diff --git a/py/plugins/vertex-ai/pyproject.toml b/py/plugins/vertex-ai/pyproject.toml index 6cca07fc7c..5cfdec5a40 100644 --- a/py/plugins/vertex-ai/pyproject.toml +++ b/py/plugins/vertex-ai/pyproject.toml @@ -18,10 +18,13 @@ classifiers = [ ] dependencies = [ "genkit", + "google-genai>=1.7.0", "google-cloud-aiplatform>=1.77.0", "pytest-mock", "structlog>=25.2.0", "strenum>=0.4.15; python_version < '3.11'", + "google-cloud-bigquery", + "google-cloud-firestore", ] description = "Genkit Google Cloud Vertex AI Plugin" license = { text = "Apache-2.0" } diff --git a/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/__init__.py b/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/__init__.py index c0ac5edf03..c635d21132 100644 --- a/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/__init__.py +++ b/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/__init__.py @@ -26,6 +26,7 @@ from genkit.plugins.vertex_ai.gemini import GeminiVersion from genkit.plugins.vertex_ai.imagen import ImagenOptions, ImagenVersion from genkit.plugins.vertex_ai.plugin_api import VertexAI, vertexai_name +from genkit.plugins.vertex_ai.vector_search.vector_search import VertexAIVectorSearch def package_name() -> str: @@ -46,4 +47,5 @@ def package_name() -> str: GeminiVersion.__name__, ImagenVersion.__name__, ImagenOptions.__name__, + VertexAIVectorSearch.__name__, ] diff --git a/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/models/retriever.py b/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/models/retriever.py new file mode 100644 index 0000000000..23e6644d95 --- /dev/null +++ b/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/models/retriever.py @@ -0,0 +1,345 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import json +from abc import ABC, abstractmethod +from collections.abc import Callable +from typing import Any + +import structlog +from google.cloud import bigquery, firestore +from google.cloud.aiplatform_v1 import FindNeighborsRequest, IndexDatapoint, Neighbor +from pydantic import BaseModel, Field, ValidationError + +from genkit.ai import Genkit +from genkit.blocks.document import Document +from genkit.core.typing import Embedding +from genkit.types import ActionRunContext, RetrieverRequest, RetrieverResponse + +logger = structlog.get_logger(__name__) + +DEFAULT_LIMIT_NEIGHBORS: int = 3 + + +class DocRetriever(ABC): + """Abstract base class for Vertex AI Vector Search document retrieval. + + This class outlines the core workflow for retrieving relevant documents. + It is not intended to be instantiated directly. Subclasses must implement + the abstract methods to provide concrete retrieval logic depending of the + technology used. + + Attributes: + ai: The Genkit instance. + name: The name of this retriever instance. + match_service_client: The Vertex AI Matching Engine client. + embedder: The name of the embedder to use for generating embeddings. + embedder_options: Options to pass to the embedder. + """ + def __init__( + self, + ai: Genkit, + name: str, + match_service_client_generator: Callable, + embedder: str, + embedder_options: dict[str, Any] | None = None, + ) -> None: + """Initializes the DocRetriever. + + Args: + ai: The Genkit application instance. + name: The name of this retriever instance. + match_service_client_generator: The Vertex AI Matching Engine client. + embedder: The name of the embedder to use for generating embeddings. + Already added plugin prefix. + embedder_options: Optional dictionary of options to pass to the embedder. + """ + self.ai = ai + self.name = name + self.embedder = embedder + self.embedder_options = embedder_options or {} + self._match_service_client_generator = match_service_client_generator + + async def retrieve(self, request: RetrieverRequest, _: ActionRunContext) -> RetrieverResponse: + """Retrieves documents based on a given query. + + Args: + request: The retrieval request containing the query. + _: The ActionRunContext (unused in this method). + + Returns: + A RetrieverResponse object containing the retrieved documents. + """ + document = Document.from_document_data(document_data=request.query) + + embeddings = await self.ai.embed( + embedder=self.embedder, + documents=[document], + options=self.embedder_options, + ) + + limit_neighbors = DEFAULT_LIMIT_NEIGHBORS + if isinstance(request.options, dict) and request.options.get('limit') is not None: + limit_neighbors = request.options.get('limit') + + docs = await self._get_closest_documents( + request=request, + top_k=limit_neighbors, + query_embeddings=embeddings.embeddings[0], + ) + + return RetrieverResponse(documents=docs) + + async def _get_closest_documents( + self, request: RetrieverRequest, top_k: int, query_embeddings: Embedding + ) -> list[Document]: + """Retrieves the closest documents from the vector search index based on query embeddings. + + Args: + request: The retrieval request containing the query and metadata. + top_k: The number of nearest neighbors to retrieve. + query_embeddings: The embedding of the query. + + Returns: + A list of Document objects representing the closest documents. + + Raises: + AttributeError: If the request does not contain the necessary + index endpoint path in its metadata. + """ + metadata = request.query.metadata + if not metadata or 'index_endpoint_path' not in metadata or 'api_endpoint' not in metadata: + raise AttributeError('Request provides no data about index endpoint path') + + api_endpoint = metadata['api_endpoint'] + index_endpoint_path = metadata['index_endpoint_path'] + deployed_index_id = metadata['deployed_index_id'] + + client_options = { + "api_endpoint": api_endpoint + } + + vector_search_client = self._match_service_client_generator( + client_options=client_options, + ) + + nn_request = FindNeighborsRequest( + index_endpoint=index_endpoint_path, + deployed_index_id=deployed_index_id, + queries=[ + FindNeighborsRequest.Query( + datapoint=IndexDatapoint(feature_vector=query_embeddings.embedding), + neighbor_count=top_k, + ) + ], + ) + + response = await vector_search_client.find_neighbors(request=nn_request) + + return await self._retrieve_neighbours_data_from_db(neighbours=response.nearest_neighbors[0].neighbors) + + @abstractmethod + async def _retrieve_neighbours_data_from_db(self, neighbours: list[Neighbor]) -> list[Document]: + """Retrieves document data from the database based on neighbor information. + + This method must be implemented by subclasses to define how document + data is fetched from the database using the provided neighbor information. + + Args: + neighbours: A list of Neighbor objects representing the nearest neighbors + found in the vector search index. + + Returns: + A list of Document objects containing the data for the retrieved documents. + """ + raise NotImplementedError + + +class BigQueryRetriever(DocRetriever): + """Retrieves documents from a BigQuery table. + + This class extends DocRetriever to fetch document data from a specified BigQuery + dataset and table. It constructs a query to retrieve documents based on the IDs + obtained from nearest neighbor search results. + + Attributes: + bq_client: The BigQuery client to use for querying. + dataset_id: The ID of the BigQuery dataset. + table_id: The ID of the BigQuery table. + """ + def __init__( + self, bq_client: bigquery.Client, dataset_id: str, table_id: str, *args, **kwargs, + ) -> None: + """Initializes the BigQueryRetriever. + + Args: + bq_client: The BigQuery client to use for querying. + dataset_id: The ID of the BigQuery dataset. + table_id: The ID of the BigQuery table. + *args: Additional positional arguments to pass to the parent class. + **kwargs: Additional keyword arguments to pass to the parent class. + """ + super().__init__(*args, **kwargs) + self.bq_client = bq_client + self.dataset_id = dataset_id + self.table_id = table_id + + async def _retrieve_neighbours_data_from_db(self, neighbours: list[Neighbor]) -> list[Document]: + """Retrieves document data from the BigQuery table for the given neighbors. + + Constructs and executes a BigQuery query to fetch document data based on + the IDs obtained. Handles potential errors during query execution and + document parsing. + + Args: + neighbours: A list of Neighbor objects representing the nearest neighbors. + Each neighbor should contain a datapoint with a datapoint_id. + + Returns: + A list of Document objects containing the retrieved document data. + Returns an empty list if no IDs are found in the neighbors or if the + query fails. + """ + ids = [ + n.datapoint.datapoint_id + for n in neighbours + if n.datapoint and n.datapoint.datapoint_id + ] + + distance_by_id = { + n.datapoint.datapoint_id: n.distance + for n in neighbours + if n.datapoint and n.datapoint.datapoint_id + } + + if not ids: + return [] + + query = f""" + SELECT * FROM `{self.dataset_id}.{self.table_id}` + WHERE id IN UNNEST(@ids) + """ + + job_config = bigquery.QueryJobConfig( + query_parameters=[bigquery.ArrayQueryParameter('ids', 'STRING', ids)], + ) + + try: + query_job = self.bq_client.query(query, job_config=job_config) + rows = query_job.result() + except Exception as e: + await logger.aerror('Failed to execute BigQuery query: %s', e) + return [] + + documents: list[Document] = [] + + for row in rows: + try: + id = row['id'] + + content = row['content'] + content = json.dumps(content) if isinstance(content, dict) else str(content) + + metadata = row.get('metadata', {}) + metadata['id'] = id + metadata['distance'] = distance_by_id[id] + + documents.append(Document.from_text(content, metadata)) + except (ValidationError, json.JSONDecodeError, Exception) as error: + doc_id = row.get('id', '') + await logger.awarning(f'Failed to parse document data for document with ID {doc_id}: {error}') + + return documents + + +class FirestoreRetriever(DocRetriever): + """Retrieves documents from a Firestore collection. + + This class extends DocRetriever to fetch document data from a specified Firestore + collection. It retrieves documents based on IDs obtained from nearest neighbor + search results. + + Attributes: + db: The Firestore client. + collection_name: The name of the Firestore collection. + """ + def __init__( + self, firestore_client: firestore.AsyncClient, collection_name: str, *args, **kwargs, + ) -> None: + """Initializes the FirestoreRetriever. + + Args: + firestore_client: The Firestore client to use for querying. + collection_name: The name of the Firestore collection. + *args: Additional positional arguments to pass to the parent class. + **kwargs: Additional keyword arguments to pass to the parent class. + """ + super().__init__(*args, **kwargs) + self.db = firestore_client + self.collection_name = collection_name + + async def _retrieve_neighbours_data_from_db(self, neighbours: list[Neighbor]) -> list[Document]: + """Retrieves document data from the Firestore collection for the given neighbors. + + Fetches document data from Firestore based on the IDs of the nearest neighbors. + Handles potential errors during document retrieval and data parsing. + + Args: + neighbours: A list of Neighbor objects representing the nearest neighbors. + Each neighbor should contain a datapoint with a datapoint_id. + + Returns: + A list of Document objects containing the retrieved document data. + Returns an empty list if no documents are found for the given IDs. + """ + documents: list[Document] = [] + + for neighbor in neighbours: + doc_ref = self.db.collection(self.collection_name).document(document_id=neighbor.datapoint.datapoint_id) + doc_snapshot = doc_ref.get() + + if doc_snapshot.exists: + doc_data = doc_snapshot.to_dict() or {} + + content = doc_data.get('content') + content = json.dumps(content) if isinstance(content, dict) else str(content) + + metadata = doc_data.get('metadata', {}) + metadata['id'] = neighbor.datapoint.datapoint_id + metadata['distance'] = neighbor.distance + + try: + documents.append( + Document.from_text( + content, + metadata, + ) + ) + except ValidationError as e: + await logger.awarning( + f'Failed to parse document data for ID {neighbor.datapoint.datapoint_id}: {e}' + ) + + return documents + + +class RetrieverOptionsSchema(BaseModel): + """Schema for retriver options. + + Attributes: + limit: Number of documents to retrieve. + """ + limit: int | None = Field(title='Number of documents to retrieve', default=None) diff --git a/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/vector_search/vector_search.py b/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/vector_search/vector_search.py new file mode 100644 index 0000000000..7126ca65b4 --- /dev/null +++ b/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/vector_search/vector_search.py @@ -0,0 +1,104 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +from functools import partial +from typing import Any + +import structlog +from google.auth.credentials import Credentials +from google.cloud import aiplatform_v1 + +from genkit.ai import GenkitRegistry, Plugin +from genkit.plugins.vertex_ai import vertexai_name +from genkit.plugins.vertex_ai.models.retriever import ( + DocRetriever, + RetrieverOptionsSchema, +) + +logger = structlog.get_logger(__name__) + + +class VertexAIVectorSearch(Plugin): + """A plugin for integrating VertexAI Vector Search. + + This class registers VertexAI Vector Stores within a registry, + and allows interaction to retrieve similar documents. + """ + + name: str = 'vertexAIVectorSearch' + + def __init__( + self, + retriever: DocRetriever, + retriever_extra_args: dict[str, Any] | None = None, + credentials: Credentials | None = None, + project: str | None = None, + location: str | None = 'us-central1', + embedder: str | None = None, + embedder_options: dict[str, Any] | None = None, + ) -> None: + """Initializes the VertexAIVectorSearch plugin. + + Args: + retriever: The DocRetriever class to use for retrieving documents. + retriever_extra_args: Optional dictionary of extra arguments to pass to the + retriever's constructor. + credentials: Optional Google Cloud credentials to use. If not provided, + the default application credentials will be used. + project: Optional Google Cloud project ID. If not provided, it will be + inferred from the credentials. + location: Optional Google Cloud location (region). Defaults to + 'us-central1'. + embedder: Optional identifier for the embedding model to use. + embedder_options: Optional dictionary of options to pass to the embedding + model. + """ + self.project = project + self.location = location + + self.embedder = embedder + self.embedder_options = embedder_options + + self.retriever_cls = retriever + self.retriever_extra_args = retriever_extra_args or {} + + self._match_service_client_generator = partial( + aiplatform_v1.MatchServiceAsyncClient, + credentials=credentials, + ) + + def initialize(self, ai: GenkitRegistry) -> None: + """Initialize plugin with the retriver specified. + + Register actions with the registry making them available for use in the Genkit framework. + + Args: + ai: The registry to register actions with. + """ + retriever = self.retriever_cls( + ai=ai, + name=self.name, + match_service_client_generator=self._match_service_client_generator, + embedder=self.embedder, + embedder_options=self.embedder_options, + **self.retriever_extra_args, + ) + + return ai.define_retriever( + name=vertexai_name(self.name), + config_schema=RetrieverOptionsSchema, + fn=retriever.retrieve, + ) diff --git a/py/samples/vertex-ai-vector-search-firestore/LICENSE b/py/samples/vertex-ai-vector-search-firestore/LICENSE new file mode 100644 index 0000000000..2205396735 --- /dev/null +++ b/py/samples/vertex-ai-vector-search-firestore/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2025 Google LLC + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/py/samples/vertex-ai-vector-search-firestore/README.md b/py/samples/vertex-ai-vector-search-firestore/README.md new file mode 100644 index 0000000000..591c705d9a --- /dev/null +++ b/py/samples/vertex-ai-vector-search-firestore/README.md @@ -0,0 +1,27 @@ +# Vertex AI - Vector Search Firestore + +An example demonstrating the use Vector Search API with Firestore retriever for Vertex AI + +## Setup environment + +1. Install [GCP CLI](https://cloud.google.com/sdk/docs/install). +2. Run the following code to connect to VertexAI. +```bash +gcloud auth application-default login +``` +3. Set the following env vars to run the sample +``` +export LOCATION='' +export PROJECT_ID='' +export FIRESTORE_COLLECTION='' +export VECTOR_SEARCH_DEPLOYED_INDEX_ID='' +export VECTOR_SEARCH_INDEX_ENDPOINT_PATH='' +export VECTOR_SEARCH_API_ENDPOINT='' +``` +4. Run the sample. + +## Run the sample + +```bash +genkit start -- uv run src/sample.py +``` diff --git a/py/samples/vertex-ai-vector-search-firestore/pyproject.toml b/py/samples/vertex-ai-vector-search-firestore/pyproject.toml new file mode 100644 index 0000000000..3413399903 --- /dev/null +++ b/py/samples/vertex-ai-vector-search-firestore/pyproject.toml @@ -0,0 +1,39 @@ +[project] +authors = [{ name = "Google" }] +classifiers = [ + "Development Status :: 3 - Alpha", + "Environment :: Console", + "Environment :: Web Environment", + "Intended Audience :: Developers", + "Operating System :: OS Independent", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Software Development :: Libraries", +] +dependencies = [ + "genkit", + "genkit-plugin-vertex-ai", + "pydantic>=2.10.5", + "structlog>=25.2.0", + "google-cloud-firestore", + "strenum>=0.4.15; python_version < '3.11'", +] +description = "An example demonstrating the use Vector Search API with Firestore retriever for Vertex AI" +license = { text = "Apache-2.0" } +name = "vertex-ai-vector-search-firestore" +readme = "README.md" +requires-python = ">=3.10" +version = "0.1.0" + +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling"] + +[tool.hatch.build.targets.wheel] +packages = ["src/sample"] diff --git a/py/samples/vertex-ai-vector-search-firestore/src/sample.py b/py/samples/vertex-ai-vector-search-firestore/src/sample.py new file mode 100644 index 0000000000..a8bd67f563 --- /dev/null +++ b/py/samples/vertex-ai-vector-search-firestore/src/sample.py @@ -0,0 +1,135 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +import os +import time + +import structlog +from google.cloud import aiplatform, firestore +from pydantic import BaseModel + +from genkit.ai import Genkit +from genkit.blocks.document import Document +from genkit.plugins.vertex_ai import ( + EmbeddingModels, + VertexAI, + VertexAIVectorSearch, + vertexai_name, +) +from genkit.plugins.vertex_ai.models.retriever import FirestoreRetriever + +LOCATION = os.getenv('LOCATION') +PROJECT_ID = os.getenv('PROJECT_ID') +EMBEDDING_MODEL = EmbeddingModels.TEXT_EMBEDDING_004_ENG + +FIRESTORE_COLLECTION = os.getenv('FIRESTORE_COLLECTION') + +VECTOR_SEARCH_DEPLOYED_INDEX_ID = os.getenv('VECTOR_SEARCH_DEPLOYED_INDEX_ID') +VECTOR_SEARCH_INDEX_ENDPOINT_PATH = os.getenv('VECVECTOR_SEARCH_INDEX_ENDPOINT_PATHTOR_SEARCH_INDEX_ENDPOINT_ID') +VECTOR_SEARCH_API_ENDPOINT = os.getenv('VECTOR_SEARCH_API_ENDPOINT') + +firestore_client = firestore.Client(project=PROJECT_ID) +aiplatform.init(project=PROJECT_ID, location=LOCATION) + +logger = structlog.get_logger(__name__) + +ai = Genkit( + plugins=[ + VertexAI(), + VertexAIVectorSearch( + retriever=FirestoreRetriever, + retriever_extra_args={ + 'firestore_client': firestore_client, + 'collection_name': FIRESTORE_COLLECTION, + }, + embedder=vertexai_name(EMBEDDING_MODEL), + embedder_options={ + 'task': 'RETRIEVAL_DOCUMENT', + 'output_dimensionality': 128, + }, + ), + ] +) + + +class QueryFlowInputSchema(BaseModel): + """Input schema.""" + query: str + k: int + + +class QueryFlowOutputSchema(BaseModel): + """Output schema.""" + result: list[dict] + length: int + time: int + + +@ai.flow(name='queryFlow') +async def query_flow(_input: QueryFlowInputSchema) -> QueryFlowOutputSchema: + """Executes a vector search with VertexAI Vector Search.""" + start_time = time.time() + + query_document = Document.from_text(text=_input.query) + query_document.metadata = { + 'api_endpoint': VECTOR_SEARCH_API_ENDPOINT, + 'index_endpoint_path': VECTOR_SEARCH_INDEX_ENDPOINT_PATH, + 'deployed_index_id': VECTOR_SEARCH_DEPLOYED_INDEX_ID, + } + + options = { + 'limit': 10, + } + + result: list[Document] = await ai.retrieve( + retriever=vertexai_name('vertexAIVectorSearch'), + query=query_document, + options=options, + ) + + end_time = time.time() + + duration = int(end_time - start_time) + + result_data = [] + for doc in result.documents: + result_data.append({ + 'id': doc.metadata.get('id'), + 'text': doc.content[0].root.text, + 'distance': doc.metadata.get('distance'), + }) + + result_data = sorted(result_data, key=lambda x: x['distance']) + + return QueryFlowOutputSchema( + result=result_data, + length=len(result_data), + time=duration, + ) + + +async def main() -> None: + """Main function.""" + query_input = QueryFlowInputSchema( + query="Content for doc", + k=3, + ) + + await logger.ainfo(await query_flow(query_input)) + + +if __name__ == '__main__': + ai.run_main(main()) diff --git a/py/uv.lock b/py/uv.lock index 0585aac076..7a8d892dca 100644 --- a/py/uv.lock +++ b/py/uv.lock @@ -36,6 +36,7 @@ members = [ "ollama-simple-embed", "short-n-long", "tool-interrupts", + "vertex-ai-vector-search-firestore", ] [[package]] @@ -1025,7 +1026,7 @@ provides-extras = ["dev-local-vectorstore", "flask", "google-genai", "ollama", " [[package]] name = "genkit-plugin-compat-oai" -version = "0.3.1" +version = "0.3.2" source = { editable = "plugins/compat-oai" } dependencies = [ { name = "genkit" }, @@ -1042,7 +1043,7 @@ requires-dist = [ [[package]] name = "genkit-plugin-dev-local-vectorstore" -version = "0.3.1" +version = "0.3.2" source = { editable = "plugins/dev-local-vectorstore" } dependencies = [ { name = "genkit" }, @@ -1061,7 +1062,7 @@ requires-dist = [ [[package]] name = "genkit-plugin-firebase" -version = "0.3.1" +version = "0.3.2" source = { editable = "plugins/firebase" } dependencies = [ { name = "genkit" }, @@ -1078,7 +1079,7 @@ requires-dist = [ [[package]] name = "genkit-plugin-flask" -version = "0.3.1" +version = "0.3.2" source = { editable = "plugins/flask" } dependencies = [ { name = "flask" }, @@ -1098,7 +1099,7 @@ requires-dist = [ [[package]] name = "genkit-plugin-google-cloud" -version = "0.3.1" +version = "0.3.2" source = { editable = "plugins/google-cloud" } dependencies = [ { name = "genkit" }, @@ -1115,7 +1116,7 @@ requires-dist = [ [[package]] name = "genkit-plugin-google-genai" -version = "0.3.1" +version = "0.3.2" source = { editable = "plugins/google-genai" } dependencies = [ { name = "genkit" }, @@ -1136,7 +1137,7 @@ requires-dist = [ [[package]] name = "genkit-plugin-ollama" -version = "0.3.1" +version = "0.3.2" source = { editable = "plugins/ollama" } dependencies = [ { name = "genkit" }, @@ -1153,11 +1154,14 @@ requires-dist = [ [[package]] name = "genkit-plugin-vertex-ai" -version = "0.3.1" +version = "0.3.2" source = { editable = "plugins/vertex-ai" } dependencies = [ { name = "genkit" }, { name = "google-cloud-aiplatform" }, + { name = "google-cloud-bigquery" }, + { name = "google-cloud-firestore" }, + { name = "google-genai" }, { name = "pytest-mock" }, { name = "strenum", marker = "python_full_version < '3.11'" }, { name = "structlog" }, @@ -1167,6 +1171,9 @@ dependencies = [ requires-dist = [ { name = "genkit", editable = "packages/genkit" }, { name = "google-cloud-aiplatform", specifier = ">=1.77.0" }, + { name = "google-cloud-bigquery" }, + { name = "google-cloud-firestore" }, + { name = "google-genai", specifier = ">=1.7.0" }, { name = "pytest-mock" }, { name = "strenum", marker = "python_full_version < '3.11'", specifier = ">=0.4.15" }, { name = "structlog", specifier = ">=25.2.0" }, @@ -4617,6 +4624,29 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/63/9a/0962b05b308494e3202d3f794a6e85abe471fe3cafdbcf95c2e8c713aabd/uvloop-0.21.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a5c39f217ab3c663dc699c04cbd50c13813e31d917642d459fdcec07555cc553", size = 4660018 }, ] +[[package]] +name = "vertex-ai-vector-search-firestore" +version = "0.1.0" +source = { editable = "samples/vertex-ai-vector-search-firestore" } +dependencies = [ + { name = "genkit" }, + { name = "genkit-plugin-vertex-ai" }, + { name = "google-cloud-firestore" }, + { name = "pydantic" }, + { name = "strenum", marker = "python_full_version < '3.11'" }, + { name = "structlog" }, +] + +[package.metadata] +requires-dist = [ + { name = "genkit", editable = "packages/genkit" }, + { name = "genkit-plugin-vertex-ai", editable = "plugins/vertex-ai" }, + { name = "google-cloud-firestore" }, + { name = "pydantic", specifier = ">=2.10.5" }, + { name = "strenum", marker = "python_full_version < '3.11'", specifier = ">=0.4.15" }, + { name = "structlog", specifier = ">=25.2.0" }, +] + [[package]] name = "virtualenv" version = "20.30.0"