mirror of
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Adding support for wildcard models in the model_providers config (#696)
* cleaning up plano cli commands * adding support for wildcard model providers * fixing compile errors * fixing bugs related to default model provider, provider hint and duplicates in the model provider list * fixed cargo fmt issues * updating tests to always include the model id * using default for the prompt_gateway path * fixed the model name, as gpt-5-mini-2025-08-07 wasn't in the config * making sure that all aliases and models match the config * fixed the config generator to allow for base_url providers LLMs to include wildcard models * re-ran the models list utility and added a shell script to run it * updating docs to mention wildcard model providers * updated provider_models.json to yaml, added that file to our docs for reference * updating the build docs to use the new root-based build --------- Co-authored-by: Salman Paracha <salmanparacha@MacBook-Pro-342.local>
This commit is contained in:
Salman Paracha
GitHub
parent
8428b06e22
commit
2941392ed1
42 changed files with 1748 additions and 202 deletions
412
crates/hermesllm/src/bin/fetch_models.rs
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412
crates/hermesllm/src/bin/fetch_models.rs
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@ -0,0 +1,412 @@
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// Fetch latest provider models from canonical provider APIs and update provider_models.yaml
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// Usage:
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// Optional: OPENAI_API_KEY, ANTHROPIC_API_KEY, DEEPSEEK_API_KEY, GROK_API_KEY,
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// DASHSCOPE_API_KEY, MOONSHOT_API_KEY, ZHIPU_API_KEY, GOOGLE_API_KEY
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// Required: AWS CLI configured for Amazon Bedrock models
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// cargo run --bin fetch_models
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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fn main() {
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// Default to writing in the same directory as this source file
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let default_path = std::path::Path::new(file!())
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.parent()
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.unwrap()
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.join("provider_models.yaml");
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let output_path = std::env::args()
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.nth(1)
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.unwrap_or_else(|| default_path.to_string_lossy().to_string());
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println!("Fetching latest models from provider APIs...");
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match fetch_all_models() {
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Ok(models) => {
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let yaml = serde_yaml::to_string(&models).expect("Failed to serialize models");
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std::fs::write(&output_path, yaml).expect("Failed to write provider_models.yaml");
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println!(
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"✓ Successfully updated {} providers ({} models) to {}",
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models.metadata.total_providers, models.metadata.total_models, output_path
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);
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}
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Err(e) => {
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eprintln!("Error fetching models: {}", e);
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eprintln!("\nMake sure required tools are set up:");
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eprintln!(" AWS CLI configured for Bedrock (for Amazon models)");
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eprintln!(" export OPENAI_API_KEY=your-key-here # Optional");
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eprintln!(" export DEEPSEEK_API_KEY=your-key-here # Optional");
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eprintln!(" cargo run --bin fetch_models");
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std::process::exit(1);
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}
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}
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}
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// OpenAI-compatible API response (used by most providers)
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#[derive(Debug, Deserialize)]
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struct OpenAICompatibleModel {
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id: String,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAICompatibleResponse {
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data: Vec<OpenAICompatibleModel>,
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}
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// Google Gemini API response
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#[derive(Debug, Deserialize)]
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struct GoogleModel {
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name: String,
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#[serde(rename = "supportedGenerationMethods")]
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supported_generation_methods: Option<Vec<String>>,
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}
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#[derive(Debug, Deserialize)]
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struct GoogleResponse {
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models: Vec<GoogleModel>,
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}
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#[derive(Debug, Serialize)]
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struct ProviderModels {
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version: String,
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source: String,
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providers: HashMap<String, Vec<String>>,
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metadata: Metadata,
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}
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#[derive(Debug, Serialize)]
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struct Metadata {
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total_providers: usize,
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total_models: usize,
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last_updated: String,
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}
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fn is_text_model(model_id: &str) -> bool {
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let id_lower = model_id.to_lowercase();
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// Filter out known non-text models
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let non_text_patterns = [
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"embedding", // Embedding models
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"whisper", // Audio transcription
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"-tts", // Text-to-speech (with dash to avoid matching in middle of words)
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"tts-", // Text-to-speech prefix
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"dall-e", // Image generation
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"sora", // Video generation
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"moderation", // Moderation models
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"babbage", // Legacy completion models
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"davinci-002", // Legacy completion models
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"transcribe", // Audio transcription models
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"realtime", // Realtime audio models
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"audio", // Audio models (gpt-audio, gpt-audio-mini)
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"-image-", // Image generation models (grok-2-image-1212)
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"-ocr-", // OCR models
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"ocr-", // OCR models prefix
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"voxtral", // Audio/voice models
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];
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// Additional pattern: models that are purely for image generation usually have "image" in the name
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// but we need to be careful not to filter vision models that can process images
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// Models like "gpt-image-1" or "chatgpt-image-latest" are image generators
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// Models like "grok-2-vision" or "gemini-vision" are vision models (text+image->text)
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if non_text_patterns
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.iter()
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.any(|pattern| id_lower.contains(pattern))
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{
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return false;
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}
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// Filter models starting with "gpt-image" (image generators)
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if id_lower.contains("/gpt-image") || id_lower.contains("/chatgpt-image") {
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return false;
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}
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true
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}
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fn fetch_openai_compatible_models(
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api_url: &str,
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api_key: &str,
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provider_prefix: &str,
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) -> Result<Vec<String>, Box<dyn std::error::Error>> {
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let response_body = ureq::get(api_url)
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.header("Authorization", &format!("Bearer {}", api_key))
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.call()?
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.body_mut()
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.read_to_string()?;
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let response: OpenAICompatibleResponse = serde_json::from_str(&response_body)?;
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Ok(response
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.data
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.into_iter()
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.filter(|m| is_text_model(&m.id))
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.map(|m| format!("{}/{}", provider_prefix, m.id))
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.collect())
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}
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fn fetch_anthropic_models(api_key: &str) -> Result<Vec<String>, Box<dyn std::error::Error>> {
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let response_body = ureq::get("https://api.anthropic.com/v1/models")
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.header("x-api-key", api_key)
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.header("anthropic-version", "2023-06-01")
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.call()?
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.body_mut()
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.read_to_string()?;
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let response: OpenAICompatibleResponse = serde_json::from_str(&response_body)?;
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let dated_models: Vec<String> = response
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.data
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.into_iter()
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.filter(|m| is_text_model(&m.id))
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.map(|m| m.id)
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.collect();
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let mut models: Vec<String> = Vec::new();
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// Add both dated versions and their aliases (without the -YYYYMMDD suffix)
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for model_id in dated_models {
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// Add the full dated model ID
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models.push(format!("anthropic/{}", model_id));
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// Generate alias by removing trailing -YYYYMMDD pattern
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// Pattern: ends with -YYYYMMDD where YYYY is year, MM is month, DD is day
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if let Some(date_pos) = model_id.rfind('-') {
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let potential_date = &model_id[date_pos + 1..];
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// Check if it's an 8-digit date (YYYYMMDD)
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if potential_date.len() == 8 && potential_date.chars().all(|c| c.is_ascii_digit()) {
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let alias = &model_id[..date_pos];
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let alias_full = format!("anthropic/{}", alias);
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// Only add if not already present
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if !models.contains(&alias_full) {
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models.push(alias_full);
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}
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}
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}
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}
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Ok(models)
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}
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fn fetch_google_models(api_key: &str) -> Result<Vec<String>, Box<dyn std::error::Error>> {
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let api_url = format!(
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"https://generativelanguage.googleapis.com/v1beta/models?key={}",
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api_key
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);
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let response_body = ureq::get(&api_url).call()?.body_mut().read_to_string()?;
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let response: GoogleResponse = serde_json::from_str(&response_body)?;
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// Only include models that support generateContent
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Ok(response
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.models
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.into_iter()
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.filter(|m| {
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m.supported_generation_methods
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.as_ref()
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.is_some_and(|methods| methods.contains(&"generateContent".to_string()))
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})
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.map(|m| {
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// Convert "models/gemini-pro" to "google/gemini-pro"
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let model_id = m.name.strip_prefix("models/").unwrap_or(&m.name);
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format!("google/{}", model_id)
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})
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.collect())
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}
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fn fetch_bedrock_amazon_models() -> Result<Vec<String>, Box<dyn std::error::Error>> {
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// Use AWS CLI to fetch Amazon models from Bedrock
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let output = std::process::Command::new("aws")
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.args([
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"bedrock",
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"list-foundation-models",
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"--by-provider",
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"amazon",
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"--by-output-modality",
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"TEXT",
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"--no-cli-pager",
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"--output",
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"json",
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])
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.output()?;
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if !output.status.success() {
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return Err(format!(
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"AWS CLI command failed: {}",
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String::from_utf8_lossy(&output.stderr)
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)
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.into());
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}
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let response_body = String::from_utf8(output.stdout)?;
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#[derive(Debug, Deserialize)]
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struct BedrockModelSummary {
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#[serde(rename = "modelId")]
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model_id: String,
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}
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#[derive(Debug, Deserialize)]
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struct BedrockResponse {
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#[serde(rename = "modelSummaries")]
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model_summaries: Vec<BedrockModelSummary>,
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}
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let bedrock_response: BedrockResponse = serde_json::from_str(&response_body)?;
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// Filter out embedding, image generation, and rerank models
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let amazon_models: Vec<String> = bedrock_response
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.model_summaries
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.into_iter()
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.filter(|model| {
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let id_lower = model.model_id.to_lowercase();
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!id_lower.contains("embed")
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&& !id_lower.contains("image")
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&& !id_lower.contains("rerank")
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})
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.map(|m| format!("amazon/{}", m.model_id))
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.collect();
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Ok(amazon_models)
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}
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fn fetch_all_models() -> Result<ProviderModels, Box<dyn std::error::Error>> {
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let mut providers: HashMap<String, Vec<String>> = HashMap::new();
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let mut errors: Vec<String> = Vec::new();
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// Configuration: provider name, env var, API URL, prefix for model IDs
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let provider_configs = vec![
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(
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"openai",
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"OPENAI_API_KEY",
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"https://api.openai.com/v1/models",
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"openai",
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),
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(
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"mistralai",
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"MISTRAL_API_KEY",
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"https://api.mistral.ai/v1/models",
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"mistralai",
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),
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(
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"deepseek",
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"DEEPSEEK_API_KEY",
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"https://api.deepseek.com/v1/models",
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"deepseek",
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),
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("x-ai", "GROK_API_KEY", "https://api.x.ai/v1/models", "x-ai"),
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(
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"moonshotai",
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"MOONSHOT_API_KEY",
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"https://api.moonshot.ai/v1/models",
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"moonshotai",
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),
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(
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"qwen",
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"DASHSCOPE_API_KEY",
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"https://dashscope-intl.aliyuncs.com/compatible-mode/v1/models",
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"qwen",
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),
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(
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"z-ai",
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"ZHIPU_API_KEY",
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"https://open.bigmodel.cn/api/paas/v4/models",
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"z-ai",
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),
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];
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// Fetch from OpenAI-compatible providers
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for (provider_name, env_var, api_url, prefix) in provider_configs {
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if let Ok(api_key) = std::env::var(env_var) {
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match fetch_openai_compatible_models(api_url, &api_key, prefix) {
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Ok(models) => {
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println!(" ✓ {}: {} models", provider_name, models.len());
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providers.insert(provider_name.to_string(), models);
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}
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Err(e) => {
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let err_msg = format!(" ✗ {}: {}", provider_name, e);
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eprintln!("{}", err_msg);
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errors.push(err_msg);
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}
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}
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} else {
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println!(" ⊘ {}: {} not set (skipped)", provider_name, env_var);
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}
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}
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// Fetch Anthropic models (different authentication)
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if let Ok(api_key) = std::env::var("ANTHROPIC_API_KEY") {
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match fetch_anthropic_models(&api_key) {
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Ok(models) => {
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println!(" ✓ anthropic: {} models", models.len());
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providers.insert("anthropic".to_string(), models);
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}
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Err(e) => {
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let err_msg = format!(" ✗ anthropic: {}", e);
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eprintln!("{}", err_msg);
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errors.push(err_msg);
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}
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}
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} else {
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println!(" ⊘ anthropic: ANTHROPIC_API_KEY not set (skipped)");
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}
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// Fetch Google models (different API format)
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if let Ok(api_key) = std::env::var("GOOGLE_API_KEY") {
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match fetch_google_models(&api_key) {
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Ok(models) => {
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println!(" ✓ google: {} models", models.len());
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providers.insert("google".to_string(), models);
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}
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Err(e) => {
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let err_msg = format!(" ✗ google: {}", e);
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eprintln!("{}", err_msg);
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errors.push(err_msg);
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}
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}
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} else {
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println!(" ⊘ google: GOOGLE_API_KEY not set (skipped)");
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}
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// Fetch Amazon models from AWS Bedrock
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match fetch_bedrock_amazon_models() {
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Ok(models) => {
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println!(" ✓ amazon: {} models (via AWS Bedrock)", models.len());
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providers.insert("amazon".to_string(), models);
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}
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Err(e) => {
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let err_msg = format!(" ✗ amazon: {} (AWS Bedrock required)", e);
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eprintln!("{}", err_msg);
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errors.push(err_msg);
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}
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}
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if providers.is_empty() {
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return Err("No models fetched from any provider. Check API keys.".into());
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}
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let total_providers = providers.len();
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let total_models: usize = providers.values().map(|v| v.len()).sum();
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println!(
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"\n✅ Successfully fetched models from {} providers",
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total_providers
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);
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if !errors.is_empty() {
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println!("⚠️ {} providers failed", errors.len());
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}
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Ok(ProviderModels {
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version: "1.0".to_string(),
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source: "canonical-apis".to_string(),
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providers,
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metadata: Metadata {
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total_providers,
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total_models,
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last_updated: chrono::Utc::now().to_rfc3339(),
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},
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})
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}
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