mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
496 lines
18 KiB
Python
496 lines
18 KiB
Python
"""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, Tuple
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from langchain_core._api.deprecation import deprecated
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from langchain_core.callbacks import CallbackManagerForRetrieverRun
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from langchain_core.documents import Document
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from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
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from langchain_core.retrievers import BaseRetriever
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from langchain_core.utils import get_from_dict_or_env
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from langchain_community.utilities.vertexai import get_client_info
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if TYPE_CHECKING:
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from google.api_core.client_options import ClientOptions
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from google.cloud.discoveryengine_v1beta import (
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ConversationalSearchServiceClient,
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SearchRequest,
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SearchResult,
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SearchServiceClient,
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)
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class _BaseGoogleVertexAISearchRetriever(BaseModel):
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project_id: str
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"""Google Cloud Project ID."""
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data_store_id: Optional[str] = None
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"""Vertex AI Search data store ID."""
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search_engine_id: Optional[str] = None
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"""Vertex AI Search app ID."""
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location_id: str = "global"
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"""Vertex AI Search data store location."""
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serving_config_id: str = "default_config"
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"""Vertex AI Search serving config ID."""
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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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engine_data_type: int = Field(default=0, ge=0, le=3)
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""" Defines the Vertex AI Search app data type
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0 - Unstructured data
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1 - Structured data
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2 - Website data
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3 - Blended search
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"""
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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 "
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"google-cloud-discoveryengine>=0.11.10"
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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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try:
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values["data_store_id"] = get_from_dict_or_env(
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values, "data_store_id", "DATA_STORE_ID"
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)
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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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)
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except Exception:
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pass
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return values
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@property
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def client_options(self) -> "ClientOptions":
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from google.api_core.client_options import ClientOptions
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return ClientOptions(
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api_endpoint=(
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f"{self.location_id}-discoveryengine.googleapis.com"
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if self.location_id != "global"
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else None
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)
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)
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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 _convert_unstructured_search_response(
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self, results: Sequence[SearchResult], chunk_type: str
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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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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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chunk_metadata = doc_metadata.copy()
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chunk_metadata["source"] = derived_struct_data.get("link", "")
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if chunk_type == "extractive_answers":
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chunk_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=chunk_metadata
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)
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)
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return documents
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def _convert_website_search_response(
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self, results: Sequence[SearchResult], chunk_type: str
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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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doc_metadata["source"] = derived_struct_data.get("link", "")
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if chunk_type not in derived_struct_data:
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continue
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text_field = "snippet" if chunk_type == "snippets" else "content"
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for chunk in derived_struct_data[chunk_type]:
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documents.append(
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Document(
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page_content=chunk.get(text_field, ""), metadata=doc_metadata
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)
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)
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if not documents:
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print(f"No {chunk_type} could be found.") # noqa: T201
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if chunk_type == "extractive_answers":
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print( # noqa: T201
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"Make sure that your data store is using Advanced Website "
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"Indexing.\n"
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"https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features#advanced-website-indexing" # noqa: E501
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)
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return documents
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@deprecated(
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since="0.0.33",
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removal="0.2.0",
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alternative_import="langchain_google_community.VertexAISearchRetriever",
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)
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class GoogleVertexAISearchRetriever(BaseRetriever, _BaseGoogleVertexAISearchRetriever):
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"""`Google Vertex AI Search` retriever.
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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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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 or Snippets.""" # noqa: E501
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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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_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.ignore
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arbitrary_types_allowed = True
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underscore_attrs_are_private = True
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def __init__(self, **kwargs: 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 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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super().__init__(**kwargs)
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# For more information, refer to:
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# https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
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self._client = SearchServiceClient(
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credentials=self.credentials,
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client_options=self.client_options,
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client_info=get_client_info(module="vertex-ai-search"),
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)
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if self.engine_data_type == 3 and not self.search_engine_id:
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raise ValueError(
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"search_engine_id must be specified for blended search apps."
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)
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if self.search_engine_id:
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self._serving_config = f"projects/{self.project_id}/locations/{self.location_id}/collections/default_collection/engines/{self.search_engine_id}/servingConfigs/default_config" # noqa: E501
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elif self.data_store_id:
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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.data_store_id,
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serving_config=self.serving_config_id,
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)
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else:
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raise ValueError(
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"Either data_store_id or search_engine_id must be specified."
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)
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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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elif self.engine_data_type in (2, 3):
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content_search_spec = SearchRequest.ContentSearchSpec(
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extractive_content_spec=SearchRequest.ContentSearchSpec.ExtractiveContentSpec(
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max_extractive_answer_count=self.max_extractive_answer_count,
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),
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snippet_spec=SearchRequest.ContentSearchSpec.SnippetSpec(
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return_snippet=True
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),
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)
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else:
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raise NotImplementedError(
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"Only data store type 0 (Unstructured), 1 (Structured),"
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"2 (Website), or 3 (Blended) 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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return self.get_relevant_documents_with_response(query)[0]
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def get_relevant_documents_with_response(
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self, query: str
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) -> Tuple[List[Document], Any]:
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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 exc:
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raise type(exc)(
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exc.message
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+ " 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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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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documents = self._convert_unstructured_search_response(
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response.results, chunk_type
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)
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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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elif self.engine_data_type in (2, 3):
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chunk_type = (
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"extractive_answers" if self.get_extractive_answers else "snippets"
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)
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documents = self._convert_website_search_response(
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response.results, chunk_type
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)
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else:
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raise NotImplementedError(
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"Only data store type 0 (Unstructured), 1 (Structured),"
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"2 (Website), or 3 (Blended) are supported currently."
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+ f" Got {self.engine_data_type}"
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)
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return documents, response
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@deprecated(
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since="0.0.33",
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removal="0.2.0",
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alternative_import="langchain_google_community.VertexAIMultiTurnSearchRetriever",
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)
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class GoogleVertexAIMultiTurnSearchRetriever(
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BaseRetriever, _BaseGoogleVertexAISearchRetriever
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):
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"""`Google Vertex AI Search` retriever for multi-turn conversations."""
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conversation_id: str = "-"
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"""Vertex AI Search Conversation ID."""
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_client: ConversationalSearchServiceClient
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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.ignore
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arbitrary_types_allowed = True
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underscore_attrs_are_private = True
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def __init__(self, **kwargs: Any):
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super().__init__(**kwargs)
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from google.cloud.discoveryengine_v1beta import (
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ConversationalSearchServiceClient,
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)
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self._client = ConversationalSearchServiceClient(
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credentials=self.credentials,
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client_options=self.client_options,
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client_info=get_client_info(module="vertex-ai-search"),
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)
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if not self.data_store_id:
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raise ValueError("data_store_id is required for MultiTurnSearchRetriever.")
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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.data_store_id,
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serving_config=self.serving_config_id,
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)
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if self.engine_data_type == 1 or self.engine_data_type == 3:
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raise NotImplementedError(
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"Data store type 1 (Structured) and 3 (Blended)"
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"is not currently supported for multi-turn search."
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+ f" Got {self.engine_data_type}"
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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.cloud.discoveryengine_v1beta import (
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ConverseConversationRequest,
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TextInput,
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)
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request = ConverseConversationRequest(
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name=self._client.conversation_path(
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self.project_id,
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self.location_id,
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self.data_store_id,
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self.conversation_id,
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),
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serving_config=self._serving_config,
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query=TextInput(input=query),
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)
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response = self._client.converse_conversation(request)
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if self.engine_data_type == 2:
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return self._convert_website_search_response(
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response.search_results, "extractive_answers"
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)
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return self._convert_unstructured_search_response(
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response.search_results, "extractive_answers"
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)
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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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def __init__(self, **data: Any):
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import warnings
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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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super().__init__(**data)
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