refactor article query parameters and improve error messages

src/routes/articles.js

@@ -11,9 +11,9 @@ function buildArticlesQuery(query) {
   const params = [];
   const includeEmbedding = String(query.include_embedding || '').toLowerCase() === 'true';
 
-  if (query.q) {
+  if (query.keyword) {
     conditions.push('(title LIKE ? OR description LIKE ? OR content LIKE ?)');
-    const keyword = `%${query.q}%`;
+    const keyword = `%${query.keyword}%`;
     params.push(keyword, keyword, keyword);
   }
 
@@ -56,16 +56,16 @@ async function articleRoutes(fastify) {
     const query = request.query || {};
     if (query.include_embedding) {
       reply.code(400);
-      return { error: 'Embeddings are not returned directly. Use similar_to for vector search.' };
+      return { error: 'Embeddings are not returned directly. Use similar_to_article for vector search.' };
     }
 
-    if (query.topic !== undefined) {
+    if (query.semantic !== undefined) {
       const limit = Number.parseInt(query.limit, 10);
-      const embedding = await getOrCreateQueryEmbedding(query.topic);
+      const embedding = await getOrCreateQueryEmbedding(query.semantic);
 
       if (!embedding) {
         reply.code(400);
-        return { error: 'Topic must not be empty' };
+        return { error: 'Semantic query must not be empty' };
       }
 
       const neighbors = findArticlesByEmbedding(
@@ -93,9 +93,9 @@ async function articleRoutes(fastify) {
         .filter(Boolean);
     }
 
-    if (query.similar_to) {
+    if (query.similar_to_article) {
       const limit = Number.parseInt(query.limit, 10);
-      const articleId = Number.parseInt(query.similar_to, 10);
+      const articleId = Number.parseInt(query.similar_to_article, 10);
       const neighbors = findSimilarArticles(
         articleId,
         Number.isFinite(limit) && limit > 0 ? Math.min(limit, 100) : 20
@@ -134,7 +134,7 @@ async function articleRoutes(fastify) {
   fastify.get('/articles/:id', async (request, reply) => {
     if (String((request.query || {}).include_embedding || '').toLowerCase() === 'true') {
       reply.code(400);
-      return { error: 'Embeddings are not returned directly. Use similar_to for vector search.' };
+      return { error: 'Embeddings are not returned directly. Use similar_to_article for vector search.' };
     }
 
     const article = db.prepare(`