Fix/init plugin (#12)
* fix: Update plugin file from linux environment. * fix: Rename plugin files * clear storage * clear packages * update workflows --------- Co-authored-by: ymislu <ymislu@163.com>pull/17608/head
fuwx
1 year ago
committed by
GitHub
parent
5e31d1c67d
commit
c16b064a6a
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
Before Width: | Height: | Size: 6.9 KiB |
@ -1 +0,0 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" fill="none" version="1.1" width="128" height="128" viewBox="0 0 128 128"><g><g style="opacity:0;"><rect x="0" y="0" width="128" height="128" rx="0" fill="#FFFFFF" fill-opacity="1"/></g><g><path d="M100.74,12L93.2335,12C69.21260000000001,12,55.3672,27.3468,55.3672,50.8672L55.3672,54.8988C52.6011,54.1056,49.7377,53.7031,46.8601,53.7031C29.816499999999998,53.7031,16,67.5196,16,84.5632C16,101.6069,29.816499999999998,115.423,46.8601,115.423C63.9037,115.423,77.72030000000001,101.6069,77.72030000000001,84.5632C77.72030000000001,82.4902,77.51140000000001,80.4223,77.0967,78.3911L77.2197,78.3911L100.74,78.3911C106.9654,78.3681,112,73.3151,112,67.08959999999999C112,60.8642,106.9654,55.8111,100.74,55.7882L100.7362,55.7882L100.6985,55.7879L100.6606,55.7882L77.2197,55.7882L77.2195,49.8663C77.2195,40.8584,83.7252,34.352900000000005,93.2335,34.352900000000005L100.5653,34.352900000000005L100.5733,34.352900000000005L100.5812,34.352900000000005L100.74,34.352900000000005L100.74,34.352900000000005C106.8469,34.2605,111.7497,29.284,111.7497,23.1764C111.7497,17.06889,106.8469,12.0923454,100.74,12L100.74,12ZM56.0347,84.5632C56.0347,79.4962,51.9271,75.3885,46.8601,75.3885C41.793099999999995,75.3885,37.6854,79.4962,37.6854,84.5632C37.6854,89.6303,41.793099999999995,93.7378,46.8601,93.7378C51.9271,93.7378,56.0347,89.6303,56.0347,84.5632Z" fill-rule="evenodd" fill="#8358F6" fill-opacity="1"/></g></g></svg>
|
||||
|
Before Width: | Height: | Size: 1.4 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 4.6 KiB |
@ -1,22 +0,0 @@
|
||||
<svg width="195.000000" height="41.359375" viewBox="0 0 195 41.3594" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
|
||||
<desc>
|
||||
Created with Pixso.
|
||||
</desc>
|
||||
<defs>
|
||||
<clipPath id="clip30_2029">
|
||||
<rect id="_图层_1" width="134.577469" height="25.511124" transform="translate(60.422485 10.022217)" fill="white"/>
|
||||
</clipPath>
|
||||
</defs>
|
||||
<g clip-path="url(#clip30_2029)">
|
||||
<path id="path" d="M119.508 30.113L117.562 30.113L117.562 27.0967L119.508 27.0967C120.713 27.0967 121.931 26.7961 122.715 25.9614C123.5 25.1265 123.796 23.8464 123.796 22.5664C123.796 21.2864 123.512 20.0063 122.715 19.1716C121.919 18.3369 120.713 18.0364 119.508 18.0364C118.302 18.0364 117.085 18.3369 116.3 19.1716C115.515 20.0063 115.219 21.2864 115.219 22.5664L115.219 34.9551L111.806 34.9551L111.806 15.031L115.219 15.031L115.219 16.2998L115.845 16.2998C115.913 16.2219 115.981 16.1553 116.049 16.0884C116.903 15.3093 118.211 15.031 119.496 15.031C121.51 15.031 123.523 15.532 124.843 16.9233C126.162 18.3145 126.629 20.4517 126.629 22.5776C126.629 24.7036 126.151 26.8296 124.843 28.2319C123.535 29.6345 121.51 30.113 119.508 30.113Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M67.5664 15.5654L69.5117 15.5654L69.5117 18.5818L67.5664 18.5818C66.3606 18.5818 65.1434 18.8823 64.3585 19.717C63.5736 20.552 63.2778 21.832 63.2778 23.1121C63.2778 24.3921 63.5623 25.6721 64.3585 26.5068C65.1548 27.3418 66.3606 27.6423 67.5664 27.6423C68.7722 27.6423 69.9895 27.3418 70.7744 26.5068C71.5593 25.6721 71.8551 24.3921 71.8551 23.1121L71.8551 10.7124L75.2677 10.7124L75.2677 30.6475L71.8551 30.6475L71.8551 29.3787L71.2294 29.3787C71.1611 29.4565 71.0929 29.5234 71.0247 29.5901C70.1715 30.3691 68.8633 30.6475 67.5779 30.6475C65.5643 30.6475 63.5509 30.1467 62.2313 28.7554C60.9117 27.364 60.4453 25.2268 60.4453 23.1008C60.4453 20.9749 60.9231 18.8489 62.2313 17.4465C63.5509 16.0552 65.5643 15.5654 67.5664 15.5654Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M92.3881 22.845L92.3881 24.0581L83.299 24.0581L83.299 21.6428L89.328 21.6428C89.1914 20.7634 88.8729 19.9397 88.3042 19.3386C87.4851 18.4705 86.2224 18.1589 84.9711 18.1589C83.7198 18.1589 82.4572 18.4705 81.6381 19.3386C80.819 20.2068 80.5232 21.5315 80.5232 22.845C80.5232 24.1582 80.819 25.4939 81.6381 26.3511C82.4572 27.208 83.7198 27.531 84.9711 27.531C86.2224 27.531 87.4851 27.2192 88.3042 26.3511C88.418 26.2285 88.5203 26.095 88.6227 25.9614L91.9899 25.9614C91.6941 27.0078 91.2277 27.9539 90.5225 28.6885C89.1573 30.1243 87.0529 30.6475 84.9711 30.6475C82.8894 30.6475 80.7849 30.1355 79.4198 28.6885C78.0547 27.2415 77.5542 25.0376 77.5542 22.845C77.5542 20.6521 78.0433 18.437 79.4198 17.0012C80.7963 15.5654 82.8894 15.0422 84.9711 15.0422C87.0529 15.0422 89.1573 15.5542 90.5225 17.0012C91.8988 18.4482 92.3881 20.6521 92.3881 22.845Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M109.52 22.845L109.52 24.0581L100.431 24.0581L100.431 21.6428L106.46 21.6428C106.323 20.7634 106.005 19.9397 105.436 19.3386C104.617 18.4705 103.354 18.1589 102.103 18.1589C100.852 18.1589 99.5889 18.4705 98.7698 19.3386C97.9507 20.2068 97.6549 21.5315 97.6549 22.845C97.6549 24.1582 97.9507 25.4939 98.7698 26.3511C99.5889 27.208 100.852 27.531 102.103 27.531C103.354 27.531 104.617 27.2192 105.436 26.3511C105.55 26.2285 105.652 26.095 105.754 25.9614L109.122 25.9614C108.826 27.0078 108.359 27.9539 107.654 28.6885C106.289 30.1243 104.185 30.6475 102.103 30.6475C100.021 30.6475 97.9166 30.1355 96.5515 28.6885C95.1864 27.2415 94.6859 25.0376 94.6859 22.845C94.6859 20.6521 95.175 18.437 96.5515 17.0012C97.928 15.5654 100.021 15.0422 102.103 15.0422C104.185 15.0422 106.289 15.5542 107.654 17.0012C109.031 18.4482 109.52 20.6521 109.52 22.845Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M136.355 30.6475C138.437 30.6475 140.541 30.3469 141.906 29.49C143.271 28.6328 143.772 27.3306 143.772 26.0393C143.772 24.7483 143.282 23.4348 141.906 22.5889C140.541 21.7429 138.437 21.4312 136.355 21.4312C135.467 21.4312 134.648 21.3088 134.068 20.9861C133.488 20.6521 133.272 20.1511 133.272 19.6504C133.272 19.1494 133.477 18.6375 134.068 18.3147C134.648 17.9807 135.547 17.8694 136.434 17.8694C137.322 17.8694 138.22 17.9919 138.801 18.3147C139.381 18.6487 139.597 19.1494 139.597 19.6504L143.066 19.6504C143.066 18.3591 142.623 17.0457 141.383 16.2C140.143 15.354 138.243 15.0422 136.355 15.0422C134.466 15.0422 132.567 15.3428 131.327 16.2C130.087 17.0569 129.643 18.3591 129.643 19.6504C129.643 20.9414 130.087 22.2549 131.327 23.1008C132.567 23.9468 134.466 24.2585 136.355 24.2585C137.333 24.2585 138.414 24.3809 139.062 24.7036C139.711 25.0266 139.938 25.5386 139.938 26.0393C139.938 26.5403 139.711 27.0522 139.062 27.375C138.414 27.6978 137.424 27.8203 136.446 27.8203C135.467 27.8203 134.466 27.6978 133.829 27.375C133.192 27.0522 132.953 26.5403 132.953 26.0393L128.949 26.0393C128.949 27.3306 129.438 28.644 130.815 29.49C132.191 30.3359 134.273 30.6475 136.355 30.6475Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M160.903 22.845L160.903 24.0581L151.814 24.0581L151.814 21.6428L157.843 21.6428C157.707 20.7634 157.388 19.9397 156.82 19.3386C156 18.4705 154.738 18.1589 153.486 18.1589C152.235 18.1589 150.972 18.4705 150.153 19.3386C149.334 20.2068 149.039 21.5315 149.039 22.845C149.039 24.1582 149.334 25.4939 150.153 26.3511C150.972 27.208 152.235 27.531 153.486 27.531C154.738 27.531 156 27.2192 156.82 26.3511C156.933 26.2285 157.036 26.095 157.138 25.9614L160.505 25.9614C160.209 27.0078 159.743 27.9539 159.038 28.6885C157.673 30.1243 155.568 30.6475 153.486 30.6475C151.405 30.6475 149.3 30.1355 147.935 28.6885C146.57 27.2415 146.07 25.0376 146.07 22.845C146.07 20.6521 146.559 18.437 147.935 17.0012C149.312 15.5654 151.405 15.0422 153.486 15.0422C155.568 15.0422 157.673 15.5542 159.038 17.0012C160.414 18.4482 160.903 20.6521 160.903 22.845Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M178.035 22.845L178.035 24.0581L168.946 24.0581L168.946 21.6428L174.975 21.6428C174.839 20.7634 174.52 19.9397 173.951 19.3386C173.132 18.4705 171.87 18.1589 170.618 18.1589C169.367 18.1589 168.104 18.4705 167.285 19.3386C166.466 20.2068 166.17 21.5315 166.17 22.845C166.17 24.1582 166.466 25.4939 167.285 26.3511C168.104 27.208 169.367 27.531 170.618 27.531C171.87 27.531 173.132 27.2192 173.951 26.3511C174.065 26.2285 174.167 26.095 174.27 25.9614L177.637 25.9614C177.341 27.0078 176.875 27.9539 176.17 28.6885C174.804 30.1243 172.7 30.6475 170.618 30.6475C168.536 30.6475 166.432 30.1355 165.067 28.6885C163.702 27.2415 163.201 25.0376 163.201 22.845C163.201 20.6521 163.69 18.437 165.067 17.0012C166.443 15.5654 168.536 15.0422 170.618 15.0422C172.7 15.0422 174.804 15.5542 176.17 17.0012C177.546 18.4482 178.035 20.6521 178.035 22.845Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<rect id="rect" x="180.321533" y="10.022217" width="3.412687" height="20.625223" fill="#4D6BFE"/>
|
||||
<path id="polygon" d="M189.559 22.3772L195.155 30.6475L190.935 30.6475L185.338 22.3772L190.935 15.7322L195.155 15.7322L189.559 22.3772Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
</g>
|
||||
<path id="path" d="M55.6128 3.47119C55.0175 3.17944 54.7611 3.73535 54.413 4.01782C54.2939 4.10889 54.1932 4.22729 54.0924 4.33667C53.2223 5.26587 52.2057 5.87646 50.8776 5.80347C48.9359 5.69409 47.2781 6.30469 45.8126 7.78979C45.5012 5.9585 44.4663 4.86499 42.8909 4.16357C42.0667 3.79907 41.2332 3.43457 40.6561 2.64185C40.2532 2.07715 40.1432 1.44849 39.9418 0.828857C39.8135 0.455322 39.6853 0.0725098 39.2548 0.00878906C38.7877 -0.0639648 38.6045 0.327637 38.4213 0.655762C37.6886 1.99512 37.4047 3.47119 37.4321 4.96533C37.4962 8.32739 38.9159 11.0059 41.7369 12.9102C42.0575 13.1289 42.1399 13.3474 42.0392 13.6665C41.8468 14.3225 41.6178 14.9602 41.4164 15.6162C41.2881 16.0354 41.0957 16.1265 40.647 15.9441C39.0991 15.2974 37.7618 14.3406 36.5803 13.1836C34.5745 11.2429 32.761 9.10181 30.4988 7.42529C29.9675 7.03345 29.4363 6.66919 28.8867 6.32275C26.5786 4.08154 29.189 2.24097 29.7935 2.02246C30.4254 1.79468 30.0133 1.01099 27.9708 1.02026C25.9283 1.0293 24.0599 1.71265 21.6786 2.62378C21.3306 2.7605 20.9641 2.8606 20.5886 2.94263C18.4271 2.53271 16.1831 2.44141 13.8384 2.70581C9.42371 3.19775 5.89758 5.28418 3.30554 8.84668C0.191406 13.1289 -0.54126 17.9941 0.356323 23.0691C1.29968 28.4172 4.02905 32.8452 8.22388 36.3076C12.5745 39.8972 17.5845 41.6558 23.2997 41.3186C26.771 41.1182 30.6361 40.6536 34.9958 36.9636C36.0948 37.5103 37.2489 37.7288 39.1632 37.8928C40.6378 38.0295 42.0575 37.8201 43.1565 37.5923C44.8784 37.2278 44.7594 35.6333 44.1366 35.3418C39.09 32.9912 40.1981 33.9478 39.1907 33.1733C41.7552 30.1394 45.6204 26.9868 47.1316 16.7732C47.2506 15.9624 47.1499 15.4521 47.1316 14.7961C47.1224 14.3953 47.214 14.2405 47.672 14.1948C48.9359 14.0491 50.1632 13.7029 51.2898 13.0833C54.5596 11.2976 55.8784 8.36377 56.1898 4.84692C56.2357 4.30933 56.1807 3.75342 55.6128 3.47119ZM27.119 35.123C22.2281 31.2783 19.856 30.0117 18.8759 30.0664C17.96 30.1211 18.1249 31.1689 18.3263 31.8523C18.537 32.5264 18.8118 32.9912 19.1964 33.5833C19.462 33.9751 19.6453 34.5581 18.9309 34.9956C17.3555 35.9705 14.6169 34.6675 14.4886 34.6038C11.3014 32.7268 8.63611 30.2485 6.75842 26.8594C4.94495 23.5974 3.89172 20.0989 3.71765 16.3633C3.67188 15.4614 3.9375 15.1423 4.83508 14.9785C6.0166 14.7598 7.23474 14.7141 8.41626 14.8872C13.408 15.6162 17.6577 17.8484 21.2206 21.3835C23.2539 23.397 24.7926 25.8025 26.3772 28.1531C28.0624 30.6494 29.8759 33.0276 32.184 34.9773C32.9991 35.6606 33.6494 36.1799 34.2722 36.5627C32.3947 36.7722 29.2622 36.8179 27.119 35.123ZM29.4637 20.0442C29.4637 19.6433 29.7843 19.3245 30.1874 19.3245C30.2789 19.3245 30.3613 19.3425 30.4346 19.3699C30.5354 19.4065 30.627 19.4612 30.7002 19.543C30.8285 19.6707 30.9017 19.8528 30.9017 20.0442C30.9017 20.4451 30.5812 20.7639 30.1782 20.7639C29.7751 20.7639 29.4637 20.4451 29.4637 20.0442ZM36.7452 23.7798C36.2781 23.9712 35.811 24.135 35.3622 24.1533C34.6661 24.1897 33.9059 23.9072 33.4938 23.561C32.8527 23.0234 32.3947 22.7229 32.2023 21.7844C32.1199 21.3835 32.1656 20.7639 32.239 20.4087C32.4038 19.6433 32.2206 19.1514 31.6803 18.7048C31.2406 18.3403 30.6819 18.2402 30.0682 18.2402C29.8392 18.2402 29.6287 18.1399 29.4729 18.0579C29.2164 17.9304 29.0059 17.6116 29.2073 17.2197C29.2714 17.0923 29.5829 16.7825 29.6561 16.7278C30.4896 16.2539 31.4513 16.4089 32.3397 16.7642C33.1641 17.1013 33.7869 17.7209 34.6844 18.5955C35.6003 19.6523 35.7651 19.9441 36.2872 20.7366C36.6995 21.3562 37.075 21.9939 37.3314 22.7229C37.4871 23.1785 37.2856 23.552 36.7452 23.7798Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 10 KiB |
@ -1,3 +0,0 @@
|
||||
<svg width="60" height="50" viewBox="0 0 60 50" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linejoin:round;stroke-miterlimit:2;">
|
||||
<path id="path" d="M55.613,3.471C55.018,3.179 54.761,3.735 54.413,4.018C54.294,4.109 54.193,4.227 54.092,4.337C53.222,5.266 52.206,5.876 50.878,5.803C48.936,5.694 47.278,6.305 45.813,7.79C45.501,5.959 44.466,4.865 42.891,4.164C42.067,3.799 41.233,3.435 40.656,2.642C40.253,2.077 40.143,1.448 39.942,0.829C39.814,0.455 39.685,0.073 39.255,0.009C38.788,-0.064 38.605,0.328 38.421,0.656C37.689,1.995 37.405,3.471 37.432,4.965C37.496,8.327 38.916,11.006 41.737,12.91C42.058,13.129 42.14,13.347 42.039,13.666C41.847,14.323 41.618,14.96 41.416,15.616C41.288,16.035 41.096,16.127 40.647,15.944C39.099,15.297 37.762,14.341 36.58,13.184C34.575,11.243 32.761,9.102 30.499,7.425C29.968,7.033 29.436,6.669 28.887,6.323C26.579,4.082 29.189,2.241 29.794,2.022C30.425,1.795 30.013,1.011 27.971,1.02C25.928,1.029 24.06,1.713 21.679,2.624C21.331,2.761 20.964,2.861 20.589,2.943C18.427,2.533 16.183,2.441 13.838,2.706C9.424,3.198 5.898,5.284 3.306,8.847C0.191,13.129 -0.541,17.994 0.356,23.069C1.3,28.417 4.029,32.845 8.224,36.308C12.575,39.897 17.584,41.656 23.3,41.319C26.771,41.118 30.636,40.654 34.996,36.964C36.095,37.51 37.249,37.729 39.163,37.893C40.638,38.03 42.058,37.82 43.157,37.592C44.878,37.228 44.759,35.633 44.137,35.342C39.09,32.991 40.198,33.948 39.191,33.173C41.755,30.139 45.62,26.987 47.132,16.773C47.251,15.962 47.15,15.452 47.132,14.796C47.122,14.395 47.214,14.241 47.672,14.195C48.936,14.049 50.163,13.703 51.29,13.083C54.56,11.298 55.878,8.364 56.19,4.847C56.236,4.309 56.181,3.753 55.613,3.471ZM27.119,35.123C22.228,31.278 19.856,30.012 18.876,30.066C17.96,30.121 18.125,31.169 18.326,31.852C18.537,32.526 18.812,32.991 19.196,33.583C19.462,33.975 19.645,34.558 18.931,34.996C17.356,35.971 14.617,34.667 14.489,34.604C11.301,32.727 8.636,30.249 6.758,26.859C4.945,23.597 3.892,20.099 3.718,16.363C3.672,15.461 3.938,15.142 4.835,14.979C6.017,14.76 7.235,14.714 8.416,14.887C13.408,15.616 17.658,17.848 21.221,21.384C23.254,23.397 24.793,25.802 26.377,28.153C28.062,30.649 29.876,33.028 32.184,34.977C32.999,35.661 33.649,36.18 34.272,36.563C32.395,36.772 29.262,36.818 27.119,35.123ZM29.464,20.044C29.464,19.643 29.784,19.325 30.187,19.325C30.279,19.325 30.361,19.343 30.435,19.37C30.535,19.407 30.627,19.461 30.7,19.543C30.828,19.671 30.902,19.853 30.902,20.044C30.902,20.445 30.581,20.764 30.178,20.764C29.775,20.764 29.464,20.445 29.464,20.044ZM36.745,23.78C36.278,23.971 35.811,24.135 35.362,24.153C34.666,24.19 33.906,23.907 33.494,23.561C32.853,23.023 32.395,22.723 32.202,21.784C32.12,21.384 32.166,20.764 32.239,20.409C32.404,19.643 32.221,19.151 31.68,18.705C31.241,18.34 30.682,18.24 30.068,18.24C29.839,18.24 29.629,18.14 29.473,18.058C29.216,17.93 29.006,17.612 29.207,17.22C29.271,17.092 29.583,16.783 29.656,16.728C30.49,16.254 31.451,16.409 32.34,16.764C33.164,17.101 33.787,17.721 34.684,18.596C35.6,19.652 35.765,19.944 36.287,20.737C36.7,21.356 37.075,21.994 37.331,22.723C37.487,23.179 37.286,23.552 36.745,23.78Z" style="fill:rgb(77,107,254);fill-rule:nonzero;"/>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 3.2 KiB |
File diff suppressed because one or more lines are too long
|
Before Width: | Height: | Size: 12 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 2.8 KiB |
File diff suppressed because one or more lines are too long
|
Before Width: | Height: | Size: 5.6 KiB |
File diff suppressed because one or more lines are too long
|
Before Width: | Height: | Size: 7.7 KiB |
File diff suppressed because one or more lines are too long
|
Before Width: | Height: | Size: 23 KiB |
@ -1 +0,0 @@
|
||||
<svg width="16" height="17" viewBox="0 0 16 17" fill="none" xmlns="http://www.w3.org/2000/svg" class="w-4 h-4 text-gray-400" data-icon="CubeOutline" aria-hidden="true"><g id="cube-outline"><g id="Solid"><path d="M8.26865 1.29003C8.09143 1.25358 7.90866 1.25358 7.73144 1.29003C7.52659 1.33216 7.3435 1.43471 7.19794 1.51624L7.15826 1.53841L6.17628 2.08395C5.85443 2.26276 5.73846 2.66863 5.91727 2.99049C6.09608 3.31234 6.50195 3.4283 6.82381 3.24949L7.80579 2.70395C7.90681 2.64782 7.95839 2.61946 7.99686 2.60091L8.00004 2.59938L8.00323 2.60091C8.0417 2.61946 8.09327 2.64782 8.1943 2.70395L9.17628 3.24949C9.49814 3.4283 9.90401 3.31234 10.0828 2.99048C10.2616 2.66863 10.1457 2.26276 9.82381 2.08395L8.84183 1.53841L8.80215 1.51624C8.65659 1.43471 8.4735 1.33216 8.26865 1.29003Z" fill="currentColor"></path><path d="M12.8238 3.75062C12.5019 3.57181 12.0961 3.68777 11.9173 4.00963C11.7385 4.33148 11.8544 4.73735 12.1763 4.91616L12.6272 5.16668L12.1763 5.41719C11.8545 5.596 11.7385 6.00186 11.9173 6.32372C12.0961 6.64558 12.502 6.76154 12.8238 6.58273L13.3334 6.29966V6.83339C13.3334 7.20158 13.6319 7.50006 14 7.50006C14.3682 7.50006 14.6667 7.20158 14.6667 6.83339V5.79435L14.6668 5.74627C14.6673 5.62441 14.6678 5.48084 14.6452 5.33482C14.6869 5.17472 14.6696 4.99892 14.5829 4.84286C14.4904 4.6764 14.3371 4.56501 14.1662 4.52099C14.0496 4.43038 13.9239 4.36116 13.8173 4.3024L13.7752 4.27915L12.8238 3.75062Z" fill="currentColor"></path><path d="M3.8238 4.91616C4.14566 4.73735 4.26162 4.33148 4.08281 4.00963C3.90401 3.68777 3.49814 3.57181 3.17628 3.75062L2.22493 4.27915L2.18284 4.3024C2.07615 4.36116 1.95045 4.4304 1.83382 4.52102C1.66295 4.56506 1.50977 4.67643 1.41731 4.84286C1.33065 4.99886 1.31323 5.17459 1.35493 5.33464C1.33229 5.48072 1.33281 5.62436 1.33326 5.74627L1.33338 5.79435V6.83339C1.33338 7.20158 1.63185 7.50006 2.00004 7.50006C2.36823 7.50006 2.66671 7.20158 2.66671 6.83339V6.29961L3.17632 6.58273C3.49817 6.76154 3.90404 6.64558 4.08285 6.32372C4.26166 6.00186 4.1457 5.596 3.82384 5.41719L3.3729 5.16666L3.8238 4.91616Z" fill="currentColor"></path><path d="M2.66671 10.1667C2.66671 9.79853 2.36823 9.50006 2.00004 9.50006C1.63185 9.50006 1.33338 9.79853 1.33338 10.1667V11.2058L1.33326 11.2538C1.33262 11.4298 1.33181 11.6509 1.40069 11.8594C1.46024 12.0397 1.55759 12.2051 1.68622 12.3447C1.835 12.5061 2.02873 12.6128 2.18281 12.6977L2.22493 12.721L3.17628 13.2495C3.49814 13.4283 3.90401 13.3123 4.08281 12.9905C4.26162 12.6686 4.14566 12.2628 3.8238 12.084L2.87245 11.5554C2.76582 11.4962 2.71137 11.4656 2.67318 11.4413L2.66995 11.4392L2.66971 11.4354C2.66699 11.3902 2.66671 11.3277 2.66671 11.2058V10.1667Z" fill="currentColor"></path><path d="M14.6667 10.1667C14.6667 9.79853 14.3682 9.50006 14 9.50006C13.6319 9.50006 13.3334 9.79853 13.3334 10.1667V11.2058C13.3334 11.3277 13.3331 11.3902 13.3304 11.4354L13.3301 11.4392L13.3269 11.4413C13.2887 11.4656 13.2343 11.4962 13.1276 11.5554L12.1763 12.084C11.8544 12.2628 11.7385 12.6686 11.9173 12.9905C12.0961 13.3123 12.5019 13.4283 12.8238 13.2495L13.7752 12.721L13.8172 12.6977C13.9713 12.6128 14.1651 12.5061 14.3139 12.3447C14.4425 12.2051 14.5398 12.0397 14.5994 11.8594C14.6683 11.6509 14.6675 11.4298 14.6668 11.2538L14.6667 11.2058V10.1667Z" fill="currentColor"></path><path d="M6.82381 13.7506C6.50195 13.5718 6.09608 13.6878 5.91727 14.0096C5.73846 14.3315 5.85443 14.7374 6.17628 14.9162L7.15826 15.4617L7.19793 15.4839C7.29819 15.54 7.41625 15.6061 7.54696 15.6556C7.66589 15.7659 7.82512 15.8333 8.00008 15.8333C8.17507 15.8333 8.33431 15.7659 8.45324 15.6556C8.58391 15.6061 8.70193 15.54 8.80215 15.4839L8.84183 15.4617L9.82381 14.9162C10.1457 14.7374 10.2616 14.3315 10.0828 14.0096C9.90401 13.6878 9.49814 13.5718 9.17628 13.7506L8.66675 14.0337V13.5C8.66675 13.1318 8.36827 12.8333 8.00008 12.8333C7.63189 12.8333 7.33341 13.1318 7.33341 13.5V14.0337L6.82381 13.7506Z" fill="currentColor"></path><path d="M6.82384 7.08385C6.50199 6.90505 6.09612 7.02101 5.91731 7.34286C5.7385 7.66472 5.85446 8.07059 6.17632 8.2494L7.33341 8.89223V10.1666C7.33341 10.5348 7.63189 10.8333 8.00008 10.8333C8.36827 10.8333 8.66675 10.5348 8.66675 10.1666V8.89223L9.82384 8.2494C10.1457 8.07059 10.2617 7.66472 10.0829 7.34286C9.90404 7.02101 9.49817 6.90505 9.17632 7.08385L8.00008 7.73732L6.82384 7.08385Z" fill="currentColor"></path></g></g></svg>
|
||||
|
Before Width: | Height: | Size: 4.2 KiB |
@ -1,4 +0,0 @@
|
||||
INSTALL_METHOD=remote
|
||||
REMOTE_INSTALL_HOST=debug-plugin.dify.dev
|
||||
REMOTE_INSTALL_PORT=5003
|
||||
REMOTE_INSTALL_KEY=ae1aa1c9-0af4-43db-b6d4-4fa9e6bfb646
|
||||
@ -1,24 +0,0 @@
|
||||
# Overview
|
||||
The Agent node in Dify Chatflow/Workflow lets LLMs autonomously use tools. This plugin features two official Dify Agent reasoning strategies, enabling LLMs to dynamically select and run tools during runtime for multi-step problem-solving.
|
||||
|
||||
## Strategies
|
||||
|
||||
### 1. Function Calling
|
||||
Function Calling maps user commands to specific functions or tools. The LLM identifies the user's intent, decides which function to call, and extracts the required parameters. It is a straightforward mechanism for invoking external capabilities.
|
||||
|
||||
|
||||
|
||||
#### Pros:
|
||||
- **Precise:** Directly calls the right tool for defined tasks, avoiding complex reasoning.
|
||||
- **Easy External Integration:** Integrates external APIs and tools as callable functions.
|
||||
- **Structured Output:** Provides structured function call information for easy processing.
|
||||
|
||||
### 2. ReAct (Reason + Act)
|
||||
ReAct alternates between the LLM reasoning about the situation and taking actions. The LLM analyzes the current state and goal, selects and uses a tool, and then uses the tool's output for the next thought and action. This cycle repeats until the problem is resolved.
|
||||
|
||||
|
||||
|
||||
#### Pros:
|
||||
- **Leverages External Information:** Effectively uses external tools to gather information for tasks the model cannot handle alone.
|
||||
- **Explainable Reasoning:** Interwoven reasoning and action steps allow some tracking of the Agent's process.
|
||||
- **Wide Applicability:** Suitable for tasks requiring external knowledge or specific actions, such as Q&A, information retrieval, and task execution.
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 162 KiB |
File diff suppressed because one or more lines are too long
|
Before Width: | Height: | Size: 23 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 157 KiB |
@ -1,10 +0,0 @@
|
||||
import sys
|
||||
|
||||
sys.path.append("../..")
|
||||
|
||||
from dify_plugin import DifyPluginEnv, Plugin
|
||||
|
||||
plugin = Plugin(DifyPluginEnv(MAX_REQUEST_TIMEOUT=240))
|
||||
|
||||
if __name__ == "__main__":
|
||||
plugin.run()
|
||||
@ -1,34 +0,0 @@
|
||||
version: 0.0.11
|
||||
type: plugin
|
||||
author: "langgenius"
|
||||
name: "agent"
|
||||
label:
|
||||
en_US: "Dify Agent Strategies"
|
||||
zh_Hans: "Dify Agent 策略"
|
||||
created_at: "2024-07-12T08:03:44.658609186Z"
|
||||
icon: icon.svg
|
||||
description:
|
||||
en_US: Dify official Agent strategies collection
|
||||
zh_Hans: Dify 官方 Agent 策略集合
|
||||
tags:
|
||||
- "agent"
|
||||
resource:
|
||||
memory: 1048576
|
||||
permission:
|
||||
tool:
|
||||
enabled: true
|
||||
model:
|
||||
enabled: true
|
||||
llm: true
|
||||
plugins:
|
||||
agent_strategies:
|
||||
- "provider/agent.yaml"
|
||||
meta:
|
||||
version: 0.0.1
|
||||
arch:
|
||||
- "amd64"
|
||||
- "arm64"
|
||||
runner:
|
||||
language: "python"
|
||||
version: "3.12"
|
||||
entrypoint: "main"
|
||||
@ -1,212 +0,0 @@
|
||||
import json
|
||||
import re
|
||||
from collections.abc import Generator
|
||||
from typing import Union
|
||||
|
||||
from dify_plugin.entities.model.llm import LLMResultChunk
|
||||
from dify_plugin.interfaces.agent import AgentScratchpadUnit
|
||||
|
||||
|
||||
class CotAgentOutputParser:
|
||||
@classmethod
|
||||
def handle_react_stream_output(
|
||||
cls, llm_response: Generator[LLMResultChunk, None, None], usage_dict: dict
|
||||
) -> Generator[Union[str, AgentScratchpadUnit.Action], None, None]:
|
||||
def parse_action(json_str):
|
||||
try:
|
||||
action = json.loads(json_str, strict=False)
|
||||
action_name = None
|
||||
action_input = None
|
||||
|
||||
# cohere always returns a list
|
||||
if isinstance(action, list) and len(action) == 1:
|
||||
action = action[0]
|
||||
|
||||
for key, value in action.items():
|
||||
if "input" in key.lower():
|
||||
action_input = value
|
||||
else:
|
||||
action_name = value
|
||||
|
||||
if action_name is not None and action_input is not None:
|
||||
return AgentScratchpadUnit.Action(
|
||||
action_name=action_name,
|
||||
action_input=action_input,
|
||||
)
|
||||
else:
|
||||
return json_str or ""
|
||||
except:
|
||||
return json_str or ""
|
||||
|
||||
def extra_json_from_code_block(
|
||||
code_block,
|
||||
) -> Generator[Union[str, AgentScratchpadUnit.Action], None, None]:
|
||||
code_blocks = re.findall(r"```(.*?)```", code_block, re.DOTALL)
|
||||
if not code_blocks:
|
||||
return
|
||||
for block in code_blocks:
|
||||
json_text = re.sub(
|
||||
r"^[a-zA-Z]+\n", "", block.strip(), flags=re.MULTILINE
|
||||
)
|
||||
yield parse_action(json_text)
|
||||
|
||||
code_block_cache = ""
|
||||
code_block_delimiter_count = 0
|
||||
in_code_block = False
|
||||
json_cache = ""
|
||||
json_quote_count = 0
|
||||
in_json = False
|
||||
got_json = False
|
||||
|
||||
action_cache = ""
|
||||
action_str = "action:"
|
||||
action_idx = 0
|
||||
|
||||
thought_cache = ""
|
||||
thought_str = "thought:"
|
||||
thought_idx = 0
|
||||
|
||||
last_character = ""
|
||||
|
||||
for response in llm_response:
|
||||
if response.delta.usage:
|
||||
usage_dict["usage"] = response.delta.usage
|
||||
response_content = response.delta.message.content
|
||||
if not isinstance(response_content, str):
|
||||
continue
|
||||
|
||||
# stream
|
||||
index = 0
|
||||
while index < len(response_content):
|
||||
steps = 1
|
||||
delta = response_content[index : index + steps]
|
||||
yield_delta = False
|
||||
|
||||
if delta == "`":
|
||||
last_character = delta
|
||||
code_block_cache += delta
|
||||
code_block_delimiter_count += 1
|
||||
else:
|
||||
if not in_code_block:
|
||||
if code_block_delimiter_count > 0:
|
||||
last_character = delta
|
||||
yield code_block_cache
|
||||
code_block_cache = ""
|
||||
else:
|
||||
last_character = delta
|
||||
code_block_cache += delta
|
||||
code_block_delimiter_count = 0
|
||||
|
||||
if not in_code_block and not in_json:
|
||||
if delta.lower() == action_str[action_idx] and action_idx == 0:
|
||||
if last_character not in {"\n", " ", ""}:
|
||||
yield_delta = True
|
||||
else:
|
||||
last_character = delta
|
||||
action_cache += delta
|
||||
action_idx += 1
|
||||
if action_idx == len(action_str):
|
||||
action_cache = ""
|
||||
action_idx = 0
|
||||
index += steps
|
||||
continue
|
||||
elif delta.lower() == action_str[action_idx] and action_idx > 0:
|
||||
last_character = delta
|
||||
action_cache += delta
|
||||
action_idx += 1
|
||||
if action_idx == len(action_str):
|
||||
action_cache = ""
|
||||
action_idx = 0
|
||||
index += steps
|
||||
continue
|
||||
else:
|
||||
if action_cache:
|
||||
last_character = delta
|
||||
yield action_cache
|
||||
action_cache = ""
|
||||
action_idx = 0
|
||||
|
||||
if delta.lower() == thought_str[thought_idx] and thought_idx == 0:
|
||||
if last_character not in {"\n", " ", ""}:
|
||||
yield_delta = True
|
||||
else:
|
||||
last_character = delta
|
||||
thought_cache += delta
|
||||
thought_idx += 1
|
||||
if thought_idx == len(thought_str):
|
||||
thought_cache = ""
|
||||
thought_idx = 0
|
||||
index += steps
|
||||
continue
|
||||
elif delta.lower() == thought_str[thought_idx] and thought_idx > 0:
|
||||
last_character = delta
|
||||
thought_cache += delta
|
||||
thought_idx += 1
|
||||
if thought_idx == len(thought_str):
|
||||
thought_cache = ""
|
||||
thought_idx = 0
|
||||
index += steps
|
||||
continue
|
||||
else:
|
||||
if thought_cache:
|
||||
last_character = delta
|
||||
yield thought_cache
|
||||
thought_cache = ""
|
||||
thought_idx = 0
|
||||
|
||||
if yield_delta:
|
||||
index += steps
|
||||
last_character = delta
|
||||
yield delta
|
||||
continue
|
||||
|
||||
if code_block_delimiter_count == 3:
|
||||
if in_code_block:
|
||||
last_character = delta
|
||||
yield from extra_json_from_code_block(code_block_cache)
|
||||
code_block_cache = ""
|
||||
|
||||
in_code_block = not in_code_block
|
||||
code_block_delimiter_count = 0
|
||||
|
||||
if not in_code_block:
|
||||
# handle single json
|
||||
if delta == "{":
|
||||
json_quote_count += 1
|
||||
in_json = True
|
||||
last_character = delta
|
||||
json_cache += delta
|
||||
elif delta == "}":
|
||||
last_character = delta
|
||||
json_cache += delta
|
||||
if json_quote_count > 0:
|
||||
json_quote_count -= 1
|
||||
if json_quote_count == 0:
|
||||
in_json = False
|
||||
got_json = True
|
||||
index += steps
|
||||
continue
|
||||
else:
|
||||
if in_json:
|
||||
last_character = delta
|
||||
json_cache += delta
|
||||
|
||||
if got_json:
|
||||
got_json = False
|
||||
last_character = delta
|
||||
yield parse_action(json_cache)
|
||||
json_cache = ""
|
||||
json_quote_count = 0
|
||||
in_json = False
|
||||
|
||||
if not in_code_block and not in_json:
|
||||
last_character = delta
|
||||
yield delta.replace("`", "")
|
||||
|
||||
index += steps
|
||||
|
||||
if code_block_cache:
|
||||
yield code_block_cache
|
||||
|
||||
if json_cache:
|
||||
yield parse_action(json_cache)
|
||||
@ -1,106 +0,0 @@
|
||||
ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
|
||||
|
||||
{{instruction}}
|
||||
|
||||
You have access to the following tools:
|
||||
|
||||
{{tools}}
|
||||
|
||||
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
|
||||
Valid "action" values: "Final Answer" or {{tool_names}}
|
||||
|
||||
Provide only ONE action per $JSON_BLOB, as shown:
|
||||
|
||||
```
|
||||
{
|
||||
"action": $TOOL_NAME,
|
||||
"action_input": $ACTION_INPUT
|
||||
}
|
||||
```
|
||||
|
||||
Follow this format:
|
||||
|
||||
Question: input question to answer
|
||||
Thought: consider previous and subsequent steps
|
||||
Action:
|
||||
```
|
||||
$JSON_BLOB
|
||||
```
|
||||
Observation: action result
|
||||
... (repeat Thought/Action/Observation N times)
|
||||
Thought: I know what to respond
|
||||
Action:
|
||||
```
|
||||
{
|
||||
"action": "Final Answer",
|
||||
"action_input": "Final response to human"
|
||||
}
|
||||
```
|
||||
|
||||
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
|
||||
{{historic_messages}}
|
||||
Question: {{query}}
|
||||
{{agent_scratchpad}}
|
||||
Thought:""" # noqa: E501
|
||||
|
||||
|
||||
ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES = """Observation: {{observation}}
|
||||
Thought:"""
|
||||
|
||||
ENGLISH_REACT_CHAT_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
|
||||
|
||||
{{instruction}}
|
||||
|
||||
You have access to the following tools:
|
||||
|
||||
{{tools}}
|
||||
|
||||
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
|
||||
Valid "action" values: "Final Answer" or {{tool_names}}
|
||||
|
||||
Provide only ONE action per $JSON_BLOB, as shown:
|
||||
|
||||
```
|
||||
{
|
||||
"action": $TOOL_NAME,
|
||||
"action_input": $ACTION_INPUT
|
||||
}
|
||||
```
|
||||
|
||||
Follow this format:
|
||||
|
||||
Question: input question to answer
|
||||
Thought: consider previous and subsequent steps
|
||||
Action:
|
||||
```
|
||||
$JSON_BLOB
|
||||
```
|
||||
Observation: action result
|
||||
... (repeat Thought/Action/Observation N times)
|
||||
Thought: I know what to respond
|
||||
Action:
|
||||
```
|
||||
{
|
||||
"action": "Final Answer",
|
||||
"action_input": "Final response to human"
|
||||
}
|
||||
```
|
||||
|
||||
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
|
||||
""" # noqa: E501
|
||||
|
||||
|
||||
ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES = ""
|
||||
|
||||
REACT_PROMPT_TEMPLATES = {
|
||||
"english": {
|
||||
"chat": {
|
||||
"prompt": ENGLISH_REACT_CHAT_PROMPT_TEMPLATES,
|
||||
"agent_scratchpad": ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES,
|
||||
},
|
||||
"completion": {
|
||||
"prompt": ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES,
|
||||
"agent_scratchpad": ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES,
|
||||
},
|
||||
}
|
||||
}
|
||||
@ -1,5 +0,0 @@
|
||||
from dify_plugin.interfaces.agent import AgentProvider
|
||||
|
||||
|
||||
class LanggeniusAgentProvider(AgentProvider):
|
||||
pass
|
||||
@ -1,18 +0,0 @@
|
||||
identity:
|
||||
author: langgenius
|
||||
name: agent
|
||||
label:
|
||||
en_US: Agent
|
||||
zh_Hans: Agent
|
||||
pt_BR: Agent
|
||||
description:
|
||||
en_US: Agent
|
||||
zh_Hans: Agent
|
||||
pt_BR: Agent
|
||||
icon: icon.svg
|
||||
strategies:
|
||||
- strategies/function_calling.yaml
|
||||
- strategies/ReAct.yaml
|
||||
extra:
|
||||
python:
|
||||
source: provider/agent.py
|
||||
@ -1 +0,0 @@
|
||||
dify_plugin==0.0.1b74
|
||||
@ -1,642 +0,0 @@
|
||||
import json
|
||||
import time
|
||||
from collections.abc import Generator, Mapping
|
||||
from typing import Any, Optional, cast
|
||||
|
||||
from dify_plugin.entities.agent import AgentInvokeMessage
|
||||
from dify_plugin.entities.model.llm import LLMModelConfig, LLMUsage
|
||||
from dify_plugin.entities.model.message import (
|
||||
AssistantPromptMessage,
|
||||
PromptMessage,
|
||||
SystemPromptMessage,
|
||||
ToolPromptMessage,
|
||||
UserPromptMessage,
|
||||
)
|
||||
from dify_plugin.entities.tool import (
|
||||
LogMetadata,
|
||||
ToolInvokeMessage,
|
||||
ToolParameter,
|
||||
ToolProviderType,
|
||||
)
|
||||
from dify_plugin.interfaces.agent import (
|
||||
AgentModelConfig,
|
||||
AgentScratchpadUnit,
|
||||
AgentStrategy,
|
||||
ToolEntity,
|
||||
)
|
||||
from output_parser.cot_output_parser import CotAgentOutputParser
|
||||
from prompt.template import REACT_PROMPT_TEMPLATES
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
ignore_observation_providers = ["wenxin"]
|
||||
|
||||
|
||||
class ReActParams(BaseModel):
|
||||
query: str
|
||||
instruction: str | None
|
||||
model: AgentModelConfig
|
||||
tools: list[ToolEntity] | None
|
||||
inputs: dict[str, Any] = {}
|
||||
maximum_iterations: int = 3
|
||||
|
||||
|
||||
class AgentPromptEntity(BaseModel):
|
||||
"""
|
||||
Agent Prompt Entity.
|
||||
"""
|
||||
|
||||
first_prompt: str
|
||||
next_iteration: str
|
||||
|
||||
|
||||
class ToolInvokeMeta(BaseModel):
|
||||
"""
|
||||
Tool invoke meta
|
||||
"""
|
||||
|
||||
time_cost: float = Field(..., description="The time cost of the tool invoke")
|
||||
error: Optional[str] = None
|
||||
tool_config: Optional[dict] = None
|
||||
|
||||
@classmethod
|
||||
def empty(cls) -> "ToolInvokeMeta":
|
||||
"""
|
||||
Get an empty instance of ToolInvokeMeta
|
||||
"""
|
||||
return cls(time_cost=0.0, error=None, tool_config={})
|
||||
|
||||
@classmethod
|
||||
def error_instance(cls, error: str) -> "ToolInvokeMeta":
|
||||
"""
|
||||
Get an instance of ToolInvokeMeta with error
|
||||
"""
|
||||
return cls(time_cost=0.0, error=error, tool_config={})
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
return {
|
||||
"time_cost": self.time_cost,
|
||||
"error": self.error,
|
||||
"tool_config": self.tool_config,
|
||||
}
|
||||
|
||||
|
||||
class ReActAgentStrategy(AgentStrategy):
|
||||
def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
|
||||
react_params = ReActParams(**parameters)
|
||||
query = react_params.query
|
||||
model = react_params.model
|
||||
agent_scratchpad = []
|
||||
history_prompt_messages: list[PromptMessage] = []
|
||||
current_session_messages = []
|
||||
self._organize_historic_prompt_messages(
|
||||
history_prompt_messages, current_session_messages=current_session_messages
|
||||
)
|
||||
tools = react_params.tools
|
||||
tool_instances = {tool.identity.name: tool for tool in tools} if tools else {}
|
||||
react_params.model.completion_params = (
|
||||
react_params.model.completion_params or {}
|
||||
)
|
||||
# check model mode
|
||||
stop = (
|
||||
react_params.model.completion_params.get("stop", [])
|
||||
if react_params.model.completion_params
|
||||
else []
|
||||
)
|
||||
|
||||
if (
|
||||
"Observation" not in stop
|
||||
and model.provider not in ignore_observation_providers
|
||||
):
|
||||
stop.append("Observation")
|
||||
# init instruction
|
||||
inputs = react_params.inputs
|
||||
instruction = react_params.instruction or ""
|
||||
self._instruction = self._fill_in_inputs_from_external_data_tools(
|
||||
instruction, inputs
|
||||
)
|
||||
|
||||
iteration_step = 1
|
||||
max_iteration_steps = react_params.maximum_iterations
|
||||
|
||||
# convert tools into ModelRuntime Tool format
|
||||
prompt_messages_tools = self._init_prompt_tools(tools)
|
||||
self._prompt_messages_tools = prompt_messages_tools
|
||||
|
||||
run_agent_state = True
|
||||
llm_usage: dict[str, Optional[LLMUsage]] = {"usage": None}
|
||||
final_answer = ""
|
||||
prompt_messages = []
|
||||
while run_agent_state and iteration_step <= max_iteration_steps:
|
||||
# continue to run until there is not any tool call
|
||||
run_agent_state = False
|
||||
round_started_at = time.perf_counter()
|
||||
round_log = self.create_log_message(
|
||||
label=f"ROUND {iteration_step}",
|
||||
data={},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: round_started_at,
|
||||
},
|
||||
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||
)
|
||||
yield round_log
|
||||
if iteration_step == max_iteration_steps:
|
||||
# the last iteration, remove all tools
|
||||
self._prompt_messages_tools = []
|
||||
|
||||
message_file_ids: list[str] = []
|
||||
|
||||
# recalc llm max tokens
|
||||
prompt_messages = self._organize_prompt_messages(agent_scratchpad, query)
|
||||
if model.completion_params:
|
||||
self.recalc_llm_max_tokens(
|
||||
model.entity, prompt_messages, model.completion_params
|
||||
)
|
||||
# invoke model
|
||||
chunks = self.session.model.llm.invoke(
|
||||
model_config=LLMModelConfig(**model.model_dump(mode="json")),
|
||||
prompt_messages=prompt_messages,
|
||||
stream=True,
|
||||
stop=stop,
|
||||
)
|
||||
|
||||
usage_dict = {}
|
||||
react_chunks = CotAgentOutputParser.handle_react_stream_output(
|
||||
chunks, usage_dict
|
||||
)
|
||||
scratchpad = AgentScratchpadUnit(
|
||||
agent_response="",
|
||||
thought="",
|
||||
action_str="",
|
||||
observation="",
|
||||
action=None,
|
||||
)
|
||||
|
||||
model_started_at = time.perf_counter()
|
||||
model_log = self.create_log_message(
|
||||
label=f"{model.model} Thought",
|
||||
data={},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: model_started_at,
|
||||
LogMetadata.PROVIDER: model.provider,
|
||||
},
|
||||
parent=round_log,
|
||||
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||
)
|
||||
yield model_log
|
||||
|
||||
for chunk in react_chunks:
|
||||
if isinstance(chunk, AgentScratchpadUnit.Action):
|
||||
action = chunk
|
||||
# detect action
|
||||
assert scratchpad.agent_response is not None
|
||||
scratchpad.agent_response += json.dumps(chunk.model_dump())
|
||||
|
||||
scratchpad.action_str = json.dumps(chunk.model_dump())
|
||||
scratchpad.action = action
|
||||
else:
|
||||
scratchpad.agent_response = scratchpad.agent_response or ""
|
||||
scratchpad.thought = scratchpad.thought or ""
|
||||
scratchpad.agent_response += chunk
|
||||
scratchpad.thought += chunk
|
||||
scratchpad.thought = (
|
||||
scratchpad.thought.strip()
|
||||
if scratchpad.thought
|
||||
else "I am thinking about how to help you"
|
||||
)
|
||||
agent_scratchpad.append(scratchpad)
|
||||
|
||||
# get llm usage
|
||||
if "usage" in usage_dict:
|
||||
if usage_dict["usage"] is not None:
|
||||
self.increase_usage(llm_usage, usage_dict["usage"])
|
||||
else:
|
||||
usage_dict["usage"] = LLMUsage.empty_usage()
|
||||
|
||||
action = (
|
||||
scratchpad.action.to_dict()
|
||||
if scratchpad.action
|
||||
else {"action": scratchpad.agent_response}
|
||||
)
|
||||
|
||||
yield self.finish_log_message(
|
||||
log=model_log,
|
||||
data={"thought": scratchpad.thought, **action},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: model_started_at,
|
||||
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||
LogMetadata.ELAPSED_TIME: time.perf_counter() - model_started_at,
|
||||
LogMetadata.PROVIDER: model.provider,
|
||||
LogMetadata.TOTAL_PRICE: usage_dict["usage"].total_price
|
||||
if usage_dict["usage"]
|
||||
else 0,
|
||||
LogMetadata.CURRENCY: usage_dict["usage"].currency
|
||||
if usage_dict["usage"]
|
||||
else "",
|
||||
LogMetadata.TOTAL_TOKENS: usage_dict["usage"].total_tokens
|
||||
if usage_dict["usage"]
|
||||
else 0,
|
||||
},
|
||||
)
|
||||
if not scratchpad.action:
|
||||
final_answer = scratchpad.thought
|
||||
else:
|
||||
if scratchpad.action.action_name.lower() == "final answer":
|
||||
# action is final answer, return final answer directly
|
||||
try:
|
||||
if isinstance(scratchpad.action.action_input, dict):
|
||||
final_answer = json.dumps(scratchpad.action.action_input)
|
||||
elif isinstance(scratchpad.action.action_input, str):
|
||||
final_answer = scratchpad.action.action_input
|
||||
else:
|
||||
final_answer = f"{scratchpad.action.action_input}"
|
||||
except json.JSONDecodeError:
|
||||
final_answer = f"{scratchpad.action.action_input}"
|
||||
else:
|
||||
run_agent_state = True
|
||||
# action is tool call, invoke tool
|
||||
tool_call_started_at = time.perf_counter()
|
||||
tool_name = scratchpad.action.action_name
|
||||
tool_call_log = self.create_log_message(
|
||||
label=f"CALL {tool_name}",
|
||||
data={},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: time.perf_counter(),
|
||||
LogMetadata.PROVIDER: tool_instances[
|
||||
tool_name
|
||||
].identity.provider
|
||||
if tool_instances.get(tool_name)
|
||||
else "",
|
||||
},
|
||||
parent=round_log,
|
||||
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||
)
|
||||
yield tool_call_log
|
||||
tool_invoke_response, tool_invoke_parameters = (
|
||||
self._handle_invoke_action(
|
||||
action=scratchpad.action,
|
||||
tool_instances=tool_instances,
|
||||
message_file_ids=message_file_ids,
|
||||
)
|
||||
)
|
||||
scratchpad.observation = tool_invoke_response
|
||||
scratchpad.agent_response = tool_invoke_response
|
||||
yield self.finish_log_message(
|
||||
log=tool_call_log,
|
||||
data={
|
||||
"tool_name": tool_name,
|
||||
"tool_call_args": tool_invoke_parameters,
|
||||
"output": tool_invoke_response,
|
||||
},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: tool_call_started_at,
|
||||
LogMetadata.PROVIDER: tool_instances[
|
||||
tool_name
|
||||
].identity.provider
|
||||
if tool_instances.get(tool_name)
|
||||
else "",
|
||||
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||
LogMetadata.ELAPSED_TIME: time.perf_counter()
|
||||
- tool_call_started_at,
|
||||
},
|
||||
)
|
||||
|
||||
# update prompt tool message
|
||||
for prompt_tool in self._prompt_messages_tools:
|
||||
self.update_prompt_message_tool(
|
||||
tool_instances[prompt_tool.name], prompt_tool
|
||||
)
|
||||
yield self.finish_log_message(
|
||||
log=round_log,
|
||||
data={
|
||||
"action_name": scratchpad.action.action_name
|
||||
if scratchpad.action
|
||||
else "",
|
||||
"action_input": scratchpad.action.action_input
|
||||
if scratchpad.action
|
||||
else "",
|
||||
"thought": scratchpad.thought,
|
||||
"observation": scratchpad.observation,
|
||||
},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: round_started_at,
|
||||
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||
LogMetadata.ELAPSED_TIME: time.perf_counter() - round_started_at,
|
||||
LogMetadata.TOTAL_PRICE: usage_dict["usage"].total_price
|
||||
if usage_dict["usage"]
|
||||
else 0,
|
||||
LogMetadata.CURRENCY: usage_dict["usage"].currency
|
||||
if usage_dict["usage"]
|
||||
else "",
|
||||
LogMetadata.TOTAL_TOKENS: usage_dict["usage"].total_tokens
|
||||
if usage_dict["usage"]
|
||||
else 0,
|
||||
},
|
||||
)
|
||||
iteration_step += 1
|
||||
|
||||
yield self.create_text_message(final_answer)
|
||||
yield self.create_json_message(
|
||||
{
|
||||
"execution_metadata": {
|
||||
LogMetadata.TOTAL_PRICE: llm_usage["usage"].total_price
|
||||
if llm_usage["usage"] is not None
|
||||
else 0,
|
||||
LogMetadata.CURRENCY: llm_usage["usage"].currency
|
||||
if llm_usage["usage"] is not None
|
||||
else "",
|
||||
LogMetadata.TOTAL_TOKENS: llm_usage["usage"].total_tokens
|
||||
if llm_usage["usage"] is not None
|
||||
else 0,
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
def _organize_system_prompt(self) -> SystemPromptMessage:
|
||||
"""
|
||||
Organize system prompt
|
||||
"""
|
||||
|
||||
prompt_entity = AgentPromptEntity(
|
||||
first_prompt=REACT_PROMPT_TEMPLATES["english"]["chat"]["prompt"],
|
||||
next_iteration=REACT_PROMPT_TEMPLATES["english"]["chat"][
|
||||
"agent_scratchpad"
|
||||
],
|
||||
)
|
||||
if not prompt_entity:
|
||||
raise ValueError("Agent prompt configuration is not set")
|
||||
first_prompt = prompt_entity.first_prompt
|
||||
|
||||
system_prompt = (
|
||||
first_prompt.replace("{{instruction}}", self._instruction)
|
||||
.replace(
|
||||
"{{tools}}",
|
||||
json.dumps(
|
||||
[
|
||||
tool.model_dump(mode="json")
|
||||
for tool in self._prompt_messages_tools
|
||||
]
|
||||
),
|
||||
)
|
||||
.replace(
|
||||
"{{tool_names}}",
|
||||
", ".join([tool.name for tool in self._prompt_messages_tools]),
|
||||
)
|
||||
)
|
||||
|
||||
return SystemPromptMessage(content=system_prompt)
|
||||
|
||||
def _organize_user_query(
|
||||
self, query, prompt_messages: list[PromptMessage]
|
||||
) -> list[PromptMessage]:
|
||||
"""
|
||||
Organize user query
|
||||
"""
|
||||
prompt_messages.append(UserPromptMessage(content=query))
|
||||
|
||||
return prompt_messages
|
||||
|
||||
def _organize_prompt_messages(
|
||||
self, agent_scratchpad: list, query: str
|
||||
) -> list[PromptMessage]:
|
||||
"""
|
||||
Organize
|
||||
"""
|
||||
# organize system prompt
|
||||
system_message = self._organize_system_prompt()
|
||||
|
||||
# organize current assistant messages
|
||||
agent_scratchpad = agent_scratchpad
|
||||
if not agent_scratchpad:
|
||||
assistant_messages = []
|
||||
else:
|
||||
assistant_message = AssistantPromptMessage(content="")
|
||||
assistant_message.content = (
|
||||
"" # FIXME: type check tell mypy that assistant_message.content is str
|
||||
)
|
||||
for unit in agent_scratchpad:
|
||||
if unit.is_final():
|
||||
assert isinstance(assistant_message.content, str)
|
||||
assistant_message.content += f"Final Answer: {unit.agent_response}"
|
||||
else:
|
||||
assert isinstance(assistant_message.content, str)
|
||||
assistant_message.content += f"Thought: {unit.thought}\n\n"
|
||||
if unit.action_str:
|
||||
assistant_message.content += f"Action: {unit.action_str}\n\n"
|
||||
if unit.observation:
|
||||
assistant_message.content += (
|
||||
f"Observation: {unit.observation}\n\n"
|
||||
)
|
||||
|
||||
assistant_messages = [assistant_message]
|
||||
|
||||
# query messages
|
||||
query_messages = self._organize_user_query(query, [])
|
||||
|
||||
if assistant_messages:
|
||||
# organize historic prompt messages
|
||||
historic_messages = self._organize_historic_prompt_messages(
|
||||
[
|
||||
system_message,
|
||||
*query_messages,
|
||||
*assistant_messages,
|
||||
UserPromptMessage(content="continue"),
|
||||
]
|
||||
)
|
||||
messages = [
|
||||
system_message,
|
||||
*historic_messages,
|
||||
*query_messages,
|
||||
*assistant_messages,
|
||||
UserPromptMessage(content="continue"),
|
||||
]
|
||||
else:
|
||||
# organize historic prompt messages
|
||||
historic_messages = self._organize_historic_prompt_messages(
|
||||
[system_message, *query_messages]
|
||||
)
|
||||
messages = [system_message, *historic_messages, *query_messages]
|
||||
|
||||
# join all messages
|
||||
return messages
|
||||
|
||||
def _handle_invoke_action(
|
||||
self,
|
||||
action: AgentScratchpadUnit.Action,
|
||||
tool_instances: Mapping[str, ToolEntity],
|
||||
message_file_ids: list[str],
|
||||
) -> tuple[str, dict[str, Any] | str]:
|
||||
"""
|
||||
handle invoke action
|
||||
:param action: action
|
||||
:param tool_instances: tool instances
|
||||
:param message_file_ids: message file ids
|
||||
:param trace_manager: trace manager
|
||||
:return: observation, meta
|
||||
"""
|
||||
# action is tool call, invoke tool
|
||||
tool_call_name = action.action_name
|
||||
tool_call_args = action.action_input
|
||||
tool_instance = tool_instances.get(tool_call_name)
|
||||
|
||||
if not tool_instance:
|
||||
answer = f"there is not a tool named {tool_call_name}"
|
||||
return answer, tool_call_args
|
||||
|
||||
if isinstance(tool_call_args, str):
|
||||
try:
|
||||
tool_call_args = json.loads(tool_call_args)
|
||||
except json.JSONDecodeError as e:
|
||||
params = [
|
||||
param.name
|
||||
for param in tool_instance.parameters
|
||||
if param.form == ToolParameter.ToolParameterForm.LLM
|
||||
]
|
||||
if len(params) > 1:
|
||||
raise ValueError("tool call args is not a valid json string") from e
|
||||
tool_call_args = {params[0]: tool_call_args} if len(params) == 1 else {}
|
||||
|
||||
tool_invoke_parameters = {**tool_instance.runtime_parameters, **tool_call_args}
|
||||
try:
|
||||
tool_invoke_responses = self.session.tool.invoke(
|
||||
provider_type=ToolProviderType(tool_instance.provider_type),
|
||||
provider=tool_instance.identity.provider,
|
||||
tool_name=tool_instance.identity.name,
|
||||
parameters=tool_invoke_parameters,
|
||||
)
|
||||
result = ""
|
||||
for response in tool_invoke_responses:
|
||||
if response.type == ToolInvokeMessage.MessageType.TEXT:
|
||||
result += cast(ToolInvokeMessage.TextMessage, response.message).text
|
||||
elif response.type == ToolInvokeMessage.MessageType.LINK:
|
||||
result += (
|
||||
f"result link: {cast(ToolInvokeMessage.TextMessage, response.message).text}."
|
||||
+ " please tell user to check it."
|
||||
)
|
||||
elif response.type in {
|
||||
ToolInvokeMessage.MessageType.IMAGE_LINK,
|
||||
ToolInvokeMessage.MessageType.IMAGE,
|
||||
}:
|
||||
result += (
|
||||
"image has been created and sent to user already, "
|
||||
+ "you do not need to create it, just tell the user to check it now."
|
||||
)
|
||||
elif response.type == ToolInvokeMessage.MessageType.JSON:
|
||||
text = json.dumps(
|
||||
cast(
|
||||
ToolInvokeMessage.JsonMessage, response.message
|
||||
).json_object,
|
||||
ensure_ascii=False,
|
||||
)
|
||||
result += f"tool response: {text}."
|
||||
else:
|
||||
result += f"tool response: {response.message!r}."
|
||||
except Exception as e:
|
||||
result = f"tool invoke error: {str(e)}"
|
||||
|
||||
return result, tool_invoke_parameters
|
||||
|
||||
def _convert_dict_to_action(self, action: dict) -> AgentScratchpadUnit.Action:
|
||||
"""
|
||||
convert dict to action
|
||||
"""
|
||||
return AgentScratchpadUnit.Action(
|
||||
action_name=action["action"], action_input=action["action_input"]
|
||||
)
|
||||
|
||||
def _fill_in_inputs_from_external_data_tools(
|
||||
self, instruction: str, inputs: Mapping[str, Any]
|
||||
) -> str:
|
||||
"""
|
||||
fill in inputs from external data tools
|
||||
"""
|
||||
for key, value in inputs.items():
|
||||
try:
|
||||
instruction = instruction.replace(f"{{{{{key}}}}}", str(value))
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
return instruction
|
||||
|
||||
def _format_assistant_message(
|
||||
self, agent_scratchpad: list[AgentScratchpadUnit]
|
||||
) -> str:
|
||||
"""
|
||||
format assistant message
|
||||
"""
|
||||
message = ""
|
||||
for scratchpad in agent_scratchpad:
|
||||
if scratchpad.is_final():
|
||||
message += f"Final Answer: {scratchpad.agent_response}"
|
||||
else:
|
||||
message += f"Thought: {scratchpad.thought}\n\n"
|
||||
if scratchpad.action_str:
|
||||
message += f"Action: {scratchpad.action_str}\n\n"
|
||||
if scratchpad.observation:
|
||||
message += f"Observation: {scratchpad.observation}\n\n"
|
||||
|
||||
return message
|
||||
|
||||
def _organize_historic_prompt_messages(
|
||||
self,
|
||||
history_prompt_messages: list[PromptMessage],
|
||||
current_session_messages: list[PromptMessage] | None = None,
|
||||
) -> list[PromptMessage]:
|
||||
"""
|
||||
organize historic prompt messages
|
||||
"""
|
||||
result: list[PromptMessage] = []
|
||||
scratchpads: list[AgentScratchpadUnit] = []
|
||||
current_scratchpad: AgentScratchpadUnit | None = None
|
||||
|
||||
for message in history_prompt_messages:
|
||||
if isinstance(message, AssistantPromptMessage):
|
||||
if not current_scratchpad:
|
||||
assert isinstance(message.content, str)
|
||||
current_scratchpad = AgentScratchpadUnit(
|
||||
agent_response=message.content,
|
||||
thought=message.content
|
||||
or "I am thinking about how to help you",
|
||||
action_str="",
|
||||
action=None,
|
||||
observation=None,
|
||||
)
|
||||
scratchpads.append(current_scratchpad)
|
||||
if message.tool_calls:
|
||||
try:
|
||||
current_scratchpad.action = AgentScratchpadUnit.Action(
|
||||
action_name=message.tool_calls[0].function.name,
|
||||
action_input=json.loads(
|
||||
message.tool_calls[0].function.arguments
|
||||
),
|
||||
)
|
||||
current_scratchpad.action_str = json.dumps(
|
||||
current_scratchpad.action.to_dict()
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
elif isinstance(message, ToolPromptMessage):
|
||||
if current_scratchpad:
|
||||
assert isinstance(message.content, str)
|
||||
current_scratchpad.observation = message.content
|
||||
else:
|
||||
raise NotImplementedError("expected str type")
|
||||
elif isinstance(message, UserPromptMessage):
|
||||
if scratchpads:
|
||||
result.append(
|
||||
AssistantPromptMessage(
|
||||
content=self._format_assistant_message(scratchpads)
|
||||
)
|
||||
)
|
||||
scratchpads = []
|
||||
current_scratchpad = None
|
||||
|
||||
result.append(message)
|
||||
|
||||
if scratchpads:
|
||||
result.append(
|
||||
AssistantPromptMessage(
|
||||
content=self._format_assistant_message(scratchpads)
|
||||
)
|
||||
)
|
||||
|
||||
return current_session_messages or []
|
||||
@ -1,58 +0,0 @@
|
||||
identity:
|
||||
name: ReAct
|
||||
author: Dify
|
||||
label:
|
||||
en_US: ReAct
|
||||
zh_Hans: ReAct
|
||||
pt_BR: ReAct
|
||||
description:
|
||||
en_US: ReAct is a basic strategy for agent, model will use the tools provided to perform the task.
|
||||
zh_Hans: ReAct 是一个基本的 Agent 策略，模型将使用提供的工具来执行任务。
|
||||
pt_BR: ReAct is a basic strategy for agent, model will use the tools provided to perform the task.
|
||||
parameters:
|
||||
- name: model
|
||||
type: model-selector
|
||||
scope: tool-call&llm
|
||||
required: true
|
||||
label:
|
||||
en_US: Model
|
||||
zh_Hans: 模型
|
||||
pt_BR: Model
|
||||
- name: tools
|
||||
type: array[tools]
|
||||
required: true
|
||||
label:
|
||||
en_US: Tool list
|
||||
zh_Hans: 工具列表
|
||||
pt_BR: Tool list
|
||||
- name: instruction
|
||||
type: string
|
||||
required: true
|
||||
label:
|
||||
en_US: Instruction
|
||||
zh_Hans: 指令
|
||||
pt_BR: Instruction
|
||||
auto_generate:
|
||||
type: prompt_instruction
|
||||
template:
|
||||
enabled: true
|
||||
- name: query
|
||||
type: string
|
||||
required: true
|
||||
label:
|
||||
en_US: Query
|
||||
zh_Hans: 查询
|
||||
pt_BR: Query
|
||||
- name: maximum_iterations
|
||||
type: number
|
||||
required: true
|
||||
label:
|
||||
en_US: Maximum Iterations
|
||||
zh_Hans: 最大迭代次数
|
||||
pt_BR: Maximum Iterations
|
||||
default: 3
|
||||
min: 1
|
||||
max: 30
|
||||
extra:
|
||||
python:
|
||||
source: strategies/ReAct.py
|
||||
@ -1,546 +0,0 @@
|
||||
import json
|
||||
import time
|
||||
from collections.abc import Generator
|
||||
from copy import deepcopy
|
||||
from typing import Any, Optional, cast
|
||||
|
||||
from dify_plugin.entities.agent import AgentInvokeMessage
|
||||
from dify_plugin.entities.model.llm import (
|
||||
LLMModelConfig,
|
||||
LLMResult,
|
||||
LLMResultChunk,
|
||||
LLMUsage,
|
||||
)
|
||||
from dify_plugin.entities.model.message import (
|
||||
AssistantPromptMessage,
|
||||
PromptMessage,
|
||||
PromptMessageContentType,
|
||||
PromptMessageRole,
|
||||
SystemPromptMessage,
|
||||
ToolPromptMessage,
|
||||
UserPromptMessage,
|
||||
)
|
||||
from dify_plugin.entities.tool import LogMetadata, ToolInvokeMessage, ToolProviderType
|
||||
from dify_plugin.interfaces.agent import AgentModelConfig, AgentStrategy, ToolEntity
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class FunctionCallingParams(BaseModel):
|
||||
query: str
|
||||
instruction: str | None
|
||||
model: AgentModelConfig
|
||||
tools: list[ToolEntity] | None
|
||||
maximum_iterations: int = 3
|
||||
|
||||
|
||||
class ToolInvokeMeta(BaseModel):
|
||||
"""
|
||||
Tool invoke meta
|
||||
"""
|
||||
|
||||
time_cost: float = Field(..., description="The time cost of the tool invoke")
|
||||
error: Optional[str] = None
|
||||
tool_config: Optional[dict] = None
|
||||
|
||||
@classmethod
|
||||
def empty(cls) -> "ToolInvokeMeta":
|
||||
"""
|
||||
Get an empty instance of ToolInvokeMeta
|
||||
"""
|
||||
return cls(time_cost=0.0, error=None, tool_config={})
|
||||
|
||||
@classmethod
|
||||
def error_instance(cls, error: str) -> "ToolInvokeMeta":
|
||||
"""
|
||||
Get an instance of ToolInvokeMeta with error
|
||||
"""
|
||||
return cls(time_cost=0.0, error=error, tool_config={})
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
return {
|
||||
"time_cost": self.time_cost,
|
||||
"error": self.error,
|
||||
"tool_config": self.tool_config,
|
||||
}
|
||||
|
||||
|
||||
class FunctionCallingAgentStrategy(AgentStrategy):
|
||||
def __init__(self, session):
|
||||
super().__init__(session)
|
||||
self.query = ""
|
||||
|
||||
def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
|
||||
"""
|
||||
Run FunctionCall agent application
|
||||
"""
|
||||
fc_params = FunctionCallingParams(**parameters)
|
||||
query = fc_params.query
|
||||
self.query = query
|
||||
instruction = fc_params.instruction
|
||||
init_prompt_messages = [
|
||||
PromptMessage(role=PromptMessageRole.SYSTEM, content=instruction)
|
||||
]
|
||||
tools = fc_params.tools
|
||||
tool_instances = {tool.identity.name: tool for tool in tools} if tools else {}
|
||||
model = fc_params.model
|
||||
stop = (
|
||||
fc_params.model.completion_params.get("stop", [])
|
||||
if fc_params.model.completion_params
|
||||
else []
|
||||
)
|
||||
# convert tools into ModelRuntime Tool format
|
||||
prompt_messages_tools = self._init_prompt_tools(tools)
|
||||
|
||||
iteration_step = 1
|
||||
max_iteration_steps = fc_params.maximum_iterations
|
||||
current_thoughts: list[PromptMessage] = []
|
||||
# continue to run until there is not any tool call
|
||||
function_call_state = True
|
||||
llm_usage: dict[str, Optional[LLMUsage]] = {"usage": None}
|
||||
final_answer = ""
|
||||
|
||||
while function_call_state and iteration_step <= max_iteration_steps:
|
||||
function_call_state = False
|
||||
round_started_at = time.perf_counter()
|
||||
round_log = self.create_log_message(
|
||||
label=f"ROUND {iteration_step}",
|
||||
data={},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: round_started_at,
|
||||
},
|
||||
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||
)
|
||||
yield round_log
|
||||
if iteration_step == max_iteration_steps:
|
||||
# the last iteration, remove all tools
|
||||
prompt_messages_tools = []
|
||||
|
||||
# recalc llm max tokens
|
||||
prompt_messages = self._organize_prompt_messages(
|
||||
history_prompt_messages=init_prompt_messages,
|
||||
current_thoughts=current_thoughts,
|
||||
)
|
||||
if model.completion_params:
|
||||
self.recalc_llm_max_tokens(
|
||||
model.entity, prompt_messages, model.completion_params
|
||||
)
|
||||
# invoke model
|
||||
model_started_at = time.perf_counter()
|
||||
model_log = self.create_log_message(
|
||||
label=f"{model.model} Thought",
|
||||
data={},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: model_started_at,
|
||||
LogMetadata.PROVIDER: model.provider,
|
||||
},
|
||||
parent=round_log,
|
||||
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||
)
|
||||
yield model_log
|
||||
chunks: Generator[LLMResultChunk, None, None] | LLMResult = (
|
||||
self.session.model.llm.invoke(
|
||||
model_config=LLMModelConfig(**model.model_dump(mode="json")),
|
||||
prompt_messages=prompt_messages,
|
||||
stream=True,
|
||||
stop=stop,
|
||||
tools=prompt_messages_tools,
|
||||
)
|
||||
)
|
||||
|
||||
tool_calls: list[tuple[str, str, dict[str, Any]]] = []
|
||||
|
||||
# save full response
|
||||
response = ""
|
||||
|
||||
# save tool call names and inputs
|
||||
tool_call_names = ""
|
||||
|
||||
current_llm_usage = None
|
||||
|
||||
if isinstance(chunks, Generator):
|
||||
for chunk in chunks:
|
||||
# check if there is any tool call
|
||||
if self.check_tool_calls(chunk):
|
||||
function_call_state = True
|
||||
tool_calls.extend(self.extract_tool_calls(chunk) or [])
|
||||
tool_call_names = ";".join(
|
||||
[tool_call[1] for tool_call in tool_calls]
|
||||
)
|
||||
|
||||
if chunk.delta.message and chunk.delta.message.content:
|
||||
if isinstance(chunk.delta.message.content, list):
|
||||
for content in chunk.delta.message.content:
|
||||
response += content.data
|
||||
if (
|
||||
not function_call_state
|
||||
or iteration_step == max_iteration_steps
|
||||
):
|
||||
yield self.create_text_message(content.data)
|
||||
else:
|
||||
response += str(chunk.delta.message.content)
|
||||
if (
|
||||
not function_call_state
|
||||
or iteration_step == max_iteration_steps
|
||||
):
|
||||
yield self.create_text_message(
|
||||
str(chunk.delta.message.content)
|
||||
)
|
||||
|
||||
if chunk.delta.usage:
|
||||
self.increase_usage(llm_usage, chunk.delta.usage)
|
||||
current_llm_usage = chunk.delta.usage
|
||||
|
||||
else:
|
||||
result = chunks
|
||||
# check if there is any tool call
|
||||
if self.check_blocking_tool_calls(result):
|
||||
function_call_state = True
|
||||
tool_calls.extend(self.extract_blocking_tool_calls(result) or [])
|
||||
tool_call_names = ";".join(
|
||||
[tool_call[1] for tool_call in tool_calls]
|
||||
)
|
||||
|
||||
if result.usage:
|
||||
self.increase_usage(llm_usage, result.usage)
|
||||
current_llm_usage = result.usage
|
||||
|
||||
if result.message and result.message.content:
|
||||
if isinstance(result.message.content, list):
|
||||
for content in result.message.content:
|
||||
response += content.data
|
||||
else:
|
||||
response += str(result.message.content)
|
||||
|
||||
if not result.message.content:
|
||||
result.message.content = ""
|
||||
yield self.finish_log_message(
|
||||
log=model_log,
|
||||
data={
|
||||
"output": response,
|
||||
"tool_name": tool_call_names,
|
||||
"tool_input": {
|
||||
tool_call[1]: tool_call[2] for tool_call in tool_calls
|
||||
},
|
||||
},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: model_started_at,
|
||||
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||
LogMetadata.ELAPSED_TIME: time.perf_counter() - model_started_at,
|
||||
LogMetadata.PROVIDER: model.provider,
|
||||
LogMetadata.TOTAL_PRICE: current_llm_usage.total_price
|
||||
if current_llm_usage
|
||||
else 0,
|
||||
LogMetadata.CURRENCY: current_llm_usage.currency
|
||||
if current_llm_usage
|
||||
else "",
|
||||
LogMetadata.TOTAL_TOKENS: current_llm_usage.total_tokens
|
||||
if current_llm_usage
|
||||
else 0,
|
||||
},
|
||||
)
|
||||
assistant_message = AssistantPromptMessage(content="", tool_calls=[])
|
||||
if tool_calls:
|
||||
assistant_message.tool_calls = [
|
||||
AssistantPromptMessage.ToolCall(
|
||||
id=tool_call[0],
|
||||
type="function",
|
||||
function=AssistantPromptMessage.ToolCall.ToolCallFunction(
|
||||
name=tool_call[1],
|
||||
arguments=json.dumps(tool_call[2], ensure_ascii=False),
|
||||
),
|
||||
)
|
||||
for tool_call in tool_calls
|
||||
]
|
||||
else:
|
||||
assistant_message.content = response
|
||||
|
||||
current_thoughts.append(assistant_message)
|
||||
|
||||
final_answer += response + "\n"
|
||||
|
||||
# call tools
|
||||
tool_responses = []
|
||||
for tool_call_id, tool_call_name, tool_call_args in tool_calls:
|
||||
tool_instance = tool_instances[tool_call_name]
|
||||
tool_call_started_at = time.perf_counter()
|
||||
tool_call_log = self.create_log_message(
|
||||
label=f"CALL {tool_call_name}",
|
||||
data={},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: time.perf_counter(),
|
||||
LogMetadata.PROVIDER: tool_instance.identity.provider,
|
||||
},
|
||||
parent=round_log,
|
||||
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||
)
|
||||
yield tool_call_log
|
||||
if not tool_instance:
|
||||
tool_response = {
|
||||
"tool_call_id": tool_call_id,
|
||||
"tool_call_name": tool_call_name,
|
||||
"tool_response": f"there is not a tool named {tool_call_name}",
|
||||
"meta": ToolInvokeMeta.error_instance(
|
||||
f"there is not a tool named {tool_call_name}"
|
||||
).to_dict(),
|
||||
}
|
||||
else:
|
||||
# invoke tool
|
||||
try:
|
||||
tool_invoke_responses = self.session.tool.invoke(
|
||||
provider_type=ToolProviderType(tool_instance.provider_type),
|
||||
provider=tool_instance.identity.provider,
|
||||
tool_name=tool_instance.identity.name,
|
||||
parameters={
|
||||
**tool_instance.runtime_parameters,
|
||||
**tool_call_args,
|
||||
},
|
||||
)
|
||||
result = ""
|
||||
for response in tool_invoke_responses:
|
||||
if response.type == ToolInvokeMessage.MessageType.TEXT:
|
||||
result += cast(
|
||||
ToolInvokeMessage.TextMessage, response.message
|
||||
).text
|
||||
elif response.type == ToolInvokeMessage.MessageType.LINK:
|
||||
result += (
|
||||
f"result link: {cast(ToolInvokeMessage.TextMessage, response.message).text}."
|
||||
+ " please tell user to check it."
|
||||
)
|
||||
elif response.type in {
|
||||
ToolInvokeMessage.MessageType.IMAGE_LINK,
|
||||
ToolInvokeMessage.MessageType.IMAGE,
|
||||
}:
|
||||
result += (
|
||||
"image has been created and sent to user already, "
|
||||
+ "you do not need to create it, just tell the user to check it now."
|
||||
)
|
||||
elif response.type == ToolInvokeMessage.MessageType.JSON:
|
||||
text = json.dumps(
|
||||
cast(
|
||||
ToolInvokeMessage.JsonMessage, response.message
|
||||
).json_object,
|
||||
ensure_ascii=False,
|
||||
)
|
||||
result += f"tool response: {text}."
|
||||
else:
|
||||
result += f"tool response: {response.message!r}."
|
||||
except Exception as e:
|
||||
result = f"tool invoke error: {str(e)}"
|
||||
tool_response = {
|
||||
"tool_call_id": tool_call_id,
|
||||
"tool_call_name": tool_call_name,
|
||||
"tool_call_input": {
|
||||
**tool_instance.runtime_parameters,
|
||||
**tool_call_args,
|
||||
},
|
||||
"tool_response": result,
|
||||
}
|
||||
|
||||
yield self.finish_log_message(
|
||||
log=tool_call_log,
|
||||
data={
|
||||
"output": tool_response,
|
||||
},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: tool_call_started_at,
|
||||
LogMetadata.PROVIDER: tool_instance.identity.provider,
|
||||
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||
LogMetadata.ELAPSED_TIME: time.perf_counter()
|
||||
- tool_call_started_at,
|
||||
},
|
||||
)
|
||||
tool_responses.append(tool_response)
|
||||
if tool_response["tool_response"] is not None:
|
||||
current_thoughts.append(
|
||||
ToolPromptMessage(
|
||||
content=str(tool_response["tool_response"]),
|
||||
tool_call_id=tool_call_id,
|
||||
name=tool_call_name,
|
||||
)
|
||||
)
|
||||
|
||||
# update prompt tool
|
||||
for prompt_tool in prompt_messages_tools:
|
||||
self.update_prompt_message_tool(
|
||||
tool_instances[prompt_tool.name], prompt_tool
|
||||
)
|
||||
yield self.finish_log_message(
|
||||
log=round_log,
|
||||
data={
|
||||
"output": {
|
||||
"llm_response": response,
|
||||
"tool_responses": tool_responses,
|
||||
},
|
||||
},
|
||||
metadata={
|
||||
LogMetadata.STARTED_AT: round_started_at,
|
||||
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||
LogMetadata.ELAPSED_TIME: time.perf_counter() - round_started_at,
|
||||
LogMetadata.TOTAL_PRICE: current_llm_usage.total_price
|
||||
if current_llm_usage
|
||||
else 0,
|
||||
LogMetadata.CURRENCY: current_llm_usage.currency
|
||||
if current_llm_usage
|
||||
else "",
|
||||
LogMetadata.TOTAL_TOKENS: current_llm_usage.total_tokens
|
||||
if current_llm_usage
|
||||
else 0,
|
||||
},
|
||||
)
|
||||
iteration_step += 1
|
||||
|
||||
yield self.create_json_message(
|
||||
{
|
||||
"execution_metadata": {
|
||||
LogMetadata.TOTAL_PRICE: llm_usage["usage"].total_price
|
||||
if llm_usage["usage"] is not None
|
||||
else 0,
|
||||
LogMetadata.CURRENCY: llm_usage["usage"].currency
|
||||
if llm_usage["usage"] is not None
|
||||
else "",
|
||||
LogMetadata.TOTAL_TOKENS: llm_usage["usage"].total_tokens
|
||||
if llm_usage["usage"] is not None
|
||||
else 0,
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
|
||||
"""
|
||||
Check if there is any tool call in llm result chunk
|
||||
"""
|
||||
return bool(llm_result_chunk.delta.message.tool_calls)
|
||||
|
||||
def check_blocking_tool_calls(self, llm_result: LLMResult) -> bool:
|
||||
"""
|
||||
Check if there is any blocking tool call in llm result
|
||||
"""
|
||||
return bool(llm_result.message.tool_calls)
|
||||
|
||||
def extract_tool_calls(
|
||||
self, llm_result_chunk: LLMResultChunk
|
||||
) -> list[tuple[str, str, dict[str, Any]]]:
|
||||
"""
|
||||
Extract tool calls from llm result chunk
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
|
||||
"""
|
||||
tool_calls = []
|
||||
for prompt_message in llm_result_chunk.delta.message.tool_calls:
|
||||
args = {}
|
||||
if prompt_message.function.arguments != "":
|
||||
args = json.loads(prompt_message.function.arguments)
|
||||
|
||||
tool_calls.append(
|
||||
(
|
||||
prompt_message.id,
|
||||
prompt_message.function.name,
|
||||
args,
|
||||
)
|
||||
)
|
||||
|
||||
return tool_calls
|
||||
|
||||
def extract_blocking_tool_calls(
|
||||
self, llm_result: LLMResult
|
||||
) -> list[tuple[str, str, dict[str, Any]]]:
|
||||
"""
|
||||
Extract blocking tool calls from llm result
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
|
||||
"""
|
||||
tool_calls = []
|
||||
for prompt_message in llm_result.message.tool_calls:
|
||||
args = {}
|
||||
if prompt_message.function.arguments != "":
|
||||
args = json.loads(prompt_message.function.arguments)
|
||||
|
||||
tool_calls.append(
|
||||
(
|
||||
prompt_message.id,
|
||||
prompt_message.function.name,
|
||||
args,
|
||||
)
|
||||
)
|
||||
|
||||
return tool_calls
|
||||
|
||||
def _init_system_message(
|
||||
self, prompt_template: str, prompt_messages: list[PromptMessage]
|
||||
) -> list[PromptMessage]:
|
||||
"""
|
||||
Initialize system message
|
||||
"""
|
||||
if not prompt_messages and prompt_template:
|
||||
return [
|
||||
SystemPromptMessage(content=prompt_template),
|
||||
]
|
||||
|
||||
if (
|
||||
prompt_messages
|
||||
and not isinstance(prompt_messages[0], SystemPromptMessage)
|
||||
and prompt_template
|
||||
):
|
||||
prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
|
||||
|
||||
return prompt_messages or []
|
||||
|
||||
def _organize_user_query(
|
||||
self, query: str, prompt_messages: list[PromptMessage]
|
||||
) -> list[PromptMessage]:
|
||||
"""
|
||||
Organize user query
|
||||
"""
|
||||
|
||||
prompt_messages.append(UserPromptMessage(content=query))
|
||||
|
||||
return prompt_messages
|
||||
|
||||
def _clear_user_prompt_image_messages(
|
||||
self, prompt_messages: list[PromptMessage]
|
||||
) -> list[PromptMessage]:
|
||||
"""
|
||||
As for now, gpt supports both fc and vision at the first iteration.
|
||||
We need to remove the image messages from the prompt messages at the first iteration.
|
||||
"""
|
||||
prompt_messages = deepcopy(prompt_messages)
|
||||
|
||||
for prompt_message in prompt_messages:
|
||||
if isinstance(prompt_message, UserPromptMessage) and isinstance(
|
||||
prompt_message.content, list
|
||||
):
|
||||
prompt_message.content = "\n".join(
|
||||
[
|
||||
content.data
|
||||
if content.type == PromptMessageContentType.TEXT
|
||||
else "[image]"
|
||||
if content.type == PromptMessageContentType.IMAGE
|
||||
else "[file]"
|
||||
for content in prompt_message.content
|
||||
]
|
||||
)
|
||||
|
||||
return prompt_messages
|
||||
|
||||
def _organize_prompt_messages(
|
||||
self,
|
||||
current_thoughts: list[PromptMessage],
|
||||
history_prompt_messages: list[PromptMessage],
|
||||
) -> list[PromptMessage]:
|
||||
prompt_template = ""
|
||||
history_prompt_messages = self._init_system_message(
|
||||
prompt_template, history_prompt_messages
|
||||
)
|
||||
query_prompt_messages = self._organize_user_query(self.query or "", [])
|
||||
|
||||
prompt_messages = [
|
||||
*history_prompt_messages,
|
||||
*query_prompt_messages,
|
||||
*current_thoughts,
|
||||
]
|
||||
if len(current_thoughts) != 0:
|
||||
# clear messages after the first iteration
|
||||
prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
|
||||
return prompt_messages
|
||||
@ -1,58 +0,0 @@
|
||||
identity:
|
||||
name: function_calling
|
||||
author: Dify
|
||||
label:
|
||||
en_US: FunctionCalling
|
||||
zh_Hans: FunctionCalling
|
||||
pt_BR: FunctionCalling
|
||||
description:
|
||||
en_US: Function Calling is a basic strategy for agent, model will use the tools provided to perform the task.
|
||||
zh_Hans: Function Calling 是一个基本的 Agent 策略，模型将使用提供的工具来执行任务。
|
||||
pt_BR: Function Calling is a basic strategy for agent, model will use the tools provided to perform the task.
|
||||
parameters:
|
||||
- name: model
|
||||
type: model-selector
|
||||
scope: tool-call&llm
|
||||
required: true
|
||||
label:
|
||||
en_US: Model
|
||||
zh_Hans: 模型
|
||||
pt_BR: Model
|
||||
- name: tools
|
||||
type: array[tools]
|
||||
required: true
|
||||
label:
|
||||
en_US: Tool list
|
||||
zh_Hans: 工具列表
|
||||
pt_BR: Tool list
|
||||
- name: instruction
|
||||
type: string
|
||||
required: true
|
||||
label:
|
||||
en_US: Instruction
|
||||
zh_Hans: 指令
|
||||
pt_BR: Instruction
|
||||
auto_generate:
|
||||
type: prompt_instruction
|
||||
template:
|
||||
enabled: true
|
||||
- name: query
|
||||
type: string
|
||||
required: true
|
||||
label:
|
||||
en_US: Query
|
||||
zh_Hans: 查询
|
||||
pt_BR: Query
|
||||
- name: maximum_iterations
|
||||
type: number
|
||||
required: true
|
||||
label:
|
||||
en_US: Maximum Iterations
|
||||
zh_Hans: 最大迭代次数
|
||||
pt_BR: Maximum Iterations
|
||||
default: 3
|
||||
max: 30
|
||||
min: 1
|
||||
extra:
|
||||
python:
|
||||
source: strategies/function_calling.py
|
||||
@ -1,4 +0,0 @@
|
||||
INSTALL_METHOD=remote
|
||||
REMOTE_INSTALL_HOST=debug-plugin.dify.dev
|
||||
REMOTE_INSTALL_PORT=5003
|
||||
REMOTE_INSTALL_KEY=********-****-****-****-************
|
||||
@ -1,7 +0,0 @@
|
||||
# Overview
|
||||
DeepSeek provides advanced AI capabilities for chats and completions. This plugin enables developers to integrate DeepSeek's models, including text generation (deepseek-chat and deepseek-code) via the API.
|
||||
|
||||
# Configure
|
||||
After installation, you need to get API keys from [Deepseek](https://platform.deepseek.com/api_keys) and setup in Settings -> Model Provider.
|
||||
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 66 KiB |
@ -1,22 +0,0 @@
|
||||
<svg width="195.000000" height="41.359375" viewBox="0 0 195 41.3594" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
|
||||
<desc>
|
||||
Created with Pixso.
|
||||
</desc>
|
||||
<defs>
|
||||
<clipPath id="clip30_2029">
|
||||
<rect id="_图层_1" width="134.577469" height="25.511124" transform="translate(60.422485 10.022217)" fill="white"/>
|
||||
</clipPath>
|
||||
</defs>
|
||||
<g clip-path="url(#clip30_2029)">
|
||||
<path id="path" d="M119.508 30.113L117.562 30.113L117.562 27.0967L119.508 27.0967C120.713 27.0967 121.931 26.7961 122.715 25.9614C123.5 25.1265 123.796 23.8464 123.796 22.5664C123.796 21.2864 123.512 20.0063 122.715 19.1716C121.919 18.3369 120.713 18.0364 119.508 18.0364C118.302 18.0364 117.085 18.3369 116.3 19.1716C115.515 20.0063 115.219 21.2864 115.219 22.5664L115.219 34.9551L111.806 34.9551L111.806 15.031L115.219 15.031L115.219 16.2998L115.845 16.2998C115.913 16.2219 115.981 16.1553 116.049 16.0884C116.903 15.3093 118.211 15.031 119.496 15.031C121.51 15.031 123.523 15.532 124.843 16.9233C126.162 18.3145 126.629 20.4517 126.629 22.5776C126.629 24.7036 126.151 26.8296 124.843 28.2319C123.535 29.6345 121.51 30.113 119.508 30.113Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M67.5664 15.5654L69.5117 15.5654L69.5117 18.5818L67.5664 18.5818C66.3606 18.5818 65.1434 18.8823 64.3585 19.717C63.5736 20.552 63.2778 21.832 63.2778 23.1121C63.2778 24.3921 63.5623 25.6721 64.3585 26.5068C65.1548 27.3418 66.3606 27.6423 67.5664 27.6423C68.7722 27.6423 69.9895 27.3418 70.7744 26.5068C71.5593 25.6721 71.8551 24.3921 71.8551 23.1121L71.8551 10.7124L75.2677 10.7124L75.2677 30.6475L71.8551 30.6475L71.8551 29.3787L71.2294 29.3787C71.1611 29.4565 71.0929 29.5234 71.0247 29.5901C70.1715 30.3691 68.8633 30.6475 67.5779 30.6475C65.5643 30.6475 63.5509 30.1467 62.2313 28.7554C60.9117 27.364 60.4453 25.2268 60.4453 23.1008C60.4453 20.9749 60.9231 18.8489 62.2313 17.4465C63.5509 16.0552 65.5643 15.5654 67.5664 15.5654Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M92.3881 22.845L92.3881 24.0581L83.299 24.0581L83.299 21.6428L89.328 21.6428C89.1914 20.7634 88.8729 19.9397 88.3042 19.3386C87.4851 18.4705 86.2224 18.1589 84.9711 18.1589C83.7198 18.1589 82.4572 18.4705 81.6381 19.3386C80.819 20.2068 80.5232 21.5315 80.5232 22.845C80.5232 24.1582 80.819 25.4939 81.6381 26.3511C82.4572 27.208 83.7198 27.531 84.9711 27.531C86.2224 27.531 87.4851 27.2192 88.3042 26.3511C88.418 26.2285 88.5203 26.095 88.6227 25.9614L91.9899 25.9614C91.6941 27.0078 91.2277 27.9539 90.5225 28.6885C89.1573 30.1243 87.0529 30.6475 84.9711 30.6475C82.8894 30.6475 80.7849 30.1355 79.4198 28.6885C78.0547 27.2415 77.5542 25.0376 77.5542 22.845C77.5542 20.6521 78.0433 18.437 79.4198 17.0012C80.7963 15.5654 82.8894 15.0422 84.9711 15.0422C87.0529 15.0422 89.1573 15.5542 90.5225 17.0012C91.8988 18.4482 92.3881 20.6521 92.3881 22.845Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M109.52 22.845L109.52 24.0581L100.431 24.0581L100.431 21.6428L106.46 21.6428C106.323 20.7634 106.005 19.9397 105.436 19.3386C104.617 18.4705 103.354 18.1589 102.103 18.1589C100.852 18.1589 99.5889 18.4705 98.7698 19.3386C97.9507 20.2068 97.6549 21.5315 97.6549 22.845C97.6549 24.1582 97.9507 25.4939 98.7698 26.3511C99.5889 27.208 100.852 27.531 102.103 27.531C103.354 27.531 104.617 27.2192 105.436 26.3511C105.55 26.2285 105.652 26.095 105.754 25.9614L109.122 25.9614C108.826 27.0078 108.359 27.9539 107.654 28.6885C106.289 30.1243 104.185 30.6475 102.103 30.6475C100.021 30.6475 97.9166 30.1355 96.5515 28.6885C95.1864 27.2415 94.6859 25.0376 94.6859 22.845C94.6859 20.6521 95.175 18.437 96.5515 17.0012C97.928 15.5654 100.021 15.0422 102.103 15.0422C104.185 15.0422 106.289 15.5542 107.654 17.0012C109.031 18.4482 109.52 20.6521 109.52 22.845Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M136.355 30.6475C138.437 30.6475 140.541 30.3469 141.906 29.49C143.271 28.6328 143.772 27.3306 143.772 26.0393C143.772 24.7483 143.282 23.4348 141.906 22.5889C140.541 21.7429 138.437 21.4312 136.355 21.4312C135.467 21.4312 134.648 21.3088 134.068 20.9861C133.488 20.6521 133.272 20.1511 133.272 19.6504C133.272 19.1494 133.477 18.6375 134.068 18.3147C134.648 17.9807 135.547 17.8694 136.434 17.8694C137.322 17.8694 138.22 17.9919 138.801 18.3147C139.381 18.6487 139.597 19.1494 139.597 19.6504L143.066 19.6504C143.066 18.3591 142.623 17.0457 141.383 16.2C140.143 15.354 138.243 15.0422 136.355 15.0422C134.466 15.0422 132.567 15.3428 131.327 16.2C130.087 17.0569 129.643 18.3591 129.643 19.6504C129.643 20.9414 130.087 22.2549 131.327 23.1008C132.567 23.9468 134.466 24.2585 136.355 24.2585C137.333 24.2585 138.414 24.3809 139.062 24.7036C139.711 25.0266 139.938 25.5386 139.938 26.0393C139.938 26.5403 139.711 27.0522 139.062 27.375C138.414 27.6978 137.424 27.8203 136.446 27.8203C135.467 27.8203 134.466 27.6978 133.829 27.375C133.192 27.0522 132.953 26.5403 132.953 26.0393L128.949 26.0393C128.949 27.3306 129.438 28.644 130.815 29.49C132.191 30.3359 134.273 30.6475 136.355 30.6475Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M160.903 22.845L160.903 24.0581L151.814 24.0581L151.814 21.6428L157.843 21.6428C157.707 20.7634 157.388 19.9397 156.82 19.3386C156 18.4705 154.738 18.1589 153.486 18.1589C152.235 18.1589 150.972 18.4705 150.153 19.3386C149.334 20.2068 149.039 21.5315 149.039 22.845C149.039 24.1582 149.334 25.4939 150.153 26.3511C150.972 27.208 152.235 27.531 153.486 27.531C154.738 27.531 156 27.2192 156.82 26.3511C156.933 26.2285 157.036 26.095 157.138 25.9614L160.505 25.9614C160.209 27.0078 159.743 27.9539 159.038 28.6885C157.673 30.1243 155.568 30.6475 153.486 30.6475C151.405 30.6475 149.3 30.1355 147.935 28.6885C146.57 27.2415 146.07 25.0376 146.07 22.845C146.07 20.6521 146.559 18.437 147.935 17.0012C149.312 15.5654 151.405 15.0422 153.486 15.0422C155.568 15.0422 157.673 15.5542 159.038 17.0012C160.414 18.4482 160.903 20.6521 160.903 22.845Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<path id="path" d="M178.035 22.845L178.035 24.0581L168.946 24.0581L168.946 21.6428L174.975 21.6428C174.839 20.7634 174.52 19.9397 173.951 19.3386C173.132 18.4705 171.87 18.1589 170.618 18.1589C169.367 18.1589 168.104 18.4705 167.285 19.3386C166.466 20.2068 166.17 21.5315 166.17 22.845C166.17 24.1582 166.466 25.4939 167.285 26.3511C168.104 27.208 169.367 27.531 170.618 27.531C171.87 27.531 173.132 27.2192 173.951 26.3511C174.065 26.2285 174.167 26.095 174.27 25.9614L177.637 25.9614C177.341 27.0078 176.875 27.9539 176.17 28.6885C174.804 30.1243 172.7 30.6475 170.618 30.6475C168.536 30.6475 166.432 30.1355 165.067 28.6885C163.702 27.2415 163.201 25.0376 163.201 22.845C163.201 20.6521 163.69 18.437 165.067 17.0012C166.443 15.5654 168.536 15.0422 170.618 15.0422C172.7 15.0422 174.804 15.5542 176.17 17.0012C177.546 18.4482 178.035 20.6521 178.035 22.845Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
<rect id="rect" x="180.321533" y="10.022217" width="3.412687" height="20.625223" fill="#4D6BFE"/>
|
||||
<path id="polygon" d="M189.559 22.3772L195.155 30.6475L190.935 30.6475L185.338 22.3772L190.935 15.7322L195.155 15.7322L189.559 22.3772Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
</g>
|
||||
<path id="path" d="M55.6128 3.47119C55.0175 3.17944 54.7611 3.73535 54.413 4.01782C54.2939 4.10889 54.1932 4.22729 54.0924 4.33667C53.2223 5.26587 52.2057 5.87646 50.8776 5.80347C48.9359 5.69409 47.2781 6.30469 45.8126 7.78979C45.5012 5.9585 44.4663 4.86499 42.8909 4.16357C42.0667 3.79907 41.2332 3.43457 40.6561 2.64185C40.2532 2.07715 40.1432 1.44849 39.9418 0.828857C39.8135 0.455322 39.6853 0.0725098 39.2548 0.00878906C38.7877 -0.0639648 38.6045 0.327637 38.4213 0.655762C37.6886 1.99512 37.4047 3.47119 37.4321 4.96533C37.4962 8.32739 38.9159 11.0059 41.7369 12.9102C42.0575 13.1289 42.1399 13.3474 42.0392 13.6665C41.8468 14.3225 41.6178 14.9602 41.4164 15.6162C41.2881 16.0354 41.0957 16.1265 40.647 15.9441C39.0991 15.2974 37.7618 14.3406 36.5803 13.1836C34.5745 11.2429 32.761 9.10181 30.4988 7.42529C29.9675 7.03345 29.4363 6.66919 28.8867 6.32275C26.5786 4.08154 29.189 2.24097 29.7935 2.02246C30.4254 1.79468 30.0133 1.01099 27.9708 1.02026C25.9283 1.0293 24.0599 1.71265 21.6786 2.62378C21.3306 2.7605 20.9641 2.8606 20.5886 2.94263C18.4271 2.53271 16.1831 2.44141 13.8384 2.70581C9.42371 3.19775 5.89758 5.28418 3.30554 8.84668C0.191406 13.1289 -0.54126 17.9941 0.356323 23.0691C1.29968 28.4172 4.02905 32.8452 8.22388 36.3076C12.5745 39.8972 17.5845 41.6558 23.2997 41.3186C26.771 41.1182 30.6361 40.6536 34.9958 36.9636C36.0948 37.5103 37.2489 37.7288 39.1632 37.8928C40.6378 38.0295 42.0575 37.8201 43.1565 37.5923C44.8784 37.2278 44.7594 35.6333 44.1366 35.3418C39.09 32.9912 40.1981 33.9478 39.1907 33.1733C41.7552 30.1394 45.6204 26.9868 47.1316 16.7732C47.2506 15.9624 47.1499 15.4521 47.1316 14.7961C47.1224 14.3953 47.214 14.2405 47.672 14.1948C48.9359 14.0491 50.1632 13.7029 51.2898 13.0833C54.5596 11.2976 55.8784 8.36377 56.1898 4.84692C56.2357 4.30933 56.1807 3.75342 55.6128 3.47119ZM27.119 35.123C22.2281 31.2783 19.856 30.0117 18.8759 30.0664C17.96 30.1211 18.1249 31.1689 18.3263 31.8523C18.537 32.5264 18.8118 32.9912 19.1964 33.5833C19.462 33.9751 19.6453 34.5581 18.9309 34.9956C17.3555 35.9705 14.6169 34.6675 14.4886 34.6038C11.3014 32.7268 8.63611 30.2485 6.75842 26.8594C4.94495 23.5974 3.89172 20.0989 3.71765 16.3633C3.67188 15.4614 3.9375 15.1423 4.83508 14.9785C6.0166 14.7598 7.23474 14.7141 8.41626 14.8872C13.408 15.6162 17.6577 17.8484 21.2206 21.3835C23.2539 23.397 24.7926 25.8025 26.3772 28.1531C28.0624 30.6494 29.8759 33.0276 32.184 34.9773C32.9991 35.6606 33.6494 36.1799 34.2722 36.5627C32.3947 36.7722 29.2622 36.8179 27.119 35.123ZM29.4637 20.0442C29.4637 19.6433 29.7843 19.3245 30.1874 19.3245C30.2789 19.3245 30.3613 19.3425 30.4346 19.3699C30.5354 19.4065 30.627 19.4612 30.7002 19.543C30.8285 19.6707 30.9017 19.8528 30.9017 20.0442C30.9017 20.4451 30.5812 20.7639 30.1782 20.7639C29.7751 20.7639 29.4637 20.4451 29.4637 20.0442ZM36.7452 23.7798C36.2781 23.9712 35.811 24.135 35.3622 24.1533C34.6661 24.1897 33.9059 23.9072 33.4938 23.561C32.8527 23.0234 32.3947 22.7229 32.2023 21.7844C32.1199 21.3835 32.1656 20.7639 32.239 20.4087C32.4038 19.6433 32.2206 19.1514 31.6803 18.7048C31.2406 18.3403 30.6819 18.2402 30.0682 18.2402C29.8392 18.2402 29.6287 18.1399 29.4729 18.0579C29.2164 17.9304 29.0059 17.6116 29.2073 17.2197C29.2714 17.0923 29.5829 16.7825 29.6561 16.7278C30.4896 16.2539 31.4513 16.4089 32.3397 16.7642C33.1641 17.1013 33.7869 17.7209 34.6844 18.5955C35.6003 19.6523 35.7651 19.9441 36.2872 20.7366C36.6995 21.3562 37.075 21.9939 37.3314 22.7229C37.4871 23.1785 37.2856 23.552 36.7452 23.7798Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 10 KiB |
@ -1,3 +0,0 @@
|
||||
<svg width="60" height="50" viewBox="0 0 60 50" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linejoin:round;stroke-miterlimit:2;">
|
||||
<path id="path" d="M55.613,3.471C55.018,3.179 54.761,3.735 54.413,4.018C54.294,4.109 54.193,4.227 54.092,4.337C53.222,5.266 52.206,5.876 50.878,5.803C48.936,5.694 47.278,6.305 45.813,7.79C45.501,5.959 44.466,4.865 42.891,4.164C42.067,3.799 41.233,3.435 40.656,2.642C40.253,2.077 40.143,1.448 39.942,0.829C39.814,0.455 39.685,0.073 39.255,0.009C38.788,-0.064 38.605,0.328 38.421,0.656C37.689,1.995 37.405,3.471 37.432,4.965C37.496,8.327 38.916,11.006 41.737,12.91C42.058,13.129 42.14,13.347 42.039,13.666C41.847,14.323 41.618,14.96 41.416,15.616C41.288,16.035 41.096,16.127 40.647,15.944C39.099,15.297 37.762,14.341 36.58,13.184C34.575,11.243 32.761,9.102 30.499,7.425C29.968,7.033 29.436,6.669 28.887,6.323C26.579,4.082 29.189,2.241 29.794,2.022C30.425,1.795 30.013,1.011 27.971,1.02C25.928,1.029 24.06,1.713 21.679,2.624C21.331,2.761 20.964,2.861 20.589,2.943C18.427,2.533 16.183,2.441 13.838,2.706C9.424,3.198 5.898,5.284 3.306,8.847C0.191,13.129 -0.541,17.994 0.356,23.069C1.3,28.417 4.029,32.845 8.224,36.308C12.575,39.897 17.584,41.656 23.3,41.319C26.771,41.118 30.636,40.654 34.996,36.964C36.095,37.51 37.249,37.729 39.163,37.893C40.638,38.03 42.058,37.82 43.157,37.592C44.878,37.228 44.759,35.633 44.137,35.342C39.09,32.991 40.198,33.948 39.191,33.173C41.755,30.139 45.62,26.987 47.132,16.773C47.251,15.962 47.15,15.452 47.132,14.796C47.122,14.395 47.214,14.241 47.672,14.195C48.936,14.049 50.163,13.703 51.29,13.083C54.56,11.298 55.878,8.364 56.19,4.847C56.236,4.309 56.181,3.753 55.613,3.471ZM27.119,35.123C22.228,31.278 19.856,30.012 18.876,30.066C17.96,30.121 18.125,31.169 18.326,31.852C18.537,32.526 18.812,32.991 19.196,33.583C19.462,33.975 19.645,34.558 18.931,34.996C17.356,35.971 14.617,34.667 14.489,34.604C11.301,32.727 8.636,30.249 6.758,26.859C4.945,23.597 3.892,20.099 3.718,16.363C3.672,15.461 3.938,15.142 4.835,14.979C6.017,14.76 7.235,14.714 8.416,14.887C13.408,15.616 17.658,17.848 21.221,21.384C23.254,23.397 24.793,25.802 26.377,28.153C28.062,30.649 29.876,33.028 32.184,34.977C32.999,35.661 33.649,36.18 34.272,36.563C32.395,36.772 29.262,36.818 27.119,35.123ZM29.464,20.044C29.464,19.643 29.784,19.325 30.187,19.325C30.279,19.325 30.361,19.343 30.435,19.37C30.535,19.407 30.627,19.461 30.7,19.543C30.828,19.671 30.902,19.853 30.902,20.044C30.902,20.445 30.581,20.764 30.178,20.764C29.775,20.764 29.464,20.445 29.464,20.044ZM36.745,23.78C36.278,23.971 35.811,24.135 35.362,24.153C34.666,24.19 33.906,23.907 33.494,23.561C32.853,23.023 32.395,22.723 32.202,21.784C32.12,21.384 32.166,20.764 32.239,20.409C32.404,19.643 32.221,19.151 31.68,18.705C31.241,18.34 30.682,18.24 30.068,18.24C29.839,18.24 29.629,18.14 29.473,18.058C29.216,17.93 29.006,17.612 29.207,17.22C29.271,17.092 29.583,16.783 29.656,16.728C30.49,16.254 31.451,16.409 32.34,16.764C33.164,17.101 33.787,17.721 34.684,18.596C35.6,19.652 35.765,19.944 36.287,20.737C36.7,21.356 37.075,21.994 37.331,22.723C37.487,23.179 37.286,23.552 36.745,23.78Z" style="fill:rgb(77,107,254);fill-rule:nonzero;"/>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 3.2 KiB |
@ -1,6 +0,0 @@
|
||||
from dify_plugin import Plugin, DifyPluginEnv
|
||||
|
||||
plugin = Plugin(DifyPluginEnv())
|
||||
|
||||
if __name__ == '__main__':
|
||||
plugin.run()
|
||||
@ -1,37 +0,0 @@
|
||||
author: langgenius
|
||||
created_at: '2024-09-20T00:13:50.29298939-04:00'
|
||||
description:
|
||||
en_US: Models provided by deepseek, such as deepseek-chat、deepseek-coder.
|
||||
zh_Hans: 深度求索提供的模型，例如 deepseek-chat、deepseek-coder 。
|
||||
icon: icon_s_en.svg
|
||||
label:
|
||||
en_US: DeepSeek
|
||||
zh_Hans: 深度求索
|
||||
meta:
|
||||
arch:
|
||||
- amd64
|
||||
- arm64
|
||||
runner:
|
||||
entrypoint: main
|
||||
language: python
|
||||
version: '3.12'
|
||||
version: 0.0.1
|
||||
name: deepseek
|
||||
plugins:
|
||||
models:
|
||||
- provider/deepseek.yaml
|
||||
resource:
|
||||
memory: 268435456
|
||||
permission:
|
||||
model:
|
||||
enabled: true
|
||||
llm: true
|
||||
moderation: false
|
||||
rerank: true
|
||||
speech2text: false
|
||||
text_embedding: true
|
||||
tts: false
|
||||
tool:
|
||||
enabled: true
|
||||
type: plugin
|
||||
version: 0.0.5
|
||||
@ -1,3 +0,0 @@
|
||||
- deepseek-chat
|
||||
- deepseek-coder
|
||||
- deepseek-reasoner
|
||||
@ -1,29 +0,0 @@
|
||||
model: deepseek-coder
|
||||
label:
|
||||
zh_Hans: deepseek-coder
|
||||
en_US: deepseek-coder
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
- tool-call
|
||||
- multi-tool-call
|
||||
- stream-tool-call
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 128000
|
||||
parameter_rules:
|
||||
- name: temperature
|
||||
use_template: temperature
|
||||
min: 0
|
||||
max: 1
|
||||
default: 0.5
|
||||
- name: top_p
|
||||
use_template: top_p
|
||||
min: 0
|
||||
max: 1
|
||||
default: 1
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
min: 1
|
||||
max: 4096
|
||||
default: 1024
|
||||
@ -1,21 +0,0 @@
|
||||
model: deepseek-reasoner
|
||||
label:
|
||||
zh_Hans: deepseek-reasoner
|
||||
en_US: deepseek-reasoner
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 128000
|
||||
parameter_rules:
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
min: 1
|
||||
max: 8192
|
||||
default: 4096
|
||||
pricing:
|
||||
input: "4"
|
||||
output: "16"
|
||||
unit: "0.000001"
|
||||
currency: RMB
|
||||
@ -1,33 +0,0 @@
|
||||
from collections.abc import Generator
|
||||
from typing import Optional, Union
|
||||
from dify_plugin.entities.model.llm import LLMMode, LLMResult
|
||||
from dify_plugin.entities.model.message import PromptMessage, PromptMessageTool
|
||||
from yarl import URL
|
||||
from dify_plugin import OAICompatLargeLanguageModel
|
||||
|
||||
|
||||
class DeepseekLargeLanguageModel(OAICompatLargeLanguageModel):
|
||||
def _invoke(
|
||||
self,
|
||||
model: str,
|
||||
credentials: dict,
|
||||
prompt_messages: list[PromptMessage],
|
||||
model_parameters: dict,
|
||||
tools: Optional[list[PromptMessageTool]] = None,
|
||||
stop: Optional[list[str]] = None,
|
||||
stream: bool = True,
|
||||
user: Optional[str] = None,
|
||||
) -> Union[LLMResult, Generator]:
|
||||
self._add_custom_parameters(credentials)
|
||||
return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
|
||||
|
||||
def validate_credentials(self, model: str, credentials: dict) -> None:
|
||||
self._add_custom_parameters(credentials)
|
||||
super().validate_credentials(model, credentials)
|
||||
|
||||
@staticmethod
|
||||
def _add_custom_parameters(credentials) -> None:
|
||||
credentials["endpoint_url"] = str(URL(credentials.get("endpoint_url", "https://api.deepseek.com")))
|
||||
credentials["mode"] = LLMMode.CHAT.value
|
||||
credentials["function_calling_type"] = "tool_call"
|
||||
credentials["stream_function_calling"] = "support"
|
||||
@ -1,24 +0,0 @@
|
||||
import logging
|
||||
from dify_plugin.entities.model import ModelType
|
||||
from dify_plugin.errors.model import CredentialsValidateFailedError
|
||||
from dify_plugin import ModelProvider
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DeepSeekProvider(ModelProvider):
|
||||
def validate_provider_credentials(self, credentials: dict) -> None:
|
||||
"""
|
||||
Validate provider credentials
|
||||
if validate failed, raise exception
|
||||
|
||||
:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
|
||||
"""
|
||||
try:
|
||||
model_instance = self.get_model_instance(ModelType.LLM)
|
||||
model_instance.validate_credentials(model="deepseek-chat", credentials=credentials)
|
||||
except CredentialsValidateFailedError as ex:
|
||||
raise ex
|
||||
except Exception as ex:
|
||||
logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
|
||||
raise ex
|
||||
@ -1,51 +0,0 @@
|
||||
background: '#c0cdff'
|
||||
configurate_methods:
|
||||
- predefined-model
|
||||
description:
|
||||
en_US: Models provided by deepseek, such as deepseek-reasoner、deepseek-chat、deepseek-coder.
|
||||
zh_Hans: 深度求索提供的模型，例如 deepseek-reasoner、deepseek-chat、deepseek-coder 。
|
||||
extra:
|
||||
python:
|
||||
model_sources:
|
||||
- models/llm/llm.py
|
||||
provider_source: provider/deepseek.py
|
||||
help:
|
||||
title:
|
||||
en_US: Get your API Key from deepseek
|
||||
zh_Hans: 从深度求索获取 API Key
|
||||
url:
|
||||
en_US: https://platform.deepseek.com/api_keys
|
||||
icon_large:
|
||||
en_US: icon_l_en.svg
|
||||
icon_small:
|
||||
en_US: icon_s_en.svg
|
||||
label:
|
||||
en_US: deepseek
|
||||
zh_Hans: 深度求索
|
||||
models:
|
||||
llm:
|
||||
position: models/llm/_position.yaml
|
||||
predefined:
|
||||
- models/llm/*.yaml
|
||||
provider: deepseek
|
||||
provider_credential_schema:
|
||||
credential_form_schemas:
|
||||
- label:
|
||||
en_US: API Key
|
||||
placeholder:
|
||||
en_US: Enter your API Key
|
||||
zh_Hans: 在此输入您的 API Key
|
||||
required: true
|
||||
type: secret-input
|
||||
variable: api_key
|
||||
- label:
|
||||
en_US: Custom API endpoint URL
|
||||
zh_Hans: 自定义 API endpoint 地址
|
||||
placeholder:
|
||||
en_US: Base URL, e.g. https://api.deepseek.com/v1 or https://api.deepseek.com
|
||||
zh_Hans: Base URL, e.g. https://api.deepseek.com/v1 or https://api.deepseek.com
|
||||
required: false
|
||||
type: text-input
|
||||
variable: endpoint_url
|
||||
supported_model_types:
|
||||
- llm
|
||||
@ -1 +0,0 @@
|
||||
dify_plugin==0.0.1b65
|
||||
@ -1,4 +0,0 @@
|
||||
INSTALL_METHOD=remote
|
||||
REMOTE_INSTALL_HOST=debug-plugin.dify.dev
|
||||
REMOTE_INSTALL_PORT=5003
|
||||
REMOTE_INSTALL_KEY=********-****-****-****-************
|
||||
File diff suppressed because one or more lines are too long
|
Before Width: | Height: | Size: 12 KiB |
File diff suppressed because one or more lines are too long
|
Before Width: | Height: | Size: 7.7 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 279 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 190 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 123 KiB |
@ -1,6 +0,0 @@
|
||||
from dify_plugin import Plugin, DifyPluginEnv
|
||||
|
||||
plugin = Plugin(DifyPluginEnv())
|
||||
|
||||
if __name__ == '__main__':
|
||||
plugin.run()
|
||||
@ -1,35 +0,0 @@
|
||||
author: langgenius
|
||||
created_at: '2024-09-20T00:13:50.29298939-04:00'
|
||||
description:
|
||||
en_US: Ollama
|
||||
icon: icon_s_en.svg
|
||||
label:
|
||||
en_US: Ollama
|
||||
meta:
|
||||
arch:
|
||||
- amd64
|
||||
- arm64
|
||||
runner:
|
||||
entrypoint: main
|
||||
language: python
|
||||
version: '3.12'
|
||||
version: 0.0.1
|
||||
name: ollama
|
||||
plugins:
|
||||
models:
|
||||
- provider/ollama.yaml
|
||||
resource:
|
||||
memory: 268435456
|
||||
permission:
|
||||
model:
|
||||
enabled: true
|
||||
llm: true
|
||||
moderation: false
|
||||
rerank: true
|
||||
speech2text: false
|
||||
text_embedding: true
|
||||
tts: false
|
||||
tool:
|
||||
enabled: true
|
||||
type: plugin
|
||||
version: 0.0.3
|
||||
@ -1,200 +0,0 @@
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from decimal import Decimal
|
||||
from typing import Optional
|
||||
from urllib.parse import urljoin
|
||||
from dify_plugin import TextEmbeddingModel
|
||||
import numpy as np
|
||||
import requests
|
||||
from dify_plugin.entities.model import (
|
||||
AIModelEntity,
|
||||
EmbeddingInputType,
|
||||
FetchFrom,
|
||||
I18nObject,
|
||||
ModelPropertyKey,
|
||||
ModelType,
|
||||
PriceConfig,
|
||||
PriceType,
|
||||
)
|
||||
from dify_plugin.entities.model.text_embedding import (
|
||||
EmbeddingUsage,
|
||||
TextEmbeddingResult,
|
||||
)
|
||||
from dify_plugin.errors.model import (
|
||||
CredentialsValidateFailedError,
|
||||
InvokeAuthorizationError,
|
||||
InvokeBadRequestError,
|
||||
InvokeConnectionError,
|
||||
InvokeError,
|
||||
InvokeRateLimitError,
|
||||
InvokeServerUnavailableError,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class OllamaEmbeddingModel(TextEmbeddingModel):
|
||||
"""
|
||||
Model class for an Ollama text embedding model.
|
||||
"""
|
||||
|
||||
def _invoke(
|
||||
self,
|
||||
model: str,
|
||||
credentials: dict,
|
||||
texts: list[str],
|
||||
user: Optional[str] = None,
|
||||
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
|
||||
) -> TextEmbeddingResult:
|
||||
"""
|
||||
Invoke text embedding model
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:param texts: texts to embed
|
||||
:param user: unique user id
|
||||
:param input_type: input type
|
||||
:return: embeddings result
|
||||
"""
|
||||
headers = {"Content-Type": "application/json"}
|
||||
endpoint_url = credentials.get("base_url", "")
|
||||
if endpoint_url and not endpoint_url.endswith("/"):
|
||||
endpoint_url += "/"
|
||||
endpoint_url = urljoin(endpoint_url, "api/embed")
|
||||
context_size = self._get_context_size(model, credentials)
|
||||
inputs = []
|
||||
used_tokens = 0
|
||||
for text in texts:
|
||||
num_tokens = self._get_num_tokens_by_gpt2(text)
|
||||
if num_tokens >= context_size:
|
||||
cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
|
||||
inputs.append(text[0:cutoff])
|
||||
else:
|
||||
inputs.append(text)
|
||||
payload = {"input": inputs, "model": model, "options": {"use_mmap": True}}
|
||||
response = requests.post(
|
||||
endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300)
|
||||
)
|
||||
response.raise_for_status()
|
||||
response_data = response.json()
|
||||
embeddings = response_data["embeddings"]
|
||||
embedding_used_tokens = self.get_num_tokens(model, credentials, inputs)
|
||||
used_tokens += sum(embedding_used_tokens)
|
||||
usage = self._calc_response_usage(
|
||||
model=model, credentials=credentials, tokens=used_tokens
|
||||
)
|
||||
return TextEmbeddingResult(embeddings=embeddings, usage=usage, model=model)
|
||||
|
||||
def get_num_tokens(
|
||||
self, model: str, credentials: dict, texts: list[str]
|
||||
) -> list[int]:
|
||||
"""
|
||||
Approximate number of tokens for given messages using GPT2 tokenizer
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:param texts: texts to embed
|
||||
:return:
|
||||
"""
|
||||
return [self._get_num_tokens_by_gpt2(text) for text in texts]
|
||||
|
||||
def validate_credentials(self, model: str, credentials: dict) -> None:
|
||||
"""
|
||||
Validate model credentials
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:return:
|
||||
"""
|
||||
try:
|
||||
self._invoke(model=model, credentials=credentials, texts=["ping"])
|
||||
except InvokeError as ex:
|
||||
raise CredentialsValidateFailedError(
|
||||
f"An error occurred during credentials validation: {ex.description}"
|
||||
)
|
||||
except Exception as ex:
|
||||
raise CredentialsValidateFailedError(
|
||||
f"An error occurred during credentials validation: {str(ex)}"
|
||||
)
|
||||
|
||||
def get_customizable_model_schema(
|
||||
self, model: str, credentials: dict
|
||||
) -> AIModelEntity:
|
||||
"""
|
||||
generate custom model entities from credentials
|
||||
"""
|
||||
entity = AIModelEntity(
|
||||
model=model,
|
||||
label=I18nObject(en_US=model),
|
||||
model_type=ModelType.TEXT_EMBEDDING,
|
||||
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
|
||||
model_properties={
|
||||
ModelPropertyKey.CONTEXT_SIZE: int(
|
||||
credentials.get("context_size", 512)
|
||||
),
|
||||
ModelPropertyKey.MAX_CHUNKS: 1,
|
||||
},
|
||||
parameter_rules=[],
|
||||
pricing=PriceConfig(
|
||||
input=Decimal(credentials.get("input_price", 0)),
|
||||
unit=Decimal(credentials.get("unit", 0)),
|
||||
currency=credentials.get("currency", "USD"),
|
||||
),
|
||||
)
|
||||
return entity
|
||||
|
||||
def _calc_response_usage(
|
||||
self, model: str, credentials: dict, tokens: int
|
||||
) -> EmbeddingUsage:
|
||||
"""
|
||||
Calculate response usage
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:param tokens: input tokens
|
||||
:return: usage
|
||||
"""
|
||||
input_price_info = self.get_price(
|
||||
model=model,
|
||||
credentials=credentials,
|
||||
price_type=PriceType.INPUT,
|
||||
tokens=tokens,
|
||||
)
|
||||
usage = EmbeddingUsage(
|
||||
tokens=tokens,
|
||||
total_tokens=tokens,
|
||||
unit_price=input_price_info.unit_price,
|
||||
price_unit=input_price_info.unit,
|
||||
total_price=input_price_info.total_amount,
|
||||
currency=input_price_info.currency,
|
||||
latency=time.perf_counter() - self.started_at,
|
||||
)
|
||||
return usage
|
||||
|
||||
@property
|
||||
def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
|
||||
"""
|
||||
Map model invoke error to unified error
|
||||
The key is the error type thrown to the caller
|
||||
The value is the error type thrown by the model,
|
||||
which needs to be converted into a unified error type for the caller.
|
||||
|
||||
:return: Invoke error mapping
|
||||
"""
|
||||
return {
|
||||
InvokeAuthorizationError: [requests.exceptions.InvalidHeader],
|
||||
InvokeBadRequestError: [
|
||||
requests.exceptions.HTTPError,
|
||||
requests.exceptions.InvalidURL,
|
||||
],
|
||||
InvokeRateLimitError: [requests.exceptions.RetryError],
|
||||
InvokeServerUnavailableError: [
|
||||
requests.exceptions.ConnectionError,
|
||||
requests.exceptions.HTTPError,
|
||||
],
|
||||
InvokeConnectionError: [
|
||||
requests.exceptions.ConnectTimeout,
|
||||
requests.exceptions.ReadTimeout,
|
||||
],
|
||||
}
|
||||
@ -1,15 +0,0 @@
|
||||
import logging
|
||||
from dify_plugin import ModelProvider
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class OpenAIProvider(ModelProvider):
|
||||
def validate_provider_credentials(self, credentials: dict) -> None:
|
||||
"""
|
||||
Validate provider credentials
|
||||
if validate failed, raise exception
|
||||
|
||||
:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
|
||||
"""
|
||||
pass
|
||||
@ -1,2 +0,0 @@
|
||||
dify_plugin==0.0.1b65
|
||||
numpy~=2.1.3
|
||||
@ -1,9 +0,0 @@
|
||||
## Overview
|
||||
|
||||
This plugin provides access to models that are OpenAI-compatible, including LLMs, reranking, text embedding, speech-to-text (STT), and text-to-speech(TTS) models. Developers can easily add models by providing configuration parameters such as the model name and API key.
|
||||
|
||||
## Configure
|
||||
|
||||
Configure the OpenAI-API-compatible model by providing its core details (Type, Name, API Key, URL) and adjusting further options like completion, context, and token limits, as well as streaming and vision settings. Save when done.
|
||||
|
||||
|
||||
Binary file not shown.
@ -1 +0,0 @@
|
||||
<svg width="16" height="17" viewBox="0 0 16 17" fill="none" xmlns="http://www.w3.org/2000/svg" class="w-4 h-4 text-gray-400" data-icon="CubeOutline" aria-hidden="true"><g id="cube-outline"><g id="Solid"><path d="M8.26865 1.29003C8.09143 1.25358 7.90866 1.25358 7.73144 1.29003C7.52659 1.33216 7.3435 1.43471 7.19794 1.51624L7.15826 1.53841L6.17628 2.08395C5.85443 2.26276 5.73846 2.66863 5.91727 2.99049C6.09608 3.31234 6.50195 3.4283 6.82381 3.24949L7.80579 2.70395C7.90681 2.64782 7.95839 2.61946 7.99686 2.60091L8.00004 2.59938L8.00323 2.60091C8.0417 2.61946 8.09327 2.64782 8.1943 2.70395L9.17628 3.24949C9.49814 3.4283 9.90401 3.31234 10.0828 2.99048C10.2616 2.66863 10.1457 2.26276 9.82381 2.08395L8.84183 1.53841L8.80215 1.51624C8.65659 1.43471 8.4735 1.33216 8.26865 1.29003Z" fill="currentColor"></path><path d="M12.8238 3.75062C12.5019 3.57181 12.0961 3.68777 11.9173 4.00963C11.7385 4.33148 11.8544 4.73735 12.1763 4.91616L12.6272 5.16668L12.1763 5.41719C11.8545 5.596 11.7385 6.00186 11.9173 6.32372C12.0961 6.64558 12.502 6.76154 12.8238 6.58273L13.3334 6.29966V6.83339C13.3334 7.20158 13.6319 7.50006 14 7.50006C14.3682 7.50006 14.6667 7.20158 14.6667 6.83339V5.79435L14.6668 5.74627C14.6673 5.62441 14.6678 5.48084 14.6452 5.33482C14.6869 5.17472 14.6696 4.99892 14.5829 4.84286C14.4904 4.6764 14.3371 4.56501 14.1662 4.52099C14.0496 4.43038 13.9239 4.36116 13.8173 4.3024L13.7752 4.27915L12.8238 3.75062Z" fill="currentColor"></path><path d="M3.8238 4.91616C4.14566 4.73735 4.26162 4.33148 4.08281 4.00963C3.90401 3.68777 3.49814 3.57181 3.17628 3.75062L2.22493 4.27915L2.18284 4.3024C2.07615 4.36116 1.95045 4.4304 1.83382 4.52102C1.66295 4.56506 1.50977 4.67643 1.41731 4.84286C1.33065 4.99886 1.31323 5.17459 1.35493 5.33464C1.33229 5.48072 1.33281 5.62436 1.33326 5.74627L1.33338 5.79435V6.83339C1.33338 7.20158 1.63185 7.50006 2.00004 7.50006C2.36823 7.50006 2.66671 7.20158 2.66671 6.83339V6.29961L3.17632 6.58273C3.49817 6.76154 3.90404 6.64558 4.08285 6.32372C4.26166 6.00186 4.1457 5.596 3.82384 5.41719L3.3729 5.16666L3.8238 4.91616Z" fill="currentColor"></path><path d="M2.66671 10.1667C2.66671 9.79853 2.36823 9.50006 2.00004 9.50006C1.63185 9.50006 1.33338 9.79853 1.33338 10.1667V11.2058L1.33326 11.2538C1.33262 11.4298 1.33181 11.6509 1.40069 11.8594C1.46024 12.0397 1.55759 12.2051 1.68622 12.3447C1.835 12.5061 2.02873 12.6128 2.18281 12.6977L2.22493 12.721L3.17628 13.2495C3.49814 13.4283 3.90401 13.3123 4.08281 12.9905C4.26162 12.6686 4.14566 12.2628 3.8238 12.084L2.87245 11.5554C2.76582 11.4962 2.71137 11.4656 2.67318 11.4413L2.66995 11.4392L2.66971 11.4354C2.66699 11.3902 2.66671 11.3277 2.66671 11.2058V10.1667Z" fill="currentColor"></path><path d="M14.6667 10.1667C14.6667 9.79853 14.3682 9.50006 14 9.50006C13.6319 9.50006 13.3334 9.79853 13.3334 10.1667V11.2058C13.3334 11.3277 13.3331 11.3902 13.3304 11.4354L13.3301 11.4392L13.3269 11.4413C13.2887 11.4656 13.2343 11.4962 13.1276 11.5554L12.1763 12.084C11.8544 12.2628 11.7385 12.6686 11.9173 12.9905C12.0961 13.3123 12.5019 13.4283 12.8238 13.2495L13.7752 12.721L13.8172 12.6977C13.9713 12.6128 14.1651 12.5061 14.3139 12.3447C14.4425 12.2051 14.5398 12.0397 14.5994 11.8594C14.6683 11.6509 14.6675 11.4298 14.6668 11.2538L14.6667 11.2058V10.1667Z" fill="currentColor"></path><path d="M6.82381 13.7506C6.50195 13.5718 6.09608 13.6878 5.91727 14.0096C5.73846 14.3315 5.85443 14.7374 6.17628 14.9162L7.15826 15.4617L7.19793 15.4839C7.29819 15.54 7.41625 15.6061 7.54696 15.6556C7.66589 15.7659 7.82512 15.8333 8.00008 15.8333C8.17507 15.8333 8.33431 15.7659 8.45324 15.6556C8.58391 15.6061 8.70193 15.54 8.80215 15.4839L8.84183 15.4617L9.82381 14.9162C10.1457 14.7374 10.2616 14.3315 10.0828 14.0096C9.90401 13.6878 9.49814 13.5718 9.17628 13.7506L8.66675 14.0337V13.5C8.66675 13.1318 8.36827 12.8333 8.00008 12.8333C7.63189 12.8333 7.33341 13.1318 7.33341 13.5V14.0337L6.82381 13.7506Z" fill="currentColor"></path><path d="M6.82384 7.08385C6.50199 6.90505 6.09612 7.02101 5.91731 7.34286C5.7385 7.66472 5.85446 8.07059 6.17632 8.2494L7.33341 8.89223V10.1666C7.33341 10.5348 7.63189 10.8333 8.00008 10.8333C8.36827 10.8333 8.66675 10.5348 8.66675 10.1666V8.89223L9.82384 8.2494C10.1457 8.07059 10.2617 7.66472 10.0829 7.34286C9.90404 7.02101 9.49817 6.90505 9.17632 7.08385L8.00008 7.73732L6.82384 7.08385Z" fill="currentColor"></path></g></g></svg>
|
||||
|
Before Width: | Height: | Size: 4.2 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 156 KiB |
@ -1,6 +0,0 @@
|
||||
from dify_plugin import Plugin, DifyPluginEnv
|
||||
|
||||
plugin = Plugin(DifyPluginEnv())
|
||||
|
||||
if __name__ == "__main__":
|
||||
plugin.run()
|
||||
@ -1,31 +0,0 @@
|
||||
version: 0.0.11
|
||||
type: plugin
|
||||
author: "langgenius"
|
||||
name: "openai_api_compatible"
|
||||
description:
|
||||
en_US: Model providers compatible with OpenAI's API standard, such as LM Studio.
|
||||
zh_Hans: 兼容 OpenAI API 的模型供应商，例如 LM Studio 。
|
||||
label:
|
||||
en_US: "OpenAI-API-compatible"
|
||||
created_at: "2024-07-12T08:03:44.658609186Z"
|
||||
icon: icon.svg
|
||||
resource:
|
||||
memory: 1048576
|
||||
permission:
|
||||
tool:
|
||||
enabled: true
|
||||
model:
|
||||
enabled: true
|
||||
llm: true
|
||||
plugins:
|
||||
models:
|
||||
- "provider/openai_api_compatible.yaml"
|
||||
meta:
|
||||
version: 0.0.1
|
||||
arch:
|
||||
- "amd64"
|
||||
- "arm64"
|
||||
runner:
|
||||
language: "python"
|
||||
version: "3.12"
|
||||
entrypoint: "main"
|
||||
@ -1,53 +0,0 @@
|
||||
from collections.abc import Mapping
|
||||
|
||||
import openai
|
||||
from httpx import Timeout
|
||||
|
||||
from dify_plugin.errors.model import InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError, InvokeError, InvokeRateLimitError, InvokeServerUnavailableError
|
||||
|
||||
|
||||
class _CommonOpenAI:
|
||||
def _to_credential_kwargs(self, credentials: Mapping) -> dict:
|
||||
"""
|
||||
Transform credentials to kwargs for model instance
|
||||
|
||||
:param credentials:
|
||||
:return:
|
||||
"""
|
||||
credentials_kwargs = {
|
||||
"api_key": credentials['openai_api_key'],
|
||||
"timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
|
||||
"max_retries": 1,
|
||||
}
|
||||
|
||||
if credentials.get("openai_api_base"):
|
||||
openai_api_base = credentials["openai_api_base"].rstrip("/")
|
||||
credentials_kwargs["base_url"] = openai_api_base + "/v1"
|
||||
|
||||
if 'openai_organization' in credentials:
|
||||
credentials_kwargs['organization'] = credentials['openai_organization']
|
||||
|
||||
return credentials_kwargs
|
||||
|
||||
@property
|
||||
def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
|
||||
"""
|
||||
Map model invoke error to unified error
|
||||
The key is the error type thrown to the caller
|
||||
The value is the error type thrown by the model,
|
||||
which needs to be converted into a unified error type for the caller.
|
||||
|
||||
:return: Invoke error mapping
|
||||
"""
|
||||
return {
|
||||
InvokeConnectionError: [openai.APIConnectionError, openai.APITimeoutError],
|
||||
InvokeServerUnavailableError: [openai.InternalServerError],
|
||||
InvokeRateLimitError: [openai.RateLimitError],
|
||||
InvokeAuthorizationError: [openai.AuthenticationError, openai.PermissionDeniedError],
|
||||
InvokeBadRequestError: [
|
||||
openai.BadRequestError,
|
||||
openai.NotFoundError,
|
||||
openai.UnprocessableEntityError,
|
||||
openai.APIError,
|
||||
],
|
||||
}
|
||||
@ -1,31 +0,0 @@
|
||||
from typing import Mapping
|
||||
|
||||
from dify_plugin.entities.model import (
|
||||
AIModelEntity,
|
||||
I18nObject,
|
||||
ModelFeature
|
||||
)
|
||||
|
||||
from dify_plugin.interfaces.model.openai_compatible.llm import (
|
||||
OAICompatLargeLanguageModel,
|
||||
)
|
||||
|
||||
|
||||
class OpenAILargeLanguageModel(OAICompatLargeLanguageModel):
|
||||
def get_customizable_model_schema(self, model: str, credentials: Mapping) -> AIModelEntity:
|
||||
entity = super().get_customizable_model_schema(model, credentials)
|
||||
|
||||
agent_though_support = credentials.get("agent_though_support", "not_supported")
|
||||
if agent_though_support == "supported":
|
||||
try:
|
||||
entity.features.index(ModelFeature.AGENT_THOUGHT)
|
||||
except ValueError:
|
||||
entity.features.append(ModelFeature.AGENT_THOUGHT)
|
||||
|
||||
if "display_name" in credentials and credentials["display_name"] != "":
|
||||
entity.label= I18nObject(
|
||||
en_US=credentials["display_name"],
|
||||
zh_Hans=credentials["display_name"]
|
||||
)
|
||||
|
||||
return entity
|
||||
@ -1,50 +0,0 @@
|
||||
from typing import Mapping
|
||||
|
||||
from dify_plugin.entities.model import (
|
||||
AIModelEntity,
|
||||
I18nObject
|
||||
)
|
||||
|
||||
from dify_plugin.interfaces.model.openai_compatible.rerank import (
|
||||
OAICompatRerankModel,
|
||||
)
|
||||
from dify_plugin.errors.model import (
|
||||
CredentialsValidateFailedError,
|
||||
)
|
||||
|
||||
class OpenAIRerankModel(OAICompatRerankModel):
|
||||
def validate_credentials(self, model: str, credentials: dict) -> None:
|
||||
"""
|
||||
Validate model credentials
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:return:
|
||||
"""
|
||||
try:
|
||||
self._invoke(
|
||||
model=model,
|
||||
credentials=credentials,
|
||||
query="What is the capital of the United States?",
|
||||
docs=[
|
||||
"Carson City is the capital city of the American state of Nevada. At the 2010 United States "
|
||||
"Census, Carson City had a population of 55,274.",
|
||||
"The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
|
||||
"are a political division controlled by the United States. Its capital is Saipan.",
|
||||
],
|
||||
score_threshold=0.8,
|
||||
top_n=3,
|
||||
)
|
||||
except Exception as ex:
|
||||
raise CredentialsValidateFailedError(str(ex)) from ex
|
||||
|
||||
def get_customizable_model_schema(self, model: str, credentials: Mapping) -> AIModelEntity:
|
||||
entity = super().get_customizable_model_schema(model, credentials)
|
||||
|
||||
if "display_name" in credentials and credentials["display_name"] != "":
|
||||
entity.label= I18nObject(
|
||||
en_US=credentials["display_name"],
|
||||
zh_Hans=credentials["display_name"]
|
||||
)
|
||||
|
||||
return entity
|
||||
@ -1,28 +0,0 @@
|
||||
from typing import Optional
|
||||
from dify_plugin.entities.model import AIModelEntity, FetchFrom, I18nObject, ModelType
|
||||
from dify_plugin.interfaces.model.openai_compatible.speech2text import (
|
||||
OAICompatSpeech2TextModel,
|
||||
)
|
||||
|
||||
|
||||
class OpenAISpeech2TextModel(OAICompatSpeech2TextModel):
|
||||
def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
|
||||
"""
|
||||
used to define customizable model schema
|
||||
"""
|
||||
entity = AIModelEntity(
|
||||
model=model,
|
||||
label=I18nObject(en_US=model),
|
||||
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
|
||||
model_type=ModelType.SPEECH2TEXT,
|
||||
model_properties={},
|
||||
parameter_rules=[],
|
||||
)
|
||||
|
||||
if "display_name" in credentials and credentials["display_name"] != "":
|
||||
entity.label= I18nObject(
|
||||
en_US=credentials["display_name"],
|
||||
zh_Hans=credentials["display_name"]
|
||||
)
|
||||
|
||||
return entity
|
||||
@ -1,24 +0,0 @@
|
||||
from typing import Mapping
|
||||
|
||||
from dify_plugin.entities.model import (
|
||||
AIModelEntity,
|
||||
I18nObject
|
||||
)
|
||||
|
||||
from dify_plugin.interfaces.model.openai_compatible.text_embedding import (
|
||||
OAICompatEmbeddingModel,
|
||||
)
|
||||
|
||||
|
||||
class OpenAITextEmbeddingModel(OAICompatEmbeddingModel):
|
||||
|
||||
def get_customizable_model_schema(self, model: str, credentials: Mapping) -> AIModelEntity:
|
||||
entity = super().get_customizable_model_schema(model, credentials)
|
||||
|
||||
if "display_name" in credentials and credentials["display_name"] != "":
|
||||
entity.label= I18nObject(
|
||||
en_US=credentials["display_name"],
|
||||
zh_Hans=credentials["display_name"]
|
||||
)
|
||||
|
||||
return entity
|
||||
@ -1,22 +0,0 @@
|
||||
from typing import Mapping
|
||||
|
||||
from dify_plugin.entities.model import (
|
||||
AIModelEntity,
|
||||
I18nObject
|
||||
)
|
||||
|
||||
from dify_plugin.interfaces.model.openai_compatible.tts import OAICompatText2SpeechModel
|
||||
|
||||
|
||||
class OpenAIText2SpeechModel(OAICompatText2SpeechModel):
|
||||
|
||||
def get_customizable_model_schema(self, model: str, credentials: Mapping) -> AIModelEntity:
|
||||
entity = super().get_customizable_model_schema(model, credentials)
|
||||
|
||||
if "display_name" in credentials and credentials["display_name"] != "":
|
||||
entity.label= I18nObject(
|
||||
en_US=credentials["display_name"],
|
||||
zh_Hans=credentials["display_name"]
|
||||
)
|
||||
|
||||
return entity
|
||||
@ -1,17 +0,0 @@
|
||||
import logging
|
||||
from collections.abc import Mapping
|
||||
|
||||
from dify_plugin import ModelProvider
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class OpenAIProvider(ModelProvider):
|
||||
def validate_provider_credentials(self, credentials: Mapping) -> None:
|
||||
"""
|
||||
Validate provider credentials
|
||||
if validate failed, raise exception
|
||||
|
||||
:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
|
||||
"""
|
||||
pass
|
||||
@ -1,240 +0,0 @@
|
||||
provider: openai_api_compatible
|
||||
label:
|
||||
en_US: OpenAI-API-compatible
|
||||
description:
|
||||
en_US: Model providers compatible with OpenAI's API standard, such as LM Studio.
|
||||
zh_Hans: 兼容 OpenAI API 的模型供应商，例如 LM Studio 。
|
||||
icon_small:
|
||||
en_US: icon.svg
|
||||
supported_model_types:
|
||||
- llm
|
||||
- rerank
|
||||
- text-embedding
|
||||
- speech2text
|
||||
- tts
|
||||
configurate_methods:
|
||||
- customizable-model
|
||||
model_credential_schema:
|
||||
model:
|
||||
label:
|
||||
en_US: Model Name
|
||||
zh_Hans: 模型名称
|
||||
placeholder:
|
||||
en_US: Enter full model name
|
||||
zh_Hans: 输入模型全称
|
||||
credential_form_schemas:
|
||||
- variable: display_name
|
||||
label:
|
||||
en_US: Model display name
|
||||
zh_Hans: 模型显示名称
|
||||
type: text-input
|
||||
required: false
|
||||
placeholder:
|
||||
zh_Hans: 模型在界面的显示名称
|
||||
en_US: The display name of the model in the interface.
|
||||
- variable: api_key
|
||||
label:
|
||||
en_US: API Key
|
||||
type: secret-input
|
||||
required: false
|
||||
placeholder:
|
||||
zh_Hans: 在此输入您的 API Key
|
||||
en_US: Enter your API Key
|
||||
- variable: endpoint_url
|
||||
label:
|
||||
zh_Hans: API endpoint URL
|
||||
en_US: API endpoint URL
|
||||
type: text-input
|
||||
required: true
|
||||
placeholder:
|
||||
zh_Hans: Base URL, e.g. https://api.openai.com/v1
|
||||
en_US: Base URL, e.g. https://api.openai.com/v1
|
||||
- variable: endpoint_model_name
|
||||
label:
|
||||
zh_Hans: API endpoint中的模型名称
|
||||
en_US: model name for API endpoint
|
||||
type: text-input
|
||||
required: false
|
||||
placeholder:
|
||||
zh_Hans: endpoint model name, e.g. chatgpt4.0
|
||||
en_US: endpoint model name, e.g. chatgpt4.0
|
||||
- variable: mode
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: llm
|
||||
label:
|
||||
en_US: Completion mode
|
||||
type: select
|
||||
required: false
|
||||
default: chat
|
||||
placeholder:
|
||||
zh_Hans: 选择对话类型
|
||||
en_US: Select completion mode
|
||||
options:
|
||||
- value: completion
|
||||
label:
|
||||
en_US: Completion
|
||||
zh_Hans: 补全
|
||||
- value: chat
|
||||
label:
|
||||
en_US: Chat
|
||||
zh_Hans: 对话
|
||||
- variable: context_size
|
||||
label:
|
||||
zh_Hans: 模型上下文长度
|
||||
en_US: Model context size
|
||||
required: true
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: llm
|
||||
type: text-input
|
||||
default: "4096"
|
||||
placeholder:
|
||||
zh_Hans: 在此输入您的模型上下文长度
|
||||
en_US: Enter your Model context size
|
||||
- variable: context_size
|
||||
label:
|
||||
zh_Hans: 模型上下文长度
|
||||
en_US: Model context size
|
||||
required: true
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: text-embedding
|
||||
type: text-input
|
||||
default: "4096"
|
||||
placeholder:
|
||||
zh_Hans: 在此输入您的模型上下文长度
|
||||
en_US: Enter your Model context size
|
||||
- variable: context_size
|
||||
label:
|
||||
zh_Hans: 模型上下文长度
|
||||
en_US: Model context size
|
||||
required: true
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: rerank
|
||||
type: text-input
|
||||
default: "4096"
|
||||
placeholder:
|
||||
zh_Hans: 在此输入您的模型上下文长度
|
||||
en_US: Enter your Model context size
|
||||
- variable: max_tokens_to_sample
|
||||
label:
|
||||
zh_Hans: 最大 token 上限
|
||||
en_US: Upper bound for max tokens
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: llm
|
||||
default: "4096"
|
||||
type: text-input
|
||||
- variable: agent_though_support
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: llm
|
||||
label:
|
||||
en_US: Agent Thought
|
||||
type: select
|
||||
required: false
|
||||
default: not_supported
|
||||
options:
|
||||
- value: supported
|
||||
label:
|
||||
en_US: Support
|
||||
zh_Hans: 支持
|
||||
- value: not_supported
|
||||
label:
|
||||
en_US: Not Support
|
||||
zh_Hans: 不支持
|
||||
- variable: function_calling_type
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: llm
|
||||
label:
|
||||
en_US: Function calling
|
||||
type: select
|
||||
required: false
|
||||
default: no_call
|
||||
options:
|
||||
- value: function_call
|
||||
label:
|
||||
en_US: Function Call
|
||||
zh_Hans: Function Call
|
||||
- value: tool_call
|
||||
label:
|
||||
en_US: Tool Call
|
||||
zh_Hans: Tool Call
|
||||
- value: no_call
|
||||
label:
|
||||
en_US: Not Support
|
||||
zh_Hans: 不支持
|
||||
- variable: stream_function_calling
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: llm
|
||||
label:
|
||||
en_US: Stream function calling
|
||||
type: select
|
||||
required: false
|
||||
default: not_supported
|
||||
options:
|
||||
- value: supported
|
||||
label:
|
||||
en_US: Support
|
||||
zh_Hans: 支持
|
||||
- value: not_supported
|
||||
label:
|
||||
en_US: Not Support
|
||||
zh_Hans: 不支持
|
||||
- variable: vision_support
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: llm
|
||||
label:
|
||||
zh_Hans: Vision 支持
|
||||
en_US: Vision Support
|
||||
type: select
|
||||
required: false
|
||||
default: no_support
|
||||
options:
|
||||
- value: support
|
||||
label:
|
||||
en_US: Support
|
||||
zh_Hans: 支持
|
||||
- value: no_support
|
||||
label:
|
||||
en_US: Not Support
|
||||
zh_Hans: 不支持
|
||||
- variable: stream_mode_delimiter
|
||||
label:
|
||||
zh_Hans: 流模式返回结果的分隔符
|
||||
en_US: Delimiter for streaming results
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: llm
|
||||
default: '\n\n'
|
||||
type: text-input
|
||||
- variable: voices
|
||||
show_on:
|
||||
- variable: __model_type
|
||||
value: tts
|
||||
label:
|
||||
en_US: Available Voices (comma-separated)
|
||||
zh_Hans: 可用声音（用英文逗号分隔）
|
||||
type: text-input
|
||||
required: false
|
||||
default: "alloy"
|
||||
placeholder:
|
||||
en_US: "alloy,echo,fable,onyx,nova,shimmer"
|
||||
zh_Hans: "alloy,echo,fable,onyx,nova,shimmer"
|
||||
help:
|
||||
en_US: "List voice names separated by commas. First voice will be used as default."
|
||||
zh_Hans: "用英文逗号分隔的声音列表。第一个声音将作为默认值。"
|
||||
extra:
|
||||
python:
|
||||
provider_source: provider/openai_api_compatible.py
|
||||
model_sources:
|
||||
- "models/llm/llm.py"
|
||||
- "models/text_embedding/text_embedding.py"
|
||||
- "models/rerank/rerank.py"
|
||||
- "models/speech2text/speech2text.py"
|
||||
- "models/tts/tts.py"
|
||||
@ -1,2 +0,0 @@
|
||||
dify_plugin==0.0.1b73
|
||||
openai~=1.66.3
|
||||
@ -1,4 +0,0 @@
|
||||
INSTALL_METHOD=remote
|
||||
REMOTE_INSTALL_HOST=debug-plugin.dify.dev
|
||||
REMOTE_INSTALL_PORT=5003
|
||||
REMOTE_INSTALL_KEY=********-****-****-****-************
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 43 KiB |
File diff suppressed because one or more lines are too long
|
Before Width: | Height: | Size: 5.6 KiB |
@ -1 +0,0 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" fill="none" version="1.1" width="128" height="128" viewBox="0 0 128 128"><g><g style="opacity:0;"><rect x="0" y="0" width="128" height="128" rx="0" fill="#FFFFFF" fill-opacity="1"/></g><g><path d="M100.74,12L93.2335,12C69.21260000000001,12,55.3672,27.3468,55.3672,50.8672L55.3672,54.8988C52.6011,54.1056,49.7377,53.7031,46.8601,53.7031C29.816499999999998,53.7031,16,67.5196,16,84.5632C16,101.6069,29.816499999999998,115.423,46.8601,115.423C63.9037,115.423,77.72030000000001,101.6069,77.72030000000001,84.5632C77.72030000000001,82.4902,77.51140000000001,80.4223,77.0967,78.3911L77.2197,78.3911L100.74,78.3911C106.9654,78.3681,112,73.3151,112,67.08959999999999C112,60.8642,106.9654,55.8111,100.74,55.7882L100.7362,55.7882L100.6985,55.7879L100.6606,55.7882L77.2197,55.7882L77.2195,49.8663C77.2195,40.8584,83.7252,34.352900000000005,93.2335,34.352900000000005L100.5653,34.352900000000005L100.5733,34.352900000000005L100.5812,34.352900000000005L100.74,34.352900000000005L100.74,34.352900000000005C106.8469,34.2605,111.7497,29.284,111.7497,23.1764C111.7497,17.06889,106.8469,12.0923454,100.74,12L100.74,12ZM56.0347,84.5632C56.0347,79.4962,51.9271,75.3885,46.8601,75.3885C41.793099999999995,75.3885,37.6854,79.4962,37.6854,84.5632C37.6854,89.6303,41.793099999999995,93.7378,46.8601,93.7378C51.9271,93.7378,56.0347,89.6303,56.0347,84.5632Z" fill-rule="evenodd" fill="#8358F6" fill-opacity="1"/></g></g></svg>
|
||||
|
Before Width: | Height: | Size: 1.4 KiB |
@ -1,6 +0,0 @@
|
||||
from dify_plugin import Plugin, DifyPluginEnv
|
||||
|
||||
plugin = Plugin(DifyPluginEnv())
|
||||
|
||||
if __name__ == '__main__':
|
||||
plugin.run()
|
||||
@ -1,44 +0,0 @@
|
||||
- Pro/deepseek-ai/DeepSeek-R1
|
||||
- Pro/deepseek-ai/DeepSeek-V3
|
||||
- deepseek-ai/DeepSeek-R1
|
||||
- deepseek-ai/DeepSeek-V3
|
||||
- deepseek-ai/DeepSeek-V2.5
|
||||
- deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
|
||||
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
|
||||
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
|
||||
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
|
||||
- deepseek-ai/DeepSeek-R1-Distill-Llama-70B
|
||||
- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
|
||||
- deepseek-ai/DeepSeek-V2.5
|
||||
- deepseek-ai/Janus-Pro-7B
|
||||
- Qwen/QwQ-32B-Preview
|
||||
- Qwen/QVQ-72B-Preview
|
||||
- Qwen/Qwen2.5-72B-Instruct
|
||||
- Qwen/Qwen2.5-72B-Instruct-128K
|
||||
- Qwen/Qwen2.5-32B-Instruct
|
||||
- Qwen/Qwen2.5-14B-Instruct
|
||||
- Qwen/Qwen2.5-7B-Instruct
|
||||
- Qwen/Qwen2.5-Coder-32B-Instruct
|
||||
- Qwen/Qwen2.5-Coder-7B-Instruct
|
||||
- Qwen/Qwen2-VL-72B-Instruct
|
||||
- Qwen/Qwen2-1.5B-Instruct
|
||||
- Qwen/Qwen2.5-72B-Instruct-128K
|
||||
- Vendor-A/Qwen/Qwen2.5-72B-Instruct
|
||||
- Pro/Qwen/Qwen2-VL-7B-Instruct
|
||||
- OpenGVLab/InternVL2-26B
|
||||
- Pro/OpenGVLab/InternVL2-8B
|
||||
- Vendor-A/Qwen/Qwen2.5-72B-Instruct
|
||||
- Pro/Qwen/Qwen2-VL-7B-Instruct
|
||||
- Pro/OpenGVLab/InternVL2-8B
|
||||
- OpenGVLab/InternVL2-26B
|
||||
- THUDM/glm-4-9b-chat
|
||||
- 01-ai/Yi-1.5-34B-Chat-16K
|
||||
- 01-ai/Yi-1.5-9B-Chat-16K
|
||||
- 01-ai/Yi-1.5-6B-Chat
|
||||
- internlm/internlm2_5-20b-chat
|
||||
- internlm/internlm2_5-7b-chat
|
||||
- meta-llama/Llama-3.3-70B-Instruct
|
||||
- meta-llama/Meta-Llama-3.1-70B-Instruct
|
||||
- meta-llama/Meta-Llama-3.1-8B-Instruct
|
||||
- google/gemma-2-27b-it
|
||||
- google/gemma-2-9b-it
|
||||
@ -1,40 +0,0 @@
|
||||
model: deepseek-ai/DeepSeek-Coder-V2-Instruct
|
||||
label:
|
||||
en_US: deepseek-ai/DeepSeek-Coder-V2-Instruct
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 32768
|
||||
parameter_rules:
|
||||
- name: temperature
|
||||
use_template: temperature
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
type: int
|
||||
default: 512
|
||||
min: 1
|
||||
max: 4096
|
||||
help:
|
||||
zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
|
||||
en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
|
||||
- name: top_p
|
||||
use_template: top_p
|
||||
- name: top_k
|
||||
label:
|
||||
zh_Hans: 取样数量
|
||||
en_US: Top k
|
||||
type: int
|
||||
help:
|
||||
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
|
||||
en_US: Only sample from the top K options for each subsequent token.
|
||||
required: false
|
||||
- name: frequency_penalty
|
||||
use_template: frequency_penalty
|
||||
pricing:
|
||||
input: '1.33'
|
||||
output: '1.33'
|
||||
unit: '0.000001'
|
||||
currency: RMB
|
||||
deprecated: true
|
||||
@ -1,21 +0,0 @@
|
||||
model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
|
||||
label:
|
||||
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
|
||||
en_US: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 32000
|
||||
parameter_rules:
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
min: 1
|
||||
max: 8192
|
||||
default: 4096
|
||||
pricing:
|
||||
input: "0.00"
|
||||
output: "4.3"
|
||||
unit: "0.000001"
|
||||
currency: RMB
|
||||
@ -1,21 +0,0 @@
|
||||
model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
|
||||
label:
|
||||
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
|
||||
en_US: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 32000
|
||||
parameter_rules:
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
min: 1
|
||||
max: 8192
|
||||
default: 4096
|
||||
pricing:
|
||||
input: "0.00"
|
||||
output: "0.00"
|
||||
unit: "0.000001"
|
||||
currency: RMB
|
||||
@ -1,21 +0,0 @@
|
||||
model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
|
||||
label:
|
||||
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
|
||||
en_US: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 32000
|
||||
parameter_rules:
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
min: 1
|
||||
max: 8192
|
||||
default: 4096
|
||||
pricing:
|
||||
input: "0.00"
|
||||
output: "1.26"
|
||||
unit: "0.000001"
|
||||
currency: RMB
|
||||
@ -1,21 +0,0 @@
|
||||
model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
|
||||
label:
|
||||
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
|
||||
en_US: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 32000
|
||||
parameter_rules:
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
min: 1
|
||||
max: 8192
|
||||
default: 4096
|
||||
pricing:
|
||||
input: "0.00"
|
||||
output: "0.70"
|
||||
unit: "0.000001"
|
||||
currency: RMB
|
||||
@ -1,21 +0,0 @@
|
||||
model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
|
||||
label:
|
||||
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
|
||||
en_US: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 32000
|
||||
parameter_rules:
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
min: 1
|
||||
max: 8192
|
||||
default: 4096
|
||||
pricing:
|
||||
input: "0.00"
|
||||
output: "1.26"
|
||||
unit: "0.000001"
|
||||
currency: RMB
|
||||
@ -1,21 +0,0 @@
|
||||
model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
|
||||
label:
|
||||
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
|
||||
en_US: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 32000
|
||||
parameter_rules:
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
min: 1
|
||||
max: 8192
|
||||
default: 4096
|
||||
pricing:
|
||||
input: "0.00"
|
||||
output: "0.00"
|
||||
unit: "0.000001"
|
||||
currency: RMB
|
||||
@ -1,21 +0,0 @@
|
||||
model: Pro/deepseek-ai/DeepSeek-R1
|
||||
label:
|
||||
zh_Hans: Pro/deepseek-ai/DeepSeek-R1
|
||||
en_US: Pro/deepseek-ai/DeepSeek-R1
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 64000
|
||||
parameter_rules:
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
min: 1
|
||||
max: 16384
|
||||
default: 16384
|
||||
pricing:
|
||||
input: "4"
|
||||
output: "16"
|
||||
unit: "0.000001"
|
||||
currency: RMB
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue