feat: Add support for user-configurable 1P embedding models and quota…

… for RAG

PiperOrigin-RevId: 642414350

tests/unit/vertex_rag/test_rag_constants.py

@@ -16,6 +16,7 @@
 #
 
 from vertexai.preview.rag.utils.resources import (
+    EmbeddingModelConfig,
     RagCorpus,
     RagFile,
     RagResource,
@@ -49,10 +50,19 @@
     display_name=TEST_CORPUS_DISPLAY_NAME,
     description=TEST_CORPUS_DISCRIPTION,
 )
+TEST_GAPIC_RAG_CORPUS.rag_embedding_model_config.vertex_prediction_endpoint.endpoint = (
+    "projects/{}/locations/{}/publishers/google/models/textembedding-gecko".format(
+        TEST_PROJECT, TEST_REGION
+    )
+)
+TEST_EMBEDDING_MODEL_CONFIG = EmbeddingModelConfig(
+    publisher_model="publishers/google/models/textembedding-gecko",
+)
 TEST_RAG_CORPUS = RagCorpus(
     name=TEST_RAG_CORPUS_RESOURCE_NAME,
     display_name=TEST_CORPUS_DISPLAY_NAME,
     description=TEST_CORPUS_DISCRIPTION,
+    embedding_model_config=TEST_EMBEDDING_MODEL_CONFIG,
 )
 TEST_PAGE_TOKEN = "test-page-token"
 
@@ -114,6 +124,8 @@
         chunk_overlap=TEST_CHUNK_OVERLAP,
     )
 )
+TEST_IMPORT_FILES_CONFIG_DRIVE_FILE.max_embedding_requests_per_min = 800
+
 TEST_IMPORT_FILES_CONFIG_DRIVE_FILE.google_drive_source.resource_ids = [
     GoogleDriveSource.ResourceId(
         resource_id=TEST_DRIVE_FILE_ID,

tests/unit/vertex_rag/test_rag_data.py

@@ -19,6 +19,7 @@
 from vertexai.preview import rag
 from vertexai.preview.rag.utils._gapic_utils import (
     prepare_import_files_request,
+    set_embedding_model_config,
 )
 from google.cloud.aiplatform_v1beta1 import (
     VertexRagDataServiceAsyncClient,
@@ -171,7 +172,10 @@ def teardown_method(self):
 
     @pytest.mark.usefixtures("create_rag_corpus_mock")
     def test_create_corpus_success(self):
-        rag_corpus = rag.create_corpus(display_name=tc.TEST_CORPUS_DISPLAY_NAME)
+        rag_corpus = rag.create_corpus(
+            display_name=tc.TEST_CORPUS_DISPLAY_NAME,
+            embedding_model_config=tc.TEST_EMBEDDING_MODEL_CONFIG,
+        )
 
         rag_corpus_eq(rag_corpus, tc.TEST_RAG_CORPUS)
 
@@ -391,6 +395,7 @@ def test_prepare_import_files_request_drive_files(self):
             paths=paths,
             chunk_size=tc.TEST_CHUNK_SIZE,
             chunk_overlap=tc.TEST_CHUNK_OVERLAP,
+            max_embedding_requests_per_min=800,
         )
         import_files_request_eq(request, tc.TEST_IMPORT_REQUEST_DRIVE_FILE)
 
@@ -415,3 +420,42 @@ def test_prepare_import_files_request_invalid_path(self):
                 chunk_overlap=tc.TEST_CHUNK_OVERLAP,
             )
         e.match("path must be a Google Cloud Storage uri or a Google Drive url")
+
+    def test_set_embedding_model_config_set_both_error(self):
+        embedding_model_config = rag.EmbeddingModelConfig(
+            publisher_model="whatever",
+            endpoint="whatever",
+        )
+        with pytest.raises(ValueError) as e:
+            set_embedding_model_config(
+                embedding_model_config,
+                tc.TEST_GAPIC_RAG_CORPUS,
+            )
+        e.match("publisher_model and endpoint cannot be set at the same time")
+
+    def test_set_embedding_model_config_not_set_error(self):
+        embedding_model_config = rag.EmbeddingModelConfig()
+        with pytest.raises(ValueError) as e:
+            set_embedding_model_config(
+                embedding_model_config,
+                tc.TEST_GAPIC_RAG_CORPUS,
+            )
+        e.match("At least one of publisher_model and endpoint must be set")
+
+    def test_set_embedding_model_config_wrong_publisher_model_format_error(self):
+        embedding_model_config = rag.EmbeddingModelConfig(publisher_model="whatever")
+        with pytest.raises(ValueError) as e:
+            set_embedding_model_config(
+                embedding_model_config,
+                tc.TEST_GAPIC_RAG_CORPUS,
+            )
+        e.match("publisher_model must be of the format ")
+
+    def test_set_embedding_model_config_wrong_endpoint_format_error(self):
+        embedding_model_config = rag.EmbeddingModelConfig(endpoint="whatever")
+        with pytest.raises(ValueError) as e:
+            set_embedding_model_config(
+                embedding_model_config,
+                tc.TEST_GAPIC_RAG_CORPUS,
+            )
+        e.match("endpoint must be of the format ")

vertexai/preview/rag/__init__.py

@@ -37,6 +37,7 @@
     VertexRagStore,
 )
 from vertexai.preview.rag.utils.resources import (
+    EmbeddingModelConfig,
     RagResource,
 )
 
@@ -53,6 +54,7 @@
     "list_files",
     "delete_file",
     "retrieval_query",
+    "EmbeddingModelConfig",
     "Retrieval",
     "VertexRagStore",
     "RagResource",

vertexai/preview/rag/rag_data.py

@@ -43,13 +43,16 @@
     _gapic_utils,
 )
 from vertexai.preview.rag.utils.resources import (
+    EmbeddingModelConfig,
     RagCorpus,
     RagFile,
 )
 
 
 def create_corpus(
-    display_name: Optional[str] = None, description: Optional[str] = None
+    display_name: Optional[str] = None,
+    description: Optional[str] = None,
+    embedding_model_config: Optional[EmbeddingModelConfig] = None,
 ) -> RagCorpus:
     """Creates a new RagCorpus resource.
 
@@ -69,6 +72,7 @@ def create_corpus(
             the RagCorpus. The name can be up to 128 characters long and can
             consist of any UTF-8 characters.
         description: The description of the RagCorpus.
+        embedding_model_config: The embedding model config.
     Returns:
         RagCorpus.
     Raises:
@@ -80,6 +84,12 @@ def create_corpus(
     parent = initializer.global_config.common_location_path(project=None, location=None)
 
     rag_corpus = GapicRagCorpus(display_name=display_name, description=description)
+    if embedding_model_config:
+        rag_corpus = _gapic_utils.set_embedding_model_config(
+            embedding_model_config,
+            rag_corpus,
+        )
+
     request = CreateRagCorpusRequest(
         parent=parent,
         rag_corpus=rag_corpus,
@@ -264,6 +274,7 @@ def import_files(
     chunk_size: int = 1024,
     chunk_overlap: int = 200,
     timeout: int = 600,
+    max_embedding_requests_per_min: int = 1000,
 ) -> ImportRagFilesResponse:
     """
     Import files to an existing RagCorpus, wait until completion.
@@ -299,6 +310,15 @@ def import_files(
             "https://drive.google.com/corp/drive/folders/...").
         chunk_size: The size of the chunks.
         chunk_overlap: The overlap between chunks.
+        max_embedding_requests_per_min:
+            Optional. The max number of queries per
+            minute that this job is allowed to make to the
+            embedding model specified on the corpus. This
+            value is specific to this job and not shared
+            across other import jobs. Consult the Quotas
+            page on the project to set an appropriate value
+            here. If unspecified, a default value of 1,000
+            QPM would be used.
         timeout: Default is 600 seconds.
     Returns:
         ImportRagFilesResponse.
@@ -309,6 +329,7 @@ def import_files(
         paths=paths,
         chunk_size=chunk_size,
         chunk_overlap=chunk_overlap,
+        max_embedding_requests_per_min=max_embedding_requests_per_min,
     )
     client = _gapic_utils.create_rag_data_service_client()
     try:
@@ -324,6 +345,7 @@ async def import_files_async(
     paths: Sequence[str],
     chunk_size: int = 1024,
     chunk_overlap: int = 200,
+    max_embedding_requests_per_min: int = 1000,
 ) -> operation_async.AsyncOperation:
     """
     Import files to an existing RagCorpus asynchronously.
@@ -361,6 +383,15 @@ async def import_files_async(
             "https://drive.google.com/corp/drive/folders/...").
         chunk_size: The size of the chunks.
         chunk_overlap: The overlap between chunks.
+        max_embedding_requests_per_min:
+            Optional. The max number of queries per
+            minute that this job is allowed to make to the
+            embedding model specified on the corpus. This
+            value is specific to this job and not shared
+            across other import jobs. Consult the Quotas
+            page on the project to set an appropriate value
+            here. If unspecified, a default value of 1,000
+            QPM would be used.
     Returns:
         operation_async.AsyncOperation.
     """
@@ -370,6 +401,7 @@ async def import_files_async(
         paths=paths,
         chunk_size=chunk_size,
         chunk_overlap=chunk_overlap,
+        max_embedding_requests_per_min=max_embedding_requests_per_min,
     )
     async_client = _gapic_utils.create_rag_data_service_async_client()
     try:

vertexai/preview/rag/utils/_gapic_utils.py

@@ -17,6 +17,7 @@
 import re
 from typing import Any, Dict, Sequence, Union
 from google.cloud.aiplatform_v1beta1 import (
+    RagEmbeddingModelConfig,
     GoogleDriveSource,
     ImportRagFilesConfig,
     ImportRagFilesRequest,
@@ -31,6 +32,7 @@
     VertexRagClientWithOverride,
 )
 from vertexai.preview.rag.utils.resources import (
+    EmbeddingModelConfig,
     RagCorpus,
     RagFile,
 )
@@ -57,12 +59,43 @@ def create_rag_service_client():
     )
 
 
+def convert_gapic_to_embedding_model_config(
+    gapic_embedding_model_config: RagEmbeddingModelConfig,
+) -> EmbeddingModelConfig:
+    """Convert GapicRagEmbeddingModelConfig to EmbeddingModelConfig."""
+    embedding_model_config = EmbeddingModelConfig()
+    path = gapic_embedding_model_config.vertex_prediction_endpoint.endpoint
+    publisher_model = re.match(
+        r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/publishers/google/models/(?P<model_id>.+?)$",
+        path,
+    )
+    endpoint = re.match(
+        r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/endpoints/(?P<endpoint>.+?)$",
+        path,
+    )
+    if publisher_model:
+        embedding_model_config.publisher_model = path
+    if endpoint:
+        embedding_model_config.endpoint = path
+        embedding_model_config.model = (
+            gapic_embedding_model_config.vertex_prediction_endpoint.model
+        )
+        embedding_model_config.model_version_id = (
+            gapic_embedding_model_config.vertex_prediction_endpoint.model_version_id
+        )
+
+    return embedding_model_config
+
+
 def convert_gapic_to_rag_corpus(gapic_rag_corpus: GapicRagCorpus) -> RagCorpus:
     """ "Convert GapicRagCorpus to RagCorpus."""
     rag_corpus = RagCorpus(
         name=gapic_rag_corpus.name,
         display_name=gapic_rag_corpus.display_name,
         description=gapic_rag_corpus.description,
+        embedding_model_config=convert_gapic_to_embedding_model_config(
+            gapic_rag_corpus.rag_embedding_model_config
+        ),
     )
     return rag_corpus
 
@@ -124,6 +157,7 @@ def prepare_import_files_request(
     paths: Sequence[str],
     chunk_size: int = 1024,
     chunk_overlap: int = 200,
+    max_embedding_requests_per_min: int = 1000,
 ) -> ImportRagFilesRequest:
     if len(corpus_name.split("/")) != 6:
         raise ValueError(
@@ -135,7 +169,8 @@ def prepare_import_files_request(
         chunk_overlap=chunk_overlap,
     )
     import_rag_files_config = ImportRagFilesConfig(
-        rag_file_chunking_config=rag_file_chunking_config
+        rag_file_chunking_config=rag_file_chunking_config,
+        max_embedding_requests_per_min=max_embedding_requests_per_min,
     )
 
     uris = []
@@ -204,3 +239,65 @@ def get_file_name(
         raise ValueError(
             "name must be of the format `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}` or `{rag_file}`"
         )
+
+
+def set_embedding_model_config(
+    embedding_model_config: EmbeddingModelConfig,
+    rag_corpus: GapicRagCorpus,
+) -> GapicRagCorpus:
+    if embedding_model_config.publisher_model and embedding_model_config.endpoint:
+        raise ValueError("publisher_model and endpoint cannot be set at the same time.")
+    if (
+        not embedding_model_config.publisher_model
+        and not embedding_model_config.endpoint
+    ):
+        raise ValueError("At least one of publisher_model and endpoint must be set.")
+    parent = initializer.global_config.common_location_path(project=None, location=None)
+
+    if embedding_model_config.publisher_model:
+        publisher_model = embedding_model_config.publisher_model
+        full_resource_name = re.match(
+            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/publishers/google/models/(?P<model_id>.+?)$",
+            publisher_model,
+        )
+        resource_name = re.match(
+            r"^publishers/google/models/(?P<model_id>.+?)$",
+            publisher_model,
+        )
+        if full_resource_name:
+            rag_corpus.rag_embedding_model_config.vertex_prediction_endpoint.endpoint = (
+                publisher_model
+            )
+        elif resource_name:
+            rag_corpus.rag_embedding_model_config.vertex_prediction_endpoint.endpoint = (
+                parent + "/" + publisher_model
+            )
+        else:
+            raise ValueError(
+                "publisher_model must be of the format `projects/{project}/locations/{location}/publishers/google/models/{model_id}` or `publishers/google/models/{model_id}`"
+            )
+
+    if embedding_model_config.endpoint:
+        endpoint = embedding_model_config.endpoint
+        full_resource_name = re.match(
+            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/endpoints/(?P<endpoint>.+?)$",
+            endpoint,
+        )
+        resource_name = re.match(
+            r"^endpoints/(?P<endpoint>.+?)$",
+            endpoint,
+        )
+        if full_resource_name:
+            rag_corpus.rag_embedding_model_config.vertex_prediction_endpoint.endpoint = (
+                endpoint
+            )
+        elif resource_name:
+            rag_corpus.rag_embedding_model_config.vertex_prediction_endpoint.endpoint = (
+                parent + "/" + endpoint
+            )
+        else:
+            raise ValueError(
+                "endpoint must be of the format `projects/{project}/locations/{location}/endpoints/{endpoint}` or `endpoints/{endpoint}`"
+            )
+
+    return rag_corpus