mirror of https://github.com/sigoden/aichat
feat: abandon replicate client (#900)
sigoden
2 weeks ago
committed by
GitHub
parent
419c626485
commit
e009f2e241
@ -1,150 +0,0 @@
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use super::message::*;
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pub struct PromptFormat<'a> {
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pub begin: &'a str,
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pub system_pre_message: &'a str,
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pub system_post_message: &'a str,
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pub user_pre_message: &'a str,
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pub user_post_message: &'a str,
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pub assistant_pre_message: &'a str,
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pub assistant_post_message: &'a str,
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pub end: &'a str,
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}
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pub const GENERIC_PROMPT_FORMAT: PromptFormat<'static> = PromptFormat {
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begin: "",
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system_pre_message: "",
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system_post_message: "\n",
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user_pre_message: "### Instruction:\n",
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user_post_message: "\n",
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assistant_pre_message: "### Response:\n",
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assistant_post_message: "\n",
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end: "### Response:\n",
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};
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pub const MISTRAL_PROMPT_FORMAT: PromptFormat<'static> = PromptFormat {
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begin: "",
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system_pre_message: "[INST] <<SYS>>",
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system_post_message: "<</SYS>> [/INST]",
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user_pre_message: "[INST]",
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user_post_message: "[/INST]",
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assistant_pre_message: "",
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assistant_post_message: "",
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end: "",
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};
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pub const LLAMA3_PROMPT_FORMAT: PromptFormat<'static> = PromptFormat {
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begin: "<|begin_of_text|>",
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system_pre_message: "<|start_header_id|>system<|end_header_id|>\n\n",
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system_post_message: "<|eot_id|>",
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user_pre_message: "<|start_header_id|>user<|end_header_id|>\n\n",
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user_post_message: "<|eot_id|>",
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assistant_pre_message: "<|start_header_id|>assistant<|end_header_id|>\n\n",
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assistant_post_message: "<|eot_id|>",
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end: "<|start_header_id|>assistant<|end_header_id|>\n\n",
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};
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pub const PHI3_PROMPT_FORMAT: PromptFormat<'static> = PromptFormat {
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begin: "",
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system_pre_message: "<|system|>\n",
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system_post_message: "<|end|>\n",
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user_pre_message: "<|user|>\n",
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user_post_message: "<|end|>\n",
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assistant_pre_message: "<|assistant|>\n",
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assistant_post_message: "<|end|>\n",
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end: "<|assistant|>\n",
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};
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pub const COMMAND_R_PROMPT_FORMAT: PromptFormat<'static> = PromptFormat {
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begin: "",
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system_pre_message: "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>",
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system_post_message: "<|END_OF_TURN_TOKEN|>",
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user_pre_message: "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>",
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user_post_message: "<|END_OF_TURN_TOKEN|>",
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assistant_pre_message: "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
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assistant_post_message: "<|END_OF_TURN_TOKEN|>",
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end: "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
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};
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pub const QWEN_PROMPT_FORMAT: PromptFormat<'static> = PromptFormat {
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begin: "",
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system_pre_message: "<|im_start|>system\n",
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system_post_message: "<|im_end|>",
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user_pre_message: "<|im_start|>user\n",
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user_post_message: "<|im_end|>",
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assistant_pre_message: "<|im_start|>assistant\n",
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assistant_post_message: "<|im_end|>",
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end: "<|im_start|>assistant\n",
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};
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pub fn generate_prompt(messages: &[Message], format: PromptFormat) -> anyhow::Result<String> {
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let PromptFormat {
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begin,
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system_pre_message,
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system_post_message,
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user_pre_message,
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user_post_message,
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assistant_pre_message,
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assistant_post_message,
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end,
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} = format;
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let mut prompt = begin.to_string();
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let mut image_urls = vec![];
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for message in messages {
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let role = &message.role;
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let content = match &message.content {
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MessageContent::Text(text) => text.clone(),
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MessageContent::Array(list) => {
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let mut parts = vec![];
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for item in list {
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match item {
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MessageContentPart::Text { text } => parts.push(text.clone()),
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MessageContentPart::ImageUrl {
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image_url: ImageUrl { url },
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} => {
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image_urls.push(url.clone());
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}
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}
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}
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parts.join("\n\n")
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}
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MessageContent::ToolResults(_) => String::new(),
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};
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match role {
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MessageRole::System => prompt.push_str(&format!(
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"{system_pre_message}{content}{system_post_message}"
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)),
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MessageRole::Assistant => prompt.push_str(&format!(
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"{assistant_pre_message}{content}{assistant_post_message}"
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)),
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MessageRole::User => {
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prompt.push_str(&format!("{user_pre_message}{content}{user_post_message}"))
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}
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}
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}
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if !image_urls.is_empty() {
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anyhow::bail!("The model does not support images: {:?}", image_urls);
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}
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prompt.push_str(end);
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Ok(prompt)
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}
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pub fn smart_prompt_format(model_name: &str) -> PromptFormat<'static> {
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if model_name.contains("llama3") || model_name.contains("llama-3") {
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LLAMA3_PROMPT_FORMAT
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} else if model_name.contains("llama2")
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|| model_name.contains("llama-2")
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|| model_name.contains("mistral")
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|| model_name.contains("mixtral")
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{
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MISTRAL_PROMPT_FORMAT
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} else if model_name.contains("phi3") || model_name.contains("phi-3") {
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PHI3_PROMPT_FORMAT
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} else if model_name.contains("command-r") {
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COMMAND_R_PROMPT_FORMAT
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} else if model_name.contains("qwen") {
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QWEN_PROMPT_FORMAT
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} else {
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GENERIC_PROMPT_FORMAT
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}
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}
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@ -1,195 +0,0 @@
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use super::prompt_format::*;
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use super::*;
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use anyhow::{anyhow, Result};
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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::time::Duration;
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const API_BASE: &str = "https://api.replicate.com/v1";
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#[derive(Debug, Clone, Deserialize, Default)]
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pub struct ReplicateConfig {
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pub name: Option<String>,
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pub api_key: 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 ReplicateClient {
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config_get_fn!(api_key, get_api_key);
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pub const PROMPTS: [PromptAction<'static>; 1] =
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[("api_key", "API Key:", true, PromptKind::String)];
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}
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#[async_trait::async_trait]
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impl Client for ReplicateClient {
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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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let request_data = prepare_chat_completions(self, data)?;
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let builder = self.request_builder(client, request_data, ApiType::ChatCompletions);
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chat_completions(builder, client, &self.get_api_key()?).await
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}
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async fn chat_completions_streaming_inner(
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&self,
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client: &ReqwestClient,
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handler: &mut SseHandler,
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data: ChatCompletionsData,
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) -> Result<()> {
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let request_data = prepare_chat_completions(self, data)?;
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let builder = self.request_builder(client, request_data, ApiType::ChatCompletions);
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chat_completions_streaming(builder, handler, client).await
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}
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}
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fn prepare_chat_completions(
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self_: &ReplicateClient,
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data: ChatCompletionsData,
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) -> Result<RequestData> {
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let api_key = self_.get_api_key()?;
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let url = format!("{API_BASE}/models/{}/predictions", self_.model.name());
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let body = build_chat_completions_body(data, &self_.model)?;
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let mut request_data = RequestData::new(url, body);
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request_data.bearer_auth(api_key);
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Ok(request_data)
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}
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async fn chat_completions(
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builder: RequestBuilder,
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client: &ReqwestClient,
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api_key: &str,
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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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let prediction_url = data["urls"]["get"]
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.as_str()
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.ok_or_else(|| anyhow!("Invalid response data: {data}"))?;
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loop {
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tokio::time::sleep(Duration::from_millis(500)).await;
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let prediction_data: Value = client
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.get(prediction_url)
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.bearer_auth(api_key)
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.send()
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.await?
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.json()
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.await?;
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debug!("non-stream-data: {prediction_data}");
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let err = || anyhow!("Invalid response data: {prediction_data}");
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let status = prediction_data["status"].as_str().ok_or_else(err)?;
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if status == "succeeded" {
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return extract_chat_completions(&prediction_data);
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} else if status == "failed" || status == "canceled" {
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return Err(err());
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}
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}
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}
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async fn chat_completions_streaming(
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builder: RequestBuilder,
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handler: &mut SseHandler,
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client: &ReqwestClient,
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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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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 stream_url = data["urls"]["stream"]
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.as_str()
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.ok_or_else(|| anyhow!("Invalid response data: {data}"))?;
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let sse_builder = client.get(stream_url).header("accept", "text/event-stream");
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let handle = |message: SseMmessage| -> Result<bool> {
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if message.event == "done" {
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return Ok(true);
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}
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debug!("stream-data: {}", message.data);
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handler.text(&message.data)?;
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Ok(false)
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};
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sse_stream(sse_builder, handle).await
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}
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fn build_chat_completions_body(data: ChatCompletionsData, model: &Model) -> Result<Value> {
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let ChatCompletionsData {
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messages,
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temperature,
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top_p,
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functions: _,
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stream,
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} = data;
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let prompt = generate_prompt(&messages, smart_prompt_format(model.name()))?;
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let mut input = json!({
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"prompt": prompt,
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"prompt_template": "{prompt}"
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});
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if let Some(v) = model.max_tokens_param() {
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input["max_tokens"] = v.into();
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input["max_new_tokens"] = v.into();
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}
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if let Some(v) = temperature {
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input["temperature"] = v.into();
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}
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if let Some(v) = top_p {
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input["top_p"] = v.into();
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}
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let mut body = json!({
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"input": input,
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});
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if stream {
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body["stream"] = true.into();
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}
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Ok(body)
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}
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fn extract_chat_completions(data: &Value) -> Result<ChatCompletionsOutput> {
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let text = data["output"]
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.as_array()
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.map(|parts| {
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parts
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.iter()
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.filter_map(|v| v.as_str().map(|v| v.to_string()))
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.collect::<Vec<String>>()
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.join("")
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})
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.ok_or_else(|| anyhow!("Invalid response data: {data}"))?;
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let output = ChatCompletionsOutput {
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text: text.to_string(),
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tool_calls: vec![],
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id: data["id"].as_str().map(|v| v.to_string()),
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input_tokens: data["metrics"]["input_token_count"].as_u64(),
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output_tokens: data["metrics"]["output_token_count"].as_u64(),
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};
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Ok(output)
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}
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