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Upload all project files and folders

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  1. .config/.last_opt_in_prompt.yaml +1 -0
  2. .config/.last_survey_prompt.yaml +1 -0
  3. .config/.last_update_check.json +1 -0
  4. .config/active_config +1 -0
  5. .config/config_sentinel +0 -0
  6. .config/configurations/config_default +6 -0
  7. .config/default_configs.db +0 -0
  8. .config/gce +1 -0
  9. .config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db +0 -0
  10. .config/logs/2025.07.10/13.33.45.486303.log +765 -0
  11. .config/logs/2025.07.10/13.34.14.671755.log +5 -0
  12. .config/logs/2025.07.10/13.34.23.548658.log +153 -0
  13. .config/logs/2025.07.10/13.34.29.179991.log +5 -0
  14. .config/logs/2025.07.10/13.34.37.820504.log +8 -0
  15. .config/logs/2025.07.10/13.34.38.537881.log +8 -0
  16. .gitattributes +5 -0
  17. chroma_db/chroma.sqlite3 +3 -0
  18. chroma_db/eecd79f6-a7d8-4f49-af31-af17bc062218/data_level0.bin +3 -0
  19. chroma_db/eecd79f6-a7d8-4f49-af31-af17bc062218/header.bin +3 -0
  20. chroma_db/eecd79f6-a7d8-4f49-af31-af17bc062218/index_metadata.pickle +3 -0
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  22. chroma_db/eecd79f6-a7d8-4f49-af31-af17bc062218/link_lists.bin +3 -0
  23. docstore/0067c469-f97a-4b83-8e90-f8f29ec88b9c +1 -0
  24. docstore/006c58ea-a83a-4cb5-8316-31c02cc01c70 +1 -0
  25. docstore/009bc6d7-eaa6-4ead-a174-e5d6f058dfea +1 -0
  26. docstore/00a07419-5f3b-46c3-9e8a-6541d35d620e +1 -0
  27. docstore/00b35170-69f6-403d-ad95-2ca64c3399f5 +1 -0
  28. docstore/00ceea18-bfce-4340-a019-233a30ab30f7 +1 -0
  29. docstore/0139abe7-0f1b-443f-b7a1-5f425686a7b8 +1 -0
  30. docstore/014320e7-ad22-4ecf-a6ed-2f461a6c1705 +1 -0
  31. docstore/014d1118-fffc-4f45-bacf-d5ff55838bf4 +1 -0
  32. docstore/01524be3-5603-4c0f-8afe-7a2590e3f4a2 +1 -0
  33. docstore/015f8fee-f8f3-4ad4-bfce-d523433342e7 +1 -0
  34. docstore/018cc2a1-fad6-4a7c-b90b-193c0662abcc +1 -0
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  36. docstore/01a3d852-18c4-4010-be44-de84fc65a7f8 +1 -0
  37. docstore/01b07962-66fe-4114-a824-4e927fbdf77f +1 -0
  38. docstore/01bd8c4d-19d8-43c2-90ff-0b99912b7b16 +1 -0
  39. docstore/01bf8b4d-c2e1-4afb-84ce-f3965f9c0d14 +1 -0
  40. docstore/01d25c80-25ba-4148-9cb6-0c19a722a776 +1 -0
  41. docstore/0201f6ab-5cd3-47a2-88b2-da4da07a432d +1 -0
  42. docstore/0210f190-906a-462c-863b-f39ab493f00c +1 -0
  43. docstore/021a0594-6ae7-4e26-bef3-5ac5494805b7 +1 -0
  44. docstore/021b3007-41c1-47b8-9b1f-63b9264653a2 +1 -0
  45. docstore/021bd55c-72f9-434d-9a17-e72a9244e94f +1 -0
  46. docstore/022e94bc-3fb7-474c-aa2d-790462d62cb0 +1 -0
  47. docstore/025538f8-2596-4e02-acd4-6e6c4b7a7880 +1 -0
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  50. docstore/026e3d2d-c1e8-4954-9946-86b8e7ea4710 +1 -0
.config/.last_opt_in_prompt.yaml ADDED
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+ {}
.config/.last_survey_prompt.yaml ADDED
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+ last_prompt_time: 1752154462.9134393
.config/.last_update_check.json ADDED
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+ {"last_update_check_time": 1752154468.6583943, "last_update_check_revision": 20250627154417, "notifications": [], "last_nag_times": {}}
.config/active_config ADDED
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+ default
.config/config_sentinel ADDED
File without changes
.config/configurations/config_default ADDED
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+ [component_manager]
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+ disable_update_check = true
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+
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+ [compute]
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+ gce_metadata_read_timeout_sec = 0
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+
.config/default_configs.db ADDED
Binary file (12.3 kB). View file
 
.config/gce ADDED
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+ False
.config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db ADDED
Binary file (12.3 kB). View file
 
.config/logs/2025.07.10/13.33.45.486303.log ADDED
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+ 2025-07-10 13:33:57,507 DEBUG root Loaded Command Group: ['gcloud', 'components']
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+ 2025-07-10 13:33:57,511 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
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+ 2025-07-10 13:33:57,513 DEBUG root Running [gcloud.components.update] with arguments: [--compile-python: "True", --quiet: "True", COMPONENT-IDS:6: "['core', 'gcloud-deps', 'bq', 'gcloud', 'gcloud-crc32c', 'gsutil']"]
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+ 2025-07-10 13:33:57,514 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
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+
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+ 2025-07-10 13:33:57,545 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2025-07-10 13:33:57,735 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/11" 200 239798
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+ 2025-07-10 13:33:57,747 INFO ___FILE_ONLY___
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+
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+ 2025-07-10 13:33:57,748 INFO ___FILE_ONLY___
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+ Your current Google Cloud CLI version is: 529.0.0
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+
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+ 2025-07-10 13:33:57,748 INFO ___FILE_ONLY___ Installing components from version: 529.0.0
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+
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+ 2025-07-10 13:33:57,748 INFO ___FILE_ONLY___
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+ 2025-07-10 13:33:57,748 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2025-07-10 13:33:57,748 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2025-07-10 13:33:57,749 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2025-07-10 13:33:57,786 INFO ___FILE_ONLY___ ┌─────────────────────────────────────────────────────────────────────────────┐
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+ 2025-07-10 13:33:57,786 INFO ___FILE_ONLY___
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+
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+ 2025-07-10 13:33:57,787 INFO ___FILE_ONLY___ │ These components will be installed. │
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+ 2025-07-10 13:33:57,787 INFO ___FILE_ONLY___
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+ 2025-07-10 13:33:57,795 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2025-07-10 13:33:59,394 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
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+ 2025-07-10 13:33:59,395 INFO ___FILE_ONLY___ Performing in place update...
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+ 2025-07-10 13:33:59,397 INFO ___FILE_ONLY___ ╠═ Downloading: BigQuery Command Line Tool ═╣
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+ 2025-07-10 13:33:59,400 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2025-07-10 13:34:05,571 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20250523104322.tar.gz HTTP/11" 200 2325
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+ 2025-07-10 13:34:05,789 INFO ___FILE_ONLY___ ═
472
+ 2025-07-10 13:34:05,789 INFO ___FILE_ONLY___ ═
473
+ 2025-07-10 13:34:05,789 INFO ___FILE_ONLY___ ╝
474
+
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+ 2025-07-10 13:34:05,791 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
476
+
477
+ 2025-07-10 13:34:05,791 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud cli dependencies (Platform Specific) ═╣
478
+
479
+ 2025-07-10 13:34:05,791 INFO ___FILE_ONLY___ ╚
480
+ 2025-07-10 13:34:05,794 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
481
+ 2025-07-10 13:34:06,857 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/11" 200 104
482
+ 2025-07-10 13:34:06,857 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
483
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484
+
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+ 2025-07-10 13:34:06,860 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
486
+
487
+ 2025-07-10 13:34:06,860 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣
488
+
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+ 2025-07-10 13:34:06,860 INFO ___FILE_ONLY___ ╚
490
+ 2025-07-10 13:34:06,972 INFO ___FILE_ONLY___ ═
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511
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530
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531
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541
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545
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546
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547
+ 2025-07-10 13:34:07,117 INFO ___FILE_ONLY___ ═
548
+ 2025-07-10 13:34:07,119 INFO ___FILE_ONLY___ ═
549
+ 2025-07-10 13:34:07,121 INFO ___FILE_ONLY___ ═
550
+ 2025-07-10 13:34:07,121 INFO ___FILE_ONLY___ ╝
551
+
552
+ 2025-07-10 13:34:07,129 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
553
+
554
+ 2025-07-10 13:34:07,129 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
555
+
556
+ 2025-07-10 13:34:07,129 INFO ___FILE_ONLY___ ╚
557
+ 2025-07-10 13:34:07,130 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
558
+ 2025-07-10 13:34:07,130 INFO ___FILE_ONLY___ ╝
559
+
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561
+
562
+ 2025-07-10 13:34:07,136 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 ═╣
563
+
564
+ 2025-07-10 13:34:07,136 INFO ___FILE_ONLY___ ╚
565
+ 2025-07-10 13:34:07,138 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
566
+ 2025-07-10 13:34:07,138 INFO ___FILE_ONLY___ ╝
567
+
568
+ 2025-07-10 13:34:07,140 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
569
+
570
+ 2025-07-10 13:34:07,140 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 (Platform Specific) ═╣
571
+
572
+ 2025-07-10 13:34:07,140 INFO ___FILE_ONLY___ ╚
573
+ 2025-07-10 13:34:09,407 INFO ___FILE_ONLY___ ═
574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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590
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591
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592
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594
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595
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602
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603
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604
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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625
+ 2025-07-10 13:34:11,861 INFO ___FILE_ONLY___ ═
626
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627
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628
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629
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630
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631
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632
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633
+ 2025-07-10 13:34:12,496 INFO ___FILE_ONLY___ ╝
634
+
635
+ 2025-07-10 13:34:12,564 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
636
+
637
+ 2025-07-10 13:34:12,564 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
638
+
639
+ 2025-07-10 13:34:12,564 INFO ___FILE_ONLY___ ╚
640
+ 2025-07-10 13:34:13,143 INFO ___FILE_ONLY___ ═
641
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642
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643
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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689
+ 2025-07-10 13:34:13,858 INFO ___FILE_ONLY___ ═
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+ 2025-07-10 13:34:13,875 INFO ___FILE_ONLY___ ═
691
+ 2025-07-10 13:34:13,889 INFO ___FILE_ONLY___ ═
692
+ 2025-07-10 13:34:13,904 INFO ___FILE_ONLY___ ═
693
+ 2025-07-10 13:34:13,919 INFO ___FILE_ONLY___ ═
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+ 2025-07-10 13:34:13,932 INFO ___FILE_ONLY___ ═
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+ 2025-07-10 13:34:13,948 INFO ___FILE_ONLY___ ═
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+ 2025-07-10 13:34:13,965 INFO ___FILE_ONLY___ ═
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+ 2025-07-10 13:34:13,987 INFO ___FILE_ONLY___ ═
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+ 2025-07-10 13:34:14,005 INFO ___FILE_ONLY___ ═
699
+ 2025-07-10 13:34:14,024 INFO ___FILE_ONLY___ ═
700
+ 2025-07-10 13:34:14,024 INFO ___FILE_ONLY___ ╝
701
+
702
+ 2025-07-10 13:34:14,056 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
703
+
704
+ 2025-07-10 13:34:14,056 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
705
+
706
+ 2025-07-10 13:34:14,056 INFO ___FILE_ONLY___ ╚
707
+ 2025-07-10 13:34:14,057 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
708
+ 2025-07-10 13:34:14,057 INFO ___FILE_ONLY___ ╝
709
+
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+ 2025-07-10 13:34:14,061 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
711
+
712
+ 2025-07-10 13:34:14,061 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
713
+
714
+ 2025-07-10 13:34:14,061 INFO ___FILE_ONLY___ ╚
715
+ 2025-07-10 13:34:14,064 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
716
+ 2025-07-10 13:34:14,064 INFO ___FILE_ONLY___ ╝
717
+
718
+ 2025-07-10 13:34:14,066 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
719
+
720
+ 2025-07-10 13:34:14,066 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
721
+
722
+ 2025-07-10 13:34:14,066 INFO ___FILE_ONLY___ ╚
723
+ 2025-07-10 13:34:14,067 INFO ___FILE_ONLY___ ══════════════════════════════
724
+ 2025-07-10 13:34:14,067 INFO ___FILE_ONLY___ ══════════════════════════════
725
+ 2025-07-10 13:34:14,067 INFO ___FILE_ONLY___ ╝
726
+
727
+ 2025-07-10 13:34:14,071 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
728
+
729
+ 2025-07-10 13:34:14,071 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
730
+
731
+ 2025-07-10 13:34:14,071 INFO ___FILE_ONLY___ ╚
732
+ 2025-07-10 13:34:14,074 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
733
+ 2025-07-10 13:34:14,074 INFO ___FILE_ONLY___ ╝
734
+
735
+ 2025-07-10 13:34:14,075 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
736
+
737
+ 2025-07-10 13:34:14,075 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣
738
+
739
+ 2025-07-10 13:34:14,076 INFO ___FILE_ONLY___ ╚
740
+ 2025-07-10 13:34:14,115 INFO ___FILE_ONLY___ ══════════════════════════════
741
+ 2025-07-10 13:34:14,116 INFO ___FILE_ONLY___ ══════════════════════════════
742
+ 2025-07-10 13:34:14,116 INFO ___FILE_ONLY___ ╝
743
+
744
+ 2025-07-10 13:34:14,121 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
745
+
746
+ 2025-07-10 13:34:14,121 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣
747
+
748
+ 2025-07-10 13:34:14,121 INFO ___FILE_ONLY___ ╚
749
+ 2025-07-10 13:34:14,121 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
750
+ 2025-07-10 13:34:14,121 INFO ___FILE_ONLY___ ╝
751
+
752
+ 2025-07-10 13:34:14,126 DEBUG root Updating notification cache...
753
+ 2025-07-10 13:34:14,127 INFO ___FILE_ONLY___
754
+
755
+ 2025-07-10 13:34:14,128 INFO ___FILE_ONLY___ Performing post processing steps...
756
+ 2025-07-10 13:34:14,129 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
757
+ 2025-07-10 13:34:22,739 DEBUG ___FILE_ONLY___
758
+ 2025-07-10 13:34:22,739 DEBUG ___FILE_ONLY___
759
+ 2025-07-10 13:34:22,909 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}]
760
+ 2025-07-10 13:34:22,909 INFO ___FILE_ONLY___
761
+ Update done!
762
+
763
+
764
+ 2025-07-10 13:34:22,912 DEBUG root Chosen display Format:none
765
+ 2025-07-10 13:34:22,912 INFO root Display format: "none"
.config/logs/2025.07.10/13.34.14.671755.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-07-10 13:34:14,672 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-07-10 13:34:14,674 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-07-10 13:34:14,676 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-07-10 13:34:22,616 DEBUG root Chosen display Format:none
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+ 2025-07-10 13:34:22,616 INFO root Display format: "none"
.config/logs/2025.07.10/13.34.23.548658.log ADDED
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1
+ 2025-07-10 13:34:23,549 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-07-10 13:34:23,551 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2025-07-10 13:34:23,553 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2025-07-10 13:34:23,554 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2025-07-10 13:34:23,562 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2025-07-10 13:34:23,747 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/11" 200 239798
8
+ 2025-07-10 13:34:23,761 WARNING root Component [compute] no longer exists.
9
+ 2025-07-10 13:34:23,762 INFO ___FILE_ONLY___
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+
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+ 2025-07-10 13:34:23,762 INFO ___FILE_ONLY___
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+ Your current Google Cloud CLI version is: 529.0.0
13
+
14
+ 2025-07-10 13:34:23,762 INFO ___FILE_ONLY___ Installing components from version: 529.0.0
15
+
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+ 2025-07-10 13:34:23,762 INFO ___FILE_ONLY___
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+
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+ 2025-07-10 13:34:23,763 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
19
+ 2025-07-10 13:34:23,763 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
20
+ 2025-07-10 13:34:23,763 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2025-07-10 13:34:23,778 INFO ___FILE_ONLY___ ┌────────────────────────────────────────────────┐
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+
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+ 2025-07-10 13:34:23,778 INFO ___FILE_ONLY___ │ These components will be installed. │
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+
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+ 2025-07-10 13:34:23,778 INFO ___FILE_ONLY___ │
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+ 2025-07-10 13:34:23,778 INFO ___FILE_ONLY___ gcloud Alpha Commands
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+ 2025-07-10 13:34:23,778 INFO ___FILE_ONLY___ 2025.06.27
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+ 2025-07-10 13:34:23,779 INFO ___FILE_ONLY___ │
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+
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+ 2025-07-10 13:34:23,779 INFO ___FILE_ONLY___ │
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+ 2025-07-10 13:34:23,779 INFO ___FILE_ONLY___ gcloud Beta Commands
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+ 2025-07-10 13:34:23,779 INFO ___FILE_ONLY___
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+ 2025-07-10 13:34:23,779 INFO ___FILE_ONLY___ │
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+ 2025-07-10 13:34:23,779 INFO ___FILE_ONLY___ │
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+
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+ 2025-07-10 13:34:23,779 INFO ___FILE_ONLY___ │
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+ 2025-07-10 13:34:23,779 INFO ___FILE_ONLY___ gcloud Preview Commands
62
+ 2025-07-10 13:34:23,780 INFO ___FILE_ONLY___
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+ 2025-07-10 13:34:23,780 INFO ___FILE_ONLY___ │
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+ 2025-07-10 13:34:23,780 INFO ___FILE_ONLY___
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+ 2025-07-10 13:34:23,780 INFO ___FILE_ONLY___ └─────────────────────────┴────────────┴─────────┘
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+
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+ 2025-07-10 13:34:23,780 INFO ___FILE_ONLY___
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+
77
+ 2025-07-10 13:34:23,783 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
78
+ 2025-07-10 13:34:24,873 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/RELEASE_NOTES HTTP/11" 200 1444035
79
+ 2025-07-10 13:34:25,334 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
80
+ https://cloud.google.com/sdk/release_notes
81
+
82
+
83
+ 2025-07-10 13:34:25,335 INFO ___FILE_ONLY___ Performing in place update...
84
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86
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+ 2025-07-10 13:34:25,337 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Alpha Commands ═╣
89
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+ 2025-07-10 13:34:25,337 INFO ___FILE_ONLY___ ╚
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+ 2025-07-10 13:34:25,340 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
92
+ 2025-07-10 13:34:26,465 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-alpha-20250627154417.tar.gz HTTP/11" 200 800
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+ 2025-07-10 13:34:26,465 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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98
+ 2025-07-10 13:34:26,467 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Beta Commands ═╣
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+ 2025-07-10 13:34:26,471 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
102
+ 2025-07-10 13:34:27,534 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-beta-20250627154417.tar.gz HTTP/11" 200 797
103
+ 2025-07-10 13:34:27,535 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+ 2025-07-10 13:34:27,537 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
107
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108
+ 2025-07-10 13:34:27,537 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Preview Commands ═╣
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110
+ 2025-07-10 13:34:27,537 INFO ___FILE_ONLY___ ╚
111
+ 2025-07-10 13:34:27,540 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
112
+ 2025-07-10 13:34:28,637 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-preview-20241115154308.tar.gz HTTP/11" 200 823
113
+ 2025-07-10 13:34:28,638 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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118
+ 2025-07-10 13:34:28,640 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Alpha Commands ═╣
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+ 2025-07-10 13:34:28,641 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+ 2025-07-10 13:34:28,647 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Beta Commands ═╣
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+ 2025-07-10 13:34:28,648 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+ 2025-07-10 13:34:28,652 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Preview Commands ═╣
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143
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+ 2025-07-10 13:34:28,660 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
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146
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+ 2025-07-10 13:34:37,191 INFO ___FILE_ONLY___
149
+ Update done!
150
+
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+ 2025-07-10 13:34:37,193 DEBUG root Chosen display Format:none
153
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.config/logs/2025.07.10/13.34.29.179991.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
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+ 2025-07-10 13:34:29,180 DEBUG root Loaded Command Group: ['gcloud', 'components']
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.config/logs/2025.07.10/13.34.37.820504.log ADDED
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.config/logs/2025.07.10/13.34.38.537881.log ADDED
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+ Gemini models | Gemini API | Google AI for Developers Skip to main content / English Deutsch Español – América Latina Français Indonesia Italiano Polski Português – Brasil Shqip Tiếng Việt Türkçe Русский עברית العربيّة فارسی हिंदी বাংলা ภาษาไทย 中文 – 简体 中文 – 繁體 日本語 한국어 Sign in Introducing Batch Mode, with higher rate limits and a 50% token discount. Learn more Home Gemini API Models Send feedback Gemini models 2.5 Pro spark Our most powerful thinking model with maximum response accuracy and state-of-the-art performance Input audio, images, video, and text, get text responses Tackle difficult problems, analyze large databases, and more Best for complex coding, reasoning, and multimodal understanding 2.5 Flash spark Our best model in terms of price-performance, offering well-rounded capabilities. Input audio, images, video, and text, and get text responses Model thinks as needed; or, you can configure a thinking budget Best for low latency, high volume tasks that require thinking 2.5 Flash-Lite experiment A Gemini 2.5 Flash model optimized for cost efficiency and low latency. Input audio, images, video, and text, and get text responses Most cost-efficient model supporting high throughput Best for real time, low latency use cases Note: Gemini 2.5 Pro and 2.5 Flash come with thinking on by default . If you're migrating from a non-thinking model such as 2.0 Pro or Flash, we recommend you to review the Thinking guide first. Model variants The Gemini API offers different models that are optimized for specific use cases. Here's a brief overview of Gemini variants that are available: Model variant Input(s) Output Optimized for Gemini 2.5 Pro gemini-2.5-pro Audio, images, videos, text, and PDF Text Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more Gemini 2.5 Flash gemini-2.5-flash Audio, images, videos, and text Text Adaptive thinking, cost efficiency Gemini 2.5 Flash-Lite Preview gemini-2.5-flash-lite-preview-06-17 Text, image, video,
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+ gemini-1.5-pro-002 calendar_month Latest update September 2024 Imagen 4 Imagen 4 is our latest image model, capable of generating highly detailed images with rich lighting, significantly better text rendering, and higher resolution output than previous models. Model details Property Description id_card Model code Gemini API imagen-4.0-generate-preview-06-06 imagen-4.0-ultra-generate-preview-06-06 save Supported data types Input Text Output Images token_auto Token limits [*] Input token limit 480 tokens (text) Output images 1 (Ultra) 1 to 4 (Standard) calendar_month Latest update June 2025 Imagen 3 Imagen 3 is our highest quality text-to-image model, capable of generating images with even better detail, richer lighting and fewer distracting artifacts than our previous models. Model details Property Description id_card Model code Gemini API imagen-3.0-generate-002 save Supported data types Input Text Output Images token_auto Token limits [*] Input token limit N/A Output images Up to 4 calendar_month Latest update February 2025 Veo 2 Veo 2 is our high quality text- and image-to-video model, capable of generating detailed videos, capturing the artistic nuance in your prompts. Model details Property Description id_card Model code Gemini API veo-2.0-generate-001 save Supported data types Input Text, image Output Video token_auto Limits Text input N/A Image input Any image resolution and aspect ratio up to 20MB file size Output video Up to 2 calendar_month Latest update April 2025 Gemini 2.5 Flash Live The Gemini 2.5 Flash Live model works with the Live API to enable low-latency bidirectional voice and video interactions with Gemini. The model can process text, audio, and video input, and it can provide text and audio output. Try in Google AI Studio Model details Property Description id_card Model code models/gemini-live-2.5-flash-preview save Supported data types Inputs Audio, video, and text Output Text, and audio token_auto Token limits [*] Input token limit 1,048,576
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+ ' $file_uri '}}] }] }' 2 > /dev/null > response.json cat response.json echo jq ".candidates[].content.parts[].text" response.json Get metadata for a file You can verify that the API successfully stored the uploaded file and get its metadata by calling files.get . Python myfile = client . files . upload ( file = 'path/to/sample.mp3' ) file_name = myfile . name myfile = client . files . get ( name = file_name ) print ( myfile ) JavaScript const myfile = await ai . files . upload ({ file : "path/to/sample.mp3" , config : { mimeType : "audio/mpeg" }, }); const fileName = myfile . name ; const fetchedFile = await ai . files . get ({ name : fileName }); console . log ( fetchedFile ); Go file , err := client . UploadFileFromPath ( ctx , "path/to/sample.mp3" , nil ) if err != nil { log . Fatal ( err ) } gotFile , err := client . GetFile ( ctx , file . Name ) if err != nil { log . Fatal ( err ) } fmt . Println ( "Got file:" , gotFile . Name ) REST # file_info.json was created in the upload example name = $( jq ".file.name" file_info.json ) # Get the file of interest to check state curl https://generativelanguage.googleapis.com/v1beta/files/ $name \ -H "x-goog-api-key: $GEMINI_API_KEY " > file_info.json # Print some information about the file you got name = $( jq ".file.name" file_info.json ) echo name = $name file_uri = $( jq ".file.uri" file_info.json ) echo file_uri = $file_uri List uploaded files You can upload multiple files using the Files API. The following code gets a list of all the files uploaded: Python print ( 'My files:' ) for f in client . files . list (): print ( ' ' , f . name ) JavaScript const listResponse = await ai . files . list ({ config : { pageSize : 10 } }); for await ( const file of listResponse ) { console . log ( file . name ); } Go iter := client . ListFiles ( ctx ) for { ifile , err := iter . Next () if err == iterator . Done { break } if err != nil { log . Fatal ( err ) } fmt . Println ( ifile . Name ) } REST echo "My files: " curl
docstore/00a07419-5f3b-46c3-9e8a-6541d35d620e ADDED
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+ URL: https://ai.google.dev/gemini-api/docs/billing Title: Billing | Gemini API | Google AI for Developers ==================================================
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+ state-of-the-art performance. Text embeddings are used to measure the relatedness of strings and are widely used in many AI applications. text-embedding-004 achieves a stronger retrieval performance and outperforms existing models with comparable dimensions, on the standard MTEB embedding benchmarks. Model details Property Description id_card Model code Gemini API models/text-embedding-004 save Supported data types Input Text Output Text embeddings token_auto Token limits [*] Input token limit 2,048 Output dimension size 768 swap_driving_apps_wheel Rate limits [**] 1,500 requests per minute encrypted Adjustable safety settings Not supported calendar_month Latest update April 2024 Embedding Note: Text Embedding is the newer version of the Embedding model. If you're creating a new project, use Text Embedding. You can use the Embedding model to generate text embeddings for input text. The Embedding model is optimized for creating embeddings with 768 dimensions for text of up to 2,048 tokens. Embedding model details Property Description id_card Model code models/embedding-001 save Supported data types Input Text Output Text embeddings token_auto Token limits [*] Input token limit 2,048 Output dimension size 768 swap_driving_apps_wheel Rate limits [**] 1,500 requests per minute encrypted Adjustable safety settings Not supported calendar_month Latest update December 2023 AQA You can use the AQA model to perform Attributed Question-Answering (AQA)–related tasks over a document, corpus, or a set of passages. The AQA model returns answers to questions that are grounded in provided sources, along with estimating answerable probability. Model details Property Description id_card Model code models/aqa save Supported data types Input Text Output Text language Supported language English token_auto Token limits [*] Input token limit 7,168 Output token limit 1,024 swap_driving_apps_wheel Rate limits [**] 1,500 requests per minute encrypted Adjustable safety settings Supported
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+ instructions, such as: "Show bounding boxes of all green objects in this image". It also support custom labels like "label the items with the allergens they can contain". For more examples, check following notebooks in the Gemini Cookbook : 2D spatial understanding notebook Experimental 3D pointing notebook Segmentation Starting with Gemini 2.5, models not only detect items but also segment them and provide their contour masks. The model predicts a JSON list, where each item represents a segmentation mask. Each item has a bounding box (" box_2d ") in the format [y0, x0, y1, x1] with normalized coordinates between 0 and 1000, a label (" label ") that identifies the object, and finally the segmentation mask inside the bounding box, as base64 encoded png that is a probability map with values between 0 and 255. The mask needs to be resized to match the bounding box dimensions, then binarized at your confidence threshold (127 for the midpoint). Note: For better results, disable thinking by setting the thinking budget to 0. See code sample below for an example. Python from google import genai from google.genai import types from PIL import Image , ImageDraw import io import base64 import json import numpy as np import os client = genai . Client () def parse_json ( json_output : str ): # Parsing out the markdown fencing lines = json_output . splitlines () for i , line in enumerate ( lines ): if line == "```json" : json_output = " \n " . join ( lines [ i + 1 :]) # Remove everything before "```json" output = json_output . split ( "```" )[ 0 ] # Remove everything after the closing "```" break # Exit the loop once "```json" is found return json_output def extract_segmentation_masks ( image_path : str , output_dir : str = "segmentation_outputs" ): # Load and resize image im = Image . open ( image_path ) im . thumbnail ([ 1024 , 1024 ], Image . Resampling . LANCZOS ) prompt = """ Give the segmentation masks for the wooden and glass items. Output a JSON list of segmentation masks
docstore/0139abe7-0f1b-443f-b7a1-5f425686a7b8 ADDED
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+ response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words. Temperature: The temperature controls the degree of randomness in token selection. The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Lower temperatures are good for prompts that require a more deterministic or less open-ended response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 is deterministic, meaning that the highest probability response is always selected. topK : The topK parameter changes how the model selects tokens for output. A topK of 1 means the selected token is the most probable among all the tokens in the model's vocabulary (also called greedy decoding), while a topK of 3 means that the next token is selected from among the 3 most probable using the temperature. For each token selection step, the topK tokens with the highest probabilities are sampled. Tokens are then further filtered based on topP with the final token selected using temperature sampling. topP : The topP parameter changes how the model selects tokens for output. Tokens are selected from the most to least probable until the sum of their probabilities equals the topP value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the topP value is 0.5, then the model will select either A or B as the next token by using the temperature and exclude C as a candidate. The default topP value is 0.95. stop_sequences : Set a stop sequence to tell the model to stop generating content. A stop sequence can be any sequence of characters. Try to avoid using a sequence of characters that may appear in the generated content. Prompt iteration strategies Prompt design can sometimes require a few iterations before you consistently get the response you're looking for. This section provides guidance on some things you can try when iterating on your prompts: Use different phrasing:
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+ where each entry contains the 2D bounding box in the key "box_2d", the segmentation mask in key "mask", and the text label in the key "label". Use descriptive labels. """ config = types . GenerateContentConfig ( thinking_config = types . ThinkingConfig ( thinking_budget = 0 ) # set thinking_budget to 0 for better results in object detection ) response = client . models . generate_content ( model = "gemini-2.5-flash" , contents = [ prompt , im ], # Pillow images can be directly passed as inputs (which will be converted by the SDK) config = config ) # Parse JSON response items = json . loads ( parse_json ( response . text )) # Create output directory os . makedirs ( output_dir , exist_ok = True ) # Process each mask for i , item in enumerate ( items ): # Get bounding box coordinates box = item [ "box_2d" ] y0 = int ( box [ 0 ] / 1000 * im . size [ 1 ]) x0 = int ( box [ 1 ] / 1000 * im . size [ 0 ]) y1 = int ( box [ 2 ] / 1000 * im . size [ 1 ]) x1 = int ( box [ 3 ] / 1000 * im . size [ 0 ]) # Skip invalid boxes if y0 > = y1 or x0 > = x1 : continue # Process mask png_str = item [ "mask" ] if not png_str . startswith ( "data:image/png;base64," ): continue # Remove prefix png_str = png_str . removeprefix ( "data:image/png;base64," ) mask_data = base64 . b64decode ( png_str ) mask = Image . open ( io . BytesIO ( mask_data )) # Resize mask to match bounding box mask = mask . resize (( x1 - x0 , y1 - y0 ), Image . Resampling . BILINEAR ) # Convert mask to numpy array for processing mask_array = np . array ( mask ) # Create overlay for this mask overlay = Image . new ( 'RGBA' , im . size , ( 0 , 0 , 0 , 0 )) overlay_draw = ImageDraw . Draw ( overlay ) # Create overlay for the mask color = ( 255 , 255 , 255 , 200 ) for y in range ( y0 , y1 ): for x in range ( x0 , x1 ): if mask_array [ y - y0 , x - x0 ] > 128 : # Threshold for mask overlay_draw . point (( x , y ), fill = color ) # Save individual mask and its overlay mask_filename = f " { item [ 'label' ] } _ { i } _mask.png"
docstore/014d1118-fffc-4f45-bacf-d5ff55838bf4 ADDED
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+ state-of-the-art performance. Text embeddings are used to measure the relatedness of strings and are widely used in many AI applications. text-embedding-004 achieves a stronger retrieval performance and outperforms existing models with comparable dimensions, on the standard MTEB embedding benchmarks. Model details Property Description id_card Model code Gemini API models/text-embedding-004 save Supported data types Input Text Output Text embeddings token_auto Token limits [*] Input token limit 2,048 Output dimension size 768 swap_driving_apps_wheel Rate limits [**] 1,500 requests per minute encrypted Adjustable safety settings Not supported calendar_month Latest update April 2024 Embedding Note: Text Embedding is the newer version of the Embedding model. If you're creating a new project, use Text Embedding. You can use the Embedding model to generate text embeddings for input text. The Embedding model is optimized for creating embeddings with 768 dimensions for text of up to 2,048 tokens. Embedding model details Property Description id_card Model code models/embedding-001 save Supported data types Input Text Output Text embeddings token_auto Token limits [*] Input token limit 2,048 Output dimension size 768 swap_driving_apps_wheel Rate limits [**] 1,500 requests per minute encrypted Adjustable safety settings Not supported calendar_month Latest update December 2023 AQA You can use the AQA model to perform Attributed Question-Answering (AQA)–related tasks over a document, corpus, or a set of passages. The AQA model returns answers to questions that are grounded in provided sources, along with estimating answerable probability. Model details Property Description id_card Model code models/aqa save Supported data types Input Text Output Text language Supported language English token_auto Token limits [*] Input token limit 7,168 Output token limit 1,024 swap_driving_apps_wheel Rate limits [**] 1,500 requests per minute encrypted Adjustable safety settings Supported
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+ gemini-1.5-pro-002 calendar_month Latest update September 2024 Imagen 4 Imagen 4 is our latest image model, capable of generating highly detailed images with rich lighting, significantly better text rendering, and higher resolution output than previous models. Model details Property Description id_card Model code Gemini API imagen-4.0-generate-preview-06-06 imagen-4.0-ultra-generate-preview-06-06 save Supported data types Input Text Output Images token_auto Token limits [*] Input token limit 480 tokens (text) Output images 1 (Ultra) 1 to 4 (Standard) calendar_month Latest update June 2025 Imagen 3 Imagen 3 is our highest quality text-to-image model, capable of generating images with even better detail, richer lighting and fewer distracting artifacts than our previous models. Model details Property Description id_card Model code Gemini API imagen-3.0-generate-002 save Supported data types Input Text Output Images token_auto Token limits [*] Input token limit N/A Output images Up to 4 calendar_month Latest update February 2025 Veo 2 Veo 2 is our high quality text- and image-to-video model, capable of generating detailed videos, capturing the artistic nuance in your prompts. Model details Property Description id_card Model code Gemini API veo-2.0-generate-001 save Supported data types Input Text, image Output Video token_auto Limits Text input N/A Image input Any image resolution and aspect ratio up to 20MB file size Output video Up to 2 calendar_month Latest update April 2025 Gemini 2.5 Flash Live The Gemini 2.5 Flash Live model works with the Live API to enable low-latency bidirectional voice and video interactions with Gemini. The model can process text, audio, and video input, and it can provide text and audio output. Try in Google AI Studio Model details Property Description id_card Model code models/gemini-live-2.5-flash-preview save Supported data types Inputs Audio, video, and text Output Text, and audio token_auto Token limits [*] Input token limit 1,048,576
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+ Gemini models | Gemini API | Google AI for Developers Skip to main content / English Deutsch Español – América Latina Français Indonesia Italiano Polski Português – Brasil Shqip Tiếng Việt Türkçe Русский עברית العربيّة فارسی हिंदी বাংলা ภาษาไทย 中文 – 简体 中文 – 繁體 日本語 한국어 Sign in Introducing Batch Mode, with higher rate limits and a 50% token discount. Learn more Home Gemini API Models Send feedback Gemini models 2.5 Pro spark Our most powerful thinking model with maximum response accuracy and state-of-the-art performance Input audio, images, video, and text, get text responses Tackle difficult problems, analyze large databases, and more Best for complex coding, reasoning, and multimodal understanding 2.5 Flash spark Our best model in terms of price-performance, offering well-rounded capabilities. Input audio, images, video, and text, and get text responses Model thinks as needed; or, you can configure a thinking budget Best for low latency, high volume tasks that require thinking 2.5 Flash-Lite experiment A Gemini 2.5 Flash model optimized for cost efficiency and low latency. Input audio, images, video, and text, and get text responses Most cost-efficient model supporting high throughput Best for real time, low latency use cases Note: Gemini 2.5 Pro and 2.5 Flash come with thinking on by default . If you're migrating from a non-thinking model such as 2.0 Pro or Flash, we recommend you to review the Thinking guide first. Model variants The Gemini API offers different models that are optimized for specific use cases. Here's a brief overview of Gemini variants that are available: Model variant Input(s) Output Optimized for Gemini 2.5 Pro gemini-2.5-pro Audio, images, videos, text, and PDF Text Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more Gemini 2.5 Flash gemini-2.5-flash Audio, images, videos, and text Text Adaptive thinking, cost efficiency Gemini 2.5 Flash-Lite Preview gemini-2.5-flash-lite-preview-06-17 Text, image, video,
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+ Gemini models | Gemini API | Google AI for Developers Skip to main content / English Deutsch Español – América Latina Français Indonesia Italiano Polski Português – Brasil Shqip Tiếng Việt Türkçe Русский עברית العربيّة فارسی हिंदी বাংলা ภาษาไทย 中文 – 简体 中文 – 繁體 日本語 한국어 Sign in Introducing Batch Mode, with higher rate limits and a 50% token discount. Learn more Home Gemini API Models Send feedback Gemini models 2.5 Pro spark Our most powerful thinking model with maximum response accuracy and state-of-the-art performance Input audio, images, video, and text, get text responses Tackle difficult problems, analyze large databases, and more Best for complex coding, reasoning, and multimodal understanding 2.5 Flash spark Our best model in terms of price-performance, offering well-rounded capabilities. Input audio, images, video, and text, and get text responses Model thinks as needed; or, you can configure a thinking budget Best for low latency, high volume tasks that require thinking 2.5 Flash-Lite experiment A Gemini 2.5 Flash model optimized for cost efficiency and low latency. Input audio, images, video, and text, and get text responses Most cost-efficient model supporting high throughput Best for real time, low latency use cases Note: Gemini 2.5 Pro and 2.5 Flash come with thinking on by default . If you're migrating from a non-thinking model such as 2.0 Pro or Flash, we recommend you to review the Thinking guide first. Model variants The Gemini API offers different models that are optimized for specific use cases. Here's a brief overview of Gemini variants that are available: Model variant Input(s) Output Optimized for Gemini 2.5 Pro gemini-2.5-pro Audio, images, videos, text, and PDF Text Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more Gemini 2.5 Flash gemini-2.5-flash Audio, images, videos, and text Text Adaptive thinking, cost efficiency Gemini 2.5 Flash-Lite Preview gemini-2.5-flash-lite-preview-06-17 Text, image, video,
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+ tokens for other Live API models Supported languages Live API supports the following languages. Note: Native audio output models automatically choose the appropriate language and don't support explicitly setting the language code. Language BCP-47 Code Language BCP-47 Code German (Germany) de-DE English (Australia)* en-AU English (UK)* en-GB English (India) en-IN English (US) en-US Spanish (US) es-US French (France) fr-FR Hindi (India) hi-IN Portuguese (Brazil) pt-BR Arabic (Generic) ar-XA Spanish (Spain)* es-ES French (Canada)* fr-CA Indonesian (Indonesia) id-ID Italian (Italy) it-IT Japanese (Japan) ja-JP Turkish (Turkey) tr-TR Vietnamese (Vietnam) vi-VN Bengali (India) bn-IN Gujarati (India)* gu-IN Kannada (India)* kn-IN Marathi (India) mr-IN Malayalam (India)* ml-IN Tamil (India) ta-IN Telugu (India) te-IN Dutch (Netherlands) nl-NL Korean (South Korea) ko-KR Mandarin Chinese (China)* cmn-CN Polish (Poland) pl-PL Russian (Russia) ru-RU Thai (Thailand) th-TH Languages marked with an asterisk (*) are not available for Native audio . What's next Read the Tool Use and Session Management guides for essential information on using the Live API effectively. Try the Live API in Google AI Studio . For more info about the Live API models, see Gemini 2.0 Flash Live and Gemini 2.5 Flash Native Audio on the Models page. Try more examples in the Live API cookbook , the Live API Tools cookbook , and the Live API Get Started script . 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. Last updated 2025-07-08 UTC.
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+ state-of-the-art performance. Text embeddings are used to measure the relatedness of strings and are widely used in many AI applications. text-embedding-004 achieves a stronger retrieval performance and outperforms existing models with comparable dimensions, on the standard MTEB embedding benchmarks. Model details Property Description id_card Model code Gemini API models/text-embedding-004 save Supported data types Input Text Output Text embeddings token_auto Token limits [*] Input token limit 2,048 Output dimension size 768 swap_driving_apps_wheel Rate limits [**] 1,500 requests per minute encrypted Adjustable safety settings Not supported calendar_month Latest update April 2024 Embedding Note: Text Embedding is the newer version of the Embedding model. If you're creating a new project, use Text Embedding. You can use the Embedding model to generate text embeddings for input text. The Embedding model is optimized for creating embeddings with 768 dimensions for text of up to 2,048 tokens. Embedding model details Property Description id_card Model code models/embedding-001 save Supported data types Input Text Output Text embeddings token_auto Token limits [*] Input token limit 2,048 Output dimension size 768 swap_driving_apps_wheel Rate limits [**] 1,500 requests per minute encrypted Adjustable safety settings Not supported calendar_month Latest update December 2023 AQA You can use the AQA model to perform Attributed Question-Answering (AQA)–related tasks over a document, corpus, or a set of passages. The AQA model returns answers to questions that are grounded in provided sources, along with estimating answerable probability. Model details Property Description id_card Model code models/aqa save Supported data types Input Text Output Text language Supported language English token_auto Token limits [*] Input token limit 7,168 Output token limit 1,024 swap_driving_apps_wheel Rate limits [**] 1,500 requests per minute encrypted Adjustable safety settings Supported
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+ client-side (browser based) applications // Consider using Ephemeral Tokens instead // More information at: https://ai.google.dev/gemini-api/docs/ephemeral-tokens // Half cascade model: // const model = "gemini-live-2.5-flash-preview" // Native audio output model: const model = "gemini-2.5-flash-preview-native-audio-dialog" const config = { responseModalities : [ Modality . AUDIO ], systemInstruction : "You are a helpful assistant and answer in a friendly tone." }; async function live () { const responseQueue = []; async function waitMessage () { let done = false ; let message = undefined ; while ( ! done ) { message = responseQueue . shift (); if ( message ) { done = true ; } else { await new Promise (( resolve ) = > setTimeout ( resolve , 100 )); } } return message ; } async function handleTurn () { const turns = []; let done = false ; while ( ! done ) { const message = await waitMessage (); turns . push ( message ); if ( message . serverContent && message . serverContent . turnComplete ) { done = true ; } } return turns ; } const session = await ai . live . connect ({ model : model , callbacks : { onopen : function () { console . debug ( 'Opened' ); }, onmessage : function ( message ) { responseQueue . push ( message ); }, onerror : function ( e ) { console . debug ( 'Error:' , e . message ); }, onclose : function ( e ) { console . debug ( 'Close:' , e . reason ); }, }, config : config , }); // Send Audio Chunk const fileBuffer = fs . readFileSync ( "sample.wav" ); // Ensure audio conforms to API requirements (16-bit PCM, 16kHz, mono) const wav = new WaveFile (); wav . fromBuffer ( fileBuffer ); wav . toSampleRate ( 16000 ); wav . toBitDepth ( "16" ); const base64Audio = wav . toBase64 (); // If already in correct format, you can use this: // const fileBuffer = fs.readFileSync("sample.pcm"); // const base64Audio = Buffer.from(fileBuffer).toString('base64'); session . sendRealtimeInput ( { audio : { data : base64Audio , mimeType : "audio/pcm;rate=16000" } } );
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+ AUDIO_PATH } " 2 > /dev/null > file_info.json file_uri = $( jq ".file.uri" file_info.json ) echo file_uri = $file_uri # Now generate content using that file curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent" \ -H "x-goog-api-key: $GEMINI_API_KEY " \ -H 'Content-Type: application/json' \ -X POST \ -d '{ "contents": [{ "parts":[ {"text": "Describe this audio clip"}, {"file_data":{"mime_type": "${MIME_TYPE}", "file_uri": ' $file_uri '}}] }] }' 2 > /dev/null > response.json cat response.json echo jq ".candidates[].content.parts[].text" response.json To learn more about working with media files, see Files API . Pass audio data inline Instead of uploading an audio file, you can pass inline audio data in the request to generateContent : Python from google.genai import types with open ( 'path/to/small-sample.mp3' , 'rb' ) as f : audio_bytes = f . read () response = client . models . generate_content ( model = 'gemini-2.5-flash' , contents = [ 'Describe this audio clip' , types . Part . from_bytes ( data = audio_bytes , mime_type = 'audio/mp3' , ) ] ) print ( response . text ) JavaScript import { GoogleGenAI } from "@google/genai" ; import * as fs from "node:fs" ; const ai = new GoogleGenAI ({}); const base64AudioFile = fs . readFileSync ( "path/to/small-sample.mp3" , { encoding : "base64" , }); const contents = [ { text : "Please summarize the audio." }, { inlineData : { mimeType : "audio/mp3" , data : base64AudioFile , }, }, ]; const response = await ai . models . generateContent ({ model : "gemini-2.5-flash" , contents : contents , }); console . log ( response . text ); Go package main import ( "context" "fmt" "os" "google.golang.org/genai" ) func main () { ctx := context . Background () client , err := genai . NewClient ( ctx , nil ) if err != nil { log . Fatal ( err ) } audioBytes , _ := os . ReadFile ( "/path/to/small-sample.mp3" ) parts := [] * genai . Part { genai . NewPartFromText ( "Describe this audio clip" ), & genai .
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+ from_cached_content ( cached_content = apollo_cache ) response = apollo_model . generate_content ( "Find a lighthearted moment from this transcript" ) JavaScript import { GoogleAICacheManager , GoogleAIFileManager } from "@google/generative-ai/server" ; import { GoogleGenerativeAI } from "@google/generative-ai" ; const cacheManager = new GoogleAICacheManager ( "GOOGLE_API_KEY" ); const fileManager = new GoogleAIFileManager ( "GOOGLE_API_KEY" ); const uploadResult = await fileManager . uploadFile ( "path/to/a11.txt" , { mimeType : "text/plain" , }); const cacheResult = await cacheManager . create ({ model : "models/gemini-1.5-flash" , contents : [ { role : "user" , parts : [ { fileData : { fileUri : uploadResult . file . uri , mimeType : uploadResult . file . mimeType , }, }, ], }, ], }); console . log ( cacheResult ); const genAI = new GoogleGenerativeAI ( "GOOGLE_API_KEY" ); const model = genAI . getGenerativeModelFromCachedContent ( cacheResult ); const result = await model . generateContent ( "Please summarize this transcript." , ); console . log ( result . response . text ()); After Python import requests import pathlib from google import genai from google.genai import types client = genai . Client () # Check which models support caching. for m in client . models . list (): for action in m . supported_actions : if action == "createCachedContent" : print ( m . name ) break # Download file response = requests . get ( 'https://storage.googleapis.com/generativeai-downloads/data/a11.txt' ) pathlib . Path ( 'a11.txt' ) . write_text ( response . text ) # Upload file document = client . files . upload ( file = 'a11.txt' ) # Create cache model = 'gemini-1.5-flash-001' apollo_cache = client . caches . create ( model = model , config = { 'contents' : [ document ], 'system_instruction' : 'You are an expert at analyzing transcripts.' , }, ) # Generate response response = client . models . generate_content ( model = model , contents = 'Find a lighthearted moment from this
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+ Japanese ( ja ) Korean ( ko ) Latvian ( lv ) Lithuanian ( lt ) Norwegian ( no ) Polish ( pl ) Portuguese ( pt ) Romanian ( ro ) Russian ( ru ) Serbian ( sr ) Slovak ( sk ) Slovenian ( sl ) Spanish ( es ) Swahili ( sw ) Swedish ( sv ) Thai ( th ) Turkish ( tr ) Ukrainian ( uk ) Vietnamese ( vi ) 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. Last updated 2025-07-07 UTC.
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+ "thinkingConfig": { "thinkingBudget": 1024 # Thinking off: # "thinkingBudget": 0 # Turn on dynamic thinking: # "thinkingBudget": -1 } } }' Thought summaries Thought summaries are synthesized versions of the model's raw thoughts and offer insights into the model's internal reasoning process. Note that thinking budgets apply to the model's raw thoughts and not to thought summaries. You can enable thought summaries by setting includeThoughts to true in your request configuration. You can then access the summary by iterating through the response parameter's parts , and checking the thought boolean. Here's an example demonstrating how to enable and retrieve thought summaries without streaming, which returns a single, final thought summary with the response: Python from google import genai from google.genai import types client = genai . Client () prompt = "What is the sum of the first 50 prime numbers?" response = client . models . generate_content ( model = "gemini-2.5-pro" , contents = prompt , config = types . GenerateContentConfig ( thinking_config = types . ThinkingConfig ( include_thoughts = True ) ) ) for part in response . candidates [ 0 ] . content . parts : if not part . text : continue if part . thought : print ( "Thought summary:" ) print ( part . text ) print () else : print ( "Answer:" ) print ( part . text ) print () JavaScript import { GoogleGenAI } from "@google/genai" ; const ai = new GoogleGenAI ({}); async function main () { const response = await ai . models . generateContent ({ model : "gemini-2.5-pro" , contents : "What is the sum of the first 50 prime numbers?" , config : { thinkingConfig : { includeThoughts : true , }, }, }); for ( const part of response . candidates [ 0 ]. content . parts ) { if ( ! part . text ) { continue ; } else if ( part . thought ) { console . log ( "Thoughts summary:" ); console . log ( part . text ); } else { console . log ( "Answer:" ); console . log ( part . text ); } } } main (); Go package main import ( "context"
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+ URL: https://ai.google.dev/gemini-api/docs/models/experimental-models#gemini-2.5-flash-native-audio Title: Gemini models | Gemini API | Google AI for Developers ==================================================
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+ declaration can include the following parameters: name (string): A unique name for the function ( get_weather_forecast , send_email ). Use descriptive names without spaces or special characters (use underscores or camelCase). description (string): A clear and detailed explanation of the function's purpose and capabilities. This is crucial for the model to understand when to use the function. Be specific and provide examples if helpful ("Finds theaters based on location and optionally movie title which is currently playing in theaters."). parameters (object): Defines the input parameters the function expects. type (string): Specifies the overall data type, such as object . properties (object): Lists individual parameters, each with: type (string): The data type of the parameter, such as string , integer , boolean, array . description (string): A description of the parameter's purpose and format. Provide examples and constraints ("The city and state, e.g., 'San Francisco, CA' or a zip code e.g., '95616'."). enum (array, optional): If the parameter values are from a fixed set, use "enum" to list the allowed values instead of just describing them in the description. This improves accuracy ("enum": ["daylight", "cool", "warm"]). required (array): An array of strings listing the parameter names that are mandatory for the function to operate. Function calling with thinking Enabling "thinking" can improve function call performance by allowing the model to reason through a request before suggesting function calls. However, because the Gemini API is stateless, this reasoning context is lost between turns, which can reduce the quality of function calls as they require multiple turn requests. To preserve this context you can use thought signatures. A thought signature is an encrypted representation of the model's internal thought process that you pass back to the model on subsequent turns. To use thought signatures: Receive the signature: When thinking is enabled, the API
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+ calendar_month Latest update December 2023 See the examples to explore the capabilities of these model variations. [*] A token is equivalent to about 4 characters for Gemini models. 100 tokens are about 60-80 English words. Model version name patterns Gemini models are available in either stable , preview , or experimental versions. In your code, you can use one of the following model name formats to specify which model and version you want to use. Latest stable Points to the most recent stable version released for the specified model generation and variation. To specify the latest stable version, use the following pattern: <model>-<generation>-<variation> . For example, gemini-2.0-flash . Stable Points to a specific stable model. Stable models usually don't change. Most production apps should use a specific stable model. To specify a stable version, use the following pattern: <model>-<generation>-<variation>-<version> . For example, gemini-2.0-flash-001 . Preview Points to a preview model which may not be suitable for production use, come with more restrictive rate limits, but may have billing enabled. To specify a preview version, use the following pattern: <model>-<generation>-<variation>-<version> . For example, gemini-2.5-pro-preview-06-05 . Experimental Points to an experimental model which may not be suitable for production use and come with more restrictive rate limits. We release experimental models to gather feedback and get our latest updates into the hands of developers quickly. To specify an experimental version, use the following pattern: <model>-<generation>-<variation>-<version> . For example, gemini-2.0-pro-exp-02-05 . Experimental models In addition to stable models, the Gemini API offers experimental models which may not be suitable for production use and come with more restrictive rate limits. We release experimental models to gather feedback, get our latest updates into the hands of developers quickly, and highlight the pace of innovation
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+ Output token limit 8,192 handyman Capabilities Structured outputs Supported Tuning Not supported Function calling Supported Code execution Supported Search Supported Image generation Not supported Audio generation Supported Thinking Not supported 123 Versions Read the model version patterns for more details. Preview: gemini-live-2.5-flash-preview calendar_month Latest update June 2025 cognition_2 Knowledge cutoff January 2025 Gemini 2.0 Flash Live The Gemini 2.0 Flash Live model works with the Live API to enable low-latency bidirectional voice and video interactions with Gemini. The model can process text, audio, and video input, and it can provide text and audio output. Try in Google AI Studio Model details Property Description id_card Model code models/gemini-2.0-flash-live-001 save Supported data types Inputs Audio, video, and text Output Text, and audio token_auto Token limits [*] Input token limit 1,048,576 Output token limit 8,192 handyman Capabilities Structured outputs Supported Tuning Not supported Function calling Supported Code execution Supported Search Supported Image generation Not supported Audio generation Supported Thinking Not supported 123 Versions Read the model version patterns for more details. Preview: gemini-2.0-flash-live-001 calendar_month Latest update April 2025 cognition_2 Knowledge cutoff August 2024 Gemini Embedding Experimental Gemini embedding achieves a SOTA performance across many key dimensions including code, multi-lingual, and retrieval. Gemini Embedding rate limits are more restricted since it is an experimental model. Model details Property Description id_card Model code Gemini API gemini-embedding-exp-03-07 save Supported data types Input Text Output Text embeddings token_auto Token limits [*] Input token limit 8,192 Output dimension size Elastic, supports: 3072, 1536, or 768 calendar_month Latest update March 2025 Text Embedding and Embedding Text Embedding Try our new experimental Gemini embedding model which achieves
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+ generateContent ({ model : 'gemini-2.5-flash' , contents : content , }); console . log ( response . text ); } main (); Go package main import ( "context" "fmt" "io" "net/http" "os" "google.golang.org/genai" ) func main () { ctx := context . Background () client , _ := genai . NewClient ( ctx , & genai . ClientConfig { APIKey : os . Getenv ( "GEMINI_API_KEY" ), Backend : genai . BackendGeminiAPI , }) docUrl1 := "https://arxiv.org/pdf/2312.11805" docUrl2 := "https://arxiv.org/pdf/2403.05530" localPath1 := "doc1_downloaded.pdf" localPath2 := "doc2_downloaded.pdf" respHttp1 , _ := http . Get ( docUrl1 ) defer respHttp1 . Body . Close () outFile1 , _ := os . Create ( localPath1 ) _ , _ = io . Copy ( outFile1 , respHttp1 . Body ) outFile1 . Close () respHttp2 , _ := http . Get ( docUrl2 ) defer respHttp2 . Body . Close () outFile2 , _ := os . Create ( localPath2 ) _ , _ = io . Copy ( outFile2 , respHttp2 . Body ) outFile2 . Close () uploadConfig1 := & genai . UploadFileConfig { MIMEType : "application/pdf" } uploadedFile1 , _ := client . Files . UploadFromPath ( ctx , localPath1 , uploadConfig1 ) uploadConfig2 := & genai . UploadFileConfig { MIMEType : "application/pdf" } uploadedFile2 , _ := client . Files . UploadFromPath ( ctx , localPath2 , uploadConfig2 ) promptParts := [] * genai . Part { genai . NewPartFromURI ( uploadedFile1 . URI , uploadedFile1 . MIMEType ), genai . NewPartFromURI ( uploadedFile2 . URI , uploadedFile2 . MIMEType ), genai . NewPartFromText ( "What is the difference between each of the " + "main benchmarks between these two papers? " + "Output these in a table." ), } contents := [] * genai . Content { genai . NewContentFromParts ( promptParts , genai . RoleUser ), } modelName := "gemini-2.5-flash" result , _ := client . Models . GenerateContent ( ctx , modelName , contents , nil , ) fmt . Println ( result . Text ()) } REST DOC_URL_1 = "https://arxiv.org/pdf/2312.11805" DOC_URL_2 = "https://arxiv.org/pdf/2403.05530" DISPLAY_NAME_1 = "Gemini_paper"
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+ Files API | Gemini API | Google AI for Developers Skip to main content / English Deutsch Español – América Latina Français Indonesia Italiano Polski Português – Brasil Shqip Tiếng Việt Türkçe Русский עברית العربيّة فارسی हिंदी বাংলা ภาษาไทย 中文 – 简体 中文 – 繁體 日本語 한국어 Sign in Introducing Batch Mode, with higher rate limits and a 50% token discount. Learn more Home Gemini API Models Send feedback Files API The Gemini family of artificial intelligence (AI) models is built to handle various types of input data, including text, images, and audio. Since these models can handle more than one type or mode of data, the Gemini models are called multimodal models or explained as having multimodal capabilities . This guide shows you how to work with media files using the Files API. The basic operations are the same for audio files, images, videos, documents, and other supported file types. For file prompting guidance, check out the File prompt guide section. Upload a file You can use the Files API to upload a media file. Always use the Files API when the total request size (including the files, text prompt, system instructions, etc.) is larger than 20 MB. The following code uploads a file and then uses the file in a call to generateContent . Python from google import genai client = genai . Client () myfile = client . files . upload ( file = "path/to/sample.mp3" ) response = client . models . generate_content ( model = "gemini-2.0-flash" , contents = [ "Describe this audio clip" , myfile ] ) print ( response . text ) JavaScript import { GoogleGenAI , createUserContent , createPartFromUri , } from "@google/genai" ; const ai = new GoogleGenAI ({}); async function main () { const myfile = await ai . files . upload ({ file : "path/to/sample.mp3" , config : { mimeType : "audio/mpeg" }, }); const response = await ai . models . generateContent ({ model : "gemini-2.0-flash" , contents : createUserContent ([ createPartFromUri ( myfile . uri , myfile . mimeType ), "Describe this audio
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+ URL: https://ai.google.dev/gemini-api/docs/live-guide#native-audio-output Title: Live API capabilities guide | Gemini API | Google AI for Developers ==================================================
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+ Japanese ( ja ) Korean ( ko ) Latvian ( lv ) Lithuanian ( lt ) Norwegian ( no ) Polish ( pl ) Portuguese ( pt ) Romanian ( ro ) Russian ( ru ) Serbian ( sr ) Slovak ( sk ) Slovenian ( sl ) Spanish ( es ) Swahili ( sw ) Swedish ( sv ) Thai ( th ) Turkish ( tr ) Ukrainian ( uk ) Vietnamese ( vi ) 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. Last updated 2025-07-07 UTC.
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+ Gemini models | Gemini API | Google AI for Developers Skip to main content / English Deutsch Español – América Latina Français Indonesia Italiano Polski Português – Brasil Shqip Tiếng Việt Türkçe Русский עברית العربيّة فارسی हिंदी বাংলা ภาษาไทย 中文 – 简体 中文 – 繁體 日本語 한국어 Sign in Introducing Batch Mode, with higher rate limits and a 50% token discount. Learn more Home Gemini API Models Send feedback Gemini models 2.5 Pro spark Our most powerful thinking model with maximum response accuracy and state-of-the-art performance Input audio, images, video, and text, get text responses Tackle difficult problems, analyze large databases, and more Best for complex coding, reasoning, and multimodal understanding 2.5 Flash spark Our best model in terms of price-performance, offering well-rounded capabilities. Input audio, images, video, and text, and get text responses Model thinks as needed; or, you can configure a thinking budget Best for low latency, high volume tasks that require thinking 2.5 Flash-Lite experiment A Gemini 2.5 Flash model optimized for cost efficiency and low latency. Input audio, images, video, and text, and get text responses Most cost-efficient model supporting high throughput Best for real time, low latency use cases Note: Gemini 2.5 Pro and 2.5 Flash come with thinking on by default . If you're migrating from a non-thinking model such as 2.0 Pro or Flash, we recommend you to review the Thinking guide first. Model variants The Gemini API offers different models that are optimized for specific use cases. Here's a brief overview of Gemini variants that are available: Model variant Input(s) Output Optimized for Gemini 2.5 Pro gemini-2.5-pro Audio, images, videos, text, and PDF Text Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more Gemini 2.5 Flash gemini-2.5-flash Audio, images, videos, and text Text Adaptive thinking, cost efficiency Gemini 2.5 Flash-Lite Preview gemini-2.5-flash-lite-preview-06-17 Text, image, video,