fix: remove pgvector dependency from AI knowledge retrieval

modules/ai/server/knowledge.test.ts

@@ -130,7 +130,7 @@ type RetrievedChunkRow = {
   title: string;
   category: string;
   content: string;
-  distance: number;
+  embedding: number[];
 };
 
 type KnowledgeDatabaseFake = {
@@ -325,7 +325,7 @@ class SelectBuilder implements PromiseLike<SelectRow[]> {
           title: chunk.sourceTitle,
           category: chunk.sourceCategory,
           content: chunk.content,
-          distance: 0.1 + rows.length * 0.1,
+          embedding: chunk.embedding,
         });
       }
     }
@@ -407,6 +407,31 @@ describe("AI knowledge service", () => {
     expect(embeddingMocks.embedDocuments).toHaveBeenCalledWith(["night fall risk"]);
   });
 
+  it("orders retrieved chunks by cosine similarity after scope filtering", async () => {
+    const weakEntry = createEntry({ id: "entry-weak", title: "Weak guidance" });
+    const strongEntry = createEntry({ id: "entry-strong", title: "Strong guidance" });
+    useDatabase(createDatabaseFake({
+      entries: [weakEntry, strongEntry],
+      chunks: [
+        createChunk(weakEntry, { embedding: [0, 1, 0] }),
+        createChunk(strongEntry, { embedding: [1, 0, 0] }),
+      ],
+    }));
+    embeddingMocks.embedDocuments.mockResolvedValueOnce([[1, 0, 0]]);
+
+    const result = await retrieveKnowledge("org-1", "night fall risk", { limit: 2 });
+
+    expect(result).toEqual({
+      success: true,
+      data: {
+        results: [
+          expect.objectContaining({ entryId: "entry-strong", score: 1 }),
+          expect.objectContaining({ entryId: "entry-weak", score: 0 }),
+        ],
+      },
+    });
+  });
+
   it("requires platform management permission before mutating platform knowledge", async () => {
     const existingPlatformEntry = createEntry({ id: "entry-platform", scope: "platform", organizationId: null });
     useDatabase(createDatabaseFake({ entries: [existingPlatformEntry] }));

modules/ai/server/knowledge.ts

@@ -1,7 +1,7 @@
 import "server-only";
 
 import { OpenAIEmbeddings } from "@langchain/openai";
-import { and, desc, eq, isNull, or, sql } from "drizzle-orm";
+import { and, desc, eq, isNull, or } from "drizzle-orm";
 
 import { getAiRuntimeConfig } from "@/modules/ai/server/config";
 import type {
@@ -316,6 +316,29 @@ export async function deleteKnowledgeEntry(context: AuthContext, id: string): Pr
   return { success: true, data: { entry: toKnowledgeEntry(row) } };
 }
 
+function calculateCosineSimilarity(left: readonly number[], right: readonly number[]): number {
+  if (left.length === 0 || right.length === 0 || left.length !== right.length) {
+    return 0;
+  }
+
+  let dotProduct = 0;
+  let leftMagnitude = 0;
+  let rightMagnitude = 0;
+  for (let index = 0; index < left.length; index += 1) {
+    const leftValue = left[index] ?? 0;
+    const rightValue = right[index] ?? 0;
+    dotProduct += leftValue * rightValue;
+    leftMagnitude += leftValue * leftValue;
+    rightMagnitude += rightValue * rightValue;
+  }
+
+  if (leftMagnitude === 0 || rightMagnitude === 0) {
+    return 0;
+  }
+
+  return dotProduct / (Math.sqrt(leftMagnitude) * Math.sqrt(rightMagnitude));
+}
+
 export async function retrieveKnowledge(
   organizationId: string,
   query: string,
@@ -333,7 +356,6 @@ export async function retrieveKnowledge(
   }
 
   const database = getDatabase();
-  const distance = sql<number>`${aiKnowledgeChunks.embedding} <=> ${JSON.stringify(embedding)}::vector`;
   const rows = await database
     .select({
       entryId: aiKnowledgeChunks.entryId,
@@ -341,25 +363,26 @@ export async function retrieveKnowledge(
       title: aiKnowledgeChunks.sourceTitle,
       category: aiKnowledgeChunks.sourceCategory,
       content: aiKnowledgeChunks.content,
-      distance,
+      embedding: aiKnowledgeChunks.embedding,
     })
     .from(aiKnowledgeChunks)
     .innerJoin(aiKnowledgeEntries, eq(aiKnowledgeEntries.id, aiKnowledgeChunks.entryId))
-    .where(getEnabledChunkWhere(organizationId))
-    .orderBy(distance)
-    .limit(options?.limit ?? DEFAULT_RETRIEVAL_LIMIT);
+    .where(getEnabledChunkWhere(organizationId));
 
   return {
     success: true,
     data: {
-      results: rows.map((row) => ({
-        entryId: row.entryId,
-        chunkId: row.chunkId,
-        title: row.title,
-        category: row.category,
-        content: row.content,
-        score: 1 - row.distance,
-      })),
+      results: rows
+        .map((row) => ({
+          entryId: row.entryId,
+          chunkId: row.chunkId,
+          title: row.title,
+          category: row.category,
+          content: row.content,
+          score: calculateCosineSimilarity(row.embedding, embedding),
+        }))
+        .sort((left, right) => right.score - left.score)
+        .slice(0, options?.limit ?? DEFAULT_RETRIEVAL_LIMIT),
     },
   };
 }

modules/core/server/schema.ts

@@ -11,7 +11,6 @@ import {
   timestamp,
   uniqueIndex,
   uuid,
-  vector,
 } from "drizzle-orm/pg-core";
 
 export const accountStatusEnum = pgEnum("account_status", ["active", "disabled", "pending"]);
@@ -555,7 +554,7 @@ export const aiKnowledgeChunks = pgTable("ai_knowledge_chunks", {
   scope: aiKnowledgeScopeEnum("scope").notNull(),
   chunkIndex: integer("chunk_index").notNull(),
   content: text("content").notNull(),
-  embedding: vector("embedding", { dimensions: AI_KNOWLEDGE_EMBEDDING_DIMENSIONS }).notNull(),
+  embedding: jsonb("embedding").$type<number[]>().notNull().default(sql`'[]'::jsonb`),
   sourceTitle: text("source_title").notNull(),
   sourceCategory: text("source_category").notNull().default(""),
   createdAt: timestamp("created_at", { withTimezone: true }).notNull().defaultNow(),