fix(ai-chat): live streaming, open-page context, any-dimension embeddings" -m "- streaming: give useChat a STABLE store id (chatId ?? per-mount generated)

so the v6 hook stops re-creating its store every render on a new chat
  (which wiped the optimistic user message + streamed deltas, so nothing
  showed until the turn finished). Also send X-Accel-Buffering:no + flushHeaders.
- context: client sends the currently-open page {id,title}; the system prompt
  tells the agent which page 'this page' refers to (it reads it via its
  CASL-scoped getPage tool; id is prompt-context only, no server-side fetch).
- embeddings: make page_embeddings.embedding dimension-agnostic (drop the
  HNSW index + ALTER to vector), remove the hard 1536 guard, filter search by
  model_dimensions — so 3072-dim (and any) models index instead of being
  skipped. Seq-scan <=> search (wiki scale); existing pages reindex on next edit.

apps/server/src/core/ai-chat/ai-chat.prompt.ts

@@ -55,6 +55,13 @@ export interface BuildSystemPromptInput {
    * used instead.
    */
   adminPrompt?: string | null;
+  /**
+   * The page the user is currently viewing (client-supplied), if any. When it
+   * has an id, a CONTEXT line is added so the agent can resolve "this page" /
+   * "the current page" to that pageId. The page is NOT fetched here — the agent
+   * uses its CASL-enforced read/write page tools with the id when needed.
+   */
+  openedPage?: { id?: string; title?: string } | null;
 }
 
 /**
@@ -65,15 +72,27 @@ export interface BuildSystemPromptInput {
 export function buildSystemPrompt({
   workspace,
   adminPrompt,
+  openedPage,
 }: BuildSystemPromptInput): string {
   const base =
     typeof adminPrompt === 'string' && adminPrompt.trim().length > 0
       ? adminPrompt.trim()
       : DEFAULT_PROMPT;
 
-  const context = workspace?.name
-    ? `\n\nWorkspace: ${workspace.name}.`
-    : '';
+  let context = workspace?.name ? `\n\nWorkspace: ${workspace.name}.` : '';
+
+  // When the user has a page open, tell the agent which page "this page" means.
+  // Context only — the agent reads/writes via its CASL-enforced page tools, so a
+  // spoofed id cannot escalate (getPage would 403). Added to the context section,
+  // never the immutable safety framework. Absent => nothing is added.
+  const pageId = openedPage?.id;
+  if (typeof pageId === 'string' && pageId.trim().length > 0) {
+    const title =
+      typeof openedPage?.title === 'string' && openedPage.title.trim().length > 0
+        ? openedPage.title.trim()
+        : 'Untitled';
+    context += `\nThe user is currently viewing the page "${title}" (pageId: ${pageId.trim()}). When they refer to "this page", "the current page", or similar, operate on that pageId — use the read/write page tools with it.`;
+  }
 
   return `${base}${context}\n${SAFETY_FRAMEWORK}`;
 }

apps/server/src/core/ai-chat/ai-chat.service.ts

@@ -24,6 +24,12 @@ import { buildSystemPrompt } from './ai-chat.prompt';
  */
 export interface AiChatStreamBody {
   chatId?: string;
+  // The page the user is currently viewing (client-supplied), or null on a
+  // non-page route. Used ONLY as prompt context so the agent knows what "this
+  // page" refers to; the page itself is never fetched server-side here. The id
+  // is attacker-controllable but harmless: the agent reads/writes via its
+  // CASL-enforced page tools, which 403 on a page the user cannot access.
+  openPage?: { id?: string; title?: string } | null;
   // useChat sends the full UIMessage list; the last one is the new user turn.
   messages?: UIMessage[];
 }
@@ -140,6 +146,7 @@ export class AiChatService {
     const system = buildSystemPrompt({
       workspace,
       adminPrompt: resolved?.systemPrompt,
+      openedPage: body.openPage,
     });
 
     // Pass the resolved chatId so the write tools can mint provenance tokens
@@ -310,7 +317,22 @@ export class AiChatService {
       // UI shows a generic failure. Surface the real provider message instead.
       // AI SDK error messages / 4xx bodies never contain the API key, so this is
       // safe; we never dump the resolved config/apiKey.
+      //
+      // SSE buffering / proxy note: pipeUIMessageStreamToResponse writes the
+      // headers immediately (res.writeHead) and each chunk incrementally, and the
+      // SDK's default UI_MESSAGE_STREAM_HEADERS already include
+      // `x-accel-buffering: no` (disables nginx response buffering) plus
+      // `content-type: text/event-stream` and `cache-control: no-cache`. We pass
+      // `headers` explicitly anyway so the intent is visible here and survives any
+      // future change to the SDK defaults (prepareHeaders only fills a header when
+      // absent, so this never clobbers the SDK's content-type). DEPLOYMENT: the
+      // reverse proxy in front of this server MUST NOT buffer this route, or the
+      // whole response is released at once and nothing streams. nginx honours the
+      // `x-accel-buffering: no` header we send (and additionally set
+      // `proxy_buffering off; proxy_cache off;` for /api/ai-chat/stream); traefik
+      // does not buffer responses by default.
       result.pipeUIMessageStreamToResponse(res.raw, {
+        headers: { 'X-Accel-Buffering': 'no' },
         onError: (error: unknown) => {
           const e = error as { statusCode?: number; message?: string };
           return e?.statusCode
@@ -318,6 +340,13 @@ export class AiChatService {
             : (e?.message ?? 'AI stream error');
         },
       });
+
+      // Force the status line + headers onto the socket NOW (before the model's
+      // first token), so the proxy sees the response start immediately even if the
+      // provider's first chunk is delayed. writeToServerResponse already called
+      // writeHead synchronously above; flushHeaders is a belt-and-braces no-op once
+      // headers are sent, and is guarded for response-likes that lack it.
+      res.raw.flushHeaders?.();
     } catch (err) {
       // Synchronous failure before/while wiring the stream: the terminal
       // callbacks will not run, so release the leased external clients here and

apps/server/src/core/ai-chat/embedding/embedding-indexer.service.ts

@@ -12,13 +12,12 @@ import { AiService } from '../../../integrations/ai/ai.service';
 import { AiEmbeddingNotConfiguredException } from '../../../integrations/ai/ai-embedding-not-configured.exception';
 import { jsonToText } from '../../../collaboration/collaboration.util';
 
-/**
- * Embedding dimension the `page_embeddings.embedding` column is fixed at
- * (`vector(1536)`). A model whose vectors have a different dimension cannot fit
- * this column — v1 limitation (§14[M7]); see the dimension guard in
- * `reindexPage`.
- */
-const EMBEDDING_DIMENSIONS = 1536;
+// NOTE: the `page_embeddings.embedding` column is now dimension-agnostic
+// (bare pgvector `vector`, see migration 20260617T140000), so the indexer
+// stores WHATEVER dimension the configured model returns and records it per row
+// in `model_dimensions`. There is no fixed-dimension guard any more; search
+// compares only same-dimension rows. Trade-off: a dimension-agnostic column has
+// no ANN index, so retrieval is a seq scan with `<=>` (fine at wiki scale).
 
 // RecursiveCharacterTextSplitter settings. ~1000 chars per chunk with 200 char
 // overlap is a reasonable default for prose retrieval (§6.7 stage D).
@@ -31,7 +30,7 @@ const CHUNK_OVERLAP = 200;
  * cosine ANN retrieval.
  *
  * Everything is workspace-scoped. Reindex HARD-replaces a page's rows (delete +
- * insert in one transaction) so the HNSW index never serves stale vectors.
+ * insert in one transaction) so search never serves stale vectors.
  */
 @Injectable()
 export class EmbeddingIndexerService {
@@ -48,9 +47,9 @@ export class EmbeddingIndexerService {
    * (Re)build the embeddings for a single page.
    *
    * No-ops quietly when embeddings are unconfigured (so the queue never dies on
-   * an unconfigured workspace) and when a non-matching embedding dimension is
-   * returned (skip + single warning — §14[M7]). Deleted/empty pages have their
-   * rows purged and return.
+   * an unconfigured workspace). Any embedding dimension is accepted; the only
+   * defensive skip is a page whose chunks somehow yield mixed vector lengths.
+   * Deleted/empty pages have their rows purged and return.
    */
   async reindexPage(pageId: string): Promise<void> {
     const page = await this.pageRepo.findById(pageId, {
@@ -115,17 +114,21 @@ export class EmbeddingIndexerService {
     // Embed all chunks in one batch.
     const vectors = await this.aiService.embedTexts(workspaceId, chunks);
 
-    // Dimension guard (§14[M7]): the column is a fixed vector(1536). A model
-    // with a different output dimension cannot be stored — skip the page and
-    // warn once rather than failing every row insert.
-    const wrongDim = vectors.find((v) => v.length !== EMBEDDING_DIMENSIONS);
-    if (wrongDim) {
-      this.logger.warn(
-        `reindexPage: embedding dimension ${wrongDim.length} != ${EMBEDDING_DIMENSIONS} ` +
-          `for workspace ${workspaceId}; skipping page ${pageId}. ` +
-          `The embedding column is fixed at ${EMBEDDING_DIMENSIONS} dims (v1 limitation §14[M7]).`,
-      );
-      return;
+    // The column is dimension-agnostic, so ANY model dimension is stored as-is.
+    // Defensive sanity check only: all chunks of ONE page come from the SAME
+    // model and must share a dimension. A page that yields mixed lengths would
+    // poison the per-dimension search filter, so skip it with a warning rather
+    // than insert inconsistent rows.
+    const expectedDim = vectors[0]?.length;
+    if (expectedDim != null) {
+      const mixed = vectors.find((v) => v.length !== expectedDim);
+      if (mixed) {
+        this.logger.warn(
+          `reindexPage: mixed embedding dimensions (${expectedDim} vs ${mixed.length}) ` +
+            `for workspace ${workspaceId}; skipping page ${pageId}.`,
+        );
+        return;
+      }
     }
 
     const rows = this.buildChunkRows(
@@ -136,8 +139,8 @@ export class EmbeddingIndexerService {
       modelName,
     );
 
-    // HARD replace in one transaction: delete then insert so the ANN index
-    // never holds stale vectors for this page.
+    // HARD replace in one transaction: delete then insert so search never
+    // returns stale vectors for this page.
     await executeTx(this.db, async (trx) => {
       await this.pageEmbeddingRepo.deleteByPage(pageId, workspaceId, trx);
       await this.pageEmbeddingRepo.insertChunks(rows, trx);

apps/server/src/database/migrations/20260617T140000-page-embeddings-dimension-agnostic.ts

@@ -0,0 +1,67 @@
+import { type Kysely, sql } from 'kysely';
+
+/**
+ * Make `page_embeddings.embedding` dimension-agnostic.
+ *
+ * The original column was `vector(1536)` — a FIXED dimension. On deployments
+ * whose embedding model emits a different dimension (e.g. OpenAI
+ * `text-embedding-3-large` = 3072, Gemini `text-embedding-004` = 768) every
+ * vector failed the indexer's dimension guard and every page was SKIPPED, so
+ * RAG / semanticSearch was never populated.
+ *
+ * pgvector's bare `vector` type (no `(N)`) accepts vectors of ANY dimension,
+ * so this migration drops the fixed dimension. The dimension is still recorded
+ * PER ROW in `model_dimensions`, and search filters on it so the `<=>` cosine
+ * operator only ever compares same-dimension vectors (pgvector errors on a
+ * dimension mismatch — possible when rows from a previous model linger).
+ *
+ * TRADE-OFF: an HNSW / ivfflat ANN index REQUIRES a fixed dimension, so a
+ * dimension-agnostic column cannot carry one. We therefore DROP the HNSW index
+ * and rely on a sequential scan with `<=>`. That is fine at wiki scale; if a
+ * single embedding dimension is ever pinned per deployment, an HNSW index can
+ * be re-added in a follow-up migration.
+ */
+export async function up(db: Kysely<any>): Promise<void> {
+  // The HNSW ANN index requires a fixed dimension; drop it before relaxing the
+  // column type. Index name mirrors 20260617T120000-page-embeddings.ts.
+  await sql`DROP INDEX IF EXISTS idx_page_embeddings_embedding_hnsw`.execute(db);
+
+  // Drop the (1536) dimension constraint so the column accepts any dimension.
+  // The identity cast `embedding::vector` is safe for existing 1536-dim rows;
+  // on the affected live stand the table is empty (everything was skipped), so
+  // there is no data risk.
+  await sql`
+    ALTER TABLE page_embeddings
+      ALTER COLUMN embedding TYPE vector USING embedding::vector
+  `.execute(db);
+
+  // Btree index supporting the scoped + dimension-filtered seq-scan search
+  // (workspace_id + space_id IN (...) + model_dimensions = queryDim).
+  await db.schema
+    .createIndex('idx_page_embeddings_ws_space_dim')
+    .ifNotExists()
+    .on('page_embeddings')
+    .columns(['workspace_id', 'space_id', 'model_dimensions'])
+    .execute();
+}
+
+export async function down(db: Kysely<any>): Promise<void> {
+  // Best-effort rollback. The `::vector(1536)` cast only succeeds if EVERY row
+  // is already 1536-dim — acceptable for a dev rollback (the up migration is
+  // the intended steady state). On non-1536 data this will (correctly) error.
+  await db.schema
+    .dropIndex('idx_page_embeddings_ws_space_dim')
+    .ifExists()
+    .execute();
+
+  await sql`
+    ALTER TABLE page_embeddings
+      ALTER COLUMN embedding TYPE vector(1536) USING embedding::vector(1536)
+  `.execute(db);
+
+  await sql`
+    CREATE INDEX IF NOT EXISTS idx_page_embeddings_embedding_hnsw
+      ON page_embeddings
+      USING hnsw (embedding vector_cosine_ops)
+  `.execute(db);
+}

apps/server/src/database/repos/ai-chat/page-embedding.repo.ts

@@ -9,11 +9,17 @@ import { dbOrTx } from '../../utils';
  * Repository for `page_embeddings` — the pgvector store backing the AI agent's
  * semantic search (§5.5 / §6.7 stage D).
  *
- * The `embedding` column is `vector(1536)`, which is NOT a native Kysely column
+ * The `embedding` column is a dimension-agnostic pgvector `vector` (no fixed
+ * `(N)`, see migration 20260617T140000), which is NOT a native Kysely column
  * type, so every read/write of a vector is serialized with the `pgvector` npm
  * helper (`pgvector.toSql(number[])` → a `'[1,2,3]'` text literal) and cast back
  * to `vector` via a raw `::vector` SQL cast. Reindex is a HARD delete + insert
- * (see `deleteByPage`) so the HNSW ANN index never returns stale vectors.
+ * (see `deleteByPage`) so search never returns stale vectors.
+ *
+ * TRADE-OFF: a dimension-agnostic column cannot carry an HNSW/ivfflat ANN index
+ * (those require a fixed dimension), so `searchByEmbedding` is a sequential scan
+ * with the `<=>` cosine operator. Fine at wiki scale; re-add an HNSW index if a
+ * single embedding dimension is ever pinned per deployment.
  */
 
 /** A single chunk row to persist for a page (page-body embeddings). */
@@ -66,8 +72,8 @@ export class PageEmbeddingRepo {
 
   /**
    * Bulk-insert chunk rows for a page. The `embedding` value is serialized with
-   * `pgvector.toSql` and cast to `vector` so Postgres stores it in the fixed
-   * `vector(1536)` column. No-op on an empty array.
+   * `pgvector.toSql` and cast to `vector` so Postgres stores it in the
+   * dimension-agnostic `vector` column (any dimension). No-op on an empty array.
    */
   async insertChunks(
     rows: PageEmbeddingChunkRow[],
@@ -97,10 +103,17 @@ export class PageEmbeddingRepo {
   }
 
   /**
-   * Cosine ANN search over the embeddings, scoped to a workspace AND a set of
+   * Cosine search over the embeddings, scoped to a workspace AND a set of
    * spaces the caller may read (see semanticSearch access-scoping). Orders by
    * `embedding <=> $query` (cosine distance) and joins the page title cheaply.
    * Returns [] when `spaceIds` is empty (no accessible spaces => no results).
+   *
+   * Because the column is dimension-agnostic (no ANN index), this is a seq scan
+   * with `<=>`. The query MUST only be compared against same-dimension rows —
+   * pgvector raises on a dimension mismatch, which can happen when rows from a
+   * previously configured embedding model still linger. We therefore filter by
+   * `model_dimensions = queryEmbedding.length` so the `<=>` operands always
+   * agree on dimension.
    */
   async searchByEmbedding(
     workspaceId: string,
@@ -112,6 +125,8 @@ export class PageEmbeddingRepo {
 
     // Serialized + cast query vector reused for the distance expression.
     const queryVector = sql`${pgvector.toSql(queryEmbedding)}::vector`;
+    // Compare only against rows produced by a model of the SAME dimension.
+    const queryDim = queryEmbedding.length;
 
     const rows = await this.db
       .selectFrom('pageEmbeddings as pe')
@@ -125,6 +140,9 @@ export class PageEmbeddingRepo {
       ])
       .where('pe.workspaceId', '=', workspaceId)
       .where('pe.spaceId', 'in', spaceIds)
+      // Same-dimension only: avoids a pgvector dimension-mismatch error against
+      // rows from a previously configured embedding model.
+      .where('pe.modelDimensions', '=', queryDim)
       // Exclude chunks whose page is in the trash (defence in depth).
       .where('p.deletedAt', 'is', null)
       .orderBy('distance', 'asc')