Google Vertex AI Search
Google Vertex AI Search (formerly known as
Enterprise Search
onGenerative AI App Builder
) is a part of the Vertex AI machine learning platform offered byGoogle Cloud
.
Vertex AI Search
lets organizations quickly build generative AI-powered search engines for customers and employees. It's underpinned by a variety ofGoogle Search
technologies, including semantic search, which helps deliver more relevant results than traditional keyword-based search techniques by using natural language processing and machine learning techniques to infer relationships within the content and intent from the userβs query input. Vertex AI Search also benefits from Googleβs expertise in understanding how users search and factors in content relevance to order displayed results.
Vertex AI Search
is available in theGoogle Cloud Console
and via an API for enterprise workflow integration.
This notebook demonstrates how to configure Vertex AI Search
and use the Vertex AI Search retriever. The Vertex AI Search retriever encapsulates the Python client library and uses it to access the Search Service API.
Install pre-requisitesβ
You need to install the google-cloud-discoveryengine
package to use the Vertex AI Search retriever.
%pip install --upgrade --quiet google-cloud-discoveryengine
Configure access to Google Cloud and Vertex AI Searchβ
Vertex AI Search is generally available without allowlist as of August 2023.
Before you can use the retriever, you need to complete the following steps:
Create a search engine and populate an unstructured data storeβ
- Follow the instructions in the Vertex AI Search Getting Started guide to set up a Google Cloud project and Vertex AI Search.
- Use the Google Cloud Console to create an unstructured data store
- Populate it with the example PDF documents from the
gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs
Cloud Storage folder. - Make sure to use the
Cloud Storage (without metadata)
option.
- Populate it with the example PDF documents from the
Set credentials to access Vertex AI Search APIβ
The Vertex AI Search client libraries used by the Vertex AI Search retriever provide high-level language support for authenticating to Google Cloud programmatically. Client libraries support Application Default Credentials (ADC); the libraries look for credentials in a set of defined locations and use those credentials to authenticate requests to the API. With ADC, you can make credentials available to your application in a variety of environments, such as local development or production, without needing to modify your application code.
If running in Google Colab authenticate with google.colab.google.auth
otherwise follow one of the supported methods to make sure that you Application Default Credentials are properly set.
import sys
if "google.colab" in sys.modules:
from google.colab import auth as google_auth
google_auth.authenticate_user()
Configure and use the Vertex AI Search retrieverβ
The Vertex AI Search retriever is implemented in the langchain.retriever.GoogleVertexAISearchRetriever
class. The get_relevant_documents
method returns a list of langchain.schema.Document
documents where the page_content
field of each document is populated the document content.
Depending on the data type used in Vertex AI Search (website, structured or unstructured) the page_content
field is populated as follows:
- Website with advanced indexing: an
extractive answer
that matches a query. Themetadata
field is populated with metadata (if any) of the document from which the segments or answers were extracted. - Unstructured data source: either an
extractive segment
or anextractive answer
that matches a query. Themetadata
field is populated with metadata (if any) of the document from which the segments or answers were extracted. - Structured data source: a string json containing all the fields returned from the structured data source. The
metadata
field is populated with metadata (if any) of the document
Extractive answers & extractive segmentsβ
An extractive answer is verbatim text that is returned with each search result. It is extracted directly from the original document. Extractive answers are typically displayed near the top of web pages to provide an end user with a brief answer that is contextually relevant to their query. Extractive answers are available for website and unstructured search.
An extractive segment is verbatim text that is returned with each search result. An extractive segment is usually more verbose than an extractive answer. Extractive segments can be displayed as an answer to a query, and can be used to perform post-processing tasks and as input for large language models to generate answers or new text. Extractive segments are available for unstructured search.
For more information about extractive segments and extractive answers refer to product documentation.
NOTE: Extractive segments require the Enterprise edition features to be enabled.
When creating an instance of the retriever you can specify a number of parameters that control which data store to access and how a natural language query is processed, including configurations for extractive answers and segments.
The mandatory parameters are:β
project_id
- Your Google Cloud Project ID.location_id
- The location of the data store.global
(default)us
eu
One of:
search_engine_id
- The ID of the search app you want to use. (Required for Blended Search)data_store_id
- The ID of the data store you want to use.
The project_id
, search_engine_id
and data_store_id
parameters can be provided explicitly in the retriever's constructor or through the environment variables - PROJECT_ID
, SEARCH_ENGINE_ID
and DATA_STORE_ID
.
You can also configure a number of optional parameters, including:
max_documents
- The maximum number of documents used to provide extractive segments or extractive answersget_extractive_answers
- By default, the retriever is configured to return extractive segments.- Set this field to
True
to return extractive answers. This is used only whenengine_data_type
set to0
(unstructured)
- Set this field to
max_extractive_answer_count
- The maximum number of extractive answers returned in each search result.- At most 5 answers will be returned. This is used only when
engine_data_type
set to0
(unstructured).
- At most 5 answers will be returned. This is used only when
max_extractive_segment_count
- The maximum number of extractive segments returned in each search result.- Currently one segment will be returned. This is used only when
engine_data_type
set to0
(unstructured).
- Currently one segment will be returned. This is used only when
filter
- The filter expression for the search results based on the metadata associated with the documents in the data store.query_expansion_condition
- 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.
engine_data_type
- Defines the Vertex AI Search data type0
- Unstructured data1
- Structured data2
- Website data3
- Blended search
Migration guide for GoogleCloudEnterpriseSearchRetriever
β
In previous versions, this retriever was called GoogleCloudEnterpriseSearchRetriever
.
To update to the new retriever, make the following changes:
- Change the import from:
from langchain.retrievers import GoogleCloudEnterpriseSearchRetriever
->from langchain.retrievers import GoogleVertexAISearchRetriever
. - Change all class references from
GoogleCloudEnterpriseSearchRetriever
->GoogleVertexAISearchRetriever
.
Configure and use the retriever for unstructured data with extractive segmentsβ
from langchain_community.retrievers import (
GoogleVertexAIMultiTurnSearchRetriever,
GoogleVertexAISearchRetriever,
)
PROJECT_ID = "<YOUR PROJECT ID>" # Set to your Project ID
LOCATION_ID = "<YOUR LOCATION>" # Set to your data store location
SEARCH_ENGINE_ID = "<YOUR SEARCH APP ID>" # Set to your search app ID
DATA_STORE_ID = "<YOUR DATA STORE ID>" # Set to your data store ID
retriever = GoogleVertexAISearchRetriever(
project_id=PROJECT_ID,
location_id=LOCATION_ID,
data_store_id=DATA_STORE_ID,
max_documents=3,
)
query = "What are Alphabet's Other Bets?"
result = retriever.invoke(query)
for doc in result:
print(doc)
Configure and use the retriever for unstructured data with extractive answersβ
retriever = GoogleVertexAISearchRetriever(
project_id=PROJECT_ID,
location_id=LOCATION_ID,
data_store_id=DATA_STORE_ID,
max_documents=3,
max_extractive_answer_count=3,
get_extractive_answers=True,
)
result = retriever.invoke(query)
for doc in result:
print(doc)
Configure and use the retriever for structured dataβ
retriever = GoogleVertexAISearchRetriever(
project_id=PROJECT_ID,
location_id=LOCATION_ID,
data_store_id=DATA_STORE_ID,
max_documents=3,
engine_data_type=1,
)
result = retriever.invoke(query)
for doc in result:
print(doc)
Configure and use the retriever for website data with Advanced Website Indexingβ
retriever = GoogleVertexAISearchRetriever(
project_id=PROJECT_ID,
location_id=LOCATION_ID,
data_store_id=DATA_STORE_ID,
max_documents=3,
max_extractive_answer_count=3,
get_extractive_answers=True,
engine_data_type=2,
)
result = retriever.invoke(query)
for doc in result:
print(doc)
Configure and use the retriever for blended dataβ
retriever = GoogleVertexAISearchRetriever(
project_id=PROJECT_ID,
location_id=LOCATION_ID,
search_engine_id=SEARCH_ENGINE_ID,
max_documents=3,
engine_data_type=3,
)
result = retriever.invoke(query)
for doc in result:
print(doc)
Configure and use the retriever for multi-turn searchβ
Search with follow-ups is based on generative AI models and it is different from the regular unstructured data search.
retriever = GoogleVertexAIMultiTurnSearchRetriever(
project_id=PROJECT_ID, location_id=LOCATION_ID, data_store_id=DATA_STORE_ID
)
result = retriever.invoke(query)
for doc in result:
print(doc)