Halaman ini memperkenalkan penelusuran dengan jawaban dan tindak lanjut untuk Vertex AI Search dan
menunjukkan cara menerapkannya untuk aplikasi penelusuran umum menggunakan panggilan metode.
Catatan: Fitur jawaban dan tindak lanjut tidak dapat diterapkan ke penyimpanan data media atau
kesehatan. Penelusuran dengan jawaban dan tindak lanjut didasarkan pada metode jawaban. Metode jawaban
menggantikan fitur ringkasan dari metode penelusuran lama
dan semua fitur metode percakapan yang tidak digunakan lagi.
Metode jawaban juga memiliki beberapa fitur tambahan penting, seperti
kemampuan untuk menangani kueri yang kompleks.
Istilah Penting: Di halaman ini, istilah jawaban mengacu pada jawaban yang dibuat AI yang
didasarkan pada hasil penelusuran dari kueri. Pada dasarnya, ini sama dengan
ringkasan , yang dapat dihasilkan oleh metode penelusuran. Fitur metode jawaban
Fitur utama metode jawaban adalah sebagai berikut:
Kemampuan untuk menghasilkan jawaban atas kueri yang kompleks. Misalnya, metode jawaban
dapat menguraikan kueri gabungan, seperti berikut,
menjadi beberapa kueri yang lebih kecil untuk menampilkan hasil yang lebih baik yang digunakan untuk
memberikan jawaban yang lebih baik:
"Berapa pendapatan Google Cloud dan Google Ads masing-masing pada tahun 2024?"
"Berapa tahun setelah didirikan, Google mencapai pendapatan sebesar 1 miliar USD?"
Kemampuan untuk menggabungkan penelusuran dan pembuatan jawaban dalam percakapan
multi-giliran dengan memanggil metode jawaban di setiap giliran.
Kemampuan untuk disambungkan dengan metode penelusuran guna mengurangi latensi penelusuran. Anda
dapat memanggil metode penelusuran dan metode jawaban secara terpisah serta merender
hasil penelusuran dan jawaban dalam iframe yang berbeda pada waktu yang berbeda. Artinya,
Anda dapat menampilkan hasil penelusuran kepada pengguna (10 link biru) dalam
milidetik. Anda tidak perlu menunggu jawaban dibuat sebelum
dapat menampilkan hasil penelusuran.
Fitur jawaban dan tindak lanjut dapat dibagi menjadi tiga fase kueri,
penelusuran, dan jawaban:
Kapan harus menggunakan jawaban dan kapan harus menggunakan penelusuran
Vertex AI Search memiliki dua metode yang digunakan untuk membuat kueri aplikasi. Keduanya memiliki fitur yang berbeda tetapi
tumpang-tindih.
Gunakan metode answer jika:
Anda menginginkan jawaban (atau ringkasan) hasil penelusuran yang dibuat AI.
Anda menginginkan penelusuran multi-giliran, yaitu penelusuran yang menyimpan konteks sehingga memungkinkan
pertanyaan lanjutan.
Gunakan metode penelusuran saat:
Anda hanya memerlukan hasil penelusuran, bukan jawaban yang dibuat.
Anda ingin lebih dari sepuluh hasil penelusuran ("link biru") ditampilkan.
Anda memiliki salah satu hal berikut:
Data media atau layanan kesehatan
Embedding Anda sendiri
Kontrol sinonim atau pengalihan
Faset
Kode negara pengguna
Gunakan metode jawaban dan penelusuran secara bersamaan jika:
Anda ingin menampilkan lebih dari sepuluh hasil penelusuran, dan Anda menginginkan jawaban
yang dihasilkan.
Anda mengalami masalah latensi dan ingin menampilkan hasil penelusuran dengan cepat
sebelum jawaban yang dihasilkan ditampilkan.
Fitur fase kueri
Fitur jawaban dan tindak lanjut mendukung pemrosesan kueri bahasa alami.
Bagian ini menjelaskan dan mengilustrasikan berbagai opsi untuk klasifikasi dan penyusunan ulang kueri.
Pemutaran ulang kueri
Pemutaran ulang kueri aktif secara default. Fitur ini memilih cara terbaik untuk menyusun ulang
kueri secara otomatis guna meningkatkan hasil penelusuran. Fitur ini juga dapat menangani
kueri yang tidak memerlukan penyusunan ulang.
Membagi kueri kompleks menjadi beberapa kueri dan melakukan sub-kueri
sinkron.
Misalnya: Kueri kompleks dibagi menjadi empat kueri yang lebih kecil dan lebih sederhana.
Input pengguna
Subkueri yang dibuat dari kueri kompleks
Apa kesamaan pekerjaan dan hobi Andie Ram dan
Arnaud Clément?
Pekerjaan Andie Ram
Pekerjaan Arnaud Clément
Hobi Andie Ram
Hobi Arnaud Clément
Sintetisasikan kueri multi-giliran, untuk membuat pertanyaan lanjutan berbasis konteks
dan stateful.
Misalnya: Kueri yang disintesis dari input pengguna pada setiap giliran mungkin terlihat
seperti ini:
Input pengguna
Kueri disintesis
Putaran 1: laptop untuk sekolah
laptop untuk sekolah
Putaran 2: not mac
laptop untuk sekolah bukan mac
Putaran 3: layar lebih besar dan saya juga memerlukan keyboard dan mouse
nirkabel
laptop layar lebih besar untuk sekolah, bukan Mac, dengan keyboard dan mouse
nirkabel
Belokan 4: dan ransel untuknya
laptop layar lebih besar untuk sekolah, bukan mac dengan keyboard dan mouse
nirkabel, serta ransel untuk laptop
Sederhanakan kueri panjang untuk meningkatkan pengambilan.
Misalnya: Kueri panjang dipersingkat menjadi kueri sederhana.
Input pengguna
Kueri yang disederhanakan
Saya mencoba mencari tahu mengapa tombol \"Tambahkan ke Keranjang"
di situs kami tidak berfungsi dengan semestinya. Sepertinya saat
pengguna mengklik tombol, item tidak ditambahkan ke keranjang dan mereka
menerima pesan error. Saya telah memeriksa kodenya dan sepertinya sudah
benar, jadi saya tidak yakin apa masalahnya. Dapatkah Anda membantu saya
memecahkan masalah ini?
Tombol "Tambahkan ke Keranjang" tidak berfungsi di situs.
Melakukan penalaran multilangkah
Istilah Penting: Langkah (juga disebut sebagai hop )
digunakan untuk menjawab pertanyaan yang kompleks. Pertanyaan ini dibagi menjadi
beberapa langkah pengambilan dan inferensi informasi.
Penalaran multi-langkah didasarkan pada paradigma ReAct (reason + act) yang
memungkinkan LLM menyelesaikan tugas kompleks menggunakan penalaran bahasa alami.
Secara default, jumlah langkah maksimum adalah lima.
Contoh:
Input pengguna
Dua langkah untuk membuat jawaban
Berapa tahun setelah didirikan, Google mencapai pendapatan sebesar 1
miliar USD?
Langkah 1: Langkah 2:
Klasifikasi kueri
Opsi klasifikasi kueri adalah untuk mengidentifikasi kueri yang tidak sah dan kueri yang tidak mencari jawaban. Secara default, opsi klasifikasi kueri nonaktif.
Untuk informasi selengkapnya tentang kueri yang mencari jawaban dan kueri yang mencari informasi yang tidak relevan, lihat Mengabaikan kueri
yang mencari jawaban dan Mengabaikan kueri
yang mencari informasi yang tidak relevan .
Fitur fase penelusuran
Untuk penelusuran, metode jawaban memiliki opsi yang sama dengan metode penelusuran. Misalnya:
Fitur fase jawaban
Selama fase jawaban, saat jawaban dihasilkan dari hasil penelusuran, Anda
dapat mengaktifkan fitur yang sama seperti dalam metode penelusuran. Contoh:
Mendapatkan kutipan untuk menunjukkan
sumber untuk setiap kalimat dalam jawaban. Untuk mengetahui informasi
selengkapnya, lihat Menyertakan kutipan .
Gunakan pengantar perintah untuk menyesuaikan jawaban untuk hal-hal seperti gaya, gaya,
dan panjangnya.
Untuk informasi selengkapnya, lihat Menentukan awalan kustom .
Pilih model Vertex AI yang akan digunakan untuk pembuatan jawaban.
Untuk mengetahui informasi selengkapnya, lihat Versi dan siklus proses
model pembuatan jawaban .
Pilih apakah akan mengabaikan kueri yang telah diklasifikasikan sebagai kueri yang tidak mencari jawaban atau kueri yang bersifat permusuhan.
Untuk informasi selengkapnya tentang kueri yang mencari jawaban dan kueri yang mencari non-jawaban, lihat
Mengabaikan kueri yang mencari jawaban dan Mengabaikan
kueri yang mencari non-ringkasan . Kueri yang tidak mencari jawaban
juga disebut kueri yang tidak mencari ringkasan.
Fitur fase jawaban tambahan yang tidak tersedia dalam metode penelusuran
adalah:
Mendapatkan skor dukungan untuk setiap klaim (kalimat dalam jawaban yang dihasilkan).
Skor dukungan adalah nilai floating point dalam rentang [0,1] yang
menunjukkan seberapa kuat dasar klaim dalam data di penyimpanan data. Untuk informasi
selengkapnya, lihat Menampilkan skor dukungan grounding .
Mendapatkan skor dukungan gabungan untuk jawaban. Skor dukungan
menunjukkan seberapa baik jawaban didasarkan pada data di penyimpanan data. Untuk
mengetahui informasi selengkapnya, lihat Menampilkan skor dukungan
penjelasan .
Hanya menampilkan jawaban yang berdasar. Anda dapat memilih untuk hanya menampilkan jawaban
yang memenuhi nilai minimum skor dukungan tertentu. Untuk informasi selengkapnya,
lihat Hanya menampilkan jawaban yang berdasar .
Sebelum memulai
Bergantung pada jenis aplikasi yang Anda miliki, selesaikan persyaratan berikut:
Jika Anda memiliki aplikasi penelusuran terstruktur atau tidak terstruktur, pastikan hal berikut
diaktifkan: Fitur LLM lanjutan
Jika Anda memiliki aplikasi penelusuran situs, pastikan hal berikut diaktifkan:
Jika Anda memiliki aplikasi penelusuran gabungan (yaitu, aplikasi yang terhubung ke lebih
dari satu penyimpanan data), hubungi tim Akun Google Anda dan minta untuk ditambahkan ke
daftar yang diizinkan untuk API jawaban dengan penelusuran gabungan.
Menelusuri dan menjawab (dasar)
Perintah berikut menunjukkan cara memanggil metode answer dan
menampilkan jawaban yang dihasilkan serta daftar hasil penelusuran, dengan link ke
sumber.
Perintah ini hanya menampilkan input yang diperlukan. Opsi dibiarkan pada
default-nya.
REST Untuk menelusuri dan mendapatkan hasil dengan jawaban yang dihasilkan, lakukan hal berikut:
Jalankan perintah curl berikut:
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 "}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY
Contoh perintah dan hasil sebagian
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{"query": { "text": "Which database is faster, bigquery or spanner?"}}'
What is the performance of BigQuery? "
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/9ab3ef91bcfde1fcd091efe9df7c699c",
"uri": "https://cloud.google.com/bigquery/docs/best-practices-performance-overview",
"title": "Introduction to optimizing query performance | BigQuery | Google Cloud",
"snippetInfo": [
{
"snippet": "After a query begins execution, \u003cb\u003eBigQuery\u003c/b\u003e calculates how many slots each query stage uses based on the stage size and complexity and the number of slots ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/4e545c5cb69b06b251265114d9099cb4",
"uri": "https://cloud.google.com/bigquery/docs/query-insights",
"title": "Get query performance insights | BigQuery | Google Cloud",
"snippetInfo": [
{
"snippet": "This document describes how to use the query execution graph to diagnose query \u003cb\u003eperformance\u003c/b\u003e issues, and to see query \u003cb\u003eperformance\u003c/b\u003e insights. \u003cb\u003eBigQuery\u003c/b\u003e offers ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d34672d877eefe596f9c7d1a3d7076b1",
"uri": "https://cloud.google.com/bigquery/docs/best-practices-performance-compute",
"title": "Optimize query computation | BigQuery | Google Cloud",
"snippetInfo": [
{
"snippet": "After addressing the query \u003cb\u003eperformance\u003c/b\u003e insights, you can further optimize your query by performing the following tasks: Reduce data that is to be processed ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/75ce2f05833683e60ddc21a11ce0466f",
"uri": "https://cloud.google.com/blog/products/data-analytics/troubleshoot-and-optimize-your-bigquery-analytics-queries-with-query-execution-graph/",
"title": "Troubleshoot and optimize your BigQuery analytics queries with query execution graph | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "Since query \u003cb\u003eperformance\u003c/b\u003e is multi-faceted, \u003cb\u003eperformance\u003c/b\u003e insights might only provide a partial picture of the overall query \u003cb\u003eperformance\u003c/b\u003e. Execution graph. When ...",
"snippetStatus": "SUCCESS"
}
]
}
]
}
},
{
"searchAction": {
"query": " What is the performance of Spanner? "
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f3d036b60379873acf7c73081c5e5b5c",
"uri": "https://cloud.google.com/spanner/docs/performance",
"title": "Performance overview | Spanner | Google Cloud",
"snippetInfo": [
{
"snippet": "These \u003cb\u003eperformance\u003c/b\u003e improvements should result in higher throughput and better latency in \u003cb\u003eSpanner\u003c/b\u003e nodes in both regional and multi-region instance configurations.",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/422496248ade354c73b4c906b8eb9b5f",
"uri": "https://cloud.google.com/blog/products/databases/announcing-cloud-spanner-price-performance-updates",
"title": "Announcing Cloud Spanner price-performance updates | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "Alongside lower costs, Cloud \u003cb\u003eSpanner\u003c/b\u003e provides single-digit ms latencies and strong consistency across multiple availability zones in the same region.",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/53c2a1a6990480ba4aa05cc6b4404562",
"uri": "https://cloud.google.com/blog/topics/developers-practitioners/understanding-cloud-spanner-performance-metrics-scale-key-visualizer",
"title": "Understanding Cloud Spanner performance metrics at scale with Key Visualizer | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "Designed for \u003cb\u003eperformance\u003c/b\u003e tuning and instance sizing, you can use Key Visualizer today in the web-based Cloud Console for all \u003cb\u003eSpanner\u003c/b\u003e databases at no additional ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/a6501ecd5d6391e3ade49097bab0ad3a",
"uri": "https://cloud.google.com/blog/products/databases/a-technical-overview-of-cloud-spanners-query-optimizer",
"title": "A technical overview of Cloud Spanner's query optimizer | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "... performance. Typically, a join will ... Google is continuously improving out-of-the-box \u003cb\u003eperformance of Spanner\u003c/b\u003e and reducing the need for manual tuning.",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "NMwKDAj1_d62BhC72_X_AhIkNjZkN2I4YWEtMDAwMC0yYTdiLWIxMmMtMDg5ZTA4MjhlNzY0"
}
Dalam contoh ini, kueri diuraikan menjadi beberapa bagian: "Apa performa Spanner?" dan "Apa performa BigQuery?"
Perintah fase kueri
Bagian ini menunjukkan cara menentukan opsi untuk fase kueri dari
panggilan metode answer .
Menelusuri dan menjawab (pernyataan ulang dinonaktifkan)
Perintah berikut menunjukkan cara memanggil metode answer dan
menampilkan jawaban yang dihasilkan serta daftar hasil penelusuran. Jawabannya dapat
berbeda dengan jawaban sebelumnya karena opsi penyusunan ulang dinonaktifkan.
REST Untuk menelusuri dan mendapatkan hasil dengan jawaban yang dihasilkan tanpa menerapkan
pernyataan ulang kueri, lakukan hal berikut:
Jalankan perintah curl berikut:
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
}
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY
Contoh perintah dan hasil sebagian
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{
"query": { "text": "Which database is faster, bigquery or spanner?"},
"queryUnderstandingSpec": { "queryRephraserSpec": { "disable": true } }
}'
Which database is faster, bigquery or spanner? "
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/ecc0e7547253f4ca3ff3328ce89995af",
"uri": "https://cloud.google.com/blog/topics/developers-practitioners/how-spanner-and-bigquery-work-together-handle-transactional-and-analytical-workloads",
"title": "How Spanner and BigQuery work together to handle transactional and analytical workloads | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "A federated \u003cb\u003equery\u003c/b\u003e might not be as \u003cb\u003efast\u003c/b\u003e as querying local \u003cb\u003eBigQuery tables\u003c/b\u003e. There may be higher latency because of the small wait time for the source \u003cb\u003edatabase\u003c/b\u003e to ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d7e238f73608a860e00b752ef80e2941",
"uri": "https://cloud.google.com/blog/products/databases/cloud-spanner-gets-stronger-with-bigquery-federated-queries",
"title": "Cloud Spanner gets stronger with BigQuery-federated queries | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "... \u003cb\u003equick\u003c/b\u003e lookup on \u003cb\u003edata\u003c/b\u003e that's in \u003cb\u003eSpanner\u003c/b\u003e -- you can ... Set up an external \u003cb\u003edata\u003c/b\u003e source for the \u003cb\u003eSpanner\u003c/b\u003e shopping \u003cb\u003edatabase\u003c/b\u003e in \u003cb\u003eBigQuery\u003c/b\u003e. ... The \u003cb\u003equery\u003c/b\u003e is executed in ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f3d036b60379873acf7c73081c5e5b5c",
"uri": "https://cloud.google.com/spanner/docs/performance",
"title": "Performance overview | Spanner | Google Cloud",
"snippetInfo": [
{
"snippet": "The information on this page applies to both GoogleSQL and PostgreSQL \u003cb\u003edatabases\u003c/b\u003e. Note: We are in the process of rolling out \u003cb\u003eperformance\u003c/b\u003e and storage changes that ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/47b09cb5ad5e3ab3b1eb93d99ecb0896",
"uri": "https://cloud.google.com/blog/products/databases/rewe-uses-cloud-spanner-to-optimize-for-speed-and-performance",
"title": "REWE uses Cloud Spanner to optimize for speed and performance | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "As a fully managed relational \u003cb\u003edatabase\u003c/b\u003e, \u003cb\u003eSpanner\u003c/b\u003e provides unlimited scale, strong consistency, and up to 99.999% availability. By choosing this approach to ...",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "M8gKCwjp_t62BhC7wOFMEiQ2NmQ3YjhhZS0wMDAwLTJhN2ItYjEyYy0wODllMDgyOGU3NjQ"
}
Menelusuri dan menjawab (tentukan langkah maksimum)
Perintah berikut menunjukkan cara memanggil metode answer dan
menampilkan jawaban yang dihasilkan serta daftar hasil penelusuran. Jawabannya
berbeda dengan jawaban sebelumnya karena jumlah langkah penyusunan ulang
telah ditingkatkan.
REST Untuk menelusuri dan mendapatkan hasil dengan jawaban yang dihasilkan yang memungkinkan hingga lima langkah
pernyataan ulang, lakukan hal berikut:
Jalankan perintah curl berikut:
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
}
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY MAX_REPHRASE 5.
Jika tidak ditetapkan atau jika ditetapkan ke kurang dari 1, nilainya adalah default,
1.
Contoh perintah
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
}
}
}'
Menelusuri dan menjawab dengan klasifikasi kueri
Perintah berikut menunjukkan cara memanggil metode answer untuk
mengajukan pertanyaan apakah kueri bersifat antagonis, tidak mencari jawaban, atau keduanya.
Respons menyertakan jenis klasifikasi untuk kueri, tetapi jawaban itu sendiri tidak terpengaruh oleh klasifikasi.
Jika ingin mengubah perilaku jawaban sesuai dengan jenis kueri, Anda dapat
melakukannya pada fase jawaban. Lihat Mengabaikan kueri
yang bersifat permusuhan dan Mengabaikan kueri pencarian
yang bukan ringkasan .
REST Untuk menentukan apakah kueri bersifat antagonis atau tidak mencari jawaban, lakukan hal berikut:
Jalankan perintah curl berikut:
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 "]
}
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY QUERY_CLASSIFICATION_TYPE ADVERSARIAL_QUERY,
NON_ANSWER_SEEKING_QUERY, atau keduanya.
Contoh perintah dan hasil sebagian
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{
"query": {
"text": "Hello!"},
"queryUnderstandingSpec": {
"queryClassificationSpec": {
"types": ["ADVERSARIAL_QUERY", "NON_ANSWER_SEEKING_QUERY"]
}
}
}'
"type": "NON_ANSWER_SEEKING_QUERY",
"positive": true
}
]
}
},
"answerQueryToken": "NMwKDAjVloK3BhCdt8u9AhIkNjZkYmFhNWItMDAwMC0yZTBkLTg0ZDAtMDg5ZTA4MmRjYjg0"
}
Dalam contoh ini, kueri "halo" tidak bersifat permusuhan, tetapi diklasifikasikan sebagai
kueri yang tidak mencari jawaban.
Perintah fase penelusuran: Menelusuri dan menjawab dengan opsi hasil penelusuran
Bagian ini menunjukkan cara menentukan opsi untuk bagian fase penelusuran dari
panggilan metode answer , opsi seperti menetapkan jumlah maksimum
dokumen yang ditampilkan, pengoptimalan, dan pemfilteran, serta cara mendapatkan jawaban saat
Anda memberikan hasil penelusuran Anda sendiri.
Perintah berikut menunjukkan cara memanggil metode answer dan
menentukan berbagai opsi untuk cara hasil penelusuran ditampilkan. (Hasil
penelusuran tidak bergantung pada jawaban.)
REST Untuk menetapkan berbagai opsi yang terkait dengan hasil penelusuran yang ditampilkan dan caranya, lakukan
hal berikut:
Jalankan perintah curl berikut:
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
}
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY MAX_RETURN_RESULTS FILTER Memfilter penelusuran umum untuk data terstruktur atau tidak terstruktur .BOOST_SPEC Meningkatkan hasil penelusuran .ORDER_BY Dokumen . Ekspresi orderBy peka huruf besar/kecil.
Jika kolom ini tidak dapat dikenali, INVALID_ARGUMENT akan
ditampilkan.SEARCH_RESULT_MODE DOCUMENTS atau CHUNKS. Untuk informasi
selengkapnya, lihat
Mengurai dan
mengelompokkan dokumen serta ContentSearchSpec .
Kolom ini hanya tersedia di API versi v1alpha.
Contoh perintah dan hasil
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 }
}
}'
Dalam contoh ini, jumlah dokumen yang ditampilkan dibatasi hingga tiga.
Perintah fase jawaban
Bagian ini menunjukkan cara menentukan opsi khusus jawaban untuk
panggilan metode answer .
Mengabaikan kueri yang tidak sah dan kueri yang tidak mencari jawaban
Perintah berikut menunjukkan cara menghindari menjawab kueri yang berniat jahat dan
kueri yang tidak mencari jawaban saat memanggil metode answer .
REST Untuk melewati jawaban kueri yang bersifat permusuhan atau tidak mencari jawaban, lakukan hal berikut:
Jalankan perintah curl berikut:
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
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY
Contoh perintah dan hasil sebagian
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)"
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{
"query": { "text": "Hello"},
"answerGenerationSpec": {
"ignoreAdversarialQuery": true ,
"ignoreNonAnswerSeekingQuery": true }
}'
A summary could not be generated for your search query. Here are some search results. ",
"steps": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "Hello"
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/69e92e5b1de5b1e583fbe95f94dd4cbf",
"uri": "https://support.google.com/voice/thread/152245405/google-voice-is-randomly-automatically-sending-hello-replies-to-incoming-texts?hl=en",
"title": "Google voice is randomly/automatically sending \"Hello!\" replies to incoming texts",
"snippetInfo": [
{
"snippet": "There IS a new "Smart reply" feature on the Android or iOS client apps, but you'd have to a) receive a SMS/MMS, b) open it up, c) look at the three suggested ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/16d65e2af7fa854d1a00995525646dc3",
"uri": "https://support.google.com/voice/thread/112990484/google-voice-sending-hello-in-response-to-text-messages?hl=en",
"title": "Google Voice sending \"Hello,\" in response to text messages",
"snippetInfo": [
{
"snippet": "When I receive text messages, a reply is instantly sent out reading "\u003cb\u003eHello\u003c/b\u003e," and I cannot figure out how this is happening. I have no linked accounts, ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/b3bdde4957f588a1458c533269626d09",
"uri": "https://support.google.com/voice/thread/4307458/lately-an-automatic-text-response-saying-hello-is-going-out-how-do-i-stop-this?hl=en",
"title": "Lately an automatic text response saying, \"Hello\" is going out. How do I stop this? - Google Voice Community",
"snippetInfo": [
{
"snippet": "You need to find out what app is causing it and deactivate or delete it. Last edited Apr 16, 2019.",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/a828eb8f442f1dfbdda06dbeb52841b0",
"uri": "https://support.google.com/a/thread/161821861/hello-hello-the-lost-phone?hl=en",
"title": "Hello.Hello the lost phone - Google Workspace Admin Community",
"snippetInfo": [
{
"snippet": "\u003cb\u003eHello\u003c/b\u003e the lost phone. My wife lost her phone but she cannot remember her emails pasward to help track .",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
],
"answerSkippedReasons": [
"NON_ANSWER_SEEKING_QUERY_IGNORED"
]
},
"answerQueryToken": "NMwKDAjFgN-2BhDlsKaZARIkNjZkN2I0NmItMDAwMC0yZmQ5LTkwMDktZjQwMzA0M2E5YTg4"
}
Dalam contoh ini, kueri ditentukan sebagai kueri yang tidak mencari jawaban sehingga tidak ada
jawaban yang dihasilkan.
Hanya tampilkan jawaban yang relevan
Vertex AI Search dapat menilai relevansi hasil terhadap
kueri. Jika tidak ada hasil yang ditentukan cukup relevan, Anda dapat memilih untuk menampilkan jawaban penggantian: "We do not have a summary for your query.", bukan menghasilkan jawaban dari hasil yang tidak relevan atau relevan secara minimal.
Perintah berikut menunjukkan cara menampilkan jawaban penggantian jika
hasilnya tidak relevan saat memanggil metode answer .
REST Untuk menampilkan jawaban penggantian jika tidak ada hasil yang relevan yang ditemukan, lakukan
hal berikut:
Jalankan perintah curl berikut:
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
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY
Contoh perintah dan hasil
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{"query": { "text": "foobar"}, "answerGenerationSpec": {
"ignoreLowRelevantContent": true
} }'
"answerText": "We do not have a summary for your query.",
"steps": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "foobar"
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/719b79786f0c143717c569eade5305d9",
"uri": "https://support.google.com/websearch/thread/261714267/google-foobar-bug-console-disappeared?hl=en",
"title": "Google Foobar Bug - Console Disappeared",
"snippetInfo": [
{
"snippet": "Google \u003cb\u003eFoobar\u003c/b\u003e Bug - Console Disappeared. After I logged in today the top bar says "The \u003cb\u003eFoobar\u003c/b\u003e Challenge will be turned down on 1 April 2024. If you run out of ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/932369826585ff45f6ab3eba01ba6933",
"uri": "https://support.google.com/websearch/thread/95251114/unable-to-contact-foobar-recruiter?hl=en",
"title": "Unable to contact Foobar Recruiter - Google Search Community",
"snippetInfo": [
{
"snippet": "Access is by invitation only so you will need to have the proper credentials to login. You can always reach out using the contact us button, but there is no ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/fb736a30ff90d058be755f0a04a522a8",
"uri": "https://support.google.com/websearch/thread/121151780/foobar-challenge-appeared-to-me-then-disappeared?hl=en",
"title": "Foobar challenge appeared to me then disappeared - Google Search Community",
"snippetInfo": [
{
"snippet": "Hi. I got the \u003cb\u003efoobar\u003c/b\u003e challenge some months ago. But then it disappeared immediately, maybe by misclick (though I don't think I misclicked).",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f56f2656b0d02b839509d0e67e60c1c9",
"uri": "https://support.google.com/chrome/thread/159931759/cannot-access-google-foobar-challenge?hl=en",
"title": "Cannot Access Google FooBar Challenge",
"snippetInfo": [
{
"snippet": "I knew I wouldn't have time for it today, so I just kept the tab in the background. Tonight, I went to go close all my tabs, but the page had changed. It said " ...",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
],
"answerSkippedReasons": [
"NO_RELEVANT_CONTENT"
]
},
"answerQueryToken": "M8gKCwiokvy2BhDtv8EDEiQ2NmQ5NDQxZC0wMDAwLTIxMGQtOWU2Yi1mNDAzMDQ1ZGJkMzA"
}
Dalam contoh ini, hasil ditentukan tidak cukup relevan
dengan kueri, sehingga jawaban penggantian ditampilkan, bukan jawaban dan hasil
yang dihasilkan.
Menampilkan skor dukungan grounding
Perintah berikut menunjukkan cara menampilkan skor dukungan dasar untuk jawaban
dan klaim.
Untuk informasi umum tentang grounding di Vertex AI, lihat Memeriksa
grounding dengan RAG . Metode
groundingConfigs.check
REST Untuk menampilkan skor dukungan untuk setiap klaim (kalimat dalam jawaban) dan
skor dukungan gabungan untuk jawaban, lakukan hal berikut:
Jalankan perintah curl berikut:
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,
"returnGroundingScore": RETURN_GROUNDING_SCORE
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY RETURN_GROUNDING_SCORE true atau
false. Tetapkan ke true jika Anda ingin menampilkan
skor dukungan: skor gabungan untuk jawaban dan skor individual untuk
klaim. Jika tidak ditetapkan, nilainya adalah false.
Contoh perintah dan hasil sebagian
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,
"returnGroundingScore": true
}
}'
"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."
}
]
}
]
}
}
]
}
]
}
}
Dalam contoh ini, skor dukungan (`groundingScore`) ditampilkan untuk setiap
kutipan.
Hanya menampilkan jawaban yang berdasar
Perintah berikut menunjukkan cara hanya menampilkan jawaban yang dianggap
berdasarkan korpus , yaitu informasi dalam penyimpanan data.
Jawaban yang tidak berdasar akan difilter.
Anda dapat memilih nilai minimum tingkat rendah atau tinggi untuk skor dukungan pentanahan. Kemudian, jawaban
hanya ditampilkan jika memenuhi atau melebihi tingkat tersebut. Anda dapat bereksperimen dengan dua nilai minimum filter dan tanpa nilai minimum untuk menentukan tingkat filter yang kemungkinan akan memberikan hasil terbaik bagi pengguna.
Untuk informasi umum tentang grounding di Vertex AI, lihat Memeriksa
grounding dengan RAG . Metode
groundingConfigs.check
REST Untuk menampilkan jawaban hanya jika memenuhi nilai minimum skor dukungan, lakukan
hal berikut:
Jalankan perintah curl berikut:
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 "
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY FILTER_LEVEL FILTERING_LEVEL_LOW dan FILTERING_LEVEL_HIGH. Jika
returnGroundingScore tidak disertakan, tidak ada
filter skor dukungan yang diterapkan ke jawaban.
Contoh perintah dan hasil sebagian
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_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
}
Dalam contoh ini, tidak ada jawaban yang ditampilkan karena nilai minimum tinggi tidak terpenuhi.
Menentukan model jawaban
Perintah berikut menunjukkan cara mengubah versi model yang digunakan untuk membuat
jawaban.
Untuk mengetahui informasi tentang model yang didukung, lihat Versi dan siklus proses model pembuatan jawaban .
REST Untuk membuat jawaban menggunakan model yang berbeda dari model default, lakukan hal berikut:
Jalankan perintah curl berikut:
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 ",
}
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY MODEL_VERSION Versi dan siklus proses
model pembuatan jawaban .
Contoh perintah dan hasil sebagian
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",
}
} }'
Dalam contoh ini, versi pratinjau model digunakan, bukan model default.
Menentukan pengantar kustom
Perintah berikut menunjukkan cara menetapkan awalan untuk jawaban yang dihasilkan. Pengantar
berisi petunjuk bahasa alami untuk menyesuaikan jawaban. Anda
dapat meminta penyesuaian seperti durasi, tingkat detail, gaya output
(seperti "sederhana"), bahasa output, fokus jawaban, dan format (seperti
tabel, butir, dan XML). Misalnya, pengantarnya mungkin "Jelaskan seperti
Anda adalah anak berusia sepuluh tahun".
Pengantar dapat memiliki efek yang signifikan terhadap kualitas jawaban
yang dihasilkan. Untuk informasi tentang apa yang harus ditulis dalam pengantar dan contoh pengantar
yang baik, lihat Tentang pengantar kustom .
REST Untuk membuat jawaban menggunakan model yang berbeda dari model default, lakukan hal berikut:
Jalankan perintah curl berikut:
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 ",
}
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY PREAMBLE show the answer format in
an ordered list atau give a very detailed answer.
Contoh perintah dan hasil
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)"
-H "Content-Type: application/json"
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{
"query": { "text": "what is bigquery?"},
"answerGenerationSpec": {
"promptSpec": {
"preamble": "Explain like you are a ten years old
kid ",
}
}
}'
BigQuery is like a super-powered storage space for your data, but it's in the cloud, not on your computer. It's like a giant warehouse for all your information, but you don't have to build or manage it yourself. You can use BigQuery to find patterns and insights in your data, like figuring out what people like to buy or how many people visited your website. It's like having a super-smart assistant that can help you understand your data better. BigQuery is really good at working with lots of data, even billions of pieces of information. \n",
"steps ": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "What is BigQuery?"
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/2d032dc582689e8c0ecea7fc7bfa3189",
"uri": "https://cloud.google.com/bigquery",
"title": "BigQuery enterprise data warehouse | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eBigQuery\u003c/b\u003e is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/4474f4a5a18ecd611dedfe323dfe55d9",
"uri": "https://cloud.google.com/bigquery/docs/introduction",
"title": "BigQuery overview | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eBigQuery\u003c/b\u003e is a fully managed, AI-ready data platform that helps you manage and analyze your data with built-in features like machine learning, search, ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/c840fdef90d86328f13bbedbdbf0ac10",
"uri": "https://cloud.google.com/bigquery/docs/query-overview",
"title": "Overview of BigQuery analytics | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eBigQuery\u003c/b\u003e lets you save queries and share queries with others. When you save a query, it can be private (visible only to you), shared at the project level ( ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/5cbfab8ce1d5f6ffe45fef22900c9a00",
"uri": "https://cloud.google.com/curated-resources/bigquery",
"title": "Guides BigQuery | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eBigQuery\u003c/b\u003e is Google Cloud's fully managed and serverless enterprise data warehouse solution, designed to help you make informed decisions quickly, so you can ...",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "NMwKDAi8hN-2BhC0jMCPARIkNjZkN2I4MzItMDAwMC0yMTliLTkxN2EtMDg5ZTA4MjA0YjFj"
}
Dalam contoh ini, pengantar meminta jawaban yang lebih sederhana daripada yang mungkin
diberikan secara default.
Menyertakan kutipan
Perintah berikut menunjukkan cara meminta kutipan untuk disertakan dengan
jawaban.
REST Untuk membuat jawaban menggunakan model yang berbeda dari model default, lakukan hal berikut:
Jalankan perintah curl berikut:
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
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY INCLUDE_CITATIONS false.
Contoh perintah dan hasil sebagian
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{"query": { "text": "What is SQL"}, "answerGenerationSpec": {
"includeCitations": true
}
}'
"citations": [
{
"endIndex": "137",
"sources": [
{
"referenceId": "0"
},
{
"referenceId": "1"
}
]
},
{
"startIndex": "138",
"endIndex": "437",
"sources": [
{
"referenceId": "3"
}
]
},
{
"startIndex": "438",
"endIndex": "575",
"sources": [
{
"referenceId": "2"
}
]
},
{
"startIndex": "576",
"endIndex": "742",
"sources": [
{
"referenceId": "3"
}
]
}
],
"references": [
{
"chunkInfo": {
"content": "There may be a second table that stores visit information. A relational database uses a unique ID for each row to maintain the linked patient information across the two tables. This way, you can quickly look up the visits of each patient. Sign up for a free trial for any of Google Cloud's SQL databases, which include AlloyDB, Cloud SQL, and Spanner. Get started for free What is SQL? SQL (Structured Query Language) is a programming language used to store, retrieve, and manage data in a relational database. SQL statements are English-like, making the language accessible to software developers, data analysts, and other practitioners. Benefits of SQL databases Enterprises choose SQL databases for being: Efficient. Relational databases are incredibly efficient in managing complex queries. Fast. SQL databases can retrieve large amounts of data, quickly. This makes them highly desirable for real-time transactional data. Reliable. SQL databases provide a high degree of data integrity and are ACID-compliant. SQL database engines There are numerous SQL database engines (products) used to build software applications. Some of the most popular include PostgreSQL, MySQL, SQL Server, and Oracle. Some database engines are open source while others are commercial offerings. ",
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"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
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"title": "SQL Databases | Google Cloud"
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}
},
{
"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. ",
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"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. ",
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"title": "SQL Databases | Google Cloud"
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}
},
{
"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. ",
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"title": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud"
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}
},
{
"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. ",
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"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. ",
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{
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"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. ",
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],
"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"
}
Menetapkan kode bahasa jawaban
Perintah berikut menunjukkan cara menetapkan kode bahasa untuk jawaban.
REST Untuk membuat jawaban menggunakan model yang berbeda dari model default, lakukan hal berikut:
Jalankan perintah curl berikut:
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 "
}
}'
Ganti kode berikut:
PROJECT_ID APP_ID QUERY ANSWER_LANGUAGE_CODE BCP47: Tag untuk
Mengidentifikasi Bahasa .
Contoh perintah dan hasil
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
-d '{"query": { "text": "What is SQL"}, "answerGenerationSpec": {
"answerLanguageCode": "es"
}
}'
"answerText": "SQL, que significa Structured Query Language, es un lenguaje de programación utilizado para almacenar, recuperar y administrar datos en una base de datos relacional. Las instrucciones de SQL son similares al inglés, lo que hace que el lenguaje sea accesible para desarrolladores de software, analistas de datos y otros profesionales. Las bases de datos SQL se utilizan para administrar y almacenar datos para apoyar numerosos casos de uso empresariales, como la inteligencia empresarial, el procesamiento de transacciones, el análisis de datos y los servicios de aprendizaje automático. SQL es el lenguaje estándar utilizado por los sistemas de gestión de bases de datos relacionales (RDBMS), incluidos PostgreSQL, SQL Server, MySQL y Oracle Database. SQL se utiliza para crear y actualizar la estructura de las tablas, leer y escribir datos, administrar los permisos de los usuarios y realizar tareas administrativas. \n",
"steps": [
{
"state": "SUCCEEDED",
"description": "Rephrase the query and search.",
"actions": [
{
"searchAction": {
"query": "What is SQL?"
},
"observation": {
"searchResults": [
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
"uri": "https://cloud.google.com/discover/what-are-sql-databases",
"title": "SQL Databases | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eSQL\u003c/b\u003e (Structured Query Language) is a programming language used to store, retrieve, and manage data in a relational database. \u003cb\u003eSQL\u003c/b\u003e statements are English-like, ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7cd9afab1282a9f57cdcee1885bb4c6",
"uri": "https://cloud.google.com/learn/postgresql-vs-sql",
"title": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eSQL\u003c/b\u003e typically uses commands written in statement format for queries and other database operations, which allow users to manipulate data in relational database ...",
"snippetStatus": "SUCCESS"
}
]
},
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3afdede140d0906c2146a2f2b3a7821e",
"uri": "https://cloud.google.com/blog/topics/developers-practitioners/what-cloud-sql",
"title": "What is Cloud SQL? | Google Cloud Blog",
"snippetInfo": [
{
"snippet": "It is a fully managed relational database for MySQL, PostgreSQL and \u003cb\u003eSQL\u003c/b\u003e Server. It reduces maintenance cost and automates database provisioning, storage ...",
"snippetStatus": "SUCCESS"
}
]
},
...
{
"document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/0c5c094170756eeb6bdfec6eb5c7d081",
"uri": "https://cloud.google.com/spanner/docs/reference/standard-sql/overview",
"title": "The GoogleSQL language in Spanner | Google Cloud",
"snippetInfo": [
{
"snippet": "\u003cb\u003eGoogleSQL\u003c/b\u003e is the new name for Google Standard \u003cb\u003eSQL\u003c/b\u003e! New name, same great \u003cb\u003eSQL\u003c/b\u003e dialect. This page provides an overview of supported statements in \u003cb\u003eGoogleSQL\u003c/b\u003e.",
"snippetStatus": "SUCCESS"
}
]
}
]
}
}
]
}
]
},
"answerQueryToken": "NMwKDAjim_y2BhDftIjEAhIkNjZkOTQ0NWQtMDAwMC0yMTBkLTllNmItZjQwMzA0NWRiZDMw"
}
Dalam contoh ini, meskipun dokumen sumbernya dalam bahasa Inggris, jawabannya
diberikan dalam bahasa Spanyol.
Perintah untuk pertanyaan lanjutan
Pertanyaan lanjutan adalah kueri multi-giliran. Setelah kueri pertama dalam sesi tindak lanjut, "giliran" berikutnya akan mempertimbangkan interaksi sebelumnya. Dengan pertanyaan lanjutan, metode jawaban
juga dapat menyarankan pertanyaan terkait, yang dapat dipilih pengguna, bukan
memasukkan pertanyaan lanjutan mereka sendiri.
Semua fitur jawaban dan pertanyaan lanjutan yang dijelaskan di bagian sebelumnya, seperti
kutipan, filter, SafeSearch, mengabaikan jenis kueri tertentu, dan menggunakan
preamble untuk menyesuaikan jawaban dapat diterapkan bersama dengan pertanyaan lanjutan.
Contoh sesi lanjutan
Berikut adalah contoh sesi dengan tindak lanjut. Misalkan Anda ingin
mengetahui informasi tentang berlibur di Meksiko:
Putaran 1:
Anda: Kapan waktu terbaik untuk berlibur di Meksiko?
Jawab dengan tindak lanjut: Waktu terbaik untuk berlibur di Meksiko adalah selama musim kemarau, yang berlangsung dari November hingga April.
Giliran 2:
Anda: Berapa nilai tukarnya?
Jawab dengan tindak lanjut: 1 USD sama dengan sekitar 17,65 peso Meksiko.
Giliran 3:
Tanpa pertanyaan lanjutan, pertanyaan Anda "Berapa nilai tukarnya?" tidak akan
dapat dijawab karena penelusuran reguler tidak akan tahu bahwa Anda menginginkan nilai tukar
Meksiko. Demikian pula, tanpa tindak lanjut, tidak akan ada konteks
yang diperlukan untuk memberi Anda suhu khusus untuk Meksiko.
Saat Anda bertanya "Kapan waktu terbaik untuk berlibur di Meksiko?", selain menjawab pertanyaan Anda, jawaban dan pertanyaan lanjutan dapat menyarankan pertanyaan lain yang mungkin Anda ajukan, seperti "Apa bulan termurah untuk berlibur di Meksiko?" dan "Apa saja bulan turis di Meksiko?".
Setelah fitur pertanyaan terkait diaktifkan, pertanyaan akan ditampilkan sebagai
string di ConverseConversationResponse .
Tentang sesi
Untuk memahami cara kerja tindak lanjut di Vertex AI Search, Anda perlu memahami sesi.
Sesi terdiri dari kueri teks yang diberikan oleh pengguna dan respons yang diberikan oleh Vertex AI Search.
Pasangan kueri dan respons ini terkadang disebut sebagai giliran . Dalam
contoh sebelumnya, giliran kedua terdiri dari "Berapa nilai tukarnya?"
dan "1 USD sama dengan sekitar 17,65 peso Meksiko".
Sesi disimpan dengan aplikasi.
Dalam aplikasi, sesi diwakili oleh resource
sesi .
Selain berisi pesan kueri dan respons, resource sesi
memiliki:
Nama unik (ID sesi).
Status (sedang berlangsung atau selesai).
ID pseudo pengguna, yang merupakan ID pengunjung yang melacak pengguna. Nilai ini dapat
ditetapkan secara terprogram.
Waktu mulai dan waktu berakhir.
Giliran, yang merupakan pasangan jawaban kueri.
Menyimpan informasi sesi dan mendapatkan respons
Anda dapat menggunakan command line untuk membuat respons dan jawaban penelusuran
serta menyimpannya, bersama dengan setiap kueri dalam sesi.
REST Untuk menggunakan command line guna membuat sesi dan menghasilkan respons dari
input pengguna, ikuti langkah-langkah berikut:
Tentukan aplikasi tempat Anda ingin menyimpan sesi:
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 "
}'
Ganti kode berikut:
PROJECT_ID : ID project Google Cloud Anda.
APP_ID : ID aplikasi Vertex AI Search.
USER_PSEUDO_ID : Ini adalah ID unik untuk melacak pengunjung penelusuran. Misalnya, Anda dapat menerapkannya dengan cookie HTTP,
yang mengidentifikasi pengunjung secara unik di satu perangkat.
Contoh perintah dan hasil
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"
}
Catat ID sesi, angka di akhir kolom name: dalam
respons JSON. Dalam contoh hasil, ID-nya adalah 5386462384953257772.
Anda memerlukan ID ini di langkah berikutnya.
Buat jawaban dan tambahkan ke sesi di aplikasi Anda:
curl -X POST \
-H "Authorization: Bearer $( gcloud auth print-access-token) " \
-H "Content-Type: application/json" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /servingConfigs/default_search:answer" \
-d '{
"query": { "text": "QUERY "},
"session": "projects/PROJECT_ID /locations/global/collections/default_collection/engines/APP_ID /sessions/SESSION_ID ",
"searchSpec":{ "searchParams": {"filter": "FILTER "} }
}'
PROJECT_ID APP_ID QUERY SESSION_ID name:, yang dicatat pada langkah 2. Untuk sesi, gunakan ID sesi yang sama di setiap giliran.
FILTER Memfilter
penelusuran umum untuk data terstruktur atau tidak terstruktur dan Memfilter
penelusuran situs .
Contoh perintah dan hasil
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"
}
Ulangi langkah 3 untuk setiap kueri baru dalam sesi.
Mendapatkan sesi dari penyimpanan data
Perintah berikut menunjukkan cara memanggil metode get
REST Untuk mendapatkan sesi dari penyimpanan data, lakukan hal berikut:
Jalankan perintah curl berikut:
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 "
Ganti kode berikut:
PROJECT_ID APP_ID SESSION_ID
Menghapus sesi dari aplikasi
Perintah berikut menunjukkan cara memanggil metode delete
Secara default, sesi yang sudah lebih dari 60 hari akan otomatis dihapus.
Namun, jika Anda ingin menghapus sesi tertentu—misalnya, jika sesi tersebut
berisi konten sensitif, gunakan panggilan API ini untuk menghapusnya.
REST Untuk menghapus sesi dari aplikasi, lakukan langkah-langkah berikut:
Jalankan perintah curl berikut:
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 "
Ganti kode berikut:
PROJECT_ID APP_ID SESSION_ID
Contoh perintah dan hasil
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"
Memperbarui sesi
Ada berbagai alasan mengapa Anda mungkin ingin memperbarui sesi. Misalnya,
untuk melakukan salah satu hal berikut:
Menandai sesi sebagai selesai
Menggabungkan pesan dari satu sesi ke sesi lainnya
Mengubah ID pseudo pengguna
Perintah berikut menunjukkan cara memanggil metode patch
REST Untuk memperbarui sesi dari aplikasi, lakukan hal berikut:
Jalankan perintah curl berikut:
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 "
}'
Ganti kode berikut:
PROJECT_ID APP_ID SESSION_ID NEW_STATE IN_PROGRESS.
Contoh perintah dan hasil
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"
}'
Contoh ini mengubah status sesi menjadi terbuka (sedang berlangsung). Ikuti pola serupa untuk mengupdate userPseudoId.
Mencantumkan semua sesi
Perintah berikut menunjukkan cara memanggil metode list
REST Untuk mencantumkan sesi untuk aplikasi, lakukan hal berikut:
Jalankan perintah curl berikut:
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"
Ganti kode berikut:
PROJECT_ID APP_ID
Contoh perintah dan hasil
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"
Respons berisi daftar sesi dan nextPageToken. Jika tidak ada nextPageToken yang ditampilkan, tidak ada lagi sesi yang akan dicantumkan. Ukuran halaman default adalah 50.
Mencantumkan sesi untuk pengguna
Perintah berikut menunjukkan cara memanggil metode list
REST Untuk mencantumkan sesi yang terkait dengan pengguna atau pengunjung, lakukan hal berikut:
Jalankan perintah curl berikut:
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 "
Ganti kode berikut:
PROJECT_ID APP_ID USER_PSEUDO_ID
Contoh perintah dan hasil
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"
Dalam contoh ini, ada satu sesi yang terkait dengan test_user. Kueri dan jawaban dalam sesi akan dicantumkan.
Mencantumkan sesi untuk pengguna dan status
Perintah berikut menunjukkan cara memanggil metode list
REST Untuk mencantumkan sesi untuk pengguna yang terbuka atau ditutup dan dikaitkan dengan pengguna atau pengunjung tertentu, lakukan hal berikut:
Jalankan perintah curl berikut:
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 "
Ganti kode berikut:
PROJECT_ID APP_ID USER_PSEUDO_ID STATE STATE_UNSPECIFIED (tertutup atau tidak diketahui) atau
IN_PROGRESS (terbuka).
Contoh perintah dan hasil
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"
Hasil yang diharapkan adalah respons kosong.
