tool_result.call.thought_signature {
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part["thoughtSignature"] = json!(sig);
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}
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part
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}).collect();
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let function_parts: Vec<Value> = tool_results.into_iter().map(|tool_result| {
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json!({
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"functionResponse": {
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"name": tool_result.call.name,
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"response": {
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"name": tool_result.call.name,
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"content": tool_result.output,
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}
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}
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})
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}).collect();
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vec![
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json!({ "role": "model", "parts": model_parts }),
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json!({ "role": "function", "parts": function_parts }),
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]
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}
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}
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})
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.collect();
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if !network_image_urls.is_empty() {
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bail!(
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"The model does not support network images: {:?}",
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network_image_urls
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);
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}
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let mut body = json!({ "contents": contents, "generationConfig": {} });
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if let Some(v) = system_message {
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body["systemInstruction"] = json!({ "parts": [{"text": v }] });
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}
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if let Some(v) = model.max_tokens_param() {
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body["generationConfig"]["maxOutputTokens"] = v.into();
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}
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if let Some(v) = temperature {
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body["generationConfig"]["temperature"] = v.into();
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}
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if let Some(v) = top_p {
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body["generationConfig"]["topP"] = v.into();
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}
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if let Some(functions) = functions {
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// Gemini doesn't support functions with parameters that have empty properties, so we need to patch it.
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let function_declarations: Vec<_> = functions
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.into_iter()
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.map(|function| {
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if function.parameters.is_empty_properties() {
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json!({
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"name": function.name,
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"description": function.description,
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})
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} else {
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json!(function)
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}
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})
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.collect();
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body["tools"] = json!([{ "functionDeclarations": function_declarations }]);
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}
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Ok(body)
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}
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#[derive(Debug, Clone, Copy, PartialEq, Eq)]
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enum ModelCategory {
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Gemini,
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Claude,
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Mistral,
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}
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|
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impl FromStr for ModelCategory {
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type Err = anyhow::Error;
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fn from_str(s: &str) -> std::result::Result<Self, Self::Err> {
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if s.starts_with("gemini") {
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Ok(ModelCategory::Gemini)
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} else if s.starts_with("claude") {
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Ok(ModelCategory::Claude)
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} else if s.starts_with("mistral") || s.starts_with("codestral") {
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Ok(ModelCategory::Mistral)
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} else {
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unsupported_model!(s)
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}
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}
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}
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|
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pub async fn prepare_gcloud_access_token(
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client: &reqwest::Client,
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client_name: &str,
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adc_file: &Option<String>,
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) -> Result<()> {
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if !is_valid_access_token(client_name) {
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let (token, expires_in) = fetch_access_token(client, adc_file)
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.await
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.with_context(|| "Failed to fetch access token")?;
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let expires_at = Utc::now()
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+ Duration::try_seconds(expires_in)
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.ok_or_else(|| anyhow!("Failed to parse expires_in of access_token"))?;
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set_access_token(client_name, token, expires_at.timestamp())
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}
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Ok(())
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}
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async fn fetch_access_token(
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client: &reqwest::Client,
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file: &Option<String>,
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) -> Result<(String, i64)> {
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let credentials = load_adc(file).await?;
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let value: Value = client
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.post("https://oauth2.googleapis.com/token")
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.json(&credentials)
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.send()
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.await?
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.json()
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.await?;
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|
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if let (Some(access_token), Some(expires_in)) =
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(value["access_token"].as_str(), value["expires_in"].as_i64())
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{
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Ok((access_token.to_string(), expires_in))
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} else if let Some(err_msg) = value["error_description"].as_str() {
|
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bail!("{err_msg}")
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} else {
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bail!("Invalid response data: {value}")
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}
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}
|
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|
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async fn load_adc(file: &Option<String>) -> Result<Value> {
|
|
let adc_file = file
|
|
.as_ref()
|
|
.map(PathBuf::from)
|
|
.or_else(default_adc_file)
|
|
.ok_or_else(|| anyhow!("No application_default_credentials.json"))?;
|
|
let data = tokio::fs::read_to_string(adc_file).await?;
|
|
let data: Value = serde_json::from_str(&data)?;
|
|
if let (Some(client_id), Some(client_secret), Some(refresh_token)) = (
|
|
data["client_id"].as_str(),
|
|
data["client_secret"].as_str(),
|
|
data["refresh_token"].as_str(),
|
|
) {
|
|
Ok(json!({
|
|
"client_id": client_id,
|
|
"client_secret": client_secret,
|
|
"refresh_token": refresh_token,
|
|
"grant_type": "refresh_token",
|
|
}))
|
|
} else {
|
|
bail!("Invalid application_default_credentials.json")
|
|
}
|
|
}
|
|
|
|
#[cfg(not(windows))]
|
|
fn default_adc_file() -> Option<PathBuf> {
|
|
let mut path = dirs::home_dir()?;
|
|
path.push(".config");
|
|
path.push("gcloud");
|
|
path.push("application_default_credentials.json");
|
|
Some(path)
|
|
}
|
|
|
|
#[cfg(windows)]
|
|
fn default_adc_file() -> Option<PathBuf> {
|
|
let mut path = dirs::config_dir()?;
|
|
path.push("gcloud");
|
|
path.push("application_default_credentials.json");
|
|
Some(path)
|
|
}
|
|
|
|
fn strip_model_version(name: &str) -> &str {
|
|
match name.split_once('@') {
|
|
Some((v, _)) => v,
|
|
None => name,
|
|
}
|
|
}
|