10c0cc007b
- Split agent crate into client/, model/, agent/ subdirs - Add billing.rs for token usage recording - Add sync.rs for upstream model sync - EmbedService: Qdrant-backed vector memory for semantic search - ChatService: wire EmbedService for memory lookup, passive skill awareness - ReAct loop: streamline with tokio::select! and proper error handling
37 lines
1.1 KiB
Rust
37 lines
1.1 KiB
Rust
//! Model sync from OpenRouter — syncs AI model metadata into the local database.
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use crate::error::AgentError;
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/// Response from `GET /v1/models`.
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#[derive(Debug, serde::Deserialize)]
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pub struct ModelsListResponse {
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pub data: Vec<ModelEntry>,
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}
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#[derive(Debug, serde::Deserialize)]
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pub struct ModelEntry {
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pub id: String,
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}
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/// List accessible model IDs from the AI endpoint.
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pub async fn list_accessible_models(
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client: &reqwest::Client,
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base_url: &str,
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api_key: &str,
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) -> Result<std::collections::HashSet<String>, AgentError> {
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let url = format!("{}/v1/models", base_url.trim_end_matches('/'));
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let resp = client
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.get(&url)
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.header("Authorization", format!("Bearer {}", api_key))
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.send()
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.await
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.map_err(|e| AgentError::Internal(format!("failed to list models: {}", e)))?;
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let body: ModelsListResponse = resp
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.json()
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.await
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.map_err(|e| AgentError::Internal(format!("failed to parse models response: {}", e)))?;
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Ok(body.data.into_iter().map(|m| m.id).collect())
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}
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