langchain/retrievers/knn.py/KNNRetriever/get_relevant_documents
class KNNRetriever:    def get_relevant_documents(self, query: str) -> List[Document]:
        query_embeds = np.array(self.embeddings.embed_query(query))
        # calc L2 norm
        index_embeds = self.index / np.sqrt((self.index**2).sum(1, keepdims=True))
        query_embeds = query_embeds / np.sqrt((query_embeds**2).sum())

        similarities = index_embeds.dot(query_embeds)
        sorted_ix = np.argsort(-similarities)

        denominator = np.max(similarities) - np.min(similarities) + 1e-6
        normalized_similarities = (similarities - np.min(similarities)) / denominator

        top_k_results = []
        for row in sorted_ix[0 : self.k]:
            if (
                self.relevancy_threshold is None
                or normalized_similarities[row] >= self.relevancy_threshold
            ):
                top_k_results.append(Document(page_content=self.texts[row]))
        return top_k_results