document_retrieval.py

import numpy as np
from mistral_api import get_embedding

def vector_similarity(x: list[float], y: list[float]) -> float:
    """
    Returns the similarity between two vectors.
    
    Because OpenAI Embeddings are normalized to length 1, the cosine similarity is the same as the dot product.
    """
    return np.dot(np.array(x), np.array(y))

def select_document_section_by_query_similarity(query: str, contexts: dict[(str, str), np.array]) -> list[(float, (str, str))]:
    """
    Find the query embedding for the supplied query, and compare it against all of the pre-calculated document embeddings
    to find the most relevant sections. 
    
    Return the list of document sections, sorted by relevance in descending order.
    """
    query_embedding = get_embedding(query)
    
    document_similarities = sorted([
        (vector_similarity(query_embedding, doc_embedding), doc_index) for doc_index, doc_embedding in contexts.items()
    ], reverse=True)
    
    return document_similarities[0]