Developing the baseline retrieval mechanism for the Retrieve module

[amrhssn]

The similarity score between the embedding of user's query and the embedding of function descriptions can be used as the baseline retrieval mechanism.

The similarity score can be implemented based on either the inner product of the embeddings or the Euclidean distance between them.

Once the scores are computed, the top-K most similar functions needs to be returned.

[amrhssn]

To improve retrieval accuracy using embeddings, you can take the following steps:

1. Enhance the Representation: Use more sophisticated embedding models or domain-specific fine-tuning to ensure that the embeddings capture the nuances of the query and the descriptions more accurately.

2. Combine Name and Description Embeddings: Sometimes, function names alone may not capture the full context or relevance of the function. By combining the embeddings of function names and descriptions, you can get a more comprehensive representation. One approach is to concatenate or average the embeddings of the function name and description before calculating similarity.

3. Contextualize the Query: Consider refining the query embedding by incorporating contextual clues or keywords extracted from the function descriptions. For instance, if the query is about visualizing data, ensure the embeddings emphasize terms like "visualize," "plot," "histogram," etc.

4. Use Multi-modal Similarity Measures: Instead of relying solely on cosine similarity, experiment with other similarity measures that might capture more subtle differences, such as Euclidean distance or more advanced neural network-based approaches like cross-attention mechanisms.

5. Implement Post-Processing Filters: After retrieving the top matches based on embeddings, apply additional filters or rules that align with your understanding of the functions. For instance, if the query mentions visualization explicitly, you might prioritize functions with descriptions containing keywords related to plotting or visualization.

6. Re-rank Based on Task-Specific Heuristics: Finally, consider re-ranking the retrieved results based on heuristics relevant to the task. For example, if the query specifically mentions histograms, functions that explicitly mention "histogram" or related concepts in their descriptions should be ranked higher.

[farhoud]

So if we have metrics like this, can we produce a score for similarity for each search result and show and sort on response?