Chroma Embeddings Aggregation Library

This Python library provides a suite of advanced methods for aggregating multiple embeddings associated with a single document or entity into a single representative embedding. It is designed to work with Chroma vector stores and is compatible with LangChain's Chroma integration.

Table of Contents

• Features
• Installation
• Usage
  • Example: Simple Average Aggregation
• Aggregation Methods
• Parameters
• Dependencies
• Contributing
• License

Features

• Multiple aggregation methods (average, weighted average, geometric mean, harmonic mean, centroid, PCA, etc.)
• Compatible with Chroma vector stores and LangChain
• Easy-to-use API for aggregating embeddings

Installation

To install the package, you can use pip:

pip install chroma_vector_aggregator

Usage

Here's an example demonstrating how to use the library to aggregate embeddings using simple averaging:

Example: Simple Average Aggregation

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import FakeEmbeddings
from langchain.schema import Document
from chroma_vector_aggregator import aggregate_embeddings

# Create a sample Chroma collection
embeddings = FakeEmbeddings(size=10)
documents = [
    Document(page_content="Test document 1", metadata={"id": "group1"}),
    Document(page_content="Test document 2", metadata={"id": "group1"}),
    Document(page_content="Test document 3", metadata={"id": "group2"}),
    Document(page_content="Test document 4", metadata={"id": "group2"}),
]
chroma_collection = Chroma.from_documents(documents, embeddings)

# Aggregate embeddings using simple averaging
aggregated_collection = aggregate_embeddings(
    chroma_collection=chroma_collection,
    column_name="id",
    method="average"
)

# Use the aggregated collection for similarity search
results = aggregated_collection.similarity_search("Test query", k=2)

Aggregation Methods

• average: Compute the arithmetic mean of embeddings.
• weighted_average: Compute a weighted average of embeddings.
• geometric_mean: Compute the geometric mean across embeddings.
• harmonic_mean: Compute the harmonic mean across embeddings.
• median: Compute the element-wise median of embeddings.
• trimmed_mean: Compute the mean after trimming outliers.
• centroid: Use K-Means clustering to find the centroid of the embeddings.
• pca: Use Principal Component Analysis to reduce embeddings.
• exemplar: Select the embedding that best represents the group.
• max_pooling: Take the maximum value for each dimension across embeddings.
• min_pooling: Take the minimum value for each dimension across embeddings.
• entropy_weighted_average: Weight embeddings by their entropy.
• attentive_pooling: Use an attention mechanism to aggregate embeddings.
• tukeys_biweight: A robust method to down-weight outliers.

Parameters

• chroma_collection: The Chroma collection to aggregate embeddings from.
• column_name: The metadata field by which to aggregate embeddings (e.g., 'id').
• method: The aggregation method to use.
• weights (optional): Weights for the weighted_average method.
• trim_percentage (optional): Fraction to trim from each end for trimmed_mean.

Dependencies

• chromadb
• numpy
• scipy
• scikit-learn
• langchain

Contributing

Contributions are welcome! Please feel free to submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.