mirror of
https://github.com/wassname/stampy-chat.git
synced 2026-09-10 12:40:44 +08:00
Daniel O'Connell
@@ -0,0 +1,25 @@
|
||||
name: Run pytest
|
||||
|
||||
on: [push]
|
||||
|
||||
jobs:
|
||||
build:
|
||||
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- name: Set up Python 3.11
|
||||
uses: actions/setup-python@v3
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
cd api
|
||||
python -m pip install --upgrade pip
|
||||
pip install pipenv
|
||||
pipenv install --dev
|
||||
- name: Test with pytest
|
||||
run: |
|
||||
cd api
|
||||
pipenv run pytest
|
||||
@@ -27,6 +27,7 @@ sqlalchemy = "*"
|
||||
mysql-connector-python = "*"
|
||||
|
||||
[dev-packages]
|
||||
pytest = "*"
|
||||
|
||||
[requires]
|
||||
python_version = "3.11"
|
||||
|
||||
Generated
+124
-90
@@ -1,7 +1,7 @@
|
||||
{
|
||||
"_meta": {
|
||||
"hash": {
|
||||
"sha256": "2b8bbf71251550eb71f393b52f3351e7a35e9ebe9bdb5d621d7f0bb2ec9bc145"
|
||||
"sha256": "da07d7e3cde89985be4705003c92dd9147b122ed78e9583e5219d909b6d9127f"
|
||||
},
|
||||
"pipfile-spec": 6,
|
||||
"requires": {
|
||||
@@ -377,11 +377,11 @@
|
||||
},
|
||||
"loguru": {
|
||||
"hashes": [
|
||||
"sha256:1612053ced6ae84d7959dd7d5e431a0532642237ec21f7fd83ac73fe539e03e1",
|
||||
"sha256:b93aa30099fa6860d4727f1b81f8718e965bb96253fa190fab2077aaad6d15d3"
|
||||
"sha256:003d71e3d3ed35f0f8984898359d65b79e5b21943f78af86aa5491210429b8eb",
|
||||
"sha256:e671a53522515f34fd406340ee968cb9ecafbc4b36c679da03c18fd8d0bd51ac"
|
||||
],
|
||||
"markers": "python_version >= '3.5'",
|
||||
"version": "==0.7.0"
|
||||
"version": "==0.7.2"
|
||||
},
|
||||
"mako": {
|
||||
"hashes": [
|
||||
@@ -604,12 +604,12 @@
|
||||
},
|
||||
"pinecone-client": {
|
||||
"hashes": [
|
||||
"sha256:21fddb752668efee4d3c6b706346d9580e36a8b06b8d97afd60bd33ef2536e7e",
|
||||
"sha256:391fe413754efd4e0ef00154b44271d63c4cdd4bedf088d23111a5725d863210"
|
||||
"sha256:2c1cc1d6648b2be66e944db2ffa59166a37b9164d1135ad525d9cd8b1e298168",
|
||||
"sha256:5bf496c01c2f82f4e5c2dc977cc5062ecd7168b8ed90743b09afcc8c7eb242ec"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==2.2.2"
|
||||
"version": "==2.2.4"
|
||||
},
|
||||
"protobuf": {
|
||||
"hashes": [
|
||||
@@ -809,11 +809,11 @@
|
||||
},
|
||||
"setuptools": {
|
||||
"hashes": [
|
||||
"sha256:3d4dfa6d95f1b101d695a6160a7626e15583af71a5f52176efa5d39a054d475d",
|
||||
"sha256:3d8083eed2d13afc9426f227b24fd1659489ec107c0e86cec2ffdde5c92e790b"
|
||||
"sha256:4ac1475276d2f1c48684874089fefcd83bd7162ddaafb81fac866ba0db282a87",
|
||||
"sha256:b454a35605876da60632df1a60f736524eb73cc47bbc9f3f1ef1b644de74fd2a"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==68.1.2"
|
||||
"version": "==68.2.2"
|
||||
},
|
||||
"six": {
|
||||
"hashes": [
|
||||
@@ -825,51 +825,51 @@
|
||||
},
|
||||
"sqlalchemy": {
|
||||
"hashes": [
|
||||
"sha256:1506e988ebeaaf316f183da601f24eedd7452e163010ea63dbe52dc91c7fc70e",
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||||
"sha256:1a58052b5a93425f656675673ef1f7e005a3b72e3f2c91b8acca1b27ccadf5f4",
|
||||
"sha256:1b74eeafaa11372627ce94e4dc88a6751b2b4d263015b3523e2b1e57291102f0",
|
||||
"sha256:1be86ccea0c965a1e8cd6ccf6884b924c319fcc85765f16c69f1ae7148eba64b",
|
||||
"sha256:1d35d49a972649b5080557c603110620a86aa11db350d7a7cb0f0a3f611948a0",
|
||||
"sha256:243d0fb261f80a26774829bc2cee71df3222587ac789b7eaf6555c5b15651eed",
|
||||
"sha256:26a3399eaf65e9ab2690c07bd5cf898b639e76903e0abad096cd609233ce5208",
|
||||
"sha256:27d554ef5d12501898d88d255c54eef8414576f34672e02fe96d75908993cf53",
|
||||
"sha256:3364b7066b3c7f4437dd345d47271f1251e0cfb0aba67e785343cdbdb0fff08c",
|
||||
"sha256:3423dc2a3b94125094897118b52bdf4d37daf142cbcf26d48af284b763ab90e9",
|
||||
"sha256:3c6aceebbc47db04f2d779db03afeaa2c73ea3f8dcd3987eb9efdb987ffa09a3",
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||||
"sha256:3ce5e81b800a8afc870bb8e0a275d81957e16f8c4b62415a7b386f29a0cb9763",
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||||
"sha256:411e7f140200c02c4b953b3dbd08351c9f9818d2bd591b56d0fa0716bd014f1e",
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||||
"sha256:4cde2e1096cbb3e62002efdb7050113aa5f01718035ba9f29f9d89c3758e7e4e",
|
||||
"sha256:5768c268df78bacbde166b48be788b83dddaa2a5974b8810af422ddfe68a9bc8",
|
||||
"sha256:599ccd23a7146e126be1c7632d1d47847fa9f333104d03325c4e15440fc7d927",
|
||||
"sha256:5ed61e3463021763b853628aef8bc5d469fe12d95f82c74ef605049d810f3267",
|
||||
"sha256:63a368231c53c93e2b67d0c5556a9836fdcd383f7e3026a39602aad775b14acf",
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||||
"sha256:63e73da7fb030ae0a46a9ffbeef7e892f5def4baf8064786d040d45c1d6d1dc5",
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||||
"sha256:6eb6d77c31e1bf4268b4d61b549c341cbff9842f8e115ba6904249c20cb78a61",
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||||
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||||
"sha256:6fe7d61dc71119e21ddb0094ee994418c12f68c61b3d263ebaae50ea8399c4d4",
|
||||
"sha256:759b51346aa388c2e606ee206c0bc6f15a5299f6174d1e10cadbe4530d3c7a98",
|
||||
"sha256:76fdfc0f6f5341987474ff48e7a66c3cd2b8a71ddda01fa82fedb180b961630a",
|
||||
"sha256:77d37c1b4e64c926fa3de23e8244b964aab92963d0f74d98cbc0783a9e04f501",
|
||||
"sha256:79543f945be7a5ada9943d555cf9b1531cfea49241809dd1183701f94a748624",
|
||||
"sha256:79fde625a0a55220d3624e64101ed68a059c1c1f126c74f08a42097a72ff66a9",
|
||||
"sha256:7d3f175410a6db0ad96b10bfbb0a5530ecd4fcf1e2b5d83d968dd64791f810ed",
|
||||
"sha256:8dd77fd6648b677d7742d2c3cc105a66e2681cc5e5fb247b88c7a7b78351cf74",
|
||||
"sha256:a3f0dd6d15b6dc8b28a838a5c48ced7455c3e1fb47b89da9c79cc2090b072a50",
|
||||
"sha256:bcb04441f370cbe6e37c2b8d79e4af9e4789f626c595899d94abebe8b38f9a4d",
|
||||
"sha256:c3d99ba99007dab8233f635c32b5cd24fb1df8d64e17bc7df136cedbea427897",
|
||||
"sha256:ca8a5ff2aa7f3ade6c498aaafce25b1eaeabe4e42b73e25519183e4566a16fc6",
|
||||
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|
||||
"sha256:d1b09ba72e4e6d341bb5bdd3564f1cea6095d4c3632e45dc69375a1dbe4e26ec",
|
||||
"sha256:d32b5ffef6c5bcb452723a496bad2d4c52b346240c59b3e6dba279f6dcc06c14",
|
||||
"sha256:d3793dcf5bc4d74ae1e9db15121250c2da476e1af8e45a1d9a52b1513a393459",
|
||||
"sha256:dd81466bdbc82b060c3c110b2937ab65ace41dfa7b18681fdfad2f37f27acdd7",
|
||||
"sha256:e4e571af672e1bb710b3cc1a9794b55bce1eae5aed41a608c0401885e3491179",
|
||||
"sha256:ea8186be85da6587456c9ddc7bf480ebad1a0e6dcbad3967c4821233a4d4df57",
|
||||
"sha256:eefebcc5c555803065128401a1e224a64607259b5eb907021bf9b175f315d2a6"
|
||||
"sha256:014794b60d2021cc8ae0f91d4d0331fe92691ae5467a00841f7130fe877b678e",
|
||||
"sha256:0268256a34806e5d1c8f7ee93277d7ea8cc8ae391f487213139018b6805aeaf6",
|
||||
"sha256:05b971ab1ac2994a14c56b35eaaa91f86ba080e9ad481b20d99d77f381bb6258",
|
||||
"sha256:141675dae56522126986fa4ca713739d00ed3a6f08f3c2eb92c39c6dfec463ce",
|
||||
"sha256:1e7dc99b23e33c71d720c4ae37ebb095bebebbd31a24b7d99dfc4753d2803ede",
|
||||
"sha256:2e617727fe4091cedb3e4409b39368f424934c7faa78171749f704b49b4bb4ce",
|
||||
"sha256:3cf229704074bce31f7f47d12883afee3b0a02bb233a0ba45ddbfe542939cca4",
|
||||
"sha256:3eb7c03fe1cd3255811cd4e74db1ab8dca22074d50cd8937edf4ef62d758cdf4",
|
||||
"sha256:3f7d57a7e140efe69ce2d7b057c3f9a595f98d0bbdfc23fd055efdfbaa46e3a5",
|
||||
"sha256:419b1276b55925b5ac9b4c7044e999f1787c69761a3c9756dec6e5c225ceca01",
|
||||
"sha256:44ac5c89b6896f4740e7091f4a0ff2e62881da80c239dd9408f84f75a293dae9",
|
||||
"sha256:4615623a490e46be85fbaa6335f35cf80e61df0783240afe7d4f544778c315a9",
|
||||
"sha256:50a69067af86ec7f11a8e50ba85544657b1477aabf64fa447fd3736b5a0a4f67",
|
||||
"sha256:513fd5b6513d37e985eb5b7ed89da5fd9e72354e3523980ef00d439bc549c9e9",
|
||||
"sha256:6ff3dc2f60dbf82c9e599c2915db1526d65415be323464f84de8db3e361ba5b9",
|
||||
"sha256:73c079e21d10ff2be54a4699f55865d4b275fd6c8bd5d90c5b1ef78ae0197301",
|
||||
"sha256:7614f1eab4336df7dd6bee05bc974f2b02c38d3d0c78060c5faa4cd1ca2af3b8",
|
||||
"sha256:785e2f2c1cb50d0a44e2cdeea5fd36b5bf2d79c481c10f3a88a8be4cfa2c4615",
|
||||
"sha256:7ca38746eac23dd7c20bec9278d2058c7ad662b2f1576e4c3dbfcd7c00cc48fa",
|
||||
"sha256:7f0c4ee579acfe6c994637527c386d1c22eb60bc1c1d36d940d8477e482095d4",
|
||||
"sha256:87bf91ebf15258c4701d71dcdd9c4ba39521fb6a37379ea68088ce8cd869b446",
|
||||
"sha256:89e274604abb1a7fd5c14867a412c9d49c08ccf6ce3e1e04fffc068b5b6499d4",
|
||||
"sha256:8c323813963b2503e54d0944813cd479c10c636e3ee223bcbd7bd478bf53c178",
|
||||
"sha256:a95aa0672e3065d43c8aa80080cdd5cc40fe92dc873749e6c1cf23914c4b83af",
|
||||
"sha256:af520a730d523eab77d754f5cf44cc7dd7ad2d54907adeb3233177eeb22f271b",
|
||||
"sha256:b19ae41ef26c01a987e49e37c77b9ad060c59f94d3b3efdfdbf4f3daaca7b5fe",
|
||||
"sha256:b4eae01faee9f2b17f08885e3f047153ae0416648f8e8c8bd9bc677c5ce64be9",
|
||||
"sha256:b69f1f754d92eb1cc6b50938359dead36b96a1dcf11a8670bff65fd9b21a4b09",
|
||||
"sha256:b977bfce15afa53d9cf6a632482d7968477625f030d86a109f7bdfe8ce3c064a",
|
||||
"sha256:bf8eebccc66829010f06fbd2b80095d7872991bfe8415098b9fe47deaaa58063",
|
||||
"sha256:c111cd40910ffcb615b33605fc8f8e22146aeb7933d06569ac90f219818345ef",
|
||||
"sha256:c2d494b6a2a2d05fb99f01b84cc9af9f5f93bf3e1e5dbdafe4bed0c2823584c1",
|
||||
"sha256:c9cba4e7369de663611ce7460a34be48e999e0bbb1feb9130070f0685e9a6b66",
|
||||
"sha256:cca720d05389ab1a5877ff05af96551e58ba65e8dc65582d849ac83ddde3e231",
|
||||
"sha256:ccb99c3138c9bde118b51a289d90096a3791658da9aea1754667302ed6564f6e",
|
||||
"sha256:d59cb9e20d79686aa473e0302e4a82882d7118744d30bb1dfb62d3c47141b3ec",
|
||||
"sha256:e36339a68126ffb708dc6d1948161cea2a9e85d7d7b0c54f6999853d70d44430",
|
||||
"sha256:ea7da25ee458d8f404b93eb073116156fd7d8c2a776d8311534851f28277b4ce",
|
||||
"sha256:f9fefd6298433b6e9188252f3bff53b9ff0443c8fde27298b8a2b19f6617eeb9",
|
||||
"sha256:fb87f763b5d04a82ae84ccff25554ffd903baafba6698e18ebaf32561f2fe4aa",
|
||||
"sha256:fc6b15465fabccc94bf7e38777d665b6a4f95efd1725049d6184b3a39fd54880"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==2.0.20"
|
||||
"version": "==2.0.21"
|
||||
},
|
||||
"stampy-chat": {
|
||||
"editable": true,
|
||||
@@ -886,39 +886,39 @@
|
||||
},
|
||||
"tiktoken": {
|
||||
"hashes": [
|
||||
"sha256:00d662de1e7986d129139faf15e6a6ee7665ee103440769b8dedf3e7ba6ac37f",
|
||||
"sha256:08efa59468dbe23ed038c28893e2a7158d8c211c3dd07f2bbc9a30e012512f1d",
|
||||
"sha256:176cad7f053d2cc82ce7e2a7c883ccc6971840a4b5276740d0b732a2b2011f8a",
|
||||
"sha256:1b6bce7c68aa765f666474c7c11a7aebda3816b58ecafb209afa59c799b0dd2d",
|
||||
"sha256:1e8fa13cf9889d2c928b9e258e9dbbbf88ab02016e4236aae76e3b4f82dd8288",
|
||||
"sha256:2ca30367ad750ee7d42fe80079d3092bd35bb266be7882b79c3bd159b39a17b0",
|
||||
"sha256:329f548a821a2f339adc9fbcfd9fc12602e4b3f8598df5593cfc09839e9ae5e4",
|
||||
"sha256:3dc3df19ddec79435bb2a94ee46f4b9560d0299c23520803d851008445671197",
|
||||
"sha256:450d504892b3ac80207700266ee87c932df8efea54e05cefe8613edc963c1285",
|
||||
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|
||||
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|
||||
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|
||||
"sha256:59b20a819969735b48161ced9b92f05dc4519c17be4015cfb73b65270a243620",
|
||||
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|
||||
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|
||||
"sha256:8d1d97f83697ff44466c6bef5d35b6bcdb51e0125829a9c0ed1e6e39fb9a08fb",
|
||||
"sha256:9c15d9955cc18d0d7ffcc9c03dc51167aedae98542238b54a2e659bd25fe77ed",
|
||||
"sha256:9c6dd439e878172dc163fced3bc7b19b9ab549c271b257599f55afc3a6a5edef",
|
||||
"sha256:9ec161e40ed44e4210d3b31e2ff426b4a55e8254f1023e5d2595cb60044f8ea6",
|
||||
"sha256:b1a038cee487931a5caaef0a2e8520e645508cde21717eacc9af3fbda097d8bb",
|
||||
"sha256:ba16698c42aad8190e746cd82f6a06769ac7edd415d62ba027ea1d99d958ed93",
|
||||
"sha256:bb2341836b725c60d0ab3c84970b9b5f68d4b733a7bcb80fb25967e5addb9920",
|
||||
"sha256:c06cd92b09eb0404cedce3702fa866bf0d00e399439dad3f10288ddc31045422",
|
||||
"sha256:c835d0ee1f84a5aa04921717754eadbc0f0a56cf613f78dfc1cf9ad35f6c3fea",
|
||||
"sha256:d0394967d2236a60fd0aacef26646b53636423cc9c70c32f7c5124ebe86f3093",
|
||||
"sha256:dae2af6f03ecba5f679449fa66ed96585b2fa6accb7fd57d9649e9e398a94f44",
|
||||
"sha256:e063b988b8ba8b66d6cc2026d937557437e79258095f52eaecfafb18a0a10c03",
|
||||
"sha256:e87751b54eb7bca580126353a9cf17a8a8eaadd44edaac0e01123e1513a33281",
|
||||
"sha256:f3020350685e009053829c1168703c346fb32c70c57d828ca3742558e94827a9"
|
||||
"sha256:1f2b3b253e22322b7f53a111e1f6d7ecfa199b4f08f3efdeb0480f4033b5cdc6",
|
||||
"sha256:1fe99953b63aabc0c9536fbc91c3c9000d78e4755edc28cc2e10825372046a2d",
|
||||
"sha256:27e773564232004f4f810fd1f85236673ec3a56ed7f1206fc9ed8670ebedb97a",
|
||||
"sha256:2b0bae3fd56de1c0a5874fb6577667a3c75bf231a6cef599338820210c16e40a",
|
||||
"sha256:2b756a65d98b7cf760617a6b68762a23ab8b6ef79922be5afdb00f5e8a9f4e76",
|
||||
"sha256:323cec0031358bc09aa965c2c5c1f9f59baf76e5b17e62dcc06d1bb9bc3a3c7c",
|
||||
"sha256:426e7def5f3f23645dada816be119fa61e587dfb4755de250e136b47a045c365",
|
||||
"sha256:43ce0199f315776dec3ea7bf86f35df86d24b6fcde1babd3e53c38f17352442f",
|
||||
"sha256:46b8554b9f351561b1989157c6bb54462056f3d44e43aa4e671367c5d62535fc",
|
||||
"sha256:5abd9436f02e2c8eda5cce2ff8015ce91f33e782a7423de2a1859f772928f714",
|
||||
"sha256:5d5a187ff9c786fae6aadd49f47f019ff19e99071dc5b0fe91bfecc94d37c686",
|
||||
"sha256:709a5220891f2b56caad8327fab86281787704931ed484d9548f65598dea9ce4",
|
||||
"sha256:714efb2f4a082635d9f5afe0bf7e62989b72b65ac52f004eb7ac939f506c03a4",
|
||||
"sha256:74c90d2be0b4c1a2b3f7dde95cd976757817d4df080d6af0ee8d461568c2e2ad",
|
||||
"sha256:779c4dea5edd1d3178734d144d32231e0b814976bec1ec09636d1003ffe4725f",
|
||||
"sha256:7ef730db4097f5b13df8d960f7fdda2744fe21d203ea2bb80c120bb58661b155",
|
||||
"sha256:8079ac065572fe0e7c696dbd63e1fdc12ce4cdca9933935d038689d4732451df",
|
||||
"sha256:92ed3bbf71a175a6a4e5fbfcdb2c422bdd72d9b20407e00f435cf22a68b4ea9b",
|
||||
"sha256:9b180a22db0bbcc447f691ffc3cf7a580e9e0587d87379e35e58b826ebf5bc7b",
|
||||
"sha256:a10488d1d1a5f9c9d2b2052fdb4cf807bba545818cb1ef724a7f5d44d9f7c3d4",
|
||||
"sha256:a84657c083d458593c0235926b5c993eec0b586a2508d6a2020556e5347c2f0d",
|
||||
"sha256:b5dcfcf9bfb798e86fbce76d40a1d5d9e3f92131aecfa3d1e5c9ea1a20f1ef1a",
|
||||
"sha256:ba9873c253ca1f670e662192a0afcb72b41e0ba3e730f16c665099e12f4dac2d",
|
||||
"sha256:c008375c0f3d97c36e81725308699116cd5804fdac0f9b7afc732056329d2790",
|
||||
"sha256:dcdc630461927718b317e6f8be7707bd0fc768cee1fdc78ddaa1e93f4dc6b2b1",
|
||||
"sha256:e21840043dbe2e280e99ad41951c00eff8ee3b63daf57cd4c1508a3fd8583ea2",
|
||||
"sha256:e4c73d47bdc1a3f1f66ffa019af0386c48effdc6e8797e5e76875f6388ff72e9",
|
||||
"sha256:e529578d017045e2f0ed12d2e00e7e99f780f477234da4aae799ec4afca89f37",
|
||||
"sha256:edd2ffbb789712d83fee19ab009949f998a35c51ad9f9beb39109357416344ff"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==0.4.0"
|
||||
"version": "==0.5.1"
|
||||
},
|
||||
"tqdm": {
|
||||
"hashes": [
|
||||
@@ -930,19 +930,19 @@
|
||||
},
|
||||
"typing-extensions": {
|
||||
"hashes": [
|
||||
"sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36",
|
||||
"sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"
|
||||
"sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0",
|
||||
"sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"
|
||||
],
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==4.7.1"
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==4.8.0"
|
||||
},
|
||||
"urllib3": {
|
||||
"hashes": [
|
||||
"sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11",
|
||||
"sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"
|
||||
"sha256:13abf37382ea2ce6fb744d4dad67838eec857c9f4f57009891805e0b5e123594",
|
||||
"sha256:ef16afa8ba34a1f989db38e1dbbe0c302e4289a47856990d0682e374563ce35e"
|
||||
],
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==2.0.4"
|
||||
"version": "==2.0.5"
|
||||
},
|
||||
"werkzeug": {
|
||||
"hashes": [
|
||||
@@ -1034,5 +1034,39 @@
|
||||
"version": "==1.9.2"
|
||||
}
|
||||
},
|
||||
"develop": {}
|
||||
"develop": {
|
||||
"iniconfig": {
|
||||
"hashes": [
|
||||
"sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3",
|
||||
"sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"
|
||||
],
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==2.0.0"
|
||||
},
|
||||
"packaging": {
|
||||
"hashes": [
|
||||
"sha256:994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61",
|
||||
"sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f"
|
||||
],
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==23.1"
|
||||
},
|
||||
"pluggy": {
|
||||
"hashes": [
|
||||
"sha256:cf61ae8f126ac6f7c451172cf30e3e43d3ca77615509771b3a984a0730651e12",
|
||||
"sha256:d89c696a773f8bd377d18e5ecda92b7a3793cbe66c87060a6fb58c7b6e1061f7"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==1.3.0"
|
||||
},
|
||||
"pytest": {
|
||||
"hashes": [
|
||||
"sha256:1d881c6124e08ff0a1bb75ba3ec0bfd8b5354a01c194ddd5a0a870a48d99b002",
|
||||
"sha256:a766259cfab564a2ad52cb1aae1b881a75c3eb7e34ca3779697c23ed47c47069"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==7.4.2"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
+95
-80
@@ -29,27 +29,105 @@ CONTEXT_FRACTION = 0.5 # the (approximate) fraction of num_tokens to use for co
|
||||
|
||||
ENCODER = tiktoken.get_encoding("cl100k_base")
|
||||
|
||||
SOURCE_PROMPT = (
|
||||
"You are a helpful assistant knowledgeable about AI Alignment and Safety. "
|
||||
"Please give a clear and coherent answer to the user's questions.(written after \"Q:\") "
|
||||
"using the following sources. Each source is labeled with a letter. Feel free to "
|
||||
"use the sources in any order, and try to use multiple sources in your answers.\n\n"
|
||||
)
|
||||
SOURCE_PROMPT_SUFFIX = (
|
||||
"\n\n"
|
||||
"Before the question (\"Q: \"), there will be a history of previous questions and answers. "
|
||||
"These sources only apply to the last question. Any sources used in previous answers "
|
||||
"are invalid."
|
||||
)
|
||||
|
||||
QUESTION_PROMPT = (
|
||||
"In your answer, please cite any claims you make back to each source "
|
||||
"using the format: [a], [b], etc. If you use multiple sources to make a claim "
|
||||
"cite all of them. For example: \"AGI is concerning [c, d, e].\"\n\n"
|
||||
)
|
||||
PROMPT_MODES = {
|
||||
'default': "",
|
||||
"concise": (
|
||||
"Answer very concisely, getting to the crux of the matter in as "
|
||||
"few words as possible. Limit your answer to 1-2 sentences.\n\n"
|
||||
),
|
||||
"rookie": (
|
||||
"This user is new to the field of AI Alignment and Safety - don't "
|
||||
"assume they know any technical terms or jargon. Still give a complete answer "
|
||||
"without patronizing the user, but take any extra time needed to "
|
||||
"explain new concepts or to illustrate your answer with examples. "
|
||||
"Put extra effort into explaining the intuition behind concepts "
|
||||
"rather than just giving a formal definition.\n\n"
|
||||
),
|
||||
}
|
||||
|
||||
# --------------------------------- prompt code --------------------------------
|
||||
|
||||
|
||||
|
||||
# limit a string to a certain number of tokens
|
||||
def cap(text: str, max_tokens: int) -> str:
|
||||
|
||||
if max_tokens <= 0: return "..."
|
||||
if max_tokens <= 0:
|
||||
return "..."
|
||||
|
||||
encoded_text = ENCODER.encode(text)
|
||||
|
||||
if len(encoded_text) <= max_tokens: return text
|
||||
else: return ENCODER.decode(encoded_text[:max_tokens]) + " ..."
|
||||
if len(encoded_text) <= max_tokens:
|
||||
return text
|
||||
return ENCODER.decode(encoded_text[:max_tokens]) + " ..."
|
||||
|
||||
|
||||
Prompt = List[Dict[str, str]]
|
||||
|
||||
def construct_prompt(query: str, mode: str, history: Prompt, context: List[Block]) -> Prompt:
|
||||
|
||||
def prompt_context(source_prompt: str, context: List[Block], max_tokens: int) -> str:
|
||||
token_count = len(ENCODER.encode(source_prompt))
|
||||
|
||||
# Context from top-k blocks
|
||||
for i, block in enumerate(context):
|
||||
block_str = f"[{chr(ord('a') + i)}] {block.title} - {','.join(block.authors)} - {block.date}\n{block.text}\n\n"
|
||||
block_tc = len(ENCODER.encode(block_str))
|
||||
|
||||
if token_count + block_tc > max_tokens:
|
||||
source_prompt += cap(block_str, max_tokens - token_count)
|
||||
break
|
||||
else:
|
||||
source_prompt += block_str
|
||||
token_count += block_tc
|
||||
return source_prompt.strip()
|
||||
|
||||
|
||||
def prompt_history(history: Prompt, max_tokens: int, n_items=10) -> Prompt:
|
||||
token_count = 0
|
||||
prompt = []
|
||||
|
||||
# Get the n_items last messages, starting from the last one. This is because it's assumed
|
||||
# that more recent messages are more important. The `-1` is because of how slicing works
|
||||
messages = history[:-n_items - 1:-1]
|
||||
for message in messages:
|
||||
if message["role"] == "user":
|
||||
prompt.append({"role": "user", "content": "Q: " + message["content"]})
|
||||
token_count += len(ENCODER.encode("Q: " + message["content"]))
|
||||
else:
|
||||
content = message["content"]
|
||||
# censor all source letters into [x]
|
||||
content = re.sub(r"\[[0-9]+\]", "[x]", content)
|
||||
content = cap(content, max_tokens - token_count)
|
||||
|
||||
prompt.append({"role": "assistant", "content": content})
|
||||
token_count += len(ENCODER.encode(content))
|
||||
|
||||
if token_count > max_tokens:
|
||||
break
|
||||
return prompt[::-1]
|
||||
|
||||
|
||||
def construct_prompt(query: str, mode: str, history: Prompt, context: List[Block]) -> Prompt:
|
||||
if mode not in PROMPT_MODES:
|
||||
raise ValueError("Invalid mode: " + mode)
|
||||
|
||||
# History takes the format: history=[
|
||||
# {"role": "user", "content": "Die monster. You don’t belong in this world!"},
|
||||
# {"role": "assistant", "content": "It was not by my hand I am once again given flesh. I was called here by humans who wished to pay me tribute."},
|
||||
@@ -59,87 +137,23 @@ def construct_prompt(query: str, mode: str, history: Prompt, context: List[Block
|
||||
# {"role": "assistant", "content": "What is a man? A miserable little pile of secrets. But enough talk... Have at you!"},
|
||||
# ]
|
||||
|
||||
source_prompt = "You are a helpful assistant knowledgeable about AI Alignment and Safety. " \
|
||||
"Please give a clear and coherent answer to the user's questions.(written after \"Q:\") " \
|
||||
"using the following sources. Each source is labeled with a letter. Feel free to " \
|
||||
"use the sources in any order, and try to use multiple sources in your answers.\n\n"
|
||||
|
||||
token_count = len(ENCODER.encode(source_prompt))
|
||||
|
||||
# Context from top-k blocks
|
||||
for i, block in enumerate(context):
|
||||
block_str = f"[{chr(ord('a') + i)}] {block.title} - {','.join(block.authors)} - {block.date}\n{block.text}\n\n"
|
||||
block_tc = len(ENCODER.encode(block_str))
|
||||
|
||||
if token_count + block_tc > int(NUM_TOKENS * CONTEXT_FRACTION):
|
||||
source_prompt += cap(block_str, int(NUM_TOKENS * CONTEXT_FRACTION) - token_count)
|
||||
break
|
||||
else:
|
||||
source_prompt += block_str
|
||||
token_count += block_tc
|
||||
|
||||
source_prompt = source_prompt.strip();
|
||||
if len(history) > 0:
|
||||
source_prompt += "\n\n"\
|
||||
"Before the question (\"Q: \"), there will be a history of previous questions and answers. " \
|
||||
"These sources only apply to the last question. Any sources used in previous answers " \
|
||||
"are invalid."
|
||||
|
||||
prompt.append({"role": "system", "content": source_prompt.strip()})
|
||||
|
||||
source_prompt = prompt_context(SOURCE_PROMPT, context, int(NUM_TOKENS * CONTEXT_FRACTION))
|
||||
if history:
|
||||
source_prompt += SOURCE_PROMPT_SUFFIX
|
||||
source_prompt = [{"role": "system", "content": source_prompt.strip()}]
|
||||
|
||||
# Write a version of the last 10 messages into history, cutting things off when we hit the token limit.
|
||||
token_count = 0
|
||||
history_trnc = []
|
||||
for message in history[:-10:-1]:
|
||||
if message["role"] == "user":
|
||||
history_trnc.append({"role": "user", "content": "Q: " + message["content"]})
|
||||
token_count += len(ENCODER.encode("Q: " + message["content"]))
|
||||
else:
|
||||
content = cap(message["content"], int(NUM_TOKENS * HISTORY_FRACTION) - token_count)
|
||||
history_prompt = prompt_history(history, int(NUM_TOKENS * HISTORY_FRACTION))
|
||||
question_prompt = [{"role": "user", "content": QUESTION_PROMPT + PROMPT_MODES[mode] + "Q: " + query}]
|
||||
|
||||
# censor all source letters into [x]
|
||||
content = re.sub(r"\[[0-9]+\]", "[x]", content)
|
||||
|
||||
history_trnc.append({"role": "assistant", "content": content})
|
||||
token_count += len(ENCODER.encode(content))
|
||||
|
||||
if token_count > int(NUM_TOKENS * HISTORY_FRACTION):
|
||||
break
|
||||
|
||||
prompt.extend(history_trnc[::-1])
|
||||
|
||||
|
||||
question_prompt = "In your answer, please cite any claims you make back to each source " \
|
||||
"using the format: [a], [b], etc. If you use multiple sources to make a claim " \
|
||||
"cite all of them. For example: \"AGI is concerning [c, d, e].\"\n\n"
|
||||
|
||||
if mode == "concise":
|
||||
question_prompt += "Answer very concisely, getting to the crux of the matter in as " \
|
||||
"few words as possible. Limit your answer to 1-2 sentences.\n\n"
|
||||
|
||||
elif mode == "rookie":
|
||||
question_prompt += "This user is new to the field of AI Alignment and Safety - don't " \
|
||||
"assume they know any technical terms or jargon. Still give a complete answer " \
|
||||
"without patronizing the user, but take any extra time needed to " \
|
||||
"explain new concepts or to illustrate your answer with examples. "\
|
||||
"Put extra effort into explaining the intuition behind concepts " \
|
||||
"rather than just giving a formal definition.\n\n"
|
||||
|
||||
elif mode != "default": raise ValueError("Invalid mode: " + mode)
|
||||
|
||||
|
||||
question_prompt += "Q: " + query
|
||||
|
||||
prompt.append({"role": "user", "content": question_prompt})
|
||||
|
||||
return prompt
|
||||
return source_prompt + history_prompt + question_prompt
|
||||
|
||||
# ------------------------------- completion code -------------------------------
|
||||
|
||||
def check_openai_moderation(prompt: Prompt, query: str):
|
||||
prompt_string = '\n\n'.join([message["content"] for message in prompt])
|
||||
mod_res = openai.Moderation.create( input = [ query, prompt_string ])
|
||||
mod_res = openai.Moderation.create(input=[query, prompt_string])
|
||||
|
||||
if any(map(lambda x: x["flagged"], mod_res["results"])):
|
||||
logger.moderation_issue(query, prompt_string, mod_res)
|
||||
@@ -153,7 +167,7 @@ def remaining_tokens(prompt: Prompt):
|
||||
len(ENCODER.encode(message["content"]) + ENCODER.encode(message["role"]))
|
||||
for message in prompt
|
||||
])
|
||||
return NUM_TOKENS - used_tokens - TOKENS_BUFFER
|
||||
return max(0, NUM_TOKENS - used_tokens - TOKENS_BUFFER)
|
||||
|
||||
|
||||
def talk_to_robot_internal(index, query: str, mode: str, history: Prompt, session_id: str, k: int = STANDARD_K):
|
||||
@@ -194,11 +208,10 @@ def talk_to_robot_internal(index, query: str, mode: str, history: Prompt, sessio
|
||||
temperature=0, # may or may not be a good idea
|
||||
):
|
||||
res = chunk["choices"][0]["delta"]
|
||||
if res is not None and res.get("content") is not None:
|
||||
if res and res.get("content"):
|
||||
response += res["content"]
|
||||
yield {"state": "streaming", "content": res["content"]}
|
||||
|
||||
|
||||
t2 = time.time()
|
||||
logger.debug(f'Time to get response: {time.time() - t1:.2f}s')
|
||||
if logger.is_debug():
|
||||
@@ -224,10 +237,12 @@ def talk_to_robot_internal(index, query: str, mode: str, history: Prompt, sessio
|
||||
logger.error(e)
|
||||
yield {'state': 'error', 'error': str(e)}
|
||||
|
||||
|
||||
# convert talk_to_robot_internal from dict generator into json generator
|
||||
def talk_to_robot(index, query: str, mode: str, history: List[Dict[str, str]], k: int = STANDARD_K):
|
||||
yield from (json.dumps(block) for block in talk_to_robot_internal(index, query, mode, history, k))
|
||||
|
||||
|
||||
# wayyy simplified api
|
||||
def talk_to_robot_simple(index, query: str):
|
||||
res = {'response': ''}
|
||||
|
||||
@@ -0,0 +1,309 @@
|
||||
import pytest
|
||||
from unittest.mock import patch, MagicMock
|
||||
|
||||
from stampy_chat.followups import Followup
|
||||
from stampy_chat.get_blocks import Block
|
||||
from stampy_chat.chat import (
|
||||
cap, construct_prompt, check_openai_moderation, remaining_tokens, talk_to_robot_internal,
|
||||
talk_to_robot, talk_to_robot_simple, prompt_context, prompt_history, ENCODER, logger
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def history():
|
||||
return [
|
||||
{"role": "user", "content": "Die monster. You don’t belong in this world!"},
|
||||
{"role": "assistant", "content": "It was not by my hand[1] I am once again given flesh. I was called here by humans who wished to pay me tribute."},
|
||||
{"role": "user", "content": "Tribute!?! You steal men's souls and make them your slaves!"},
|
||||
{"role": "assistant", "content": "Perhaps the same could be said[321] of all religions..."},
|
||||
{"role": "user", "content": "Your words are as empty as your soul! Mankind ill needs a savior such as you!"},
|
||||
{"role": "assistant", "content": "What is a man? A[4234] miserable little pile of secrets. But enough talk... Have at you!"},
|
||||
]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def context():
|
||||
return [
|
||||
Block(
|
||||
id=i,
|
||||
url=f"http://bla.bla/{i}",
|
||||
tags=[],
|
||||
title=f"Block{i}",
|
||||
authors=[f"Author{i}"],
|
||||
date=f"2021-01-0{i + 1}",
|
||||
text=f"Block text {i}"
|
||||
) for i in range(5)
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"text, max_tokens, expected",
|
||||
[
|
||||
("", 10, ""), # case when input text is empty
|
||||
("Hello, world!", -1, "..."), # case when max_tokens are negative
|
||||
("Hello, world!", 0, "..."), # case when max_tokens is zero
|
||||
("Hello, world!", 10, "Hello, world!"), # case when input text is less than max_tokens
|
||||
("Hello, world! This is a long text string that exceeds the token limit.", 5,
|
||||
ENCODER.decode(ENCODER.encode("Hello, world! This is a long text string that exceeds the token limit.")[:5]) + " ..."), # case when input text is more than max_tokens
|
||||
],
|
||||
)
|
||||
def test_cap(text, max_tokens, expected):
|
||||
assert cap(text, max_tokens) == expected
|
||||
|
||||
|
||||
EXPECTED_CONTEXT = """bla bla: [a] Block0 - Author0 - 2021-01-01
|
||||
Block text 0
|
||||
|
||||
[b] Block1 - Author1 - 2021-01-02
|
||||
Block text 1
|
||||
|
||||
[c] Block2 - Author2 - 2021-01-03
|
||||
Block text 2
|
||||
|
||||
[d] Block3 - Author3 - 2021-01-04
|
||||
Block text 3
|
||||
|
||||
[e] Block4 - Author4 - 2021-01-05
|
||||
Block text 4"""
|
||||
|
||||
|
||||
def test_prompt_context(context):
|
||||
assert prompt_context("bla bla: ", context, 1000) == EXPECTED_CONTEXT
|
||||
|
||||
|
||||
def test_prompt_context_cutoff(context):
|
||||
formatted = prompt_context("bla bla: ", context, 50)
|
||||
|
||||
assert len(ENCODER.encode(formatted)) == 50 + 1 # the "..." is a single token
|
||||
assert prompt_context("bla bla: ", context, 50) == EXPECTED_CONTEXT[:116] + '...'
|
||||
|
||||
|
||||
def test_prompt_history(history):
|
||||
assert prompt_history(history, 1000) == [
|
||||
{'content': 'Q: Die monster. You don’t belong in this world!', 'role': 'user'},
|
||||
{'content': 'It was not by my hand[x] I am once again given flesh. I was called here by humans who wished to pay me tribute.', 'role': 'assistant'},
|
||||
{'content': "Q: Tribute!?! You steal men's souls and make them your slaves!", 'role': 'user'},
|
||||
{'content': 'Perhaps the same could be said[x] of all religions...', 'role': 'assistant'},
|
||||
{'content': 'Q: Your words are as empty as your soul! Mankind ill needs a savior such as you!', 'role': 'user'},
|
||||
{'content': 'What is a man? A[x] miserable little pile of secrets. But enough talk... Have at you!', 'role': 'assistant'},
|
||||
]
|
||||
|
||||
|
||||
def test_prompt_history_cutoffs(history):
|
||||
assert prompt_history(history, 50) == [
|
||||
{'content': 'Perhaps the same could be said ...', 'role': 'assistant'},
|
||||
{'content': 'Q: Your words are as empty as your soul! Mankind ill needs a savior such as you!', 'role': 'user'},
|
||||
{'content': 'What is a man? A[x] miserable little pile of secrets. But enough talk... Have at you!', 'role': 'assistant'},
|
||||
]
|
||||
|
||||
|
||||
def test_prompt_history_limit_items():
|
||||
history = [{'content': f'content {i}', 'role': 'assistant'} for i in range(30)]
|
||||
|
||||
assert len(prompt_history(history, 1000)) == 10
|
||||
assert prompt_history(history, 1000) == history[-10:]
|
||||
|
||||
|
||||
def test_construct_prompt(history, context):
|
||||
assert construct_prompt("to be or not to be?", "default", history, context) == [
|
||||
{
|
||||
'content': (
|
||||
'You are a helpful assistant knowledgeable about AI Alignment and '
|
||||
"Safety. Please give a clear and coherent answer to the user's "
|
||||
'questions.(written after "Q:") using the following sources. Each '
|
||||
'source is labeled with a letter. Feel free to use the sources in '
|
||||
'any order, and try to use multiple sources in your answers.\n'
|
||||
'\n'
|
||||
'[a] Block0 - Author0 - 2021-01-01\n'
|
||||
'Block text 0\n'
|
||||
'\n'
|
||||
'[b] Block1 - Author1 - 2021-01-02\n'
|
||||
'Block text 1\n'
|
||||
'\n'
|
||||
'[c] Block2 - Author2 - 2021-01-03\n'
|
||||
'Block text 2\n'
|
||||
'\n'
|
||||
'[d] Block3 - Author3 - 2021-01-04\n'
|
||||
'Block text 3\n'
|
||||
'\n'
|
||||
'[e] Block4 - Author4 - 2021-01-05\n'
|
||||
'Block text 4\n'
|
||||
'\n'
|
||||
'Before the question ("Q: "), there will be a history of previous '
|
||||
'questions and answers. These sources only apply to the last '
|
||||
'question. Any sources used in previous answers are invalid.'
|
||||
),
|
||||
'role': 'system'
|
||||
}, {
|
||||
'content': 'Q: Die monster. You don’t belong in this world!', 'role': 'user'
|
||||
}, {
|
||||
'content': 'It was not by my hand[x] I am once again given flesh. I was called'
|
||||
' here by humans who wished to pay me tribute.',
|
||||
'role': 'assistant'
|
||||
},
|
||||
{'content': "Q: Tribute!?! You steal men's souls and make them your slaves!", 'role': 'user'},
|
||||
{'content': 'Perhaps the same could be said[x] of all religions...', 'role': 'assistant'},
|
||||
{'content': 'Q: Your words are as empty as your soul! Mankind ill needs a savior such as you!', 'role': 'user'},
|
||||
{
|
||||
'content': 'What is a man? A[x] miserable little pile of secrets. But enough '
|
||||
'talk... Have at you!',
|
||||
'role': 'assistant'
|
||||
},
|
||||
{
|
||||
'content': (
|
||||
'In your answer, please cite any claims you make back to each '
|
||||
'source using the format: [a], [b], etc. If you use multiple '
|
||||
'sources to make a claim cite all of them. For example: "AGI is '
|
||||
'concerning [c, d, e]."\n'
|
||||
'\n'
|
||||
'Q: to be or not to be?'
|
||||
),
|
||||
'role': 'user'
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
def test_construct_prompt_no_history_or_context():
|
||||
assert construct_prompt("to be or not to be?", "default", [], []) == [
|
||||
{
|
||||
'content': (
|
||||
'You are a helpful assistant knowledgeable about AI Alignment and '
|
||||
"Safety. Please give a clear and coherent answer to the user's "
|
||||
'questions.(written after "Q:") using the following sources. Each '
|
||||
'source is labeled with a letter. Feel free to use the sources in '
|
||||
'any order, and try to use multiple sources in your answers.'
|
||||
),
|
||||
'role': 'system'
|
||||
},
|
||||
{
|
||||
'content': (
|
||||
'In your answer, please cite any claims you make back to each '
|
||||
'source using the format: [a], [b], etc. If you use multiple '
|
||||
'sources to make a claim cite all of them. For example: "AGI is '
|
||||
'concerning [c, d, e]."\n'
|
||||
'\n'
|
||||
'Q: to be or not to be?'
|
||||
),
|
||||
'role': 'user'
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
|
||||
def test_check_openai_moderation_flagged():
|
||||
prompt = [{"content": "message 1"}, {"content": "message 2"}]
|
||||
query = "test query"
|
||||
|
||||
# Create a mock for openai.Moderation.create return value
|
||||
results = {
|
||||
'results': [
|
||||
{'flagged': False, 'text': 'bla bla 1'},
|
||||
{'flagged': True, 'text': 'bla bla 2'},
|
||||
{'flagged': False, 'text': 'bla bla 3'},
|
||||
]
|
||||
}
|
||||
|
||||
# Patch openai.Moderation.create and logger.moderation_issue
|
||||
with patch('openai.Moderation.create', return_value=results), patch.object(logger, 'moderation_issue'):
|
||||
with pytest.raises(ValueError):
|
||||
check_openai_moderation(prompt, query)
|
||||
|
||||
|
||||
def test_check_openai_moderation_not_flagged():
|
||||
prompt = [{"content": "message 1"}, {"content": "message 2"}]
|
||||
query = "test query"
|
||||
|
||||
results = {
|
||||
'results': [
|
||||
{'flagged': False, 'text': 'bla bla 1'},
|
||||
{'flagged': False, 'text': 'bla bla 2'},
|
||||
{'flagged': False, 'text': 'bla bla 3'},
|
||||
]
|
||||
}
|
||||
|
||||
# Patch openai.Moderation.create and logger.moderation_issue
|
||||
with patch('openai.Moderation.create', return_value=results), patch.object(logger, 'moderation_issue'):
|
||||
assert check_openai_moderation(prompt, query) is None
|
||||
|
||||
|
||||
@pytest.mark.parametrize('prompt, remaining', (
|
||||
([{'role': 'system', 'content': 'bla'}], 4043),
|
||||
(
|
||||
[
|
||||
{'role': 'system', 'content': 'bla'},
|
||||
{'role': 'user', 'content': 'message 1'},
|
||||
{'role': 'assistant', 'content': 'response 1'},
|
||||
],
|
||||
4035
|
||||
),
|
||||
(
|
||||
[
|
||||
{'role': 'system', 'content': 'bla'},
|
||||
{'role': 'user', 'content': 'message 1'},
|
||||
{'role': 'assistant', 'content': 'response 1'},
|
||||
] * 1999,
|
||||
0
|
||||
),
|
||||
))
|
||||
def test_remaining_tokens(prompt, remaining):
|
||||
assert remaining_tokens(prompt) == remaining
|
||||
|
||||
|
||||
@patch('stampy_chat.chat.check_openai_moderation')
|
||||
@patch('stampy_chat.chat.logger')
|
||||
def test_talk_to_robot_internal(history, context):
|
||||
chunks = [
|
||||
{'choices': [{'delta': {'content': f"response 1"}}]},
|
||||
{'choices': [{'delta': {'content': f"response 2"}}]},
|
||||
{'choices': [{'delta': {'content': f"response 3"}}]},
|
||||
{'choices': [{'delta': {}}]},
|
||||
{'choices': [{'delta': {'content': None}}]},
|
||||
{'choices': [{'delta': {'content': f"response 4"}}]},
|
||||
]
|
||||
followups = [
|
||||
Followup('followup 1', '1', 0.231),
|
||||
Followup('followup 2', '2', 0.231),
|
||||
Followup('followup 3', '3', 0.231),
|
||||
]
|
||||
with patch('stampy_chat.chat.get_top_k_blocks', return_value=context):
|
||||
with patch('stampy_chat.chat.multisearch_authored', return_value=followups):
|
||||
with patch('openai.ChatCompletion.create', return_value=chunks):
|
||||
assert list(talk_to_robot_internal("index", "what is this about?", "default", history)) == [
|
||||
{'phase': 'semantic', 'state': 'loading'},
|
||||
{'citations': [], 'phase': 'semantic', 'state': 'loading'},
|
||||
{'phase': 'prompt', 'state': 'loading'},
|
||||
{'phase': 'llm', 'state': 'loading'},
|
||||
{'content': 'response 1', 'state': 'streaming'},
|
||||
{'content': 'response 2', 'state': 'streaming'},
|
||||
{'content': 'response 3', 'state': 'streaming'},
|
||||
{'content': 'response 4', 'state': 'streaming'},
|
||||
{
|
||||
'followup_0': {'pageid': '1', 'score': 0.231, 'text': 'followup 1'},
|
||||
'followup_1': {'pageid': '2', 'score': 0.231, 'text': 'followup 2'},
|
||||
'followup_2': {'pageid': '3', 'score': 0.231, 'text': 'followup 3'},
|
||||
'state': 'done'
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
@patch('stampy_chat.chat.check_openai_moderation')
|
||||
@patch('stampy_chat.chat.logger')
|
||||
def test_talk_to_robot_internal_error(history, context):
|
||||
chunks = [
|
||||
{'choices': [{'delta': {'content': f"response 1"}}]},
|
||||
{'choices': [{'delta': {'content': f"response 2"}}]},
|
||||
{'choices': [{'delta': {'content': f"response 3"}}]},
|
||||
{'choices': []},
|
||||
]
|
||||
with patch('stampy_chat.chat.get_top_k_blocks', return_value=context):
|
||||
with patch('openai.ChatCompletion.create', return_value=chunks):
|
||||
assert list(talk_to_robot_internal("index", "what is this about?", "default", history)) == [
|
||||
{'phase': 'semantic', 'state': 'loading'},
|
||||
{'citations': [], 'phase': 'semantic', 'state': 'loading'},
|
||||
{'phase': 'prompt', 'state': 'loading'},
|
||||
{'phase': 'llm', 'state': 'loading'},
|
||||
{'content': 'response 1', 'state': 'streaming'},
|
||||
{'content': 'response 2', 'state': 'streaming'},
|
||||
{'content': 'response 3', 'state': 'streaming'},
|
||||
{'error': 'list index out of range', 'state': 'error'},
|
||||
]
|
||||
Reference in New Issue
Block a user