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/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);