Questa pagina introduce la ricerca con risposta e follow-up per Vertex AI Search e mostra come implementarla per le app di ricerca generica utilizzando le chiamate ai metodi.
Nota: le funzionalità di risposta e follow-up non possono essere applicate ai depositi di dati multimediali o sanitari.
La ricerca con risposta e domande di follow-up si basa sul metodo di risposta. Il metodo di risposta
sostituisce le funzionalità di sintesi del metodo search precedente
e tutte le funzionalità del metodo converse deprecato.
Il metodo di risposta presenta anche alcune funzionalità aggiuntive importanti, come la capacità di gestire query complesse.
Termine chiave: in questa pagina, il termine risposta fa riferimento a una risposta generata dall'AI che si basa sui risultati di ricerca di una query. È essenzialmente lo stesso del
riepilogo , che può essere generato dal metodo di ricerca.
Funzionalità del metodo di risposta
Le funzionalità principali del metodo di risposta sono le seguenti:
La capacità di generare risposte a query complesse. Ad esempio, il metodo answer può suddividere le query complesse, come la seguente, in più query più piccole per restituire risultati migliori che vengono utilizzati per fornire risposte migliori:
"Quali sono le rispettive entrate di Google Cloud e Google Ads nel 2024?"
"Dopo quanti anni dalla sua fondazione, Google ha raggiunto un miliardo di dollari di entrate?"
La possibilità di combinare la ricerca e la generazione di risposte in una conversazione con più turni chiamando il metodo di risposta in ogni turno.
La possibilità di accoppiarsi con il metodo di ricerca per ridurre la latenza della ricerca. Puoi chiamare il metodo di ricerca e il metodo di risposta separatamente e visualizzare i risultati di ricerca e le risposte in iframe diversi in momenti diversi. Ciò significa che puoi mostrare ai tuoi utenti i risultati di ricerca (i 10 link blu) in millisecondi. Non è necessario attendere che le risposte vengano generate prima di poter mostrare i risultati di ricerca.
Le funzionalità di risposta e follow-up possono essere suddivise in tre fasi della query,
della ricerca e della risposta:
Quando utilizzare la risposta e quando utilizzare la ricerca
Vertex AI Search dispone di due metodi utilizzati per eseguire query sulle app. Hanno funzionalità diverse, ma parzialmente sovrapposte.
Utilizza il metodo answer quando:
Vuoi una risposta (o un riepilogo) dei risultati di ricerca generata dall'AI.
Vuoi effettuare ricerche in più passaggi, ovvero ricerche che mantengono il contesto in modo da consentire domande di follow-up.
Utilizza il metodo search quando:
Ti servono solo i risultati di ricerca, non una risposta generata.
Vuoi che vengano restituiti più di dieci risultati di ricerca ("link blu").
Hai uno dei seguenti problemi:
Dati multimediali o sanitari
I tuoi embedding
Controlli sinonimi o reindirizzamento
Facet
Codici paese dell'utente
Utilizza i metodi di risposta e di ricerca insieme quando:
Vuoi restituire più di dieci risultati di ricerca e vuoi che venga generata una risposta.
Hai problemi di latenza e vuoi restituire e visualizzare rapidamente i risultati di ricerca
prima che venga restituita la risposta generata.
Funzionalità della fase di query
La funzionalità di risposta e follow-up supporta l'elaborazione delle query in linguaggio naturale.
Questa sezione descrive e illustra le varie opzioni per la riformulazione e la classificazione delle query.
Riformulazione delle query
La riformulazione delle query è attiva per impostazione predefinita. Questa funzionalità sceglie automaticamente il modo migliore per riformulare le query al fine di migliorare i risultati di ricerca. Questa funzionalità può gestire anche le query che non richiedono una nuova formulazione.
Suddividi le query complesse in più query ed esegui query secondarie sincrone.
Ad esempio, una query complessa viene suddivisa in quattro query più piccole e semplici.
Input utente
Subquery create dalla query complessa
Quali lavori e hobby hanno in comune Andie Ram e Arnaud
Clément?
Professione di Andie Ram
Professione di Arnaud Clément
Andie Ram hobby
Hobby di Arnaud Clément
Sintetizza le query con più turni per rendere le domande di follow-up sensibili al contesto e stateful.
Ad esempio, le query sintetizzate dall'input utente'utente a ogni turno potrebbero avere il seguente aspetto:
Input utente
Query sintetizzata
Primo turno: laptop per la scuola
laptop per la scuola
Turn 2: not mac
laptop per la scuola non mac
Terza svolta: schermo più grande e ho bisogno anche di tastiera e mouse wireless
laptop con schermo più grande per la scuola non Mac con tastiera e mouse wireless
Gira a 4: e uno zaino
laptop con schermo più grande per la scuola non Mac con tastiera e mouse wireless e uno zaino
Semplifica le query lunghe per migliorare il recupero.
Ad esempio, una query lunga viene abbreviata in una semplice query.
Input utente
Query semplificata
Sto cercando di capire perché il pulsante "Aggiungi al carrello"
sul nostro sito web non funziona correttamente. Sembra che quando un
utente fa clic sul pulsante, l'articolo non venga aggiunto al carrello e l'utente
riceva un messaggio di errore. Ho controllato il codice e sembra essere corretto, quindi non so quale potrebbe essere il problema. Puoi aiutarmi
a risolvere il problema?
Il pulsante "Aggiungi al carrello" non funziona sul sito web.
Eseguire ragionamenti in più passaggi
Termini chiave: i passaggi (chiamati anche hop ) vengono utilizzati per rispondere a domande complesse. La domanda è suddivisa in diversi passaggi di recupero e deduzione delle informazioni.
Il ragionamento multi-passaggio si basa sul paradigma ReAct (reason + act) che consente ai modelli linguistici di grandi dimensioni di risolvere attività complesse utilizzando il ragionamento in linguaggio naturale.
Per impostazione predefinita, il numero massimo di passaggi è cinque.
Ad esempio:
Input utente
Due passaggi per generare la risposta
Dopo quanti anni dalla sua fondazione Google ha raggiunto un fatturato di 1 miliardo di dollari?
Passaggio 1:
[Riflessione]: devo sapere quando è stata fondata Google, quindi posso eseguire una query sulle entrate da allora.
[Azione] Ricerca: quando è stata fondata Google? [Osserva i risultati di ricerca]: "1998"
Passaggio 2:
[Pensiero]: ora devo trovare le entrate annuali di Google
dal 1998 e scoprire quando hanno superato 1 miliardo per la prima volta.
[Azione] Ricerca: entrate di Google dal 1998
[Osserva i risultati di ricerca] Entrate di Google nel 1998, entrate di Google nel 1999…..
[Risposta]: Google ha raggiunto un fatturato di oltre 1 miliardo di dollari nel 2003 [1], 5 anni dopo la sua fondazione nel 1998 [2].
Classificazione delle query
Le opzioni di classificazione delle query consentono di identificare le query dirette e quelle che non cercano risposte. Per impostazione predefinita, le opzioni di classificazione delle query sono disattivate.
Per ulteriori informazioni sulle query ostili e di ricerca di risposte, consulta Ignorare le query ostili e Ignorare le query di ricerca di contenuti diversi dai riepiloghi .
Funzionalità della fase di ricerca
Per la ricerca, il metodo di risposta ha le stesse opzioni del metodo di ricerca. Ad
esempio:
Funzionalità della fase di risposta
Durante la fase di risposta, quando le risposte vengono generate dai risultati di ricerca, puoi attivare le stesse funzionalità del metodo di ricerca. Ad esempio:
Ulteriori funzionalità della fase di risposta non disponibili nel metodo di ricerca:
Ottenere un punteggio di supporto per ogni affermazione (frase nella risposta generata).
Un punteggio di supporto è un valore in virgola mobile nell'intervallo [0,1] che indica quanto la rivendicazione sia basata sui dati nello datastore. Per ulteriori informazioni, consulta Restituire i punteggi di assistenza per l'impianto di messa a terra .
Ottenere un punteggio di assistenza aggregato per la risposta. Il punteggio di supporto
indica quanto la risposta è basata sui dati nello datastore. Per maggiori informazioni, consulta Scori di assistenza per il reso in base al grounding .
Fornisci solo risposte ben fondate. Puoi scegliere di restituire solo le risposte che soddisfano una determinata soglia del punteggio di assistenza. Per ulteriori informazioni, consulta Mostrare solo risposte ben fondate .
Prima di iniziare
A seconda del tipo di app, completa i seguenti requisiti:
Se hai un'app di ricerca strutturata o non strutturata, assicurati che sia attivata la seguente funzionalità: Funzionalità LLM avanzate
Se hai un'app di ricerca di siti web, assicurati che siano attive le seguenti opzioni:
Se hai un'app di ricerca combinata (ovvero un'app collegata a più datastore), contatta il team dedicato all'Account Google e richiedi di essere aggiunto alla lista consentita per l'API Answer con la ricerca combinata.
Ricerca e risposta (di base)
Il seguente comando mostra come chiamare il metodo answer e
restituire una risposta generata e un elenco di risultati di ricerca, con link alle
fonti.
Questo comando mostra solo l'input richiesto. Le opzioni rimangono predefinite.
REST
Per eseguire ricerche e ottenere risultati con una risposta generata:
Esegui il seguente comando curl:
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 "}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca. Ad esempio, "Confrontare i database BigQuery e Spanner?".
Comando di esempio e risultato parziale
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?"}}'
{
"answer": {
"state": "SUCCEEDED",
"answerText": "The provided sources do not directly compare the speed of BigQuery and Spanner. However, they do highlight the performance capabilities of each database. BigQuery is described as having strong query performance, particularly for short and complex queries. It also offers a serverless architecture that provides consistent performance regardless of query complexity. Spanner is described as having high performance at virtually unlimited scale, with single-digit millisecond latency for strongly-consistent reads and writes. It also offers a five-nines availability SLA. Ultimately, the best database for a particular use case will depend on the specific requirements of the application. \n",
"steps": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": " 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 questo esempio, la query è decomposta in parti: "Quali sono le prestazioni di Spanner?" e "Quali sono le prestazioni di BigQuery?"
Comandi della fase di query
Questa sezione mostra come specificare le opzioni per la fase di query della chiamata al metodo
answer .
Ricerca e risposta (riformulazione disattivata)
Il seguente comando mostra come chiamare il metodo answer e
restituire una risposta generata e un elenco di risultati di ricerca. La risposta potrebbe essere diversa da quella precedente perché l'opzione di riformulazione è disattivata.
REST
Per eseguire ricerche e ottenere risultati con una risposta generata senza applicare la riformulazione della query, segui questi passaggi:
Esegui il seguente comando curl:
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
}
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca. Ad esempio, "Confrontare i database BigQuery e Spanner?".
Comando di esempio e risultato parziale
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 } }
}'
{
"answer": {
"state": "SUCCEEDED",
"answerText": "The sources provided do not directly compare the speed of BigQuery and Spanner. They do mention that Spanner is optimized for transactional workloads and BigQuery is optimized for analytical workloads. Spanner is a fully managed relational database that provides seamless replication across regions in Google Cloud. BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse. Spanner is designed to scale horizontally across multiple regions and continents. BigQuery is designed for business agility. \n",
"steps": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "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"
}
Ricerca e risposta (specifica il numero massimo di passaggi)
Il seguente comando mostra come chiamare il metodo answer e
restituire una risposta generata e un elenco di risultati di ricerca. La risposta è diversa dalle risposte precedenti perché è stato aumentato il numero di passaggi di riformulazione.
REST
Per eseguire ricerche e ottenere risultati con una risposta generata che consente fino a cinque passaggi di riformulazione, svolgi i seguenti passaggi:
Esegui il seguente comando curl:
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
}
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca. Ad esempio, "Confrontare i database BigQuery e Spanner?".
MAX_REPHRASE
: il numero massimo di passaggi per la riformulazione. Il valore massimo consentito è 5
.
Se non viene impostato o se viene impostato su un valore inferiore a 1
, il valore è predefinito,
1
.
Comando di esempio
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
}
}
}'
Ricerca e risposta con classificazione delle query
Il seguente comando mostra come chiamare il metodo answer per verificare se una query è ostile, non cerca una risposta o nessuna delle due.
La risposta include il tipo di classificazione per la query, ma la risposta stessa non è interessata dalla classificazione.
Se vuoi modificare il comportamento della risposta in base al tipo di query, puoi farlo nella fase di risposta. Consulta Ignorare le query dannose e Ignorare le query di ricerca non di riepilogo .
REST
Per determinare se una query è ostile o non cerca una risposta:
Esegui il seguente comando curl:
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 "]
}
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca. Ad esempio, "ciao".
QUERY_CLASSIFICATION_TYPE
: i tipi di query
che vuoi identificare: ADVERSARIAL_QUERY
,
NON_ANSWER_SEEKING_QUERY
o entrambi.
Comando di esempio e risultato parziale
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"]
}
}
}'
{
"answer": {
"state": "SUCCEEDED",
"answerText": "A user reported that their Google Voice account was randomly sending \"Hello!\" replies to incoming texts. The user was frustrated because they did not want to send these replies and found the behavior random. The user was unable to find any linked accounts, Google extensions, or other settings that could be causing the issue. The user confirmed that Google Voice does not have auto-reply functions. The user was seeking help to stop the automatic replies. \n",
"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/44fb313bcc09877e7239f3810ddb132b",
"uri": "https://support.google.com/mail/thread/58174131/gmail-sends-random-email-saying-hello-to-my-emails-without-me-touching-it?hl=en",
"title": "Gmail sends random email saying \"Hello!!\" to my emails without me touching it",
"snippetInfo": [
{
"snippet": "Gmail sends random email saying "\u003cb\u003eHello\u003c/b\u003e!!" to my emails without me touching it. Whenever I email somebody and they reply, a random email from my Gmail is sent to ...",
"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/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"
}
]
}
]
}
}
]
}
],
"queryUnderstandingInfo": {
"queryClassificationInfo": [
{
"type": "ADVERSARIAL_QUERY"
},
{
"type": "NON_ANSWER_SEEKING_QUERY",
"positive": true
}
]
}
},
"answerQueryToken": "NMwKDAjVloK3BhCdt8u9AhIkNjZkYmFhNWItMDAwMC0yZTBkLTg0ZDAtMDg5ZTA4MmRjYjg0"
}
In questo esempio, la query "ciao" non è ostile, ma è classificata come query che non richiede una risposta.
Comandi della fase di ricerca: cerca e rispondi con le opzioni dei risultati di ricerca
Questa sezione mostra come specificare le opzioni per la parte della fase di ricerca della chiamata al metodo answer , ad esempio l'impostazione del numero massimo di documenti restituiti, il potenziamento e i filtri, nonché come ottenere una risposta quando fornisci i tuoi risultati di ricerca.
Il comando seguente mostra come chiamare il metodo answer e
specificare varie opzioni per la modalità di restituzione del risultato di ricerca. I risultati di ricerca sono indipendenti dalla risposta.
REST
Per impostare varie opzioni relative a quali e come vengono restituiti i risultati di ricerca, svolgi i seguenti passaggi:
Esegui il seguente comando curl:
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
}
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca. Ad esempio, "Confrontare i database BigQuery e Spanner?"
MAX_RETURN_RESULTS
: il numero di risultati di ricerca da restituire. Il valore predefinito è 10.
FILTER
: il filtro specifica i documenti su cui viene eseguita la query. Se i metadati di un documento soddisfano la specifica del filtro, verrà eseguita una query sul documento. Per ulteriori informazioni, inclusa la sintassi dei filtri, consulta Filtra la ricerca generica per dati strutturati o non strutturati .
BOOST_SPEC
: la specifica del miglioramento consente di migliorare determinati documenti nei risultati di ricerca, il che può influire sulla risposta.
Per ulteriori informazioni, inclusa la sintassi per la specifica dell'incremento, consulta
Migliorare i risultati di ricerca .
ORDER_BY
: l'ordine in cui vengono restituiti i documenti. I documenti possono essere ordinati in base a un campo in un oggetto
Document . L'espressione orderBy
è sensibile alle maiuscole.
Se questo campo non è riconoscibile, viene restituito un valore INVALID_ARGUMENT
.
SEARCH_RESULT_MODE
: specifica la modalità di risultato di ricerca: DOCUMENTS
o CHUNKS
. Per ulteriori informazioni, consulta Eseguire l'analisi e suddividere i documenti e ContentSearchSpec .
Questo campo è disponibile solo nella versione v1alpha dell'API.
Comando e risultato di esempio
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 }
}
}'
{
"answer": {
"state": "SUCCEEDED",
"answerText": "Spanner database has an API that provides programmatic access to the database. The API is available through client libraries, RPC, and REST. The client libraries allow you to interact with Spanner in your preferred language. The RPC API and REST API provide programmatic access to Spanner. The Cloud Spanner API is a managed, mission-critical, globally consistent and scalable relational database service. \n",
"steps": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "Does spanner database have an API?"
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d135b46c4a44d0cc6b652538c1887f4d",
"uri": "https://cloud.google.com/spanner/docs/apis",
"title": "APIs & reference | Spanner | Google Cloud",
"snippetInfo": [
{
"snippet": "The client libraries, the RPC \u003cb\u003eAPI\u003c/b\u003e, and the REST \u003cb\u003eAPI\u003c/b\u003e provide programmatic access to \u003cb\u003eSpanner\u003c/b\u003e. \u003cb\u003eSpanner\u003c/b\u003e client libraries. \u003cb\u003eGet\u003c/b\u003e started with \u003cb\u003eSpanner\u003c/b\u003e in your language ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7a744d43e61ccd33539de74d5c1f6313",
"uri": "https://cloud.google.com/spanner/docs/reference/rest",
"title": "Cloud Spanner API",
"snippetInfo": [
{
"snippet": "Returns permissions that the caller \u003cb\u003ehas\u003c/b\u003e on the specified \u003cb\u003edatabase\u003c/b\u003e or backup resource. updateDdl, PATCH /v1/{\u003cb\u003edatabase\u003c/b\u003e=projects/*/instances/*/\u003cb\u003edatabases\u003c/b\u003e/*}/ddl",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/70834ebf4b72b6dc69e06c44ee80f90b",
"uri": "https://cloud.google.com/spanner/docs/reference/rpc",
"title": "Cloud Spanner API",
"snippetInfo": [
{
"snippet": "ChangeQuorum \u003cb\u003eis\u003c/b\u003e strictly restricted to \u003cb\u003edatabases\u003c/b\u003e ... Returns the schema of a Cloud \u003cb\u003eSpanner database\u003c/b\u003e ... Returns permissions that the caller \u003cb\u003ehas\u003c/b\u003e on the specified ...",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "NMwKDAj2l4K3BhCqiv66ARIkNjZkYmFhNmMtMDAwMC0yZTBkLTg0ZDAtMDg5ZTA4MmRjYjg0"
}
In questo esempio, il numero di documenti restituiti è limitato a tre.
Comandi della fase di risposta
Questa sezione mostra come specificare opzioni specifiche per la risposta per la chiamata del metodo answer .
Ignora le query contraddittorie e quelle che non cercano una risposta
Il seguente comando mostra come evitare di rispondere alle query dirette e alle query che non cercano una risposta quando viene chiamato il metodo answer .
REST
Per saltare la risposta alle query ostili o che non cercano una risposta, procedi come segue:
Esegui il seguente comando curl:
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
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca.
Comando di esempio e risultato parziale
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 }
}'
{
"answer": {
"state": "SUCCEEDED",
"answerText": "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 questo esempio, la query viene determinata come non in cerca di una risposta, pertanto non viene generata alcuna risposta.
Mostra solo risposte pertinenti
Vertex AI Search può valutare la pertinenza dei risultati per una query. Se nessun risultato viene considerato sufficientemente pertinente, anziché generare una risposta da risultati non pertinenti o minimamente pertinenti, puoi scegliere di restituire una risposta di riserva: "We do not have a summary for your query.
"
Il seguente comando mostra come restituire la risposta di riserva in caso di risultati irrilevanti quando viene chiamato il metodo answer .
REST
Per restituire una risposta di riserva se non vengono trovati risultati pertinenti, svolgi i seguenti passaggi:
Esegui il seguente comando curl:
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
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca.
Comando e risultato di esempio
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
} }'
{
"answer": {
"state": "SUCCEEDED",
"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 questo esempio, è stato stabilito che i risultati non sono sufficientemente pertinenti alla query, pertanto è stata restituita la risposta di riserva anziché una risposta e risultati generati.
Restituire i punteggi di supporto per l'isolamento a terra
Il seguente comando mostra come restituire i punteggi di supporto per le risposte
e le rivendicazioni.
Per informazioni generali sulla grounding in Vertex AI, consulta Verificare la grounding con RAG . Il metodo
groundingConfigs.check
viene chiamato dal metodo
answer.
REST
Per restituire un punteggio di supporto per ogni affermazione (frase nella risposta) e un
punteggio di supporto aggregato per la risposta, svolgi i seguenti passaggi:
Esegui il seguente comando curl:
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 "},
"groundingSpec": {
"includeGroundingSupports": true,
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca.
Comando di esempio e risultato parziale
curl -X POST -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/servingConfigs/default_search:answer"
-d '{
"query": { "text": "What is SQL?"},
"groundingSpec": {
"includeGroundingSupports": true,
}
}'
{
"answer": {
"state": "SUCCEEDED",
"answerText": "SQL stands for Structured Query Language. It is a database management programming language that is used to access and manage data in a database. SQL is used to create, update, and delete data in a database. It can also be used to query data and retrieve information. SQL is a standard language that is used by many different database systems.",
"groundingScore" 0.9
"groundingSupports": [
{
"endIndex": "41",
"sources": [
{
"referenceId": "1"
}
]
"groundingScore": 0.9
"groundingCheckRequired": true
},
{
"startIndex": "42",
"endIndex": "144",
"sources": [
{
"referenceId": "1"
}
]
"groundingScore": 0.8
"groundingCheckRequired": true
},
{
"startIndex": "267",
"endIndex": "342",
"sources": [
{
"referenceId": "2"
}
]
"groundingScore": 0.6
"groundingCheckRequired": true
}
],
"references": [
{
"chunkInfo": {
"content": "There are a lot of Databases available in the market such as MS Access, Oracle and many others.For you to write programs that interact with these databases easily, there has to be a way where you could get information from all these databases using the same method.For this purpose SQL was developed.It is a kind of language (simple when compared to the likes of C or C++) which enables you to ask all your queries to a database without bothering about the exact type of database.When you use this Query the database engine would first find the table called people.Then it would find a column called firstname.Next it would compare all the values in that column with 'Reena'.Finally it would return all the details wherever it finds a match for the firstname.When you write a database program in VC++ or Java or any other language for that matter, you would make a database connection to your database and then you would query the database using SQL queries.When you query the database with any SQL query the database returns a recordset.A recordset is basically a set of records (all the entries that your query returns).This recordset is received in your program and all languages have a data structure which represents a recordset.",
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d993d922043374f5ef7ba297c158b106",
"uri": "gs://my-bucket-123/documents/058dee0ec23a3e92f9bfd7cd29840e8f.txt"
"structData": {
"fields": [
{
"key": "cdoc_url"
"value": { "stringValue": "058dee0ec23a3e92f9bfd7cd29840e8f" }
},
{
"key": "doc_id"
"value": { "stringValue": "d993d922043374f5ef7ba297c158b106" }
}
]
}
}
}
},
{
"chunkInfo": {
"content": "The Structured Query Language (SQL) is a database management programming language.SQL is a tool for accessing databases, and more specifically, relational databases, and can be used with different database products.This chapter will prepare you to learn basic database management using this language.SQLite – To implement SQL as a library, you need SQLite.SQLite is intended to provide users and programs a way to store data using a SQL interface within the program.SQLite3 can be used to manipulate SQLite databases for major Linux distros.SQL is used to access relational databases.Each database contains more or less tables which in turn contain more or less rows and columns.Hereby a single row is seen as a separate object with features represented by the tables' columns.To access a table's data you first have to connect to its database.With the same table, the query SELECT * FROM T WHERE C1 = 1 will result in all the elements of all the rows where the value of column C1 is '1' being shown.A WHERE clause specifies that a SQL statement should only affect rows that meet specified criteria.The criteria are expressed in the form of predicates.WHERE clauses are not mandatory clauses of SQL statements, but should be used to limit the number of rows affected by a SQL DML statement or returned by a query.",
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3825eac51ef9e934bbc558faa42f4c71",
"uri": "gs://my-bucket-123/documents/26f5872b0719790cb966a697bfa1ea27.txt"
"structData": {
"fields": [
{
"key": "cdoc_url"
"value": { "stringValue": "26f5872b0719790cb966a697bfa1ea27" }
},
{
"key": "doc_id"
"value": { "stringValue": "3825eac51ef9e934bbc558faa42f4c71" }
}
]
}
}
}
},
{
"chunkInfo": {
"content": "This chapter focuses on using Paradox as a client/server development tool.It does not talk about connecting; it is assumed you have already connected.If you are having trouble connecting to a particular SQL server, then refer to the Connection Guide for that particular server.This chapter does review what a user can do interactively with Paradox and how to use ObjectPAL with SQL servers.Structured Query Language (SQL) was developed to create a standard for accessing database information.The ANSI standard for SQL allows a user to become familiar with the commands needed to query many different types of data.After you learn ANSI SQL, you then can query many different databases.Is SQL a solid standard?Yes and no.Yes, the core ANSI SQL commands are solid and consistent from vendor to vendor.Every vendor, however, adds capability to its version of SQL.These improvements are expected because ANSI SQL does not go far enough to cover every feature of every high-end DBMS.The SQL standard is used by many companies for their high-end products.They include Oracle, Sybase, Microsoft SQL, Informix, and Interbase.Paradox also provides the capability to use standard ANSI SQL commands on local Paradox and dBASE tables.Although SQL by definition is a standard, various flavors are on the market.",
"documentMetadata": {
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/b3e88db8676b87b99af1e6ecc7d8757f",
"uri": "gs://my-bucket-123/documents/073c21335d37d8d14982cb3437a721c0.txt"
"structData": {
"fields": [
{
"key": "cdoc_url"
"value": { "stringValue": "073c21335d37d8d14982cb3437a721c0" }
},
{
"key": "doc_id"
"value": { "stringValue": "b3e88db8676b87b99af1e6ecc7d8757f" }
}
]
}
}
}
}
],
...
"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/d993d922043374f5ef7ba297c158b106",
"uri": "gs://my-bucket-123/documents/058dee0ec23a3e92f9bfd7cd29840e8f.txt",
"chunkInfo": [
{
"content": "There are a lot of Databases available in the market such as MS Access, Oracle and many others.For you to write programs that interact with these databases easily, there has to be a way where you could get information from all these databases using the same method.For this purpose SQL was developed.It is a kind of language (simple when compared to the likes of C or C++) which enables you to ask all your queries to a database without bothering about the exact type of database.When you use this Query the database engine would first find the table called people.Then it would find a column called firstname.Next it would compare all the values in that column with 'Reena'.Finally it would return all the details wherever it finds a match for the firstname.When you write a database program in VC++ or Java or any other language for that matter, you would make a database connection to your database and then you would query the database using SQL queries.When you query the database with any SQL query the database returns a recordset.A recordset is basically a set of records (all the entries that your query returns).This recordset is received in your program and all languages have a data structure which represents a recordset."
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3825eac51ef9e934bbc558faa42f4c71",
"uri": "gs://my-bucket-123/documents/26f5872b0719790cb966a697bfa1ea27.txt",
"chunkInfo": [
{
"content": "The Structured Query Language (SQL) is a database management programming language.SQL is a tool for accessing databases, and more specifically, relational databases, and can be used with different database products.This chapter will prepare you to learn basic database management using this language.SQLite – To implement SQL as a library, you need SQLite.SQLite is intended to provide users and programs a way to store data using a SQL interface within the program.SQLite3 can be used to manipulate SQLite databases for major Linux distros.SQL is used to access relational databases.Each database contains more or less tables which in turn contain more or less rows and columns.Hereby a single row is seen as a separate object with features represented by the tables' columns.To access a table's data you first have to connect to its database.With the same table, the query SELECT * FROM T WHERE C1 = 1 will result in all the elements of all the rows where the value of column C1 is '1' being shown.A WHERE clause specifies that a SQL statement should only affect rows that meet specified criteria.The criteria are expressed in the form of predicates.WHERE clauses are not mandatory clauses of SQL statements, but should be used to limit the number of rows affected by a SQL DML statement or returned by a query."
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/b3e88db8676b87b99af1e6ecc7d8757f",
"uri": "gs://my-bucket-123/documents/073c21335d37d8d14982cb3437a721c0.txt",
"chunkInfo": [
{
"content": "This chapter focuses on using Paradox as a client/server development tool.It does not talk about connecting; it is assumed you have already connected.If you are having trouble connecting to a particular SQL server, then refer to the Connection Guide for that particular server.This chapter does review what a user can do interactively with Paradox and how to use ObjectPAL with SQL servers.Structured Query Language (SQL) was developed to create a standard for accessing database information.The ANSI standard for SQL allows a user to become familiar with the commands needed to query many different types of data.After you learn ANSI SQL, you then can query many different databases.Is SQL a solid standard?Yes and no.Yes, the core ANSI SQL commands are solid and consistent from vendor to vendor.Every vendor, however, adds capability to its version of SQL.These improvements are expected because ANSI SQL does not go far enough to cover every feature of every high-end DBMS.The SQL standard is used by many companies for their high-end products.They include Oracle, Sybase, Microsoft SQL, Informix, and Interbase.Paradox also provides the capability to use standard ANSI SQL commands on local Paradox and dBASE tables.Although SQL by definition is a standard, various flavors are on the market."
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3dd4014e41044c5dd6a0fe380847f369",
"uri": "gs://my-bucket-123/documents/76245cb33a66f4fbd9030a2a11eea00d.txt",
"chunkInfo": [
{
"content": "SQL injection is a code injection technique that might destroy your database.You can read more here OWASP sql injection testing sheet.Description: SQL injection ( second order) SQL injection vulnerabilities arise when user- controllable data is incorporated sheet into database SQL queries in an unsafe manner.This sheet cheat wiki assumes you have a basic understanding of SQL injection, please go here for an introduction if you are unfamiliar.Bypass login page with sql SQL injection [ closed].Gone are the days when knowledge sheet of just sql SQL Injection or XSS could help you land a lucrative high- paying InfoSec job.There is many sheet differnet variations you would login have probably have to try to make this exploit work ( sql especially if it is sql a blind SQL exploit).SQL injection usually occurs when you ask a user for input, like their.ゲストブック/ コメントの例.Submit Text Post.Get an ad- free experience with special benefits, and directly support Reddit.get reddit premium.SQL Injection Cheat.Many web applications have an authentication system: a user provides a user name and password, the web application checks them and stores the corresponding user id in the session hash.Login # 1 Login # 2 Login # 3 Login # 4."
}
]
}
]
}
}
]
}
]
}
}
In questo esempio, viene restituito un punteggio di supporto ("groundingScore") per ogni citata.
Mostra solo risposte ben fondate
Il seguente comando mostra come restituire solo le risposte ritenute ben fondate nel corpus , le informazioni nello datastore.
Le risposte poco fondate vengono filtrate.
Scegli una soglia di livello basso o alto per il punteggio di supporto alla messa a terra. La risposta viene poi restituita solo se soddisfa o supera questo livello. Puoi fare esperimenti con le due soglie di filtro e senza alcuna soglia per determinare quale livello di filtro è più probabile che fornisca i risultati migliori per i tuoi utenti.
Per informazioni generali sulla grounding in Vertex AI, consulta Verificare la grounding con RAG . Il metodo
groundingConfigs.check
viene chiamato dal metodo
answer.
REST
Per restituire una risposta solo se soddisfa una soglia del punteggio di assistenza, svolgi i seguenti passaggi:
Esegui il seguente comando curl:
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 "},
"groundingSpec": {
"filteringLevel": "FILTER_LEVEL "
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca.
FILTER_LEVEL
: un'enumerazione per filtrare le risposte in base al punteggio di assistenza per l'orientamento. Le opzioni sono:
FILTERING_LEVEL_LOW
e FILTERING_LEVEL_HIGH
. Se filteringLevel
non è incluso, alla risposta non viene applicato alcun filtro del punteggio di assistenza.
Comando di esempio e risultato parziale
curl -X POST -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/servingConfigs/default_search:answer" \
-d '{
"query": { "text": "When can an NCD be made?"},
"groundingSpec": {
"filtering_level": "FILTERING_LEVEL_HIGH"
}
}'
{
answer {
state: SUCCEEDED
answer_text: "We do not have a summary for your query."
steps {
state: SUCCEEDED
description: "Rephrase the query and search."
actions {
search_action {
query: "test?"
}
observation {
search_results {
document: "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7f5cfde02"
uri: "gs://my-bucket-123/data/CoverageDocumentation.pdf"
title: "ABC345_0101"
chunk_info {
content: "This notice implements part of section 731 of the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 by describing a method of developing, and making available to the public, guidance documents under the Medicare program… "
}
...
search_results {
document: "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7f5cfde02"
uri: "gs://my-bucket-123/data/CoverageDocumentation.pdf"
title: "ABC345_0101"
chunk_info {
content: "For the purposes of this notice, the term guidance documents means documents prepared for our staff, potential requestors of National Coverage Determinations, and other interested parties explaining the NCD process… "
}
}
}
}
}
answer_skipped_reasons: LOW_GROUNDED_CONTENT
}
In questo esempio non viene restituita alcuna risposta perché la soglia alta non è stata raggiunta.
Specifica il modello di risposta
Il seguente comando mostra come modificare la versione del modello utilizzata per generare le risposte.
Per informazioni sui modelli supportati, consulta Versioni e ciclo di vita dei modelli di generazione di risposte .
REST
Per generare una risposta utilizzando un modello diverso da quello predefinito:
Esegui il seguente comando curl:
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 ",
}
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca.
MODEL_VERSION
: la versione del modello che vuoi utilizzare per generare la risposta. Per ulteriori informazioni, consulta la sezione Versioni e ciclo di vita del modello di generazione di risposte .
Comando di esempio e risultato parziale
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",
}
} }'
{
"answer": {
"state": "SUCCEEDED",
"answerText": "Cloud Spanner is a fully managed relational database optimized for transactional workloads. BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility. BigQuery is optimized for ad-hoc analysis and reporting. Both Spanner and BigQuery are built on Google's distributed storage system, Colossus, and their internal cluster management system, Borg. They are also built on Jupiter, Google's in-house custom network hardware and software.\n\nBigQuery can query data stored in Spanner in real time without moving or copying the data. This is possible with BigQuery's query federation support. To run a federated query, you need to configure an external data source in BigQuery that points to the intended Spanner instance. You can then write queries that can be used to populate a BigQuery table on demand or scheduled to run as needed. You can also join the query with another BigQuery result set dynamically.\n\nYou can also use Dataflow to copy data from Spanner to BigQuery. Dataflow is a service that can be used to ingest Spanner data into BigQuery. This is useful for more complex transformations or external dependencies. For example, an online gaming company might use Spanner to store game data and BigQuery to perform analytics on player behavior. They can replicate data from Spanner into BigQuery and perform analytics against local data, or they can use federated queries to retrieve data from Spanner on-demand.\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/648c220055c1d2ac369165007d9f6650",
"uri": "https://cloud.google.com/blog/products/databases/choosing-cloud-spanner-for-game-development",
"title": "Choosing Cloud Spanner for game development | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "To get started with \u003cb\u003eSpanner\u003c/b\u003e, create a \u003cb\u003edatabase\u003c/b\u003e, or try it out with a \u003cb\u003eSpanner\u003c/b\u003e Qwiklab. ... AlloyDB \u003cb\u003evs\u003c/b\u003e. ... SQL for SQL Server to \u003cb\u003eBigQuery\u003c/b\u003e. By Alexander ...",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "NMwKDAivmvy2BhCxnsqdARIkNjZkOTQ0NWEtMDAwMC0yMTBkLTllNmItZjQwMzA0NWRiZDMw"
}
In questo esempio, viene utilizzata la versione di anteprima del modello anziché il modello predefinito.
Specifica un preambolo personalizzato
Il seguente comando mostra come impostare un preambolo per la risposta generata. Un preambulo contiene istruzioni in linguaggio naturale per personalizzare la risposta. Puoi richiedere personalizzazioni come lunghezza, livello di dettaglio, stile di output (ad es. "semplice"), lingua di output, punto focale della risposta e formato (ad es. tabelle, elenchi puntati e XML). Ad esempio, un preambolo potrebbe essere "Spiega come se fossi un bambino di dieci anni".
Il preambolo può avere un effetto significativo sulla qualità della risposta generata. Per informazioni su cosa scrivere nei preamboli ed esempi di preamboli efficaci, consulta Informazioni sui preamboli personalizzati .
REST
Per generare una risposta utilizzando un modello diverso da quello predefinito:
Esegui il seguente comando curl:
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 ",
}
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca.
PREAMBLE
: un'istruzione in linguaggio naturale per personalizzare la risposta. Ad esempio, prova show the answer format in
an ordered list
o give a very detailed answer
.
Comando e risultato di esempio
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 ",
}
}
}'
{
"answer": {
"state": "SUCCEEDED",
"answerText": "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 questo esempio, il preambolo richiede una risposta più semplice rispetto a quella che potrebbe essere fornita per impostazione predefinita.
Includi citazioni
Il seguente comando mostra come richiedere l'inclusione delle citazioni nella risposta.
REST
Per generare una risposta utilizzando un modello diverso da quello predefinito:
Esegui il seguente comando curl:
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
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca.
INCLUDE_CITATIONS
: specifica se includere
i metadati della citazione nella risposta. Il valore predefinito è false
.
Comando di esempio e risultato parziale
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
}
}'
{
"answer": {
"state": "SUCCEEDED",
"answerText": "SQL stands for Structured Query Language and is a programming language used to manage, query, and retrieve data in a relational database. It is the standard language used by relational database management systems (RDBMS) such as PostgreSQL, SQL Server, MySQL, and Oracle Database. SQL statements are written in a statement format for queries and other database operations, allowing users to manipulate data in relational database tables. SQL is used to create and update the structure of tables, read and write data, manage user permissions, and perform administrative tasks. While originally created for relational databases, SQL is now a foundation for many technology capabilities, making SQL knowledge essential for many technology roles. \n",
"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"
}
Impostare il codice lingua della risposta
Il seguente comando mostra come impostare il codice lingua per le risposte.
REST
Per generare una risposta utilizzando un modello diverso da quello predefinito:
Esegui il seguente comando curl:
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 "
}
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search su cui vuoi eseguire una query.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca.
ANSWER_LANGUAGE_CODE
: un codice lingua per la risposta. Utilizza i tag lingua definiti da BCP47: Tag per identificare le lingue .
Comando e risultato di esempio
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"
}
}'
{
"answer": {
"state": "SUCCEEDED",
"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 questo esempio, anche se i documenti di origine sono in inglese, la risposta viene fornita in spagnolo.
Comandi per le domande aggiuntive
I follow-up sono query con più turni. Dopo la prima query in una sessione di follow-up, le "volte" successive prendono in considerazione le interazioni precedenti. Con le domande di follow-up, il metodo di risposta può anche suggerire domande correlate, che gli utenti possono scegliere anziché inserire le proprie domande di follow-up.
Tutte le funzionalità di risposta e di follow-up descritte nelle sezioni precedenti, come citazioni, filtri, SafeSearch, l'ignorare determinati tipi di query e l'utilizzo di un preambolo per personalizzare le risposte, possono essere applicate insieme ai follow-up.
Esempio di sessione di follow-up
Di seguito è riportato un esempio di una sessione con follow-up. Supponiamo che tu voglia informazioni sulle vacanze in Messico:
Curva 1:
Tu: Qual è il periodo migliore dell'anno per fare una vacanza in Messico?
Rispondi con i follow-up: il periodo migliore per fare le vacanze in Messico è durante la stagione secca, da novembre ad aprile.
Gira 2:
Tu: Qual è il tasso di cambio?
Rispondi con domande di follow-up: 1 dollaro statunitense equivale a circa 17,65 pesos messicani.
Curva 3:
Senza domande successive, la tua domanda "Qual è il tasso di cambio?" non avrebbe risposta perché la ricerca normale non saprebbe che ti interessa il tasso di cambio messicano. Analogamente, senza i follow-up, non avremmo il contesto necessario per fornirti le temperature specifiche per il Messico.
Quando chiedi "Qual è il periodo migliore dell'anno per fare le vacanze in Messico?", oltre a rispondere alla tua domanda, la risposta e le domande di follow-up possono suggerire altre domande che potresti porre, ad esempio "Qual è il mese più economico per fare le vacanze in Messico?" e "Quali sono i mesi di alta stagione in Messico?".
Una volta attivata la funzionalità delle domande correlate, le domande vengono restituite come stringhe in ConverseConversationResponse .
Informazioni sulle sessioni
Per capire come funzionano i follow-up in Vertex AI Search, devi conoscere le sessioni.
Una sessione è composta da query di testo fornite da un
utente e da risposte fornite da Vertex AI Search.
Queste coppie di query e risposta sono a volte chiamate turn . Nell'esempio precedente, il secondo turno è composto da "Qual è il tasso di cambio?" e "1 dollaro statunitense equivale a circa 17,65 pesos messicani".
Le sessioni vengono archiviate con l'app.
Nell'app, una sessione è rappresentata dalla risorsa
sessione .
Oltre a contenere i messaggi di query e risposta, la risorsa sessione ha:
Un nome univoco (l'ID sessione).
Uno stato (in corso o completato).
Uno pseudo ID utente, ovvero un ID visitatore che monitora l'utente. Può essere assegnata in modo programmatico.
Un'ora di inizio e un'ora di fine.
Una svolta, ovvero una coppia di query e risposta.
Memorizzare le informazioni sulla sessione e ricevere risposte
Puoi utilizzare la riga di comando per generare risposte e risposte di ricerca
e per memorizzarle, insieme a ogni query in una sessione.
REST
Per utilizzare la riga di comando per creare una sessione e generare risposte dall'input dell'utente, segui questi passaggi:
Specifica l'app in cui vuoi archiviare la sessione:
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 "
}'
Sostituisci quanto segue:
PROJECT_ID : l'ID del tuo progetto Google Cloud.
APP_ID : l'ID dell'app Vertex AI Search.
USER_PSEUDO_ID : si tratta di un identificatore univoco per monitorare un visitatore proveniente dalla rete di ricerca. Ad esempio, puoi implementarlo con un cookie HTTP,
che identifica in modo univoco un visitatore su un singolo dispositivo.
Comando e risultato di esempio
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"
}
Prendi nota dell'ID sessione, i numeri alla fine del campo name:
nella risposta JSON. Nel risultato dell'esempio, l'ID è 5386462384953257772
.
Ti servirà questo ID nel passaggio successivo.
Genera una risposta e aggiungila a una sessione nella tua 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
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search.
QUERY
: una stringa di testo libero contenente la domanda o la query di ricerca.
SESSION_ID
: l'ID della sessione che hai creato nel passaggio 1. Si tratta delle cifre alla fine del
name:
campo, indicato nel passaggio 2. Per una sessione, utilizza lo stesso ID sessione in ogni turno.
FILTER
: un campo di testo per filtrare la ricerca
utilizzando un'espressione di filtro. Il valore predefinito è una stringa vuota. Il modo in cui crei il filtro varia a seconda che tu abbia dati non strutturati con metadati, dati strutturati o dati del sito web. Per ulteriori informazioni, consulta Filtrare la ricerca generica per dati strutturati o non strutturati e Filtrare la ricerca sul sito web .
Comando e risultato di esempio
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"
}
Ripeti il passaggio 3 per ogni nuova query nella sessione.
Recupera una sessione dal datastore
Il seguente comando mostra come chiamare il metodo get
e recuperare una sessione dalldatastore.
REST
Per ottenere una sessione da un datastore:
Esegui il seguente comando curl:
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 "
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search.
SESSION_ID
: l'ID della sessione che vuoi recuperare.
Eliminare una sessione dall'app
Il comando seguente mostra come chiamare il metodo delete
ed eliminare una sessione dall'datastore.
Per impostazione predefinita, le sessioni più vecchie di 60 giorni vengono eliminate automaticamente.
Tuttavia, se vuoi eliminare una determinata sessione, ad esempio se contiene contenuti sensibili, utilizza questa chiamata API per eliminarla.
REST
Per eliminare una sessione da un'app:
Esegui il seguente comando curl:
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 "
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search.
SESSION_ID
: l'ID della sessione che vuoi eliminare.
Comando e risultato di esempio
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"
{}
Aggiornare una sessione
Esistono diversi motivi per cui potresti voler aggiornare una sessione. Ad esempio,
per eseguire una delle seguenti operazioni:
Contrassegnare una sessione come completata
Unire i messaggi di una sessione in un'altra
Modificare lo pseudo ID di un utente
Il seguente comando mostra come chiamare il metodo patch
e
aggiornare una sessione nelldatastore.
REST
Per aggiornare una sessione da un'app:
Esegui il seguente comando curl:
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 "
}'
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search.
SESSION_ID
: l'ID della sessione che vuoi aggiornare.
NEW_STATE
: il nuovo valore per lo stato, ad esempio IN_PROGRESS
.
Comando e risultato di esempio
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"
}'
{
"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": "2024-09-13T18:49:41.579151Z"
}
Questo esempio modifica lo stato della sessione in Aperta (in corso). Segui una procedura simile per aggiornare userPseudoId
.
Elenca tutte le sessioni
Il seguente comando mostra come chiamare il metodo list
e elencare le sessioni nelldatastore.
REST
Per elencare le sessioni di un'app:
Esegui il seguente comando curl:
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"
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search.
Comando e risultato di esempio
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"
{
"sessions": [
{
"name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10000135306311111817",
"state": "IN_PROGRESS",
"turns": [
{
"query": {
"queryId": "projects/123456/locations/global/questions/10000135306311114276",
"text": "bugs reported by tiktok on grounding"
}
}
],
"startTime": "2024-09-03T00:38:40.338623Z",
"endTime": "2024-09-03T00:38:40.338623Z"
},
{
"name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10000827040519035859",
"state": "IN_PROGRESS",
"turns": [
{
"query": {
"queryId": "projects/123456/locations/global/questions/10000827040519033518",
"text": "GDM models"
}
}
],
"startTime": "2024-07-19T15:53:06.521775Z"
},
{
"name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10003910515245149877",
"state": "IN_PROGRESS",
"turns": [
{
"query": {
"queryId": "projects/123456/locations/global/questions/10003910515245148378",
"text": "gyorgyattila"
},
"answer": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10003910515245149877/answers/17036357111873257990"
}
],
"startTime": "2024-08-08T11:40:04.632463Z",
"endTime": "2024-08-08T11:40:04.632463Z"
},
...
{
"name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10198752942940073431",
"state": "IN_PROGRESS",
"turns": [
{
"query": {
"queryId": "projects/123456/locations/global/questions/10198752942940071818",
"text": "hello"
},
"answer": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10198752942940073431/answers/13411441797796265380"
}
],
"startTime": "2024-08-14T17:30:21.203439Z",
"endTime": "2024-08-14T17:30:21.203439Z"
}
],
"nextPageToken": "IDEDgIwL_vuieLC"
}
La risposta contiene un elenco di sessioni e il token nextPageToken. Se non viene restituito alcun valore nextPageToken, non ci sono altre sessioni da elencare. La dimensione predefinita della pagina è 50.
Elenca le sessioni per un utente
Il seguente comando mostra come chiamare il metodo list
per elencare le sessioni associate a un utente o un visitatore.
REST
Per elencare le sessioni associate a un utente o un visitatore:
Esegui il seguente comando curl:
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 "
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search.
USER_PSEUDO_ID
: lo pseudo ID dell'utente
di cui vuoi elencare le sessioni.
Comando e risultato di esempio
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"
{
"sessions": [
{
"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": "2024-09-13T18:49:41.579151Z"
}
]
}
In questo esempio, è presente una sessione associata a test_user. Le query e le risposte nella sessione sono elencate.
Elenca le sessioni per un utente e uno stato
Il seguente comando mostra come chiamare il metodo list
per elencare le sessioni in un determinato stato per un determinato utente.
REST
Per elencare le sessioni aperte o chiuse di un utente associate a un determinato utente o visitatore, procedi nel seguente modo:
Esegui il seguente comando curl:
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 "
Sostituisci quanto segue:
PROJECT_ID
: l'ID del tuo progetto Google Cloud.
APP_ID
: l'ID dell'app Vertex AI Search.
USER_PSEUDO_ID
: lo pseudo ID dell'utente
di cui vuoi elencare le sessioni.
STATE
: lo stato della sessione:
STATE_UNSPECIFIED
(chiusa o sconosciuta) o
IN_PROGRESS
(aperta).
Comando e risultato di esempio
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"
{
"sessions": [
{
"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": "2024-09-13T18:49:41.579151Z"
}
]
}
Il risultato previsto è una risposta vuota.