mirror of
https://github.com/hwchase17/langchain.git
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fix: Update Google Cloud Enterprise Search to Vertex AI Search (#10513)
- Description: Google Cloud Enterprise Search was renamed to Vertex AI Search - https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-search-and-conversation-is-now-generally-available - This PR updates the documentation and Retriever class to use the new terminology. - Changed retriever class from `GoogleCloudEnterpriseSearchRetriever` to `GoogleVertexAISearchRetriever` - Updated documentation to specify that `extractive_segments` requires the new [Enterprise edition](https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features#enterprise-features) to be enabled. - Fixed spelling errors in documentation. - Change parameter for Retriever from `search_engine_id` to `data_store_id` - When this retriever was originally implemented, there was no distinction between a data store and search engine, but now these have been split. - Fixed an issue blocking some users where the api_endpoint can't be set
This commit is contained in:
Holt Skinner
GitHub
@@ -30,6 +30,9 @@ from langchain.retrievers.ensemble import EnsembleRetriever
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from langchain.retrievers.google_cloud_enterprise_search import (
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GoogleCloudEnterpriseSearchRetriever,
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)
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from langchain.retrievers.google_vertex_ai_search import (
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GoogleVertexAISearchRetriever,
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)
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from langchain.retrievers.kay import KayAiRetriever
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from langchain.retrievers.kendra import AmazonKendraRetriever
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from langchain.retrievers.knn import KNNRetriever
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@@ -70,6 +73,7 @@ __all__ = [
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"ChaindeskRetriever",
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"ElasticSearchBM25Retriever",
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"GoogleCloudEnterpriseSearchRetriever",
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"GoogleVertexAISearchRetriever",
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"KayAiRetriever",
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"KNNRetriever",
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"LlamaIndexGraphRetriever",
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@@ -1,275 +1,22 @@
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"""Retriever wrapper for Google Cloud Enterprise Search on Gen App Builder."""
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from __future__ import annotations
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"""Retriever wrapper for Google Vertex AI Search.
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DEPRECATED: Maintained for backwards compatibility.
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"""
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from typing import Any
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence
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from langchain.callbacks.manager import CallbackManagerForRetrieverRun
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from langchain.pydantic_v1 import Extra, Field, root_validator
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from langchain.schema import BaseRetriever, Document
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from langchain.utils import get_from_dict_or_env
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if TYPE_CHECKING:
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from google.cloud.discoveryengine_v1beta import (
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SearchRequest,
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SearchResult,
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SearchServiceClient,
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)
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from langchain.retrievers.google_vertex_ai_search import GoogleVertexAISearchRetriever
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class GoogleCloudEnterpriseSearchRetriever(BaseRetriever):
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"""`Google Cloud Enterprise Search API` retriever.
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For a detailed explanation of the Enterprise Search concepts
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and configuration parameters, refer to the product documentation.
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https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction
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class GoogleCloudEnterpriseSearchRetriever(GoogleVertexAISearchRetriever):
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"""`Google Vertex Search API` retriever alias for backwards compatibility.
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DEPRECATED: Use `GoogleVertexAISearchRetriever` instead.
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"""
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project_id: str
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"""Google Cloud Project ID."""
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search_engine_id: str
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"""Enterprise Search engine ID."""
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serving_config_id: str = "default_config"
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"""Enterprise Search serving config ID."""
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location_id: str = "global"
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"""Enterprise Search engine location."""
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filter: Optional[str] = None
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"""Filter expression."""
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get_extractive_answers: bool = False
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"""If True return Extractive Answers, otherwise return Extractive Segments."""
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max_documents: int = Field(default=5, ge=1, le=100)
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"""The maximum number of documents to return."""
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max_extractive_answer_count: int = Field(default=1, ge=1, le=5)
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"""The maximum number of extractive answers returned in each search result.
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At most 5 answers will be returned for each SearchResult.
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"""
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max_extractive_segment_count: int = Field(default=1, ge=1, le=1)
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"""The maximum number of extractive segments returned in each search result.
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Currently one segment will be returned for each SearchResult.
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"""
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query_expansion_condition: int = Field(default=1, ge=0, le=2)
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"""Specification to determine under which conditions query expansion should occur.
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0 - Unspecified query expansion condition. In this case, server behavior defaults
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to disabled
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1 - Disabled query expansion. Only the exact search query is used, even if
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SearchResponse.total_size is zero.
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2 - Automatic query expansion built by the Search API.
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"""
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spell_correction_mode: int = Field(default=2, ge=0, le=2)
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"""Specification to determine under which conditions query expansion should occur.
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0 - Unspecified spell correction mode. In this case, server behavior defaults
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to auto.
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1 - Suggestion only. Search API will try to find a spell suggestion if there is any
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and put in the `SearchResponse.corrected_query`.
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The spell suggestion will not be used as the search query.
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2 - Automatic spell correction built by the Search API.
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Search will be based on the corrected query if found.
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"""
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credentials: Any = None
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"""The default custom credentials (google.auth.credentials.Credentials) to use
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when making API calls. If not provided, credentials will be ascertained from
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the environment."""
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def __init__(self, **data: Any):
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import warnings
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# TODO: Add extra data type handling for type website
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engine_data_type: int = Field(default=0, ge=0, le=1)
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""" Defines the enterprise search data type
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0 - Unstructured data
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1 - Structured data
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"""
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_client: SearchServiceClient
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_serving_config: str
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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underscore_attrs_are_private = True
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validates the environment."""
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try:
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from google.cloud import discoveryengine_v1beta # noqa: F401
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except ImportError as exc:
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raise ImportError(
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"google.cloud.discoveryengine is not installed."
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"Please install it with pip install google-cloud-discoveryengine"
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) from exc
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try:
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from google.api_core.exceptions import InvalidArgument # noqa: F401
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except ImportError as exc:
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raise ImportError(
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"google.api_core.exceptions is not installed. "
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"Please install it with pip install google-api-core"
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) from exc
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values["project_id"] = get_from_dict_or_env(values, "project_id", "PROJECT_ID")
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values["search_engine_id"] = get_from_dict_or_env(
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values, "search_engine_id", "SEARCH_ENGINE_ID"
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warnings.warn(
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"GoogleCloudEnterpriseSearchRetriever is deprecated, use GoogleVertexAISearchRetriever", # noqa: E501
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DeprecationWarning,
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)
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return values
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def __init__(self, **data: Any) -> None:
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"""Initializes private fields."""
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try:
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from google.cloud.discoveryengine_v1beta import SearchServiceClient
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except ImportError:
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raise ImportError(
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"google.cloud.discoveryengine is not installed."
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"Please install it with pip install google-cloud-discoveryengine"
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)
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super().__init__(**data)
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self._client = SearchServiceClient(credentials=self.credentials)
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self._serving_config = self._client.serving_config_path(
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project=self.project_id,
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location=self.location_id,
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data_store=self.search_engine_id,
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serving_config=self.serving_config_id,
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)
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def _convert_unstructured_search_response(
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self, results: Sequence[SearchResult]
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) -> List[Document]:
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"""Converts a sequence of search results to a list of LangChain documents."""
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from google.protobuf.json_format import MessageToDict
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documents: List[Document] = []
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for result in results:
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document_dict = MessageToDict(
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result.document._pb, preserving_proto_field_name=True
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)
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derived_struct_data = document_dict.get("derived_struct_data")
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if not derived_struct_data:
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continue
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doc_metadata = document_dict.get("struct_data", {})
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doc_metadata["id"] = document_dict["id"]
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chunk_type = (
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"extractive_answers"
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if self.get_extractive_answers
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else "extractive_segments"
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)
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if chunk_type not in derived_struct_data:
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continue
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for chunk in derived_struct_data[chunk_type]:
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doc_metadata["source"] = derived_struct_data.get("link", "")
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if chunk_type == "extractive_answers":
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doc_metadata["source"] += f":{chunk.get('pageNumber', '')}"
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documents.append(
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Document(
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page_content=chunk.get("content", ""), metadata=doc_metadata
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)
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)
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return documents
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def _convert_structured_search_response(
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self, results: Sequence[SearchResult]
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) -> List[Document]:
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"""Converts a sequence of search results to a list of LangChain documents."""
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import json
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from google.protobuf.json_format import MessageToDict
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documents: List[Document] = []
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for result in results:
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document_dict = MessageToDict(
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result.document._pb, preserving_proto_field_name=True
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)
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documents.append(
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Document(
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page_content=json.dumps(document_dict.get("struct_data", {})),
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metadata={"id": document_dict["id"], "name": document_dict["name"]},
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)
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)
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return documents
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def _create_search_request(self, query: str) -> SearchRequest:
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"""Prepares a SearchRequest object."""
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from google.cloud.discoveryengine_v1beta import SearchRequest
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query_expansion_spec = SearchRequest.QueryExpansionSpec(
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condition=self.query_expansion_condition,
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)
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spell_correction_spec = SearchRequest.SpellCorrectionSpec(
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mode=self.spell_correction_mode
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)
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if self.engine_data_type == 0:
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if self.get_extractive_answers:
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extractive_content_spec = (
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SearchRequest.ContentSearchSpec.ExtractiveContentSpec(
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max_extractive_answer_count=self.max_extractive_answer_count,
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)
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)
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else:
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extractive_content_spec = (
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SearchRequest.ContentSearchSpec.ExtractiveContentSpec(
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max_extractive_segment_count=self.max_extractive_segment_count,
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)
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)
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content_search_spec = SearchRequest.ContentSearchSpec(
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extractive_content_spec=extractive_content_spec
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)
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elif self.engine_data_type == 1:
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content_search_spec = None
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else:
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# TODO: Add extra data type handling for type website
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raise NotImplementedError(
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"Only engine data type 0 (Unstructured) or 1 (Structured)"
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+ " are supported currently."
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+ f" Got {self.engine_data_type}"
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)
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return SearchRequest(
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query=query,
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filter=self.filter,
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serving_config=self._serving_config,
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page_size=self.max_documents,
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content_search_spec=content_search_spec,
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query_expansion_spec=query_expansion_spec,
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spell_correction_spec=spell_correction_spec,
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)
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun
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) -> List[Document]:
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"""Get documents relevant for a query."""
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from google.api_core.exceptions import InvalidArgument
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search_request = self._create_search_request(query)
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try:
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response = self._client.search(search_request)
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except InvalidArgument as e:
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raise type(e)(
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e.message + " This might be due to engine_data_type not set correctly."
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)
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if self.engine_data_type == 0:
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documents = self._convert_unstructured_search_response(response.results)
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elif self.engine_data_type == 1:
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documents = self._convert_structured_search_response(response.results)
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else:
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# TODO: Add extra data type handling for type website
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raise NotImplementedError(
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"Only engine data type 0 (Unstructured) or 1 (Structured)"
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+ " are supported currently."
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+ f" Got {self.engine_data_type}"
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)
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return documents
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314
libs/langchain/langchain/retrievers/google_vertex_ai_search.py
Normal file
314
libs/langchain/langchain/retrievers/google_vertex_ai_search.py
Normal file
@@ -0,0 +1,314 @@
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"""Retriever wrapper for Google Vertex AI Search."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence
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from langchain.callbacks.manager import CallbackManagerForRetrieverRun
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from langchain.pydantic_v1 import Extra, Field, root_validator
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from langchain.schema import BaseRetriever, Document
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from langchain.utils import get_from_dict_or_env
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if TYPE_CHECKING:
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from google.cloud.discoveryengine_v1beta import (
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SearchRequest,
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SearchResult,
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SearchServiceClient,
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)
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class GoogleVertexAISearchRetriever(BaseRetriever):
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"""`Google Vertex AI Search` retriever.
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|
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For a detailed explanation of the Vertex AI Search concepts
|
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and configuration parameters, refer to the product documentation.
|
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https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction
|
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"""
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project_id: str
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"""Google Cloud Project ID."""
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data_store_id: str
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"""Vertex AI Search data store ID."""
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serving_config_id: str = "default_config"
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"""Vertex AI Search serving config ID."""
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location_id: str = "global"
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"""Vertex AI Search data store location."""
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filter: Optional[str] = None
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"""Filter expression."""
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get_extractive_answers: bool = False
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"""If True return Extractive Answers, otherwise return Extractive Segments."""
|
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max_documents: int = Field(default=5, ge=1, le=100)
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"""The maximum number of documents to return."""
|
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max_extractive_answer_count: int = Field(default=1, ge=1, le=5)
|
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"""The maximum number of extractive answers returned in each search result.
|
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At most 5 answers will be returned for each SearchResult.
|
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"""
|
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max_extractive_segment_count: int = Field(default=1, ge=1, le=1)
|
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"""The maximum number of extractive segments returned in each search result.
|
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Currently one segment will be returned for each SearchResult.
|
||||
"""
|
||||
query_expansion_condition: int = Field(default=1, ge=0, le=2)
|
||||
"""Specification to determine under which conditions query expansion should occur.
|
||||
0 - Unspecified query expansion condition. In this case, server behavior defaults
|
||||
to disabled
|
||||
1 - Disabled query expansion. Only the exact search query is used, even if
|
||||
SearchResponse.total_size is zero.
|
||||
2 - Automatic query expansion built by the Search API.
|
||||
"""
|
||||
spell_correction_mode: int = Field(default=2, ge=0, le=2)
|
||||
"""Specification to determine under which conditions query expansion should occur.
|
||||
0 - Unspecified spell correction mode. In this case, server behavior defaults
|
||||
to auto.
|
||||
1 - Suggestion only. Search API will try to find a spell suggestion if there is any
|
||||
and put in the `SearchResponse.corrected_query`.
|
||||
The spell suggestion will not be used as the search query.
|
||||
2 - Automatic spell correction built by the Search API.
|
||||
Search will be based on the corrected query if found.
|
||||
"""
|
||||
credentials: Any = None
|
||||
"""The default custom credentials (google.auth.credentials.Credentials) to use
|
||||
when making API calls. If not provided, credentials will be ascertained from
|
||||
the environment."""
|
||||
|
||||
# TODO: Add extra data type handling for type website
|
||||
engine_data_type: int = Field(default=0, ge=0, le=1)
|
||||
""" Defines the Vertex AI Search data type
|
||||
0 - Unstructured data
|
||||
1 - Structured data
|
||||
"""
|
||||
|
||||
_client: SearchServiceClient
|
||||
_serving_config: str
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.ignore
|
||||
arbitrary_types_allowed = True
|
||||
underscore_attrs_are_private = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validates the environment."""
|
||||
try:
|
||||
from google.cloud import discoveryengine_v1beta # noqa: F401
|
||||
except ImportError as exc:
|
||||
raise ImportError(
|
||||
"google.cloud.discoveryengine is not installed."
|
||||
"Please install it with pip install google-cloud-discoveryengine"
|
||||
) from exc
|
||||
|
||||
try:
|
||||
from google.api_core.exceptions import InvalidArgument # noqa: F401
|
||||
except ImportError as exc:
|
||||
raise ImportError(
|
||||
"google.api_core.exceptions is not installed. "
|
||||
"Please install it with pip install google-api-core"
|
||||
) from exc
|
||||
|
||||
values["project_id"] = get_from_dict_or_env(values, "project_id", "PROJECT_ID")
|
||||
|
||||
try:
|
||||
# For backwards compatibility
|
||||
search_engine_id = get_from_dict_or_env(
|
||||
values, "search_engine_id", "SEARCH_ENGINE_ID"
|
||||
)
|
||||
|
||||
if search_engine_id:
|
||||
import warnings
|
||||
|
||||
warnings.warn(
|
||||
"The `search_engine_id` parameter is deprecated. Use `data_store_id` instead.", # noqa: E501
|
||||
DeprecationWarning,
|
||||
)
|
||||
values["data_store_id"] = search_engine_id
|
||||
except: # noqa: E722
|
||||
pass
|
||||
|
||||
values["data_store_id"] = get_from_dict_or_env(
|
||||
values, "data_store_id", "DATA_STORE_ID"
|
||||
)
|
||||
|
||||
return values
|
||||
|
||||
def __init__(self, **data: Any) -> None:
|
||||
"""Initializes private fields."""
|
||||
try:
|
||||
from google.cloud.discoveryengine_v1beta import SearchServiceClient
|
||||
except ImportError as exc:
|
||||
raise ImportError(
|
||||
"google.cloud.discoveryengine is not installed."
|
||||
"Please install it with pip install google-cloud-discoveryengine"
|
||||
) from exc
|
||||
try:
|
||||
from google.api_core.client_options import ClientOptions
|
||||
except ImportError as exc:
|
||||
raise ImportError(
|
||||
"google.api_core.client_options is not installed."
|
||||
"Please install it with pip install google-api-core"
|
||||
) from exc
|
||||
|
||||
super().__init__(**data)
|
||||
|
||||
# For more information, refer to:
|
||||
# https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
|
||||
api_endpoint = (
|
||||
"discoveryengine.googleapis.com"
|
||||
if self.location_id == "global"
|
||||
else f"{self.location_id}-discoveryengine.googleapis.com"
|
||||
)
|
||||
|
||||
self._client = SearchServiceClient(
|
||||
credentials=self.credentials,
|
||||
client_options=ClientOptions(api_endpoint=api_endpoint),
|
||||
)
|
||||
|
||||
self._serving_config = self._client.serving_config_path(
|
||||
project=self.project_id,
|
||||
location=self.location_id,
|
||||
data_store=self.data_store_id,
|
||||
serving_config=self.serving_config_id,
|
||||
)
|
||||
|
||||
def _convert_unstructured_search_response(
|
||||
self, results: Sequence[SearchResult]
|
||||
) -> List[Document]:
|
||||
"""Converts a sequence of search results to a list of LangChain documents."""
|
||||
from google.protobuf.json_format import MessageToDict
|
||||
|
||||
documents: List[Document] = []
|
||||
|
||||
for result in results:
|
||||
document_dict = MessageToDict(
|
||||
result.document._pb, preserving_proto_field_name=True
|
||||
)
|
||||
derived_struct_data = document_dict.get("derived_struct_data")
|
||||
if not derived_struct_data:
|
||||
continue
|
||||
|
||||
doc_metadata = document_dict.get("struct_data", {})
|
||||
doc_metadata["id"] = document_dict["id"]
|
||||
|
||||
chunk_type = (
|
||||
"extractive_answers"
|
||||
if self.get_extractive_answers
|
||||
else "extractive_segments"
|
||||
)
|
||||
|
||||
if chunk_type not in derived_struct_data:
|
||||
continue
|
||||
|
||||
for chunk in derived_struct_data[chunk_type]:
|
||||
doc_metadata["source"] = derived_struct_data.get("link", "")
|
||||
|
||||
if chunk_type == "extractive_answers":
|
||||
doc_metadata["source"] += f":{chunk.get('pageNumber', '')}"
|
||||
|
||||
documents.append(
|
||||
Document(
|
||||
page_content=chunk.get("content", ""), metadata=doc_metadata
|
||||
)
|
||||
)
|
||||
|
||||
return documents
|
||||
|
||||
def _convert_structured_search_response(
|
||||
self, results: Sequence[SearchResult]
|
||||
) -> List[Document]:
|
||||
"""Converts a sequence of search results to a list of LangChain documents."""
|
||||
import json
|
||||
|
||||
from google.protobuf.json_format import MessageToDict
|
||||
|
||||
documents: List[Document] = []
|
||||
|
||||
for result in results:
|
||||
document_dict = MessageToDict(
|
||||
result.document._pb, preserving_proto_field_name=True
|
||||
)
|
||||
|
||||
documents.append(
|
||||
Document(
|
||||
page_content=json.dumps(document_dict.get("struct_data", {})),
|
||||
metadata={"id": document_dict["id"], "name": document_dict["name"]},
|
||||
)
|
||||
)
|
||||
|
||||
return documents
|
||||
|
||||
def _create_search_request(self, query: str) -> SearchRequest:
|
||||
"""Prepares a SearchRequest object."""
|
||||
from google.cloud.discoveryengine_v1beta import SearchRequest
|
||||
|
||||
query_expansion_spec = SearchRequest.QueryExpansionSpec(
|
||||
condition=self.query_expansion_condition,
|
||||
)
|
||||
|
||||
spell_correction_spec = SearchRequest.SpellCorrectionSpec(
|
||||
mode=self.spell_correction_mode
|
||||
)
|
||||
|
||||
if self.engine_data_type == 0:
|
||||
if self.get_extractive_answers:
|
||||
extractive_content_spec = (
|
||||
SearchRequest.ContentSearchSpec.ExtractiveContentSpec(
|
||||
max_extractive_answer_count=self.max_extractive_answer_count,
|
||||
)
|
||||
)
|
||||
else:
|
||||
extractive_content_spec = (
|
||||
SearchRequest.ContentSearchSpec.ExtractiveContentSpec(
|
||||
max_extractive_segment_count=self.max_extractive_segment_count,
|
||||
)
|
||||
)
|
||||
content_search_spec = SearchRequest.ContentSearchSpec(
|
||||
extractive_content_spec=extractive_content_spec
|
||||
)
|
||||
elif self.engine_data_type == 1:
|
||||
content_search_spec = None
|
||||
else:
|
||||
# TODO: Add extra data type handling for type website
|
||||
raise NotImplementedError(
|
||||
"Only engine data type 0 (Unstructured) or 1 (Structured)"
|
||||
+ " are supported currently."
|
||||
+ f" Got {self.engine_data_type}"
|
||||
)
|
||||
|
||||
return SearchRequest(
|
||||
query=query,
|
||||
filter=self.filter,
|
||||
serving_config=self._serving_config,
|
||||
page_size=self.max_documents,
|
||||
content_search_spec=content_search_spec,
|
||||
query_expansion_spec=query_expansion_spec,
|
||||
spell_correction_spec=spell_correction_spec,
|
||||
)
|
||||
|
||||
def _get_relevant_documents(
|
||||
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
|
||||
) -> List[Document]:
|
||||
"""Get documents relevant for a query."""
|
||||
from google.api_core.exceptions import InvalidArgument
|
||||
|
||||
search_request = self._create_search_request(query)
|
||||
|
||||
try:
|
||||
response = self._client.search(search_request)
|
||||
except InvalidArgument as exc:
|
||||
raise type(exc)(
|
||||
exc.message
|
||||
+ " This might be due to engine_data_type not set correctly."
|
||||
)
|
||||
|
||||
if self.engine_data_type == 0:
|
||||
documents = self._convert_unstructured_search_response(response.results)
|
||||
elif self.engine_data_type == 1:
|
||||
documents = self._convert_structured_search_response(response.results)
|
||||
else:
|
||||
# TODO: Add extra data type handling for type website
|
||||
raise NotImplementedError(
|
||||
"Only engine data type 0 (Unstructured) or 1 (Structured)"
|
||||
+ " are supported currently."
|
||||
+ f" Got {self.engine_data_type}"
|
||||
)
|
||||
|
||||
return documents
|
||||
@@ -1,32 +0,0 @@
|
||||
"""Test Google Cloud Enterprise Search retriever.
|
||||
|
||||
You need to create a Gen App Builder search app and populate it
|
||||
with data to run the integration tests.
|
||||
Follow the instructions in the example notebook:
|
||||
google_cloud_enterprise_search.ipynb
|
||||
to set up the app and configure authentication.
|
||||
|
||||
Set the following environment variables before the tests:
|
||||
PROJECT_ID - set to your Google Cloud project ID
|
||||
SEARCH_ENGINE_ID - the ID of the search engine to use for the test
|
||||
"""
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain.retrievers.google_cloud_enterprise_search import (
|
||||
GoogleCloudEnterpriseSearchRetriever,
|
||||
)
|
||||
from langchain.schema import Document
|
||||
|
||||
|
||||
@pytest.mark.requires("google_api_core")
|
||||
def test_google_cloud_enterprise_search_get_relevant_documents() -> None:
|
||||
"""Test the get_relevant_documents() method."""
|
||||
retriever = GoogleCloudEnterpriseSearchRetriever()
|
||||
documents = retriever.get_relevant_documents("What are Alphabet's Other Bets?")
|
||||
assert len(documents) > 0
|
||||
for doc in documents:
|
||||
assert isinstance(doc, Document)
|
||||
assert doc.page_content
|
||||
assert doc.metadata["id"]
|
||||
assert doc.metadata["source"]
|
||||
@@ -0,0 +1,61 @@
|
||||
"""Test Google Vertex AI Search retriever.
|
||||
|
||||
You need to create a Vertex AI Search app and populate it
|
||||
with data to run the integration tests.
|
||||
Follow the instructions in the example notebook:
|
||||
google_vertex_ai_search.ipynb
|
||||
to set up the app and configure authentication.
|
||||
|
||||
Set the following environment variables before the tests:
|
||||
PROJECT_ID - set to your Google Cloud project ID
|
||||
DATA_STORE_ID - the ID of the search engine to use for the test
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain.retrievers.google_cloud_enterprise_search import (
|
||||
GoogleCloudEnterpriseSearchRetriever,
|
||||
)
|
||||
from langchain.retrievers.google_vertex_ai_search import GoogleVertexAISearchRetriever
|
||||
from langchain.schema import Document
|
||||
|
||||
|
||||
@pytest.mark.requires("google_api_core")
|
||||
def test_google_vertex_ai_search_get_relevant_documents() -> None:
|
||||
"""Test the get_relevant_documents() method."""
|
||||
retriever = GoogleVertexAISearchRetriever()
|
||||
documents = retriever.get_relevant_documents("What are Alphabet's Other Bets?")
|
||||
assert len(documents) > 0
|
||||
for doc in documents:
|
||||
assert isinstance(doc, Document)
|
||||
assert doc.page_content
|
||||
assert doc.metadata["id"]
|
||||
assert doc.metadata["source"]
|
||||
|
||||
|
||||
@pytest.mark.requires("google_api_core")
|
||||
def test_google_vertex_ai_search_enterprise_search_deprecation() -> None:
|
||||
"""Test the deprecation of GoogleCloudEnterpriseSearchRetriever."""
|
||||
with pytest.warns(
|
||||
DeprecationWarning,
|
||||
match="GoogleCloudEnterpriseSearchRetriever is deprecated, use GoogleVertexAISearchRetriever", # noqa: E501
|
||||
):
|
||||
retriever = GoogleCloudEnterpriseSearchRetriever()
|
||||
|
||||
os.environ["SEARCH_ENGINE_ID"] = os.getenv("DATA_STORE_ID", "data_store_id")
|
||||
with pytest.warns(
|
||||
DeprecationWarning,
|
||||
match="The `search_engine_id` parameter is deprecated. Use `data_store_id` instead.", # noqa: E501
|
||||
):
|
||||
retriever = GoogleCloudEnterpriseSearchRetriever()
|
||||
|
||||
# Check that mapped methods still work.
|
||||
documents = retriever.get_relevant_documents("What are Alphabet's Other Bets?")
|
||||
assert len(documents) > 0
|
||||
for doc in documents:
|
||||
assert isinstance(doc, Document)
|
||||
assert doc.page_content
|
||||
assert doc.metadata["id"]
|
||||
assert doc.metadata["source"]
|
||||
Reference in New Issue
Block a user