diff --git a/litellm/llms/s3_vectors/vector_stores/transformation.py b/litellm/llms/s3_vectors/vector_stores/transformation.py index e074d1ebce2..044315168d2 100644 --- a/litellm/llms/s3_vectors/vector_stores/transformation.py +++ b/litellm/llms/s3_vectors/vector_stores/transformation.py @@ -8,6 +8,7 @@ from litellm.llms.base_llm.vector_store.transformation import ( VectorStoreEmbeddingExecutor, ) from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.types.rag import RAGIngestEmbeddingOptions from litellm.types.router import GenericLiteLLMParams from litellm.types.vector_stores import ( VECTOR_STORE_OPENAI_PARAMS, @@ -57,6 +58,21 @@ def s3_vectors_ingest_target(vector_store_config: Mapping[str, object]) -> tuple return explicit_bucket_name or derived_bucket_name, explicit_index_name or derived_index_name +def s3_vectors_configured_embedding_model(litellm_params: Mapping[str, object]) -> str | None: + return _non_empty_str(litellm_params.get("litellm_embedding_model") or litellm_params.get("embedding_model")) + + +def s3_vectors_ingest_embedding_options( + vector_store_config: Mapping[str, object], + embedding_options: RAGIngestEmbeddingOptions | None, +) -> RAGIngestEmbeddingOptions | None: + store_embedding_model: Final = s3_vectors_configured_embedding_model(vector_store_config) + if store_embedding_model is None: + return embedding_options + store_embedding_options: Final[RAGIngestEmbeddingOptions] = {"model": store_embedding_model} + return store_embedding_options + + class S3VectorsVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig, BaseAWSLLM): """Vector store configuration for AWS S3 Vectors.""" @@ -98,8 +114,7 @@ class S3VectorsVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig, BaseAWSLLM @staticmethod def query_embedding_model(litellm_params: Mapping[str, object]) -> str: - configured: Final = litellm_params.get("litellm_embedding_model") or litellm_params.get("embedding_model") - return configured if isinstance(configured, str) and configured else _DEFAULT_QUERY_EMBEDDING_MODEL + return s3_vectors_configured_embedding_model(litellm_params) or _DEFAULT_QUERY_EMBEDDING_MODEL @staticmethod def _query_target(vector_store_id: str, litellm_params: Mapping[str, object]) -> tuple[str, str]: diff --git a/litellm/rag/ingestion/s3_vectors_ingestion.py b/litellm/rag/ingestion/s3_vectors_ingestion.py index 8f362c146c3..e2aa5555eec 100644 --- a/litellm/rag/ingestion/s3_vectors_ingestion.py +++ b/litellm/rag/ingestion/s3_vectors_ingestion.py @@ -33,7 +33,10 @@ from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) -from litellm.llms.s3_vectors.vector_stores.transformation import s3_vectors_ingest_target +from litellm.llms.s3_vectors.vector_stores.transformation import ( + s3_vectors_ingest_embedding_options, + s3_vectors_ingest_target, +) from litellm.rag.ingestion.base_ingestion import BaseRAGIngestion if TYPE_CHECKING: @@ -91,6 +94,7 @@ class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM): BaseAWSLLM.__init__(self) self.vector_bucket_name, self.index_name = s3_vectors_ingest_target(self.vector_store_config) + self.embedding_config = s3_vectors_ingest_embedding_options(self.vector_store_config, self.embedding_config) self.distance_metric: str = self.vector_store_config.get("distance_metric", S3_VECTORS_DEFAULT_DISTANCE_METRIC) self.non_filterable_metadata_keys: Sequence[str] = self.vector_store_config.get( "non_filterable_metadata_keys", diff --git a/tests/test_litellm/rag/ingestion/test_s3_vectors_ingestion.py b/tests/test_litellm/rag/ingestion/test_s3_vectors_ingestion.py index 24dfc392bbe..07fd2b765f3 100644 --- a/tests/test_litellm/rag/ingestion/test_s3_vectors_ingestion.py +++ b/tests/test_litellm/rag/ingestion/test_s3_vectors_ingestion.py @@ -1,18 +1,68 @@ +from types import SimpleNamespace + import pytest from litellm.rag.ingestion.s3_vectors_ingestion import S3VectorsRAGIngestion STORE_ID_FORMAT_ERROR = "vector_store_id must be in format 'bucket_name:index_name'" +REQUEST_EMBEDDING_MODEL = "text-embedding-3-small" +STORE_EMBEDDING_MODEL = "text-embedding-3-large" +REQUEST_EMBEDDING = {"model": REQUEST_EMBEDDING_MODEL} -def _ingestion(**vector_store): - return S3VectorsRAGIngestion( - ingest_options={ - "embedding": {"model": "text-embedding-3-small"}, - "vector_store": {"custom_llm_provider": "s3_vectors", "aws_region_name": "us-west-2", **vector_store}, - } +class _RecordingRouter: + def __init__(self): + self.embedding_models = [] + + async def aembedding(self, model, input): + self.embedding_models.append(model) + return SimpleNamespace(data=[{"embedding": [0.1, 0.2]} for _ in input]) + + +def _ingestion(embedding=REQUEST_EMBEDDING, router=None, **vector_store): + vector_store_options = {"custom_llm_provider": "s3_vectors", "aws_region_name": "us-west-2", **vector_store} + ingest_options = {"vector_store": vector_store_options} if embedding is None else { + "embedding": embedding, + "vector_store": vector_store_options, + } + return S3VectorsRAGIngestion(ingest_options=ingest_options, router=router) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("store_model_key", ["embedding_model", "litellm_embedding_model"]) +async def test_a_registered_store_embedding_model_wins_over_the_request_on_ingest(store_model_key): + router = _RecordingRouter() + ingestion = _ingestion( + router=router, vector_store_id="my-embeddings:my-index", **{store_model_key: STORE_EMBEDDING_MODEL} ) + await ingestion.embed(["chunk one", "chunk two"]) + + assert router.embedding_models == [STORE_EMBEDDING_MODEL] + + +@pytest.mark.asyncio +async def test_a_registered_store_embedding_model_is_used_when_the_request_names_none(): + router = _RecordingRouter() + ingestion = _ingestion( + embedding=None, router=router, vector_store_id="my-embeddings:my-index", embedding_model=STORE_EMBEDDING_MODEL + ) + + await ingestion.embed(["chunk"]) + + assert router.embedding_models == [STORE_EMBEDDING_MODEL] + + +@pytest.mark.asyncio +@pytest.mark.parametrize("store_model", [{}, {"embedding_model": ""}]) +async def test_the_request_embedding_model_is_kept_when_the_store_names_none(store_model): + router = _RecordingRouter() + ingestion = _ingestion(router=router, vector_store_id="my-embeddings:my-index", **store_model) + + await ingestion.embed(["chunk"]) + + assert router.embedding_models == [REQUEST_EMBEDDING_MODEL] + def test_store_id_alone_names_the_bucket_and_index(): ingestion = _ingestion(vector_store_id="my-embeddings:my-index")