feat: async insight generation with SQLite job tracking
- Add insight_generation_jobs table migration and DAO - Implement job lifecycle: create_or_get_active, complete, fail, cancel - Refactor POST /insights/generate and /agentic to async spawn with timeout - Add GET /insights/generation/status endpoint with job_id and file_path lookup - Use String for enum fields in Diesel models to avoid private Bound type - Add from_str() helpers on InsightJobStatus and InsightGenerationType - Fix update_training_messages to return Result<usize, DbError> - 7/7 DAO unit tests passing
This commit is contained in:
Cameron Cordes
+332
-160
@@ -5,8 +5,9 @@ use serde::{Deserialize, Serialize};
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use crate::ai::insight_chat::{ChatStreamEvent, ChatTurnRequest};
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use crate::ai::ollama::ChatMessage;
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use crate::ai::{InsightGenerator, ModelCapabilities, OllamaClient};
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use crate::ai::{ModelCapabilities, OllamaClient};
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use crate::data::Claims;
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use crate::database::models::{InsightGenerationType, InsightJobStatus};
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use crate::database::{ExifDao, InsightDao};
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use crate::libraries;
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use crate::otel::{extract_context_from_request, global_tracer};
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@@ -64,6 +65,101 @@ pub struct GetPhotoInsightQuery {
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pub library: Option<String>,
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}
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#[derive(Debug, Deserialize)]
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pub struct GenerationStatusQuery {
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/// If provided, look up the job by id.
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#[serde(default)]
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pub job_id: Option<i32>,
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/// If provided with `library`, look up the latest running job for this
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/// file. Used when the client doesn't have a persisted job_id.
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#[serde(default)]
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pub file_path: Option<String>,
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#[serde(default)]
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pub library: Option<String>,
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}
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/// GET /insights/generation/status - Check status of a generation job.
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/// Accepts either `?job_id=<id>` or `?file_path=<path>&library=<name>`.
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#[get("/insights/generation/status")]
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pub async fn generation_status_handler(
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_claims: Claims,
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query: web::Query<GenerationStatusQuery>,
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app_state: web::Data<AppState>,
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) -> impl Responder {
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let ctx = opentelemetry::Context::new();
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if let Some(jid) = query.job_id {
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let mut dao = app_state
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.insight_job_dao
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.lock()
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.expect("Unable to lock InsightJobDao");
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match dao.get_job_by_id(&ctx, jid) {
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Ok(Some(job)) => {
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return HttpResponse::Ok().json(GenerationStatusResponse {
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job_id: job.id,
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status: InsightJobStatus::from_str(&job.status),
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started_at: job.started_at,
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completed_at: job.completed_at,
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result_insight_id: job.result_insight_id,
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error_message: job.error_message,
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});
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}
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Ok(None) => {
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return HttpResponse::NotFound().json(serde_json::json!({
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"error": format!("Job {} not found", jid)
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}));
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}
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Err(e) => {
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log::error!("Failed to look up job {}: {:?}", jid, e);
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return HttpResponse::InternalServerError().json(serde_json::json!({
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"error": "Failed to look up job"
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}));
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}
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}
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}
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if let Some(ref fp) = query.file_path {
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let library = libraries::resolve_library_param(&app_state, query.library.as_deref())
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.ok()
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.flatten()
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.unwrap_or_else(|| app_state.primary_library());
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let normalized = normalize_path(fp);
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let mut dao = app_state
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.insight_job_dao
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.lock()
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.expect("Unable to lock InsightJobDao");
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match dao.get_active_job(&ctx, library.id, &normalized) {
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Ok(Some(job)) => {
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return HttpResponse::Ok().json(GenerationStatusResponse {
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job_id: job.id,
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status: InsightJobStatus::from_str(&job.status),
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started_at: job.started_at,
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completed_at: job.completed_at,
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result_insight_id: job.result_insight_id,
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error_message: job.error_message,
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});
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}
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Ok(None) => {
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return HttpResponse::Ok().json(serde_json::json!({
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"status": "idle",
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"message": "No running generation job for this file"
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}));
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}
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Err(e) => {
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log::error!("Failed to look up active job for {}: {:?}", normalized, e);
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return HttpResponse::InternalServerError().json(serde_json::json!({
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"error": "Failed to look up active job"
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}));
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}
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}
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}
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HttpResponse::BadRequest().json(serde_json::json!({
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"error": "Provide either job_id or file_path query parameter"
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}))
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}
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#[derive(Debug, Deserialize)]
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pub struct RateInsightRequest {
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pub file_path: String,
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@@ -76,6 +172,24 @@ pub struct ExportTrainingDataQuery {
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pub approved_only: Option<bool>,
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}
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#[derive(Debug, Serialize)]
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pub struct JobIdResponse {
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pub job_id: i32,
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}
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#[derive(Debug, Serialize)]
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pub struct GenerationStatusResponse {
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pub job_id: i32,
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pub status: InsightJobStatus,
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pub started_at: i64,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub completed_at: Option<i64>,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub result_insight_id: Option<i32>,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub error_message: Option<String>,
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}
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#[derive(Debug, Serialize)]
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pub struct PhotoInsightResponse {
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pub id: i32,
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@@ -110,70 +224,123 @@ pub struct ServerModels {
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pub default_model: String,
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}
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/// POST /insights/generate - Generate insight for a specific photo
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/// POST /insights/generate - Generate insight for a specific photo (async)
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#[post("/insights/generate")]
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pub async fn generate_insight_handler(
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http_request: HttpRequest,
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_http_request: HttpRequest,
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_claims: Claims,
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request: web::Json<GeneratePhotoInsightRequest>,
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insight_generator: web::Data<InsightGenerator>,
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app_state: web::Data<AppState>,
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) -> impl Responder {
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let parent_context = extract_context_from_request(&http_request);
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let tracer = global_tracer();
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let mut span = tracer.start_with_context("http.insights.generate", &parent_context);
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let normalized_path = normalize_path(&request.file_path);
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span.set_attribute(KeyValue::new("file_path", normalized_path.clone()));
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if let Some(ref model) = request.model {
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span.set_attribute(KeyValue::new("model", model.clone()));
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}
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if let Some(ref prompt) = request.system_prompt {
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span.set_attribute(KeyValue::new("has_custom_prompt", true));
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span.set_attribute(KeyValue::new("prompt_length", prompt.len() as i64));
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}
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if let Some(ctx) = request.num_ctx {
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span.set_attribute(KeyValue::new("num_ctx", ctx as i64));
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}
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let library = app_state.primary_library();
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let gen_type = InsightGenerationType::Standard;
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log::info!(
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"Manual insight generation triggered for photo: {} with model: {:?}, custom_prompt: {}, num_ctx: {:?}",
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"Manual insight generation triggered for photo: {} with model: {:?}",
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normalized_path,
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request.model,
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request.system_prompt.is_some(),
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request.num_ctx
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request.model
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);
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// Generate insight with optional custom model, system prompt, and context size
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let result = insight_generator
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.generate_insight_for_photo_with_config(
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// Cancel any running job for this file, then create a fresh one
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{
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let mut dao = app_state
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.insight_job_dao
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.lock()
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.expect("Unable to lock InsightJobDao");
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let _ = dao.cancel_active_job(
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&opentelemetry::Context::new(),
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library.id,
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&normalized_path,
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request.model.clone(),
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request.system_prompt.clone(),
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request.num_ctx,
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request.temperature,
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request.top_p,
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request.top_k,
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request.min_p,
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gen_type,
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);
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}
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let job_id = {
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let mut dao = app_state
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.insight_job_dao
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.lock()
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.expect("Unable to lock InsightJobDao");
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match dao.create_or_get_active_job(
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&opentelemetry::Context::new(),
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library.id,
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&normalized_path,
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gen_type,
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) {
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Ok(id) => id,
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Err(e) => {
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log::error!("Failed to create generation job: {:?}", e);
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return HttpResponse::InternalServerError().json(serde_json::json!({
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"error": "Failed to create generation job"
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}));
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}
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}
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};
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// Spawn background task with timeout
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let generator = app_state.insight_generator.clone();
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let job_dao = app_state.insight_job_dao.clone();
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let lib_id = library.id;
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let path = normalized_path.clone();
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tokio::spawn(async move {
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let timeout_secs: u64 = std::env::var("INSIGHT_GENERATION_TIMEOUT_SECS")
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.ok()
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.and_then(|v| v.parse().ok())
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.unwrap_or(120);
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let result = tokio::time::timeout(
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std::time::Duration::from_secs(timeout_secs),
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generator.generate_insight_for_photo_with_config(
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&path,
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request.model.clone(),
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request.system_prompt.clone(),
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request.num_ctx,
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request.temperature,
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request.top_p,
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request.top_k,
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request.min_p,
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),
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)
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.await;
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match result {
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Ok(()) => {
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span.set_status(Status::Ok);
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HttpResponse::Ok().json(serde_json::json!({
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"success": true,
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"message": "Insight generated successfully"
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}))
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let ctx = opentelemetry::Context::new();
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let mut dao = job_dao.lock().expect("Unable to lock InsightJobDao");
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match result {
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Ok(Ok(())) => {
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// Look up the stored insight id to record on the job
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let mut insight_dao = generator
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.insight_dao()
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.lock()
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.expect("Unable to lock InsightDao");
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let insight_id = insight_dao
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.get_insight(&ctx, &path)
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.ok()
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.flatten()
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.map(|i| i.id);
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if let Some(id) = insight_id {
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let _ = dao.complete_job(&ctx, job_id, id);
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} else {
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let _ = dao.fail_job(&ctx, job_id, "generation returned no insight");
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}
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}
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Ok(Err(e)) => {
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log::error!("Insight generation failed for {}: {:?}", path, e);
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let _ = dao.fail_job(&ctx, job_id, &format!("{:?}", e));
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}
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Err(_) => {
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log::error!(
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"Insight generation timed out for {} after {}s",
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path,
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timeout_secs
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);
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let _ = dao.fail_job(&ctx, job_id, &format!("timeout after {}s", timeout_secs));
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}
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}
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Err(e) => {
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log::error!("Failed to generate insight: {:?}", e);
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span.set_status(Status::error(e.to_string()));
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HttpResponse::InternalServerError().json(serde_json::json!({
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"error": format!("Failed to generate insight: {:?}", e)
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}))
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}
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}
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});
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HttpResponse::Ok().json(JobIdResponse { job_id })
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}
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/// GET /insights?path=/path/to/photo.jpg - Fetch insight for specific photo
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@@ -301,56 +468,60 @@ pub async fn get_all_insights_handler(
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}
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}
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/// POST /insights/generate/agentic - Generate insight using agentic tool-calling loop
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/// POST /insights/generate/agentic - Generate insight using agentic tool-calling loop (async)
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#[post("/insights/generate/agentic")]
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pub async fn generate_agentic_insight_handler(
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http_request: HttpRequest,
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_http_request: HttpRequest,
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claims: Claims,
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request: web::Json<GeneratePhotoInsightRequest>,
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insight_generator: web::Data<InsightGenerator>,
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insight_dao: web::Data<std::sync::Mutex<Box<dyn InsightDao>>>,
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app_state: web::Data<AppState>,
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) -> impl Responder {
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// Service tokens (sub: "service:apollo") fall through to user_id=1
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// — the operator convention. Mobile/web clients have a numeric sub.
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let user_id = claims.sub.parse::<i32>().unwrap_or(1);
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let parent_context = extract_context_from_request(&http_request);
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let tracer = global_tracer();
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let mut span = tracer.start_with_context("http.insights.generate_agentic", &parent_context);
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let normalized_path = normalize_path(&request.file_path);
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span.set_attribute(KeyValue::new("file_path", normalized_path.clone()));
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if let Some(ref model) = request.model {
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span.set_attribute(KeyValue::new("model", model.clone()));
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}
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if let Some(ref prompt) = request.system_prompt {
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span.set_attribute(KeyValue::new("has_custom_prompt", true));
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span.set_attribute(KeyValue::new("prompt_length", prompt.len() as i64));
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}
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if let Some(ctx) = request.num_ctx {
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span.set_attribute(KeyValue::new("num_ctx", ctx as i64));
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}
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let max_iterations: usize = std::env::var("AGENTIC_MAX_ITERATIONS")
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.ok()
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.and_then(|v| v.parse().ok())
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.unwrap_or(12);
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span.set_attribute(KeyValue::new("max_iterations", max_iterations as i64));
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let library = app_state.primary_library();
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let gen_type = InsightGenerationType::Agentic;
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log::info!(
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"Agentic insight generation triggered for photo: {} with model: {:?}, max_iterations: {}",
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"Agentic insight generation triggered for photo: {} with model: {:?}",
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normalized_path,
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request.model,
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max_iterations
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request.model
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);
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if let Some(ref b) = request.backend {
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span.set_attribute(KeyValue::new("backend", b.clone()));
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// Cancel any running job for this file, then create a fresh one
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{
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let mut dao = app_state
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.insight_job_dao
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.lock()
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.expect("Unable to lock InsightJobDao");
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let _ = dao.cancel_active_job(
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&opentelemetry::Context::new(),
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library.id,
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&normalized_path,
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gen_type,
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);
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}
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// Resolve few-shot ids: request-provided ids take precedence when
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// non-empty; otherwise fall back to the hardcoded defaults.
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let job_id = {
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let mut dao = app_state
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.insight_job_dao
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.lock()
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.expect("Unable to lock InsightJobDao");
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match dao.create_or_get_active_job(
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&opentelemetry::Context::new(),
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library.id,
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&normalized_path,
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gen_type,
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) {
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Ok(id) => id,
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Err(e) => {
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log::error!("Failed to create agentic generation job: {:?}", e);
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return HttpResponse::InternalServerError().json(serde_json::json!({
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"error": "Failed to create generation job"
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}));
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}
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}
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};
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// Resolve few-shot ids for the background task
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let fewshot_ids: Vec<i32> = match request.fewshot_insight_ids.as_deref() {
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Some(ids) if !ids.is_empty() => ids.iter().take(2).copied().collect(),
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_ => DEFAULT_FEWSHOT_INSIGHT_IDS
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@@ -359,11 +530,14 @@ pub async fn generate_agentic_insight_handler(
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.copied()
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.collect(),
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};
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span.set_attribute(KeyValue::new("fewshot_count", fewshot_ids.len() as i64));
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let fewshot_examples: Vec<Vec<ChatMessage>> = {
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let otel_context = opentelemetry::Context::new();
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let mut dao = insight_dao.lock().expect("Unable to lock InsightDao");
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let mut dao = app_state
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.insight_chat
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.insight_dao()
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.lock()
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.expect("Unable to lock InsightDao");
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fewshot_ids
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.iter()
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.filter_map(|id| {
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@@ -384,90 +558,88 @@ pub async fn generate_agentic_insight_handler(
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.collect()
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};
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let user_id = claims.sub.parse::<i32>().unwrap_or(1);
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let persona_id = request
|
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.persona_id
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.clone()
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.filter(|s| !s.trim().is_empty())
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.unwrap_or_else(|| "default".to_string());
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span.set_attribute(KeyValue::new("persona_id", persona_id.clone()));
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let result = insight_generator
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.generate_agentic_insight_for_photo(
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&normalized_path,
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request.model.clone(),
|
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request.system_prompt.clone(),
|
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request.num_ctx,
|
||||
request.temperature,
|
||||
request.top_p,
|
||||
request.top_k,
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||||
request.min_p,
|
||||
max_iterations,
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request.backend.clone(),
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fewshot_examples,
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||||
fewshot_ids,
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user_id,
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persona_id,
|
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let max_iterations: usize = std::env::var("AGENTIC_MAX_ITERATIONS")
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.ok()
|
||||
.and_then(|v| v.parse().ok())
|
||||
.unwrap_or(12);
|
||||
|
||||
// Spawn background task with timeout
|
||||
let generator = app_state.insight_generator.clone();
|
||||
let job_dao = app_state.insight_job_dao.clone();
|
||||
let lib_id = library.id;
|
||||
let path = normalized_path.clone();
|
||||
|
||||
tokio::spawn(async move {
|
||||
let timeout_secs: u64 = std::env::var("INSIGHT_GENERATION_TIMEOUT_SECS")
|
||||
.ok()
|
||||
.and_then(|v| v.parse().ok())
|
||||
.unwrap_or(180);
|
||||
|
||||
let result = tokio::time::timeout(
|
||||
std::time::Duration::from_secs(timeout_secs),
|
||||
generator.generate_agentic_insight_for_photo(
|
||||
&path,
|
||||
request.model.clone(),
|
||||
request.system_prompt.clone(),
|
||||
request.num_ctx,
|
||||
request.temperature,
|
||||
request.top_p,
|
||||
request.top_k,
|
||||
request.min_p,
|
||||
max_iterations,
|
||||
request.backend.clone(),
|
||||
fewshot_examples,
|
||||
fewshot_ids,
|
||||
user_id,
|
||||
persona_id,
|
||||
),
|
||||
)
|
||||
.await;
|
||||
|
||||
match result {
|
||||
Ok((prompt_eval_count, eval_count)) => {
|
||||
span.set_status(Status::Ok);
|
||||
// Fetch the stored insight to return it
|
||||
let otel_context = opentelemetry::Context::new();
|
||||
let mut dao = insight_dao.lock().expect("Unable to lock InsightDao");
|
||||
match dao.get_insight(&otel_context, &normalized_path) {
|
||||
Ok(Some(insight)) => {
|
||||
let response = PhotoInsightResponse {
|
||||
id: insight.id,
|
||||
file_path: insight.file_path,
|
||||
title: insight.title,
|
||||
summary: insight.summary,
|
||||
generated_at: insight.generated_at,
|
||||
model_version: insight.model_version,
|
||||
prompt_eval_count,
|
||||
eval_count,
|
||||
approved: insight.approved,
|
||||
has_training_messages: insight.training_messages.is_some(),
|
||||
backend: insight.backend,
|
||||
};
|
||||
HttpResponse::Ok().json(response)
|
||||
}
|
||||
Ok(None) => HttpResponse::Ok().json(serde_json::json!({
|
||||
"success": true,
|
||||
"message": "Agentic insight generated successfully"
|
||||
})),
|
||||
Err(e) => {
|
||||
log::warn!("Insight stored but failed to retrieve: {:?}", e);
|
||||
HttpResponse::Ok().json(serde_json::json!({
|
||||
"success": true,
|
||||
"message": "Agentic insight generated successfully"
|
||||
}))
|
||||
}
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
let error_msg = format!("{:?}", e);
|
||||
log::error!("Failed to generate agentic insight: {}", error_msg);
|
||||
span.set_status(Status::error(error_msg.clone()));
|
||||
let ctx = opentelemetry::Context::new();
|
||||
let mut dao = job_dao.lock().expect("Unable to lock InsightJobDao");
|
||||
|
||||
if error_msg.contains("tool calling not supported")
|
||||
|| error_msg.contains("model not available")
|
||||
{
|
||||
HttpResponse::BadRequest().json(serde_json::json!({
|
||||
"error": format!("Failed to generate agentic insight: {}", error_msg)
|
||||
}))
|
||||
} else if error_msg.contains("error parsing tool call") {
|
||||
HttpResponse::BadRequest().json(serde_json::json!({
|
||||
"error": "Model is not compatible with Ollama's tool calling protocol. Try a model known to support native tool calling (e.g. llama3.1, llama3.2, qwen2.5, mistral-nemo)."
|
||||
}))
|
||||
} else {
|
||||
HttpResponse::InternalServerError().json(serde_json::json!({
|
||||
"error": format!("Failed to generate agentic insight: {}", error_msg)
|
||||
}))
|
||||
match result {
|
||||
Ok(Ok(_)) => {
|
||||
// Fetch the stored insight id to record on the job
|
||||
let mut insight_dao = generator
|
||||
.insight_dao()
|
||||
.lock()
|
||||
.expect("Unable to lock InsightDao");
|
||||
let insight_id = insight_dao
|
||||
.get_insight(&ctx, &path)
|
||||
.ok()
|
||||
.flatten()
|
||||
.map(|i| i.id);
|
||||
if let Some(id) = insight_id {
|
||||
let _ = dao.complete_job(&ctx, job_id, id);
|
||||
} else {
|
||||
let _ = dao.fail_job(&ctx, job_id, "generation returned no insight");
|
||||
}
|
||||
}
|
||||
Ok(Err(e)) => {
|
||||
log::error!("Agentic insight generation failed for {}: {:?}", path, e);
|
||||
let _ = dao.fail_job(&ctx, job_id, &format!("{:?}", e));
|
||||
}
|
||||
Err(_) => {
|
||||
log::error!(
|
||||
"Agentic insight generation timed out for {} after {}s",
|
||||
path,
|
||||
timeout_secs
|
||||
);
|
||||
let _ = dao.fail_job(&ctx, job_id, &format!("timeout after {}s", timeout_secs));
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
HttpResponse::Ok().json(JobIdResponse { job_id })
|
||||
}
|
||||
|
||||
/// GET /insights/models - Local-backend models with capabilities. Returns
|
||||
|
||||
Reference in New Issue
Block a user