fix(oss): auto-detect embedding dimension to fix Qdrant mismatch with non-OpenAI embedders (#4297)

Co-authored-by: utkarsh240799 <utkarsh240799@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-12 21:45:09 +05:30
parent 5b3acf416b
commit 59c3b050bd
13 changed files with 2816 additions and 87 deletions
@@ -0,0 +1,88 @@
+/// <reference types="jest" />
+import { ConfigManager } from "../src/config/manager";
+
+describe("ConfigManager", () => {
+  describe("mergeConfig - dimension handling", () => {
+    const baseLlm = {
+      provider: "openai",
+      config: { apiKey: "test-key" },
+    };
+
+    it("should leave dimension undefined when no explicit dimension or embeddingDims provided", () => {
+      const config = ConfigManager.mergeConfig({
+        embedder: { provider: "openai", config: { apiKey: "test-key" } },
+        vectorStore: { provider: "memory", config: { collectionName: "test" } },
+        llm: baseLlm,
+      });
+
+      // Dimension should be undefined so Memory._autoInitialize() will
+      // auto-detect it via a probe embedding at runtime.
+      expect(config.vectorStore.config.dimension).toBeUndefined();
+    });
+
+    it("should use embeddingDims from embedder config when provided", () => {
+      const config = ConfigManager.mergeConfig({
+        embedder: {
+          provider: "ollama",
+          config: { model: "nomic-embed-text", embeddingDims: 768 },
+        },
+        vectorStore: { provider: "qdrant", config: { collectionName: "test" } },
+        llm: baseLlm,
+      });
+
+      expect(config.vectorStore.config.dimension).toBe(768);
+    });
+
+    it("should prefer explicit vector store dimension over embedder dims", () => {
+      const config = ConfigManager.mergeConfig({
+        embedder: {
+          provider: "ollama",
+          config: { model: "nomic-embed-text", embeddingDims: 768 },
+        },
+        vectorStore: {
+          provider: "qdrant",
+          config: { collectionName: "test", dimension: 1024 },
+        },
+        llm: baseLlm,
+      });
+
+      expect(config.vectorStore.config.dimension).toBe(1024);
+    });
+
+    it("should leave dimension undefined when using a custom client without explicit dims", () => {
+      const mockClient = { someMethod: () => {} };
+      const config = ConfigManager.mergeConfig({
+        embedder: {
+          provider: "ollama",
+          config: { model: "nomic-embed-text" },
+        },
+        vectorStore: {
+          provider: "qdrant",
+          config: { collectionName: "test", client: mockClient },
+        },
+        llm: baseLlm,
+      });
+
+      // No embeddingDims and no explicit dimension → should be undefined
+      // for auto-detection at runtime.
+      expect(config.vectorStore.config.dimension).toBeUndefined();
+    });
+
+    it("should use embeddingDims when using a custom client", () => {
+      const mockClient = { someMethod: () => {} };
+      const config = ConfigManager.mergeConfig({
+        embedder: {
+          provider: "ollama",
+          config: { model: "nomic-embed-text", embeddingDims: 768 },
+        },
+        vectorStore: {
+          provider: "qdrant",
+          config: { collectionName: "test", client: mockClient },
+        },
+        llm: baseLlm,
+      });
+
+      expect(config.vectorStore.config.dimension).toBe(768);
+    });
+  });
+});