From b8f3563b8428f25f9c9b5c4d403f41fc4bc15a17 Mon Sep 17 00:00:00 2001
From: ikechan8370 <geyinchibuaa@gmail.com>
Date: Wed, 5 Nov 2025 16:13:01 +0800
Subject: [PATCH] fix: one more attempt by codex

---
 models/chaite/vectorizer.js       | 18 ++++++++-------
 models/memory/database.js         |  6 +++++
 models/memory/groupMemoryStore.js | 37 ++++++++++++++++++++++++++-----
 3 files changed, 48 insertions(+), 13 deletions(-)

diff --git a/models/chaite/vectorizer.js b/models/chaite/vectorizer.js
index a67ffae..60b8f60 100644
--- a/models/chaite/vectorizer.js
+++ b/models/chaite/vectorizer.js
@@ -45,10 +45,11 @@ export function createChaiteVectorizer (model, dimensions) {
   return {
     async textToVector (text) {
       const { client } = await getClientForModel(model)
-      const result = await client.getEmbedding(text, {
-        model,
-        dimensions
-      })
+      const options = { model }
+      if (Number.isFinite(dimensions) && dimensions > 0) {
+        options.dimensions = dimensions
+      }
+      const result = await client.getEmbedding(text, options)
       return result.embeddings[0]
     },
     async batchTextToVector (texts) {
@@ -63,10 +64,11 @@ export function createChaiteVectorizer (model, dimensions) {
       for (let i = 0; i < channels.length; i++) {
         const { quantity } = channels[i]
         const slice = texts.slice(startIndex, startIndex + quantity)
-        const embeddings = await clients[i].getEmbedding(slice, {
-          model,
-          dimensions
-        })
+        const options = { model }
+        if (Number.isFinite(dimensions) && dimensions > 0) {
+          options.dimensions = dimensions
+        }
+        const embeddings = await clients[i].getEmbedding(slice, options)
         results.push(...embeddings.embeddings)
         startIndex += quantity
       }
diff --git a/models/memory/database.js b/models/memory/database.js
index fd2b739..75316d1 100644
--- a/models/memory/database.js
+++ b/models/memory/database.js
@@ -617,6 +617,12 @@ function ensureVectorTable (db) {
     dimension = parseDimension(preferredDimension)
   }
 
+  if (!needsTableReset && preferredDimension > 0 && dimension > 0 && dimension !== preferredDimension) {
+    logger?.info?.('[Memory] vector dimension change detected (stored=%s, preferred=%s), rebuilding vec_group_facts', dimension, preferredDimension)
+    needsTableReset = true
+    dimension = preferredDimension
+  }
+
   if (dimension <= 0 && preferredDimension > 0) {
     dimension = preferredDimension
   }
diff --git a/models/memory/groupMemoryStore.js b/models/memory/groupMemoryStore.js
index 42b68d7..459c3c0 100644
--- a/models/memory/groupMemoryStore.js
+++ b/models/memory/groupMemoryStore.js
@@ -307,16 +307,43 @@ export class GroupMemoryStore {
       return []
     }
     try {
-      const dimension = getVectorDimension()
-      if (!dimension || dimension <= 0) {
-        logger.debug('[Memory] vector search skipped: vector dimension unavailable')
-        return []
+      let tableDimension = getVectorDimension() || 0
+      if (!tableDimension || tableDimension <= 0) {
+        logger.debug('[Memory] vector table dimension unavailable, attempting to infer from embedding')
       }
-      const [embedding] = await embedTexts([queryText], this.embeddingModel, dimension)
+      const requestedDimension = tableDimension > 0 ? tableDimension : undefined
+      const [embedding] = await embedTexts([queryText], this.embeddingModel, requestedDimension)
       if (!embedding) {
         return []
       }
       const embeddingVector = ArrayBuffer.isView(embedding) ? embedding : Float32Array.from(embedding)
+      const actualDimension = embeddingVector.length
+      if (!actualDimension) {
+        logger.debug('[Memory] vector search skipped: empty embedding returned')
+        return []
+      }
+      if (tableDimension > 0 && actualDimension !== tableDimension) {
+        logger.warn(`[Memory] vector dimension mismatch detected during search, table=${tableDimension}, embedding=${actualDimension}. Rebuilding vector table.`)
+        try {
+          resetVectorTableDimension(actualDimension)
+          this.prepareVectorStatements()
+          tableDimension = actualDimension
+        } catch (resetErr) {
+          logger.error('Failed to reset vector table dimension during search:', resetErr)
+          return []
+        }
+        logger.info('[Memory] vector table rebuilt; old vectors must be regenerated before vector search can return results')
+        return []
+      } else if (tableDimension <= 0 && actualDimension > 0) {
+        try {
+          resetVectorTableDimension(actualDimension)
+          this.prepareVectorStatements()
+          tableDimension = actualDimension
+        } catch (resetErr) {
+          logger.error('Failed to initialise vector table dimension during search:', resetErr)
+          return []
+        }
+      }
       const rows = this.db.prepare(`
         SELECT gf.*, vec_group_facts.distance AS distance
         FROM vec_group_facts