From ebf12d0ab08baa147b42c951d6ad98ab86fcc971 Mon Sep 17 00:00:00 2001
From: skamenan7 <skamenan@redhat.com>
Date: Thu, 17 Jul 2025 13:45:50 -0400
Subject: [PATCH] chore(tests): replace unicode punctuation in configurable
 embeddings tests

---
 .../vector_io/test_configurable_embeddings.py | 81 +++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100644 tests/integration/vector_io/test_configurable_embeddings.py

diff --git a/tests/integration/vector_io/test_configurable_embeddings.py b/tests/integration/vector_io/test_configurable_embeddings.py
new file mode 100644
index 000000000..de684626a
--- /dev/null
+++ b/tests/integration/vector_io/test_configurable_embeddings.py
@@ -0,0 +1,81 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import pytest
+from llama_stack_client import BadRequestError  # type: ignore
+
+# Local copy of the sample_chunks fixture so this module can run independently
+from llama_stack.apis.vector_io import Chunk
+
+
+# Re-use the same sample data defined in test_openai_vector_stores.py
+@pytest.fixture(scope="session")
+def sample_chunks():
+    return [
+        Chunk(
+            content="Python is a high-level programming language that emphasizes code readability.",
+            metadata={"document_id": "doc1", "topic": "programming"},
+        ),
+        Chunk(
+            content="Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience.",
+            metadata={"document_id": "doc2", "topic": "ai"},
+        ),
+        Chunk(
+            content="Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently.",
+            metadata={"document_id": "doc3", "topic": "computer_science"},
+        ),
+        Chunk(
+            content="Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information.",
+            metadata={"document_id": "doc4", "topic": "ai"},
+        ),
+    ]
+
+
+# Re-use the existing integration fixtures:
+#   * `client_with_models` - a LlamaStackClient with providers & models registered
+#   * `sample_chunks` - four small text chunks defined in test_openai_vector_stores.py
+
+
+@pytest.mark.integration
+def test_vector_db_custom_embedding_happy_path(client_with_models, sample_chunks):
+    """Register a vector DB with an explicit, non-default embedding model and verify e2e search works."""
+    vector_db_id = "embed_happy_test"
+
+    # Register the vector DB with a custom embedding model.  The model already exists
+    # in the default run-config so we don't need to register it separately.
+    client_with_models.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model="all-MiniLM-L6-v2",  # explicit even though it is default
+        embedding_dimension=384,
+    )
+
+    try:
+        # Insert one chunk so the DB is not empty
+        client_with_models.vector_io.insert(vector_db_id=vector_db_id, chunks=sample_chunks[:1])
+
+        # Simple similarity query - should return the chunk we just inserted
+        res = client_with_models.vector_io.query(vector_db_id=vector_db_id, query="programming language")
+
+        assert res is not None
+        assert len(res.chunks) > 0, "Expected at least one chunk to be retrieved"
+        assert res.scores[0] > 0, "Top score should be positive"
+    finally:
+        # Always clean up so later tests start fresh
+        client_with_models.vector_dbs.unregister(vector_db_id=vector_db_id)
+
+
+@pytest.mark.integration
+def test_vector_db_register_rejects_non_embedding_model(client_with_models):
+    """Attempting to register a vector DB with a non-embedding model should raise an error."""
+    vector_db_id = "embed_failure_test"
+
+    with pytest.raises((BadRequestError, ValueError)):
+        client_with_models.vector_dbs.register(
+            vector_db_id=vector_db_id,
+            # This is an LLM, *not* an embedding model ⇒ should fail
+            embedding_model="meta-llama/Llama-3.1-8B-Instruct",
+            embedding_dimension=4096,
+        )