# embeddings_queue

created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: d2ea2bce-1b1a-49d9-ad66-3d08fc7f96b6
encoding: FLOAT32
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Ideal.\n    primary_image      https://m.media-amazon.com/images/I/410lp8Rwjv.\n    style                                                   Contemporary\n    material                                                        Wood\n    color                                                          Brown\n    url                             https://www.amazon.com/dp/B07BQHWWRW\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 273, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              BRIAN & DANY Foldable Storage Ottoman Footrest.\n    primary_image      https://m.media-amazon.com/images/I/413YS7nQBn.\n    style                                                         Modern\n    material                                                        Wood\n    color                                                          Khaki\n    url                             https://www.amazon.com/dp/B08BNDNGHJ\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 274, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              ReplacementScrews Bed Frame Rail Screws Compat.\n    primary_image      https://m.media-amazon.com/images/I/31-vY+TuWO.\n    style                                                           Flat\n    material                                                       Metal\n    color                                                   Multicolored\n    url                             https://www.amazon.com/dp/B0CMXYMDH4\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 275, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              mDesign Round Metal in-Lay Accent Table with H.\n    primary_image      https://m.media-amazon.com/images/I/413u0H2o1I.\n    style                                                         Modern\n    material                                                Steel/Mirror\n    color                                              Soft Brass/Mirror\n    url                             https://www.amazon."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: a28f185e-f3bc-436b-ad8e-266a3434c15a
encoding: FLOAT32
metadata:
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\"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "10ea5ba1-792b-4899-9949-2def50553156", "doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "ref_doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "chroma:document": "primary_image      https://m.media-amazon.com/images/I/410lp8Rwjv.\n    style                                                   Contemporary\n    material                                                        Wood\n    color                                                          Brown\n    url                             https://www.amazon.com/dp/B07BQHWWRW\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 273, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              BRIAN & DANY Foldable Storage Ottoman Footrest.\n    primary_image      https://m.media-amazon.com/images/I/413YS7nQBn.\n    style                                                         Modern\n    material                                                        Wood\n    color                                                          Khaki\n    url                             https://www.amazon.com/dp/B08BNDNGHJ\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 274, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              ReplacementScrews Bed Frame Rail Screws Compat.\n    primary_image      https://m.media-amazon.com/images/I/31-vY+TuWO.\n    style                                                           Flat\n    material                                                       Metal\n    color                                                   Multicolored\n    url                             https://www.amazon.com/dp/B0CMXYMDH4\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 275, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              mDesign Round Metal in-Lay Accent Table with H.\n    primary_image      https://m.media-amazon.com/images/I/413u0H2o1I.\n    style                                                         Modern\n    material                                                Steel/Mirror\n    color                                              Soft Brass/Mirror\n    url                             https://www.amazon.com/dp/B08XPR7662\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 276, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              NSFRCLHO Round End Table, Tempered Glass End T.\n    primary_image      https://m.media-amazon.com/images/I/41z8YktAkG.\n    style                                                        Classic\n    material                                              Tempered Glass\n    color                                                          Black\n    url                             https://www.amazon.com/dp/B089YWCTN2\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 277, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              pranovo Metal Sofa Handle Cable Recliner Chair.\n    primary_image      https://m.media-amazon.com/images/I/3144eTNpeE.\n    style                                                            NaN\n    material                                                    Aluminum\n    color                                                          Black\n    url                             https://www.amazon.com/dp/B00R5VYYIG\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 278, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Stuffed Animal Storage Bean Bag Chair Cover fo.\n    primary_image      https://m.media-amazon.com/images/I/41dBlMhHTh.\n    style                                                            NaN\n    material                                                      velvet\n    color                                                     Cover Only\n    url                             https://www.amazon."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 1a9acf2f-799e-4d67-b0d6-88a1a551e5cb
encoding: FLOAT32
metadata:
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\"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "10ea5ba1-792b-4899-9949-2def50553156", "doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "ref_doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "chroma:document": "primary_image      https://m.media-amazon.com/images/I/413u0H2o1I.\n    style                                                         Modern\n    material                                                Steel/Mirror\n    color                                              Soft Brass/Mirror\n    url                             https://www.amazon.com/dp/B08XPR7662\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 276, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              NSFRCLHO Round End Table, Tempered Glass End T.\n    primary_image      https://m.media-amazon.com/images/I/41z8YktAkG.\n    style                                                        Classic\n    material                                              Tempered Glass\n    color                                                          Black\n    url                             https://www.amazon.com/dp/B089YWCTN2\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 277, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              pranovo Metal Sofa Handle Cable Recliner Chair.\n    primary_image      https://m.media-amazon.com/images/I/3144eTNpeE.\n    style                                                            NaN\n    material                                                    Aluminum\n    color                                                          Black\n    url                             https://www.amazon.com/dp/B00R5VYYIG\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 278, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Stuffed Animal Storage Bean Bag Chair Cover fo.\n    primary_image      https://m.media-amazon.com/images/I/41dBlMhHTh.\n    style                                                            NaN\n    material                                                      velvet\n    color                                                     Cover Only\n    url                             https://www.amazon.com/dp/B08JLH2PVH\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 279, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Pinkpum Shoe Ogranizer for Closet, 12 Pack Sho.\n    primary_image      https://m.media-amazon.com/images/I/41huFJxt+F.\n    style                                                            NaN\n    material                             Acrylonitrile Butadiene Styrene\n    color                                                          Clear\n    url                             https://www.amazon.com/dp/B0B6P65LGH\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 280, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              BOOSDEN Padded Folding Chair 2 Pack, Foldable .\n    primary_image      https://m.media-amazon.com/images/I/41H64LdIQ8.\n    style                                                            NaN\n    material                                                         NaN\n    color                                       2 Pack Thick Chair | Red\n    url                             https://www.amazon.com/dp/B0CC4SZBQ9\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 281, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Kingston Brass SCC8247 Edenscape Pedestal Stee.\n    primary_image      https://m.media-amazon.com/images/I/31FOa-k+Et.\n    style                                                         Modern\n    material                                                 Alloy Steel\n    color                                                  Brushed Brass\n    url                             https://www.amazon."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 39717a2a-b825-4edd-bf23-2c2039135e35
encoding: FLOAT32
metadata:
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\"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "10ea5ba1-792b-4899-9949-2def50553156", "doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "ref_doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "chroma:document": "style                                                            NaN\n    material                                                      velvet\n    color                                                     Cover Only\n    url                             https://www.amazon.com/dp/B08JLH2PVH\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 279, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Pinkpum Shoe Ogranizer for Closet, 12 Pack Sho.\n    primary_image      https://m.media-amazon.com/images/I/41huFJxt+F.\n    style                                                            NaN\n    material                             Acrylonitrile Butadiene Styrene\n    color                                                          Clear\n    url                             https://www.amazon.com/dp/B0B6P65LGH\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 280, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              BOOSDEN Padded Folding Chair 2 Pack, Foldable .\n    primary_image      https://m.media-amazon.com/images/I/41H64LdIQ8.\n    style                                                            NaN\n    material                                                         NaN\n    color                                       2 Pack Thick Chair | Red\n    url                             https://www.amazon.com/dp/B0CC4SZBQ9\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 281, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Kingston Brass SCC8247 Edenscape Pedestal Stee.\n    primary_image      https://m.media-amazon.com/images/I/31FOa-k+Et.\n    style                                                         Modern\n    material                                                 Alloy Steel\n    color                                                  Brushed Brass\n    url                             https://www.amazon.com/dp/B0B5VJNZHL\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 282, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Industrial Rolling Bar 3-Tier Kitchen Serving .\n    primary_image      https://m.media-amazon.com/images/I/51rjiq645t.\n    style                                                            NaN\n    material                                             Solid Wood,Iron\n    color                                                    Brown+black\n    url                             https://www.amazon.com/dp/B07RGDWW5C\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 283, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Chill Sack Bean Bag Chair: Giant 5' Memory Foa.\n    primary_image      https://m.media-amazon.com/images/I/51fQFu92ts.\n    style                                                 Furniture Foam\n    material                                                         NaN\n    color                                              Microsuede - Lime\n    url                             https://www.amazon.com/dp/B00P21TM2O\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 284, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Caroline's Treasures BB5130JMAT Day of The Dea.\n    primary_image      https://m.media-amazon.com/images/I/41Q15C0DMD.\n    style                              Day of the Dead Red Flowers Skull\n    material                                                      Rubber\n    color                              Day of the Dead Red Flowers Skull\n    url                             https://www.amazon."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 6fe20b32-9b7e-488e-9430-ed97b15559b9
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created_at: 2024-08-26 20:31:21
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creating embedding for title              Noori Rug Home - Lux Collection Modern Ava Rou.\n    primary_image      https://m.media-amazon.com/images/I/21Uq9uJEE5.\n    style                                                           Glam\n    material                                             Engineered Wood\n    color                                                 Ivory/Gold Ava\n    url                             https://www.amazon.com/dp/B097FC9C27\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 310, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Modway Parcel Upholstered Fabric Parsons Dinin.\n    primary_image      https://m.media-amazon.com/images/I/41f8WNXejU.\n    style                                                         Modern\n    material                                                        Foam\n    color                                                          Beige\n    url                             https://www.amazon.com/dp/B00SMM4H98\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 311, dtype: object: 'float' object is not iterable\n\n\n\n```python\ndf_search.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;"}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
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Lux Collection Modern Ava Rou.\n    primary_image      https://m.media-amazon.com/images/I/21Uq9uJEE5.\n    style                                                           Glam\n    material                                             Engineered Wood\n    color                                                 Ivory/Gold Ava\n    url                             https://www.amazon.com/dp/B097FC9C27\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 310, dtype: object: 'float' object is not iterable\n    Error creating embedding for title              Modway Parcel Upholstered Fabric Parsons Dinin.\n    primary_image      https://m.media-amazon.com/images/I/41f8WNXejU.\n    style                                                         Modern\n    material                                                        Foam\n    color                                                          Beige\n    url                             https://www.amazon.com/dp/B00SMM4H98\n    keywords                                                         NaN\n    img_description                                                  NaN\n    caption                                                          NaN\n    Name: 311, dtype: object: 'float' object is not iterable\n\n\n\n```python\ndf_search.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>title</th>\n      <th>primary_image</th>\n      <th>style</th>\n      <th>material</th>\n      <th>color</th>\n      <th>url</th>\n      <th>keywords</th>\n      <th>img_description</th>\n      <th>caption</th>\n      <th>embedding</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>GOYMFK 1pc Free Standing Shoe Rack, Multi-laye.</td>\n      <td>https://m.media-amazon.com/images/I/416WaLx10j.</td>\n      <td>Modern</td>\n      <td>Metal</td>\n      <td>White</td>\n      <td>https://www.amazon.com/dp/B0CJHKVG6P</td>\n      <td>['shoe rack', 'metal', 'white', 'multi-layer',</td>\n      <td>The GOYMFK Free Standing Shoe Rack is a versat.</td>\n      <td>Sleek white multi-layer metal free-standing sh.</td>\n      <td>[-0.06301482, -0.038354326, -0.0108071, -0.015.</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>subrtex Leather ding Room, Dining Chairs Set o.</td>\n      <td>https://m.media-amazon.com/images/I/31SejUEWY7."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: a5756a05-fa9c-4e49-a584-f0f87d307211
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created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 1665b5df-f356-4027-80cf-089e743941e2
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created_at: 2024-08-26 20:31:21
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created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 2956eec7-7301-485d-b601-7cad02d05bbf
encoding: FLOAT32
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(https://www.amazon.com/dp/B0CJHKVG6P) - Similarity: 0.57\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/416WaLx10jL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    MAEPA RV Shoe Storage for Bedside - 8 Extra Large ... (https://www.amazon.com/dp/B0C4PL1R3F) - Similarity: 0.55\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31bcwiowcBL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: black metal side table\n    \n    FLYJOE Narrow Side Table with PU Leather Magazine ... (https://www.amazon.com/dp/B0CHYDTQKN) - Similarity: 0.58\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41Hsse9SYsL._SS522_.jpg\"/>"}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 6109f705-a7f9-4b87-80cb-6d6f85a28210
encoding: FLOAT32
metadata:
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created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: c34f6f91-f109-4a02-b592-0bc39a093098
encoding: FLOAT32
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb", "title": "Using GPT-4o mini to tag & caption images", "tokens": 43074, "retrieve_doc": false, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"c34f6f91-f109-4a02-b592-0bc39a093098\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"10ea5ba1-792b-4899-9949-2def50553156\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"8a35c83b4ca97a55dbfb4c665f169096f360b8af9befbf8d15dd085af5f31e89\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"6109f705-a7f9-4b87-80cb-6d6f85a28210\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"9459b7e8cbdfbc1e50333a5b4bd8886290ee203f20252ca0a97bf10a6ac0db55\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"fba9990c-ff51-4162-a4f7-cb15bca0f420\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"48e54aee9ae3df88579667c29c563355655ed4212b4a719c276bd911e04140b2\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 238006, \"end_char_idx\": 240043, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "10ea5ba1-792b-4899-9949-2def50553156", "doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "ref_doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "chroma:document": "<img src=\"https://m.media-amazon.com/images/I/41Hsse9SYsL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    HomePop Metal Accent Table Triangle Base Round Mir... (https://www.amazon.com/dp/B08N5H868H) - Similarity: 0.57\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41cG70UIWTL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: doormat\n    \n    GXFC ZHAO Welcome Funny Door Mat Shoes and Bras Of... (https://www.amazon.com/dp/B07X61R7N8) - Similarity: 0.52\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/51z8ko3rsiL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Pickleball Doormat, Welcome Doormat Absorbent Non-... (https://www.amazon.com/dp/B0C1MRB2M8) - Similarity: 0.49\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/61vz1IglerL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: step bookshelf\n    \n    Leick Home 70007-WTGD Mixed Metal and Wood Stepped... (https://www.amazon.com/dp/B098KNRNLQ) - Similarity: 0.57\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31XhtLE1F1L._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Wildkin Kids Canvas Sling Bookshelf with Storage f... (https://www.amazon.com/dp/B07GBVFZ1Y) - Similarity: 0.46\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/51-GsdoM+IS._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: ottoman\n    \n    Moroccan Leather Pouf Ottoman for Living Room - Ro... (https://www.amazon.com/dp/B0CP45784G) - Similarity: 0.49\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/51UKACPPL9L._SS522_.jpg\"/>\n\n\n    \n    \n    \n    HomePop Home Decor | K2380-YDQY-2 | Luxury Large F... (https://www.amazon.com/dp/B0B94T1TZ1) - Similarity: 0.46\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/416lZwKs-SL._SS522_.jpg\"/>\n\n\n    \n    \n    \n\n\n### Search from image\n\nIf the input is an image, we can find similar images by first turning images into captions, and embedding those captions to compare them to the already created embeddings.\n\n\n```python\n# We'll take a mix of images: some we haven't seen and some that are already in the dataset\nexample_images = df.iloc[306:]['primary_image'].to_list() + df.iloc[5:10]['primary_image'].to_list()\n```"}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: fba9990c-ff51-4162-a4f7-cb15bca0f420
encoding: FLOAT32
metadata:
{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb", "title": "Using GPT-4o mini to tag & caption images", "tokens": 43074, "retrieve_doc": false, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"fba9990c-ff51-4162-a4f7-cb15bca0f420\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"10ea5ba1-792b-4899-9949-2def50553156\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"8a35c83b4ca97a55dbfb4c665f169096f360b8af9befbf8d15dd085af5f31e89\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"c34f6f91-f109-4a02-b592-0bc39a093098\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"8828a831dcd608b5f089ab07f653846831d08335213d9e688059da93e4f12a39\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"66e23b18-7e40-4fec-8300-1b6ab28e2400\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"111e4c62568425c5a99112645480d3035e65f3bdb4730a5cff2478014c700031\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 238751, \"end_char_idx\": 241266, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "10ea5ba1-792b-4899-9949-2def50553156", "doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "ref_doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "chroma:document": "Input: step bookshelf\n    \n    Leick Home 70007-WTGD Mixed Metal and Wood Stepped... (https://www.amazon.com/dp/B098KNRNLQ) - Similarity: 0.57\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31XhtLE1F1L._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Wildkin Kids Canvas Sling Bookshelf with Storage f... (https://www.amazon.com/dp/B07GBVFZ1Y) - Similarity: 0.46\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/51-GsdoM+IS._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: ottoman\n    \n    Moroccan Leather Pouf Ottoman for Living Room - Ro... (https://www.amazon.com/dp/B0CP45784G) - Similarity: 0.49\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/51UKACPPL9L._SS522_.jpg\"/>\n\n\n    \n    \n    \n    HomePop Home Decor | K2380-YDQY-2 | Luxury Large F... (https://www.amazon.com/dp/B0B94T1TZ1) - Similarity: 0.46\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/416lZwKs-SL._SS522_.jpg\"/>\n\n\n    \n    \n    \n\n\n### Search from image\n\nIf the input is an image, we can find similar images by first turning images into captions, and embedding those captions to compare them to the already created embeddings.\n\n\n```python\n# We'll take a mix of images: some we haven't seen and some that are already in the dataset\nexample_images = df.iloc[306:]['primary_image'].to_list() + df.iloc[5:10]['primary_image'].to_list()\n```\n\n\n```python\nfor i in example_images:\n    img_description = describe_image(i, '')\n    caption = caption_image(img_description)\n    img = Image(url=i)\n    print('Input: \\n')\n    display(img)\n    res = search_from_input_text(caption, 1).iloc[0]\n    similarity_score = res['similarity']\n    if isinstance(similarity_score, np.ndarray):\n        similarity_score = similarity_score[0][0]\n    print(f\"{res['title'][:50]}{'...' if len(res['title']) > 50 else ''} ({res['url']}) - Similarity: {similarity_score:.2f}\")\n    img_res = Image(url=res['primary_image'])\n    display(img_res)\n    print(\"\\n\\n\")\n    \n```\n\n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31dCSKQ14YL._SS522_.jpg\"/>\n\n\n    Black Leather Office Chair Mid Back Leather Desk C... (https://www.amazon.com/dp/B0BVQSPCCF) - Similarity: 0.54\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/317sVlhzMLL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41CPL03Y-WL._SS522_.jpg\"/>\n\n\n    subrtex Leather ding Room, Dining Chairs Set of 2,... (https://www.amazon.com/dp/B0B66QHB23) - Similarity: 0.52\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input:"}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 66e23b18-7e40-4fec-8300-1b6ab28e2400
encoding: FLOAT32
metadata:
{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb", "title": "Using GPT-4o mini to tag & caption images", "tokens": 43074, "retrieve_doc": false, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"66e23b18-7e40-4fec-8300-1b6ab28e2400\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"10ea5ba1-792b-4899-9949-2def50553156\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"8a35c83b4ca97a55dbfb4c665f169096f360b8af9befbf8d15dd085af5f31e89\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"fba9990c-ff51-4162-a4f7-cb15bca0f420\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"00884240b31e5386d603d3d8ec13146e9999618e4294ae62488d5a0d8406a2dd\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"55d02c6b-88f7-422e-aa64-50a4b8211ca0\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"0beaede2dff74e5066c957096e19e725e56e97f498e0603bbb6cca273b798fee\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 240046, \"end_char_idx\": 242378, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "10ea5ba1-792b-4899-9949-2def50553156", "doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "ref_doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "chroma:document": "```python\nfor i in example_images:\n    img_description = describe_image(i, '')\n    caption = caption_image(img_description)\n    img = Image(url=i)\n    print('Input: \\n')\n    display(img)\n    res = search_from_input_text(caption, 1).iloc[0]\n    similarity_score = res['similarity']\n    if isinstance(similarity_score, np.ndarray):\n        similarity_score = similarity_score[0][0]\n    print(f\"{res['title'][:50]}{'...' if len(res['title']) > 50 else ''} ({res['url']}) - Similarity: {similarity_score:.2f}\")\n    img_res = Image(url=res['primary_image'])\n    display(img_res)\n    print(\"\\n\\n\")\n    \n```\n\n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31dCSKQ14YL._SS522_.jpg\"/>\n\n\n    Black Leather Office Chair Mid Back Leather Desk C... (https://www.amazon.com/dp/B0BVQSPCCF) - Similarity: 0.54\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/317sVlhzMLL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41CPL03Y-WL._SS522_.jpg\"/>\n\n\n    subrtex Leather ding Room, Dining Chairs Set of 2,... (https://www.amazon.com/dp/B0B66QHB23) - Similarity: 0.52\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31qQ2tZPv-L._SS522_.jpg\"/>\n\n\n    MAEPA RV Shoe Storage for Bedside - 8 Extra Large ... (https://www.amazon.com/dp/B0C4PL1R3F) - Similarity: 0.65\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31bcwiowcBL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41TkLI3K2-L._SS522_.jpg\"/>\n\n\n    Chief Mfg.Swing-Arm Wall Mount Hardware Mount Blac... (https://www.amazon.com/dp/B007E40Z5K) - Similarity: 0.66\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41HxUoRXloL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/21Uq9uJEE5L._SS522_.jpg\"/>\n\n\n    Homebeez 39.1\" Length Bedroom Storage Bench, End B... (https://www.amazon.com/dp/B0BWQ8M4Q3) - Similarity: 0.52\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31eBuhJ0NDL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41f8WNXejUL._SS522_.jpg\"/>\n\n\n    subrtex Leather ding Room, Dining Chairs Set of 2,... (https://www.amazon.com/dp/B0B66QHB23) - Similarity: 0.51"}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 55d02c6b-88f7-422e-aa64-50a4b8211ca0
encoding: FLOAT32
metadata:
{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb", "title": "Using GPT-4o mini to tag & caption images", "tokens": 43074, "retrieve_doc": false, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"55d02c6b-88f7-422e-aa64-50a4b8211ca0\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"10ea5ba1-792b-4899-9949-2def50553156\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"8a35c83b4ca97a55dbfb4c665f169096f360b8af9befbf8d15dd085af5f31e89\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"66e23b18-7e40-4fec-8300-1b6ab28e2400\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Tag_caption_images_with_GPT4V.ipynb\", \"title\": \"Using GPT-4o mini to tag & caption images\", \"tokens\": 43074, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"ead5bdf3c2c1c94d5b5dd0c6a0bcee6186715314c5f81fac8791dbd12fb9493c\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"2a62dce4-49f7-4725-a2d1-0edf66cd5ac1\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"a7abb7be3ab2ce883d576389f40f9ac77a249848386f69397354d62f7deb65eb\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 241260, \"end_char_idx\": 243476, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "10ea5ba1-792b-4899-9949-2def50553156", "doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "ref_doc_id": "10ea5ba1-792b-4899-9949-2def50553156", "chroma:document": "Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31qQ2tZPv-L._SS522_.jpg\"/>\n\n\n    MAEPA RV Shoe Storage for Bedside - 8 Extra Large ... (https://www.amazon.com/dp/B0C4PL1R3F) - Similarity: 0.65\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31bcwiowcBL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41TkLI3K2-L._SS522_.jpg\"/>\n\n\n    Chief Mfg.Swing-Arm Wall Mount Hardware Mount Blac... (https://www.amazon.com/dp/B007E40Z5K) - Similarity: 0.66\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41HxUoRXloL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/21Uq9uJEE5L._SS522_.jpg\"/>\n\n\n    Homebeez 39.1\" Length Bedroom Storage Bench, End B... (https://www.amazon.com/dp/B0BWQ8M4Q3) - Similarity: 0.52\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31eBuhJ0NDL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41f8WNXejUL._SS522_.jpg\"/>\n\n\n    subrtex Leather ding Room, Dining Chairs Set of 2,... (https://www.amazon.com/dp/B0B66QHB23) - Similarity: 0.51\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41zMuj2wvvL._SS522_.jpg\"/>\n\n\n    LOVMOR 30'' Bathroom Vanity Sink Base Cabine, Stor... (https://www.amazon.com/dp/B0C9WYYFLB) - Similarity: 0.58\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41zMuj2wvvL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41ixgM73DgL._SS522_.jpg\"/>\n\n\n    Folews Bathroom Organizer Over The Toilet Storage,... (https://www.amazon.com/dp/B09NZY3R1T) - Similarity: 0.73\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/41ixgM73DgL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/416WaLx10jL._SS522_.jpg\"/>\n\n\n    GOYMFK 1pc Free Standing Shoe Rack, Multi-layer Me... (https://www.amazon.com/dp/B0CJHKVG6P) - Similarity: 0.72\n\n\n\n<img src=\"https://m.media-amazon.com/images/I/416WaLx10jL._SS522_.jpg\"/>\n\n\n    \n    \n    \n    Input: \n    \n\n\n\n<img src=\"https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg\"/>"}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 2a62dce4-49f7-4725-a2d1-0edf66cd5ac1
encoding: FLOAT32
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created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 6c12abd0-32f7-42b1-af0a-f031937a0ea6
encoding: FLOAT32
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb", "title": "evaluate embeddings as recommendations on X_test", "tokens": 1239, "retrieve_doc": true, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"6c12abd0-32f7-42b1-af0a-f031937a0ea6\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb\", \"title\": \"evaluate embeddings as recommendations on X_test\", \"tokens\": 1239, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"90dc67d9-9abf-4cce-9e17-dfdf33f0ff2e\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb\", \"title\": \"evaluate embeddings as recommendations on X_test\", \"tokens\": 1239, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"hash\": \"a51b579907cd6b4cb690e2ab48f39e8915ca6146ee99018ad7624ffccad29fe1\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"f4674fd8-49ef-40f6-8096-5d42e5a13912\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"17af8fe7d208ed3dc72c9a9ba516ef62a819ce59393a95b632e4a034c6282660\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 0, \"end_char_idx\": 2599, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "90dc67d9-9abf-4cce-9e17-dfdf33f0ff2e", "doc_id": "90dc67d9-9abf-4cce-9e17-dfdf33f0ff2e", "ref_doc_id": "90dc67d9-9abf-4cce-9e17-dfdf33f0ff2e", "chroma:document": "## User and product embeddings\n\nWe calculate user and product embeddings based on the training set, and evaluate the results on the unseen test set. We will evaluate the results by plotting the user and product similarity versus the review score. The dataset is created in the [Get_embeddings_from_dataset Notebook](Get_embeddings_from_dataset.ipynb).\n\n### 1. Calculate user and product embeddings\n\nWe calculate these embeddings simply by averaging all the reviews about the same product or written by the same user within the training set.\n\n\n```python\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom ast import literal_eval\n\ndf = pd.read_csv('data/fine_food_reviews_with_embeddings_1k.csv', index_col=0)  # note that you will need to generate this file to run the code below\ndf.head(2)\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>Score</th>\n      <th>Summary</th>\n      <th>Text</th>\n      <th>combined</th>\n      <th>n_tokens</th>\n      <th>embedding</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B003XPF9BO</td>\n      <td>A3R7JR3FMEBXQB</td>\n      <td>5</td>\n      <td>where does one  start...and stop... with a tre...</td>\n      <td>Wanted to save some to bring to my Chicago fam...</td>\n      <td>Title: where does one  start...and stop... wit...</td>\n      <td>52</td>\n      <td>[0.03599238395690918, -0.02116263099014759, -0...</td>\n    </tr>\n    <tr>\n      <th>297</th>\n      <td>B003VXHGPK</td>\n      <td>A21VWSCGW7UUAR</td>\n      <td>4</td>\n      <td>Good, but not Wolfgang Puck good</td>\n      <td>Honestly, I have to admit that I expected a li...</td>\n      <td>Title: Good, but not Wolfgang Puck good; Conte...</td>\n      <td>178</td>\n      <td>[-0.07042013108730316, -0.03175969794392586, -...</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\ndf['babbage_similarity'] = df[\"embedding\"].apply(literal_eval).apply(np.array)\nX_train, X_test, y_train, y_test = train_test_split(df, df.Score, test_size = 0.2, random_state=42)\n\nuser_embeddings = X_train.groupby('UserId').babbage_similarity.apply(np.mean)\nprod_embeddings = X_train.groupby('ProductId').babbage_similarity.apply(np.mean)\nlen(user_embeddings), len(prod_embeddings)\n\n```\n\n\n\n\n    (577, 706)"}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: f4674fd8-49ef-40f6-8096-5d42e5a13912
encoding: FLOAT32
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb", "title": "evaluate embeddings as recommendations on X_test", "tokens": 1239, "retrieve_doc": true, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"f4674fd8-49ef-40f6-8096-5d42e5a13912\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb\", \"title\": \"evaluate embeddings as recommendations on X_test\", \"tokens\": 1239, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"90dc67d9-9abf-4cce-9e17-dfdf33f0ff2e\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb\", \"title\": \"evaluate embeddings as recommendations on X_test\", \"tokens\": 1239, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"hash\": \"a51b579907cd6b4cb690e2ab48f39e8915ca6146ee99018ad7624ffccad29fe1\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"6c12abd0-32f7-42b1-af0a-f031937a0ea6\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb\", \"title\": \"evaluate embeddings as recommendations on X_test\", \"tokens\": 1239, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"hash\": \"2b66414376794da8e858523e51b42a700b48fcecb00b975f7ecc93542efca246\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 2184, \"end_char_idx\": 5018, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "90dc67d9-9abf-4cce-9e17-dfdf33f0ff2e", "doc_id": "90dc67d9-9abf-4cce-9e17-dfdf33f0ff2e", "ref_doc_id": "90dc67d9-9abf-4cce-9e17-dfdf33f0ff2e", "chroma:document": "```python\ndf['babbage_similarity'] = df[\"embedding\"].apply(literal_eval).apply(np.array)\nX_train, X_test, y_train, y_test = train_test_split(df, df.Score, test_size = 0.2, random_state=42)\n\nuser_embeddings = X_train.groupby('UserId').babbage_similarity.apply(np.mean)\nprod_embeddings = X_train.groupby('ProductId').babbage_similarity.apply(np.mean)\nlen(user_embeddings), len(prod_embeddings)\n\n```\n\n\n\n\n    (577, 706)\n\n\n\nWe can see that most of the users and products appear within the 50k examples only once.\n\n### 2. Evaluate the embeddings\n\nTo evaluate the recommendations, we look at the similarity of the user and product embeddings amongst the reviews in the unseen test set. We calculate the cosine distance between the user and product embeddings, which gives us a similarity score between 0 and 1. We then normalize the scores to be evenly split between 0 and 1, by calculating the percentile of the similarity score amongst all predicted scores.\n\n\n```python\nfrom utils.embeddings_utils import cosine_similarity\n\n# evaluate embeddings as recommendations on X_test\ndef evaluate_single_match(row):\n    user_id = row.UserId\n    product_id = row.ProductId\n    try:\n        user_embedding = user_embeddings[user_id]\n        product_embedding = prod_embeddings[product_id]\n        similarity = cosine_similarity(user_embedding, product_embedding)\n        return similarity\n    except Exception as e:\n        return np.nan\n\nX_test['cosine_similarity'] = X_test.apply(evaluate_single_match, axis=1)\nX_test['percentile_cosine_similarity'] = X_test.cosine_similarity.rank(pct=True)\n\n```\n\n#### 2.1 Visualize cosine similarity by review score\n\nWe group the cosine similarity scores by the review score, and plot the distribution of cosine similarity scores for each review score.\n\n\n```python\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\n\n\ncorrelation = X_test[['percentile_cosine_similarity', 'Score']].corr().values[0,1]\nprint('Correlation between user & vector similarity percentile metric and review number of stars (score): %.2f%%' % (100*correlation))\n\n# boxplot of cosine similarity for each score\nX_test.boxplot(column='percentile_cosine_similarity', by='Score')\nplt.title('')\nplt.show()\nplt.close()\n\n```\n\n    Correlation between user & vector similarity percentile metric and review number of stars (score): 29.56%\n\n\n\n    \n![png](output_8_1.png)\n    \n\n\nWe can observe a weak trend, showing that the higher the similarity score between the user and the product embedding, the higher the review score. Therefore, the user and product embeddings can weakly predict the review score - even before the user receives the product!\n\nBecause this signal works in a different way than the more commonly used collaborative filtering, it can act as an additional feature to slightly improve the performance on existing problems."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 62dd0c92-621d-42e5-9123-c02ed2aa978b
encoding: FLOAT32
metadata:
{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/Visualizing_embeddings_in_3D.ipynb", "title": "Visualizing embeddings in 3D", "tokens": 104116, "retrieve_doc": false, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"62dd0c92-621d-42e5-9123-c02ed2aa978b\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Visualizing_embeddings_in_3D.ipynb\", \"title\": \"Visualizing embeddings in 3D\", \"tokens\": 104116, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"e0a91f9c-03ee-4f2b-913a-0f87b160562e\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Visualizing_embeddings_in_3D.ipynb\", \"title\": \"Visualizing embeddings in 3D\", \"tokens\": 104116, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"f5f29ee5958ac7f50fe2351cdbf79f4f368ead1908c66bad45aad0c0b6fe5d3a\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"232de465-5485-451f-a775-a26df80f42e3\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"dd30df47364fa964e92cf422993306fc8287e916dc11fca41fa3bd55d2cc0232\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 0, \"end_char_idx\": 2813, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "e0a91f9c-03ee-4f2b-913a-0f87b160562e", "doc_id": "e0a91f9c-03ee-4f2b-913a-0f87b160562e", "ref_doc_id": "e0a91f9c-03ee-4f2b-913a-0f87b160562e", "chroma:document": "# Visualizing embeddings in 3D\n\nThe example uses [PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) to reduce the dimensionality fo the embeddings from 1536 to 3. Then we can visualize the data points in a 3D plot. The small dataset `dbpedia_samples.jsonl` is curated by randomly sampling 200 samples from [DBpedia validation dataset](https://www.kaggle.com/danofer/dbpedia-classes?select=DBPEDIA_val.csv).\n\n### 1. Load the dataset and query embeddings\n\n\n```python\nimport pandas as pd\nsamples = pd.read_json(\"data/dbpedia_samples.jsonl\", lines=True)\ncategories = sorted(samples[\"category\"].unique())\nprint(\"Categories of DBpedia samples:\", samples[\"category\"].value_counts())\nsamples.head()\n\n```\n\n    Categories of DBpedia samples: Artist                    21\n    Film                      19\n    Plant                     19\n    OfficeHolder              18\n    Company                   17\n    NaturalPlace              16\n    Athlete                   16\n    Village                   12\n    WrittenWork               11\n    Building                  11\n    Album                     11\n    Animal                    11\n    EducationalInstitution    10\n    MeanOfTransportation       8\n    Name: category, dtype: int64\n\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>text</th>\n      <th>category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Morada Limited is a textile company based in ...</td>\n      <td>Company</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>The Armenian Mirror-Spectator is a newspaper ...</td>\n      <td>WrittenWork</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Mt. Kinka (\u91d1\u83ef\u5c71 Kinka-zan) also known as Kinka...</td>\n      <td>NaturalPlace</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Planning the Play of a Bridge Hand is a book ...</td>\n      <td>WrittenWork</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Wang Yuanping (born 8 December 1976) is a ret...</td>\n      <td>Athlete</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom utils.embeddings_utils import get_embeddings\n# NOTE: The following code will send a query of batch size 200 to /embeddings\nmatrix = get_embeddings(samples[\"text\"].to_list(), model=\"text-embedding-3-small\")\n\n```\n\n### 2. Reduce the embedding dimensionality\n\n\n```python\nfrom sklearn.decomposition import PCA\npca = PCA(n_components=3)\nvis_dims = pca.fit_transform(matrix)\nsamples[\"embed_vis\"] = vis_dims.tolist()\n\n```\n\n### 3. Plot the embeddings of lower dimensionality"}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 232de465-5485-451f-a775-a26df80f42e3
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