├── LICENSE ├── RAG ├── __pycache__ │ └── utils.cpython-310.pyc ├── artwork_data.tsv ├── artwork_img │ ├── .ipynb_checkpoints │ │ ├── 10161-checkpoint.jpg │ │ ├── 35574-checkpoint.jpg │ │ ├── 37121-checkpoint.jpg │ │ ├── 38682-checkpoint.jpg │ │ ├── 42039-checkpoint.jpg │ │ └── 43121-checkpoint.jpg │ ├── 10161.jpg │ ├── 10601.jpg │ ├── 10671.jpg │ ├── 1072.jpg │ ├── 10896.jpg │ ├── 12020.jpg │ ├── 13577.jpg │ ├── 13778.jpg │ ├── 13939.jpg │ ├── 14283.jpg │ ├── 14427.jpg │ ├── 15167.jpg │ ├── 15870.jpg │ ├── 16101.jpg │ ├── 16215.jpg │ ├── 16715.jpg │ ├── 16970.jpg │ ├── 17669.jpg │ ├── 17760.jpg │ ├── 17960.jpg │ ├── 18130.jpg │ ├── 18149.jpg │ ├── 19159.jpg │ ├── 19341.jpg │ ├── 20326.jpg │ ├── 21230.jpg │ ├── 21905.jpg │ ├── 22186.jpg │ ├── 22651.jpg │ ├── 22740.jpg │ ├── 23438.jpg │ ├── 23588.jpg │ ├── 23901.jpg │ ├── 24321.jpg │ ├── 2436.jpg │ ├── 25400.jpg │ ├── 25427.jpg │ ├── 2545.jpg │ ├── 25484.jpg │ ├── 2550.jpg │ ├── 25609.jpg │ ├── 26587.jpg │ ├── 27400.jpg │ ├── 27493.jpg │ ├── 27521.jpg │ ├── 27782.jpg │ ├── 28167.jpg │ ├── 283.jpg │ ├── 29519.jpg │ ├── 29537.jpg │ ├── 30119.jpg │ ├── 30738.jpg │ ├── 30878.jpg │ ├── 3100.jpg │ ├── 3118.jpg │ ├── 31182.jpg │ ├── 32091.jpg │ ├── 32275.jpg │ ├── 32845.jpg │ ├── 34486.jpg │ ├── 34602.jpg │ ├── 3483.jpg │ ├── 34926.jpg │ ├── 35186.jpg │ ├── 35574.jpg │ ├── 36035.jpg │ ├── 36711.jpg │ ├── 37121.jpg │ ├── 37504.jpg │ ├── 3764.jpg │ ├── 38682.jpg │ ├── 3917.jpg │ ├── 3945.jpg │ ├── 39561.jpg │ ├── 40334.jpg │ ├── 4073.jpg │ ├── 40827.jpg │ ├── 40945.jpg │ ├── 41303.jpg │ ├── 41805.jpg │ ├── 4195.jpg │ ├── 42039.jpg │ ├── 43111.jpg │ ├── 43121.jpg │ ├── 466.jpg │ ├── 4880.jpg │ ├── 5522.jpg │ ├── 5570.jpg │ ├── 7440.jpg │ ├── 7460.jpg │ ├── 7852.jpg │ ├── 8106.jpg │ ├── 8154.jpg │ ├── 8308.jpg │ ├── 8432.jpg │ ├── 8439.jpg │ ├── 8657.jpg │ ├── 9086.jpg │ ├── 9512.jpg │ └── 9740.jpg ├── image_rag.ipynb ├── multimodal_rag.ipynb ├── test_img.jpg ├── text_rag.ipynb └── utils.py ├── README.md ├── README_zh.md ├── figures ├── SODAtitle_crop.pdf ├── Soda.png ├── image_retrieve.mp4 ├── soda_architecture.png ├── soda_title.png ├── test_pics │ ├── 1.jpg │ ├── 2.jpg │ ├── 3.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ └── 9.jpg ├── text_retrieve.mp4 ├── web_search.gif └── web_search.mp4 ├── mllm ├── IXC2.py ├── __pycache__ │ └── soda_mllm.cpython-310.pyc ├── soda_mllm.py └── test_img.jpg ├── requirements.txt ├── service ├── __pycache__ │ └── utils.cpython-310.pyc ├── rerank.ipynb └── utils.py ├── web_search ├── Bing_API.ipynb ├── Google_API.ipynb ├── Serper_API.ipynb ├── __pycache__ │ └── utils.cpython-310.pyc └── utils.py └── web_ui ├── database ├── 1713354159_521 │ └── RAR_arXiv.pdf ├── 1713354159_521_database │ ├── 936abf26-b455-4d19-a124-b9ffeb5be592 │ │ ├── data_level0.bin │ │ ├── header.bin │ │ ├── length.bin │ │ └── link_lists.bin │ └── chroma.sqlite3 ├── ff122a0aed7ded202b4cdad150eb8aff35500a8c │ └── soda_title.png └── tmp7hym5i1j ├── soda_title.png └── web_ui.py /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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Paul Getty Museum, Los Angeles, FORM: painting, TYPE: portrait, SCHOOL: Italian, TIMELINE: 1701-1750 12 | 3917 3917.jpg AUTHOR: BERJON, Antoine, BORN-DIED: (b. 1754, Lyon, d. 1843, Lyon), TITLE: Still-Life, DATE: -, TECHNIQUE: Oil on canvas, 46 x 56 cm, LOCATION: Private collection, FORM: painting, TYPE: still-life, SCHOOL: French, TIMELINE: 1801-1850 13 | 3945 3945.jpg AUTHOR: BERNINI, Gian Lorenzo, BORN-DIED: (b. 1598, Napoli, d. 1680, Roma), TITLE: Interior view, DATE: 1658-61, TECHNIQUE: Photo, LOCATION: Sant'Andrea al Quirinale, Rome, FORM: architecture, TYPE: religious, SCHOOL: Italian, TIMELINE: 1601-1650 14 | 4073 4073.jpg AUTHOR: BERRUGUETE, Alonso, BORN-DIED: (b. 1488, Paredes de Nava, d. 1561, Valladolid), TITLE: Adoration of the Magi, DATE: 1526-32, TECHNIQUE: Polychrome wood, LOCATION: National Museum of Religious Carvings, Valladolid, FORM: sculpture, TYPE: religious, SCHOOL: Spanish, TIMELINE: 1501-1550 15 | 4195 4195.jpg AUTHOR: BEZZUOLI, Giuseppe, BORN-DIED: (b. 1784, Firenze, d. 1855, Firenze), TITLE: Venus Crossing the Sea on a Shell, DATE: 1830s, TECHNIQUE: Oil on wood panel, 33 x 41 cm, LOCATION: Private collection, FORM: painting, TYPE: mythological, SCHOOL: Italian, TIMELINE: 1801-1850 16 | 4880 4880.jpg AUTHOR: BOSCH, Hieronymus, BORN-DIED: (b. ca. 1450, 's-Hertogenbosch, d. 1516, 's-Hertogenbosch), TITLE: Triptych of Garden of Earthly Delights (detail), DATE: c. 1500, TECHNIQUE: Oil on panel, LOCATION: Museo del Prado, Madrid, FORM: painting, TYPE: religious, SCHOOL: Netherlandish, TIMELINE: 1451-1500 17 | 5522 5522.jpg AUTHOR: BÖCKLIN, Arnold, BORN-DIED: (b. 1827, Basel, d. 1901, Firenze), TITLE: Campagna Landscape, DATE: 1857-58, TECHNIQUE: Oil on canvas, 88 x 105 cm, LOCATION: Nationalgalerie, Berlin, FORM: painting, TYPE: landscape, SCHOOL: Swiss, TIMELINE: 1851-1900 18 | 5570 5570.jpg AUTHOR: BRAMANTE, Donato, BORN-DIED: (b. 1444, Fermignano, d. 1514, Roma), TITLE: Umbrella vault, DATE: 1493, TECHNIQUE: -, LOCATION: Santa Maria delle Grazie, Milan, FORM: architecture, TYPE: interior, SCHOOL: Italian, TIMELINE: 1451-1500 19 | 7440 7440.jpg AUTHOR: CARPACCIO, Vittore, BORN-DIED: (b. 1472, Venezia, d. 1526, Capodistria), TITLE: The Lion of St Mark (detail), DATE: 1516, TECHNIQUE: Tempera on canvas, LOCATION: Palazzo Ducale, Venice, FORM: painting, TYPE: historical, SCHOOL: Italian, TIMELINE: 1501-1550 20 | 7460 7460.jpg AUTHOR: CARPEAUX, Jean-Baptiste, BORN-DIED: (b. 1827, Valenciennes, d. 1875, Courbevoie), TITLE: Ugolino and His Sons (detail), DATE: 1865-67, TECHNIQUE: Marble, LOCATION: Metropolitan Museum of Art, New York, FORM: sculpture, TYPE: other, SCHOOL: French, TIMELINE: 1851-1900 21 | 7852 7852.jpg AUTHOR: CAVALLINO, Bernardo, BORN-DIED: (b. 1616, Napoli, d. ca. 1656, Napoli), TITLE: Curing of Tobias, DATE: -, TECHNIQUE: Oil on canvas, 76 x 103 cm, LOCATION: Museo del Prado, Madrid, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1601-1650 22 | 8106 8106.jpg AUTHOR: CÉZANNE, Paul, BORN-DIED: (b. 1839, Aix-en-Provence, d. 1906, Aix-en-Provence), TITLE: Mont Sainte-Victoire, DATE: c. 1887, TECHNIQUE: Oil on canvas, 67 x 92 cm, LOCATION: Courtauld Gallery, London, FORM: painting, TYPE: landscape, SCHOOL: French, TIMELINE: 1851-1900 23 | 8154 8154.jpg AUTHOR: CÉZANNE, Paul, BORN-DIED: (b. 1839, Aix-en-Provence, d. 1906, Aix-en-Provence), TITLE: The Large Bathers, DATE: 1898-1905, TECHNIQUE: Oil on canvas, 208 x 249 cm, LOCATION: Museum of Art, Philadelphia, FORM: painting, TYPE: other, SCHOOL: French, TIMELINE: 1851-1900 24 | 8308 8308.jpg AUTHOR: CHAUVEAU, François, BORN-DIED: (b. 1613, Paris, d. 1676, Paris), TITLE: Frontispiece to 'Cabinet de M. de Scudéry', DATE: 1646, TECHNIQUE: Engraving, LOCATION: Bibliothèque Nationale, Paris, FORM: graphics, TYPE: other, SCHOOL: French, TIMELINE: 1651-1700 25 | 8432 8432.jpg AUTHOR: CIMA da Conegliano, BORN-DIED: (b. ca. 1459, Conegliano, d. 1517/18, Conegliano), TITLE: St Christopher with the Infant Christ and St Peter, DATE: 1504-06, TECHNIQUE: Oil on poplar panel, 73 x 56 cm, LOCATION: Private collection, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1451-1500 26 | 8439 8439.jpg AUTHOR: CIMA da Conegliano, BORN-DIED: (b. ca. 1459, Conegliano, d. 1517/18, Conegliano), TITLE: St John the Baptist, DATE: c. 1500, TECHNIQUE: Stained glass, LOCATION: Basilica dei Santi Giovanni e Paolo, Venice, FORM: stained-glass, TYPE: religious, SCHOOL: Italian, TIMELINE: 1451-1500 27 | 8657 8657.jpg AUTHOR: CLEVE, Joos van, BORN-DIED: (b. ca. 1485, Antwerpen, d. 1540, Antwerpen), TITLE: Self-Portrait, DATE: c. 1519, TECHNIQUE: Oil on panel, 38 x 27 cm, LOCATION: Museo Thyssen-Bornemisza, Madrid, FORM: painting, TYPE: portrait, SCHOOL: Flemish, TIMELINE: 1501-1550 28 | 9086 9086.jpg AUTHOR: CORNELISZ VAN OOSTSANEN, Jacob, BORN-DIED: (b. ca. 1472, Oostzan, d. 1533, Amsterdam), TITLE: Mary Magdalen, DATE: 1519, TECHNIQUE: Panel, 49 x 40 cm, LOCATION: Art Museum, Saint Louis, FORM: painting, TYPE: religious, SCHOOL: Netherlandish, TIMELINE: 1501-1550 29 | 9512 9512.jpg AUTHOR: COUSTOU, Nicolas, BORN-DIED: (b. 1658, Lyon, d. 1733, Paris), TITLE: Louis XIII Kneeling, DATE: 1712-28, TECHNIQUE: Marble, LOCATION: Notre-Dame Cathedral, Paris, FORM: sculpture, TYPE: religious, SCHOOL: French, TIMELINE: 1701-1750 30 | 9740 9740.jpg AUTHOR: CRANACH, Lucas the Elder, BORN-DIED: (b. 1472, Kronach, d. 1553, Weimar), TITLE: The Paradise, DATE: 1530, TECHNIQUE: Limewood, 81 x 114 cm, LOCATION: Kunsthistorisches Museum, Vienna, FORM: painting, TYPE: religious, SCHOOL: German, TIMELINE: 1501-1550 31 | 10161 10161.jpg AUTHOR: CUYP, Aelbert, BORN-DIED: (b. 1620, Dordrecht, d. 1691, Dordrecht), TITLE: River Scene with Milking Woman, DATE: c. 1646, TECHNIQUE: Oil on wood, 48,3 x 74,6 cm, LOCATION: Staatliche Kunsthalle, Karlsruhe, FORM: painting, TYPE: landscape, SCHOOL: Dutch, TIMELINE: 1651-1700 32 | 10601 10601.jpg AUTHOR: DECAMPS, Alexandre Gabriel, BORN-DIED: (b. 1803, Paris, d. 1860, Fontainebleau), TITLE: The Defeat of the Cimbri, DATE: 1833, TECHNIQUE: Oil on canvas, 130 x 195 cm, LOCATION: Musée du Louvre, Paris, FORM: painting, TYPE: historical, SCHOOL: French, TIMELINE: 1801-1850 33 | 10671 10671.jpg AUTHOR: DEGAS, Edgar, BORN-DIED: (b. 1834, Paris, d. 1917, Paris), TITLE: The Dance Class, DATE: 1874, TECHNIQUE: Oil on canvas, 84 x 77 cm, LOCATION: Metropolitan Museum of Art, New York, FORM: painting, TYPE: genre, SCHOOL: French, TIMELINE: 1851-1900 34 | 10896 10896.jpg AUTHOR: DELEMER, Jean, BORN-DIED: (active mid 15th century in Tournai and Brussels), TITLE: Female Figure from the Tomb of Isabella of Bourbon, DATE: 1476, TECHNIQUE: Bronze with black laquer patina, height 58 cm, LOCATION: Rijksmuseum, Amsterdam, FORM: sculpture, TYPE: other, SCHOOL: Flemish, TIMELINE: 1401-1450 35 | 12020 12020.jpg AUTHOR: DÜRER, Albrecht, BORN-DIED: (b. 1471, Nürnberg, d. 1528, Nürnberg), TITLE: Adoration of the Magi (detail), DATE: 1504, TECHNIQUE: Oil on wood, LOCATION: Galleria degli Uffizi, Florence, FORM: painting, TYPE: religious, SCHOOL: German, TIMELINE: 1501-1550 36 | 13577 13577.jpg AUTHOR: FRAGONARD, Jean-Honoré, BORN-DIED: (b. 1732, Grasse, d. 1806, Paris), TITLE: Marie-Madeleine Guimard (Fanciful Figure), DATE: 1769, TECHNIQUE: Oil on canvas, 82 x 65 cm, LOCATION: Musée du Louvre, Paris, FORM: painting, TYPE: portrait, SCHOOL: French, TIMELINE: 1751-1800 37 | 13778 13778.jpg AUTHOR: FRANCKEN, Frans II, BORN-DIED: (b. 1581, Antwerpen, d. 1642, Antwerpen), TITLE: Feast of Esther, DATE: -, TECHNIQUE: Oil on copper, 55 x 69 cm, LOCATION: Národní Galerie, Prague, FORM: painting, TYPE: mythological, SCHOOL: Flemish, TIMELINE: 1601-1650 38 | 13939 13939.jpg AUTHOR: FRUEAUF, Rueland the Elder, BORN-DIED: (b. 1440/45, Passau, d. 1507, Passau), TITLE: The Annunciation (detail), DATE: c. 1495, TECHNIQUE: Tempera on pine panel, LOCATION: Szépmûvészeti Múzeum, Budapest, FORM: painting, TYPE: religious, SCHOOL: Austrian, TIMELINE: 1451-1500 39 | 14283 14283.jpg AUTHOR: GAUGUIN, Paul, BORN-DIED: (b. 1848, Paris, d. 1903, Atuona, Hiva Oa, French Polynesia), TITLE: Study of a Nude (Suzanne Sewing), DATE: 1880, TECHNIQUE: Oil on canvas, 115 x 80 cm, LOCATION: Ny Carlsberg Glyptotek, Copenhagen, FORM: painting, TYPE: study, SCHOOL: French, TIMELINE: 1851-1900 40 | 14427 14427.jpg AUTHOR: GAUGUIN, Paul, BORN-DIED: (b. 1848, Paris, d. 1903, Atuona, Hiva Oa, French Polynesia), TITLE: The God Taaroa with One of His Wifes, DATE: 1892-93, TECHNIQUE: Watercolour, 215 x 170 mm, LOCATION: Musée du Louvre, Paris, FORM: graphics, TYPE: other, SCHOOL: French, TIMELINE: 1851-1900 41 | 15167 15167.jpg AUTHOR: GIJSELS, Pieter, BORN-DIED: (b. 1621, Antwerpen, d. 1690, Antwerpen), TITLE: Village Scene, DATE: -, TECHNIQUE: Oil on copper, 13 x 17 cm, LOCATION: Private collection, FORM: painting, TYPE: landscape, SCHOOL: Flemish, TIMELINE: 1651-1700 42 | 15870 15870.jpg AUTHOR: GIOTTO di Bondone, BORN-DIED: (b. 1267, Vespignano, d. 1337, Firenze), TITLE: The Stefaneschi Triptych: Martyrdom of St Paul, DATE: c. 1330, TECHNIQUE: Tempera on panel, LOCATION: Pinacoteca, Vatican, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1301-1350 43 | 16101 16101.jpg AUTHOR: GIROMETTI, Giuseppe, BORN-DIED: (b. 1760, Roma, d. 1851, Roma), TITLE: Nessus Abducting Deianira, DATE: 1815-25, TECHNIQUE: Sardonyx, mounted in gold as a pendant, 405 x 480 mm, LOCATION: Metropolitan Museum of Art, New York, FORM: sculpture, TYPE: mythological, SCHOOL: Italian, TIMELINE: 1801-1850 44 | 16215 16215.jpg AUTHOR: GIUSTI, Antonio, BORN-DIED: (b. 1479, San Martino, d. 1519, Tours), TITLE: Head of St Peter, DATE: 1509, TECHNIQUE: Terracotta, height 30 cm, LOCATION: Musée du Louvre, Paris, FORM: sculpture, TYPE: religious, SCHOOL: Italian, TIMELINE: 1501-1550 45 | 16715 16715.jpg AUTHOR: GOGH, Vincent van, BORN-DIED: (b. 1853, Groot Zundert, d. 1890, Auvers-sur-Oise), TITLE: Wheat Field with Cypresses, DATE: June 1889, Saint-Rémy, TECHNIQUE: Black chalk and pen, 470 x 620 mm, LOCATION: Rijksmuseum Vincent van Gogh, Amsterdam, FORM: graphics, TYPE: landscape, SCHOOL: Dutch, TIMELINE: 1851-1900 46 | 16970 16970.jpg AUTHOR: GOSSART, Jan, BORN-DIED: (b. ca. 1478, Maubeuge, d. 1532, Middelburg), TITLE: The Mocking of Christ or the Man of Sorrows, DATE: c. 1525, TECHNIQUE: Etching on iron, second state, 200 x 148 mm, LOCATION: Museum of Fine Arts, Boston, FORM: graphics, TYPE: religious, SCHOOL: Flemish, TIMELINE: 1501-1550 47 | 17669 17669.jpg AUTHOR: GRECO, El, BORN-DIED: (b. 1541, Candia, d. 1614, Toledo), TITLE: The Crucifixion, DATE: 1596-1600, TECHNIQUE: Oil on canvas, 312 x 169 cm, LOCATION: Museo del Prado, Madrid, FORM: painting, TYPE: religious, SCHOOL: Spanish, TIMELINE: 1551-1600 48 | 17760 17760.jpg AUTHOR: GRECO, El, BORN-DIED: (b. 1541, Candia, d. 1614, Toledo), TITLE: Laocoön (detail), DATE: 1610, TECHNIQUE: Oil on canvas, LOCATION: National Gallery of Art, Washington, FORM: painting, TYPE: mythological, SCHOOL: Spanish, TIMELINE: 1551-1600 49 | 17960 17960.jpg AUTHOR: GRÜNEWALD, Matthias, BORN-DIED: (b. 1470/80, Würzburg, d. 1528, Halle), TITLE: The Annunciation (detail), DATE: c. 1515, TECHNIQUE: Oil on wood, LOCATION: Musée d'Unterlinden, Colmar, FORM: painting, TYPE: religious, SCHOOL: German, TIMELINE: 1501-1550 50 | 18130 18130.jpg AUTHOR: GUARDI, Francesco, BORN-DIED: (b. 1712, Venezia, d. 1793, Venezia), TITLE: The Canal Grande with San Simeone Piccolo, DATE: after 1780, TECHNIQUE: Oil on canvas, 63 x 89 cm, LOCATION: Akademie der bildenden Künste, Vienna, FORM: painting, TYPE: landscape, SCHOOL: Italian, TIMELINE: 1751-1800 51 | 18149 18149.jpg AUTHOR: GUARDI, Gianantonio, BORN-DIED: (b. 1699, Wien, d. 1760, Venezia), TITLE: Madonna and Child with Saints, DATE: 1746-48, TECHNIQUE: Oil on canvas, 234 x 154 cm, LOCATION: Parish Church, Belvedere di Aquileia, FORM: painting, TYPE: landscape, SCHOOL: Italian, TIMELINE: 1701-1750 52 | 19159 19159.jpg AUTHOR: HOLBEIN, Hans the Younger, BORN-DIED: (b. 1497, Augsburg, d. 1543, London), TITLE: Robert Cheseman, DATE: 1533, TECHNIQUE: Oil on oak, 59 x 63 cm, LOCATION: Mauritshuis, The Hague, FORM: painting, TYPE: portrait, SCHOOL: German, TIMELINE: 1501-1550 53 | 19341 19341.jpg AUTHOR: HONTHORST, Gerrit van, BORN-DIED: (b. 1590, Utrecht, d. 1656, Utrecht), TITLE: Portrait of a Gentleman, DATE: 1631, TECHNIQUE: Oil on canvas, 70 x 58 cm, LOCATION: Private collection, FORM: painting, TYPE: portrait, SCHOOL: Dutch, TIMELINE: 1601-1650 54 | 20326 20326.jpg AUTHOR: KNYFF, Jacob, BORN-DIED: (b. 1639, Haarlem, d. 1681, London), TITLE: An English Ship and other Shipping off Castle Cornet, Guernsey, DATE: -, TECHNIQUE: Oil on canvas, 218 x 165 cm, LOCATION: Private collection, FORM: painting, TYPE: landscape, SCHOOL: Dutch, TIMELINE: 1651-1700 55 | 21230 21230.jpg AUTHOR: LEONARDO da Vinci, BORN-DIED: (b. 1452, Vinci, d. 1519, Cloux, near Amboise), TITLE: Landscape near Pisa, DATE: 1502-03, TECHNIQUE: Red chalk on paper, 211 x 150 mm, LOCATION: Biblioteca Nacional, Madrid, FORM: graphics, TYPE: landscape, SCHOOL: Italian, TIMELINE: 1451-1500 56 | 21905 21905.jpg AUTHOR: LISS, Johann, BORN-DIED: (b. ca. 1590, Oldenburg, d. 1631, Verona), TITLE: The Ecstasy of St Paul, DATE: 1628-29, TECHNIQUE: Oil on canvas, 80 x 58 cm, LOCATION: Staatliche Museen, Berlin, FORM: painting, TYPE: religious, SCHOOL: German, TIMELINE: 1601-1650 57 | 22186 22186.jpg AUTHOR: LORENZETTI, Ambrogio, BORN-DIED: (b. ca. 1290, Siena, d. 1348, Siena), TITLE: Nursing Madonna, DATE: c. 1330, TECHNIQUE: Tempera on wood, 90 x 48 cm, LOCATION: Palazzo Arcivescovile, Siena, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1301-1350 58 | 22651 22651.jpg AUTHOR: MACTAGGART, William, BORN-DIED: (b. 1835, Aros, Kintyre, Strathclyde, d. 1910, Broomiknowe, Lothian), TITLE: The Storm, DATE: 1890, TECHNIQUE: Oil on canvas, 122 x 183 cm, LOCATION: National Gallery of Scotland, Edinburgh, FORM: painting, TYPE: landscape, SCHOOL: Scottish, TIMELINE: 1851-1900 59 | 22740 22740.jpg AUTHOR: MAGNASCO, Alessandro, BORN-DIED: (b. 1667, Genova, d. 1749, Genova), TITLE: The Seashore, DATE: 1720s, TECHNIQUE: Oil on canvas, 158 x 211 cm, LOCATION: The Hermitage, St. Petersburg, FORM: painting, TYPE: landscape, SCHOOL: Italian, TIMELINE: 1701-1750 60 | 23438 23438.jpg AUTHOR: MARREL, Jacob, BORN-DIED: (b. ca. 1613, Frankenthal, d. 1681, Frankfurt), TITLE: Still-Life, DATE: 1669, TECHNIQUE: Oil on canvas, 36 x 43 cm, LOCATION: Private collection, FORM: painting, TYPE: still-life, SCHOOL: German, TIMELINE: 1651-1700 61 | 23588 23588.jpg AUTHOR: MASO DI BANCO, BORN-DIED: (active 1320-50 in Firenze), TITLE: Descent of Mary's Girdle to the Apostle Thomas, DATE: c. 1337-39, TECHNIQUE: Panel, LOCATION: Staatliche Museen, Berlin, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1301-1350 62 | 23901 23901.jpg AUTHOR: MASTER of Frankfurt, BORN-DIED: (b. 1460, d. 1533, Antwerpen), TITLE: St Christopher (detail), DATE: c. 1495, TECHNIQUE: Oil on panel, LOCATION: Private collection, FORM: painting, TYPE: religious, SCHOOL: Flemish, TIMELINE: 1501-1550 63 | 24321 24321.jpg AUTHOR: MATHAM, Theodor, BORN-DIED: (b. ca. 1605, Haarlem, d. 1676, Amsterdam), TITLE: Merry Toper, DATE: 1629-30, TECHNIQUE: Engraving, 216 x 170 mm, LOCATION: Graphische Sammlung Albertina, Vienna, FORM: graphics, TYPE: genre, SCHOOL: Dutch, TIMELINE: 1601-1650 64 | 25400 25400.jpg AUTHOR: METSU, Gabriel, BORN-DIED: (b. 1629, Leiden, d. 1667, Amsterdam), TITLE: The Dismissal of Hagar, DATE: 1653-54, TECHNIQUE: Oil on canvas, 112 x 86 cm, LOCATION: Stedelijk Museum De Lakenhal, Leiden, FORM: painting, TYPE: religious, SCHOOL: Dutch, TIMELINE: 1651-1700 65 | 25427 25427.jpg AUTHOR: METSU, Gabriel, BORN-DIED: (b. 1629, Leiden, d. 1667, Amsterdam), TITLE: Visit of the Physician, DATE: 1660-67, TECHNIQUE: Oil on canvas, 61 x 48 cm, LOCATION: The Hermitage, St. Petersburg, FORM: painting, TYPE: genre, SCHOOL: Dutch, TIMELINE: 1651-1700 66 | 25484 25484.jpg AUTHOR: MICHELANGELO Buonarroti, BORN-DIED: (b. 1475, Caprese, d. 1564, Roma), TITLE: Madonna and Child (detail), DATE: 1501-05, TECHNIQUE: Marble, LOCATION: O.L. Vrouwekerk, Bruges, FORM: sculpture, TYPE: religious, SCHOOL: Italian, TIMELINE: 1501-1550 67 | 25609 25609.jpg AUTHOR: MICHELANGELO Buonarroti, BORN-DIED: (b. 1475, Caprese, d. 1564, Roma), TITLE: The third bay of the ceiling, DATE: 1508-12, TECHNIQUE: Fresco, LOCATION: Cappella Sistina, Vatican, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1501-1550 68 | 26587 26587.jpg AUTHOR: MINIATURIST, French, BORN-DIED: (active around 870 in Saint-Denis), TITLE: Codex Aureus, DATE: 870, TECHNIQUE: Manuscript (Clm. 14000), 420 x 330 mm, LOCATION: Bayerische Staatsbibliothek, Munich, FORM: illumination, TYPE: religious, SCHOOL: French, TIMELINE: 0851-0900 69 | 27400 27400.jpg AUTHOR: MISERONI, Ottavio, BORN-DIED: (b. 1567, Milano, d. 1624, Praha), TITLE: Bowl in the Form of a Lion Skin, DATE: 1590s, TECHNIQUE: Cairngorm, 9 x 25 x 10 cm, LOCATION: Kunsthistorisches Museum, Vienna, FORM: metalwork, TYPE: other, SCHOOL: Italian, TIMELINE: 1601-1650 70 | 27493 27493.jpg AUTHOR: MOLENAER, Klaes, BORN-DIED: (b. before 1630, Haarlem, d. 1676, Haarlem), TITLE: Winter Landscape, DATE: -, TECHNIQUE: Oil on oak panel, 37 x 49 cm, LOCATION: Private collection, FORM: painting, TYPE: landscape, SCHOOL: Dutch, TIMELINE: 1651-1700 71 | 27521 27521.jpg AUTHOR: MOMPER, Joos de, BORN-DIED: (b. 1564, Antwerpen, d. 1634/35, Antwerpen), TITLE: Christ Healing the Blind Man, DATE: -, TECHNIQUE: Oil on canvas, 138 x 205 cm, LOCATION: Private collection, FORM: painting, TYPE: religious, SCHOOL: Flemish, TIMELINE: 1601-1650 72 | 27782 27782.jpg AUTHOR: MONTAÑÉS, Juan Martínez, BORN-DIED: (b. 1568, Alcala la Real, d. 1649, Sevilla), TITLE: The Merciful Christ, DATE: c. 1605, TECHNIQUE: Polychrome wood, height 190 cm, LOCATION: Cathedral, Seville, FORM: sculpture, TYPE: religious, SCHOOL: Spanish, TIMELINE: 1601-1650 73 | 28167 28167.jpg AUTHOR: MOSAIC ARTIST, Italian, BORN-DIED: (active 1200-1250 in Lucca), TITLE: Façade, DATE: 1200-50, TECHNIQUE: Mosaic, LOCATION: San Frediano, Lucca, FORM: mosaic, TYPE: religious, SCHOOL: Italian, TIMELINE: 1201-1250 74 | 29519 29519.jpg AUTHOR: PATENIER, Joachim, BORN-DIED: (b. ca. 1480, Bouvignes, d. 1524, Antwerpen), TITLE: Baptism of Christ, DATE: -, TECHNIQUE: Oil on oak, 59,5 x 77 cm, LOCATION: Kunsthistorisches Museum, Vienna, FORM: painting, TYPE: religious, SCHOOL: Flemish, TIMELINE: 1501-1550 75 | 29537 29537.jpg AUTHOR: PATER, Jean Baptiste Joseph, BORN-DIED: (b. 1695, Valenciennes, d. 1736, Paris), TITLE: Concert Champêtre, DATE: 1730-35, TECHNIQUE: Oil on canvas, LOCATION: Musée des Beaux-Arts, Valenciennes, FORM: painting, TYPE: genre, SCHOOL: French, TIMELINE: 1701-1750 76 | 30119 30119.jpg AUTHOR: PIERO DELLA FRANCESCA, BORN-DIED: (b. 1416, Borgo San Sepolcro, d. 1492, Borgo San Sepolcro), TITLE: 6. Torture of the Jew (detail), DATE: 1452-66, TECHNIQUE: Fresco, LOCATION: San Francesco, Arezzo, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1451-1500 77 | 30738 30738.jpg AUTHOR: PITTONI, Giambattista, BORN-DIED: (b. 1687, Venezia, d. 1767, Venezia), TITLE: Hagar in the Desert, DATE: -, TECHNIQUE: Oil on canvas, LOCATION: Santa Maria Gloriosa dei Frari, Venice, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1701-1750 78 | 30878 30878.jpg AUTHOR: POMPEI, Alessandro, Conte, BORN-DIED: (b. 1705, Verona, d. 1772, Garda), TITLE: Villa Pompei Carlotti: Façade, DATE: 1731-37, TECHNIQUE: Photo, LOCATION: Illasi, Verona, FORM: architecture, TYPE: other, SCHOOL: Italian, TIMELINE: 1701-1750 79 | 31182 31182.jpg AUTHOR: POUSSIN, Nicolas, BORN-DIED: (b. 1594, Les Andelys, d. 1665, Roma), TITLE: The Triumph of David, DATE: c. 1630, TECHNIQUE: Oil on canvas, 100 x 130 cm, LOCATION: Museo del Prado, Madrid, FORM: painting, TYPE: mythological, SCHOOL: French, TIMELINE: 1601-1650 80 | 32091 32091.jpg AUTHOR: RAFFAELLO Sanzio, BORN-DIED: (b. 1483, Urbino, d. 1520, Roma), TITLE: Head of an Angel, DATE: 1519-20, TECHNIQUE: Black chalk and brownish charcoal, heightened with white, 308 x 254 mm, LOCATION: Szépmûvészeti Múzeum, Budapest, FORM: graphics, TYPE: study, SCHOOL: Italian, TIMELINE: 1501-1550 81 | 32275 32275.jpg AUTHOR: REGNAULT, Henri, BORN-DIED: (b. 1843, Paris, d. 1871, Buzenval), TITLE: Salome, DATE: 1870, TECHNIQUE: Oil on canvas, 160 x 103 cm, LOCATION: Metropolitan Museum of Art, New York, FORM: painting, TYPE: religious, SCHOOL: French, TIMELINE: 1851-1900 82 | 32845 32845.jpg AUTHOR: REMBRANDT Harmenszoon van Rijn, BORN-DIED: (b. 1606, Leiden, d. 1669, Amsterdam), TITLE: Man in Armour (Mars?), DATE: 1650s, TECHNIQUE: Oil on canvas, 102 x 91 cm, LOCATION: Metropolitan Museum of Art, New York, FORM: painting, TYPE: portrait, SCHOOL: Dutch, TIMELINE: 1601-1650 83 | 34486 34486.jpg AUTHOR: RUBENS, Peter Paul, BORN-DIED: (b. 1577, Siegen, d. 1640, Antwerpen), TITLE: Portrait of Helene Fourment, DATE: c. 1630, TECHNIQUE: Oil on canvas, LOCATION: Musées Royaux des Beaux-Arts, Brussels, FORM: painting, TYPE: portrait, SCHOOL: Flemish, TIMELINE: 1601-1650 84 | 34602 34602.jpg AUTHOR: RUISDAEL, Jacob Isaackszon van, BORN-DIED: (b. ca. 1628, Haarlem, d. 1682, Amsterdam), TITLE: The Dam Square in Amsterdam (detail), DATE: c. 1670, TECHNIQUE: Oil on canvas, LOCATION: Staatliche Museen, Berlin, FORM: painting, TYPE: landscape, SCHOOL: Dutch, TIMELINE: 1651-1700 85 | 34926 34926.jpg AUTHOR: SALVIATI, Cecchino del, BORN-DIED: (b. 1510, Firenze, d. 1563, Roma), TITLE: Charity, DATE: 1554-58, TECHNIQUE: Oil on wood, 156 x 122 cm, LOCATION: Galleria degli Uffizi, Florence, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1501-1550 86 | 35186 35186.jpg AUTHOR: SARACENI, Carlo, BORN-DIED: (b. 1579, Venezia, d. 1620, Venezia), TITLE: The Rest on the Flight into Egypt, DATE: 1606, TECHNIQUE: Oil on canvas, 180 x 125 cm, LOCATION: Eremo dei Camaldolesi, Frascati, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1601-1650 87 | 35574 35574.jpg AUTHOR: SCHUFFENECKER, Emile, BORN-DIED: (b. 1851, Fresne-Saint-Mames, d. 1934, Paris), TITLE: Female Nude Seated on a Bed, DATE: 1885, TECHNIQUE: Oil on canvas, 65 x 45 cm, LOCATION: Private collection, FORM: painting, TYPE: other, SCHOOL: French, TIMELINE: 1851-1900 88 | 36035 36035.jpg AUTHOR: SIMONE MARTINI, BORN-DIED: (b. 1280/85, Siena, d. 1344, Avignon), TITLE: Maestà (detail), DATE: 1315, TECHNIQUE: Fresco, 99 x 85 cm (size of detail), LOCATION: Palazzo Pubblico, Siena, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1301-1350 89 | 36711 36711.jpg AUTHOR: STEEN, Jan, BORN-DIED: (b. 1626, Leiden, d. 1679, Leiden), TITLE: Self-Portrait as a Lutenist, DATE: 1652-55, TECHNIQUE: Oil on panel, 55 x 44 cm, LOCATION: Museo Thyssen-Bornemisza, Madrid, FORM: painting, TYPE: portrait, SCHOOL: Dutch, TIMELINE: 1651-1700 90 | 37121 37121.jpg AUTHOR: SUSTERMANS, Justus, BORN-DIED: (b. 1597, Antwerpen, d. 1681, Firenze), TITLE: Portrait of Vittoria della Rovere, DATE: 1628-30, TECHNIQUE: Oil on canvas, 114 x 92 cm, LOCATION: Museo di Palazzo Martelli, Florence, FORM: painting, TYPE: portrait, SCHOOL: Flemish, TIMELINE: 1601-1650 91 | 37504 37504.jpg AUTHOR: TETRODE, Willem Danielsz van, BORN-DIED: (b. ca. 1525, Delft, d. ca. 1587, Delft), TITLE: Hercules Pomarius, DATE: 1545-65, TECHNIQUE: Bronze, height 39 cm, LOCATION: Private collection, FORM: sculpture, TYPE: mythological, SCHOOL: Dutch, TIMELINE: 1551-1600 92 | 38682 38682.jpg AUTHOR: TORRITI, Jacopo, BORN-DIED: (active c. 1270-1300), TITLE: Deësis vault, DATE: 1290s, TECHNIQUE: Fresco, LOCATION: Upper Church, San Francesco, Assisi, FORM: painting, TYPE: religious, SCHOOL: Italian, TIMELINE: 1251-1300 93 | 39561 39561.jpg AUTHOR: UNKNOWN MASTER, Dutch, BORN-DIED: (active around 1650), TITLE: Wall Tile Painter, DATE: c. 1650, TECHNIQUE: Black charcoal, LOCATION: Amsterdam Museum, Amsterdam, FORM: graphics, TYPE: study, SCHOOL: Dutch, TIMELINE: 1601-1650 94 | 40334 40334.jpg AUTHOR: UNKNOWN MASTER, Italian, BORN-DIED: (active in 1500-1510 in Padua), TITLE: Toad, DATE: 1500-10, TECHNIQUE: Bronze, diameter cm, LOCATION: Kunsthistorisches Museum, Vienna, FORM: sculpture, TYPE: other, SCHOOL: Italian, TIMELINE: 1501-1550 95 | 40827 40827.jpg AUTHOR: VASARI, Giorgio, BORN-DIED: (b. 1511, Arezzo, d. 1574, Firenze), TITLE: Allegory Related to Alchemy, DATE: c. 1570, TECHNIQUE: Oil on slate, LOCATION: Palazzo Vecchio, Florence, FORM: painting, TYPE: mythological, SCHOOL: Italian, TIMELINE: 1551-1600 96 | 40945 40945.jpg AUTHOR: VELÁZQUEZ, Diego Rodriguez de Silva y, BORN-DIED: (b. 1599, Sevilla, d. 1660, Madrid), TITLE: Young Man, DATE: c. 1629, TECHNIQUE: Oil on canvas, 89 x 69 cm, LOCATION: Alte Pinakothek, Munich, FORM: painting, TYPE: portrait, SCHOOL: Spanish, TIMELINE: 1601-1650 97 | 41303 41303.jpg AUTHOR: VERMEER, Johannes, BORN-DIED: (b. 1632, Delft, d. 1675, Delft), TITLE: View of Delft, DATE: 1659-60, TECHNIQUE: Oil on canvas, 97 x 116 cm, LOCATION: Mauritshuis, The Hague, FORM: painting, TYPE: landscape, SCHOOL: Dutch, TIMELINE: 1651-1700 98 | 41805 41805.jpg AUTHOR: VERWILT, François, BORN-DIED: (b. ca. 1620, Rotterdam, d. 1691, Rotterdam), TITLE: Adoration of the Shepherds, DATE: c. 1660, TECHNIQUE: Oil on panel, 42 x 62 cm, LOCATION: Private collection, FORM: painting, TYPE: religious, SCHOOL: Dutch, TIMELINE: 1651-1700 99 | 42039 42039.jpg AUTHOR: VITTORIA, Alessandro, BORN-DIED: (b. 1525, Trento, d. 1608, Venezia), TITLE: St Sebastian, DATE: 1566, TECHNIQUE: Bronze, height 54 cm, LOCATION: Metropolitan Museum of Art, New York, FORM: sculpture, TYPE: religious, SCHOOL: Italian, TIMELINE: 1551-1600 100 | 43111 43111.jpg AUTHOR: WTEWAEL, Peter, BORN-DIED: (b. 1596, Utrecht, d. 1660, Utrecht), TITLE: Allegory of Love, DATE: -, TECHNIQUE: Oil on canvas, 68 x 106 cm, LOCATION: Private collection, FORM: painting, TYPE: mythological, 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"bf037b19-3475-4850-9fd0-d99a713331ce", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import matplotlib.pyplot as plt\n", 11 | "from PIL import Image\n", 12 | "from utils import build_image_database" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "id": "72c0d164-4aa0-40c0-9ecb-60fa731e97fb", 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "### give a image fold path，build image database\n", 23 | "img_collection = build_image_database(fold_path = '/mnt/petrelfs/liuziyu/V3Det/Soda/RAG/artwork_img/',database_name = 'test')" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": null, 29 | "id": "4175c116-96ed-4279-a440-4474d4f40c41", 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [ 33 | "### input a image, then retrieve\n", 34 | "test_image_path = \"./test_img.jpg\"\n", 35 | "n_pictures = 1\n", 36 | "ans = img_collection.query(\n", 37 | " query_uris=[test_image_path], # A list of strings representing URIs to data\n", 38 | " n_results=n_pictures\n", 39 | " )" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": null, 45 | "id": "e434db62-c47a-4b56-8784-503e2ed14526", 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "retrieved_image_path = '/mnt/petrelfs/liuziyu/V3Det/Soda/RAG/artwork_img/'+ ans[\"metadatas\"][0][0][\"ID\"]+'.jpg'\n", 50 | "retrieved_image_path" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": null, 56 | "id": "154c4c94-c71a-4bba-8135-dd9a443ada5e", 57 | "metadata": {}, 58 | "outputs": [], 59 | "source": [ 60 | "### show results\n", 61 | "image_path1 = test_image_path\n", 62 | "image_path2 = retrieved_image_path\n", 63 | "\n", 64 | "image1 = Image.open(image_path1)\n", 65 | "image2 = Image.open(image_path2)\n", 66 | "\n", 67 | "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n", 68 | "\n", 69 | "axes[0].imshow(image1)\n", 70 | "axes[0].set_title('Original Image')\n", 71 | "axes[0].axis('off')\n", 72 | "\n", 73 | "axes[1].imshow(image2)\n", 74 | "axes[1].set_title('Retrieved Image')\n", 75 | "axes[1].axis('off')\n", 76 | "\n", 77 | "plt.show()" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": null, 83 | "id": "f921cd03-f5d6-4809-b47e-6c2d2da32dc8", 84 | "metadata": {}, 85 | "outputs": [], 86 | "source": [] 87 | } 88 | ], 89 | "metadata": { 90 | "kernelspec": { 91 | "display_name": "Python 3 (ipykernel)", 92 | "language": "python", 93 | "name": "python3" 94 | }, 95 | "language_info": { 96 | "codemirror_mode": { 97 | "name": "ipython", 98 | "version": 3 99 | }, 100 | "file_extension": ".py", 101 | "mimetype": "text/x-python", 102 | "name": "python", 103 | "nbconvert_exporter": "python", 104 | "pygments_lexer": "ipython3", 105 | "version": "3.10.13" 106 | } 107 | }, 108 | "nbformat": 4, 109 | "nbformat_minor": 5 110 | } 111 | -------------------------------------------------------------------------------- /RAG/test_img.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/RAG/test_img.jpg -------------------------------------------------------------------------------- /RAG/text_rag.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "54c60bec-0433-4590-b51a-265a31162bd7", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "from utils import preprocess_files, build_text_database" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "id": "d382be04-6140-4749-a6f9-071d4a70b524", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "### upload PDF TXT or DOCX file\n", 21 | "docs = preprocess_files(file_path=\"RAR-pr.docx\",chunk_size=1000, chunk_overlap=50)" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": null, 27 | "id": "1972207e-2fe1-4346-ad2c-f4dfa77c686c", 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "docs" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "id": "780ef08c-a9d7-4da6-9be5-f7876c2d3b81", 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "### build database\n", 42 | "txt_collection = build_text_database(docs, batch_size=40000, encoder=\"intfloat/multilingual-e5-base\", database_name = \"test\")" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": null, 48 | "id": "a4b7cac3-fdd2-4871-a617-4cff0899af7e", 49 | "metadata": {}, 50 | "outputs": [], 51 | "source": [ 52 | "### change query_text to retrieve\n", 53 | "query_text = \"What is the database consisted of\"\n", 54 | "n_text = 10\n", 55 | "ans = txt_collection.query(\n", 56 | " query_texts=[query_text],\n", 57 | " n_results=n_text\n", 58 | ")\n", 59 | "print(ans)" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": null, 65 | "id": "4b98ed5d-2180-45b2-823f-54cd2ce95ec9", 66 | "metadata": {}, 67 | "outputs": [], 68 | "source": [] 69 | } 70 | ], 71 | "metadata": { 72 | "kernelspec": { 73 | "display_name": "Python 3 (ipykernel)", 74 | "language": "python", 75 | "name": "python3" 76 | }, 77 | "language_info": { 78 | "codemirror_mode": { 79 | "name": "ipython", 80 | "version": 3 81 | }, 82 | "file_extension": ".py", 83 | "mimetype": "text/x-python", 84 | "name": "python", 85 | "nbconvert_exporter": "python", 86 | "pygments_lexer": "ipython3", 87 | "version": "3.10.13" 88 | } 89 | }, 90 | "nbformat": 4, 91 | "nbformat_minor": 5 92 | } 93 | -------------------------------------------------------------------------------- /RAG/utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import glob 3 | import shutil 4 | import logging 5 | import chromadb 6 | import numpy as np 7 | import pandas as pd 8 | import sentence_transformers 9 | from chromadb.utils import embedding_functions 10 | from chromadb.utils.data_loaders import ImageLoader 11 | from langchain.document_loaders import PyMuPDFLoader 12 | from langchain.embeddings import HuggingFaceEmbeddings 13 | from langchain_community.document_loaders import PyPDFLoader 14 | from chromadb.utils.embedding_functions import OpenCLIPEmbeddingFunction 15 | from langchain.text_splitter import RecursiveCharacterTextSplitter, TokenTextSplitter 16 | 17 | logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') 18 | logging.info("initial") 19 | 20 | """ 21 | Load Files Function 22 | """ 23 | ### return file format 24 | def identify_file_type(file_path): 25 | _, file_extension = os.path.splitext(file_path) 26 | file_extension = file_extension.lower() 27 | 28 | if file_extension == '.pdf': 29 | return 'PDF' 30 | elif file_extension == '.txt': 31 | return 'Text' 32 | elif file_extension == '.tsv': 33 | return 'TSV' 34 | elif file_extension == '.csv': 35 | return 'CSV' 36 | else: 37 | return None 38 | 39 | ### load PDF，TXT，DOCX 40 | def documents_load_local(doc_path): 41 | try: 42 | docs = [] 43 | docs.extend(PyMuPDFLoader(doc_path).load()) 44 | return docs 45 | except Exception as e: 46 | print(f"Documentes loaded failed: {e}") 47 | raise e 48 | 49 | ### load PDF 50 | def documents_load_pdf(pdf_path): 51 | loader = PyPDFLoader(pdf_path) 52 | docs = loader.load_and_split() 53 | return docs 54 | 55 | ### load CVS 56 | def documents_load_csv(path): 57 | docs = pd.read_csv(path, sep='\t', header=None) 58 | return docs 59 | 60 | """ 61 | Preprocess loaded text files 62 | """ 63 | ### split documents for different chunk_size 64 | def documents_split(docs,chunk_size, chunk_overlap): 65 | try: 66 | text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) 67 | # text_splitter = TokenTextSplitter(encoding_name=encoder, chunk_size=chunk_size, chunk_overlap=chunk_overlap) 68 | docs_split = text_splitter.split_documents(docs) 69 | return docs_split 70 | except Exception as e: 71 | print(f"Documentes split failed: {e}") 72 | raise e 73 | 74 | def documents_to_str(docs): 75 | docs_str = [] 76 | for doc in docs: 77 | docs_str.append(doc.dict()["page_content"]) 78 | return docs_str 79 | 80 | 81 | def preprocess_files(file_path, chunk_size, chunk_overlap): 82 | docs = documents_load_local(file_path) 83 | docs_split = documents_split(docs, chunk_size, chunk_overlap) 84 | docs_split[0].dict()["page_content"] 85 | docs_split_str = documents_to_str(docs_split) 86 | return docs_split_str 87 | 88 | """ 89 | Build text database 90 | """ 91 | def build_text_database(docs, batch_size, encoder="intfloat/multilingual-e5-base", database_name = "test", database_path = "./database/test"): 92 | if os.path.exists(database_path): 93 | print(f"Database '{database_name}' exists. Change a new name.") 94 | return None 95 | else: 96 | ### text encoder 97 | embeddings = HuggingFaceEmbeddings(model_name=encoder, encode_kwargs={'normalize_embeddings': True}) 98 | ### client 99 | client = chromadb.PersistentClient(path=database_path) 100 | ### text encoder funtino 101 | text_emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=encoder, normalize_embeddings = True) 102 | 103 | ### create collection 104 | txt_collection = client.create_collection(name=database_name, embedding_function=text_emb_fn, metadata={"hnsw:space": "cosine"}) 105 | 106 | ### load collection 107 | txt_collection = client.get_collection(name=database_name, embedding_function=text_emb_fn) 108 | 109 | for start_idx in range(0, len(docs), batch_size): 110 | end_idx = min(start_idx + batch_size, len(docs)) 111 | batch_docs = docs[start_idx:end_idx] 112 | ids = [str(num) for num in range(start_idx, end_idx)] 113 | # embeddings 114 | logging.info("Begin embedding docs.") 115 | embed_split = embeddings.embed_documents(batch_docs) 116 | logging.info("End embedding docs.") 117 | 118 | # add to database 119 | metadatas = [{"ID": num} for num in ids] 120 | logging.info("Begin adding documents.") 121 | txt_collection.add( 122 | documents=batch_docs, 123 | embeddings=embed_split, 124 | ids=ids, 125 | ) 126 | logging.info(f"Batch {start_idx // batch_size + 1} done!") 127 | logging.info("All done!") 128 | 129 | return txt_collection 130 | 131 | 132 | ### delete database 133 | def delete_folder(folder_path): 134 | # check the fold 135 | if os.path.exists(folder_path): 136 | # delete the fold 137 | shutil.rmtree(folder_path) 138 | print(f"Folder '{folder_path}' has been deleted.") 139 | else: 140 | print(f"Folder '{folder_path}' does not exist.") 141 | 142 | 143 | """ 144 | Read fold with images 145 | """ 146 | def read_image_files(folder_path): 147 | # format 148 | image_formats = ["*.jpg", "*.jpeg", "*.png", "*.gif", "*.bmp", "*.tiff"] 149 | image_paths = [] 150 | for format in image_formats: 151 | image_paths.extend(glob.glob(os.path.join(folder_path, format))) 152 | 153 | return image_paths 154 | 155 | 156 | 157 | """ 158 | Build image database 159 | """ 160 | def build_image_database(fold_path, database_name = "test", database_path = "./database/test"): 161 | 162 | ### client 163 | client = chromadb.PersistentClient(path=database_path) 164 | ### image encoder function 165 | img_emb_fn = OpenCLIPEmbeddingFunction() 166 | ### data loader 167 | data_loader = ImageLoader() 168 | ### creat collection 169 | img_collection = client.create_collection(name=database_name, embedding_function=img_emb_fn, metadata={"hnsw:space": "cosine"}) 170 | ### load collection 171 | img_collection = client.get_collection(name=database_name, embedding_function=img_emb_fn, data_loader=data_loader) 172 | 173 | image_paths = [os.path.join(fold_path, file) for file in os.listdir(fold_path) if file.lower().endswith(('.jpg', '.jpeg', '.png', '.gif', '.bmp'))] 174 | print("total image number is: "+str(len(image_paths))) 175 | ids = list(range(0, len(image_paths))) 176 | ids = [str(num) for num in ids] 177 | image_names = [os.path.splitext(os.path.basename(url))[0] for url in image_paths] 178 | metadatas = [{"ID":metadata} for metadata in image_names] 179 | logging.info("Begin building database") 180 | img_collection.add( 181 | ids=ids, 182 | uris = image_paths, 183 | metadatas=metadatas, 184 | ) 185 | logging.info("Done!") 186 | 187 | return img_collection 188 | 189 | 190 | """ 191 | Build multimodal database 192 | """ 193 | def build_multimodal_database(tsv_path, image_folder, encoder="intfloat/multilingual-e5-base", database_name = "test"): 194 | ### text encoder 195 | embeddings = HuggingFaceEmbeddings(model_name=encoder, encode_kwargs={'normalize_embeddings': True}) 196 | ### client 197 | client = chromadb.PersistentClient(path=f"./database/{database_name}") 198 | ### text/image encoder function 199 | text_emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="intfloat/multilingual-e5-base") 200 | img_emb_fn = OpenCLIPEmbeddingFunction() 201 | ### data loader 202 | data_loader = ImageLoader() 203 | 204 | 205 | txt_collection = client.create_collection(name=f"{database_name}_text", embedding_function=text_emb_fn, metadata={"hnsw:space": "cosine"}) 206 | img_collection = client.create_collection(name=f"{database_name}_image", embedding_function=img_emb_fn, metadata={"hnsw:space": "cosine"}) 207 | 208 | ### load collection 209 | txt_collection = client.get_collection(name=f"{database_name}_text", embedding_function=text_emb_fn) 210 | img_collection = client.get_collection(name=f"{database_name}_image", embedding_function=img_emb_fn, data_loader=data_loader) 211 | 212 | 213 | ### add data 214 | ### embed_text [[float],[float],[float],...]; docs_split [str,str,str,...] 215 | df = pd.read_csv(tsv_path, sep='\t') 216 | 217 | docs_split = df["INFO"] 218 | docs_split = [s for s in docs_split] 219 | 220 | metadatas = df["PATH"] 221 | metadatas = [s for s in metadatas] 222 | image_paths = metadatas.copy() 223 | metadatas = [{"ID":metadata} for metadata in metadatas] 224 | 225 | ids = list(range(0, len(docs_split))) 226 | ids = [str(num) for num in ids] 227 | 228 | logging.info("Len of data is: "+str(len(ids))) 229 | 230 | 231 | logging.info("Begin add text.") 232 | txt_collection.add( 233 | documents = docs_split, 234 | ids = ids, 235 | metadatas = metadatas 236 | ) 237 | logging.info("End add text.") 238 | 239 | image_paths = [image_folder+image_path for image_path in image_paths] 240 | logging.info("Begin add images.") 241 | img_collection.add( 242 | ids=ids, 243 | uris = image_paths, 244 | metadatas=metadatas 245 | ) 246 | logging.info("End add images.") 247 | logging.info("ALL DONE!") 248 | 249 | return txt_collection, img_collection -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

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SODA: Search, Organize, Discovery Anything

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15 | English | 16 | 简体中文 17 |

18 | 19 | 🌟 Welcome to my GitHub project! If you like what you see, don't hesitate to hit that star button! More stars, faster updates, more fun! 20 | 21 | ## 📣 Introduction 22 | With the advent and extensive deployment of Large Language Models (LLMs), these sophisticated systems have showcased immense potential in a variety of application domains. Nevertheless, even highly advanced models such as GPT-4 are not without their limitations; they aren't omniscient and are susceptible to the so-called 'hallucination problem'. 23 | 24 | Acknowledging these constraints, we have innovated **SODA (Search, Organize, Discover Anything) - a cutting-edge information integration Tool**, propelled by the power of large language models(LLMs). SODA leverages an LLM at its core for processing information, adeptly sourcing data from a multitude of channels in response to user queries. This enables it to provide nuanced and comprehensive answers. Through SODA, users gain access to a sophisticated web search mechanism that fetches pertinent information from the internet. This integrates seamlessly with the innate knowledge of the LLM and external sources, ensuring answers are not only accurate but also reliable. Furthermore, SODA empowers users to upload personal files, facilitating the creation of a private, secure, and robust local knowledge database. This feature allows LLMs to assimilate new information effortlessly, eliminating the need for pre-training or fine-tuning, and to utilize this knowledge effectively in response to queries. 25 | 26 | Overall, SODA is envisioned as a **secure**, **dependable**, and **intelligently sourced** tool. It's strategically designed to enable users to proficiently handle and interpret information gleaned from expansive models, the web, and your own database. 27 | 28 | ## 🔭 Architecture 29 | SODA's architecture is show below: 30 |
31 | soda_system 32 |
33 | 34 | We support **web search**, **text retrieval(local database)** and **image retrieval(local database)** now. 35 | In text retrieval, we have implemented a **two-stage** retrieval process, consisting of initial database retrieval and subsequent reranking. 36 | 37 | ## 📢 News 38 | - 🚀 [04/18/2024] We have open-sourced the first version of SODA, and more updates will be coming soon!!! 39 | 40 | ## 💡 Highlights 41 | - 🔥 **New technology framework.** We have developed an LLM-driven information integration tool, which provides a technical framework for retrieval argumented generation(RAG) and tool use directions for AI Agents. 42 | - 🔥 **Good compatibility.** SODA is capable of easily swapping components, utilizing various search engines, vector databases or LLMs, and exhibits good compatibility. 43 | - 🔥 **Reliable&traceable.** SODA effectively addresses partial hallucination issues of LLM, providing reliable and accurate answers with traceable information sources. 44 | - 🔥 **Data privacy.** SODA supports local databases, allowing the model to acquire new knowledge without pretraining or finetuning, while effectively protecting user data privacy. 45 | 46 | ## 🛠️ Usage 47 | 48 | ### Contents 49 | - [Install](#Install) 50 | - [Web Search Pipeline with Various APIs](#Web-Search-Pipeline-with-Various-APIs) 51 | - [Retrieve-Pipeline-Based-on-Local-Database](#Retrieve-Pipeline-Based-on-Local-Database) 52 | - [LLMs](#LLMs) 53 | 54 | ### Install 55 | To run SODA locally, clone the repository and set up the environment. 56 | ```bash 57 | mkdir SODA 58 | cd SODA 59 | git clone https://github.com/Liuziyu77/Soda.git 60 | pip install requirements.txt 61 | ``` 62 | To experiment with individual functions of SODA, navigate through various directories to execute `.ipynb` files. To run Gradio locally, please follow these instructions. 63 | ```bash 64 | cd web_ui 65 | python web_ui.py 66 | ``` 67 | Please note that you need to **modify the base_directory** path in `web_ui.py`. Intermediate files generated (such as databases built from local files) will be temporarily stored there. These files will be periodically cleaned up. If needed, please adjust the code accordingly. 68 | 69 | To enable web search and utilize OpenAI's API, please enter the corresponding API keys in the `./web_search/utils.py` and `./mllm/soda_mllm.py` files. 70 | 71 | ### 🌐 Web Search Pipeline with Various APIs 72 | The code related to web search is stored in the `web_search` folder. This folder contains a collection of code that utilizes various search engine APIs to retrieve relevant information based on user input. This process demonstrates an efficient integration of multiple search tools to optimize the relevance and accuracy of search results. 73 | 74 | #### Using API 75 | We have suported APIs of Google, Bing and Serper. You can run `./web_search/Google_API.ipynb`, `./web_search/Serper_API.ipynb` and `./web_search/Bing_API.ipynb` to test the usage of these search engines. But first of all, an API is necessary. Here are the links to get various search engine APIs. 76 | 77 | * You can get Google APIs: [Google API](https://cloud.google.com/apis/docs/overview) 78 | * You can get Bing APIs: [Bing API](https://serper.dev/) 79 | * You can get Serper APIs: [Serper API](https://www.microsoft.com/en-us/bing/apis/bing-web-search-api) 80 | 81 | Additionally, we will offer comprehensive search capabilities beyond **text**, including support for both **image and video searches** soon! 82 | 83 | Here is the [Web Search Example](figures/web_search.mp4). 84 |
Web Search Example 85 | 86 | 87 | 88 | https://github.com/Liuziyu77/Soda_Dev/assets/137670115/7bc73223-eaa9-44f5-a379-8bf204d4380c 89 | 90 | 91 | 92 | 93 |
94 | 95 | ### 🔎 Retrieve Pipeline Based on Local Database 96 | The code related to RAG on local database is stored in the `RAG` folder. This folder is the implement of building your own local database and retrieve information from it. It includes `text-text` retrieve, `image-image` retrieve and `image-image&text pair` retrieve. You can test the retrieval functionalities by running different `.ipynb` files, we provide three scripts as examples. 97 | 98 | #### 1. Text-text retrieve 99 | User can run the `./RAG/text_rag.ipynb` to build a text database and retrieve information from it. The only thing you need to do is just providing a text file path. We support TXT, DOCX, PDF format now. 100 | 101 | We use **Sentense transfomer** as the text encoder. More encoder will be supported soon! 102 | 103 | Here is the [Text Retrieve Example](figures/text_retrieve.mp4). 104 |
Text Retrieve Example 105 | 106 | 107 | 108 | 109 | 110 | https://github.com/Liuziyu77/Soda_Dev/assets/137670115/7a2042b9-7c03-44f4-9e36-abb7f244da19 111 | 112 | 113 | 114 | 115 | 116 |
117 | 118 | #### 2. Image-Image retrieve 119 | User can run the `./RAG/image_rag.ipynb` to build a image database and retrieve information from it. The only thing you need to do is just providing a folder path. 120 | 121 | We use **CLIP-B/32** as the image encoder. More visual encoder will be supported soon! 122 | 123 | Here is the [Image Retrieve Example](figures/image_retrieve.mp4). 124 |
Image Retrieve Example 125 | 126 | 127 | 128 | 129 | 130 | 131 | https://github.com/Liuziyu77/Soda_Dev/assets/137670115/761e489c-d572-4070-bb29-bb31d891f661 132 | 133 | 134 | 135 | 136 | 137 |
138 | 139 | #### 3. Image-Image&Text retrieve 140 | User can run the `./RAG/multimodal_rag.ipynb` to build a multimodal database and retrieve information from it. Here, you need to provide a `.tsv` file which include your data's `ID`, `PATH`, `INFO`. An example TSV file is `./RAG/artwork_data.tsv`. 141 | 142 | ### 🐑 LLMs 143 | We use the InternLM-Xcomposer2(a vision-language large model (VLLM) based on InternLM2-7B) or GPT-4 to process the information from web and database, and feedback to users. We will soon support more LLMs as the information processing core for SODA. 144 | 145 | ### ✒️ Citation 146 | ``` 147 | @misc{2024SODA, 148 | title={SODA: Search, Organize, Discovery Anything}, 149 | author={SODA Team}, 150 | howpublished = {\url{https://github.com/Liuziyu77/Soda}}, 151 | year={2024} 152 | } 153 | ``` 154 | 155 | ## 📜 License 156 | ![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg) ![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg) **Usage and License Notices**: The data and code are intended and licensed for research use only. 157 | -------------------------------------------------------------------------------- /README_zh.md: -------------------------------------------------------------------------------- 1 |

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SODA: Search, Organize, Discovery Anything

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13 | 14 |

15 | English | 16 | 简体中文 17 |

18 | 19 | 🌟 欢迎来到我的GitHub项目！如果这个项目让你心动，不妨赏个星星吧！点赞越多，更新越快，快乐越多！ 20 | 21 | ## 📣 简介 22 | 随着大型语言模型（LLM）的出现和广泛部署，这些高级系统在多种应用领域展现了巨大的潜力。然而，即使是像GPT-4这样的先进模型也不是没有局限性；它们并非全知全能，且容易出现所谓的“幻觉问题”。 23 | 24 | 为了解决这些局限，我们创新了一款尖端的信息整合工具——**SODA(Search, Organize, Discover Anything)**。SODA以大型语言模型为核心，灵活地从众多渠道获取数据以响应用户查询，从而提供精细和全面的答案。通过SODA，用户可以利用一个高级的网络搜索机制，从互联网上提取相关信息。这使得LLM的内在知识和外部资源无缝整合，确保提供的答案不仅准确而且可靠。此外，SODA还支持用户上传个人文件，便于创建一个私密、安全且强大的本地知识数据库。这一功能使得LLM能够轻松吸收新信息，无需预训练或微调，有效地利用这些知识来响应用户输入。 25 | 26 | 总体而言，SODA被设计为一个安全、可靠、且智能信息采集和处理的工具。它的设计使用户能够处理和解释从大语言模型、网络和您自己的数据库中获得的信息。 27 | 28 | ## 🔭 SODA架构 29 | SODA的架构如下所示： 30 |
31 | soda_system 32 |
33 | 我们现在支持 **网络检索**, **文本检索(本地数据库)** and **图像检索(本地数据库)**。 34 | 在文本检索阶段，我们实现了**两阶段**检索过程，第一阶段从数据库中检索信息，第二阶段对检索到的文本进行重新排序。 35 | 36 | ## 📢 New 37 | - 🚀 [04/18/2024] 我们开源了第一版的SODA，即将发布更多更新！！！ 38 | 39 | ## 💡 Highlights 40 | - 🔥 **全新的技术框架** 我们开发了一款由LLM（大型语言模型）驱动的信息整合工具，它提供了一个用于检索增强生成（RAG）的技术框架，并为AI Agent提供了使用工具的指导。 41 | - 🔥 **良好的兼容性** SODA能够轻松切换组件，使用不同的搜索引擎、向量数据库或LLM，并展现出良好的兼容性。 42 | - 🔥 **可靠&信息来源可追溯** SODA有效地解决了LLM的部分幻觉问题，提供了可追溯信息源的可靠且准确的答案。 43 | - 🔥 **数据隐私** SODA支持本地数据库，允许模型在不进行预训练或微调的情况下获取新知识，同时有效保护用户数据隐私。 44 | 45 | ## 🛠️ 使用方法 46 | 47 | ### 目录 48 | - [安装](#安装) 49 | - [网络搜索](#网络搜索) 50 | - [本地数据库检索](#本地数据库检索) 51 | - [大语言模型](#大语言模型) 52 | 53 | ### 安装 54 | 要在本地运行SODA，首先按照以下的命令行将项目克隆到本地，并安装依赖的包。 55 | ```bash 56 | mkdir SODA 57 | cd SODA 58 | git clone https://github.com/Liuziyu77/Soda.git 59 | pip install requirements.txt 60 | ``` 61 | 如果要体验SODA的全部功能，请运行我们基于gradio构建的webui程序，命令行如下所示。如果要体验SODA中单一功能的效果，可以进入SODA子文件夹中寻找`.ipynb`文件，并运行。 62 | ```bash 63 | cd web_ui 64 | python web_ui.py 65 | ``` 66 | 请注意，您需要在 `web_ui.py` 文件中修改 `base_directory` 路径。此路径用于临时存储中间文件（例如从本地文件构建的数据库）。这些文件将自动定期进行清理。如有需要，请相应调整代码。 67 | 68 | 为启用网络搜索功能并使用 OpenAI 的API，请在 `./web_search/utils.py` 和 `./mllm/soda_mllm.py` 文件夹中输入相应的 API 密钥。 69 | 70 | ### 🌐 网络搜索 71 | 与网络搜索相关的代码存放在 `web_search` 文件夹中。该文件夹包含了一系列代码，这些代码利用各种搜索引擎的API来根据用户输入检索相关信息。这一过程展示了多种搜索工具的有效整合，优化了搜索结果的相关性和准确性。 72 | 73 | #### 使用API 74 | 我们现在已经支持谷歌，必应和serper的API。你可以通过运行`./web_search/Google_API.ipynb`， `./web_search/Serper_API.ipynb` 和 `./web_search/Bing_API.ipynb` 去测试不同搜索引擎。但是，你首先需要获取对应的API。以下是获取API的网页连接。 75 | 76 | * 你可以获取 Google APIs: [Google API](https://cloud.google.com/apis/docs/overview) 77 | * 你可以获取 Bing APIs: [Bing API](https://serper.dev/) 78 | * 你可以获取 Serper APIs: [Serper API](https://www.microsoft.com/en-us/bing/apis/bing-web-search-api) 79 | 80 | 以下是网络搜索功能的示例： [Web Search Example](figures/web_search.mp4). 81 |
Web Search Example 82 | 83 | 84 | 85 | https://github.com/Liuziyu77/Soda_Dev/assets/137670115/7bc73223-eaa9-44f5-a379-8bf204d4380c 86 | 87 | 88 | 89 | 90 |
91 | 92 | 93 | ### 🔎 本地数据库检索 94 | 与本地数据库上的检索增强生成（RAG）相关的代码存放在 `RAG` 文件夹中。这个文件夹实现了构建您自己的本地数据库并从中检索信息。它包括 `text-text` 检索、`image-image` 检索以及 `image-image&text pair` 检索。您可以通过运行不同的 `.ipynb` 文件来测试检索功能，我们提供了三个脚本作为示例。 95 | 96 | #### 1. 文本到文本的检索 97 | 运行 `./RAG/text_rag.ipynb` 来构建本地文本数据库并从中检索信息，你所需要做的唯一一件事就是修改上传的文件的路径。 SODA现在支持 TXT, DOCX, PDF 等文件格式。 98 | 99 | SODA使用 **Sentense transfomer** 作文文本编码器，不久将会支持更多的文本编码器! 100 | 101 | 以下是文本检索的示例： [Text Retrieve Example](figures/text_retrieve.mp4). 102 |
Text Retrieve Example 103 | 104 | 105 | 106 | 107 | 108 | https://github.com/Liuziyu77/Soda_Dev/assets/137670115/7a2042b9-7c03-44f4-9e36-abb7f244da19 109 | 110 | 111 | 112 | 113 | 114 |
115 | 116 | #### 2. 图像到图像的检索 117 | 运行 `./RAG/image_rag.ipynb` 构建本地图像数据库并从中检索图像。 你所需要做的唯一一件事就是修改上传的文件夹的路径。 118 | 119 | 我们使用 **CLIP-B/32** 作为图像编码器。不久将会支持更多的图像编码器! 120 | 121 | 以下是图像检索的示例： [Image Retrieve Example](figures/image_retrieve.mp4). 122 |
Image Retrieve Example 123 | 124 | 125 | 126 | 127 | 128 | 129 | https://github.com/Liuziyu77/Soda_Dev/assets/137670115/761e489c-d572-4070-bb29-bb31d891f661 130 | 131 | 132 | 133 | 134 | 135 |
136 | 137 | #### 3. 图像到图文对的检索 138 | 运行 `./RAG/multimodal_rag.ipynb` 构建多模态数据库并从中检索信息。 在这里，用户需要提供了一个 `.tsv` 文件，包括`ID`, `PATH`, `INFO`。一个示例的 TSV 文件是 `./RAG/artwork_data.tsv`。 139 | 140 | ### 🐑 大语言模型 141 | 我们使用 InternLM-Xcomposer2 （一个基于 InternLM2-7B 的视觉语言模型）或者 GPT-4 来处理来自网络或者本地数据库的信息，并反馈给使用者。其中 InternLM-Xcomposer2 在本地运行。不久我们将会支持更多类型的大语言模型作为 SODA 的信息处理大脑。 142 | 143 | ### ✒️ Citation 144 | ``` 145 | @misc{2024SODA, 146 | title={SODA: Search, Organize, Discovery Anything}, 147 | author={SODA Team}, 148 | howpublished = {\url{https://github.com/Liuziyu77/Soda}}, 149 | year={2024} 150 | } 151 | ``` 152 | 153 | ## 📜 License 154 | ![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg) ![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg) **Usage and License Notices**: The data and code are intended and licensed for research use only. 155 | -------------------------------------------------------------------------------- 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StoppingCriteriaList) 13 | from transformers.generation import GenerationConfig 14 | from peft import AutoPeftModelForCausalLM 15 | 16 | torch.manual_seed(1234) 17 | 18 | torch.set_grad_enabled(False) 19 | model = AutoModelForCausalLM.from_pretrained('internlm/internlm-xcomposer2-vl-7b', trust_remote_code=True).cuda().eval() 20 | tokenizer = AutoTokenizer.from_pretrained('internlm/internlm-xcomposer2-vl-7b', trust_remote_code=True) 21 | 22 | 23 | query = ''+ "What do you see in the picture?" 24 | with torch.no_grad(): 25 | query = query 26 | image = "./test_img.jpg" 27 | with torch.cuda.amp.autocast(): 28 | response, _ = model.chat(tokenizer, query=query, image=image, history=[], do_sample=False) 29 | print("\033[92m" + response + "\033[0m") -------------------------------------------------------------------------------- /mllm/__pycache__/soda_mllm.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/mllm/__pycache__/soda_mllm.cpython-310.pyc -------------------------------------------------------------------------------- /mllm/soda_mllm.py: -------------------------------------------------------------------------------- 1 | from openai import OpenAI 2 | import torch 3 | 4 | # Enter your OpenAI API here: 5 | api_base = "***********************************" 6 | api_key = "***********************************" 7 | 8 | # call gpt4v 9 | def mllm_openai(query, search_results): 10 | conversation_history = [] 11 | client = OpenAI(api_key=api_key, base_url=api_base) 12 | response = client.chat.completions.create( 13 | model="gpt-4-0125-preview", 14 | messages=[ 15 | {"role": "system", "content": "You are a helpful assistant."}, 16 | {"role": "user", "content": f"Given a input text and web information, please response to the input text with these information.\n Here is the input text: {query}.\n Here is the materials: {search_results}"}, 17 | ] 18 | ) 19 | # print(response.choices[0].message.content) 20 | return response.choices[0].message.content 21 | 22 | # call InternLM-Xcomposer2 23 | def mllm_IXC2(IXC2_model, IXC2_tokenizer, query, search_results): 24 | text_inputs = f"Given a input text and web information, please response to the input text with these information.\n Here is the input text: {query}.\n Here is the materials: {search_results}" 25 | with torch.no_grad(): 26 | query = text_inputs 27 | with torch.cuda.amp.autocast(): 28 | response, _ = IXC2_model.chat(IXC2_tokenizer, query=text_inputs, history=[], do_sample=False) 29 | # print("\033[92m" + response + "\033[0m") 30 | return response -------------------------------------------------------------------------------- /mllm/test_img.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/mllm/test_img.jpg -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | aiohttp==3.9.3 2 | chromadb==0.4.24 3 | clip==1.0 4 | gradio==4.26.0 5 | html2text==2024.2.26 6 | ipython==8.12.3 7 | langchain==0.1.16 8 | langchain_community==0.0.32 9 | llama_index==0.10.28 10 | llava==0.0.1.dev0 11 | matplotlib==3.8.3 12 | numpy==1.26.4 13 | openai==1.17.0 14 | pandas==2.2.2 15 | peft==0.10.0 16 | Pillow==10.3.0 17 | Requests==2.31.0 18 | sentence_transformers==2.6.1 19 | torch==2.1.2+cu118 20 | tqdm==4.66.2 21 | transformers==4.39.2 22 | -------------------------------------------------------------------------------- /service/__pycache__/utils.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/service/__pycache__/utils.cpython-310.pyc -------------------------------------------------------------------------------- /service/rerank.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "4875508a", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "\"\"\"\n", 11 | "test code for rerank\n", 12 | "\"\"\"" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 1, 18 | "id": "0644bef7-478a-4eb3-ab09-0f48ac2953d4", 19 | "metadata": {}, 20 | "outputs": [ 21 | { 22 | "name": "stderr", 23 | "output_type": "stream", 24 | "text": [ 25 | "/mnt/petrelfs/liuziyu/miniconda3/envs/soda/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", 26 | " from .autonotebook import tqdm as notebook_tqdm\n", 27 | "/mnt/petrelfs/liuziyu/miniconda3/envs/soda/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", 28 | " return self.fget.__get__(instance, owner)()\n" 29 | ] 30 | } 31 | ], 32 | "source": [ 33 | "from sentence_transformers import CrossEncoder\n", 34 | "cross_encoder_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', max_length=512)" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 7, 40 | "id": "93af7e00-0f27-4b24-bd83-1cf1dbf52552", 41 | "metadata": {}, 42 | "outputs": [ 43 | { 44 | "name": "stdout", 45 | "output_type": "stream", 46 | "text": [ 47 | "[-9.597101 -8.573202 -7.995801 -0.78380984]\n", 48 | "['Thank you', 'you are welcome', 'Good afternoon', 'Today is Friday']\n" 49 | ] 50 | } 51 | ], 52 | "source": [ 53 | "import numpy as np\n", 54 | "query_text = \"Thanks\"\n", 55 | "str_list = [\"Today is Friday\", \"Good afternoon\", \"you are welcome\", \"Thank you\"]\n", 56 | "input_cross_encoder = [(query_text, s) for s in str_list]\n", 57 | "rerank_scores = cross_encoder_model.predict(input_cross_encoder)\n", 58 | "print(rerank_scores)\n", 59 | "sorted_str_list = [x for _, x in sorted(zip(rerank_scores, str_list), reverse=True)]\n", 60 | "print(sorted_str_list)" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": null, 66 | "id": "c29a3b38-f975-4c34-ad5c-df16095a1b8c", 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [] 70 | } 71 | ], 72 | "metadata": { 73 | "kernelspec": { 74 | "display_name": "Python 3 (ipykernel)", 75 | "language": "python", 76 | "name": "python3" 77 | }, 78 | "language_info": { 79 | "codemirror_mode": { 80 | "name": "ipython", 81 | "version": 3 82 | }, 83 | "file_extension": ".py", 84 | "mimetype": "text/x-python", 85 | "name": "python", 86 | "nbconvert_exporter": "python", 87 | "pygments_lexer": "ipython3", 88 | "version": "3.10.13" 89 | } 90 | }, 91 | "nbformat": 4, 92 | "nbformat_minor": 5 93 | } 94 | -------------------------------------------------------------------------------- /service/utils.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append("../") 3 | from mllm.soda_mllm import mllm_openai 4 | from web_search.utils import search, preprocess_search_results, merge_snippet, get_web_contents 5 | 6 | ### input snippet to LLM 7 | async def web_search_snippet(query, search_engine='google', search_num=10, search_type=None): 8 | try: 9 | results = search(search_engine, query) 10 | urls, title, snippet = preprocess_search_results(search_engine, results, search_num, search_type) 11 | web_contents_combined = merge_snippet(snippet) 12 | answer = mllm_openai(query,web_contents_combined) 13 | return answer 14 | except Exception as e: 15 | print(f"Web search error: {e}") 16 | 17 | ### input whole web page to LLM 18 | async def web_search_pagehtml(query, search_engine='google', search_num=3, search_type=None): 19 | try: 20 | results = search(search_engine, query) 21 | urls, title, snippet = preprocess_search_results(search_engine, results, search_num, search_type) 22 | web_contents = await get_web_contents(urls) 23 | print(len((web_contents))) 24 | web_contents_combined = " ".join(web_contents) 25 | answer = mllm_openai(query,web_contents_combined) 26 | return answer 27 | except Exception as e: 28 | print(f"Web search error: {e}") 29 | 30 | ### retrieve database 31 | def rag_database(query, text_num, text_collection): 32 | try: 33 | ans = text_collection.query( 34 | query_texts=[query], 35 | n_results=text_num 36 | ) 37 | ans = ans["documents"][0] 38 | ans_combined = merge_snippet(ans) 39 | print("RAG text:") 40 | print(ans_combined) 41 | answer = mllm_openai(query,ans_combined) 42 | return answer 43 | except Exception as e: 44 | print(f"RAG database error: {e}") -------------------------------------------------------------------------------- /web_search/Bing_API.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "\"\"\"\n", 10 | "input your Bing API and test web search\n", 11 | "\"\"\"" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 1, 17 | "metadata": { 18 | "executionInfo": { 19 | "elapsed": 1375, 20 | "status": "ok", 21 | "timestamp": 1712481598835, 22 | "user": { 23 | "displayName": "刘某", 24 | "userId": "17997477929294889377" 25 | }, 26 | "user_tz": -480 27 | }, 28 | "id": "o56ILroftBdi" 29 | }, 30 | "outputs": [], 31 | "source": [ 32 | "subscription_key = \"********************************\"\n", 33 | "assert subscription_key" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 5, 39 | "metadata": { 40 | "executionInfo": { 41 | "elapsed": 315, 42 | "status": "ok", 43 | "timestamp": 1712481618047, 44 | "user": { 45 | "displayName": "刘某", 46 | "userId": "17997477929294889377" 47 | }, 48 | "user_tz": -480 49 | }, 50 | "id": "pf8pIZzVulyN" 51 | }, 52 | "outputs": [], 53 | "source": [ 54 | "search_url = \"https://api.bing.microsoft.com/v7.0/search\"\n", 55 | "search_term = \"LLaVa\"" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 6, 61 | "metadata": { 62 | "executionInfo": { 63 | "elapsed": 524, 64 | "status": "ok", 65 | "timestamp": 1712481620257, 66 | "user": { 67 | "displayName": "刘某", 68 | "userId": "17997477929294889377" 69 | }, 70 | "user_tz": -480 71 | }, 72 | "id": "-3Cmm9Gf23fr" 73 | }, 74 | "outputs": [], 75 | "source": [ 76 | "import requests\n", 77 | "\n", 78 | "headers = {\"Ocp-Apim-Subscription-Key\": subscription_key}\n", 79 | "params = {\"q\": search_term, \"textDecorations\": True, \"textFormat\": \"HTML\"}\n", 80 | "response = requests.get(search_url, headers=headers, params=params)\n", 81 | "response.raise_for_status()\n", 82 | "search_results = response.json()" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 11, 88 | "metadata": { 89 | "colab": { 90 | "base_uri": "https://localhost:8080/", 91 | "height": 436 92 | }, 93 | "executionInfo": { 94 | "elapsed": 316, 95 | "status": "ok", 96 | "timestamp": 1712481635040, 97 | "user": { 98 | "displayName": "刘某", 99 | "userId": "17997477929294889377" 100 | }, 101 | "user_tz": -480 102 | }, 103 | "id": "DcXJq5lf3AKc", 104 | "outputId": "6e8c8bdc-c371-4139-b6c7-18a01a5c5b5a" 105 | }, 106 | "outputs": [ 107 | { 108 | "data": { 109 | "text/html": [ 110 | "\n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | "\n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | "\n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | "\n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | "\n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | "\n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | "\n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | "\n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | "\n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | "\n", 147 | " \n", 148 | " \n", 149 | "
haotian-liu/LLaVA: [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond. - GitHub[10/26] 🔥 LLaVA-1.5 with LoRA achieves comparable performance as full-model finetuning, with a reduced GPU RAM requirement (ckpts, script). We also provide a doc on how to finetune LLaVA-1.5 on your own dataset with LoRA. [10/12] Check out the Korean]
LLaVALLaVA represents a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding, achieving impressive chat capabilities mimicking spirits of the multimodal GPT-4 and setting a new state-of-the-art accuracy on Science QA.
LLaVAImage. Drop Image Here - or - Click to Upload. Examples. What is unusual about this image? What are the things I should be cautious about when I visit here? Parameters . LLaVA Chatbot. 👍 Upvote.
LLaVA（Large Language and Vision Assistant）大模型 - 知乎LLaVA（Large Language and Vision Assistant）是一个由威斯康星大学麦迪逊分校、微软研究院和哥伦比亚大学研究者共同发布的多模态大模型。. 该模型展示出了一些接近多模态 GPT-4 的图文理解能力：相对于 GPT-4 获得了 85.1% 的相对得分。. 当在科学问答（Science QA）上进行 ...
LLaVA-1.6: Improved reasoning, OCR, and world knowledgeToday, we are thrilled to present LLaVA-1.6, with improved reasoning, OCR, and world knowledge. LLaVA-1.6 even exceeds Gemini Pro on several benchmarks. Compared with LLaVA-1.5, LLaVA-1.6 has several improvements: Increasing the input image to 4x
LLaVA: Large Language and Vision Assistant - Microsoft ResearchLLaVA is an open-source project, collaborating with research community to advance the state-of-the-art in AI. LLaVA represents the first end-to-end trained large multimodal model (LMM) that achieves impressive chat capabilities mimicking spirits of the multimodal GPT-4.
LLaVA-InteractiveLLaVA-Interactive is a system-level synergy of the inference stages of three models, without additional model training. It is surprisingly cheap to build. Checkout our code release on GitHub. For better demo experience, please play LLaVA-Interactive in a seperate tab by clicking me.
[2304.08485] Visual Instruction Tuning - arXiv.orgBy instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.Our early
Releases · haotian-liu/LLaVA · GitHubRelease v1.1.0. 🔥 LLaVA-1.5 is out! This release supports LLaVA-1.5 model inference and serving. We will release the training scripts, data, and evaluation scripts on benchmarks in the coming week.
LLaVA/README.md at main · haotian-liu/LLaVA · GitHub[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond. - LLaVA/README.md at main · haotian-liu/LLaVA
" 150 | ], 151 | "text/plain": [ 152 | "" 153 | ] 154 | }, 155 | "execution_count": 11, 156 | "metadata": {}, 157 | "output_type": "execute_result" 158 | } 159 | ], 160 | "source": [ 161 | "from IPython.display import HTML\n", 162 | "\n", 163 | "rows = \"\\n\".join([\"\"\"\n", 164 | " {1}\n", 165 | " {2}\n", 166 | " \"\"\".format(v[\"url\"], v[\"name\"], v[\"snippet\"])\n", 167 | " for v in search_results[\"webPages\"][\"value\"]])\n", 168 | "HTML(\"{0}
\".format(rows))" 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": null, 174 | "metadata": {}, 175 | "outputs": [], 176 | "source": [] 177 | } 178 | ], 179 | "metadata": { 180 | "colab": { 181 | "authorship_tag": "ABX9TyNg5Jz6VfBt+1bKMOC2VI7z", 182 | "provenance": [] 183 | }, 184 | "kernelspec": { 185 | "display_name": "Python 3 (ipykernel)", 186 | "language": "python", 187 | "name": "python3" 188 | }, 189 | "language_info": { 190 | "codemirror_mode": { 191 | "name": "ipython", 192 | "version": 3 193 | }, 194 | "file_extension": ".py", 195 | "mimetype": "text/x-python", 196 | "name": "python", 197 | "nbconvert_exporter": "python", 198 | "pygments_lexer": "ipython3", 199 | "version": "3.10.13" 200 | } 201 | }, 202 | "nbformat": 4, 203 | "nbformat_minor": 4 204 | } 205 | -------------------------------------------------------------------------------- /web_search/Google_API.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "bd73e766", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "\"\"\"\n", 11 | "input your Google API and test web search\n", 12 | "\"\"\"" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 4, 18 | "id": "a0ea24df-ef76-40a5-9d68-52fef825d7d6", 19 | "metadata": {}, 20 | "outputs": [ 21 | { 22 | "name": "stdout", 23 | "output_type": "stream", 24 | "text": [ 25 | "Enter your search query: llava\n" 26 | ] 27 | } 28 | ], 29 | "source": [ 30 | "import requests\n", 31 | "\n", 32 | "def google_search(query):\n", 33 | " api_key = '**************************************'\n", 34 | " cx = '**********************************'\n", 35 | " url = 'https://www.googleapis.com/customsearch/v1'\n", 36 | " params = {'key': api_key, 'cx': cx, 'q': query}\n", 37 | " response = requests.get(url, params=params)\n", 38 | " data = response.json()\n", 39 | " return data['items']\n", 40 | "\n", 41 | "query = input('Enter your search query: ')\n", 42 | "search_results = google_search(query)" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 5, 48 | "id": "4ad27ded-c9af-43d3-93b4-a67b4e78c825", 49 | "metadata": {}, 50 | "outputs": [ 51 | { 52 | "data": { 53 | "text/html": [ 54 | "\n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | "\n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | "\n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | "\n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | "\n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | "\n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | "\n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | "\n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | "\n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | "\n", 91 | " \n", 92 | " \n", 93 | "
LLaVALLaVA Model. We introduce LLaVA (Large Language-and-Vision Assistant) , an end-to-end trained large multimodal model that connects a vision encoder and LLM for ...
haotian-liu/LLaVA: [NeurIPS'23 Oral] Visual Instruction ... - GitHubChat about images using LLaVA without the need of Gradio interface. It also supports multiple GPUs, 4-bit and 8-bit quantized inference. With 4-bit quantization ...
Visual Instruction TuningApr 17, 2023 ... Our early experiments show that LLaVA demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal ...
LLaVAThe service is a research preview intended for non-commercial use only, subject to the model License of LLaMA, Terms of Use of the data generated by OpenAI, and ...
LLaVA: Large Language and Vision Assistant - Microsoft ResearchLLaVA is an open-source project, collaborating with research community to advance the state-of-the-art in AI. LLaVA represents the first end-to-end trained ...
Image descriptions with LLAVA : r/LocalLLaMADec 30, 2023 ... Image descriptions with LLAVA ... Here is a \"good\" label: The image features a comic strip with two main characters: an angel and a runner. The ...
LLaVaLLaVa is an open-source chatbot trained by fine-tuning LlamA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language ...
[llama.cpp] Experimental LLaVA 1.6 Quants (34B and Mistral 7B) : r ...Feb 2, 2024 ... [llama.cpp] Experimental LLaVA 1.6 Quants (34B and Mistral 7B) ... They were prepared through this hacky script and is likely missing some of the ...
How to use llava with huggingface - Transformers - Hugging Face ...Aug 27, 2023 ... Create a Visual Chatbot on AWS EC2 with LLaVA-1.5 and Runhouse. Get started with multimodal conversational models using the open-source LLaVA- ...
Has Anyone Encountered Issues with LLaVA 1.6 Models on Ollama ...Feb 9, 2024 ... Make sure you are running latest version of LLaVA, for me it runs superfast, even tho sometimes server can crash, when the response requires ...
" 94 | ], 95 | "text/plain": [ 96 | "" 97 | ] 98 | }, 99 | "execution_count": 5, 100 | "metadata": {}, 101 | "output_type": "execute_result" 102 | } 103 | ], 104 | "source": [ 105 | "from IPython.display import HTML\n", 106 | "\n", 107 | "rows = \"\\n\".join([\"\"\"\n", 108 | " {1}\n", 109 | " {2}\n", 110 | " \"\"\".format(v[\"link\"], v[\"title\"], v[\"snippet\"])\n", 111 | " for v in search_results])\n", 112 | "HTML(\"{0}
\".format(rows))" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": null, 118 | "id": "c58aea72-a4a8-4131-b03a-e86d5d7707c9", 119 | "metadata": {}, 120 | "outputs": [], 121 | "source": [] 122 | } 123 | ], 124 | "metadata": { 125 | "kernelspec": { 126 | "display_name": "Python 3 (ipykernel)", 127 | "language": "python", 128 | "name": "python3" 129 | }, 130 | "language_info": { 131 | "codemirror_mode": { 132 | "name": "ipython", 133 | "version": 3 134 | }, 135 | "file_extension": ".py", 136 | "mimetype": "text/x-python", 137 | "name": "python", 138 | "nbconvert_exporter": "python", 139 | "pygments_lexer": "ipython3", 140 | "version": "3.10.13" 141 | } 142 | }, 143 | "nbformat": 4, 144 | "nbformat_minor": 5 145 | } 146 | -------------------------------------------------------------------------------- /web_search/Serper_API.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 30, 6 | "id": "3e7cb826-e7da-4bc1-8024-73bce8cbb410", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "name": "stdout", 11 | "output_type": "stream", 12 | "text": [ 13 | "{\"searchParameters\":{\"q\":\"lions\",\"type\":\"videos\",\"num\":10,\"engine\":\"google\"},\"videos\":[{\"title\":\"Jake Bates DOES IT AGAIN....The Detroit Lions ... - YouTube\",\"link\":\"https://www.youtube.com/watch?v=kLX17H48ios\",\"snippet\":\"... Lions resonates with every fan, \\\"Lions Syndicate\\\" brings you another electrifying update that you simply can't miss. \\\"Jake Bates DOES ...\",\"imageUrl\":\"https://i.ytimg.com/vi/kLX17H48ios/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3mQ8U8YW-sY56CrG1KlKj-m2v9-iw\",\"duration\":\"6:51\",\"source\":\"YouTube\",\"channel\":\"Lions Syndicate\",\"date\":\"10 hours ago\",\"position\":1},{\"title\":\"Detroit Lions News And A Mock Draft - YouTube\",\"link\":\"https://www.youtube.com/watch?v=KvxOKLOkOAQ\",\"snippet\":\"Detroit Lions News And A Mock Draft In this episode of the Detroit Lions Podcast, Chris and Jeff are finally back! Its been a bit since the ...\",\"imageUrl\":\"https://i.ytimg.com/vi/KvxOKLOkOAQ/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3ll1UxiFsfNUmOnZF2k7xnLT3O6bA\",\"duration\":\"1:33:36\",\"source\":\"YouTube\",\"channel\":\"Detroit Lions Podcast\",\"date\":\"1 day ago\",\"position\":2},{\"title\":\"Diving into Detroit Lions 7-Round Mock Draft from Erik Schlitt\",\"link\":\"https://www.youtube.com/watch?v=JpaR3ACFNeE\",\"snippet\":\"Pride of Detroit's very own Erik Schlitt conducted a 7-round mock draft for the Detroit Lions. Tune in to see if Meko and Morgan agree with ...\",\"imageUrl\":\"https://i.ytimg.com/vi/JpaR3ACFNeE/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nTOOIw-4vS-OPilwrELuGG3mJoAg\",\"duration\":\"31:10\",\"source\":\"YouTube\",\"channel\":\"Pride of Detroit\",\"date\":\"1 day ago\",\"position\":3},{\"title\":\"Alim McNeill Is A BEAST For The Lions - YouTube\",\"link\":\"https://www.youtube.com/watch?v=wWF71AtKm7E\",\"snippet\":\"Alim doesn't get a lot of hype but he's a top 10 DT imo. I think he's gonna have a huge 2024 season for The Lions with double digit sacks.\",\"imageUrl\":\"https://i.ytimg.com/vi/wWF71AtKm7E/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nsm_9Q3gP0XlJP3xacRtERJRA5Sg\",\"duration\":\"22:15\",\"source\":\"YouTube\",\"channel\":\"Green Light with Chris Long\",\"date\":\"14 hours ago\",\"position\":4},{\"title\":\"Opening Day and the Detroit Lions still get the loudest ovation\",\"link\":\"https://www.youtube.com/watch?v=Difxt8qW0cs\",\"snippet\":\"Opening Day and the Detroit Lions still get the loudest ovation. 1.8K views · 5 hours ago Locked On Lions Podcast ...more. Locked On Lions. 10.7 ...\",\"imageUrl\":\"https://i.ytimg.com/vi/Difxt8qW0cs/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nVHORSmC8Qp8liO59MNEfkPBPkaQ\",\"duration\":\"25:56\",\"source\":\"YouTube\",\"channel\":\"Locked On Lions\",\"date\":\"2 days ago\",\"position\":5},{\"title\":\"Detroit Lions Mailbag Rumors: Sign Xavien Howard & Tyler ...\",\"link\":\"https://www.youtube.com/watch?v=TCUpU3dcZvU\",\"snippet\":\"Detroit Lions rumors begin with the Detroit Lions signing Free Agenct CB Xavien Howard. The Lions need help on defense and get a veteran CB ...\",\"imageUrl\":\"https://i.ytimg.com/vi/TCUpU3dcZvU/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nPek0pKF5q9EJo-Kkc6llMyh5QuA\",\"duration\":\"13:59\",\"source\":\"YouTube\",\"channel\":\"Lions Talk by Chat Sports\",\"date\":\"1 day ago\",\"position\":6},{\"title\":\"How Much BETTER Are the Detroit Lions? - YouTube\",\"link\":\"https://www.youtube.com/watch?v=IKruZqpva4s\",\"snippet\":\"Ryan Ermanni, Braylon Edwards and Tom Mazawey discuss the Detroit Lions free agent signings and how the crop of new players will affect the ...\",\"imageUrl\":\"https://i.ytimg.com/vi/IKruZqpva4s/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3mjl4P_pMfRT16kFOXcI1dvmNyq6w\",\"duration\":\"14:32\",\"source\":\"YouTube\",\"channel\":\"WoodwardSports\",\"date\":\"3 weeks ago\",\"position\":7},{\"title\":\"I Don't Think We Realize What The Detroit Lions Just Did..\",\"link\":\"https://www.youtube.com/watch?v=dYSSHgYPERM\",\"snippet\":\"No mention of Amik Robertson coming to the lions from the raiders. I'm excited to see what he can do too he wanted to be a lion baaaad.\",\"imageUrl\":\"https://i.ytimg.com/vi/dYSSHgYPERM/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3ly3aAsp2Ma_rifiEf4PsddliNd0A\",\"duration\":\"8:35\",\"source\":\"YouTube\",\"channel\":\"Football Logic\",\"date\":\"1 week ago\",\"position\":8},{\"title\":\"Detroit Lions Are Setting Up A Huge Draft Move - YouTube\",\"link\":\"https://www.youtube.com/watch?v=Fvxq3w_jR8A\",\"snippet\":\"Detroit Lions Are Setting Up A Huge Draft Move. detroit lions news and rumors. detroit lions are for sure going to trade their pick in the ...\",\"imageUrl\":\"https://i.ytimg.com/vi/Fvxq3w_jR8A/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nrmm2_-p_OJruEODf9H9armk6Btg\",\"duration\":\"8:06\",\"source\":\"YouTube\",\"channel\":\"Sports Talk Detroit\",\"date\":\"7 hours ago\",\"position\":9},{\"title\":\"Zeitler on living up to the standard that's been set - Detroit Lions\",\"link\":\"https://www.detroitlions.com/video/zeitler-on-living-up-to-the-standard-that-s-been-set\",\"snippet\":\"Wood on continued growth throughout the organization. President and CEO Rod Wood speaks to the media about the future of the Lions organization at the 2024 NFL ...\",\"imageUrl\":\"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSVOSzC6dO3nIo29HBfspJQObbJVNOR7qBiQLeSEZeCcjsp&s\",\"source\":\"Detroit Lions\",\"date\":\"3 weeks ago\",\"position\":10}]}\n" 14 | ] 15 | } 16 | ], 17 | "source": [ 18 | "import requests\n", 19 | "import json\n", 20 | "from IPython.display import HTML\n", 21 | "\n", 22 | "### choose between images or videos\n", 23 | "# search_type = \"images\"\n", 24 | "search_type = \"videos\"\n", 25 | "url = f\"https://google.serper.dev/{search_type}\"\n", 26 | "\n", 27 | "payload = json.dumps({\n", 28 | " \"q\": \"lions\",\n", 29 | " \"num\": 10\n", 30 | "})\n", 31 | "headers = {\n", 32 | " 'X-API-KEY': '*************************************',\n", 33 | " 'Content-Type': 'application/json'\n", 34 | "}\n", 35 | "\n", 36 | "response = requests.request(\"POST\", url, headers=headers, data=payload)\n", 37 | "\n", 38 | "print(response.text)" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 31, 44 | "id": "6c70f0ff-3e96-4974-b97e-12a3d5f85813", 45 | "metadata": {}, 46 | "outputs": [ 47 | { 48 | "name": "stdout", 49 | "output_type": "stream", 50 | "text": [ 51 | "\n" 52 | ] 53 | } 54 | ], 55 | "source": [ 56 | "search_results = json.loads(response.text)\n", 57 | "print(type(search_results))" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 32, 63 | "id": "6a90bde5-d68e-409e-8e8a-06b56c6b4afc", 64 | "metadata": {}, 65 | "outputs": [ 66 | { 67 | "name": "stdout", 68 | "output_type": "stream", 69 | "text": [ 70 | "dict_keys(['searchParameters', 'videos'])\n" 71 | ] 72 | } 73 | ], 74 | "source": [ 75 | "print(search_results.keys())" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": 33, 81 | "id": "220491af-2c00-48c5-96be-d52e7ec91f7c", 82 | "metadata": {}, 83 | "outputs": [ 84 | { 85 | "name": "stdout", 86 | "output_type": "stream", 87 | "text": [ 88 | "{'q': 'lions', 'type': 'videos', 'num': 10, 'engine': 'google'}\n", 89 | "10\n", 90 | "[{'title': 'Jake Bates DOES IT AGAIN....The Detroit Lions ... - YouTube', 'link': 'https://www.youtube.com/watch?v=kLX17H48ios', 'snippet': '... Lions resonates with every fan, \"Lions Syndicate\" brings you another electrifying update that you simply can\\'t miss. \"Jake Bates DOES ...', 'imageUrl': 'https://i.ytimg.com/vi/kLX17H48ios/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3mQ8U8YW-sY56CrG1KlKj-m2v9-iw', 'duration': '6:51', 'source': 'YouTube', 'channel': 'Lions Syndicate', 'date': '10 hours ago', 'position': 1}, {'title': 'Detroit Lions News And A Mock Draft - YouTube', 'link': 'https://www.youtube.com/watch?v=KvxOKLOkOAQ', 'snippet': 'Detroit Lions News And A Mock Draft In this episode of the Detroit Lions Podcast, Chris and Jeff are finally back! Its been a bit since the ...', 'imageUrl': 'https://i.ytimg.com/vi/KvxOKLOkOAQ/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3ll1UxiFsfNUmOnZF2k7xnLT3O6bA', 'duration': '1:33:36', 'source': 'YouTube', 'channel': 'Detroit Lions Podcast', 'date': '1 day ago', 'position': 2}, {'title': 'Diving into Detroit Lions 7-Round Mock Draft from Erik Schlitt', 'link': 'https://www.youtube.com/watch?v=JpaR3ACFNeE', 'snippet': \"Pride of Detroit's very own Erik Schlitt conducted a 7-round mock draft for the Detroit Lions. Tune in to see if Meko and Morgan agree with ...\", 'imageUrl': 'https://i.ytimg.com/vi/JpaR3ACFNeE/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nTOOIw-4vS-OPilwrELuGG3mJoAg', 'duration': '31:10', 'source': 'YouTube', 'channel': 'Pride of Detroit', 'date': '1 day ago', 'position': 3}, {'title': 'Alim McNeill Is A BEAST For The Lions - YouTube', 'link': 'https://www.youtube.com/watch?v=wWF71AtKm7E', 'snippet': \"Alim doesn't get a lot of hype but he's a top 10 DT imo. I think he's gonna have a huge 2024 season for The Lions with double digit sacks.\", 'imageUrl': 'https://i.ytimg.com/vi/wWF71AtKm7E/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nsm_9Q3gP0XlJP3xacRtERJRA5Sg', 'duration': '22:15', 'source': 'YouTube', 'channel': 'Green Light with Chris Long', 'date': '14 hours ago', 'position': 4}, {'title': 'Opening Day and the Detroit Lions still get the loudest ovation', 'link': 'https://www.youtube.com/watch?v=Difxt8qW0cs', 'snippet': 'Opening Day and the Detroit Lions still get the loudest ovation. 1.8K views · 5 hours ago Locked On Lions Podcast ...more. Locked On Lions. 10.7 ...', 'imageUrl': 'https://i.ytimg.com/vi/Difxt8qW0cs/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nVHORSmC8Qp8liO59MNEfkPBPkaQ', 'duration': '25:56', 'source': 'YouTube', 'channel': 'Locked On Lions', 'date': '2 days ago', 'position': 5}, {'title': 'Detroit Lions Mailbag Rumors: Sign Xavien Howard & Tyler ...', 'link': 'https://www.youtube.com/watch?v=TCUpU3dcZvU', 'snippet': 'Detroit Lions rumors begin with the Detroit Lions signing Free Agenct CB Xavien Howard. The Lions need help on defense and get a veteran CB ...', 'imageUrl': 'https://i.ytimg.com/vi/TCUpU3dcZvU/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nPek0pKF5q9EJo-Kkc6llMyh5QuA', 'duration': '13:59', 'source': 'YouTube', 'channel': 'Lions Talk by Chat Sports', 'date': '1 day ago', 'position': 6}, {'title': 'How Much BETTER Are the Detroit Lions? - YouTube', 'link': 'https://www.youtube.com/watch?v=IKruZqpva4s', 'snippet': 'Ryan Ermanni, Braylon Edwards and Tom Mazawey discuss the Detroit Lions free agent signings and how the crop of new players will affect the ...', 'imageUrl': 'https://i.ytimg.com/vi/IKruZqpva4s/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3mjl4P_pMfRT16kFOXcI1dvmNyq6w', 'duration': '14:32', 'source': 'YouTube', 'channel': 'WoodwardSports', 'date': '3 weeks ago', 'position': 7}, {'title': \"I Don't Think We Realize What The Detroit Lions Just Did..\", 'link': 'https://www.youtube.com/watch?v=dYSSHgYPERM', 'snippet': \"No mention of Amik Robertson coming to the lions from the raiders. I'm excited to see what he can do too he wanted to be a lion baaaad.\", 'imageUrl': 'https://i.ytimg.com/vi/dYSSHgYPERM/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3ly3aAsp2Ma_rifiEf4PsddliNd0A', 'duration': '8:35', 'source': 'YouTube', 'channel': 'Football Logic', 'date': '1 week ago', 'position': 8}, {'title': 'Detroit Lions Are Setting Up A Huge Draft Move - YouTube', 'link': 'https://www.youtube.com/watch?v=Fvxq3w_jR8A', 'snippet': 'Detroit Lions Are Setting Up A Huge Draft Move. detroit lions news and rumors. detroit lions are for sure going to trade their pick in the ...', 'imageUrl': 'https://i.ytimg.com/vi/Fvxq3w_jR8A/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nrmm2_-p_OJruEODf9H9armk6Btg', 'duration': '8:06', 'source': 'YouTube', 'channel': 'Sports Talk Detroit', 'date': '7 hours ago', 'position': 9}, {'title': \"Zeitler on living up to the standard that's been set - Detroit Lions\", 'link': 'https://www.detroitlions.com/video/zeitler-on-living-up-to-the-standard-that-s-been-set', 'snippet': 'Wood on continued growth throughout the organization. President and CEO Rod Wood speaks to the media about the future of the Lions organization at the 2024 NFL ...', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSVOSzC6dO3nIo29HBfspJQObbJVNOR7qBiQLeSEZeCcjsp&s', 'source': 'Detroit Lions', 'date': '3 weeks ago', 'position': 10}]\n", 91 | "{'title': 'Jake Bates DOES IT AGAIN....The Detroit Lions ... - YouTube', 'link': 'https://www.youtube.com/watch?v=kLX17H48ios', 'snippet': '... Lions resonates with every fan, \"Lions Syndicate\" brings you another electrifying update that you simply can\\'t miss. \"Jake Bates DOES ...', 'imageUrl': 'https://i.ytimg.com/vi/kLX17H48ios/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3mQ8U8YW-sY56CrG1KlKj-m2v9-iw', 'duration': '6:51', 'source': 'YouTube', 'channel': 'Lions Syndicate', 'date': '10 hours ago', 'position': 1}\n" 92 | ] 93 | } 94 | ], 95 | "source": [ 96 | "if search_type == \"images\":\n", 97 | " print(search_results[\"searchParameters\"])\n", 98 | " print(len(search_results[\"images\"]))\n", 99 | " print(search_results[\"images\"])\n", 100 | " print(search_results[\"images\"][0])\n", 101 | "elif search_type == \"videos\":\n", 102 | " print(search_results[\"searchParameters\"])\n", 103 | " print(len(search_results[\"videos\"]))\n", 104 | " print(search_results[\"videos\"])\n", 105 | " print(search_results[\"videos\"][0])" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 38, 111 | "id": "020c7e8c-a5a3-4ee9-af13-7aa3fd8ebb5c", 112 | "metadata": {}, 113 | "outputs": [ 114 | { 115 | "data": { 116 | "text/html": [ 117 | "\n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | "\n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | "\n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | "\n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | "\n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | "\n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | "\n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | "\n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | "\n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | "\n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | "
Jake Bates DOES IT AGAIN....The Detroit Lions ... - YouTube... Lions resonates with every fan, \"Lions Syndicate\" brings you another electrifying update that you simply can't miss. \"Jake Bates DOES ...https://i.ytimg.com/vi/kLX17H48ios/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3mQ8U8YW-sY56CrG1KlKj-m2v9-iw
Detroit Lions News And A Mock Draft - YouTubeDetroit Lions News And A Mock Draft In this episode of the Detroit Lions Podcast, Chris and Jeff are finally back! Its been a bit since the ...https://i.ytimg.com/vi/KvxOKLOkOAQ/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3ll1UxiFsfNUmOnZF2k7xnLT3O6bA
Diving into Detroit Lions 7-Round Mock Draft from Erik SchlittPride of Detroit's very own Erik Schlitt conducted a 7-round mock draft for the Detroit Lions. Tune in to see if Meko and Morgan agree with ...https://i.ytimg.com/vi/JpaR3ACFNeE/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nTOOIw-4vS-OPilwrELuGG3mJoAg
Alim McNeill Is A BEAST For The Lions - YouTubeAlim doesn't get a lot of hype but he's a top 10 DT imo. I think he's gonna have a huge 2024 season for The Lions with double digit sacks.https://i.ytimg.com/vi/wWF71AtKm7E/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nsm_9Q3gP0XlJP3xacRtERJRA5Sg
Opening Day and the Detroit Lions still get the loudest ovationOpening Day and the Detroit Lions still get the loudest ovation. 1.8K views · 5 hours ago Locked On Lions Podcast ...more. Locked On Lions. 10.7 ...https://i.ytimg.com/vi/Difxt8qW0cs/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nVHORSmC8Qp8liO59MNEfkPBPkaQ
Detroit Lions Mailbag Rumors: Sign Xavien Howard & Tyler ...Detroit Lions rumors begin with the Detroit Lions signing Free Agenct CB Xavien Howard. The Lions need help on defense and get a veteran CB ...https://i.ytimg.com/vi/TCUpU3dcZvU/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nPek0pKF5q9EJo-Kkc6llMyh5QuA
How Much BETTER Are the Detroit Lions? - YouTubeRyan Ermanni, Braylon Edwards and Tom Mazawey discuss the Detroit Lions free agent signings and how the crop of new players will affect the ...https://i.ytimg.com/vi/IKruZqpva4s/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3mjl4P_pMfRT16kFOXcI1dvmNyq6w
I Don't Think We Realize What The Detroit Lions Just Did..No mention of Amik Robertson coming to the lions from the raiders. I'm excited to see what he can do too he wanted to be a lion baaaad.https://i.ytimg.com/vi/dYSSHgYPERM/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3ly3aAsp2Ma_rifiEf4PsddliNd0A
Detroit Lions Are Setting Up A Huge Draft Move - YouTubeDetroit Lions Are Setting Up A Huge Draft Move. detroit lions news and rumors. detroit lions are for sure going to trade their pick in the ...https://i.ytimg.com/vi/Fvxq3w_jR8A/mqdefault.jpg?sqp=-oaymwEFCJQBEFM&rs=AMzJL3nrmm2_-p_OJruEODf9H9armk6Btg
Zeitler on living up to the standard that's been set - Detroit LionsWood on continued growth throughout the organization. President and CEO Rod Wood speaks to the media about the future of the Lions organization at the 2024 NFL ...https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSVOSzC6dO3nIo29HBfspJQObbJVNOR7qBiQLeSEZeCcjsp&s
" 167 | ], 168 | "text/plain": [ 169 | "" 170 | ] 171 | }, 172 | "metadata": {}, 173 | "output_type": "display_data" 174 | } 175 | ], 176 | "source": [ 177 | "if search_type == \"images\":\n", 178 | " rows = \"\\n\".join([\"\"\"\n", 179 | " {1}\n", 180 | " {2}\n", 181 | " \"\"\".format(v[\"link\"], v[\"title\"], v[\"imageUrl\"])\n", 182 | " for v in search_results[\"images\"]])\n", 183 | " display(HTML(\"{0}
\".format(rows)))\n", 184 | "elif search_type == \"videos\":\n", 185 | " rows = \"\\n\".join([\"\"\"\n", 186 | " {1}\n", 187 | " {2}\n", 188 | " {3}\n", 189 | " \"\"\".format(v[\"link\"], v[\"title\"], v[\"snippet\"], v[\"imageUrl\"])\n", 190 | " for v in search_results[\"videos\"]])\n", 191 | " display(HTML(\"{0}
\".format(rows)))" 192 | ] 193 | }, 194 | { 195 | "cell_type": "code", 196 | "execution_count": null, 197 | "id": "f7e23d70-e969-40de-8a1d-06b44610ccbb", 198 | "metadata": {}, 199 | "outputs": [], 200 | "source": [] 201 | } 202 | ], 203 | "metadata": { 204 | "kernelspec": { 205 | "display_name": "Python 3 (ipykernel)", 206 | "language": "python", 207 | "name": "python3" 208 | }, 209 | "language_info": { 210 | "codemirror_mode": { 211 | "name": "ipython", 212 | "version": 3 213 | }, 214 | "file_extension": ".py", 215 | "mimetype": "text/x-python", 216 | "name": "python", 217 | "nbconvert_exporter": "python", 218 | "pygments_lexer": "ipython3", 219 | "version": "3.10.13" 220 | } 221 | }, 222 | "nbformat": 4, 223 | "nbformat_minor": 5 224 | } 225 | -------------------------------------------------------------------------------- /web_search/__pycache__/utils.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/web_search/__pycache__/utils.cpython-310.pyc -------------------------------------------------------------------------------- /web_search/utils.py: -------------------------------------------------------------------------------- 1 | import re 2 | import json 3 | import urllib 4 | import urllib.request 5 | import requests 6 | import html2text 7 | import urllib.request 8 | from IPython.display import HTML 9 | import asyncio 10 | import aiohttp 11 | 12 | """ 13 | Input your search engine API here 14 | """ 15 | def google_search(query): 16 | try: 17 | api_key = '********************************' 18 | cx = '****************************' 19 | url = 'https://www.googleapis.com/customsearch/v1' 20 | params = {'key': api_key, 'cx': cx, 'q': query} 21 | response = requests.get(url, params=params) 22 | data = response.json() 23 | ### ["link"], ["title"], ["snippet"] 24 | return data['items'] 25 | except Exception as e: 26 | print(f"Google API Search Failed: {e}") 27 | raise e 28 | 29 | """ 30 | Input your search engine API here 31 | """ 32 | def bing_search(query): 33 | try: 34 | subscription_key = "*****************************" 35 | assert subscription_key 36 | search_url = "https://api.bing.microsoft.com/v7.0/search" 37 | 38 | search_term = query 39 | 40 | headers = {"Ocp-Apim-Subscription-Key": subscription_key} 41 | params = {"q": search_term, "textDecorations": True, "textFormat": "HTML"} 42 | response = requests.get(search_url, headers=headers, params=params) 43 | response.raise_for_status() 44 | search_results = response.json() 45 | 46 | ### ["url"], ["name"], ["snippet"] 47 | return search_results["webPages"]["value"] 48 | except Exception as e: 49 | print(f"Bing API Search Failed: {e}") 50 | raise e 51 | 52 | """ 53 | Input your search engine API here 54 | """ 55 | def serper_search(query, search_type): 56 | try: 57 | url = f"https://google.serper.dev/{search_type}" 58 | payload = json.dumps({ 59 | "q": query, 60 | "num": 10 61 | }) 62 | headers = { 63 | 'X-API-KEY': '**********************************', 64 | 'Content-Type': 'application/json' 65 | } 66 | response = requests.request("POST", url, headers=headers, data=payload) 67 | search_results = json.loads(response.text) 68 | 69 | if search_type == "images": 70 | ### ["link"], ["title"], ["imageUrl"] 71 | return search_results["images"] 72 | 73 | elif search_type == "videos": 74 | ### ["link"], ["title"], ["snippet"], ["imageUrl"] 75 | return search_results["videos"] 76 | except Exception as e: 77 | print(f"Serper API Search Failed: {e}") 78 | raise e 79 | 80 | """ 81 | Web search 82 | """ 83 | def search(search_engine, query, search_type=None): 84 | if search_engine == "google": 85 | search_results = google_search(query) 86 | return search_results 87 | elif search_engine == "bing": 88 | search_results = bing_search(query) 89 | return search_results 90 | elif search_engine == "serper": 91 | search_results = serper_search(query, search_type) 92 | return search_results 93 | else: 94 | print("Search engine is not support.") 95 | 96 | def preprocess_search_results(search_engine, search_results, search_num=5, search_type=None): 97 | if search_engine == "google": 98 | url = [] 99 | title = [] 100 | snippet = [] 101 | for item in search_results: 102 | url.append(item["link"]) 103 | title.append(item["title"]) 104 | snippet.append(item["snippet"]) 105 | if len(search_results)>=search_num: 106 | return url[:search_num], title[:search_num], snippet[:search_num] 107 | else: 108 | return url, title, snippet 109 | 110 | elif search_engine == "bing": 111 | url = [] 112 | title = [] 113 | snippet = [] 114 | for item in search_results: 115 | url.append(item["url"]) 116 | title.append(item["name"]) 117 | snippet.append(item["snippet"]) 118 | if len(search_results)>=search_num: 119 | return url[:search_num], title[:search_num], snippet[:search_num] 120 | else: 121 | return url, title, snippet 122 | 123 | elif search_engine == "serper": 124 | url = [] 125 | title = [] 126 | image_url = [] 127 | snippet = [] 128 | if search_type=="images": 129 | for item in search_results: 130 | url.append(item["link"]) 131 | title.append(item["title"]) 132 | image_url.append(item["imageUrl"]) 133 | if len(search_results)>=search_num: 134 | return url[:search_num], title[:search_num], image_url[:search_num] 135 | else: 136 | return url, title, image_url 137 | 138 | elif search_type == "videos": 139 | for item in search_results: 140 | url.append(item["link"]) 141 | title.append(item["title"]) 142 | image_url.append(item["imageUrl"]) 143 | snippet.append(item["snippet"]) 144 | if len(search_results)>=search_num: 145 | return url[:search_num], title[:search_num], image_url[:search_num], snippet[:search_num] 146 | else: 147 | return url, title, image_url, snippet 148 | else: 149 | print("Search engine is not support.") 150 | 151 | """ 152 | 抓取页面方法，调用该方法返回抓取到数据 153 | """ 154 | async def get_web_contents(urls): 155 | try: 156 | async with aiohttp.ClientSession(trust_env = True) as session: 157 | # tasks = [get_web_content(session, url) for url in urls] 158 | tasks = [read_pageHtml(session, url) for url in urls] 159 | web_contents = await asyncio.gather(*tasks, return_exceptions=False) 160 | return web_contents 161 | except aiohttp.ClientResponseError as e: 162 | print(f"get web contents failed: {e}") 163 | return [] 164 | 165 | async def read_pageHtml(session, url): 166 | async with session.get(url) as response: 167 | try: 168 | response.raise_for_status() 169 | print(response) 170 | try: 171 | response.encoding = 'utf-8' 172 | html = await response.text() 173 | except UnicodeDecodeError: 174 | try: 175 | response.encoding = 'gbk' 176 | html = await response.text() 177 | except Exception as e: 178 | print(f"An error occurred during decode html: {e}") 179 | 180 | # HTML(html) 181 | h = html2text.HTML2Text() 182 | h.ignore_links = True 183 | h.ignore_images = True 184 | page_string = h.handle(html) 185 | page_string = page_string.replace("\n","") 186 | return page_string 187 | 188 | except Exception as e: 189 | print(f"An error occurred during read web pages: {e}") 190 | return None 191 | 192 | def read_single_pageHtml(url): 193 | try: 194 | file = urllib.request.urlopen(url) 195 | data = file.read() 196 | # decode to string 197 | try: 198 | decoded_html = data.decode('utf-8') 199 | except UnicodeDecodeError: 200 | try: 201 | decoded_html = data.decode('gbk') 202 | except Exception as e: 203 | print(f"An error occurred during decode html: {e}") 204 | 205 | # string to html 206 | HTML(decoded_html) 207 | # html to markdown 208 | h = html2text.HTML2Text() 209 | h.ignore_links = True 210 | h.ignore_images = True 211 | page_string = h.handle(decoded_html) 212 | page_string = page_string.replace("\n","") 213 | return page_string 214 | 215 | except Exception as e: 216 | print(f"An error occurred during read web pages: {e}") 217 | return None 218 | 219 | def merge_snippet(str_list): 220 | numbered_str = "\n".join(f"{i+1}. {s}" for i, s in enumerate(str_list)) 221 | return numbered_str 222 | 223 | """ 224 | rerank 225 | """ -------------------------------------------------------------------------------- /web_ui/database/1713354159_521/RAR_arXiv.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/web_ui/database/1713354159_521/RAR_arXiv.pdf -------------------------------------------------------------------------------- /web_ui/database/1713354159_521_database/936abf26-b455-4d19-a124-b9ffeb5be592/header.bin: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/web_ui/database/1713354159_521_database/936abf26-b455-4d19-a124-b9ffeb5be592/header.bin -------------------------------------------------------------------------------- /web_ui/database/1713354159_521_database/936abf26-b455-4d19-a124-b9ffeb5be592/length.bin: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/web_ui/database/1713354159_521_database/936abf26-b455-4d19-a124-b9ffeb5be592/length.bin -------------------------------------------------------------------------------- /web_ui/database/1713354159_521_database/936abf26-b455-4d19-a124-b9ffeb5be592/link_lists.bin: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/web_ui/database/1713354159_521_database/936abf26-b455-4d19-a124-b9ffeb5be592/link_lists.bin -------------------------------------------------------------------------------- /web_ui/database/1713354159_521_database/chroma.sqlite3: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/web_ui/database/1713354159_521_database/chroma.sqlite3 -------------------------------------------------------------------------------- /web_ui/database/ff122a0aed7ded202b4cdad150eb8aff35500a8c/soda_title.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/web_ui/database/ff122a0aed7ded202b4cdad150eb8aff35500a8c/soda_title.png -------------------------------------------------------------------------------- /web_ui/database/tmp7hym5i1j: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/web_ui/database/tmp7hym5i1j -------------------------------------------------------------------------------- /web_ui/soda_title.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liuziyu77/Soda/f126ff09515c8b907b805d3fc8e9f2ba3529def1/web_ui/soda_title.png -------------------------------------------------------------------------------- /web_ui/web_ui.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append("../") 3 | import torch 4 | import gradio as gr 5 | from mllm.soda_mllm import mllm_openai, mllm_IXC2 6 | from web_search.utils import search, preprocess_search_results, merge_snippet 7 | from web_search.utils import bing_search 8 | import os 9 | import uuid 10 | os.environ["no_proxy"] = "127.0.0.1,localhost" 11 | os.environ["GRADIO_TEMP_DIR"] = "./database" 12 | import shutil 13 | import time 14 | import threading 15 | import random 16 | from RAG.utils import preprocess_files, build_text_database 17 | from RAG.utils import build_image_database 18 | from service.utils import rag_database 19 | from sentence_transformers import CrossEncoder 20 | from transformers import AutoModel, AutoTokenizer 21 | from transformers import (AutoModelForCausalLM, AutoTokenizer, 22 | StoppingCriteria, StoppingCriteriaList) 23 | from transformers.generation import GenerationConfig 24 | from peft import AutoPeftModelForCausalLM 25 | 26 | torch.manual_seed(1234) 27 | 28 | ### globle variable 29 | user_directory = None 30 | image_user_directory = None 31 | txt_collection = None 32 | img_collection = None 33 | 34 | ### init model 35 | ## crose_encoder 36 | cross_encoder_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', max_length=512) 37 | ## internlm-xcomposer 38 | torch.set_grad_enabled(False) 39 | IXC2_model = AutoModelForCausalLM.from_pretrained('internlm/internlm-xcomposer2-vl-7b', trust_remote_code=True).cuda().eval() 40 | IXC2_tokenizer = AutoTokenizer.from_pretrained('internlm/internlm-xcomposer2-vl-7b', trust_remote_code=True) 41 | 42 | """ 43 | set your database path here 44 | """ 45 | base_directory = "/mnt/petrelfs/liuziyu/V3Det/Soda_Dev/web_ui/database/" 46 | # max keep time（s） 47 | max_file_age = 3600 # 1 hour 48 | 49 | custom_css = """ 50 | body { font-family: 'Arial'; background-color: #f4f4f4; color: #333; } 51 | .header { background-color: #5D1049; padding: 20px; color: #fff; text-align: center; } 52 | .header h1 { margin: 0; } 53 | .button, .tab { background-color: #EAC435; color: #5D1049; border: none; border-radius: 5px; } 54 | .button:hover, .tab:hover { background-color: #F3D70B; } 55 | input, textarea, .textbox { border-radius: 5px; } 56 | img { border: none; display: block; margin: 0 auto; } 57 | .intro p { color: #333; font-size: 18px; text-align: center; } 58 | # 59 | """ 60 | # h1 { 61 | # color: black; 62 | # font-size: 36px; 63 | # text-align: center; 64 | # } 65 | # p { 66 | # color: #555; 67 | # font-size: 24px; 68 | # text-align: center; 69 | # } 70 | 71 | def clear_old_files(): 72 | while True: 73 | now = time.time() 74 | for filename in os.listdir(base_directory): 75 | file_path = os.path.join(base_directory, filename) 76 | if os.path.isdir(file_path): 77 | creation_time = os.path.getctime(file_path) 78 | if now - creation_time > max_file_age: 79 | shutil.rmtree(file_path) 80 | time.sleep(3600) # check 1 time per hour 81 | # clear old data 82 | threading.Thread(target=clear_old_files, daemon=True).start() 83 | 84 | 85 | def web_search_gradio(query, search_engine, mllm, search_num): 86 | results = search(search_engine, query) 87 | print(search_engine) 88 | urls, title, snippet = preprocess_search_results(search_engine, results, search_num) 89 | 90 | ### rerank 91 | input_cross_encoder = [(query, s) for s in snippet] 92 | rerank_scores = cross_encoder_model.predict(input_cross_encoder) 93 | sorted_str_list = [x for _, x in sorted(zip(rerank_scores, snippet), reverse=True)] 94 | 95 | web_contents_combined = merge_snippet(sorted_str_list) 96 | 97 | if mllm == "GPT4-V": 98 | answer = mllm_openai(query, web_contents_combined) 99 | elif mllm =="InternLM-Xcomposer2": 100 | answer = mllm_IXC2(IXC2_model, IXC2_tokenizer, query, web_contents_combined) 101 | links = [] 102 | for i in range(len(urls)): 103 | links.append((title[i], urls[i])) 104 | links = "\n".join([f"[{title}]({url})" for title, url in links]) 105 | return links, answer 106 | 107 | def process_uploaded_text_file(file_upload): 108 | global user_directory 109 | global txt_collection 110 | 111 | try: 112 | session_id = f"{int(time.time())}_{random.randint(0, 1000)}" 113 | user_directory = os.path.join(base_directory, session_id) 114 | os.makedirs(user_directory, exist_ok=True) 115 | target_path = os.path.join(user_directory, file_upload.name.split("/")[-1]) 116 | shutil.move(file_upload.name, target_path) 117 | 118 | docs = preprocess_files(file_path = target_path,chunk_size=500, chunk_overlap=50) 119 | txt_collection = build_text_database(docs, batch_size=40000, encoder="intfloat/multilingual-e5-base", database_name = session_id, database_path = user_directory+"_database") 120 | return "Database has been built." 121 | except Exception as e: 122 | print("Error:", e) 123 | return "Database built error" 124 | 125 | def text_rag_gradio(query, n_results, mllm): 126 | global txt_collection 127 | try: 128 | ans = txt_collection.query( 129 | query_texts=[query], 130 | n_results=n_results 131 | ) 132 | ans = ans["documents"][0] 133 | 134 | ### rerank 135 | input_cross_encoder = [(query, s) for s in ans] 136 | rerank_scores = cross_encoder_model.predict(input_cross_encoder) 137 | sorted_str_list = [x for _, x in sorted(zip(rerank_scores, ans), reverse=True)] 138 | 139 | ans_combined = merge_snippet(sorted_str_list) 140 | if mllm == "GPT4-V": 141 | answer = mllm_openai(query, ans_combined) 142 | elif mllm =="InternLM-Xcomposer2": 143 | answer = mllm_IXC2(IXC2_model, IXC2_tokenizer, query, ans_combined) 144 | return ans_combined,answer 145 | except Exception as e: 146 | print(f"RAG database error: {e}") 147 | return f"{e}" 148 | 149 | 150 | def process_uploaded_image_fold_file(images_upload): 151 | global img_collection 152 | global image_user_directory 153 | file_paths = [file.name for file in images_upload] 154 | session_id = f"{int(time.time())}_{random.randint(0, 1000)}" 155 | image_user_directory = os.path.join(base_directory, session_id) 156 | os.makedirs(image_user_directory, exist_ok=True) 157 | 158 | for file_path in file_paths: 159 | target_path = os.path.join(image_user_directory, file_path.split("/")[-1]) 160 | shutil.move(file_path, target_path) 161 | 162 | img_collection = build_image_database(fold_path = image_user_directory, database_name = session_id, database_path = image_user_directory+"_database") 163 | 164 | return "Database has been built." 165 | 166 | def image_rag_gradio(image_upload): 167 | test_image_path = image_upload.name 168 | n_pictures = 4 169 | ans = img_collection.query( 170 | query_uris=[test_image_path], # A list of strings representing URIs to data 171 | n_results=n_pictures 172 | ) 173 | retrieved_image_path = [] 174 | retrieved_image_path.append(image_user_directory+"/"+ans["metadatas"][0][0]["ID"]+'.jpg') 175 | retrieved_image_path.append(image_user_directory+"/"+ans["metadatas"][0][1]["ID"]+'.jpg') 176 | retrieved_image_path.append(image_user_directory+"/"+ans["metadatas"][0][2]["ID"]+'.jpg') 177 | return [test_image_path],retrieved_image_path 178 | 179 | 180 | """ 181 | Gradio main function 182 | """ 183 | def main(): 184 | # SimplyRetrieve App 185 | with gr.Blocks(title="SODA Agent",css=custom_css) as app: 186 | gr.Markdown(""" 187 |
188 |

189 |

190 |
191 | """) 192 | with gr.Row(): 193 | gr.Markdown("") 194 | gr.Image(value="./soda_title.png", show_download_button=False, 195 | container=False) 196 | gr.Markdown("") 197 | 198 | gr.Markdown(""" 199 | 200 |
201 |

202 | 🚀🚀🚀Welcome to the SODA: Search, Organize, Discover Anything. This multi-functional tool helps you search the web, process text and images, and leverage large language models to derive insights. 🚀🚀🚀 203 |

204 |
205 | 206 | Choose a tab to begin using specific functionalities: 207 | 208 | 🌐 **Web Search**: Search the internet using your preferred search engine. 209 | 210 | 🔎 **Text Retrieve**: Upload and process text files, and ask questions based on their content. 211 | 212 | 🌅 **Image Retrieve**: Manage and retrieve images based on uploaded content. 213 | 214 | 215 | Link to our Github: [SODA](https://github.com/Liuziyu77/Soda) 216 | """) 217 | 218 | with gr.Tab("Web Search"): 219 | with gr.Row(): 220 | # Input section - where users can type their query 221 | web_search_query = gr.Textbox(label="Enter your query here", placeholder="Type your query...", lines=2) 222 | search_engine_dropdown = gr.Dropdown(label="Select Search Engine", choices=["google", "bing"], 223 | value="bing") 224 | web_mllm_dropdown = gr.Dropdown(label="Select mllms", choices=["GPT4-V", "InternLM-Xcomposer2"], 225 | value="GPT4-V") 226 | search_num_slider = gr.Slider(label="Number of results", minimum=0, maximum=10, step=1, value=10) 227 | web_search_button = gr.Button("Search") 228 | 229 | with gr.Row(): 230 | # Output section - divided into two parts: search results and LLM response 231 | with gr.Column(scale=1): 232 | web_search_results = gr.Textbox(label="Web Search Results", placeholder="Search results from web will appear here...", lines=10, interactive=False) 233 | with gr.Column(scale=1): 234 | web_search_answer = gr.Textbox(label="LLM Response", placeholder="LLM's response will appear here...", lines=10, interactive=False) 235 | 236 | with gr.Tab("Text Retrieve"): 237 | with gr.Row(): 238 | with gr.Column(scale=1): 239 | text_file_upload = gr.components.File(label="Upload a file or folder") 240 | text_file_upload_status = gr.Textbox(label="Database status:", placeholder="No database...") 241 | process_text_file_button = gr.Button("Process File") 242 | with gr.Row(): 243 | text_mllm_dropdown = gr.Dropdown(label="Select mllms", choices=["GPT4-V", "InternLM-Xcomposer2"], 244 | value="GPT4-V") 245 | text_rag_n_results_slider = gr.Slider(label="Number of Retrieved Texts", minimum=1, maximum=10,step=1, value=3) 246 | with gr.Column(scale=1): 247 | text_question_input = gr.Textbox(label="Enter your question", placeholder="Type your question here...", lines=2) 248 | text_rag_button = gr.Button("Submit Question") 249 | with gr.Row(): 250 | # Display section for search results and answer 251 | with gr.Column(scale=1): 252 | text_rag_results = gr.Textbox(label="Local Database Search Results", placeholder="Search results from local database will appear here...", lines=10, interactive=False) 253 | with gr.Column(scale=1): 254 | text_rag_answer = gr.Textbox(label="LLM Response", placeholder="LLM's response will appear here....", lines=10, interactive=False) 255 | 256 | with gr.Tab("Image Retrieve"): 257 | with gr.Row(): 258 | with gr.Column(scale=1): 259 | image_file_input = gr.components.File(label="Upload a file or folder", file_count="multiple") 260 | image_file_input_status = gr.Textbox(label="Database status:", placeholder="No database...") 261 | process_image_file_button = gr.Button("Process Images") 262 | with gr.Column(scale=1): 263 | image_question_upload = gr.components.File(label="Upload a single image for retrieval") 264 | image_rag_button = gr.Button("Retrieve Images") 265 | with gr.Row(): 266 | with gr.Column(scale=1): 267 | origin_images = gr.Gallery(label="Original Images", show_label=True, columns=[3], rows=[1], object_fit="contain", height="auto") 268 | with gr.Column(scale=1): 269 | retrieved_images = gr.Gallery(label="Retrieved Images", show_label=True, columns=[3], rows=[1], object_fit="contain", height="auto") 270 | 271 | # Web Search Event 272 | web_search_button.click( 273 | fn=web_search_gradio, 274 | inputs=[web_search_query, search_engine_dropdown, web_mllm_dropdown, search_num_slider], 275 | outputs=[web_search_results, web_search_answer] 276 | ) 277 | # Text RAG Event 278 | process_text_file_button.click(process_uploaded_text_file, inputs = text_file_upload, outputs = text_file_upload_status) 279 | text_rag_button.click( 280 | fn=text_rag_gradio, 281 | inputs=[text_question_input, text_rag_n_results_slider, text_mllm_dropdown], 282 | outputs=[text_rag_results, text_rag_answer] 283 | ) 284 | # Image RAG Event 285 | process_image_file_button.click(process_uploaded_image_fold_file, inputs = image_file_input, outputs = image_file_input_status) 286 | image_rag_button.click(image_rag_gradio, inputs=image_question_upload, outputs=[origin_images,retrieved_images]) 287 | 288 | # App Main Settings 289 | app.queue(max_size=100) 290 | app.launch(share=True, server_name="0.0.0.0", server_port=10078) 291 | 292 | if __name__ == "__main__": 293 | main() 294 | --------------------------------------------------------------------------------