EmbeddingSimilarityCalculator

EmbeddingSimilarityCalculator is a class in the automata.embedding.base module. It takes an instance of EmbeddingVectorProvider and calculates the similarity score between a query text and symbol embeddings.

Overview

EmbeddingSimilarityCalculator leverages embeddings representation to quantify the similarity between code symbols and a given query text. It uses the dot product of the query embedding and the symbol embeddings. If required, the resulting similarity scores can be sorted in descending order by default.

Every instance of EmbeddingSimilarityCalculator is initialized with an EmbeddingVectorProvider and a type of norm for vector normalization (EmbeddingNormType). Initially, it sets these parameters with the corresponding values.

The main method in this class is calculate_query_similarity_dict. This method retrieves the embedding for a provided query text, calculates the similarity scores with the existing symbol embeddings, constructs a dictionary with these scores indexed by the symbols and optionally sorts the dictionary.

Related Symbols

• automata.embedding.base.EmbeddingVectorProvider

• automata.embedding.base.EmbeddingNormType

• automata.embedding.base.Embedding

• automata.core.base.database.vector.VectorDatabaseProvider

• automata.symbol.base.Symbol

Example:

In this example, EmbeddingSimilarityCalculator is utilized to find the symbol most similar to a given query text:

from automata.embedding.base import EmbeddingSimilarityCalculator, EmbeddingVectorProvider
from automata.symbol.base import Symbol
from numpy import array

# Create an instance of the class
mock_provider = EmbeddingVectorProvider()
embedding_sim_calc = EmbeddingSimilarityCalculator(mock_provider)

# Define query_text, and embeddings
query_text = "symbol1"
ordered_embeddings = [Embedding(Symbol('symbol1'), 'symbol1', array([1,0,0,0])),
                      Embedding(Symbol('symbol2'), 'symbol2', array([0,1,0,0])),
                      Embedding(Symbol('symbol3'), 'symbol3', array([0,0,1,0]))]

# Use the calculate_query_similarity_dict method
result = embedding_sim_calc.calculate_query_similarity_dict(ordered_embeddings, query_text)

print(result)

Note: In real scenario EmbeddingVectorProvider would be an instance of class that provides actual embeddings like OpenAIEmbedding.

Limitations

The accuracy of EmbeddingSimilarityCalculator heavily depends on the quality of the embeddings produced by EmbeddingVectorProvider. Poor embeddings can result in inaccurate similarity scores. Additionally, it does not inherently handle cases where symbols might have the same embedding values.

Follow-up Questions:

• If two symbols end up having the same embedding, how does the EmbeddingSimilarityCalculator differentiate between them?

• How are the results affected if a different norm type (EmbeddingNormType) is used?