vembed 0.24

Package providing methods to create Vector Embeddings from Strings, calculate similarities between lists of Strings, and Generate Visualizations such as Heatmaps from simple Lists.

vembed

Library to generate and serialize Vector Embeddings, extract Semantic Similarity, and create Visualizations.

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Strings to Embeddings

• Convert a String to a Vector Embedding

from vembed import string_to_embedding

input_string = "This is a test sentence."
embedding = string_to_embedding(input_string)
print(embedding)

• Use Batching to Convert Several Strings to their Vector Float Representation Efficiently.

from vembed import lists_to_embeddings

embeddings = lists_to_embeddings(["Convert to a List[Float]", "Another String","More Strings!"])
print(embeddings)  # Output: [[0.123, 0.456, ...], [0.789, 0.012, ...]]

Serialization

Functions for Embedding Serialization for Network Transfer.

• Protobuf Serializable Format to use with gRPC Services
• JSON Serialization for usage with REST API's

from vembed import lists_to_embeddings, embeddings_to_proto_format, embeddings_to_json_format

embeddings = lists_to_embeddings(["CSV,Row,1" , "CSV,Row,2"])

# Convert to a Protobuf Serializable Format to send over a gRPC Service
proto_embedding = embeddings_to_proto_format(embeddings)

# Convert to a JSON String for usage with REST API's
json_embedding = embeddings_to_json_format(embeddings)

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Similarity

Semantic Similarity Between Entities

Extract Insights such as Patterns or Relevancy from your Data.

• Calculating Similarity for Entities.

from vembed import calculate_similarities, plot_similarities

customer_feedback = ["Loved the recent update","The app is user-friendly",
                    "Facing issues after the update","The new interface is great"]

themes            = ["positive feedback","negative feedback","app interface","app functionality"]

cos_df, dot_df = calculate_similarities(customer_feedback, themes, print_results=True)

# Prints and Returns Results

Results

Cosine Similarities:

Query: 'Loved the recent update'
  Data: 'positive feedback' => Similarity Score: 0.45
  Data: 'app functionality' => Similarity Score: 0.22
  Data: 'app interface'     => Similarity Score: 0.19
  Data: 'negative feedback' => Similarity Score: 0.11

Query: 'Facing issues after the update'
  Data: 'negative feedback' => Similarity Score: 0.31
  Data: 'positive feedback' => Similarity Score: 0.27
  Data: 'app interface'     => Similarity Score: 0.24
  Data: 'app functionality' => Similarity Score: 0.21

Dot Product Similarities:

Query: 'Loved the recent update'
  Data: 'positive feedback' => Similarity Score: 4.51
  Data: 'app functionality' => Similarity Score: 2.06
  Data: 'negative feedback' => Similarity Score: 1.91
  Data: 'app interface'     => Similarity Score: 1.80

Query: 'Facing issues after the update'
  Data: 'negative feedback' => Similarity Score: 2.92
  Data: 'positive feedback' => Similarity Score: 2.51
  Data: 'app interface'     => Similarity Score: 2.07
  Data: 'app functionality' => Similarity Score: 1.82

• Generating Beautiful, Clean Visualizations from Results.

from vembed import plot_similarities

# .... cos_df, dot_df = calculate_similarities(queries, data)

# Create HeapMap for Visualizing Relationships
plot_similarities(cos_df, dot_df, save_path="heatmaps/customer_feedback_similarity.png")

# View and access the Heatmap at /heatmaps/customer_feedback_similarity.png

Cosine Similarity

• Ranges between -1 and 1

• Recommended when the Context and Similarity is important - and Frequency is not important (Magnitude)

• Use Case for Cosine Similarity

  • Here, Direction - thematic orienation (climate change, agriculture) is relevant

  • Cosine Similarity is useful here as we want to find the relevancy of documents discussing similar topics (direction) - irrespective of the length of frequency of specific words (Magnitude)

@Usage

queries = ["Climate change effects on agriculture"]
data = [
    "Effects of climate change on wheat production",
    "Agriculture in developing countries",
    "Climate change and its impact on global food security",
    "Advances in agricultural technology"
]

# Calculate cosine similarities
cos_df, _ = calculate_similarities(queries, data, sorted=True, print_results=True)

Dot Product Similarity

• Ranges between any Real Number

• When both the magnitude and direction of the vectors are important, and you are dealing with vectors in a similar scale.

• When the Frequency (Magnitude) as well as the Direction (Relevancy) is both important.

• Use Case for Dot Product

  • Direction (Types of Articles) and Magnitude (Frequency of Reading Habits) are both important.

@Usage

user_reading_profile = ["Read many articles on machine learning", "Occasionally reads about space exploration"]
article_options = [
    "Latest trends in machine learning",
    "Beginner's guide to space travel",
    "In-depth analysis of neural networks",
    "Recent discoveries in astronomy"
]

# Calculate dot product similarities
_, dot_df = calculate_similarities(user_reading_profile, article_options, sorted=True, print_results=True)

• Calculating Similarity

@Usage

queries = ["What is the capital of France?", "How is the weather today?"]
    data = [
        "Paris is the capital of France.",
        "The weather is sunny.",
        "Berlin is the capital of Germany.",
        "It is raining in Berlin.",
    ]

    # Calculate similarities and Print Results
    cos_df, dot_df = calculate_similarities(
        queries, data, sorted=True, print_results=True
    )

Visualization for Relevance

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• Create a visualization to display the Simalirities using a Heatmap.

@Usage

customer_feedback = [
    "Loved the recent update",
    "The app is user-friendly",
    "Facing issues after the update",
    "The new interface is great",
]
themes = [
    "positive feedback",
    "negative feedback",
    "app interface",
    "app functionality",
]

# Heatmap of Both Cosine and Dot Product
cos_df, dot_df = calculate_similarities(customer_feedback, themes, sorted=True)
plot_similarities(cos_df, dot_df, save_path="customer_feedback_similarity.png")

# Heatmap of Only Cosine Similarity
cos_df, _ = calculate_similarities(customer_feedback, themes, sorted=True)
plot_similarities(cos_df, None, save_path="customer_feedback_similarity.png")

# Heatmap of Only Dot Product Similarity
_, dot_df = calculate_similarities(customer_feedback, themes, sorted=True)
plot_similarities(None, dot_df, save_path="customer_feedback_similarity.png")

# View customer_feedback_similarity.png to see the Heatmap

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Tests

Latest Test Run

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Build and Run Locally from Source

git clone [e-mail]:kuro337/vembed.git

# Create Isolated Virtual Env
python3 -m venv venv
source venv/bin/activate

# Install Deps
pip install -e .

# Run Tests
chmod +x RUN_TESTS.sh
./RUN_TESTS.sh

# Create Dist 
pip3 install build && python3 -m build

# Use Built Dist in any project
pip3 install ./vembed/dist/vembed-0.24-py3-none-any.whl

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Dependencies

• sentence_transformers
• torch
• transformers
• pandas
• matplotlib
• seaborn

Note: This package uses Nvidia Cuda and Torch.

# Check Disk Allocation for Packages 
du -h venv | sort -hr | head -n 10

2.8G    venv/lib/python3.11/site-packages/nvidia
1.4G    venv/lib/python3.11/site-packages/torch
1.3G    venv/lib/python3.11/site-packages/torch/lib
1.2G    venv/lib/python3.11/site-packages/nvidia/cudnn/lib
1.2G    venv/lib/python3.11/site-packages/nvidia/cudnn
596M    venv/lib/python3.11/site-packages/nvidia/cublas

# Checking System Cache

# Show pip cache location
pip cache dir # /home/user/.cache/pip

# Getting Top Folders from Cache by Size
du -h /home/user/.cache/pip | sort -hr | head -n 10

# Remove Cached Files
pip cache purge 

# Cached Files
pip cache list

# Installing Packages without Cache
pip install --no-cache-dir <package_name>

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Author: kuro337