File size: 104,678 Bytes
b585c7f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 |
import gzip
import io
import json
import os
import shutil
import tempfile
import uuid
import pytest
from src.gen import get_model_retry
from tests.test_client_calls import texts_helium1, texts_helium2, texts_helium3, texts_helium4, texts_helium5, \
texts_simple
from tests.utils import wrap_test_forked, kill_weaviate, make_user_path_test
from src.enums import DocumentSubset, LangChainAction, LangChainMode, LangChainTypes, DocumentChoice, \
docs_joiner_default, docs_token_handling_default, db_types, db_types_full
from src.gpt_langchain import get_persist_directory, get_db, get_documents, length_db1, _run_qa_db, split_merge_docs
from src.utils import zip_data, download_simple, get_ngpus_vis, get_mem_gpus, have_faiss, remove, get_kwargs, \
FakeTokenizer, get_token_count, flatten_list, tar_data
have_openai_key = os.environ.get('OPENAI_API_KEY') is not None
have_replicate_key = os.environ.get('REPLICATE_API_TOKEN') is not None
have_gpus = get_ngpus_vis() > 0
mem_gpus = get_mem_gpus()
# FIXME:
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
@pytest.mark.skipif(not have_openai_key, reason="requires OpenAI key to run")
@wrap_test_forked
def test_qa_wiki_openai():
return run_qa_wiki_fork(use_openai_model=True)
@pytest.mark.need_gpu
@wrap_test_forked
def test_qa_wiki_stuff_hf():
# NOTE: total context length makes things fail when n_sources * text_limit >~ 2048
return run_qa_wiki_fork(use_openai_model=False, text_limit=256, chain_type='stuff', prompt_type='human_bot')
@pytest.mark.xfail(strict=False,
reason="Too long context, improve prompt for map_reduce. Until then hit: The size of tensor a (2048) must match the size of tensor b (2125) at non-singleton dimension 3")
@wrap_test_forked
def test_qa_wiki_map_reduce_hf():
return run_qa_wiki_fork(use_openai_model=False, text_limit=None, chain_type='map_reduce', prompt_type='human_bot')
def run_qa_wiki_fork(*args, **kwargs):
# disable fork to avoid
# RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
# because some other tests use cuda in parent
# from tests.utils import call_subprocess_onetask
# return call_subprocess_onetask(run_qa_wiki, args=args, kwargs=kwargs)
return run_qa_wiki(*args, **kwargs)
def run_qa_wiki(use_openai_model=False, first_para=True, text_limit=None, chain_type='stuff', prompt_type=None):
from src.gpt_langchain import get_wiki_sources, get_llm
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
sources = get_wiki_sources(first_para=first_para, text_limit=text_limit)
llm, model_name, streamer, prompt_type_out, async_output, only_new_text, gradio_server = \
get_llm(use_openai_model=use_openai_model, prompt_type=prompt_type, llamacpp_dict={},
exllama_dict={})
chain = load_qa_with_sources_chain(llm, chain_type=chain_type)
question = "What are the main differences between Linux and Windows?"
from src.gpt_langchain import get_answer_from_sources
answer = get_answer_from_sources(chain, sources, question)
print(answer)
def check_ret(ret):
"""
check generator
:param ret:
:return:
"""
rets = []
for ret1 in ret:
rets.append(ret1)
print(ret1)
assert rets
return rets
@pytest.mark.skipif(not have_openai_key, reason="requires OpenAI key to run")
@wrap_test_forked
def test_qa_wiki_db_openai():
from src.gpt_langchain import _run_qa_db
query = "What are the main differences between Linux and Windows?"
langchain_mode = 'wiki'
ret = _run_qa_db(query=query, use_openai_model=True, use_openai_embedding=True, text_limit=None,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
db_type='faiss',
langchain_mode_types=dict(langchain_mode=LangChainTypes.SHARED.value),
langchain_mode=langchain_mode,
langchain_action=LangChainAction.QUERY.value, langchain_agents=[], llamacpp_dict={})
check_ret(ret)
@pytest.mark.need_gpu
@wrap_test_forked
def test_qa_wiki_db_hf():
from src.gpt_langchain import _run_qa_db
# if don't chunk, still need to limit
# but this case can handle at least more documents, by picking top k
# FIXME: but spitting out garbage answer right now, all fragmented, or just 1-word answer
query = "What are the main differences between Linux and Windows?"
langchain_mode = 'wiki'
ret = _run_qa_db(query=query, use_openai_model=False, use_openai_embedding=False, text_limit=256,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
db_type='faiss',
langchain_mode_types=dict(langchain_mode=LangChainTypes.SHARED.value),
langchain_mode=langchain_mode,
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[], llamacpp_dict={})
check_ret(ret)
@pytest.mark.need_gpu
@wrap_test_forked
def test_qa_wiki_db_chunk_hf():
from src.gpt_langchain import _run_qa_db
query = "What are the main differences between Linux and Windows?"
langchain_mode = 'wiki'
ret = _run_qa_db(query=query, use_openai_model=False, use_openai_embedding=False, text_limit=256, chunk=True,
chunk_size=256,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
db_type='faiss',
langchain_mode_types=dict(langchain_mode=LangChainTypes.SHARED.value),
langchain_mode=langchain_mode,
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[], llamacpp_dict={})
check_ret(ret)
@pytest.mark.skipif(not have_openai_key, reason="requires OpenAI key to run")
@wrap_test_forked
def test_qa_wiki_db_chunk_openai():
from src.gpt_langchain import _run_qa_db
# don't need 256, just seeing how compares to hf
query = "What are the main differences between Linux and Windows?"
langchain_mode = 'wiki'
ret = _run_qa_db(query=query, use_openai_model=True, use_openai_embedding=True, text_limit=256, chunk=True,
chunk_size=256,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
db_type='faiss',
langchain_mode_types=dict(langchain_mode=LangChainTypes.SHARED.value),
langchain_mode=langchain_mode,
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[], llamacpp_dict={})
check_ret(ret)
@pytest.mark.skipif(not have_openai_key, reason="requires OpenAI key to run")
@wrap_test_forked
def test_qa_github_db_chunk_openai():
from src.gpt_langchain import _run_qa_db
# don't need 256, just seeing how compares to hf
query = "what is a software defined asset"
langchain_mode = 'github h2oGPT'
ret = _run_qa_db(query=query, use_openai_model=True, use_openai_embedding=True, text_limit=256, chunk=True,
chunk_size=256,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
db_type='faiss',
langchain_mode_types=dict(langchain_mode=LangChainTypes.SHARED.value),
langchain_mode=langchain_mode,
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[], llamacpp_dict={})
check_ret(ret)
@pytest.mark.need_gpu
@wrap_test_forked
def test_qa_daidocs_db_chunk_hf():
from src.gpt_langchain import _run_qa_db
# FIXME: doesn't work well with non-instruct-tuned Cerebras
query = "Which config.toml enables pytorch for NLP?"
langchain_mode = 'DriverlessAI docs'
ret = _run_qa_db(query=query, use_openai_model=False, use_openai_embedding=False, text_limit=None, chunk=True,
chunk_size=128,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
db_type='faiss',
langchain_mode_types=dict(langchain_mode=LangChainTypes.SHARED.value),
langchain_mode=langchain_mode,
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[], llamacpp_dict={})
check_ret(ret)
@pytest.mark.skipif(not have_faiss, reason="requires FAISS")
@wrap_test_forked
def test_qa_daidocs_db_chunk_hf_faiss():
from src.gpt_langchain import _run_qa_db
query = "Which config.toml enables pytorch for NLP?"
# chunk_size is chars for each of k=4 chunks
langchain_mode = 'DriverlessAI docs'
ret = _run_qa_db(query=query, use_openai_model=False, use_openai_embedding=False, text_limit=None, chunk=True,
chunk_size=128 * 1, # characters, and if k=4, then 4*4*128 = 2048 chars ~ 512 tokens
langchain_mode_types=dict(langchain_mode=LangChainTypes.SHARED.value),
langchain_mode=langchain_mode,
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[],
llamacpp_dict={},
db_type='faiss',
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
)
check_ret(ret)
@pytest.mark.need_gpu
@pytest.mark.parametrize("db_type", db_types)
@pytest.mark.parametrize("top_k_docs", [-1, 3])
@wrap_test_forked
def test_qa_daidocs_db_chunk_hf_dbs(db_type, top_k_docs):
kill_weaviate(db_type)
langchain_mode = 'DriverlessAI docs'
langchain_action = LangChainAction.QUERY.value
langchain_agents = []
persist_directory, langchain_type = get_persist_directory(langchain_mode,
langchain_type=LangChainTypes.SHARED.value)
assert langchain_type == LangChainTypes.SHARED.value
remove(persist_directory)
from src.gpt_langchain import _run_qa_db
query = "Which config.toml enables pytorch for NLP?"
# chunk_size is chars for each of k=4 chunks
if top_k_docs == -1:
# else OOMs on generation immediately when generation starts, even though only 1600 tokens and 256 new tokens
model_name = 'h2oai/h2ogpt-oig-oasst1-512-6_9b'
else:
model_name = None
ret = _run_qa_db(query=query, use_openai_model=False, use_openai_embedding=False, text_limit=None, chunk=True,
chunk_size=128 * 1, # characters, and if k=4, then 4*4*128 = 2048 chars ~ 512 tokens
langchain_mode=langchain_mode,
langchain_action=langchain_action,
langchain_agents=langchain_agents,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
db_type=db_type,
top_k_docs=top_k_docs,
model_name=model_name,
llamacpp_dict={},
)
check_ret(ret)
kill_weaviate(db_type)
def get_test_model(base_model='h2oai/h2ogpt-oig-oasst1-512-6_9b',
tokenizer_base_model='',
prompt_type='human_bot',
inference_server='',
max_seq_len=None):
# need to get model externally, so don't OOM
from src.gen import get_model
all_kwargs = dict(load_8bit=False,
load_4bit=False,
low_bit_mode=1,
load_half=True,
load_gptq='',
use_autogptq=False,
load_awq='',
load_exllama=False,
use_safetensors=False,
revision=None,
use_gpu_id=True,
base_model=base_model,
tokenizer_base_model=tokenizer_base_model,
inference_server=inference_server,
regenerate_clients=True,
lora_weights='',
gpu_id=0,
n_jobs=1,
n_gpus=None,
reward_type=False,
local_files_only=False,
resume_download=True,
use_auth_token=False,
trust_remote_code=True,
offload_folder=None,
rope_scaling=None,
max_seq_len=max_seq_len,
compile_model=True,
llamacpp_dict={},
exllama_dict={},
gptq_dict={},
attention_sinks=False,
sink_dict={},
truncation_generation=False,
hf_model_dict={},
use_flash_attention_2=False,
llamacpp_path='llamacpp_path',
verbose=False)
model, tokenizer, device = get_model_retry(reward_type=False,
**get_kwargs(get_model, exclude_names=['reward_type'], **all_kwargs))
return model, tokenizer, base_model, prompt_type
@pytest.mark.need_gpu
@pytest.mark.parametrize("db_type", ['chroma'])
@wrap_test_forked
def test_qa_daidocs_db_chunk_hf_dbs_switch_embedding(db_type):
model, tokenizer, base_model, prompt_type = get_test_model()
langchain_mode = 'DriverlessAI docs'
langchain_action = LangChainAction.QUERY.value
langchain_agents = []
persist_directory, langchain_type = get_persist_directory(langchain_mode,
langchain_type=LangChainTypes.SHARED.value)
assert langchain_type == LangChainTypes.SHARED.value
remove(persist_directory)
from src.gpt_langchain import _run_qa_db
query = "Which config.toml enables pytorch for NLP?"
# chunk_size is chars for each of k=4 chunks
ret = _run_qa_db(query=query, use_openai_model=False, use_openai_embedding=False,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
migrate_embedding_model=True,
model=model,
tokenizer=tokenizer,
model_name=base_model,
prompt_type=prompt_type,
text_limit=None, chunk=True,
chunk_size=128 * 1, # characters, and if k=4, then 4*4*128 = 2048 chars ~ 512 tokens
langchain_mode=langchain_mode,
langchain_action=langchain_action,
langchain_agents=langchain_agents,
db_type=db_type,
llamacpp_dict={},
)
check_ret(ret)
query = "Which config.toml enables pytorch for NLP?"
# chunk_size is chars for each of k=4 chunks
ret = _run_qa_db(query=query, use_openai_model=False, use_openai_embedding=False,
hf_embedding_model='hkunlp/instructor-large',
migrate_embedding_model=True,
model=model,
tokenizer=tokenizer,
model_name=base_model,
prompt_type=prompt_type,
text_limit=None, chunk=True,
chunk_size=128 * 1, # characters, and if k=4, then 4*4*128 = 2048 chars ~ 512 tokens
langchain_mode=langchain_mode,
langchain_action=langchain_action,
langchain_agents=langchain_agents,
db_type=db_type,
llamacpp_dict={},
)
check_ret(ret)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_qa_wiki_db_chunk_hf_dbs_llama(db_type):
kill_weaviate(db_type)
from src.gpt4all_llm import get_model_tokenizer_gpt4all
model_name = 'llama'
model, tokenizer, device = get_model_tokenizer_gpt4all(model_name,
n_jobs=8,
max_seq_len=512,
llamacpp_dict=dict(
model_path_llama='https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q6_K.gguf?download=true',
n_gpu_layers=100,
use_mlock=True,
n_batch=1024))
from src.gpt_langchain import _run_qa_db
query = "What are the main differences between Linux and Windows?"
# chunk_size is chars for each of k=4 chunks
langchain_mode = 'wiki'
ret = _run_qa_db(query=query, use_openai_model=False, use_openai_embedding=False, text_limit=None, chunk=True,
chunk_size=128 * 1, # characters, and if k=4, then 4*4*128 = 2048 chars ~ 512 tokens
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
langchain_mode_types=dict(langchain_mode=LangChainTypes.SHARED.value),
langchain_mode=langchain_mode,
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[],
db_type=db_type,
prompt_type='llama2',
langchain_only_model=True,
model_name=model_name, model=model, tokenizer=tokenizer,
llamacpp_dict=dict(n_gpu_layers=100, use_mlock=True, n_batch=1024),
)
check_ret(ret)
kill_weaviate(db_type)
@pytest.mark.skipif(not have_openai_key, reason="requires OpenAI key to run")
@wrap_test_forked
def test_qa_daidocs_db_chunk_openai():
from src.gpt_langchain import _run_qa_db
query = "Which config.toml enables pytorch for NLP?"
langchain_mode = 'DriverlessAI docs'
ret = _run_qa_db(query=query, use_openai_model=True, use_openai_embedding=True, text_limit=256, chunk=True,
db_type='faiss',
hf_embedding_model="",
chunk_size=256,
langchain_mode_types=dict(langchain_mode=LangChainTypes.SHARED.value),
langchain_mode=langchain_mode,
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[], llamacpp_dict={})
check_ret(ret)
@pytest.mark.skipif(not have_openai_key, reason="requires OpenAI key to run")
@wrap_test_forked
def test_qa_daidocs_db_chunk_openaiembedding_hfmodel():
from src.gpt_langchain import _run_qa_db
query = "Which config.toml enables pytorch for NLP?"
langchain_mode = 'DriverlessAI docs'
ret = _run_qa_db(query=query, use_openai_model=False, use_openai_embedding=True, text_limit=None, chunk=True,
chunk_size=128,
hf_embedding_model="",
db_type='faiss',
langchain_mode_types=dict(langchain_mode=LangChainTypes.SHARED.value),
langchain_mode=langchain_mode,
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[], llamacpp_dict={})
check_ret(ret)
@pytest.mark.need_tokens
@wrap_test_forked
def test_get_dai_pickle():
from src.gpt_langchain import get_dai_pickle
with tempfile.TemporaryDirectory() as tmpdirname:
get_dai_pickle(dest=tmpdirname)
assert os.path.isfile(os.path.join(tmpdirname, 'dai_docs.pickle'))
@pytest.mark.need_tokens
@wrap_test_forked
def test_get_dai_db_dir():
from src.gpt_langchain import get_some_dbs_from_hf
with tempfile.TemporaryDirectory() as tmpdirname:
get_some_dbs_from_hf(tmpdirname)
# repeat is to check if first case really deletes, else assert will fail if accumulates wrongly
@pytest.mark.parametrize("repeat", [0, 1])
@pytest.mark.parametrize("db_type", db_types_full)
@wrap_test_forked
def test_make_add_db(repeat, db_type):
kill_weaviate(db_type)
from src.gpt_langchain import get_source_files, get_source_files_given_langchain_mode, get_any_db, update_user_db, \
get_sources, update_and_get_source_files_given_langchain_mode
from src.make_db import make_db_main
from src.gpt_langchain import path_to_docs
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
with tempfile.TemporaryDirectory() as tmp_persist_directory_my:
with tempfile.TemporaryDirectory() as tmp_user_path_my:
msg1 = "Hello World"
test_file1 = os.path.join(tmp_user_path, 'test.txt')
with open(test_file1, "wt") as f:
f.write(msg1)
chunk = True
chunk_size = 512
langchain_mode = 'UserData'
db, collection_name = make_db_main(persist_directory=tmp_persist_directory,
user_path=tmp_user_path,
add_if_exists=False,
collection_name=langchain_mode,
fail_any_exception=True, db_type=db_type)
assert db is not None
docs = db.similarity_search("World")
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert docs[0].page_content == msg1
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
test_file1my = os.path.join(tmp_user_path_my, 'test.txt')
with open(test_file1my, "wt") as f:
f.write(msg1)
dbmy, collection_namemy = make_db_main(persist_directory=tmp_persist_directory_my,
user_path=tmp_user_path_my,
add_if_exists=False,
collection_name='MyData',
fail_any_exception=True, db_type=db_type)
db1 = {LangChainMode.MY_DATA.value: [dbmy, 'foouuid', 'foousername']}
assert dbmy is not None
docs1 = dbmy.similarity_search("World")
assert len(docs1) == 1 + (1 if db_type == 'chroma' else 0)
assert docs1[0].page_content == msg1
assert os.path.normpath(docs1[0].metadata['source']) == os.path.normpath(test_file1my)
# some db testing for gradio UI/client
get_source_files(db=db)
get_source_files(db=dbmy)
selection_docs_state1 = dict(langchain_modes=[langchain_mode], langchain_mode_paths={},
langchain_mode_types={})
requests_state1 = dict()
get_source_files_given_langchain_mode(db1, selection_docs_state1, requests_state1, None,
langchain_mode, dbs={langchain_mode: db})
get_source_files_given_langchain_mode(db1, selection_docs_state1, requests_state1, None,
langchain_mode='MyData', dbs={})
get_any_db(db1, langchain_mode='UserData',
langchain_mode_paths=selection_docs_state1['langchain_mode_paths'],
langchain_mode_types=selection_docs_state1['langchain_mode_types'],
dbs={langchain_mode: db})
get_any_db(db1, langchain_mode='MyData',
langchain_mode_paths=selection_docs_state1['langchain_mode_paths'],
langchain_mode_types=selection_docs_state1['langchain_mode_types'],
dbs={})
msg1up = "Beefy Chicken"
test_file2 = os.path.join(tmp_user_path, 'test2.txt')
with open(test_file2, "wt") as f:
f.write(msg1up)
test_file2_my = os.path.join(tmp_user_path_my, 'test2my.txt')
with open(test_file2_my, "wt") as f:
f.write(msg1up)
kwargs = dict(use_openai_embedding=False,
hf_embedding_model='hkunlp/instructor-large',
migrate_embedding_model=True,
auto_migrate_db=False,
caption_loader=False,
doctr_loader=False,
asr_loader=False,
enable_captions=False,
enable_doctr=False,
enable_pix2struct=False,
enable_llava=False,
enable_transcriptions=False,
captions_model="Salesforce/blip-image-captioning-base",
llava_model=None,
llava_prompt=None,
asr_model='openai/whisper-medium',
enable_ocr=False,
enable_pdf_ocr='auto',
enable_pdf_doctr=False,
gradio_upload_to_chatbot_num_max=1,
verbose=False,
is_url=False, is_txt=False)
langchain_mode2 = 'MyData'
selection_docs_state2 = dict(langchain_modes=[langchain_mode2],
langchain_mode_paths={},
langchain_mode_types={})
requests_state2 = dict()
z1, z2, source_files_added, exceptions, last_file, last_dict = update_user_db(test_file2_my, db1,
selection_docs_state2,
requests_state2,
langchain_mode2,
chunk=chunk,
chunk_size=chunk_size,
dbs={},
db_type=db_type,
**kwargs)
assert z1 is None
assert 'MyData' == z2
assert 'test2my' in str(source_files_added)
assert len(exceptions) == 0
langchain_mode = 'UserData'
selection_docs_state1 = dict(langchain_modes=[langchain_mode],
langchain_mode_paths={langchain_mode: tmp_user_path},
langchain_mode_types={langchain_mode: LangChainTypes.SHARED.value})
z1, z2, source_files_added, exceptions, last_file, last_dict = update_user_db(test_file2, db1,
selection_docs_state1,
requests_state1,
langchain_mode,
chunk=chunk,
chunk_size=chunk_size,
dbs={
langchain_mode: db},
db_type=db_type,
**kwargs)
assert 'test2' in str(source_files_added)
assert langchain_mode == z2
assert z1 is None
docs_state0 = [x.name for x in list(DocumentSubset)]
get_sources(db1, selection_docs_state1, {}, langchain_mode, dbs={langchain_mode: db},
docs_state0=docs_state0)
get_sources(db1, selection_docs_state1, {}, 'MyData', dbs={}, docs_state0=docs_state0)
selection_docs_state1['langchain_mode_paths'] = {langchain_mode: tmp_user_path}
kwargs2 = dict(first_para=False,
text_limit=None, chunk=chunk, chunk_size=chunk_size,
db_type=db_type,
hf_embedding_model=kwargs['hf_embedding_model'],
migrate_embedding_model=kwargs['migrate_embedding_model'],
auto_migrate_db=kwargs['auto_migrate_db'],
load_db_if_exists=True,
n_jobs=-1, verbose=False)
update_and_get_source_files_given_langchain_mode(db1,
selection_docs_state1, requests_state1,
langchain_mode, dbs={langchain_mode: db},
**kwargs2)
update_and_get_source_files_given_langchain_mode(db1,
selection_docs_state2, requests_state2,
'MyData', dbs={}, **kwargs2)
assert path_to_docs(test_file2_my, db_type=db_type)[0].metadata['source'] == test_file2_my
extra = 1 if db_type == 'chroma' else 0
assert os.path.normpath(
path_to_docs(os.path.dirname(test_file2_my), db_type=db_type)[1 + extra].metadata[
'source']) == os.path.normpath(
os.path.abspath(test_file2_my))
assert path_to_docs([test_file1, test_file2, test_file2_my], db_type=db_type)[0].metadata[
'source'] == test_file1
assert path_to_docs(None, url='arxiv:1706.03762', db_type=db_type)[0].metadata[
'source'] == 'http://arxiv.org/abs/1706.03762v7'
assert path_to_docs(None, url='http://h2o.ai', db_type=db_type)[0].metadata[
'source'] == 'http://h2o.ai'
assert 'user_paste' in path_to_docs(None,
text='Yufuu is a wonderful place and you should really visit because there is lots of sun.',
db_type=db_type)[0].metadata['source']
if db_type == 'faiss':
# doesn't persist
return
# now add using new source path, to original persisted
with tempfile.TemporaryDirectory() as tmp_user_path3:
msg2 = "Jill ran up the hill"
test_file2 = os.path.join(tmp_user_path3, 'test2.txt')
with open(test_file2, "wt") as f:
f.write(msg2)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory,
user_path=tmp_user_path3,
add_if_exists=True,
fail_any_exception=True, db_type=db_type,
collection_name=collection_name)
assert db is not None
docs = db.similarity_search("World")
assert len(docs) == 3 + (1 if db_type == 'chroma' else 0)
assert docs[0].page_content == msg1
assert docs[1 + extra].page_content in [msg2, msg1up]
assert docs[2 + extra].page_content in [msg2, msg1up]
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
docs = db.similarity_search("Jill")
assert len(docs) == 3 + (1 if db_type == 'chroma' else 0)
assert docs[0].page_content == msg2
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file2)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_zip_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
msg1 = "Hello World"
test_file1 = os.path.join(tmp_user_path, 'test.txt')
with open(test_file1, "wt") as f:
f.write(msg1)
zip_file = './tmpdata/data.zip'
zip_data(tmp_user_path, zip_file=zip_file, fail_any_exception=True)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("World")
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert docs[0].page_content == msg1
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@pytest.mark.parametrize("tar_type", ["tar.gz", "tgz"])
@wrap_test_forked
def test_tar_add(db_type, tar_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
msg1 = "Hello World"
test_file1 = os.path.join(tmp_user_path, 'test.txt')
with open(test_file1, "wt") as f:
f.write(msg1)
tar_file = f'./tmpdata/data.{tar_type}'
tar_data(tmp_user_path, tar_file=tar_file, fail_any_exception=True)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("World")
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert docs[0].page_content == msg1
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_url_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
url = 'https://h2o.ai/company/team/leadership-team/'
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, url=url, fail_any_exception=True,
db_type=db_type)
assert db is not None
docs = db.similarity_search("list founding team of h2o.ai")
assert len(docs) == 4
assert 'Sri Ambati' in docs[0].page_content
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_urls_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
urls = ['https://h2o.ai/company/team/leadership-team/',
'https://arxiv.org/abs/1706.03762',
'https://github.com/h2oai/h2ogpt',
'https://h2o.ai'
]
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, url=urls,
fail_any_exception=True,
db_type=db_type)
assert db is not None
if db_type == 'chroma':
assert len(db.get()['documents']) > 100
docs = db.similarity_search("list founding team of h2o.ai")
assert len(docs) == 4
assert 'Sri Ambati' in docs[0].page_content
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_urls_file_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
urls = ['https://h2o.ai/company/team/leadership-team/',
'https://arxiv.org/abs/1706.03762',
'https://github.com/h2oai/h2ogpt',
'https://h2o.ai'
]
with open(os.path.join(tmp_user_path, 'list.urls'), 'wt') as f:
f.write('\n'.join(urls))
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, url=urls,
user_path=tmp_user_path,
fail_any_exception=True,
db_type=db_type)
assert db is not None
if db_type == 'chroma':
assert len(db.get()['documents']) > 100
docs = db.similarity_search("list founding team of h2o.ai")
assert len(docs) == 4
assert 'Sri Ambati' in docs[0].page_content
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_html_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
html_content = """
<!DOCTYPE html>
<html>
<body>
<h1>Yugu is a wonderful place</h1>
<p>Animals love to run in the world of Yugu. They play all day long in the alien sun.</p>
</body>
</html>
"""
test_file1 = os.path.join(tmp_user_path, 'test.html')
with open(test_file1, "wt") as f:
f.write(html_content)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("Yugu")
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert 'Yugu' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_docx_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://calibre-ebook.com/downloads/demos/demo.docx'
test_file1 = os.path.join(tmp_user_path, 'demo.docx')
download_simple(url, dest=test_file1)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type)
assert db is not None
docs = db.similarity_search("What is calibre DOCX plugin do?")
assert len(docs) == 4
assert 'calibre' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_xls_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
test_file1 = os.path.join(tmp_user_path, 'example.xlsx')
shutil.copy('data/example.xlsx', tmp_user_path)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type)
assert db is not None
docs = db.similarity_search("What is Profit?")
assert len(docs) == 4
assert '16185' in docs[0].page_content or \
'Small Business' in docs[0].page_content or \
'United States of America' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_md_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
test_file1 = 'README.md'
if not os.path.isfile(test_file1):
# see if ran from tests directory
test_file1 = '../README.md'
test_file1 = os.path.abspath(test_file1)
shutil.copy(test_file1, tmp_user_path)
test_file1 = os.path.join(tmp_user_path, os.path.basename(test_file1))
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type)
assert db is not None
docs = db.similarity_search("What is h2oGPT?")
assert len(docs) == 4
assert 'Query and summarize your documents' in docs[1].page_content or 'document Q/A' in docs[
1].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_rst_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://gist.githubusercontent.com/javiertejero/4585196/raw/21786e2145c0cc0a202ffc4f257f99c26985eaea/README.rst'
test_file1 = os.path.join(tmp_user_path, 'demo.rst')
download_simple(url, dest=test_file1)
test_file1 = os.path.join(tmp_user_path, os.path.basename(test_file1))
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type)
assert db is not None
docs = db.similarity_search("Font Faces - Emphasis and Examples")
assert len(docs) == 4
assert 'Within paragraphs, inline markup' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_xml_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://gist.githubusercontent.com/theresajayne/1409545/raw/a8b46e7799805e86f4339172c9778fa55afb0f30/gistfile1.txt'
test_file1 = os.path.join(tmp_user_path, 'demo.xml')
download_simple(url, dest=test_file1)
test_file1 = os.path.join(tmp_user_path, os.path.basename(test_file1))
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type)
assert db is not None
docs = db.similarity_search("Entrance Hall")
assert len(docs) == 4 if db_type == 'chroma' else 3
assert 'Ensuite Bathroom' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_eml_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://raw.githubusercontent.com/FlexConfirmMail/Thunderbird/master/sample.eml'
test_file1 = os.path.join(tmp_user_path, 'sample.eml')
download_simple(url, dest=test_file1)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("What is subject?")
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert 'testtest' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_simple_eml_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
html_content = """
Date: Sun, 1 Apr 2012 14:25:25 -0600
From: file@fyicenter.com
Subject: Welcome
To: someone@somewhere.com
Dear Friend,
Welcome to file.fyicenter.com!
Sincerely,
FYIcenter.com Team"""
test_file1 = os.path.join(tmp_user_path, 'test.eml')
with open(test_file1, "wt") as f:
f.write(html_content)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("Subject")
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert 'Welcome' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_odt_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://github.com/owncloud/example-files/raw/master/Documents/Example.odt'
test_file1 = os.path.join(tmp_user_path, 'sample.odt')
download_simple(url, dest=test_file1)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type)
assert db is not None
docs = db.similarity_search("What is ownCloud?")
assert len(docs) == 4
assert 'ownCloud' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_pptx_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://www.unm.edu/~unmvclib/powerpoint/pptexamples.ppt'
test_file1 = os.path.join(tmp_user_path, 'sample.pptx')
download_simple(url, dest=test_file1)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("Suggestions")
assert len(docs) == 4
assert 'Presentation' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("use_pypdf", ['auto', 'on', 'off'])
@pytest.mark.parametrize("use_unstructured_pdf", ['auto', 'on', 'off'])
@pytest.mark.parametrize("use_pymupdf", ['auto', 'on', 'off'])
@pytest.mark.parametrize("enable_pdf_doctr", ['auto', 'on', 'off'])
@pytest.mark.parametrize("enable_pdf_ocr", ['auto', 'on', 'off'])
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_pdf_add(db_type, enable_pdf_ocr, enable_pdf_doctr, use_pymupdf, use_unstructured_pdf, use_pypdf):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
if True:
if False:
url = 'https://www.africau.edu/images/default/sample.pdf'
test_file1 = os.path.join(tmp_user_path, 'sample.pdf')
download_simple(url, dest=test_file1)
else:
test_file1 = os.path.join(tmp_user_path, 'sample2.pdf')
shutil.copy(os.path.join('tests', 'sample.pdf'), test_file1)
else:
if False:
name = 'CityofTshwaneWater.pdf'
location = "tests"
else:
name = '555_593.pdf'
location = '/home/jon/Downloads/'
test_file1 = os.path.join(location, name)
shutil.copy(test_file1, tmp_user_path)
test_file1 = os.path.join(tmp_user_path, name)
default_mode = use_pymupdf in ['auto', 'on'] and \
use_pypdf in ['auto'] and \
use_unstructured_pdf in ['auto'] and \
enable_pdf_doctr in ['off', 'auto'] and \
enable_pdf_ocr in ['off', 'auto']
no_doc_mode = use_pymupdf in ['off'] and \
use_pypdf in ['off'] and \
use_unstructured_pdf in ['off'] and \
enable_pdf_doctr in ['off'] and \
enable_pdf_ocr in ['off', 'auto']
try:
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
use_pymupdf=use_pymupdf,
enable_pdf_ocr=enable_pdf_ocr,
enable_pdf_doctr=enable_pdf_doctr,
use_unstructured_pdf=use_unstructured_pdf,
use_pypdf=use_pypdf,
add_if_exists=False)
except Exception as e:
if 'had no valid text and no meta data was parsed' in str(
e) or 'had no valid text, but meta data was parsed' in str(e):
if no_doc_mode:
return
else:
raise
raise
assert db is not None
docs = db.similarity_search("Suggestions")
if default_mode:
assert len(docs) == 3 + (1 if db_type == 'chroma' else 0) or len(docs) == 4 # weaviate madness
else:
# ocr etc. end up with different pages, overly complex to test exact count
assert len(docs) >= 2
assert 'And more text. And more text.' in docs[0].page_content
if db_type == 'weaviate':
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1) or os.path.basename(
docs[0].metadata['source']) == os.path.basename(test_file1)
else:
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("use_pypdf", ['auto', 'on', 'off'])
@pytest.mark.parametrize("use_unstructured_pdf", ['auto', 'on', 'off'])
@pytest.mark.parametrize("use_pymupdf", ['auto', 'on', 'off'])
@pytest.mark.parametrize("enable_pdf_doctr", ['auto', 'on', 'off'])
@pytest.mark.parametrize("enable_pdf_ocr", ['auto', 'on', 'off'])
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_image_pdf_add(db_type, enable_pdf_ocr, enable_pdf_doctr, use_pymupdf, use_unstructured_pdf, use_pypdf):
if enable_pdf_ocr == 'off' and not enable_pdf_doctr:
return
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
name = 'CityofTshwaneWater.pdf'
location = "tests"
test_file1 = os.path.join(location, name)
shutil.copy(test_file1, tmp_user_path)
test_file1 = os.path.join(tmp_user_path, name)
str_test = [db_type, enable_pdf_ocr, enable_pdf_doctr, use_pymupdf, use_unstructured_pdf, use_pypdf]
str_test = [str(x) for x in str_test]
str_test = '-'.join(str_test)
default_mode = use_pymupdf in ['auto', 'on'] and \
use_pypdf in ['off', 'auto'] and \
use_unstructured_pdf in ['auto'] and \
enable_pdf_doctr in ['off', 'auto'] and \
enable_pdf_ocr in ['off', 'auto']
no_doc_mode = use_pymupdf in ['off'] and \
use_pypdf in ['off'] and \
use_unstructured_pdf in ['off'] and \
enable_pdf_doctr in ['off'] and \
enable_pdf_ocr in ['off', 'auto']
no_docs = ['off-off-auto-off-auto', 'off-off-on-off-on', 'off-off-auto-off-off', 'off-off-off-off-auto',
'off-off-on-off-off', 'off-off-on-off-auto', 'off-off-auto-off-on', 'off-off-off-off-on',
]
no_doc_mode |= any([x in str_test for x in no_docs])
try:
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
use_pymupdf=use_pymupdf,
enable_pdf_ocr=enable_pdf_ocr,
enable_pdf_doctr=enable_pdf_doctr,
use_unstructured_pdf=use_unstructured_pdf,
use_pypdf=use_pypdf,
add_if_exists=False)
except Exception as e:
if 'had no valid text and no meta data was parsed' in str(
e) or 'had no valid text, but meta data was parsed' in str(e):
if no_doc_mode:
return
else:
raise
raise
if default_mode:
assert db is not None
docs = db.similarity_search("List Tshwane's concerns about water.")
assert len(docs) == 4
assert 'we appeal to residents that do have water to please use it sparingly.' in docs[
1].page_content or 'OFFICE OF THE MMC FOR UTILITIES AND REGIONAL' in docs[1].page_content
else:
assert db is not None
docs = db.similarity_search("List Tshwane's concerns about water.")
assert len(docs) >= 2
assert docs[0].page_content
assert docs[1].page_content
if db_type == 'weaviate':
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1) or os.path.basename(
docs[0].metadata['source']) == os.path.basename(test_file1)
else:
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_simple_pptx_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://www.suu.edu/webservices/styleguide/example-files/example.pptx'
test_file1 = os.path.join(tmp_user_path, 'sample.pptx')
download_simple(url, dest=test_file1)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("Example")
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert 'Powerpoint' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_epub_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://contentserver.adobe.com/store/books/GeographyofBliss_oneChapter.epub'
test_file1 = os.path.join(tmp_user_path, 'sample.epub')
download_simple(url, dest=test_file1)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("Grump")
assert len(docs) == 4
assert 'happy' in docs[0].page_content or 'happiness' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.skip(reason="Not supported, GPL3, and msg-extractor code fails too often")
@pytest.mark.xfail(strict=False,
reason="fails with AttributeError: 'Message' object has no attribute '_MSGFile__stringEncoding'. Did you mean: '_MSGFile__overrideEncoding'? even though can use online converter to .eml fine.")
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_msg_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'http://file.fyicenter.com/b/sample.msg'
test_file1 = os.path.join(tmp_user_path, 'sample.msg')
download_simple(url, dest=test_file1)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type)
assert db is not None
docs = db.similarity_search("Grump")
assert len(docs) == 4 + (1 if db_type == 'chroma' else 0)
assert 'Happy' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
os.system('cd tests ; unzip -o driverslicense.jpeg.zip')
@pytest.mark.parametrize("file", ['data/pexels-evg-kowalievska-1170986_small.jpg',
'data/Sample-Invoice-printable.png',
'tests/driverslicense.jpeg.zip',
'tests/driverslicense.jpeg'])
@pytest.mark.parametrize("db_type", db_types)
@pytest.mark.parametrize("enable_pix2struct", [False, True])
@pytest.mark.parametrize("enable_doctr", [False, True])
@pytest.mark.parametrize("enable_ocr", [False, True])
@pytest.mark.parametrize("enable_captions", [False, True])
@pytest.mark.parametrize("pre_load_image_audio_models", [False, True])
@pytest.mark.parametrize("caption_gpu", [False, True])
@pytest.mark.parametrize("captions_model", [None, 'Salesforce/blip2-flan-t5-xl'])
@wrap_test_forked
@pytest.mark.parallel10
def test_png_add(captions_model, caption_gpu, pre_load_image_audio_models, enable_captions,
enable_doctr, enable_pix2struct, enable_ocr, db_type, file):
if not have_gpus and caption_gpu:
# if have no GPUs, don't enable caption on GPU
return
if not caption_gpu and captions_model == 'Salesforce/blip2-flan-t5-xl':
# RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'
return
if not enable_captions and pre_load_image_audio_models:
# nothing to preload if not enabling captions
return
if captions_model == 'Salesforce/blip2-flan-t5-xl' and not (have_gpus and mem_gpus[0] > 20 * 1024 ** 3):
# requires GPUs and enough memory to run
return
if not (enable_ocr or enable_doctr or enable_pix2struct or enable_captions):
# nothing enabled for images
return
# FIXME (too many permutations):
if enable_pix2struct and (
pre_load_image_audio_models or enable_captions or enable_ocr or enable_doctr or captions_model or caption_gpu):
return
if enable_pix2struct and 'kowalievska' in file:
# FIXME: Not good for this
return
kill_weaviate(db_type)
try:
return run_png_add(captions_model=captions_model, caption_gpu=caption_gpu,
pre_load_image_audio_models=pre_load_image_audio_models,
enable_captions=enable_captions,
enable_ocr=enable_ocr,
enable_doctr=enable_doctr,
enable_pix2struct=enable_pix2struct,
db_type=db_type,
file=file)
except Exception as e:
if not enable_captions and 'data/pexels-evg-kowalievska-1170986_small.jpg' in file and 'had no valid text and no meta data was parsed' in str(
e):
pass
else:
raise
kill_weaviate(db_type)
def run_png_add(captions_model=None, caption_gpu=False,
pre_load_image_audio_models=False,
enable_captions=True,
enable_ocr=False,
enable_doctr=False,
enable_pix2struct=False,
db_type='chroma',
file='data/pexels-evg-kowalievska-1170986_small.jpg'):
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
test_file1 = file
if not os.path.isfile(test_file1):
# see if ran from tests directory
test_file1 = os.path.join('../', file)
assert os.path.isfile(test_file1)
test_file1 = os.path.abspath(test_file1)
shutil.copy(test_file1, tmp_user_path)
test_file1 = os.path.join(tmp_user_path, os.path.basename(test_file1))
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True,
enable_ocr=enable_ocr,
enable_pdf_ocr='auto',
enable_pdf_doctr=False,
caption_gpu=caption_gpu,
pre_load_image_audio_models=pre_load_image_audio_models,
captions_model=captions_model,
enable_captions=enable_captions,
enable_doctr=enable_doctr,
enable_pix2struct=enable_pix2struct,
db_type=db_type,
add_if_exists=False,
fail_if_no_sources=False)
if (enable_captions or enable_pix2struct) and not enable_doctr and not enable_ocr:
if 'kowalievska' in file:
docs = db.similarity_search("cat", k=10)
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert 'a cat sitting on a window' in docs[0].page_content
check_source(docs, test_file1)
elif 'Sample-Invoice-printable' in file:
docs = db.similarity_search("invoice", k=10)
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
# weak test
assert 'plumbing' in docs[0].page_content.lower() or 'invoice' in docs[0].page_content.lower()
check_source(docs, test_file1)
else:
docs = db.similarity_search("license", k=10)
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
check_content_captions(docs, captions_model, enable_pix2struct)
check_source(docs, test_file1)
elif not (enable_captions or enable_pix2struct) and not enable_doctr and enable_ocr:
if 'kowalievska' in file:
assert db is None
elif 'Sample-Invoice-printable' in file:
# weak test
assert db is not None
else:
docs = db.similarity_search("license", k=10)
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
check_content_ocr(docs)
check_source(docs, test_file1)
elif not (enable_captions or enable_pix2struct) and enable_doctr and not enable_ocr:
if 'kowalievska' in file:
assert db is None
elif 'Sample-Invoice-printable' in file:
# weak test
assert db is not None
else:
docs = db.similarity_search("license", k=10)
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
check_content_doctr(docs)
check_source(docs, test_file1)
elif not (enable_captions or enable_pix2struct) and enable_doctr and enable_ocr:
if 'kowalievska' in file:
assert db is None
elif 'Sample-Invoice-printable' in file:
# weak test
assert db is not None
else:
docs = db.similarity_search("license", k=10)
assert len(docs) == 2 + (2 if db_type == 'chroma' else 0)
check_content_doctr(docs)
check_content_ocr(docs)
check_source(docs, test_file1)
elif (enable_captions or enable_pix2struct) and not enable_doctr and enable_ocr:
if 'kowalievska' in file:
docs = db.similarity_search("cat", k=10)
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert 'a cat sitting on a window' in docs[0].page_content
check_source(docs, test_file1)
elif 'Sample-Invoice-printable' in file:
# weak test
assert db is not None
else:
docs = db.similarity_search("license", k=10)
assert len(docs) == 2 + (2 if db_type == 'chroma' else 0)
check_content_ocr(docs)
check_content_captions(docs, captions_model, enable_pix2struct)
check_source(docs, test_file1)
elif (enable_captions or enable_pix2struct) and enable_doctr and not enable_ocr:
if 'kowalievska' in file:
docs = db.similarity_search("cat", k=10)
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert 'a cat sitting on a window' in docs[0].page_content
check_source(docs, test_file1)
elif 'Sample-Invoice-printable' in file:
# weak test
assert db is not None
else:
docs = db.similarity_search("license", k=10)
assert len(docs) == 2 + (2 if db_type == 'chroma' else 0)
check_content_doctr(docs)
check_content_captions(docs, captions_model, enable_pix2struct)
check_source(docs, test_file1)
elif (enable_captions or enable_pix2struct) and enable_doctr and enable_ocr:
if 'kowalievska' in file:
docs = db.similarity_search("cat", k=10)
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert 'a cat sitting on a window' in docs[0].page_content
check_source(docs, test_file1)
elif 'Sample-Invoice-printable' in file:
# weak test
assert db is not None
else:
if db_type == 'chroma':
assert len(db.get()['documents']) == 6
docs = db.similarity_search("license", k=10)
# because search can't find DRIVERLICENSE from DocTR one
assert len(docs) == 4 + (2 if db_type == 'chroma' else 1)
check_content_ocr(docs)
# check_content_doctr(docs)
check_content_captions(docs, captions_model, enable_pix2struct)
check_source(docs, test_file1)
else:
raise NotImplementedError()
def check_content_captions(docs, captions_model, enable_pix2struct):
assert any(['license' in docs[ix].page_content.lower() for ix in range(len(docs))])
if captions_model is not None and 'blip2' in captions_model:
str_expected = """california driver license with a woman's face on it california driver license"""
elif enable_pix2struct:
str_expected = """california license"""
else:
str_expected = """a california driver's license with a picture of a woman's face and a picture of a man's face"""
assert any([str_expected in docs[ix].page_content.lower() for ix in range(len(docs))])
def check_content_doctr(docs):
assert any(['DRIVER LICENSE' in docs[ix].page_content for ix in range(len(docs))])
assert any(['California' in docs[ix].page_content for ix in range(len(docs))])
assert any(['ExP08/31/2014' in docs[ix].page_content for ix in range(len(docs))])
assert any(['VETERAN' in docs[ix].page_content for ix in range(len(docs))])
def check_content_ocr(docs):
# hi_res
# assert any(['Californias' in docs[ix].page_content for ix in range(len(docs))])
# ocr_only
assert any(['DRIVER LICENSE' in docs[ix].page_content for ix in range(len(docs))])
def check_source(docs, test_file1):
if test_file1.endswith('.zip'):
# when zip, adds dir etc.:
# AssertionError: assert '/tmp/tmp63h5dxxv/driverslicense.jpeg.zip_d7d5f561-6/driverslicense.jpeg' == '/tmp/tmp63h5dxxv/driverslicense.jpeg.zip'
assert os.path.basename(os.path.normpath(test_file1)) in os.path.normpath(docs[0].metadata['source'])
else:
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
@pytest.mark.parametrize("image_file", ['./models/anthropic.png', 'data/pexels-evg-kowalievska-1170986_small.jpg'])
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_llava_add(image_file, db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
file = os.path.basename(image_file)
test_file1 = os.path.join(tmp_user_path, file)
shutil.copy(image_file, test_file1)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False,
enable_llava=True,
llava_model=os.getenv('H2OGPT_LLAVA_MODEL', 'http://192.168.1.46:7861'),
llava_prompt=None,
enable_doctr=False,
enable_captions=False,
enable_ocr=False,
enable_transcriptions=False,
enable_pdf_ocr=False,
enable_pdf_doctr=False,
enable_pix2struct=False,
)
assert db is not None
if 'anthropic' in image_file:
docs = db.similarity_search("circle")
assert len(docs) == 2 if db_type == 'chroma' else 1
assert 'letter "A"' in docs[0].page_content
else:
docs = db.similarity_search("cat")
assert len(docs) == 2 if db_type == 'chroma' else 1
assert 'cat' in docs[0].page_content
assert 'birds' in docs[0].page_content or 'outdoors' in docs[0].page_content or 'outside' in docs[
0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_simple_rtf_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
rtf_content = """
{\rtf1\mac\deff2 {\fonttbl{\f0\fswiss Chicago;}{\f2\froman New York;}{\f3\fswiss Geneva;}{\f4\fmodern Monaco;}{\f11\fnil Cairo;}{\f13\fnil Zapf Dingbats;}{\f16\fnil Palatino;}{\f18\fnil Zapf Chancery;}{\f20\froman Times;}{\f21\fswiss Helvetica;}
{\f22\fmodern Courier;}{\f23\ftech Symbol;}{\f24\fnil Mobile;}{\f100\fnil FoxFont;}{\f107\fnil MathMeteor;}{\f164\fnil Futura;}{\f1024\fnil American Heritage;}{\f2001\fnil Arial;}{\f2005\fnil Courier New;}{\f2010\fnil Times New Roman;}
{\f2011\fnil Wingdings;}{\f2515\fnil MT Extra;}{\f3409\fnil FoxPrint;}{\f11132\fnil InsigniaLQmono;}{\f11133\fnil InsigniaLQprop;}{\f14974\fnil LB Helvetica Black;}{\f14976\fnil L Helvetica Light;}}{\colortbl\red0\green0\blue0;\red0\green0\blue255;
\red0\green255\blue255;\red0\green255\blue0;\red255\green0\blue255;\red255\green0\blue0;\red255\green255\blue0;\red255\green255\blue255;}{\stylesheet{\f4\fs18 \sbasedon222\snext0 Normal;}}{\info{\title samplepostscript.msw}{\author
Computer Science Department}}\widowctrl\ftnbj \sectd \sbknone\linemod0\linex0\cols1\endnhere \pard\plain \qc \f4\fs18 {\plain \b\f21 Sample Rich Text Format Document\par
}\pard {\plain \f20 \par
}\pard \ri-80\sl-720\keep\keepn\absw570 {\caps\f20\fs92\dn6 T}{\plain \f20 \par
}\pard \qj {\plain \f20 his is a sample rich text format (RTF), document. This document was created using Microsoft Word and then printing the document to a RTF file. It illustrates the very basic text formatting effects that can be achieved using RTF.
\par
\par
}\pard \qj\li1440\ri1440\box\brdrs \shading1000 {\plain \f20 RTF }{\plain \b\f20 contains codes for producing advanced editing effects. Such as this indented, boxed, grayed background, entirely boldfaced paragraph.\par
}\pard \qj {\plain \f20 \par
Microsoft Word developed RTF for document transportability and gives a user access to the complete set of the effects that can be achieved using RTF. \par
}}
"""
test_file1 = os.path.join(tmp_user_path, 'test.rtf')
with open(test_file1, "wt") as f:
f.write(rtf_content)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("How was this document created?")
assert len(docs) == 4
assert 'Microsoft' in docs[1].page_content
assert os.path.normpath(docs[1].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
# Windows is not supported with EmbeddedDB. Please upvote the feature request if you want this: https://github.com/weaviate/weaviate-python-client/issues/239
@pytest.mark.parametrize("db_type", ['chroma'])
@wrap_test_forked
def test_url_more_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
url = 'https://edition.cnn.com/2023/08/19/europe/ukraine-f-16s-counteroffensive-intl/index.html'
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, url=url, fail_any_exception=True,
db_type=db_type)
assert db is not None
docs = db.similarity_search("Ukraine")
assert len(docs) == 4
assert 'Ukraine' in docs[0].page_content
kill_weaviate(db_type)
json_data = {
"quiz": {
"sport": {
"q1": {
"question": "Which one is correct team name in NBA?",
"options": [
"New York Bulls",
"Los Angeles Kings",
"Golden State Warriros",
"Huston Rocket"
],
"answer": "Huston Rocket"
}
},
"maths": {
"q1": {
"question": "5 + 7 = ?",
"options": [
"10",
"11",
"12",
"13"
],
"answer": "12"
},
"q2": {
"question": "12 - 8 = ?",
"options": [
"1",
"2",
"3",
"4"
],
"answer": "4"
}
}
}
}
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_json_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
# too slow:
# eval_filename = 'ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json'
# url = "https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/%s" % eval_filename
test_file1 = os.path.join(tmp_user_path, 'sample.json')
# download_simple(url, dest=test_file1)
with open(test_file1, 'wt') as f:
f.write(json.dumps(json_data))
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("NBA")
assert len(docs) == 2 if db_type == 'chroma' else 1
assert 'Bulls' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1)
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_jsonl_gz_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
# url = "https://huggingface.co/datasets/OpenAssistant/oasst1/resolve/main/2023-04-12_oasst_spam.messages.jsonl.gz"
test_file1 = os.path.join(tmp_user_path, 'sample.jsonl.gz')
# download_simple(url, dest=test_file1)
with gzip.open(test_file1, 'wb') as f:
f.write(json.dumps(json_data).encode())
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("NBA")
assert len(docs) == 2 if db_type == 'chroma' else 1
assert 'Bulls' in docs[0].page_content
assert os.path.normpath(docs[0].metadata['source']) == os.path.normpath(test_file1).replace('.gz', '')
kill_weaviate(db_type)
@wrap_test_forked
def test_url_more_subunit():
url = 'https://edition.cnn.com/2023/08/19/europe/ukraine-f-16s-counteroffensive-intl/index.html'
from langchain.document_loaders import UnstructuredURLLoader
docs1 = UnstructuredURLLoader(urls=[url]).load()
docs1 = [x for x in docs1 if x.page_content]
assert len(docs1) > 0
# Playwright and Selenium fails on cnn url
url_easy = 'https://github.com/h2oai/h2ogpt'
from langchain.document_loaders import PlaywrightURLLoader
docs1 = PlaywrightURLLoader(urls=[url_easy]).load()
docs1 = [x for x in docs1 if x.page_content]
assert len(docs1) > 0
from langchain.document_loaders import SeleniumURLLoader
docs1 = SeleniumURLLoader(urls=[url_easy]).load()
docs1 = [x for x in docs1 if x.page_content]
assert len(docs1) > 0
@wrap_test_forked
@pytest.mark.parametrize("db_type", db_types_full)
@pytest.mark.parametrize("num", [1000, 100000])
def test_many_text(db_type, num):
from langchain.docstore.document import Document
sources = [Document(page_content=str(i)) for i in range(0, num)]
hf_embedding_model = "fake"
# hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
# hf_embedding_model = 'hkunlp/instructor-large'
db = get_db(sources, db_type=db_type, langchain_mode='ManyTextData', hf_embedding_model=hf_embedding_model)
documents = get_documents(db)['documents']
assert len(documents) == num
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_youtube_audio_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://www.youtube.com/watch?v=cwjs1WAG9CM'
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, url=url,
fail_any_exception=True, db_type=db_type,
add_if_exists=False,
extract_frames=0)
assert db is not None
docs = db.similarity_search("Example")
assert len(docs) == 3 + (1 if db_type == 'chroma' else 0) or len(docs) == 4
assert 'structured output' in docs[0].page_content
assert url in docs[0].metadata['source']
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_youtube_full_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://www.youtube.com/shorts/JjdqlglRxrU'
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, url=url,
fail_any_exception=True, db_type=db_type,
add_if_exists=False)
assert db is not None
docs = db.similarity_search("cat")
assert len(docs) == 3 + (1 if db_type == 'chroma' else 0) or len(docs) == 4
assert 'couch' in str([x.page_content for x in docs])
assert url in docs[0].metadata['source'] or url in docs[0].metadata['original_source']
docs = db.similarity_search("cat", 100)
assert 'So I heard if you give a cat an egg' in str([x.page_content for x in docs])
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_mp3_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
test_file1 = os.path.join(tmp_user_path, 'sample.mp3.zip')
shutil.copy('tests/porsche.mp3.zip', test_file1)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type)
assert db is not None
docs = db.similarity_search("Porsche")
assert len(docs) == 1 + (1 if db_type == 'chroma' else 0)
assert 'Porsche Macan' in docs[0].page_content
assert 'porsche.mp3' in os.path.normpath(docs[0].metadata['source'])
kill_weaviate(db_type)
@pytest.mark.parametrize("db_type", db_types)
@wrap_test_forked
def test_mp4_add(db_type):
kill_weaviate(db_type)
from src.make_db import make_db_main
with tempfile.TemporaryDirectory() as tmp_persist_directory:
with tempfile.TemporaryDirectory() as tmp_user_path:
url = 'https://h2o-release.s3.amazonaws.com/h2ogpt/iG_jeMeUPBnUO6sx.mp4'
test_file1 = os.path.join(tmp_user_path, 'demo.mp4')
download_simple(url, dest=test_file1)
db, collection_name = make_db_main(persist_directory=tmp_persist_directory, user_path=tmp_user_path,
fail_any_exception=True, db_type=db_type,
enable_captions=True)
assert db is not None
docs = db.similarity_search("Gemini")
assert len(docs) == 3 + (1 if db_type == 'chroma' else 0)
assert 'Gemini' in str([x.page_content for x in docs])
assert 'demo.mp4' in os.path.normpath(docs[0].metadata['source'])
docs = db.similarity_search("AI", 100)
assert 'fun birthday party' in str([x.page_content for x in docs])
assert 'Gemini tries to design' in str([x.page_content for x in docs])
assert 'H2OAudioCaptionLoader' in str([x.metadata for x in docs])
assert 'H2OImageCaptionLoader' in str([x.metadata for x in docs])
assert '.jpg' in str([x.metadata for x in docs])
kill_weaviate(db_type)
@wrap_test_forked
def test_chroma_filtering():
# get test model so don't have to reload it each time
model, tokenizer, base_model, prompt_type = get_test_model()
# generic settings true for all cases
requests_state1 = {'username': 'foo'}
verbose1 = True
max_raw_chunks = None
api = False
n_jobs = -1
db_type1 = 'chroma'
load_db_if_exists1 = True
use_openai_embedding1 = False
migrate_embedding_model_or_db1 = False
auto_migrate_db1 = False
def get_userid_auth_fake(requests_state1, auth_filename=None, auth_access=None, guest_name=None, **kwargs):
return str(uuid.uuid4())
other_kwargs = dict(load_db_if_exists1=load_db_if_exists1,
db_type1=db_type1,
use_openai_embedding1=use_openai_embedding1,
migrate_embedding_model_or_db1=migrate_embedding_model_or_db1,
auto_migrate_db1=auto_migrate_db1,
verbose1=verbose1,
get_userid_auth1=get_userid_auth_fake,
max_raw_chunks=max_raw_chunks,
api=api,
n_jobs=n_jobs,
)
mydata_mode1 = LangChainMode.MY_DATA.value
from src.make_db import make_db_main
for chroma_new in [False, True]:
print("chroma_new: %s" % chroma_new, flush=True)
if chroma_new:
# fresh, so chroma >= 0.4
user_path = make_user_path_test()
from langchain.vectorstores import Chroma
db, collection_name = make_db_main(user_path=user_path)
assert isinstance(db, Chroma)
hf_embedding_model = 'hkunlp/instructor-xl'
langchain_mode1 = collection_name
query = 'What is h2oGPT?'
else:
# old, was with chroma < 0.4
# has no user_path
db, collection_name = make_db_main(download_some=True)
from src.gpt_langchain import ChromaMig
assert isinstance(db, ChromaMig)
assert ChromaMig.__name__ in str(db)
query = 'What is whisper?'
hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
langchain_mode1 = collection_name
db1s = {langchain_mode1: [None] * length_db1(), mydata_mode1: [None] * length_db1()}
dbs1 = {langchain_mode1: db}
langchain_modes = [langchain_mode1]
langchain_mode_paths = dict(langchain_mode1=None)
langchain_mode_types = dict(langchain_modes='shared')
selection_docs_state1 = dict(langchain_modes=langchain_modes,
langchain_mode_paths=langchain_mode_paths,
langchain_mode_types=langchain_mode_types)
run_db_kwargs = dict(query=query,
db=db,
use_openai_model=False, use_openai_embedding=False, text_limit=None,
hf_embedding_model=hf_embedding_model,
db_type=db_type1,
langchain_mode_paths=langchain_mode_paths,
langchain_mode_types=langchain_mode_types,
langchain_mode=langchain_mode1,
langchain_agents=[],
llamacpp_dict={},
model=model,
tokenizer=tokenizer,
model_name=base_model,
prompt_type=prompt_type,
top_k_docs=10, # 4 leaves out docs for test in some cases, so use 10
cut_distance=1.8, # default leaves out some docs in some cases
)
# GET_CHAIN etc.
for answer_with_sources in [-1, True]:
print("answer_with_sources: %s" % answer_with_sources, flush=True)
# mimic nochat-API or chat-UI
append_sources_to_answer = answer_with_sources != -1
for doc_choice in ['All', 1, 2]:
if doc_choice == 'All':
document_choice = [DocumentChoice.ALL.value]
else:
docs = [x['source'] for x in db.get()['metadatas']]
if doc_choice == 1:
document_choice = docs[:doc_choice]
else:
# ensure don't get dup
docs = sorted(set(docs))
document_choice = docs[:doc_choice]
print("doc_choice: %s" % doc_choice, flush=True)
for langchain_action in [LangChainAction.QUERY.value, LangChainAction.SUMMARIZE_MAP.value]:
print("langchain_action: %s" % langchain_action, flush=True)
for document_subset in [DocumentSubset.Relevant.name, DocumentSubset.TopKSources.name,
DocumentSubset.RelSources.name]:
print("document_subset: %s" % document_subset, flush=True)
ret = _run_qa_db(**run_db_kwargs,
langchain_action=langchain_action,
document_subset=document_subset,
document_choice=document_choice,
answer_with_sources=answer_with_sources,
append_sources_to_answer=append_sources_to_answer,
)
rets = check_ret(ret)
rets1 = rets[0]
if chroma_new:
if answer_with_sources == -1:
assert len(rets1) == 8 and (
'h2oGPT' in rets1['response'] or 'H2O GPT' in rets1['response'] or 'H2O.ai' in
rets1['response'])
else:
assert len(rets1) == 8 and (
'h2oGPT' in rets1['response'] or 'H2O GPT' in rets1['response'] or 'H2O.ai' in
rets1['response'])
if document_subset == DocumentSubset.Relevant.name:
assert 'h2oGPT' in rets1['sources']
else:
if answer_with_sources == -1:
assert len(rets1) == 8 and (
'whisper' in rets1['response'].lower() or
'phase' in rets1['response'].lower() or
'generate' in rets1['response'].lower() or
'statistic' in rets1['response'].lower() or
'a chat bot that' in rets1['response'].lower() or
'non-centrality parameter' in rets1['response'].lower() or
'.pdf' in rets1['response'].lower() or
'gravitational' in rets1['response'].lower()
)
else:
assert len(rets1) == 8 and (
'whisper' in rets1['response'].lower() or
'phase' in rets1['response'].lower() or
'generate' in rets1['response'].lower() or
'statistic' in rets1['response'].lower() or
'.pdf' in rets1['response'].lower())
if document_subset == DocumentSubset.Relevant.name:
assert 'whisper' in rets1['sources'] or 'unbiased' in rets1[
'sources'] or 'approximate' in rets1['sources']
if answer_with_sources == -1:
if document_subset == DocumentSubset.Relevant.name:
assert 'score' in rets1['sources'][0] and 'content' in rets1['sources'][
0] and 'source' in rets1['sources'][0]
if doc_choice in [1, 2]:
if langchain_action == 'Summarize':
assert len(set(flatten_list([x['source'].split(docs_joiner_default) for x in
rets1['sources']]))) >= doc_choice
else:
assert len(set([x['source'] for x in rets1['sources']])) == doc_choice
else:
assert len(set([x['source'] for x in rets1['sources']])) >= 1
elif document_subset == DocumentSubset.RelSources.name:
if doc_choice in [1, 2]:
assert len(set([x['source'] for x in rets1['sources']])) <= doc_choice
else:
if langchain_action == 'Summarize':
assert len(set(flatten_list(
[x['source'].split(docs_joiner_default) for x in rets1['sources']]))) >= 2
else:
assert len(set([x['source'] for x in rets1['sources']])) >= 2
else:
# TopK may just be 1 doc because of many chunks from that doc
# if top_k_docs=-1 might get more
assert len(set([x['source'] for x in rets1['sources']])) >= 1
# SHOW DOC
single_document_choice1 = [x['source'] for x in db.get()['metadatas']][0]
text_context_list1 = []
pdf_height = 800
for view_raw_text_checkbox1 in [True, False]:
print("view_raw_text_checkbox1: %s" % view_raw_text_checkbox1, flush=True)
from src.gradio_runner import show_doc
show_ret = show_doc(db1s, selection_docs_state1, requests_state1,
langchain_mode1,
single_document_choice1,
view_raw_text_checkbox1,
text_context_list1,
pdf_height,
dbs1=dbs1,
hf_embedding_model1=hf_embedding_model,
**other_kwargs
)
assert len(show_ret) == 5
if chroma_new:
assert1 = show_ret[4]['value'] is not None and 'README.md' in show_ret[4]['value']
assert2 = show_ret[3]['value'] is not None and 'h2oGPT' in show_ret[3]['value']
assert assert1 or assert2
else:
assert1 = show_ret[4]['value'] is not None and single_document_choice1 in show_ret[4]['value']
assert2 = show_ret[3]['value'] is not None and single_document_choice1 in show_ret[3]['value']
assert assert1 or assert2
@pytest.mark.parametrize("data_kind", [
'simple',
'helium1',
'helium2',
'helium3',
'helium4',
'helium5',
])
@wrap_test_forked
def test_merge_docs(data_kind):
model_max_length = 4096
max_input_tokens = 1024
docs_joiner = docs_joiner_default
docs_token_handling = docs_token_handling_default
tokenizer = FakeTokenizer(model_max_length=model_max_length)
from langchain.docstore.document import Document
if data_kind == 'simple':
texts = texts_simple
elif data_kind == 'helium1':
texts = texts_helium1
elif data_kind == 'helium2':
texts = texts_helium2
elif data_kind == 'helium3':
texts = texts_helium3
elif data_kind == 'helium4':
texts = texts_helium4
elif data_kind == 'helium5':
texts = texts_helium5
else:
raise RuntimeError("BAD")
docs_with_score = [(Document(page_content=page_content, metadata={"source": "%d" % pi}), 1.0) for pi, page_content
in enumerate(texts)]
docs_with_score_new, max_docs_tokens = (
split_merge_docs(docs_with_score, tokenizer=tokenizer, max_input_tokens=max_input_tokens,
docs_token_handling=docs_token_handling, joiner=docs_joiner, verbose=True))
text_context_list = [x[0].page_content for x in docs_with_score_new]
tokens = [get_token_count(x + docs_joiner, tokenizer) for x in text_context_list]
print(tokens)
if data_kind == 'simple':
assert len(docs_with_score_new) == 1
assert all([x < max_input_tokens for x in tokens])
elif data_kind == 'helium1':
assert len(docs_with_score_new) == 4
assert all([x < max_input_tokens for x in tokens])
elif data_kind == 'helium2':
assert len(docs_with_score_new) == 8
assert all([x < max_input_tokens for x in tokens])
elif data_kind == 'helium3':
assert len(docs_with_score_new) == 5
assert all([x < max_input_tokens for x in tokens])
elif data_kind == 'helium4':
assert len(docs_with_score_new) == 5
assert all([x < max_input_tokens for x in tokens])
elif data_kind == 'helium5':
assert len(docs_with_score_new) == 3
assert all([x < max_input_tokens for x in tokens])
@wrap_test_forked
def test_crawl():
from src.gpt_langchain import Crawler
final_urls = Crawler(urls=['https://github.com/h2oai/h2ogpt'], verbose=True).run()
assert 'https://github.com/h2oai/h2ogpt/blob/main/docs/README_GPU.md' in final_urls
print(final_urls)
if __name__ == '__main__':
pass
|