This page introduces search with answer and follow-ups for Vertex AI Search and
shows you how to implement it for website data and unstructured data using
method calls.
Note: The answer and follow-ups features cannot be applied to data stores with structured
data. Search with answer and follow-ups is based on the answer method. The answer method
replaces the summarization features of the older search method
and all of the features of the deprecated converse method.
The answer method also has some important additional features, such as the
ability to handle complex queries.
Key Term: On this page, the term answer refers to an AI generated answer which
is based on search results from a query. It is essentially the same as the
summary , which can be generated by the search method. Features of the answer method
Key features of the answer method are as follows:
The ability to generate answers to complex queries. For example, the answer
method can break down compound queries, such as the following,
into multiple, smaller queries to return better results that are used to
power better answers:
"What is Google Cloud and Google Ads respective revenue in 2024?"
"After how many years since its founding, did Google reach 1 billion USD
revenue?"
The ability to combine search and answer generation in a multi-turn
conversation by calling the answer method in each turn.
The ability to pair with the search method to reduce search latency. You
can call the search method and the answer method separately and render
the search results and answers in different iframes at different times. This
means that you can show your users search results (the 10 blue links) within
milliseconds. You don't need to wait for answers to be generated before you
can show search results.
The features of answer and follow-ups can be divided into three phases of the query,
search and answer:
When to use answer and when to use search
Vertex AI Search has two methods that are used for querying apps. They have different but
overlapping features.
Use the answer method when:
You want an AI generated answer (or summary) of the search results.
You want multi-turn searching, that is, searches that hold context so allow
for follow-up questions.
Use the search method when:
You only need search results; not a generated answer.
You want more than ten search results ("blue links") returned.
You have any of the following:
Media, healthcare, or generic structured data
Your own embeddings
Synonym or redirect controls
Facets
User country codes
Use the answer and search methods together when:
You want to return more than ten search results, and you want a generated
answer.
You have latency issues and want to return and display search results quickly
before the generated answer is returned.
Query phase features
The answer and follow-ups feature supports natural language query processing.
This section describes and illustrates the various options for query rephrasing and classification.
Query rephrasing
Query rephrasing is on by default. This feature chooses the best way to rephrase
queries automatically to improve search results. This feature can also handle
queries that don't require rephrasing.
Break down complex queries into multiple queries and perform synchronous
sub-querying.
For example: A complex query is broken down into four smaller, simpler
queries.
User input
Sub-queries created from the complex query
What jobs and hobbies do Andie Ram and Arnaud
Clément have in common?
Andie Ram occupation
Arnaud Clément occupation
Andie Ram hobby
Arnaud Clément hobby
Synthesize multi-turn queries, to make follow-up questions context aware
and stateful.
For example: Queries synthesized from user input at each turn might look
like this:
User input
Query synthesized
Turn 1: laptops for school
laptops for school
Turn 2: not mac
laptops for school not mac
Turn 3: bigger screen and i also need wireless keyboard and
mouse
bigger screen laptops for school not mac with wireless keyboard and
mouse
Turn 4: and a backpack for it
bigger screen laptops for school not mac with wireless keyboard and
mouse and a backpack for it
Simplify long queries to improve retrieval.
For example: A long query is shortened to a simple query.
User input
Query simplified
I am trying to find out why the \"Add to Cart\"
button on our website is not working properly. It seems that when a
user clicks the button, the item is not added to the cart and they
receive an error message. I have checked the code and it seems to be
correct, so I am not sure what the issue could be. Can you help me
troubleshoot this problem?
"Add to Cart" button not working on website.
Perform multi-step reasoning
Key Terms: Steps (also referred to as hops ) are
used to answer complex questions. The question is broken down into
multiple steps of information retrieval and inference.
Multi-step reasoning is based on the ReAct (reason + act) paradigm which
enables LLMs to solve complex tasks using natural language reasoning.
By default, the maximum number of steps is five.
For example:
User input
Two steps to generate the answer
After how many years since its founding, did Google reach 1
billion USD revenue?
Step 1: Step 2:
Query classification
Query classification options are to identify adversarial queries and non-answer
seeking queries. By default, query classification options are off.
For more information about adversarial and non-answer seeking queries, see Ignore adversarial
queries and Ignore non-summary seeking
queries .
Search phase features
For searching, the answer method has the same options as the search method. For
example:
Answer phase features
During the answer phase, when answers are generated from the search results, you
can enable the same features as in the search method. For example:
Before you begin
Depending on the type of app you have, complete the following requirements:
If you have an unstructured search app, make sure the following is turned on:
Advanced LLM features
If you have a website search app, make sure the following are turned on:
Search and answer (basic)
The following command shows how to call the answer method and
return a generated answer and a list of search results, with links to the
sources.
This command shows the required input only. The options are left at their
defaults.
REST To search and get results with a generated answer, do the following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "}
}'
Replace the following:
PROJECT_ID APP_ID QUERY
Example command and partial result
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{"query": { "text": "Which database is faster, bigquery or spanner?"}}'
What is the performance of BigQuery? "
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/9ab3ef91bcfde1fcd091efe9df7c699c",
"uri": "https://cloud.google.com/bigquery/docs/best-practices-performance-overview",
"title": "Introduction to optimizing query performance | BigQuery | Google Cloud",
"snippetInfo": [
{
"snippet": "After a query begins execution, \u003cb\u003eBigQuery\u003c/b\u003e calculates how many slots each query stage uses based on the stage size and complexity and the number of slots ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/4e545c5cb69b06b251265114d9099cb4",
"uri": "https://cloud.google.com/bigquery/docs/query-insights",
"title": "Get query performance insights | BigQuery | Google Cloud",
"snippetInfo": [
{
"snippet": "This document describes how to use the query execution graph to diagnose query \u003cb\u003eperformance\u003c/b\u003e issues, and to see query \u003cb\u003eperformance\u003c/b\u003e insights. \u003cb\u003eBigQuery\u003c/b\u003e offers ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d34672d877eefe596f9c7d1a3d7076b1",
"uri": "https://cloud.google.com/bigquery/docs/best-practices-performance-compute",
"title": "Optimize query computation | BigQuery | Google Cloud",
"snippetInfo": [
{
"snippet": "After addressing the query \u003cb\u003eperformance\u003c/b\u003e insights, you can further optimize your query by performing the following tasks: Reduce data that is to be processed ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/75ce2f05833683e60ddc21a11ce0466f",
"uri": "https://cloud.google.com/blog/products/data-analytics/troubleshoot-and-optimize-your-bigquery-analytics-queries-with-query-execution-graph/",
"title": "Troubleshoot and optimize your BigQuery analytics queries with query execution graph | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "Since query \u003cb\u003eperformance\u003c/b\u003e is multi-faceted, \u003cb\u003eperformance\u003c/b\u003e insights might only provide a partial picture of the overall query \u003cb\u003eperformance\u003c/b\u003e. Execution graph. When ...",
"snippetStatus": "SUCCESS"
}
]
}
]
}
},
{
"searchAction": {
"query": " What is the performance of Spanner? "
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f3d036b60379873acf7c73081c5e5b5c",
"uri": "https://cloud.google.com/spanner/docs/performance",
"title": "Performance overview | Spanner | Google Cloud",
"snippetInfo": [
{
"snippet": "These \u003cb\u003eperformance\u003c/b\u003e improvements should result in higher throughput and better latency in \u003cb\u003eSpanner\u003c/b\u003e nodes in both regional and multi-region instance configurations.",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/422496248ade354c73b4c906b8eb9b5f",
"uri": "https://cloud.google.com/blog/products/databases/announcing-cloud-spanner-price-performance-updates",
"title": "Announcing Cloud Spanner price-performance updates | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "Alongside lower costs, Cloud \u003cb\u003eSpanner\u003c/b\u003e provides single-digit ms latencies and strong consistency across multiple availability zones in the same region.",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/53c2a1a6990480ba4aa05cc6b4404562",
"uri": "https://cloud.google.com/blog/topics/developers-practitioners/understanding-cloud-spanner-performance-metrics-scale-key-visualizer",
"title": "Understanding Cloud Spanner performance metrics at scale with Key Visualizer | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "Designed for \u003cb\u003eperformance\u003c/b\u003e tuning and instance sizing, you can use Key Visualizer today in the web-based Cloud Console for all \u003cb\u003eSpanner\u003c/b\u003e databases at no additional ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/a6501ecd5d6391e3ade49097bab0ad3a",
"uri": "https://cloud.google.com/blog/products/databases/a-technical-overview-of-cloud-spanners-query-optimizer",
"title": "A technical overview of Cloud Spanner's query optimizer | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "... performance. Typically, a join will ... Google is continuously improving out-of-the-box \u003cb\u003eperformance of Spanner\u003c/b\u003e and reducing the need for manual tuning.",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "NMwKDAj1_d62BhC72_X_AhIkNjZkN2I4YWEtMDAwMC0yYTdiLWIxMmMtMDg5ZTA4MjhlNzY0"
}
In this example, the query is decomposed into parts: "What is the
performance of Spanner?" and "What is the performance of BigQuery?"
Query phase commands
This section shows how to specify options for the query phase of the
answer method call.
Search and answer (rephrasing disabled)
The following command shows how to call the answer method and
return a generated answer and a list of search results. The answer could be
different from the preceding answer because the rephrasing option is disabled.
REST To search and get results with a generated answer without applying query
rephrasing, do the following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"queryUnderstandingSpec": {
"queryRephraserSpec": {
"disable": true
}
}
}'
Replace the following:
PROJECT_ID APP_ID QUERY
Example command and partial result
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{
"query": { "text": "Which database is faster, bigquery or spanner?"},
"queryUnderstandingSpec": { "queryRephraserSpec": { "disable": true } }
}'
Which database is faster, bigquery or spanner? "
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/ecc0e7547253f4ca3ff3328ce89995af",
"uri": "https://cloud.google.com/blog/topics/developers-practitioners/how-spanner-and-bigquery-work-together-handle-transactional-and-analytical-workloads",
"title": "How Spanner and BigQuery work together to handle transactional and analytical workloads | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "A federated \u003cb\u003equery\u003c/b\u003e might not be as \u003cb\u003efast\u003c/b\u003e as querying local \u003cb\u003eBigQuery tables\u003c/b\u003e. There may be higher latency because of the small wait time for the source \u003cb\u003edatabase\u003c/b\u003e to ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d7e238f73608a860e00b752ef80e2941",
"uri": "https://cloud.google.com/blog/products/databases/cloud-spanner-gets-stronger-with-bigquery-federated-queries",
"title": "Cloud Spanner gets stronger with BigQuery-federated queries | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "... \u003cb\u003equick\u003c/b\u003e lookup on \u003cb\u003edata\u003c/b\u003e that's in \u003cb\u003eSpanner\u003c/b\u003e -- you can ... Set up an external \u003cb\u003edata\u003c/b\u003e source for the \u003cb\u003eSpanner\u003c/b\u003e shopping \u003cb\u003edatabase\u003c/b\u003e in \u003cb\u003eBigQuery\u003c/b\u003e. ... The \u003cb\u003equery\u003c/b\u003e is executed in ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f3d036b60379873acf7c73081c5e5b5c",
"uri": "https://cloud.google.com/spanner/docs/performance",
"title": "Performance overview | Spanner | Google Cloud",
"snippetInfo": [
{
"snippet": "The information on this page applies to both GoogleSQL and PostgreSQL \u003cb\u003edatabases\u003c/b\u003e. Note: We are in the process of rolling out \u003cb\u003eperformance\u003c/b\u003e and storage changes that ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/47b09cb5ad5e3ab3b1eb93d99ecb0896",
"uri": "https://cloud.google.com/blog/products/databases/rewe-uses-cloud-spanner-to-optimize-for-speed-and-performance",
"title": "REWE uses Cloud Spanner to optimize for speed and performance | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "As a fully managed relational \u003cb\u003edatabase\u003c/b\u003e, \u003cb\u003eSpanner\u003c/b\u003e provides unlimited scale, strong consistency, and up to 99.999% availability. By choosing this approach to ...",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "M8gKCwjp_t62BhC7wOFMEiQ2NmQ3YjhhZS0wMDAwLTJhN2ItYjEyYy0wODllMDgyOGU3NjQ"
}
Search and answer (specify maximum steps)
The following command shows how to call the answer method and
return a generated answer and a list of search results. The answer is
different from the preceding answers because the number of rephrasing steps has
been increased.
REST To search and get results with a generated answer allowing up to five rephrasing
steps, do the following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"queryUnderstandingSpec": {
"queryRephraserSpec": {
"maxRephraseSteps": MAX_REPHRASE
}
}
}'
Replace the following:
PROJECT_ID APP_ID QUERY MAX_REPHRASE 5.
If not set or if set to less than 1 the value is the default,
1.
Example command
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{
"query": { "text": "How much longer does it take to train a recommendations model than a search model"},
"queryUnderstandingSpec": {
"queryRephraserSpec": {
"maxRephraseSteps": 5
}
}
}'
Search and answer with query classification
The following command shows how to call the answer method to
inquire whether a query is adversarial, non-answer seeking, or neither.
The response includes the classification type for the query, but the answer itself is not affected by the classification.
If you want to change the answer behavior according to the query type, you can
do this in the answer phase. See Ignore adversarial
queries and Ignore non-summary seeking
queries .
REST To determine if a query is adversarial or non-answer seeking, do the following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"queryUnderstandingSpec": {
"queryClassificationSpec": {
"types": ["QUERY_CLASSIFICATION_TYPE "]
}
}
}'
Replace the following:
PROJECT_ID APP_ID QUERY QUERY_CLASSIFICATION_TYPE ADVERSARIAL_QUERY,
NON_ANSWER_SEEKING_QUERY, or both.
Example command and partial result
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{
"query": {
"text": "Hello!"},
"queryUnderstandingSpec": {
"queryClassificationSpec": {
"types": ["ADVERSARIAL_QUERY", "NON_ANSWER_SEEKING_QUERY"]
}
}
}'
"type": "NON_ANSWER_SEEKING_QUERY",
"positive": true
}
]
}
},
"answerQueryToken": "NMwKDAjVloK3BhCdt8u9AhIkNjZkYmFhNWItMDAwMC0yZTBkLTg0ZDAtMDg5ZTA4MmRjYjg0"
}
In this example, the query "hello" is not adversarial but it is classified as
non-answer-seeking.
Search phase commands: Search and answer with search result options
This section shows how to specify options for the search phase portion of the
answer method call, options such as setting the maximum number
of documents returned, boosting, and filtering, and how to get an answer when
you supply your own search results.
The following command shows how to call the answer method and
specify various options for how the search result is returned. (The search
results are independent of the answer.)
REST To set various options related to which and how search results are returned, do
the following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"searchSpec": {
"searchParams": {
"maxReturnResults": MAX_RETURN_RESULTS ,
"filter": "FILTER ",
"boostSpec": BOOST_SPEC ,
"orderBy": "ORDER_BY ",
"searchResultMode": SEARCH_RESULT_MODE
}
}
}'
Replace the following:
PROJECT_ID APP_ID QUERY MAX_RETURN_RESULTS FILTER Filter
generic search for structured or unstructured data .BOOST_SPEC Boost search results .ORDER_BY Document
object. The orderBy expression is case-sensitive.
If this field is unrecognizable, an INVALID_ARGUMENT is
returned.SEARCH_RESULT_MODE DOCUMENTS or CHUNKS. For more
information, see
Parse and
chunk documents and ContentSearchSpec .
This field is only available in the v1alpha version of the API.
Example command and result
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{
"query": {
"text": "Does spanner database have an API?"},
"searchSpec": {
"searchParams": { "maxReturnResults": 3 }
}
}'
In this example, the number of documents returned is capped at three.
Answer phase commands
This section shows how to specify answer-specific options for the
answer method call.
Ignore adversarial queries and non-answer-seeking queries
The following command shows how to avoid answering adversarial queries and
non-answer-seeking queries when calling the answer method.
REST To skip answering queries that are adversarial or non-answer-seeking, do the
following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"answerGenerationSpec": {
"ignoreAdversarialQuery": true,
"ignoreNonAnswerSeekingQuery": true
}
}'
Replace the following:
PROJECT_ID APP_ID QUERY
Example command and partial result
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)"
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{
"query": { "text": "Hello"},
"answerGenerationSpec": {
"ignoreAdversarialQuery": true ,
"ignoreNonAnswerSeekingQuery": true }
}'
A summary could not be generated for your search query. Here are some search results. ",
"steps": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "Hello"
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/69e92e5b1de5b1e583fbe95f94dd4cbf",
"uri": "https://support.google.com/voice/thread/152245405/google-voice-is-randomly-automatically-sending-hello-replies-to-incoming-texts?hl=en",
"title": "Google voice is randomly/automatically sending \"Hello!\" replies to incoming texts",
"snippetInfo": [
{
"snippet": "There IS a new "Smart reply" feature on the Android or iOS client apps, but you'd have to a) receive a SMS/MMS, b) open it up, c) look at the three suggested ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/16d65e2af7fa854d1a00995525646dc3",
"uri": "https://support.google.com/voice/thread/112990484/google-voice-sending-hello-in-response-to-text-messages?hl=en",
"title": "Google Voice sending \"Hello,\" in response to text messages",
"snippetInfo": [
{
"snippet": "When I receive text messages, a reply is instantly sent out reading "\u003cb\u003eHello\u003c/b\u003e," and I cannot figure out how this is happening. I have no linked accounts, ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/b3bdde4957f588a1458c533269626d09",
"uri": "https://support.google.com/voice/thread/4307458/lately-an-automatic-text-response-saying-hello-is-going-out-how-do-i-stop-this?hl=en",
"title": "Lately an automatic text response saying, \"Hello\" is going out. How do I stop this? - Google Voice Community",
"snippetInfo": [
{
"snippet": "You need to find out what app is causing it and deactivate or delete it. Last edited Apr 16, 2019.",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/a828eb8f442f1dfbdda06dbeb52841b0",
"uri": "https://support.google.com/a/thread/161821861/hello-hello-the-lost-phone?hl=en",
"title": "Hello.Hello the lost phone - Google Workspace Admin Community",
"snippetInfo": [
{
"snippet": "\u003cb\u003eHello\u003c/b\u003e the lost phone. My wife lost her phone but she cannot remember her emails pasward to help track .",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
],
"answerSkippedReasons": [
"NON_ANSWER_SEEKING_QUERY_IGNORED"
]
},
"answerQueryToken": "NMwKDAjFgN-2BhDlsKaZARIkNjZkN2I0NmItMDAwMC0yZmQ5LTkwMDktZjQwMzA0M2E5YTg4"
}
In this example, the query is determined to be non-answer-seeking and so no
answer is generated.
Show only relevant answers
Vertex AI Search can assess how relevant the results are to a
query. If no results are determined to be sufficiently relevant, then instead
of generating an answer from non-relevant or minimally-relevant results, you can
choose to return a fallback answer: "We do not have a summary for your query."
The following command shows how to return the fallback answer in the case of
irrelevant results when calling the answer method.
REST To return a fallback answer if no relevant results are found, do the
following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"answerGenerationSpec": {
"ignoreLowRelevantContent": true
}
}'
Replace the following:
PROJECT_ID APP_ID QUERY
Example command and result
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{"query": { "text": "foobar"}, "answerGenerationSpec": {
"ignoreLowRelevantContent": true
} }'
"answerText": "We do not have a summary for your query.",
"steps": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "foobar"
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/719b79786f0c143717c569eade5305d9",
"uri": "https://support.google.com/websearch/thread/261714267/google-foobar-bug-console-disappeared?hl=en",
"title": "Google Foobar Bug - Console Disappeared",
"snippetInfo": [
{
"snippet": "Google \u003cb\u003eFoobar\u003c/b\u003e Bug - Console Disappeared. After I logged in today the top bar says "The \u003cb\u003eFoobar\u003c/b\u003e Challenge will be turned down on 1 April 2024. If you run out of ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/932369826585ff45f6ab3eba01ba6933",
"uri": "https://support.google.com/websearch/thread/95251114/unable-to-contact-foobar-recruiter?hl=en",
"title": "Unable to contact Foobar Recruiter - Google Search Community",
"snippetInfo": [
{
"snippet": "Access is by invitation only so you will need to have the proper credentials to login. You can always reach out using the contact us button, but there is no ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/fb736a30ff90d058be755f0a04a522a8",
"uri": "https://support.google.com/websearch/thread/121151780/foobar-challenge-appeared-to-me-then-disappeared?hl=en",
"title": "Foobar challenge appeared to me then disappeared - Google Search Community",
"snippetInfo": [
{
"snippet": "Hi. I got the \u003cb\u003efoobar\u003c/b\u003e challenge some months ago. But then it disappeared immediately, maybe by misclick (though I don't think I misclicked).",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f56f2656b0d02b839509d0e67e60c1c9",
"uri": "https://support.google.com/chrome/thread/159931759/cannot-access-google-foobar-challenge?hl=en",
"title": "Cannot Access Google FooBar Challenge",
"snippetInfo": [
{
"snippet": "I knew I wouldn't have time for it today, so I just kept the tab in the background. Tonight, I went to go close all my tabs, but the page had changed. It said " ...",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
],
"answerSkippedReasons": [
"NO_RELEVANT_CONTENT"
]
},
"answerQueryToken": "M8gKCwiokvy2BhDtv8EDEiQ2NmQ5NDQxZC0wMDAwLTIxMGQtOWU2Yi1mNDAzMDQ1ZGJkMzA"
}
In this example, the results were determined to be not sufficiently relevant
to the query, and so the fallback answer was returned instead of a generated
answer and results.
Specify the answer model
The following command shows how to change the model version used to generate
answers.
For information about the supported models, see Answer generation model versions and lifecycle .
REST To generate an answer using a model different from the default model, do the following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"answerGenerationSpec": {
"modelSpec": {
"modelVersion": "MODEL_VERSION ",
}
}
}'
Replace the following:
PROJECT_ID APP_ID QUERY MODEL_VERSION Answer
generation model versions and lifecycle .
Example command and partial result
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{"query": { "text": "Compare bigquery with spanner database?"}, "answerGenerationSpec": {
"modelSpec": {
"modelVersion": "preview",
}
} }'
In this example, the preview version of the model is used instead of the default model.
Specify a custom preamble
The following command shows how to set a preamble for the generated answer. A
preamble contains natural language instructions for customizing the answer. You
can request customizations such as length, level of detail, style of output
(such as "simple"), language of output, focus of answer, and format (such as
tables, bullets, and XML). For example, a preamble might be "Explain like you
are a ten years old kid."
REST To generate an answer using a model different from the default model, do the following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"answerGenerationSpec": {
"promptSpec": {
"preamble": "PREAMBLE ",
}
}
}'
Replace the following:
PROJECT_ID APP_ID QUERY PREAMBLE show the answer format in
an ordered list or give a very detailed answer.
Example command and result
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)"
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{
"query": { "text": "what is bigquery?"},
"answerGenerationSpec": {
"promptSpec": {
"preamble": "Explain like you are a ten years old
kid ",
}
}
}'
BigQuery is like a super-powered storage space for your data, but it's in the cloud, not on your computer. It's like a giant warehouse for all your information, but you don't have to build or manage it yourself. You can use BigQuery to find patterns and insights in your data, like figuring out what people like to buy or how many people visited your website. It's like having a super-smart assistant that can help you understand your data better. BigQuery is really good at working with lots of data, even billions of pieces of information. \n",
"steps ": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "What is BigQuery?"
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/2d032dc582689e8c0ecea7fc7bfa3189",
"uri": "https://cloud.google.com/bigquery",
"title": "BigQuery enterprise data warehouse | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eBigQuery\u003c/b\u003e is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/4474f4a5a18ecd611dedfe323dfe55d9",
"uri": "https://cloud.google.com/bigquery/docs/introduction",
"title": "BigQuery overview | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eBigQuery\u003c/b\u003e is a fully managed, AI-ready data platform that helps you manage and analyze your data with built-in features like machine learning, search, ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/c840fdef90d86328f13bbedbdbf0ac10",
"uri": "https://cloud.google.com/bigquery/docs/query-overview",
"title": "Overview of BigQuery analytics | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eBigQuery\u003c/b\u003e lets you save queries and share queries with others. When you save a query, it can be private (visible only to you), shared at the project level ( ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/5cbfab8ce1d5f6ffe45fef22900c9a00",
"uri": "https://cloud.google.com/curated-resources/bigquery",
"title": "Guides BigQuery | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eBigQuery\u003c/b\u003e is Google Cloud's fully managed and serverless enterprise data warehouse solution, designed to help you make informed decisions quickly, so you can ...",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "NMwKDAi8hN-2BhC0jMCPARIkNjZkN2I4MzItMDAwMC0yMTliLTkxN2EtMDg5ZTA4MjA0YjFj"
}
In this example, the preamble requests a simpler answer than the default
might provide.
Include citations
The following command shows how to request citations to be included with the
answer.
REST To generate an answer using a model different from the default model, do the following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"answerGenerationSpec": {
"includeCitations": INCLUDE_CITATIONS
}
}'
Replace the following:
PROJECT_ID APP_ID QUERY INCLUDE_CITATIONS false.
Example command and partial result
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{"query": { "text": "What is SQL"}, "answerGenerationSpec": {
"includeCitations": true
}
}'
"citations": [
{
"endIndex": "137",
"sources": [
{
"referenceId": "0"
},
{
"referenceId": "1"
}
]
},
{
"startIndex": "138",
"endIndex": "437",
"sources": [
{
"referenceId": "3"
}
]
},
{
"startIndex": "438",
"endIndex": "575",
"sources": [
{
"referenceId": "2"
}
]
},
{
"startIndex": "576",
"endIndex": "742",
"sources": [
{
"referenceId": "3"
}
]
}
],
"references": [
{
"chunkInfo": {
"content": "There may be a second table that stores visit information. A relational database uses a unique ID for each row to maintain the linked patient information across the two tables. This way, you can quickly look up the visits of each patient. Sign up for a free trial for any of Google Cloud's SQL databases, which include AlloyDB, Cloud SQL, and Spanner. Get started for free What is SQL? SQL (Structured Query Language) is a programming language used to store, retrieve, and manage data in a relational database. SQL statements are English-like, making the language accessible to software developers, data analysts, and other practitioners. Benefits of SQL databases Enterprises choose SQL databases for being: Efficient. Relational databases are incredibly efficient in managing complex queries. Fast. SQL databases can retrieve large amounts of data, quickly. This makes them highly desirable for real-time transactional data. Reliable. SQL databases provide a high degree of data integrity and are ACID-compliant. SQL database engines There are numerous SQL database engines (products) used to build software applications. Some of the most popular include PostgreSQL, MySQL, SQL Server, and Oracle. Some database engines are open source while others are commercial offerings. ",
"relevanceScore": 0.9,
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
"uri": "https://cloud.google.com/discover/what-are-sql-databases",
"title": "SQL Databases | Google Cloud"
}
}
},
{
"chunkInfo": {
"content": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud Page Contents Topics PostgreSQL vs. SQL PostgreSQL vs SQL Server: What are the key differences? Trying to find the right database for your applications? When it comes to choosing a database technology, the most common SQL options to consider are PostgreSQL vs. SQL Server. While both systems share many core features, there are some key differences—the major one being that PostgreSQL is open source and SQL Server is owned by Microsoft. Today, it is more vital than ever for companies to be able to manage, store, and activate data for modern business operations. With the growing assortment of databases available to choose from, it can be overwhelming to pick the right one for your applications. The most important thing to remember is that no single database will be a good match for every project requirement, so it's critical to understand the option that will work best for your specific use case. So, what is PostgreSQL vs. SQL Server? In this short guide, we'll discuss the basic differences between PostgreSQL and SQL Server. Get started for freeStay informed What is SQL? Structured Query Language or SQL, as it's more commonly known, is a programming language used to manage, query, and retrieve data in a relational database. ",
"relevanceScore": 0.8,
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7cd9afab1282a9f57cdcee1885bb4c6",
"uri": "https://cloud.google.com/learn/postgresql-vs-sql",
"title": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud"
}
}
},
{
"chunkInfo": {
"content": "SQL Databases | Google Cloud Page Contents Topics What are SQL databases? What are SQL databases? A SQL database, also known as a relational database, is a system that stores and organizes data into highly structured tables of rows and columns. These databases offer Structured Query Language (SQL) to read and write the data, and are categorized as relational database management systems (RDBMS). SQL statements are used to create and update the structure of tables, read and write data, manage user permissions, and perform administrative tasks. For example, a CREATE statement is used to create a table, an INSERT statement adds a new row to a table, and a SELECT statement performs a database query. Statements that make structural or administrative changes are usually reserved for software developers and administrators, while read and write operations are performed by end-user applications. A relational database maintains the ability to link information across multiple tables. This format makes it easy to quickly gain insights about the relationships between various columns or data points in these tables. A relational database can create indexes for particular columns for faster lookups. For example, a healthcare facility might maintain a table containing rows of patient information, where each row is one patient and the columns contain data points, such as the patient's name, insurance information, and contact details. ",
"relevanceScore": 0.8,
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
"uri": "https://cloud.google.com/discover/what-are-sql-databases",
"title": "SQL Databases | Google Cloud"
}
}
},
{
"chunkInfo": {
"content": "It is the standard language used by relational database management systems (RDBMS), including PostgreSQL, SQL Server, MySQL, and Oracle Database. SQL typically uses commands written in statement format for queries and other database operations, which allow users to manipulate data in relational database tables. While originally created for relational databases, SQL acts as a foundation for many of today's technology capabilities, making SQL knowledge an essential skill for many technology roles today, including data analysts, database engineers, and even backend programming. However, you will find that there are different variants of SQL depending on the database or database management system that you choose. What is Microsoft SQL Server? SQL Server is a leading RDBMS that is built on top of SQL and developed by Microsoft. It is used to manage and store data to support numerous enterprise use cases for business intelligence, transaction processing, data analytics, and machine learning services. SQL Server has a row-based table structure that allows you to connect related data elements from different tables without having to store data multiple times in a database. In general, Microsoft SQL Server is known for its high availability, fast performance when handling large workloads, and easy integration with other applications to gain business intelligence across your entire data estate. For more information, we recommend reviewing the official SQL Server documentation. ",
"relevanceScore": 0.8,
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7cd9afab1282a9f57cdcee1885bb4c6",
"uri": "https://cloud.google.com/learn/postgresql-vs-sql",
"title": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud"
}
}
},
{
"chunkInfo": {
"content": "Send feedback The GoogleSQL language in Spanner bookmark_borderbookmark Stay organized with collections Save and categorize content based on your preferences. Dismiss Got it GoogleSQL is the new name for Google Standard SQL! New name, same great SQL dialect. This page provides an overview of supported statements in GoogleSQL. GoogleSQL is an ANSI compliant Structured Query Language (SQL) which includes the following types of supported statements: Query statements, also known as Data Query Language (DQL) statements, are the primary method to analyze data in Spanner. They scan one or more tables or expressions and return the computed result rows. Data Definition Language (DDL) statements let you create and modify database objects such as tables, views, and database roles. Data Manipulation Language (DML) statements enable you to update, insert, and delete data from your Spanner tables. Data Access Control statements let you grant and revoke access privileges at the table and column level. Transaction Control statements allow you to manage transactions for data modifications. Was this helpful? Send feedback Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. ",
"relevanceScore": 0.7,
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/0c5c094170756eeb6bdfec6eb5c7d081",
"uri": "https://cloud.google.com/spanner/docs/reference/standard-sql/overview",
"title": "The GoogleSQL language in Spanner | Google Cloud"
}
}
},
{
"chunkInfo": {
"content": "FAQ Expand all What is Cloud SQL? Cloud SQL is a service that delivers fully managed relational databases in the cloud. It offers MySQL, PostgreSQL, and SQL Server database engines. How is Cloud SQL different from other cloud databases? Cloud SQL is valued for its openness, ease of use, security, cost-efficiency, and Google Cloud integration—in fact, more than 95% of Google Cloud's top 100 customers use it. If you're comparing PostgreSQL options on Google Cloud, view our comparison chart. What's the difference between the Enterprise and Enterprise Plus editions? For PostgreSQL, the Enterprise Plus edition brings enhanced availability, performance, and data protection capabilities. Specifically, it provides a 99.99% availability SLA with near-zero downtime maintenance, optimized hardware and software configurations, intelligent data caching for read-intensive transactional workloads, a configurable data cache option and 35 days of log retention. For MySQL, the Enterprise Plus edition brings enhanced availability, performance, and data protection capabilities. Specifically, it provides a 99.99% availability SLA with near-zero downtime maintenance, optimized hardware and software configurations, intelligent data caching for read-intensive transactional workloads, a configurable data cache option, 35 days of log retention and advanced disaster recovery capabilities like orchestrated failover and switchback. ",
"relevanceScore": 0.7,
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/931f2c8e19ed54a407857f1cad3b5aaa",
"uri": "https://cloud.google.com/sql",
"title": "Cloud SQL for MySQL, PostgreSQL, and SQL Server | Google Cloud"
}
}
},
{
"chunkInfo": {
"content": "PostgreSQL versus SQL PostgreSQL is an open-source, object-relational database (ORDBMS) designed for enterprise-level performance and is valued for its reliability and robust features. Its long history of development and its use of SQL makes it one of the most popular open source databases worldwide. Its default procedural language is an extension of pgSQL (PL/pgSQL), with procedural language extensions of Tcl, Perl, and Python included in the standard distribution (written as PL/Tcl, PL/Perl, and PL/Python). Many more languages are supported through extensions, including Java, Ruby, C, C++, Delphi, and JavaScript. For a more in-depth comparison, visit our PostgreSQL versus SQL guide. MySQL versus SQL MySQL is a popular open source relational database created in 1995 and currently sponsored by Oracle. It supports SQL queries and can be administered either through a graphical user interface (GUI) or a command line. MySQL can be deployed manually on a physical machine or through a cloud service provider. Enterprises are increasingly choosing fully managed services to reduce the maintenance burden of their databases. What is SQL Server? SQL Server is a Microsoft-owned database that runs SQL queries. Dive into the differences between PostgreSQL and SQL Server. ",
"relevanceScore": 0.6,
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
"uri": "https://cloud.google.com/discover/what-are-sql-databases",
"title": "SQL Databases | Google Cloud"
}
}
},
{
"chunkInfo": {
"content": "Send feedback On this page BigQuery SQL dialects Changing from the default dialect What's next Introduction to SQL in BigQuery bookmark_borderbookmark Stay organized with collections Save and categorize content based on your preferences. Dismiss Got it GoogleSQL is the new name for Google Standard SQL! New name, same great SQL dialect. This document provides an overview of supported statements and SQL dialects in BigQuery. GoogleSQL is an ANSI compliant Structured Query Language (SQL) which includes the following types of supported statements: Query statements, also known as Data Query Language (DQL) statements, are the primary method to analyze data in BigQuery. They scan one or more tables or expressions and return the computed result rows. Procedural language statements are procedural extensions to GoogleSQL that allow you to execute multiple SQL statements in one request. Procedural statements can use variables and control-flow statements, and can have side effects. Data Definition Language (DDL) statements let you create and modify database objects such as tables, views, functions, and row-level access policies. Data Manipulation Language (DML) statements enable you to update, insert, and delete data from your BigQuery tables. Data Control Language (DCL) statements let you control BigQuery system resources such as access and capacity. Transaction Control Language (TCL) statements allow you to manage transactions for data modifications. ",
"relevanceScore": 0.6,
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/2f6fc3e29873518196cb50195d7ded45",
"uri": "https://cloud.google.com/bigquery/docs/introduction-sql",
"title": "Introduction to SQL in BigQuery | Google Cloud"
}
}
},
{
"chunkInfo": {
"content": "Database administration Cloud SQL pricing Connect to a Cloud SQL managed database Cloud SQL updates Configuration updates System updates What's next Home Cloud SQL Documentation Guides Was this helpful? Send feedback Cloud SQL overview bookmark_borderbookmark Stay organized with collections Save and categorize content based on your preferences. Dismiss Got it On this page Database configurations with Cloud SQL Use cases for Cloud SQL What Cloud SQL provides What is a Cloud SQL instance? Database administration Cloud SQL pricing Connect to a Cloud SQL managed database Cloud SQL updates Configuration updates System updates What's next Cloud SQL is a fully managed relational database service for MySQL, PostgreSQL, and SQL Server. This frees you from database administration tasks so that you have more time to manage your data. This page discusses basic concepts and terminology for Cloud SQL, which provides SQL data storage for Google Cloud. For a more in-depth explanation of key concepts, see the key terms and features pages. For information about how Cloud SQL databases compare with one another, see Cloud SQL feature support by database engine. Database configurations with Cloud SQL The following video shows you the benefits of using Cloud SQL. ",
"relevanceScore": 0.6,
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/4098ae11bfa400e8f1b8e9ba59d2b71b",
"uri": "https://cloud.google.com/sql/docs/introduction",
"title": "Cloud SQL overview"
}
}
},
{
"chunkInfo": {
"content": "Cloud SQL documentation View all product documentation Cloud SQL is a fully-managed database service that helps you set up, maintain, manage, and administer your relational databases on Google Cloud Platform. You can use Cloud SQL with MySQL, PostgreSQL, or SQL Server. Not sure what database option is right for you? Learn more about our database services. Learn more about Cloud SQL. Documentation resources Find quickstarts and guides, review key references, and get help with common issues. format_list_numbered Guides Cloud SQL overview Database engine feature support MySQL PostgreSQL SQL Server find_in_page Reference gcloud commands REST API Client libraries info Resources Pricing Release notes Resources Try Cloud SQL for yourself Create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads. Try Cloud SQL free Was this helpful? Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Last updated 2024-08-29 UTC. ",
"relevanceScore": 0.5,
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/37935181d99a6ad3b4897e673a7a7986",
"uri": "https://cloud.google.com/sql/docs",
"title": "Cloud SQL documentation"
}
}
}
],
"steps": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "What is SQL?"
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
"uri": "https://cloud.google.com/discover/what-are-sql-databases",
"title": "SQL Databases | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eSQL\u003c/b\u003e (Structured Query Language) is a programming language used to store, retrieve, and manage data in a relational database. \u003cb\u003eSQL\u003c/b\u003e statements are English-like, ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7cd9afab1282a9f57cdcee1885bb4c6",
"uri": "https://cloud.google.com/learn/postgresql-vs-sql",
"title": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eSQL\u003c/b\u003e typically uses commands written in statement format for queries and other database operations, which allow users to manipulate data in relational database ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3afdede140d0906c2146a2f2b3a7821e",
"uri": "https://cloud.google.com/blog/topics/developers-practitioners/what-cloud-sql",
"title": "What is Cloud SQL? | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "It is a fully managed relational database for MySQL, PostgreSQL and \u003cb\u003eSQL\u003c/b\u003e Server. It reduces maintenance cost and automates database provisioning, storage ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/0c5c094170756eeb6bdfec6eb5c7d081",
"uri": "https://cloud.google.com/spanner/docs/reference/standard-sql/overview",
"title": "The GoogleSQL language in Spanner | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eGoogleSQL\u003c/b\u003e is the new name for Google Standard \u003cb\u003eSQL\u003c/b\u003e! New name, same great \u003cb\u003eSQL\u003c/b\u003e dialect. This page provides an overview of supported statements in \u003cb\u003eGoogleSQL\u003c/b\u003e.",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "NMwKDAiFm_y2BhC_nfrYAxIkNjZkYjg3NjItMDAwMC0yZTBkLTg0ZDAtMDg5ZTA4MmRjYjg0"
}
Set the answer language code
The following command shows how to set the language code for answers.
REST To generate an answer using a model different from the default model, do the following:
Run the following curl command:
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"answerGenerationSpec": {
"answerLanguageCode": "ANSWER_LANGUAGE_CODE "
}
}'
Replace the following:
PROJECT_ID APP_ID QUERY ANSWER_LANGUAGE_CODE BCP47: Tags for
Identifying Languages .
Example command and result
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{"query": { "text": "What is SQL"}, "answerGenerationSpec": {
"answerLanguageCode": "es"
}
}'
"answerText": "SQL, que significa Structured Query Language, es un lenguaje de programación utilizado para almacenar, recuperar y administrar datos en una base de datos relacional. Las instrucciones de SQL son similares al inglés, lo que hace que el lenguaje sea accesible para desarrolladores de software, analistas de datos y otros profesionales. Las bases de datos SQL se utilizan para administrar y almacenar datos para apoyar numerosos casos de uso empresariales, como la inteligencia empresarial, el procesamiento de transacciones, el análisis de datos y los servicios de aprendizaje automático. SQL es el lenguaje estándar utilizado por los sistemas de gestión de bases de datos relacionales (RDBMS), incluidos PostgreSQL, SQL Server, MySQL y Oracle Database. SQL se utiliza para crear y actualizar la estructura de las tablas, leer y escribir datos, administrar los permisos de los usuarios y realizar tareas administrativas. \n",
"steps": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "What is SQL?"
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
"uri": "https://cloud.google.com/discover/what-are-sql-databases",
"title": "SQL Databases | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eSQL\u003c/b\u003e (Structured Query Language) is a programming language used to store, retrieve, and manage data in a relational database. \u003cb\u003eSQL\u003c/b\u003e statements are English-like, ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7cd9afab1282a9f57cdcee1885bb4c6",
"uri": "https://cloud.google.com/learn/postgresql-vs-sql",
"title": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eSQL\u003c/b\u003e typically uses commands written in statement format for queries and other database operations, which allow users to manipulate data in relational database ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3afdede140d0906c2146a2f2b3a7821e",
"uri": "https://cloud.google.com/blog/topics/developers-practitioners/what-cloud-sql",
"title": "What is Cloud SQL? | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "It is a fully managed relational database for MySQL, PostgreSQL and \u003cb\u003eSQL\u003c/b\u003e Server. It reduces maintenance cost and automates database provisioning, storage ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/0c5c094170756eeb6bdfec6eb5c7d081",
"uri": "https://cloud.google.com/spanner/docs/reference/standard-sql/overview",
"title": "The GoogleSQL language in Spanner | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eGoogleSQL\u003c/b\u003e is the new name for Google Standard \u003cb\u003eSQL\u003c/b\u003e! New name, same great \u003cb\u003eSQL\u003c/b\u003e dialect. This page provides an overview of supported statements in \u003cb\u003eGoogleSQL\u003c/b\u003e.",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "NMwKDAjim_y2BhDftIjEAhIkNjZkOTQ0NWQtMDAwMC0yMTBkLTllNmItZjQwMzA0NWRiZDMw"
}
In this example, although the source documents are in English, the answer is
provided in Spanish.
Commands for follow-up questions
Follow-ups are multi-turn queries. After the first query in a follow-up session, subsequent
"turns" take into account prior interactions. With follow-ups, the answer method
can also suggest related questions, which your users can choose instead of
entering their own follow-up questions.
All the answer and follow-ups features described in the preceding sections, such as
citations, filters, SafeSearch, ignoring certain types of queries, and using a
preamble to customize answers can be applied along with follow-ups.
Example of a follow-up session
The following is an example of a session with follow-ups. Suppose that you want
to know about vacationing in Mexico:
Turn 1:
You: When is the best time of the year to vacation in Mexico?
Answer with follow-ups: The best time to vacation in Mexico is during the dry
season, which runs from November to April.
Turn 2:
Turn 3:
Without follow-ups, your question "What is the exchange rate?" wouldn't be
answerable because regular search wouldn't know that you wanted the Mexican
exchange rate. Similarly, without follow-ups, there wouldn't be the context
needed to give you temperatures specifially for Mexico.
When you ask "What is the best time of the year to vacation in
Mexico?", in addition to answering your question, answer and follow-ups can suggest other
questions that you might ask, such as "What is the cheapest month
to vacation in Mexico?" and "What are the tourist months in Mexico?".
After the related questions feature is enabled, questions are returned as
strings in the ConverseConversationResponse .
About sessions
To understand how follow-ups work in Vertex AI Search, you need
to understand about sessions.
A session is made up of text queries provided by a
user and responses provided by Vertex AI Search.
These query and response pairs are sometimes referred to as turns . In the
preceding example, the second turn is made up of "What is the exchange rate?"
and "1 USD is equal to approximately 17.65 Mexican pesos."
Sessions are stored with the app.
In the app, a session is represented by the session
resource .
In addition to containing the query and response messages, the session
resource has:
A unique name (the session ID).
A state (in-progress or completed).
A user pseudo ID, which is a visitor ID that tracks the user. It can be
assigned programmatically.
A start time and an end time.
A turn, which is a query answer pair.
Store session information and get responses
You can use the command line to generate search responses and answers
and to store these, along with each query in a session.
REST To use the command line to create a session and generate responses from the
user's input, follow these steps:
Specify the app where you want to store the session:
curl -X POST \
-H "Authorization: Bearer $( gcloud auth print-access-token) " \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /sessions" \
-d '{
"userPseudoId": "USER_PSEUDO_ID "
} '
Replace the following:
PROJECT_ID : the ID of your Google Cloud project.
APP_ID : the ID of the Vertex AI Search app.
USER_PSEUDO_ID : This is a unique identifier for tracking a
search visitor. For example, you can implement this with an HTTP cookie,
which uniquely identifies a visitor on a single device.
Example command and result
curl -X POST -H "Authorization: Bearer $( gcloud auth print-access-token) "
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/sessions"
-d '{
"userPseudoId": "test_user"
}'
{
"name" : "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943" ,
"state" : "IN_PROGRESS" ,
"userPseudoId" : "test_user" ,
"startTime" : "2024-09-13T18:47:10.465311Z" ,
"endTime" : "2024-09-13T18:47:10.465311Z"
}
Note down the session ID, the numbers at the end of the name: field in the
JSON response. In the example result, the ID is 5386462384953257772.
You need this ID in the next step.
Generate an answer and add it to a session in your app:
curl -X POST \
-H "Authorization: Bearer $( gcloud auth print-access-token) " \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "} ,
"session": "projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /sessions/SESSION_ID ",
"searchSpec":{ "searchParams": { "filter": "FILTER "} }
} '
PROJECT_ID APP_ID QUERY SESSION_ID name: field, noted step 2. For a session, use the same
session ID in every turn.
FILTER Filter
generic search for structured or unstructured data .
Example command and result
curl -X POST -H "Authorization: Bearer $( gcloud auth print-access-token) "
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer"
-d '{
"query": { "text": "Compare bigquery with spanner database?"},
"session": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943",
}'
{
"answer" : {
"name" : "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943/answers/4861507376861383072" ,
"state" : "SUCCEEDED" ,
"answerText" : "BigQuery and Spanner are both powerful tools that can be used together to handle transactional and analytical workloads. Spanner is a fully managed relational database optimized for transactional workloads, while BigQuery is a serverless data warehouse designed for business agility. Spanner provides seamless replication across regions in Google Cloud and processes over 1 billion requests per second at peak. BigQuery analyzes over 110 terabytes of data per second. Users can leverage federated queries to read data from Spanner and write to a native BigQuery table. \n" ,
"steps" : [
{
"state" : "SUCCEEDED" ,
"description" : "Rephrase the query and search." ,
"actions" : [
{
"searchAction" : {
"query" : "Compare bigquery with spanner database?"
} ,
"observation" : {
"searchResults" : [
{
"document" : "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/ecc0e7547253f4ca3ff3328ce89995af" ,
"uri" : "https://cloud.google.com/blog/topics/developers-practitioners/how-spanner-and-bigquery-work-together-handle-transactional-and-analytical-workloads" ,
"title" : "How Spanner and BigQuery work together to handle transactional and analytical workloads | Google Cloud Blog" ,
"snippetInfo" : [
{
"snippet" : "Using Cloud \u003cb\u003eSpanner\u003c/b\u003e and \u003cb\u003eBigQuery\u003c/b\u003e also allows customers to build their \u003cb\u003edata\u003c/b\u003e clouds using Google Cloud, a unified, open approach to \u003cb\u003edata\u003c/b\u003e-driven transformation ..." ,
"snippetStatus" : "SUCCESS"
}
]
} ,
{
"document" : "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d7e238f73608a860e00b752ef80e2941" ,
"uri" : "https://cloud.google.com/blog/products/databases/cloud-spanner-gets-stronger-with-bigquery-federated-queries" ,
"title" : "Cloud Spanner gets stronger with BigQuery-federated queries | Google Cloud Blog" ,
"snippetInfo" : [
{
"snippet" : "As enterprises compete for market share, their need for real-time insights has given rise to increased demand for transactional \u003cb\u003edatabases\u003c/b\u003e to support \u003cb\u003edata\u003c/b\u003e ..." ,
"snippetStatus" : "SUCCESS"
}
]
} ,
{
"document" : "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/e10a5a3c267dc61579e7c00fefe656eb" ,
"uri" : "https://cloud.google.com/blog/topics/developers-practitioners/replicating-cloud-spanner-bigquery-scale" ,
"title" : "Replicating from Cloud Spanner to BigQuery at scale | Google Cloud Blog" ,
"snippetInfo" : [
{
"snippet" : "... \u003cb\u003eSpanner data\u003c/b\u003e into \u003cb\u003eBigQuery\u003c/b\u003e for analytics. In this post, you will learn how to efficiently use this feature to replicate large tables with high throughput ..." ,
"snippetStatus" : "SUCCESS"
}
]
} ,
...
{
"document" : "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/8100ad36e1cac149eb9fc180a41d8f25" ,
"uri" : "https://cloud.google.com/blog/products/gcp/from-nosql-to-new-sql-how-spanner-became-a-global-mission-critical-database" ,
"title" : "How Spanner became a global, mission-critical database | Google Cloud Blog" ,
"snippetInfo" : [
{
"snippet" : "... SQL \u003cb\u003evs\u003c/b\u003e. NoSQL dichotomy may no longer be relevant." The \u 003cb\u 003eSpanner\u 003c/b\u 003e SQL query processor, while recognizable as a standard implementation, has unique ...",
" snippetStatus": " SUCCESS"
}
]
}
]
}
}
]
}
]
},
" session": {
" name": " projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943",
" state": " IN_PROGRESS",
" userPseudoId": " test_user",
" turns": [
{
" query": {
" queryId": " projects/123456/locations/global/questions/741830",
" text": " Compare bigquery with spanner database?"
},
" answer": " projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943/answers/4861507376861383072"
}
],
" startTime": " 2024 -09-13T18:47:10.465311Z",
" endTime": " 2024 -09-13T18:47:10.465311Z"
},
" answerQueryToken": " NMwKDAjFkpK3BhDU24uZAhIkNjZlNDIyZWYtMDAwMC0yMjVmLWIxMmQtZjQwMzA0M2FkYmNj"
}
Repeat step 3 for each new query in the session.
Get a session from the data store
The following command shows how to call the get
REST To get a session from a data store, do the following:
Run the following curl command:
curl -X GET -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /sessions/SESSION_ID "
Replace the following:
PROJECT_ID APP_ID SESSION_ID
Delete a session from the app
The following command shows how to call the delete
By default, sessions older than 60 days are automatically deleted.
However, if you want to delete a particular session—for example, if it
contains sensitive content, then use this API call to delete it.
REST To delete a session from an app, do the following:
Run the following curl command:
curl -X DELETE -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /sessions/SESSION_ID "
Replace the following:
PROJECT_ID APP_ID SESSION_ID
Example command and result
curl -X DELETE -H "Authorization: Bearer $(gcloud auth print-access-token)"
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943"
Update a session
There are various reasons that you might want to update a session. For example,
to do one of the following:
Mark a session as completed
Merge the messages from one session into another
Change a user's pseudo ID
The following command shows how to call the patch
REST To update a session from an app, do the following:
Run the following curl command:
curl -X PATCH \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /sessions/SESSION_ID ?updateMask=state" \
-d '{
"state": "NEW_STATE "
}'
Replace the following:
PROJECT_ID APP_ID SESSION_ID NEW_STATE IN_PROGRESS.
Example command and result
curl -X PATCH -H "Authorization: Bearer $(gcloud auth print-access-token)"
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943?updateMask=state"
-d '{
"state": "IN_PROGRESS"
}'
This example changes the state of the session to open (in progress). Follow a similar pattern to update the userPseudoId.
List all sessions
The following command shows how to call the list
REST To list the sessions for an app, do the following:
Run the following curl command:
curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /sessions"
Replace the following:
PROJECT_ID APP_ID
Example command and result
curl -X GET -H "Authorization: Bearer $(gcloud auth print-access-token)"
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/123456/locations/global/collections/default_collection/engines/my-app/sessions"
The response contains a list of sessions and the nextPageToken. If no
nextPageToken is returned, there are no more sessions to list. Default page
size is 50.
List sessions for a user
The following command shows how to call the list
REST To list sessions associated with a user or visitor, do the following:
Run the following curl command:
curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /sessions?filter=userPseudoId=USER_PSEUDO_ID "
Replace the following:
PROJECT_ID APP_ID USER_PSEUDO_ID
Example command and result
curl -X GET -H "Authorization: Bearer $(gcloud auth print-access-token)"
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/123456/locations/global/collections/default_collection/engines/my-app/sessions?filter=userPseudoId=test_user"
In this example, there is one session associated with the test_user. The
queries and answers in the session are listed.
List sessions for a user and state
The following command shows how to call the list
REST To list sessions for a user that are open or closed and associated with a given
user or visitor, do the following:
Run the following curl command:
curl -X GET -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /sessions?filter=userPseudoId=USER_PSEUDO_ID %20AND%20state=STATE "
Replace the following:
PROJECT_ID APP_ID USER_PSEUDO_ID STATE STATE_UNSPECIFIED (closed or unknown) or
IN_PROGRESS (open).
Example command and result
curl -X GET -H "Authorization: Bearer $(gcloud auth print-access-token)"
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/123456/locations/global/collections/default_collection/engines/my-app/sessions?filter=userPseudoId=test_user%20AND%20state=IN_PROGRESS"
The expected result is an empty response.
