feat: add per-article classify/summarize prompt and schema

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/src/services/llm/schema.rs

@@ -107,6 +107,20 @@ pub fn build_classification_schema() -> Value {
     })
 }
 
+/// Build a JSON Schema for per-article classification and summarization.
+pub fn build_article_classify_schema() -> Value {
+    serde_json::json!({
+        "type": "object",
+        "properties": {
+            "title": { "type": "string", "description": "Article title" },
+            "summary": { "type": "string", "description": "4-5 line summary of the article" },
+            "category": { "type": "string", "description": "Category name from the provided list" }
+        },
+        "required": ["title", "summary", "category"],
+        "additionalProperties": false
+    })
+}
+
 /// Build a JSON Schema for LLM link extraction response.
 pub fn build_link_extraction_schema() -> Value {
     serde_json::json!({
@@ -330,6 +344,16 @@ mod tests {
         assert_eq!(schema["additionalProperties"], false);
     }
 
+    #[test]
+    fn article_classify_schema_has_all_fields() {
+        let schema = build_article_classify_schema();
+        let props = schema["properties"].as_object().unwrap();
+        assert!(props.contains_key("title"));
+        assert!(props.contains_key("summary"));
+        assert!(props.contains_key("category"));
+        assert_eq!(schema["additionalProperties"], false);
+    }
+
     #[test]
     fn link_extraction_schema_has_urls_array() {
         let schema = build_link_extraction_schema();

backend/src/services/prompts.rs

@@ -205,6 +205,43 @@ pub fn build_article_extraction_prompt(head_html: &str, body_text: &str) -> (Str
     (system_prompt, user_prompt)
 }
 
+/// Build a prompt for per-article classification and summarization.
+///
+/// The LLM classifies the article into a category and generates a title + summary.
+pub fn build_article_classify_prompt(
+    title: &str,
+    body_snippet: &str,
+    categories: &[String],
+) -> (String, String) {
+    let system_prompt =
+        "Tu es un assistant qui analyse des articles d'actualite. \
+         Tu dois classer l'article dans une categorie et generer un titre et un resume. \
+         Reponds uniquement au format JSON demande."
+            .to_string();
+
+    let categories_list = categories
+        .iter()
+        .map(|c| format!("- \"{}\"", c))
+        .collect::<Vec<_>>()
+        .join("\n");
+
+    let user_prompt = format!(
+        "Voici un article d'actualite.\n\n\
+         Titre : {title}\n\n\
+         Contenu (extrait) :\n{body}\n\n\
+         Categories disponibles :\n{categories}\n\n\
+         Classe cet article dans la categorie la plus appropriee.\n\
+         Si aucune categorie ne correspond, utilise \"Autre\".\n\
+         Genere un titre clair et un resume de 4 a 5 lignes.\n\
+         Si le titre fourni est vide, genere un titre a partir du contenu.",
+        title = if title.is_empty() { "(pas de titre)" } else { title },
+        body = body_snippet,
+        categories = categories_list,
+    );
+
+    (system_prompt, user_prompt)
+}
+
 /// Build a prompt for classifying scraped articles into categories.
 ///
 /// # Arguments
@@ -528,6 +565,25 @@ mod tests {
         assert!(user.len() < 15000);
     }
 
+    #[test]
+    fn article_classify_prompt_includes_content() {
+        let (sys, user) = build_article_classify_prompt(
+            "GPT-5 Released",
+            "OpenAI released GPT-5 today",
+            &["AI News".into(), "Autre".into()],
+        );
+        assert!(user.contains("GPT-5 Released"));
+        assert!(user.contains("AI News"));
+        assert!(user.contains("Autre"));
+        assert!(sys.contains("classer"));
+    }
+
+    #[test]
+    fn article_classify_prompt_handles_empty_title() {
+        let (_, user) = build_article_classify_prompt("", "Some content", &["Tech".into(), "Autre".into()]);
+        assert!(user.contains("(pas de titre)"));
+    }
+
     #[test]
     fn article_extraction_prompt_includes_content() {
         let (_, user) = build_article_extraction_prompt("<meta name='date'>", "Article body here");