embeddings_without_metadatas = (
                    [embeddings[j] for j in empty_ids] if embeddings else None
                )
                ids_without_metadatas = [ids[j] for j in empty_ids]
                self._collection.upsert(
                    embeddings=embeddings_without_metadatas,
                    documents=texts_without_metadatas,
                    ids=ids_without_metadatas,
                )
        else:
            self._collection.upsert(
                embeddings=embeddings,
                documents=texts,
                ids=ids,
            )
        return ids
[docs]    def similarity_search(
        self,
        query: str,
        k: int = DEFAULT_K,
        filter: Optional[Dict[str, str]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Run similarity search with Chroma.
        Args:
            query (str): Query text to search for.
            k (int): Number of results to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
        Returns:
            List[Document]: List of documents most similar to the query text.
        """
        docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
        return [doc for doc, _ in docs_and_scores]
[docs]    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = DEFAULT_K,
        filter: Optional[Dict[str, str]] = None,
        where_document: Optional[Dict[str, str]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.
        Args: