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```python
df_search.head()
```




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```python
df_search.head()
```




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  <tbody>
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      <th>0</th>
      <td>GOYMFK 1pc Free Standing Shoe Rack, Multi-laye.</td>
      <td>https://m.media-amazon.com/images/I/416WaLx10j.</td>
      <td>Modern</td>
      <td>Metal</td>
      <td>White</td>
      <td>https://www.amazon.com/dp/B0CJHKVG6P</td>
      <td>['shoe rack', 'metal', 'white', 'multi-layer',</td>
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      <td>Sleek white multi-layer metal free-standing sh.</td>
      <td>[-0.06301482, -0.038354326, -0.0108071, -0.015.</td>
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      <th>1</th>
      <td>subrtex Leather ding Room, Dining Chairs Set o.</td>
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      <td>White</td>
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      <th>3</th>
      <td>Pickleball Doormat, Welcome Doormat Absorbent .</td>
      <td>https://m.


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string_value:
media-amazon.com/images/I/31SejUEWY7.</td>
      <td>Black Rubber Wood</td>
      <td>Sponge</td>
      <td>Black</td>
      <td>https://www.amazon.com/dp/B0B66QHB23</td>
      <td>['dining chair', 'leather', 'black']</td>
      <td>The Subrtex Leather Dining Chairs come in a se.</td>
      <td>Set of 2 modern black faux leather dining chai.</td>
      <td>[-0.018292552, -0.006216094, -0.009373649, -0.</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Plant Repotting Mat MUYETOL Waterproof Transpl.</td>
      <td>https://m.media-amazon.com/images/I/41RgefVq70.</td>
      <td>Modern</td>
      <td>Polyethylene</td>
      <td>Green</td>
      <td>https://www.amazon.com/dp/B0BXRTWLYK</td>
      <td>['repotting mat', 'waterproof', 'portable', 'f.</td>
      <td>The Plant Repotting Mat is a portable and fold.</td>
      <td>Vibrant green waterproof plant repotting mat</td>
      <td>[-0.010247701, 0.0074028056, -0.00037697714, -.</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Pickleball Doormat, Welcome Doormat Absorbent .</td>
      <td>https://m.media-amazon.com/images/I/61vz1Igler.</td>
      <td>Modern</td>
      <td>Rubber</td>
      <td>A5589</td>
      <td>https://www.amazon.com/dp/B0C1MRB2M8</td>
      <td>['doormat', 'absorbent', 'non-slip', 'coconut .</td>
      <td>The Pickleball Doormat is a charming welcome m.</td>
      <td>Coir welcome mat featuring a playful "It's a g.</td>
      <td>[-0.0033125042, -0.02689817, -0.009523449, 0.0.</td>
    </tr>
    <tr>
      <th>4</th>
      <td>JOIN IRON Foldable TV Trays for Eating Set of .</td>
      <td>https://m.media-amazon.com/images/I/41p4d4VJnN.</td>
      <td>X Classic Style</td>
      <td>Iron</td>
      <td>Grey Set of 4</td>
      <td>https://www.amazon.com/dp/B0CG1N9QRC</td>
      <td>['tv tray', 'foldable', 'metal', 'grey']</td>
      <td>The JOIN IRON Foldable TV Tray Set includes fo.</td>
      <td>Set of 4 foldable grey TV trays with durable b.</td>
      <td>[-0.020860892, -0.0053859027, -0.019131333,


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key: chroma:document
string_value:
Welcome Doormat Absorbent .</td>
      <td>https://m.media-amazon.com/images/I/61vz1Igler.</td>
      <td>Modern</td>
      <td>Rubber</td>
      <td>A5589</td>
      <td>https://www.amazon.com/dp/B0C1MRB2M8</td>
      <td>['doormat', 'absorbent', 'non-slip', 'coconut .</td>
      <td>The Pickleball Doormat is a charming welcome m.</td>
      <td>Coir welcome mat featuring a playful "It's a g.</td>
      <td>[-0.0033125042, -0.02689817, -0.009523449, 0.0.</td>
    </tr>
    <tr>
      <th>4</th>
      <td>JOIN IRON Foldable TV Trays for Eating Set of .</td>
      <td>https://m.media-amazon.com/images/I/41p4d4VJnN.</td>
      <td>X Classic Style</td>
      <td>Iron</td>
      <td>Grey Set of 4</td>
      <td>https://www.amazon.com/dp/B0CG1N9QRC</td>
      <td>['tv tray', 'foldable', 'metal', 'grey']</td>
      <td>The JOIN IRON Foldable TV Tray Set includes fo.</td>
      <td>Set of 4 foldable grey TV trays with durable b.</td>
      <td>[-0.020860892, -0.0053859027, -0.019131333, -0.</td>
    </tr>
  </tbody>
</table>
</div>




```python
# Keep only the lines where we have embeddings
df_search = df_search.dropna(subset=['embedding'])
print(df_search.shape)
```

    (50, 10)



```python
data_embeddings_path = "data/items_tagged_and_captioned_embeddings.csv"
```


```python
# Saving locally for later - optional: do not execute if you prefer to use the provided file
df_search.to_csv(data_embeddings_path, index=False)
```


```python
# Optional: load data from saved file if you haven't processed the whole dataset
from ast import literal_eval
df_search = pd.read_csv(data_embeddings_path)
df_search["embedding"] = df_search.embedding.apply(literal_eval).apply(np.array)
```

### Search from input text    

We can compare the input text from a user directly to the embeddings we just created.


```python
# Searching for N most similar results
def search_from_input_text(query, n = 2):
    embedded_value = get_embedding(query)
    df_search['similarity'] = df_search['embedding'].apply(lambda x: cosine_similarity(np.array(x).reshape(1,-1), np.array(embedded_value).reshape(1, -1)))
    most_similar = df_search.sort_values('similarity', ascending=False).iloc[:n]
    return most_similar
```


```python
user_inputs = ['shoe storage', 'black metal side table', 'doormat', 'step bookshelf', 'ottoman']
```


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string_value:
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key: chroma:document
string_value:
'grey']</td>
      <td>The JOIN IRON Foldable TV Tray Set includes fo.</td>
      <td>Set of 4 foldable grey TV trays with durable b.</td>
      <td>[-0.020860892, -0.0053859027, -0.019131333, -0.</td>
    </tr>
  </tbody>
</table>
</div>




```python
# Keep only the lines where we have embeddings
df_search = df_search.dropna(subset=['embedding'])
print(df_search.shape)
```

    (50, 10)



```python
data_embeddings_path = "data/items_tagged_and_captioned_embeddings.csv"
```


```python
# Saving locally for later - optional: do not execute if you prefer to use the provided file
df_search.to_csv(data_embeddings_path, index=False)
```


```python
# Optional: load data from saved file if you haven't processed the whole dataset
from ast import literal_eval
df_search = pd.read_csv(data_embeddings_path)
df_search["embedding"] = df_search.embedding.apply(literal_eval).apply(np.array)
```

### Search from input text    

We can compare the input text from a user directly to the embeddings we just created.


```python
# Searching for N most similar results
def search_from_input_text(query, n = 2):
    embedded_value = get_embedding(query)
    df_search['similarity'] = df_search['embedding'].apply(lambda x: cosine_similarity(np.array(x).reshape(1,-1), np.array(embedded_value).reshape(1, -1)))
    most_similar = df_search.sort_values('similarity', ascending=False).iloc[:n]
    return most_similar
```


```python
user_inputs = ['shoe storage', 'black metal side table', 'doormat', 'step bookshelf', 'ottoman']
```


```python
for i in user_inputs:
    print(f"Input: {i}\n")
    res = search_from_input_text(i)
    for index, row in res.iterrows():
        similarity_score = row['similarity']
        if isinstance(similarity_score, np.ndarray):
            similarity_score = similarity_score[0][0]
        print(f"{row['title'][:50]}{'...' if len(row['title']) > 50 else ''} ({row['url']}) - Similarity: {similarity_score:.2f}")
        img = Image(url=row['primary_image'])
        display(img)
        print("\n\n")
```

    Input: shoe storage
    
    GOYMFK 1pc Free Standing Shoe Rack, Multi-layer Me... (https://www.amazon.com/dp/B0CJHKVG6P) - Similarity: 0.57



<img src="https://m.media-amazon.com/images/I/416WaLx10jL._SS522_.jpg"/>


    
    
    
    MAEPA RV Shoe Storage for Bedside - 8 Extra Large ... (https://www.amazon.com/dp/B0C4PL1R3F) - Similarity: 0.55



<img src="https://m.media-amazon.com/images/I/31bcwiowcBL._SS522_.jpg"/>


    
    
    
    Input: black metal side table
    
    FLYJOE Narrow Side Table with PU Leather Magazine ... (https://www.amazon.com/dp/B0CHYDTQKN) - Similarity: 0.58



<img src="https://m.media-amazon.com/images/I/41Hsse9SYsL._SS522_.jpg"/>


key: _node_content
string_value:
{"id_": "f44e0d00-b9cd-478c-9abf-81fdc35f799b", "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": "445cd3a9-3b52-4059-b5b8-98d9fd6df70f", "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": "68ccc828-ed50-4e3c-9dd5-8bf6437b8747", "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": "ce9130f5e812d331f7c2715e69bc14853c3c6f569a209082c714cabcc80a7471", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "82e0c8b3-bef7-4ffd-a2fb-f01eabdbd78a", "node_type": "1", "metadata": {}, "hash": "5fa9823e88235ca6be016e6b68b1eeee0fe2c940f587c97368c66729fe1e03f7", "class_name": "RelatedNodeInfo"}}, "text": "", "mimetype": "text/plain", "start_char_idx": 236778, "end_char_idx": 239178, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}


key: chroma:document
string_value:
```python
user_inputs = ['shoe storage', 'black metal side table', 'doormat', 'step bookshelf', 'ottoman']
```


```python
for i in user_inputs:
    print(f"Input: {i}\n")
    res = search_from_input_text(i)
    for index, row in res.iterrows():
        similarity_score = row['similarity']
        if isinstance(similarity_score, np.ndarray):
            similarity_score = similarity_score[0][0]
        print(f"{row['title'][:50]}{'...' if len(row['title']) > 50 else ''} ({row['url']}) - Similarity: {similarity_score:.2f}")
        img = Image(url=row['primary_image'])
        display(img)
        print("\n\n")
```

    Input: shoe storage
    
    GOYMFK 1pc Free Standing Shoe Rack, Multi-layer Me... (https://www.amazon.com/dp/B0CJHKVG6P) - Similarity: 0.57



<img src="https://m.media-amazon.com/images/I/416WaLx10jL._SS522_.jpg"/>


    
    
    
    MAEPA RV Shoe Storage for Bedside - 8 Extra Large ... (https://www.amazon.com/dp/B0C4PL1R3F) - Similarity: 0.55



<img src="https://m.media-amazon.com/images/I/31bcwiowcBL._SS522_.jpg"/>


    
    
    
    Input: black metal side table
    
    FLYJOE Narrow Side Table with PU Leather Magazine ... (https://www.amazon.com/dp/B0CHYDTQKN) - Similarity: 0.58



<img src="https://m.media-amazon.com/images/I/41Hsse9SYsL._SS522_.jpg"/>


    
    
    
    HomePop Metal Accent Table Triangle Base Round Mir... (https://www.amazon.com/dp/B08N5H868H) - Similarity: 0.57



<img src="https://m.media-amazon.com/images/I/41cG70UIWTL._SS522_.jpg"/>


    
    
    
    Input: doormat
    
    GXFC ZHAO Welcome Funny Door Mat Shoes and Bras Of... (https://www.amazon.com/dp/B07X61R7N8) - Similarity: 0.52



<img src="https://m.media-amazon.com/images/I/51z8ko3rsiL._SS522_.jpg"/>


    
    
    
    Pickleball Doormat, Welcome Doormat Absorbent Non-... (https://www.amazon.com/dp/B0C1MRB2M8) - Similarity: 0.49



<img src="https://m.media-amazon.com/images/I/61vz1IglerL._SS522_.jpg"/>


    
    
    
    Input: step bookshelf
    
    Leick Home 70007-WTGD Mixed Metal and Wood Stepped... (https://www.amazon.com/dp/B098KNRNLQ) - Similarity: 0.57



<img src="https://m.media-amazon.com/images/I/31XhtLE1F1L._SS522_.jpg"/>


    
    
    
    Wildkin Kids Canvas Sling Bookshelf with Storage f... (https://www.amazon.com/dp/B07GBVFZ1Y) - Similarity: 0.46



<img src="https://m.media-amazon.com/images/I/51-GsdoM+IS._SS522_.jpg"/>


key: _node_content
string_value:
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key: chroma:document
string_value:
<img src="https://m.media-amazon.com/images/I/41Hsse9SYsL._SS522_.jpg"/>


    
    
    
    HomePop Metal Accent Table Triangle Base Round Mir... (https://www.amazon.com/dp/B08N5H868H) - Similarity: 0.57



<img src="https://m.media-amazon.com/images/I/41cG70UIWTL._SS522_.jpg"/>


    
    
    
    Input: doormat
    
    GXFC ZHAO Welcome Funny Door Mat Shoes and Bras Of... (https://www.amazon.com/dp/B07X61R7N8) - Similarity: 0.52



<img src="https://m.media-amazon.com/images/I/51z8ko3rsiL._SS522_.jpg"/>


    
    
    
    Pickleball Doormat, Welcome Doormat Absorbent Non-... (https://www.amazon.com/dp/B0C1MRB2M8) - Similarity: 0.49



<img src="https://m.media-amazon.com/images/I/61vz1IglerL._SS522_.jpg"/>


    
    
    
    Input: step bookshelf
    
    Leick Home 70007-WTGD Mixed Metal and Wood Stepped... (https://www.amazon.com/dp/B098KNRNLQ) - Similarity: 0.57



<img src="https://m.media-amazon.com/images/I/31XhtLE1F1L._SS522_.jpg"/>


    
    
    
    Wildkin Kids Canvas Sling Bookshelf with Storage f... (https://www.amazon.com/dp/B07GBVFZ1Y) - Similarity: 0.46



<img src="https://m.media-amazon.com/images/I/51-GsdoM+IS._SS522_.jpg"/>


    
    
    
    Input: ottoman
    
    Moroccan Leather Pouf Ottoman for Living Room - Ro... (https://www.amazon.com/dp/B0CP45784G) - Similarity: 0.49



<img src="https://m.media-amazon.com/images/I/51UKACPPL9L._SS522_.jpg"/>


    
    
    
    HomePop Home Decor | K2380-YDQY-2 | Luxury Large F... (https://www.amazon.com/dp/B0B94T1TZ1) - Similarity: 0.46



<img src="https://m.media-amazon.com/images/I/416lZwKs-SL._SS522_.jpg"/>


    
    
    


### Search from image

If 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.


```python
# We'll take a mix of images: some we haven't seen and some that are already in the dataset
example_images = df.iloc[306:]['primary_image'].to_list() + df.iloc[5:10]['primary_image'].to_list()
```


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key: chroma:document
string_value:
Input: step bookshelf
    
    Leick Home 70007-WTGD Mixed Metal and Wood Stepped... (https://www.amazon.com/dp/B098KNRNLQ) - Similarity: 0.57



<img src="https://m.media-amazon.com/images/I/31XhtLE1F1L._SS522_.jpg"/>


    
    
    
    Wildkin Kids Canvas Sling Bookshelf with Storage f... (https://www.amazon.com/dp/B07GBVFZ1Y) - Similarity: 0.46



<img src="https://m.media-amazon.com/images/I/51-GsdoM+IS._SS522_.jpg"/>


    
    
    
    Input: ottoman
    
    Moroccan Leather Pouf Ottoman for Living Room - Ro... (https://www.amazon.com/dp/B0CP45784G) - Similarity: 0.49



<img src="https://m.media-amazon.com/images/I/51UKACPPL9L._SS522_.jpg"/>


    
    
    
    HomePop Home Decor | K2380-YDQY-2 | Luxury Large F... (https://www.amazon.com/dp/B0B94T1TZ1) - Similarity: 0.46



<img src="https://m.media-amazon.com/images/I/416lZwKs-SL._SS522_.jpg"/>


    
    
    


### Search from image

If 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.


```python
# We'll take a mix of images: some we haven't seen and some that are already in the dataset
example_images = df.iloc[306:]['primary_image'].to_list() + df.iloc[5:10]['primary_image'].to_list()
```


```python
for i in example_images:
    img_description = describe_image(i, '')
    caption = caption_image(img_description)
    img = Image(url=i)
    print('Input: \n')
    display(img)
    res = search_from_input_text(caption, 1).iloc[0]
    similarity_score = res['similarity']
    if isinstance(similarity_score, np.ndarray):
        similarity_score = similarity_score[0][0]
    print(f"{res['title'][:50]}{'...' if len(res['title']) > 50 else ''} ({res['url']}) - Similarity: {similarity_score:.2f}")
    img_res = Image(url=res['primary_image'])
    display(img_res)
    print("\n\n")
    
```

    Input: 
    



<img src="https://m.media-amazon.com/images/I/31dCSKQ14YL._SS522_.jpg"/>


    Black Leather Office Chair Mid Back Leather Desk C... (https://www.amazon.com/dp/B0BVQSPCCF) - Similarity: 0.54



<img src="https://m.media-amazon.com/images/I/317sVlhzMLL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41CPL03Y-WL._SS522_.jpg"/>


    subrtex Leather ding Room, Dining Chairs Set of 2,... (https://www.amazon.com/dp/B0B66QHB23) - Similarity: 0.52



<img src="https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg"/>


    
    
    
    Input:


key: _node_content
string_value:
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key: chroma:document
string_value:
```python
for i in example_images:
    img_description = describe_image(i, '')
    caption = caption_image(img_description)
    img = Image(url=i)
    print('Input: \n')
    display(img)
    res = search_from_input_text(caption, 1).iloc[0]
    similarity_score = res['similarity']
    if isinstance(similarity_score, np.ndarray):
        similarity_score = similarity_score[0][0]
    print(f"{res['title'][:50]}{'...' if len(res['title']) > 50 else ''} ({res['url']}) - Similarity: {similarity_score:.2f}")
    img_res = Image(url=res['primary_image'])
    display(img_res)
    print("\n\n")
    
```

    Input: 
    



<img src="https://m.media-amazon.com/images/I/31dCSKQ14YL._SS522_.jpg"/>


    Black Leather Office Chair Mid Back Leather Desk C... (https://www.amazon.com/dp/B0BVQSPCCF) - Similarity: 0.54



<img src="https://m.media-amazon.com/images/I/317sVlhzMLL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41CPL03Y-WL._SS522_.jpg"/>


    subrtex Leather ding Room, Dining Chairs Set of 2,... (https://www.amazon.com/dp/B0B66QHB23) - Similarity: 0.52



<img src="https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/31qQ2tZPv-L._SS522_.jpg"/>


    MAEPA RV Shoe Storage for Bedside - 8 Extra Large ... (https://www.amazon.com/dp/B0C4PL1R3F) - Similarity: 0.65



<img src="https://m.media-amazon.com/images/I/31bcwiowcBL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41TkLI3K2-L._SS522_.jpg"/>


    Chief Mfg.Swing-Arm Wall Mount Hardware Mount Blac... (https://www.amazon.com/dp/B007E40Z5K) - Similarity: 0.66



<img src="https://m.media-amazon.com/images/I/41HxUoRXloL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/21Uq9uJEE5L._SS522_.jpg"/>


    Homebeez 39.1" Length Bedroom Storage Bench, End B... (https://www.amazon.com/dp/B0BWQ8M4Q3) - Similarity: 0.52



<img src="https://m.media-amazon.com/images/I/31eBuhJ0NDL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41f8WNXejUL._SS522_.jpg"/>


    subrtex Leather ding Room, Dining Chairs Set of 2,... (https://www.amazon.com/dp/B0B66QHB23) - Similarity: 0.51


key: _node_content
string_value:
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key: chroma:document
string_value:
Input: 
    



<img src="https://m.media-amazon.com/images/I/31qQ2tZPv-L._SS522_.jpg"/>


    MAEPA RV Shoe Storage for Bedside - 8 Extra Large ... (https://www.amazon.com/dp/B0C4PL1R3F) - Similarity: 0.65



<img src="https://m.media-amazon.com/images/I/31bcwiowcBL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41TkLI3K2-L._SS522_.jpg"/>


    Chief Mfg.Swing-Arm Wall Mount Hardware Mount Blac... (https://www.amazon.com/dp/B007E40Z5K) - Similarity: 0.66



<img src="https://m.media-amazon.com/images/I/41HxUoRXloL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/21Uq9uJEE5L._SS522_.jpg"/>


    Homebeez 39.1" Length Bedroom Storage Bench, End B... (https://www.amazon.com/dp/B0BWQ8M4Q3) - Similarity: 0.52



<img src="https://m.media-amazon.com/images/I/31eBuhJ0NDL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41f8WNXejUL._SS522_.jpg"/>


    subrtex Leather ding Room, Dining Chairs Set of 2,... (https://www.amazon.com/dp/B0B66QHB23) - Similarity: 0.51



<img src="https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41zMuj2wvvL._SS522_.jpg"/>


    LOVMOR 30'' Bathroom Vanity Sink Base Cabine, Stor... (https://www.amazon.com/dp/B0C9WYYFLB) - Similarity: 0.58



<img src="https://m.media-amazon.com/images/I/41zMuj2wvvL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41ixgM73DgL._SS522_.jpg"/>


    Folews Bathroom Organizer Over The Toilet Storage,... (https://www.amazon.com/dp/B09NZY3R1T) - Similarity: 0.73



<img src="https://m.media-amazon.com/images/I/41ixgM73DgL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/416WaLx10jL._SS522_.jpg"/>


    GOYMFK 1pc Free Standing Shoe Rack, Multi-layer Me... (https://www.amazon.com/dp/B0CJHKVG6P) - Similarity: 0.72



<img src="https://m.media-amazon.com/images/I/416WaLx10jL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg"/>


key: _node_content
string_value:
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key: chroma:document
string_value:
<img src="https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41zMuj2wvvL._SS522_.jpg"/>


    LOVMOR 30'' Bathroom Vanity Sink Base Cabine, Stor... (https://www.amazon.com/dp/B0C9WYYFLB) - Similarity: 0.58



<img src="https://m.media-amazon.com/images/I/41zMuj2wvvL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41ixgM73DgL._SS522_.jpg"/>


    Folews Bathroom Organizer Over The Toilet Storage,... (https://www.amazon.com/dp/B09NZY3R1T) - Similarity: 0.73



<img src="https://m.media-amazon.com/images/I/41ixgM73DgL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/416WaLx10jL._SS522_.jpg"/>


    GOYMFK 1pc Free Standing Shoe Rack, Multi-layer Me... (https://www.amazon.com/dp/B0CJHKVG6P) - Similarity: 0.72



<img src="https://m.media-amazon.com/images/I/416WaLx10jL._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg"/>


    subrtex Leather ding Room, Dining Chairs Set of 2,... (https://www.amazon.com/dp/B0B66QHB23) - Similarity: 0.77



<img src="https://m.media-amazon.com/images/I/31SejUEWY7L._SS522_.jpg"/>


    
    
    
    Input: 
    



<img src="https://m.media-amazon.com/images/I/41RgefVq70L._SS522_.jpg"/>


    Plant Repotting Mat MUYETOL Waterproof Transplanti... (https://www.amazon.com/dp/B0BXRTWLYK) - Similarity: 0.64



<img src="https://m.media-amazon.com/images/I/41RgefVq70L._SS522_.jpg"/>


    
    
    


## Wrapping up


In this notebook, we explored how to leverage the multimodal capabilities of `gpt-4o-mini` to tag and caption images. By providing images along with contextual information to the model, we were able to generate tags and descriptions that can be further refined to create captions. This process has practical applications in various scenarios, particularly in enhancing search functionalities.

The search use case illustrated can be directly applied to applications such as recommendation systems, but the techniques covered in this notebook can be extended beyond items search and used in multiple use cases, for example RAG applications leveraging unstructured image data.

As a next step, you could explore using a combination of rule-based filtering with keywords and embeddings search with captions to retrieve more relevant results.


key: _node_content
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key: chroma:document
string_value:
## User and product embeddings

We 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).

### 1. Calculate user and product embeddings

We calculate these embeddings simply by averaging all the reviews about the same product or written by the same user within the training set.


```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from ast import literal_eval

df = 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
df.head(2)
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ProductId</th>
      <th>UserId</th>
      <th>Score</th>
      <th>Summary</th>
      <th>Text</th>
      <th>combined</th>
      <th>n_tokens</th>
      <th>embedding</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>B003XPF9BO</td>
      <td>A3R7JR3FMEBXQB</td>
      <td>5</td>
      <td>where does one  start...and stop... with a tre...</td>
      <td>Wanted to save some to bring to my Chicago fam...</td>
      <td>Title: where does one  start...and stop... wit...</td>
      <td>52</td>
      <td>[0.03599238395690918, -0.02116263099014759, -0...</td>
    </tr>
    <tr>
      <th>297</th>
      <td>B003VXHGPK</td>
      <td>A21VWSCGW7UUAR</td>
      <td>4</td>
      <td>Good, but not Wolfgang Puck good</td>
      <td>Honestly, I have to admit that I expected a li...</td>
      <td>Title: Good, but not Wolfgang Puck good; Conte...</td>
      <td>178</td>
      <td>[-0.07042013108730316, -0.03175969794392586, -...</td>
    </tr>
  </tbody>
</table>
</div>




```python
df['babbage_similarity'] = df["embedding"].apply(literal_eval).apply(np.array)
X_train, X_test, y_train, y_test = train_test_split(df, df.Score, test_size = 0.2, random_state=42)

user_embeddings = X_train.groupby('UserId').babbage_similarity.apply(np.mean)
prod_embeddings = X_train.groupby('ProductId').babbage_similarity.apply(np.mean)
len(user_embeddings), len(prod_embeddings)

```




    (577, 706)


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key: chroma:document
string_value:
```python
df['babbage_similarity'] = df["embedding"].apply(literal_eval).apply(np.array)
X_train, X_test, y_train, y_test = train_test_split(df, df.Score, test_size = 0.2, random_state=42)

user_embeddings = X_train.groupby('UserId').babbage_similarity.apply(np.mean)
prod_embeddings = X_train.groupby('ProductId').babbage_similarity.apply(np.mean)
len(user_embeddings), len(prod_embeddings)

```




    (577, 706)



We can see that most of the users and products appear within the 50k examples only once.

### 2. Evaluate the embeddings

To 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.


```python
from utils.embeddings_utils import cosine_similarity

# evaluate embeddings as recommendations on X_test
def evaluate_single_match(row):
    user_id = row.UserId
    product_id = row.ProductId
    try:
        user_embedding = user_embeddings[user_id]
        product_embedding = prod_embeddings[product_id]
        similarity = cosine_similarity(user_embedding, product_embedding)
        return similarity
    except Exception as e:
        return np.nan

X_test['cosine_similarity'] = X_test.apply(evaluate_single_match, axis=1)
X_test['percentile_cosine_similarity'] = X_test.cosine_similarity.rank(pct=True)

```

#### 2.1 Visualize cosine similarity by review score

We group the cosine similarity scores by the review score, and plot the distribution of cosine similarity scores for each review score.


```python
import matplotlib.pyplot as plt
import statsmodels.api as sm


correlation = X_test[['percentile_cosine_similarity', 'Score']].corr().values[0,1]
print('Correlation between user & vector similarity percentile metric and review number of stars (score): %.2f%%' % (100*correlation))

# boxplot of cosine similarity for each score
X_test.boxplot(column='percentile_cosine_similarity', by='Score')
plt.title('')
plt.show()
plt.close()

```

    Correlation between user & vector similarity percentile metric and review number of stars (score): 29.56%



    
![png](output_8_1.png)
    


We 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!

Because 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.


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key: chroma:document
string_value:
# Visualizing embeddings in 3D

The 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).

### 1. Load the dataset and query embeddings


```python
import pandas as pd
samples = pd.read_json("data/dbpedia_samples.jsonl", lines=True)
categories = sorted(samples["category"].unique())
print("Categories of DBpedia samples:", samples["category"].value_counts())
samples.head()

```

    Categories of DBpedia samples: Artist                    21
    Film                      19
    Plant                     19
    OfficeHolder              18
    Company                   17
    NaturalPlace              16
    Athlete                   16
    Village                   12
    WrittenWork               11
    Building                  11
    Album                     11
    Animal                    11
    EducationalInstitution    10
    MeanOfTransportation       8
    Name: category, dtype: int64





<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>text</th>
      <th>category</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Morada Limited is a textile company based in ...</td>
      <td>Company</td>
    </tr>
    <tr>
      <th>1</th>
      <td>The Armenian Mirror-Spectator is a newspaper ...</td>
      <td>WrittenWork</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Mt. Kinka (金華山 Kinka-zan) also known as Kinka...</td>
      <td>NaturalPlace</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Planning the Play of a Bridge Hand is a book ...</td>
      <td>WrittenWork</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Wang Yuanping (born 8 December 1976) is a ret...</td>
      <td>Athlete</td>
    </tr>
  </tbody>
</table>
</div>




```python
from utils.embeddings_utils import get_embeddings
# NOTE: The following code will send a query of batch size 200 to /embeddings
matrix = get_embeddings(samples["text"].to_list(), model="text-embedding-3-small")

```

### 2. Reduce the embedding dimensionality


```python
from sklearn.decomposition import PCA
pca = PCA(n_components=3)
vis_dims = pca.fit_transform(matrix)
samples["embed_vis"] = vis_dims.tolist()

```

### 3. Plot the embeddings of lower dimensionality


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key: chroma:document
string_value:
```python
from utils.embeddings_utils import get_embeddings
# NOTE: The following code will send a query of batch size 200 to /embeddings
matrix = get_embeddings(samples["text"].to_list(), model="text-embedding-3-small")

```

### 2. Reduce the embedding dimensionality


```python
from sklearn.decomposition import PCA
pca = PCA(n_components=3)
vis_dims = pca.fit_transform(matrix)
samples["embed_vis"] = vis_dims.tolist()

```

### 3. Plot the embeddings of lower dimensionality


```python
%matplotlib widget
import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(projection='3d')
cmap = plt.get_cmap("tab20")

# Plot each sample category individually such that we can set label name.
for i, cat in enumerate(categories):
    sub_matrix = np.array(samples[samples["category"] == cat]["embed_vis"].to_list())
    x=sub_matrix[:, 0]
    y=sub_matrix[:, 1]
    z=sub_matrix[:, 2]
    colors = [cmap(i/len(categories))] * len(sub_matrix)
    ax.scatter(x, y, zs=z, zdir='z', c=colors, label=cat)

ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.legend(bbox_to_anchor=(1.1, 1))

```




    <matplotlib.legend.Legend at 0x1622180a0>





<div style="display: inline-block;">
    <div class="jupyter-widgets widget-label" style="text-align: center;">
        Figure
    </div>


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