Accusys
2cfcfdd1af
Phase 2.6.1: co_occurrence_edges migration - build_co_occurrence_edges_from_qdrant() - Qdrant embeddings → frame grouping → YOLO objects - Result: 6679 edges (vs 6701 PostgreSQL) Phase 2.6.2: face_face_edges migration - build_face_face_edges_from_qdrant() - Qdrant embeddings → frame grouping → face pairs - mutual_gaze detection preserved - Result: 6 edges (exact match) Phase 2.6.3: speaker_face_edges migration - build_speaker_face_edges_from_qdrant() - Qdrant embeddings → trace_id frame ranges - SPEAKS_AS edge creation Architecture: - All edges use Qdrant payload (no face_detections queries) - PostgreSQL fallback for empty Qdrant - Estimated 3.6x performance improvement Testing: - Playground (3003): ✓ All Phase 2.6 logs verified - Edge counts: ✓ Close match with PostgreSQL - Fallback: ✓ Working Docs: - docs_v1.0/DESIGN/TKG_PHASE2_6_EDGES_MIGRATION.md - docs_v1.0/M4_workspace/2026-06-21_phase2_6_test.md
90 lines
2.8 KiB
Rust
90 lines
2.8 KiB
Rust
use axum::{extract::State, http::StatusCode, response::Json, routing::post, Router};
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use serde::{Deserialize, Serialize};
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use crate::api::types::AppState;
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use crate::core::llm::function_calling::LLM_CLIENT;
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pub fn agent_routes() -> Router<AppState> {
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Router::new().route("/api/v1/agents/translate", post(translate_text))
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}
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#[derive(Debug, Deserialize)]
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pub struct TranslationRequest {
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pub text: String,
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pub target_language: String,
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pub source_language: Option<String>, // "auto" if not specified
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}
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#[derive(Debug, Serialize)]
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pub struct TranslationResponse {
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pub success: bool,
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pub translated_text: String,
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pub source_language_detected: String,
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pub model_used: String,
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}
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async fn translate_text(
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State(_state): State<AppState>,
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Json(req): Json<TranslationRequest>,
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) -> Result<Json<TranslationResponse>, (StatusCode, String)> {
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let system_prompt = "You are a professional translator for Momentry Core, a digital asset management system specializing in video analysis.
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## Guidelines:
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1. **Accuracy**: Translate the meaning accurately, maintaining the original tone.
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2. **Style**:
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- For subtitles: Keep it concise and natural for reading.
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- For technical terms (e.g., 5W1H, metadata): Use standard industry translations.
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3. **Output**: Return ONLY the translated text. Do not include explanations or notes.";
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let prompt = format!(
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"Translate the following text to {}: \n\n{}",
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req.target_language, req.text
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);
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// Call LLM via configurable endpoint
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let llm_url = crate::core::config::llm::CHAT_URL.as_str();
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let model = crate::core::config::llm::CHAT_MODEL.as_str();
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let body = serde_json::json!({
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"model": model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt}
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],
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"stream": false,
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"max_tokens": 1024,
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"temperature": 0.1
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});
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let response = LLM_CLIENT
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.post(llm_url)
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.json(&body)
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.send()
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.await
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.map_err(|e| {
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(
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StatusCode::INTERNAL_SERVER_ERROR,
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format!("Failed to call LLM: {}", e),
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)
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})?;
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let llm_resp: serde_json::Value = response.json().await.map_err(|e| {
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(
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StatusCode::INTERNAL_SERVER_ERROR,
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format!("Failed to parse LLM response: {}", e),
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)
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})?;
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let translated_text = llm_resp["choices"][0]["message"]["content"]
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.as_str()
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.unwrap_or("")
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.to_string();
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Ok(Json(TranslationResponse {
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success: true,
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translated_text,
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source_language_detected: req.source_language.unwrap_or_else(|| "auto".to_string()),
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model_used: model.to_string(),
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}))
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}
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