deno.lock

@@ -21,6 +21,7 @@
     "jsr:@zod/zod@^4.3.5": "4.3.5",
     "npm:@supabase/supabase-js@2": "2.90.1",
     "npm:@supabase/supabase-js@^2.90.1": "2.90.1",
+    "npm:@toon-format/toon@^2.1.0": "2.1.0",
     "npm:@types/bun@^1.3.5": "1.3.5",
     "npm:deno@^2.6.4": "2.6.4",
     "npm:openai@6.16.0": "6.16.0_zod@3.25.76",
@@ -186,6 +187,9 @@
         "@supabase/storage-js"
       ]
     },
+    "@toon-format/toon@2.1.0": {
+      "integrity": "sha512-JwWptdF5eOA0HaQxbKAzkpQtR4wSWTEfDlEy/y3/4okmOAX1qwnpLZMmtEWr+ncAhTTY1raCKH0kteHhSXnQqg=="
+    },
     "@types/bun@1.3.5": {
       "integrity": "sha512-RnygCqNrd3srIPEWBd5LFeUYG7plCoH2Yw9WaZGyNmdTEei+gWaHqydbaIRkIkcbXwhBT94q78QljxN0Sk838w==",
       "dependencies": [
@@ -519,8 +523,7 @@
         "minipass",
         "minizlib",
         "yallist"
-      ],
+      ]
-      "deprecated": true
     },
     "tr46@0.0.3": {
       "integrity": "sha512-N3WMsuqV66lT30CrXNbEjx4GEwlow3v6rr4mCcv6prnfwhS01rkgyFdjPNBYd9br7LpXV1+Emh01fHnq2Gdgrw=="
@@ -591,6 +594,7 @@
     "packageJson": {
       "dependencies": [
         "npm:@supabase/supabase-js@^2.90.1",
+        "npm:@toon-format/toon@^2.1.0",
         "npm:@types/bun@^1.3.5",
         "npm:deno@^2.6.4",
         "npm:openai@^6.16.0",

notebooks/definitions-openai.ipynb

@@ -83,11 +83,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "1403c789",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import { title } from \"node:process\";\n",
+    "\n",
     "const combinedSchema = {\n",
     "    type: \"object\",\n",
     "    $defs: {\n",
@@ -126,10 +128,12 @@
     "        fabricante: { type: \"string\", maxLength: 100 },\n",
     "\n",
     "        // Agregar campo del preset\n",
-    "        perishables: { $ref: \"#/$defs/perishables\" },\n",
+    "        perishables: {\n",
-    "        origen: { $ref: \"#/$defs/origen\" },\n",
+    "            $ref: \"#/$defs/perishables\",\n",
+    "            \"x-column\": \"perishables\",\n",
+    "        },\n",
     "\n",
-    "        perecedero: { type: \"null\", \"x-column\": \"perishable\", const: null },\n",
+    "        origen: { $ref: \"#/$defs/origen\" },\n",
     "    },\n",
     "    required: [\"nombre\", \"fabricante\", \"perishables\", \"origen\", \"perecedero\"],\n",
     "    additionalProperties: false,\n",

notebooks/embeddings-openai.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "902bff53",
    "metadata": {},
    "outputs": [],
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "3b0acbc3",
    "metadata": {},
    "outputs": [],
@@ -32,63 +32,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "id": "dae842ad",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "ename": "ReferenceError",
-     "output_type": "stream",
+     "evalue": "openai is not defined",
-     "text": [
+     "output_type": "error",
-      "{\n",
+     "traceback": [
-      "  object: \"list\",\n",
+      "Stack trace:",
-      "  data: [\n",
+      "ReferenceError: openai is not defined",
-      "    {\n",
+      "    at <anonymous>:5:15"
-      "      object: \"embedding\",\n",
-      "      index: 0,\n",
-      "      embedding: [\n",
-      "          0.017360063269734383,   0.022553985938429832,   0.025286488234996796,\n",
-      "         -0.009742671623826027,  -0.016438385471701622,  -0.014725150540471077,\n",
-      "         -0.020840751007199287,    0.02215278521180153,   -0.02024437114596367,\n",
-      "          0.007834257557988167,  0.0029981620609760284,   -0.04109596461057663,\n",
-      "          0.030165957286953926,   -0.03402615711092949,  0.0008369643473997712,\n",
-      "        0.00044016868923790753,  -0.027129843831062317,   -0.04248390346765518,\n",
-      "         -0.058683738112449646,   0.034655068069696426,   0.013738414272665977,\n",
-      "           0.02060219831764698,   0.016687780618667603,     0.0059637944214046,\n",
-      "         -0.010306521318852901,   0.014898642897605896,  -0.008826415985822678,\n",
-      "          0.042136918753385544,   0.009477011859416962,   -0.03953453525900841,\n",
-      "         -0.012198670767247677,  -0.040055014193058014,  -0.019409440457820892,\n",
-      "           -0.0210576169192791,    -0.0392959825694561,  -0.005996324121952057,\n",
-      "        -0.0022486215457320213,    0.05209103599190712,  0.0009325206046923995,\n",
-      "          0.013163721188902855,     0.0505296029150486,   0.014540815725922585,\n",
-      "         0.0027921402361243963,    0.03190087899565697, 0.00004917589103570208,\n",
-      "           0.03209605813026428,  -0.038363464176654816,   -0.02424553595483303,\n",
-      "          0.014096241444349289,    0.02559009939432144,   -0.06089576333761215,\n",
-      "         -0.011016755364835262,  -0.026696112006902695,   0.050659723579883575,\n",
-      "          0.019561246037483215,   0.050833214074373245,   0.014529972337186337,\n",
-      "         -0.023204581812024117,  -0.014399852603673935,  -0.009292676113545895,\n",
-      "          0.003149967873468995,    0.02007087878882885,   0.025503354147076607,\n",
-      "            0.0230094026774168,    0.04471761733293533,    0.01479020994156599,\n",
-      "          -0.06692461669445038,       0.03591288626194,  -0.037127330899238586,\n",
-      "          0.029558734968304634,  -0.017576929181814194,    0.01953955926001072,\n",
-      "         -0.027888871729373932, -0.0041095963679254055,  0.0004940461367368698,\n",
-      "         -0.011266149580478668,   -0.04200679808855057,    0.03537072241306305,\n",
-      "        -0.0015058581484481692,   -0.05551750585436821,   -0.05287174880504608,\n",
-      "          0.016373327001929283,   0.005573437083512545,      -0.01235047634691,\n",
-      "         -0.009005329571664333,  -0.034047845751047134,  -0.018368486315011978,\n",
-      "          -0.00271217105910182,   0.007731246296316385,  -0.020981714129447937,\n",
-      "         -0.040900785475969315,   -0.03727913647890091,  -0.059030722826719284,\n",
-      "          -0.04536821320652962,    0.02257567271590233,    0.08579189330339432,\n",
-      "          0.014605875127017498,  -0.030686434358358383, -0.0019084142986685038,\n",
-      "           0.02216362953186035,\n",
-      "        ... 1436 more items\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  model: \"text-embedding-3-small\",\n",
-      "  usage: { prompt_tokens: 13, total_tokens: 13 }\n",
-      "}\n",
-      "✅ Insertados textos con embeddings\n"
      ]
     }
    ],
@@ -120,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "2d8fa9a9",
    "metadata": {},
    "outputs": [
@@ -129,20 +84,26 @@
      "output_type": "stream",
      "text": [
       "🔎 Consulta: ¿Cómo son buscados vectores?\n",
-      "\n",
-      "- Los vectores se usan con el cliente de OPENAI embeddings\n",
-      "  Similaridad: 0.5639\n",
-      "\n",
-      "- Supabase permite almacenar vectores usando pgvector.\n",
-      "  Similaridad: 0.4758\n",
-      "\n",
-      "- Los embeddings convierten texto en representaciones numéricas.\n",
-      "  Similaridad: 0.3645\n",
       "\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[32m\"code: PGRST202\\n\"\u001b[39m +\n",
+       "  \u001b[32m'details: \"Searched for the function public.buscar_documentos with parameters match_count, query_embedding or with a single unnamed json/jsonb parameter, but no matches were found in the schema cache.\"\\n'\u001b[39m +\n",
+       "  \u001b[32m\"hint: Perhaps you meant to call the function public.unaccent\\n\"\u001b[39m +\n",
+       "  \u001b[32m'message: \"Could not find the function public.buscar_documentos(match_count, query_embedding) in the schema cache\"'\u001b[39m"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
+    "import { encode } from \"@toon-format/toon\";\n",
+    "\n",
     "const consulta = \"¿Cómo son buscados vectores?\";\n",
     "\n",
     "const emb = await openai.embeddings.create({\n",
@@ -157,22 +118,42 @@
     "  match_count: 3,\n",
     "});\n",
     "\n",
-    "if (error) throw error;\n",
+    "/* if (error) throw error; */\n",
     "\n",
     "console.log(`🔎 Consulta: ${consulta}\\n`);\n",
-    "for (const r of data ?? []) {\n",
+    "encode(error, {\n",
-    "  console.log(`- ${r.contenido}`);\n",
+    "  indent: 2,\n",
-    "  console.log(`  Similaridad: ${Number(r.similarity).toFixed(4)}\\n`);\n",
+    "  delimiter: \",\",\n",
-    "}\n"
+    "  keyFolding: \"off\",\n",
+    "  flattenDepth: Infinity,\n",
+    "});\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "791a94d8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
-   "source": []
+    {
+     "data": {
+      "text/plain": [
+       "{\n",
+       "  code: \u001b[32m\"PGRST202\"\u001b[39m,\n",
+       "  details: \u001b[32m\"Searched for the function public.buscar_documentos with parameters match_count, query_embedding or with a single unnamed json/jsonb parameter, but no matches were found in the schema cache.\"\u001b[39m,\n",
+       "  hint: \u001b[32m\"Perhaps you meant to call the function public.unaccent\"\u001b[39m,\n",
+       "  message: \u001b[32m\"Could not find the function public.buscar_documentos(match_count, query_embedding) in the schema cache\"\u001b[39m\n",
+       "}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error"
+   ]
   }
  ],
  "metadata": {

package.json

@@ -1,6 +1,7 @@
 {
   "dependencies": {
     "@supabase/supabase-js": "^2.90.1",
+    "@toon-format/toon": "^2.1.0",
     "deno": "^2.6.4",
     "openai": "^6.16.0",
     "supabase": "^2.72.6"

supabase/functions/create-chat-conversation/index.ts

@@ -123,81 +123,7 @@ app.post(`${prefix}/conversations`, async (c) => {
   }
 });
 
-/**
- * GET /conversations/:conversation_plan_id/messages
- * Lista mensajes (assistant/user) desde OpenAI
- */
-app.get(`${prefix}/conversations/:id/messages`, async (c) => {
-  try {
-    /*     const auth = c.req.header("authorization");
-    await requireUser(auth); */
 
-    const conversation_plan_id = c.req.param("id");
-    assertUuid(conversation_plan_id, "conversation_plan_id");
-
-    const supabase = getSupabaseServiceClient();
-    const openai = getOpenAI();
-
-    const { data: convRow, error } = await supabase
-      .from("conversaciones_plan")
-      .select("openai_conversation_id, estado")
-      .eq("id", conversation_plan_id)
-      .single();
-
-    if (error || !convRow) {
-      throw new HttpError(
-        404,
-        "conversation_not_found",
-        "Conversación no encontrada",
-        error,
-      );
-    }
-    if (convRow.estado === "ARCHIVADA") {
-      // si ya está archivada, devolvemos lo guardado
-      const { data: archived } = await supabase
-        .from("conversaciones_plan")
-        .select("conversacion_json")
-        .eq("id", conversation_plan_id)
-        .single();
-      return withCors(
-        jsonResponse({
-          source: "supabase",
-          items: archived?.conversacion_json ?? null,
-        }),
-      );
-    }
-
-    const items = await openai.conversations.items.list(
-      convRow.openai_conversation_id,
-    );
-
-    const conversacion = items.data
-      .filter((it: any) =>
-        it.type === "message" &&
-        (it.role === "assistant" || it.role === "user")
-      )
-      .map((it: any) => {
-        const rawText = it.content.map((c: any) => c.text).join("");
-
-        let parsedContent;
-
-        try {
-          parsedContent = JSON.parse(rawText); // 👈 aquí lo convertimos
-        } catch {
-          parsedContent = rawText; // si no es JSON, lo dejamos normal
-        }
-
-        return {
-          role: it.role,
-          content: parsedContent,
-        };
-      });
-
-    return withCors(jsonResponse({ source: "openai", items: conversacion }));
-  } catch (err) {
-    return withCors(handleErr(err));
-  }
-});
 
 /**
  * POST /conversations/:conversation_plan_id/messages
@@ -285,6 +211,11 @@ app.post(`${prefix}/conversations/:id/messages`, async (c) => {
                     "Excelente, actualmente tu plan de estudio tiene una redacción clara, pero podrías mejorar el perfil de ingreso para hacerlo más atractivo.",
                   ],
                 },
+                "is_refusal": {
+                  type: "boolean",
+                  description:
+                    "Indica si la respuesta es un refusal (es decir, la pregunta no tiene que ver con el plan de estudio)",
+                },
               },
             },
           },
@@ -295,12 +226,62 @@ app.post(`${prefix}/conversations/:id/messages`, async (c) => {
     }
 
     // Pedimos respuesta estructurada con responses.create
-    const schema = pickSchemaFields(definicion, body.campos);
+    const schema = pickSchemaFields(definicion, body.campos ?? []);
     const planForPrompt = safePlanForPrompt(plan);
 
     const model = CREATE_CHAT_CONVERSATION_STRUCTURED_MODELO;
     const prompt = body.user_prompt ?? body.content;
 
+    // append message of the user to conversacion_json (which guarantees a JSONB default to '[]')
+    /**
+     * appended includes timestamp, user, prompt and fields (if any)
+     */
+
+    type AppendedMessage = {
+      timestamp: string;
+      user: string;
+      prompt: string;
+      fields?: string[];
+    };
+
+    type AppendedResponse = {
+      timestamp: string;
+      user: "assistant";
+      refusal: boolean;
+      message: string;
+      recommendations?: {
+        texto_mejora: string;
+        campo_afectado: string;
+        aplicada: false;
+      };
+    };
+
+    type AppendedItem = AppendedMessage | AppendedResponse;
+
+    let appended: AppendedItem = {
+      timestamp: new Date().toISOString(),
+      user: /* user.email ?? user.id  ??*/ "unknown",
+      prompt,
+      fields: body.campos,
+    };
+
+    const { error: appendErr } = await supabase.rpc(
+      "append_conversacion_plan",
+      {
+        p_id: conversation_plan_id,
+        p_append: appended,
+      },
+    );
+
+    if (appendErr) {
+      throw new HttpError(
+        500,
+        "append_conversation_failed",
+        "No se pudo agregar el mensaje a la conversación",
+        appendErr,
+      );
+    }
+
     const resp = await openai.responses.create({
       conversation: row.openai_conversation_id,
       model,
@@ -322,6 +303,47 @@ app.post(`${prefix}/conversations/:id/messages`, async (c) => {
       ],
     });
 
+    const respuestaJSON = JSON.parse(resp.output_text ?? "{}");
+    const refusal = respuestaJSON["is-refusal"] === true;
+    //remove the is-refusal field from respuestaJSON to avoid confusion
+    delete respuestaJSON["is-refusal"];
+
+    // Now an item with the assistant response and the structured data (if any) should be
+    appended = {
+      timestamp: new Date().toISOString(),
+      user: "assistant",
+      refusal,
+      // the ai-message field is the response
+      message: respuestaJSON?.["ai-message"] ?? "",
+      recommendations: resp.output_text
+        ? Object.entries(respuestaJSON).filter(([k]) => k !== "ai-message")
+          .map(
+            ([campo_afectado, texto_mejora]) => ({
+              campo_afectado,
+              texto_mejora,
+              aplicada: false,
+            }),
+          )
+        : undefined,
+    } as AppendedResponse;
+
+    const { error: appendRespErr } = await supabase.rpc(
+      "append_conversacion_plan",
+      {
+        p_id: conversation_plan_id,
+        p_append: appended,
+      },
+    );
+
+    if (appendRespErr) {
+      throw new HttpError(
+        500,
+        "append_response_failed",
+        "No se pudo agregar la respuesta a la conversación",
+        appendRespErr,
+      );
+    }
+
     return withCors(jsonResponse({
       ok: true,
       openai_response_id: resp.id,
@@ -332,91 +354,7 @@ app.post(`${prefix}/conversations/:id/messages`, async (c) => {
   }
 });
 
-/**
- * DELETE /conversations/:conversation_plan_id/archive
- * Guarda items en Supabase y elimina la conversación de OpenAI
- */
-app.delete(`${prefix}/conversations/:id/archive`, async (c) => {
-  try {
-    /*     const auth = c.req.header("authorization");
-    await requireUser(auth); */
 
-    const conversation_plan_id = c.req.param("id");
-    assertUuid(conversation_plan_id, "conversation_plan_id");
-
-    const supabase = getSupabaseServiceClient();
-    const openai = getOpenAI();
-
-    const { data: row, error } = await supabase
-      .from("conversaciones_plan")
-      .select("id, openai_conversation_id, estado")
-      .eq("id", conversation_plan_id)
-      .single();
-
-    if (error || !row) {
-      throw new HttpError(
-        404,
-        "conversation_not_found",
-        "Conversación no encontrada",
-        error,
-      );
-    }
-
-    if (row.estado === "ARCHIVADA") {
-      return withCors(jsonResponse({ ok: true, already: true }));
-    }
-
-    // Marcar estado
-    await supabase.from("conversaciones_plan")
-      .update({ estado: "ARCHIVANDO" })
-      .eq("id", conversation_plan_id);
-
-    // Descargar items de OpenAI
-    const items = await openai.conversations.items.list(
-      row.openai_conversation_id,
-    );
-
-    const conversacion = items.data.filter((it: any) =>
-      it.type === "message" && (it.role === "assistant" || it.role === "user")
-    ).map((it: any) => ({
-      role: it.role,
-      content: it.content.map((c: any) => c.text).join(""),
-    }));
-
-    // Guardar y marcar como ARCHIVADA
-    const { error: upErr } = await supabase.from("conversaciones_plan")
-      .update({
-        estado: "ARCHIVADA",
-        conversacion_json: conversacion,
-      })
-      .eq("id", conversation_plan_id);
-
-    if (upErr) {
-      throw new HttpError(
-        500,
-        "archive_save_failed",
-        "No se pudo guardar el archivo en Supabase",
-        upErr,
-      );
-    }
-
-    // Borrar conversación en OpenAI (best effort)
-    try {
-      await openai.conversations.delete(row.openai_conversation_id);
-    } catch (delErr) {
-      // Queda archivada en Supabase, pero reportamos warning
-      return withCors(jsonResponse({
-        ok: true,
-        warning: "Archivada en Supabase, pero no se pudo borrar en OpenAI",
-        details: String(delErr),
-      }, 200));
-    }
-
-    return withCors(jsonResponse({ ok: true }));
-  } catch (err) {
-    return withCors(handleErr(err));
-  }
-});
 
 /**
  * Unknown routes

supabase/functions/create-chat-conversation/lib/plan.ts

@@ -2,7 +2,7 @@ import { HttpError } from "./errors.ts";
 
 export function pickSchemaFields(
   definicion: any,
-  campos?: string[],
+  campos: string[],
 ) {
   if (!definicion || definicion.type !== "object" || !definicion.properties) {
     return definicion;
@@ -18,10 +18,15 @@ export function pickSchemaFields(
           "Listo: mejoré la redacción del perfil de ingreso y propuse un tema de investigación alineado al plan.",
         ],
       },
+      "is-refusal": {
+        type: "boolean",
+        description:
+          "Indica si el plan fue rechazado por el modelo. En caso de ser true, se espera un mensaje de rechazo en `ai-message`.",
+      },
     },
   };
 
-  let out = structuredClone(definicion);
+  const out = structuredClone(definicion);
 
   // Si piden campos, filtramos propiedades/required a esos campos
   const entries = Object.entries(out.properties).filter(([k]) =>

supabase/migrations/20260223212325_agregar_nombre_conversacion.sql

@@ -1,5 +1,3 @@
 alter table "public"."conversaciones_plan" add column "nombre" text default ('Chat '::text || CURRENT_DATE);
 
 alter table "public"."conversaciones_plan" alter column "conversacion_json" set default '[]'::jsonb;
-
-

supabase/migrations/20260224211719_agregar_conversaciones.sql

@@ -0,0 +1,21 @@
+CREATE OR REPLACE FUNCTION public.append_conversacion_asignatura(p_id uuid, p_append jsonb)
+ RETURNS void
+ LANGUAGE sql
+AS $function$
+  update conversaciones_asignatura
+  set conversacion_json = coalesce(conversacion_json, '[]'::jsonb) || p_append
+  where id = p_id;
+$function$
+;
+
+CREATE OR REPLACE FUNCTION public.append_conversacion_plan(p_id uuid, p_append jsonb)
+ RETURNS void
+ LANGUAGE sql
+AS $function$
+  update conversaciones_plan
+  set conversacion_json = coalesce(conversacion_json, '[]'::jsonb) || p_append
+  where id = p_id;
+$function$
+;
+
+