change Reranker to WeightedInMemoryAggregator

docs/source/providers/batches/index.md

@@ -3,15 +3,15 @@
 ## Overview
 
 The Batches API enables efficient processing of multiple requests in a single operation,
-    particularly useful for processing large datasets, batch evaluation workflows, and
+particularly useful for processing large datasets, batch evaluation workflows, and
-    cost-effective inference at scale.
+cost-effective inference at scale.
 
-    The API is designed to allow use of openai client libraries for seamless integration.
+The API is designed to allow use of openai client libraries for seamless integration.
 
-    This API provides the following extensions:
+This API provides the following extensions:
  - idempotent batch creation
 
-    Note: This API is currently under active development and may undergo changes.
+Note: This API is currently under active development and may undergo changes.
 
 This section contains documentation for all available providers for the **batches** API.
 

llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -31,7 +31,7 @@ from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
 )
-from llama_stack.providers.utils.vector_io.vector_utils import Reranker
+from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator
 
 from .config import ChromaVectorIOConfig as RemoteChromaVectorIOConfig
 
@@ -192,7 +192,9 @@ class ChromaIndex(EmbeddingIndex):
         }
 
         # Combine scores using the reranking utility
-        combined_scores = Reranker.combine_search_results(vector_scores, keyword_scores, reranker_type, reranker_params)
+        combined_scores = WeightedInMemoryAggregator.combine_search_results(
+            vector_scores, keyword_scores, reranker_type, reranker_params
+        )
 
         # Efficient top-k selection because it only tracks the k best candidates it's seen so far
         top_k_items = heapq.nlargest(k, combined_scores.items(), key=lambda x: x[1])

llama_stack/providers/utils/vector_io/vector_utils.py

@@ -39,7 +39,6 @@ def sanitize_collection_name(name: str, weaviate_format=False) -> str:
     return s
 
 
-
 class WeightedInMemoryAggregator:
     @staticmethod
     def _normalize_scores(scores: dict[str, float]) -> dict[str, float]:

pyproject.toml

@@ -49,6 +49,7 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http>=1.30.0", # server
     "aiosqlite>=0.21.0", # server - for metadata store
     "asyncpg", # for metadata store
+    "pre-commit>=4.2.0",
 ]
 
 [project.optional-dependencies]

tests/unit/providers/vector_io/remote/test_chroma.py

@@ -1,124 +0,0 @@
-# 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 json
-from unittest.mock import MagicMock, patch
-
-import numpy as np
-import pytest
-
-from llama_stack.apis.vector_io import QueryChunksResponse
-
-# Mock the entire chromadb module
-chromadb_mock = MagicMock()
-chromadb_mock.AsyncHttpClient = MagicMock
-chromadb_mock.PersistentClient = MagicMock
-
-# Apply the mock before importing ChromaIndex
-with patch.dict("sys.modules", {"chromadb": chromadb_mock}):
-    from llama_stack.providers.remote.vector_io.chroma.chroma import ChromaIndex
-
-# This test is a unit test for the ChromaVectorIOAdapter class. This should only contain
-# tests which are specific to this class. More general (API-level) tests should be placed in
-# tests/integration/vector_io/
-#
-# How to run this test:
-#
-# pytest tests/unit/providers/vector_io/test_chroma.py \
-# -v -s --tb=short --disable-warnings --asyncio-mode=auto
-
-CHROMA_PROVIDER = "chromadb"
-
-
-@pytest.fixture
-async def mock_chroma_collection() -> MagicMock:
-    """Create a mock Chroma collection with common method behaviors."""
-    collection = MagicMock()
-    collection.name = "test_collection"
-
-    # Mock add operation
-    collection.add.return_value = None
-
-    # Mock query operation for vector search
-    collection.query.return_value = {
-        "distances": [[0.1, 0.2]],
-        "documents": [
-            [
-                json.dumps({"content": "mock chunk 1", "metadata": {"document_id": "doc1"}}),
-                json.dumps({"content": "mock chunk 2", "metadata": {"document_id": "doc2"}}),
-            ]
-        ],
-    }
-
-    # Mock delete operation
-    collection.delete.return_value = None
-
-    return collection
-
-
-@pytest.fixture
-async def mock_chroma_client(mock_chroma_collection):
-    """Create a mock Chroma client with common method behaviors."""
-    client = MagicMock()
-
-    # Mock collection operations
-    client.get_or_create_collection.return_value = mock_chroma_collection
-    client.get_collection.return_value = mock_chroma_collection
-    client.delete_collection.return_value = None
-
-    return client
-
-
-@pytest.fixture
-async def chroma_index(mock_chroma_client, mock_chroma_collection):
-    """Create a ChromaIndex with mocked client and collection."""
-    index = ChromaIndex(client=mock_chroma_client, collection=mock_chroma_collection)
-    yield index
-    # No real cleanup needed since we're using mocks
-
-
-async def test_add_chunks(chroma_index, sample_chunks, sample_embeddings, mock_chroma_collection):
-    await chroma_index.add_chunks(sample_chunks, sample_embeddings)
-
-    # Verify data was inserted
-    mock_chroma_collection.add.assert_called_once()
-
-    # Verify the add call had the right number of chunks
-    add_call = mock_chroma_collection.add.call_args
-    assert len(add_call[1]["documents"]) == len(sample_chunks)
-
-
-async def test_query_chunks_vector(
-    chroma_index, sample_chunks, sample_embeddings, embedding_dimension, mock_chroma_collection
-):
-    # Setup: Add chunks first
-    await chroma_index.add_chunks(sample_chunks, sample_embeddings)
-
-    # Test vector search
-    query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
-    response = await chroma_index.query_vector(query_embedding, k=2, score_threshold=0.0)
-
-    assert isinstance(response, QueryChunksResponse)
-    assert len(response.chunks) == 2
-    mock_chroma_collection.query.assert_called_once()
-
-
-async def test_query_chunks_keyword_search(chroma_index, sample_chunks, sample_embeddings, mock_chroma_collection):
-    await chroma_index.add_chunks(sample_chunks, sample_embeddings)
-
-    # Test keyword search
-    query_string = "Sentence 5"
-    response = await chroma_index.query_keyword(query_string=query_string, k=2, score_threshold=0.0)
-
-    assert isinstance(response, QueryChunksResponse)
-    assert len(response.chunks) == 2
-
-
-async def test_delete_collection(chroma_index, mock_chroma_client):
-    # Test collection deletion
-    await chroma_index.delete()
-
-    mock_chroma_client.delete_collection.assert_called_once_with(chroma_index.collection.name)

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
     "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
@@ -1767,6 +1767,7 @@ dependencies = [
     { name = "opentelemetry-exporter-otlp-proto-http" },
     { name = "opentelemetry-sdk" },
     { name = "pillow" },
+    { name = "pre-commit" },
     { name = "prompt-toolkit" },
     { name = "pydantic" },
     { name = "python-dotenv" },
@@ -1892,6 +1893,7 @@ requires-dist = [
     { name = "opentelemetry-sdk", specifier = ">=1.30.0" },
     { name = "pandas", marker = "extra == 'ui'" },
     { name = "pillow" },
+    { name = "pre-commit", specifier = ">=4.2.0" },
     { name = "prompt-toolkit" },
     { name = "pydantic", specifier = ">=2" },
     { name = "python-dotenv" },