Improve playground scope and answer model control

RAG/docs/API_RAG.md

@@ -47,6 +47,10 @@ Respuesta esperada aproximada:
     "provider": "openrouter",
     "model": "qwen/qwen3-embedding-8b"
   },
+  "answer": {
+    "provider": "openrouter",
+    "model": "openai/gpt-4.1-mini"
+  },
   "vectorStore": {
     "ok": true,
     "kind": "qdrant"
@@ -137,6 +141,7 @@ Payload base:
 {
   "mode": "documental",
   "intent": "specific",
+  "model": "openai/gpt-4.1-mini",
   "query": "que tenemos pendiente por hacer en este workspace",
   "scope": {
     "sourceRef": "/home/pancho/Documentos/Empresa/Desarrollo/IA/docs"
@@ -242,12 +247,15 @@ Es util para:
 - respuestas listas para usuario
 - pruebas rapidas desde n8n o aplicaciones
 
+Tambien permite indicar el modelo de respuesta explicitamente mediante el campo opcional `model`.
+
 Payload base:
 
 ```json
 {
   "mode": "documental",
   "intent": "specific",
+  "model": "openai/gpt-4.1-mini",
   "query": "que tenemos pendiente por hacer en este workspace",
   "scope": {
     "sourceRef": "/home/pancho/Documentos/Empresa/Desarrollo/IA/docs"
@@ -291,6 +299,7 @@ Respuesta resumida esperada:
 {
   "mode": "codigo",
   "intent": "specific",
+  "model": "openai/gpt-4.1-mini",
   "answer": "...",
   "summary": "...",
   "topics": ["ids.ts"],
@@ -311,6 +320,32 @@ Respuesta resumida esperada:
 }
 ```
 
+### 5. `POST /answer/direct`
+
+Sirve para pedir una respuesta directa al modelo sin usar contexto del RAG.
+
+Es util para comparar:
+- respuesta sin RAG
+- respuesta con RAG
+
+Payload base:
+
+```json
+{
+  "query": "que es este servicio",
+  "model": "openai/gpt-4.1-mini"
+}
+```
+
+Respuesta esperada:
+
+```json
+{
+  "model": "openai/gpt-4.1-mini",
+  "answer": "..."
+}
+```
+
 ---
 
 ## Recomendacion practica para n8n

RAG/docs/PLAYGROUND.md

@@ -50,12 +50,14 @@ El backend lo sirve desde:
 3. `retrieve`
 4. `answer`
 5. `answer` sin RAG para comparar impacto del contexto
+6. seleccion explicita del modelo de `answer`
 
 Tambien permite:
 
 - cambiar `mode`
 - cambiar `intent`
 - ajustar `scope`
+- seleccionar el modelo de respuesta
 - usar presets para docs, docs del modulo y codigo del RAG
 
 ---

RAG/public/playground/app.js

@@ -13,6 +13,8 @@ const queryInput = document.getElementById("queryInput");
 const queryMode = document.getElementById("queryMode");
 const queryIntent = document.getElementById("queryIntent");
 const queryOperation = document.getElementById("queryOperation");
+const answerModel = document.getElementById("answerModel");
+const scopePresetSelect = document.getElementById("scopePresetSelect");
 const scopeSourceRef = document.getElementById("scopeSourceRef");
 const scopeTags = document.getElementById("scopeTags");
 const compareWithoutRag = document.getElementById("compareWithoutRag");
@@ -30,6 +32,11 @@ function splitTags(value) {
   return value.split(",").map((entry) => entry.trim()).filter(Boolean);
 }
 
+function buildScopeLabel(scope) {
+  const modes = scope.chunkModes.join(", ") || "sin modo";
+  return `${scope.sourceRef} [${modes}]`;
+}
+
 async function request(url, payload, method = "POST") {
   const response = await fetch(url, {
     method,
@@ -47,6 +54,51 @@ async function request(url, payload, method = "POST") {
   return data;
 }
 
+async function loadScopes() {
+  try {
+    const scopes = await fetch("/sources").then((response) => response.json());
+    scopePresetSelect.innerHTML = "";
+
+    const placeholder = document.createElement("option");
+    placeholder.value = "";
+    placeholder.textContent = "Selecciona un scope disponible";
+    scopePresetSelect.appendChild(placeholder);
+
+    for (const scope of scopes) {
+      const option = document.createElement("option");
+      option.value = JSON.stringify(scope);
+      option.textContent = buildScopeLabel(scope);
+      scopePresetSelect.appendChild(option);
+    }
+
+    if (scopes.length === 0) {
+      placeholder.textContent = "No hay scopes detectados";
+    }
+  } catch (error) {
+    scopePresetSelect.innerHTML = "";
+    const option = document.createElement("option");
+    option.value = "";
+    option.textContent = `Error cargando scopes: ${error}`;
+    scopePresetSelect.appendChild(option);
+  }
+}
+
+scopePresetSelect.addEventListener("change", () => {
+  if (!scopePresetSelect.value) {
+    return;
+  }
+
+  const scope = JSON.parse(scopePresetSelect.value);
+  scopeSourceRef.value = scope.sourceRef || "";
+  scopeTags.value = (scope.tags || []).join(", ");
+
+  if (scope.chunkModes.includes("codigo")) {
+    queryMode.value = "codigo";
+  } else if (scope.chunkModes.includes("documental")) {
+    queryMode.value = "documental";
+  }
+});
+
 healthButton.addEventListener("click", async () => {
   healthResult.textContent = "Comprobando...";
   try {
@@ -81,6 +133,7 @@ queryButton.addEventListener("click", async () => {
     mode: queryMode.value,
     intent: queryIntent.value,
     query: queryInput.value,
+    model: answerModel.value,
     scope: {
       sourceRef: scopeSourceRef.value,
       tags: splitTags(scopeTags.value)
@@ -92,12 +145,13 @@ queryButton.addEventListener("click", async () => {
     const data = await request(endpoint, payload);
     mainResult.textContent = format(data);
 
-    if (compareWithoutRag.checked && queryOperation.value === "answer") {
+      if (compareWithoutRag.checked && queryOperation.value === "answer") {
-      const comparison = await request("/answer/direct", {
+        const comparison = await request("/answer/direct", {
-        query: queryInput.value
+          query: queryInput.value,
-      });
+          model: answerModel.value
-      compareResult.textContent = format(comparison);
+        });
-    }
+        compareResult.textContent = format(comparison);
+      }
   } catch (error) {
     mainResult.textContent = String(error);
     compareResult.textContent = compareWithoutRag.checked ? String(error) : "Desactivada.";
@@ -110,6 +164,7 @@ presetDocs.addEventListener("click", () => {
   queryOperation.value = "answer";
   queryInput.value = "que tenemos pendiente por hacer en este workspace";
   scopeSourceRef.value = "/home/pancho/Documentos/Empresa/Desarrollo/IA/docs";
+  answerModel.value = "openai/gpt-4.1-mini";
 });
 
 presetRagDocs.addEventListener("click", () => {
@@ -118,6 +173,7 @@ presetRagDocs.addEventListener("click", () => {
   queryOperation.value = "retrieve";
   queryInput.value = "dame un mapa inicial del modulo RAG, su arquitectura, decisiones, estado actual y documentos clave";
   scopeSourceRef.value = "/home/pancho/Documentos/Empresa/Desarrollo/IA/RAG/docs";
+  answerModel.value = "openai/gpt-4.1-mini";
 });
 
 presetCode.addEventListener("click", () => {
@@ -126,4 +182,7 @@ presetCode.addEventListener("click", () => {
   queryOperation.value = "answer";
   queryInput.value = "como se construye source_id en el rag";
   scopeSourceRef.value = "/home/pancho/Documentos/Empresa/Desarrollo/IA/RAG/src";
+  answerModel.value = "openai/gpt-4.1-mini";
 });
+
+loadScopes();

RAG/public/playground/index.html

@@ -67,9 +67,17 @@
                 <option value="answer">answer</option>
               </select>
             </label>
+            <label>Modelo answer
+              <input id="answerModel" value="openai/gpt-4.1-mini" />
+            </label>
           </div>
 
           <div class="row">
+            <label>Scope disponible
+              <select id="scopePresetSelect">
+                <option value="">Cargando scopes...</option>
+              </select>
+            </label>
             <label>Scope por sourceRef
               <input id="scopeSourceRef" value="/home/pancho/Documentos/Empresa/Desarrollo/IA/docs" />
             </label>

RAG/src/app.ts

@@ -39,12 +39,25 @@ export function createApp() {
         provider: embeddingProvider.providerName,
         model: embeddingProvider.modelName
       },
+      answer: {
+        provider: env.answerProvider,
+        model: env.answerModel
+      },
       vectorStore: vectorHealth,
       parsers: supportedParserExtensions(),
       chunking: documentalChunkingPolicy
     });
   });
 
+  app.get("/sources", async (_req, res) => {
+    try {
+      const scopes = await vectorStore.listScopes();
+      res.json(scopes);
+    } catch (error) {
+      res.status(500).json({ ok: false, error: error instanceof Error ? error.message : "Unknown sources error" });
+    }
+  });
+
   app.post("/ingest", async (req, res) => {
     try {
       const result = await ingestService.ingest(req.body);
@@ -85,7 +98,8 @@ export function createApp() {
             tags: Array.isArray(req.body.scope.tags) ? req.body.scope.tags.map(String) : undefined
           }
         : undefined;
-      const result = await answerService.answer(mode, intent, query, scope);
+      const model = req.body.model ? String(req.body.model) : undefined;
+      const result = await answerService.answer(mode, intent, query, scope, model);
       res.json(result);
     } catch (error) {
       res.status(500).json({ ok: false, error: error instanceof Error ? error.message : "Unknown answer error" });
@@ -95,7 +109,8 @@ export function createApp() {
   app.post("/answer/direct", async (req, res) => {
     try {
       const query = String(req.body.query ?? "");
-      const result = await answerService.answerWithoutRag(query);
+      const model = req.body.model ? String(req.body.model) : undefined;
+      const result = await answerService.answerWithoutRag(query, model);
       res.json(result);
     } catch (error) {
       res.status(500).json({ ok: false, error: error instanceof Error ? error.message : "Unknown direct answer error" });

RAG/src/modules/answer/service.ts

@@ -36,14 +36,15 @@ export class AnswerService {
     });
   }
 
-  async answer(mode: ChunkMode, intent: RetrieveIntent, query: string, scope?: RetrieveScope): Promise<AnswerResponse> {
+  async answer(mode: ChunkMode, intent: RetrieveIntent, query: string, scope?: RetrieveScope, modelOverride?: string): Promise<AnswerResponse> {
     if (!env.answerApiKey) {
       throw new Error("Missing ANSWER_API_KEY for answer provider");
     }
 
     const retrieved = await this.retrieveService.retrieve(mode, intent, query, scope);
+    const answerModel = modelOverride?.trim() || env.answerModel;
     const completion = await this.client.chat.completions.create({
-      model: env.answerModel,
+      model: answerModel,
       temperature: 0.2,
       messages: [
         {
@@ -60,6 +61,7 @@ export class AnswerService {
     return {
       mode: retrieved.mode,
       intent: retrieved.intent,
+      model: answerModel,
       answer: completion.choices[0]?.message?.content?.trim() || "No se pudo generar respuesta.",
       summary: retrieved.summary,
       topics: retrieved.topics,
@@ -76,13 +78,14 @@ export class AnswerService {
     };
   }
 
-  async answerWithoutRag(query: string): Promise<DirectAnswerResponse> {
+  async answerWithoutRag(query: string, modelOverride?: string): Promise<DirectAnswerResponse> {
     if (!env.answerApiKey) {
       throw new Error("Missing ANSWER_API_KEY for answer provider");
     }
 
+    const answerModel = modelOverride?.trim() || env.answerModel;
     const completion = await this.client.chat.completions.create({
-      model: env.answerModel,
+      model: answerModel,
       temperature: 0.2,
       messages: [
         {
@@ -97,7 +100,7 @@ export class AnswerService {
     });
 
     return {
-      model: env.answerModel,
+      model: answerModel,
       answer: completion.choices[0]?.message?.content?.trim() || "No se pudo generar respuesta."
     };
   }

RAG/src/modules/vectorstore/client.ts

@@ -1,6 +1,6 @@
 import { QdrantClient } from "@qdrant/js-client-rest";
 import { env } from "../../config/env.js";
-import type { IngestedChunk, RetrieveScope, RetrievedItem } from "../../shared/types/rag.js";
+import type { AvailableScope, IngestedChunk, RetrieveScope, RetrievedItem } from "../../shared/types/rag.js";
 
 function buildQdrantClient(): QdrantClient {
   const url = new URL(env.qdrantUrl);
@@ -19,6 +19,7 @@ export interface VectorStoreClient {
   ensureCollection(vectorSize: number): Promise<void>;
   upsert(chunks: IngestedChunk[]): Promise<void>;
   search(queryVector: number[], limit: number, mode?: string, scope?: RetrieveScope): Promise<RetrievedItem[]>;
+  listScopes(): Promise<AvailableScope[]>;
 }
 
 function buildSearchFilter(mode?: string, scope?: RetrieveScope) {
@@ -133,4 +134,59 @@ export class QdrantVectorStoreClient implements VectorStoreClient {
       endLine: point.payload?.end_line ? Number(point.payload.end_line) : undefined
     }));
   }
+
+  async listScopes(): Promise<AvailableScope[]> {
+    const collections = await this.client.getCollections();
+    const exists = collections.collections.some((collection) => collection.name === env.qdrantCollection);
+
+    if (!exists) {
+      return [];
+    }
+
+    const scopeMap = new Map<string, AvailableScope>();
+    let nextOffset = undefined;
+
+    do {
+      const response = await this.client.scroll(env.qdrantCollection, {
+        limit: 128,
+        offset: nextOffset,
+        with_payload: ["source_id", "source_ref", "chunk_mode", "tags"]
+      });
+
+      for (const point of response.points) {
+        const sourceId = String(point.payload?.source_id ?? "");
+        const sourceRef = String(point.payload?.source_ref ?? "");
+        const chunkMode = String(point.payload?.chunk_mode ?? "");
+        const tags = Array.isArray(point.payload?.tags) ? point.payload.tags.map(String) : [];
+
+        if (!sourceId || !sourceRef) {
+          continue;
+        }
+
+        const key = `${sourceId}::${sourceRef}`;
+        const existing = scopeMap.get(key) ?? {
+          sourceId,
+          sourceRef,
+          chunkModes: [],
+          tags: []
+        };
+
+        if ((chunkMode === "documental" || chunkMode === "codigo" || chunkMode === "auto") && !existing.chunkModes.includes(chunkMode)) {
+          existing.chunkModes.push(chunkMode);
+        }
+
+        for (const tag of tags) {
+          if (!existing.tags.includes(tag)) {
+            existing.tags.push(tag);
+          }
+        }
+
+        scopeMap.set(key, existing);
+      }
+
+      nextOffset = response.next_page_offset;
+    } while (nextOffset);
+
+    return [...scopeMap.values()].sort((left, right) => left.sourceRef.localeCompare(right.sourceRef));
+  }
 }

RAG/src/shared/types/rag.ts

@@ -48,6 +48,13 @@ export interface RetrieveScope {
   tags?: string[];
 }
 
+export interface AvailableScope {
+  sourceId: string;
+  sourceRef: string;
+  chunkModes: ChunkMode[];
+  tags: string[];
+}
+
 export interface RetrieveResponse {
   mode: ChunkMode;
   intent: RetrieveIntent;
@@ -62,6 +69,7 @@ export interface RetrieveResponse {
 export interface AnswerResponse {
   mode: ChunkMode;
   intent: RetrieveIntent;
+  model: string;
   answer: string;
   summary: string;
   topics: string[];

docs/HISTORIAL_SESIONES.md

@@ -60,8 +60,10 @@ Este archivo registra agentes y sesiones de trabajo de este workspace.
 - Pruebas satisfactorias en produccion de `health`, `retrieve` y `answer`, tanto en modo documental como en modo codigo.
 - Deteccion y anotacion como pendiente del cambio futuro del modelo actual de `answer`.
 - Creacion de `RAG/docs/API_RAG.md` como referencia operativa de la API para conectar rapidamente el servicio desde n8n, agentes y otras aplicaciones.
+- Alineacion de las variables de entorno para EasyPanel, dejando `.env.example` ajustado al despliegue esperado y `RAG/.env.easypanel.local` como respaldo operativo local ignorado por Git.
 - Implementacion de un playground web interno servido por el propio backend del RAG para probar `health`, `ingest`, `retrieve`, `answer` y comparacion sin RAG.
 - Creacion de `RAG/docs/PLAYGROUND.md` para documentar la tecnologia elegida, su ubicacion y su papel dentro del modulo.
+- Ajuste de la API y del playground para hacer visible y seleccionable el modelo de `answer`, evitando dejarlo oculto como una decision fija del backend.
 - Reorganizacion de RAG como modulo raiz independiente con documentacion propia en `RAG/docs/`.
 - Ajuste del indice documental global para reflejar la separacion entre documentacion global y documentacion por tool.
 - Creacion de `docs/TASK.md` para descomponer lineas de trabajo amplias en puntos de analisis y acuerdos.