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389 | class VertexAISearchRetriever(BaseRetriever):
"""`Vertex AI Search` retrieval.
For a detailed explanation of the Vertex AI Search concepts
and configuration parameters, refer to the product documentation.
https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction
Args:
project_id: str
#Google Cloud Project ID
data_store_id: str
#Vertex AI Search data store ID.
location_id: str = "global"
#Vertex AI Search data store location.
serving_config_id: str = "default_config"
#Vertex AI Search serving config ID
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
engine_data_type: int = 0
Defines the Vertex AI Search data type
0 - Unstructured data
1 - Structured data
2 - Website data
Example:
retriever = VertexAISearchRetriever(
project_id=PROJECT_ID,
data_store_id=DATA_STORE_ID,
location_id=LOCATION_ID,
engine_data_type=0
)
"""
"""
The following parameter explanation can be found here:
https://cloud.google.com/generative-ai-app-builder/docs/reference/rpc/google.cloud.discoveryengine.v1#contentsearchspec
"""
filter: Optional[str] = None
"""Filter expression."""
get_extractive_answers: bool = False
"""If True return Extractive Answers, otherwise return Extractive Segments or Snippets."""
max_documents: int = 5
"""The maximum number of documents to return."""
max_extractive_answer_count: int = 1
"""The maximum number of extractive answers returned in each search result.
At most 5 answers will be returned for each SearchResult.
"""
max_extractive_segment_count: int = 1
"""The maximum number of extractive segments returned in each search result.
Currently one segment will be returned for each SearchResult.
"""
query_expansion_condition: int = 1
"""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 = 1
"""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.
"""
boost_spec: Optional[Dict[Any, Any]] = None
"""BoostSpec for boosting search results. A protobuf should be provided.
https://cloud.google.com/generative-ai-app-builder/docs/boost-search-results
https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1beta/BoostSpec
"""
return_extractive_segment_score: bool = True
"""
Specifies whether to return the confidence score from the extractive segments in each search result.
This feature is available only for new or allowlisted data stores.
"""
_client: SearchServiceClient
_serving_config: str
def __init__(
self,
project_id: str,
data_store_id: str,
location_id: str = "global",
serving_config_id: str = "default_config",
credentials: Any = None,
engine_data_type: int = 0,
max_documents: int = 5,
user_agent: Optional[str] = None,
**kwargs: Any,
) -> None:
"""Initializes private fields."""
self.project_id = project_id
self.location_id = location_id
self.data_store_id = data_store_id
self.serving_config_id = serving_config_id
self.engine_data_type = engine_data_type
self.credentials = credentials
self.max_documents = max_documents
self._user_agent = user_agent or "llama-index/0.0.0"
self.client_options = ClientOptions(
api_endpoint=(
f"{self.location_id}-discoveryengine.googleapis.com"
if self.location_id != "global"
else None
)
)
try:
from google.cloud.discoveryengine_v1beta import SearchServiceClient
except ImportError as exc:
raise ImportError(
"Could not import google-cloud-discoveryengine python package. "
"Please, install vertexaisearch dependency group: "
) from exc
try:
super().__init__(**kwargs)
except ValueError as e:
print(f"Error initializing GoogleVertexAISearchRetriever: {e!s}")
raise
# For more information, refer to:
# https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
self._client = SearchServiceClient(
credentials=self.credentials,
client_options=self.client_options,
client_info=get_client_info(module="vertex-ai-search"),
)
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 _get_content_spec_kwargs(self) -> Optional[Dict[str, Any]]:
"""Prepares a ContentSpec object."""
from google.cloud.discoveryengine_v1beta import SearchRequest
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,
return_extractive_segment_score=self.return_extractive_segment_score,
)
else:
extractive_content_spec = SearchRequest.ContentSearchSpec.ExtractiveContentSpec(
max_extractive_segment_count=self.max_extractive_segment_count,
return_extractive_segment_score=self.return_extractive_segment_score,
)
content_search_spec = {"extractive_content_spec": extractive_content_spec}
elif self.engine_data_type == 1:
content_search_spec = None
elif self.engine_data_type == 2:
content_search_spec = {
"extractive_content_spec": SearchRequest.ContentSearchSpec.ExtractiveContentSpec(
max_extractive_segment_count=self.max_extractive_segment_count,
max_extractive_answer_count=self.max_extractive_answer_count,
return_extractive_segment_score=self.return_extractive_segment_score,
),
"snippet_spec": SearchRequest.ContentSearchSpec.SnippetSpec(
return_snippet=True
),
}
else:
raise NotImplementedError(
"Only data store type 0 (Unstructured), 1 (Structured),"
"or 2 (Website) are supported currently."
+ f" Got {self.engine_data_type}"
)
return content_search_spec
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
)
content_search_spec_kwargs = self._get_content_spec_kwargs()
if content_search_spec_kwargs is not None:
content_search_spec = SearchRequest.ContentSearchSpec(
**content_search_spec_kwargs
)
else:
content_search_spec = None
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,
boost_spec=SearchRequest.BoostSpec(**self.boost_spec)
if self.boost_spec
else None,
)
def _convert_structured_datastore_response(
self, results: Sequence[SearchResult]
) -> List[NodeWithScore]:
"""Converts a sequence of search results to a list of Llamaindex note_with_score."""
note_with_score: List[NodeWithScore] = []
for i, result in enumerate(results):
# Structured datastore does not have relevance score. The results are ranked
# in order. score is calculated by below. Index 0 has the highest score
score = (len(results) - i) / len(results)
document_dict = MessageToDict(
result.document._pb, preserving_proto_field_name=True
)
note_with_score.append(
NodeWithScore(
node=TextNode(
text=json.dumps(document_dict.get("struct_data", {}))
),
score=score,
)
)
return note_with_score
def _convert_unstructured_datastore_response(
self, results: Sequence[SearchResult], chunk_type: str
) -> List[NodeWithScore]:
"""Converts a sequence of search results to a list of LLamaindex note_with_score."""
note_with_score: List[NodeWithScore] = []
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
if chunk_type not in derived_struct_data:
continue
for chunk in derived_struct_data[chunk_type]:
score = chunk.get("relevanceScore", 0)
note_with_score.append(
NodeWithScore(
node=TextNode(text=chunk.get("content", "")),
score=score,
)
)
return note_with_score
def _convert_website_datastore_response(
self, results: Sequence[SearchResult], chunk_type: str
) -> List[NodeWithScore]:
"""Converts a sequence of search results to a list of LLamaindex note_with_score."""
note_with_score: List[NodeWithScore] = []
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
if chunk_type not in derived_struct_data:
continue
text_field = "snippet" if chunk_type == "snippets" else "content"
for chunk in derived_struct_data[chunk_type]:
score = chunk.get("relevanceScore", 0)
note_with_score.append(
NodeWithScore(
node=TextNode(text=chunk.get(text_field, "")),
score=score,
)
)
if not note_with_score:
print(f"No {chunk_type} could be found.")
if chunk_type == "extractive_answers":
print(
"Make sure that your data store is using Advanced Website "
"Indexing.\n"
"https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features#advanced-website-indexing"
)
return note_with_score
def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Retrieve from the platform."""
"""Get note_with_score relevant for a query."""
search_request = self._create_search_request(query_bundle.query_str)
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:
chunk_type = (
"extractive_answers"
if self.get_extractive_answers
else "extractive_segments"
)
note_with_score = self._convert_unstructured_datastore_response(
response.results, chunk_type
)
elif self.engine_data_type == 1:
note_with_score = self._convert_structured_datastore_response(
response.results
)
elif self.engine_data_type == 2:
chunk_type = (
"extractive_answers"
if self.get_extractive_answers
else "extractive_segments"
)
note_with_score = self._convert_website_datastore_response(
response.results, chunk_type
)
else:
raise NotImplementedError(
"Only data store type 0 (Unstructured), 1 (Structured),"
"or 2 (Website) are supported currently."
+ f" Got {self.engine_data_type}"
)
return note_with_score
async def _aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Asynchronously retrieve from the platform."""
return self._retrieve(query_bundle=query_bundle)
|
