Can vectorstore.as_retriever() just return the filltered documents? not limited the top-k parameter?

[SetonLiang]

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Example Code

vs_retriever = document_vs.as_retriever(search_kwargs={"filter": {"":""}})

def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
        """Return VectorStoreRetriever initialized from this VectorStore.

        Args:
            **kwargs: Keyword arguments to pass to the search function.
                Can include:
                search_type (Optional[str]): Defines the type of search that
                    the Retriever should perform.
                    Can be "similarity" (default), "mmr", or
                    "similarity_score_threshold".
                search_kwargs (Optional[Dict]): Keyword arguments to pass to the
                    search function. Can include things like:
                        k: Amount of documents to return (Default: 4)
                        score_threshold: Minimum relevance threshold
                            for similarity_score_threshold
                        fetch_k: Amount of documents to pass to MMR algorithm
                            (Default: 20)
                        lambda_mult: Diversity of results returned by MMR;
                            1 for minimum diversity and 0 for maximum. (Default: 0.5)
                        filter: Filter by document metadata

        Returns:
            VectorStoreRetriever: Retriever class for VectorStore.

        Examples:

        .. code-block:: python

            # Retrieve more documents with higher diversity
            # Useful if your dataset has many similar documents
            docsearch.as_retriever(
                search_type="mmr",
                search_kwargs={'k': 6, 'lambda_mult': 0.25}
            )

            # Fetch more documents for the MMR algorithm to consider
            # But only return the top 5
            docsearch.as_retriever(
                search_type="mmr",
                search_kwargs={'k': 5, 'fetch_k': 50}
            )

            # Only retrieve documents that have a relevance score
            # Above a certain threshold
            docsearch.as_retriever(
                search_type="similarity_score_threshold",
                search_kwargs={'score_threshold': 0.8}
            )

            # Only get the single most similar document from the dataset
            docsearch.as_retriever(search_kwargs={'k': 1})

            # Use a filter to only retrieve documents from a specific paper
            docsearch.as_retriever(
                search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
            )
        """
        tags = kwargs.pop("tags", None) or [] + self._get_retriever_tags()
        return VectorStoreRetriever(vectorstore=self, tags=tags, **kwargs)

Description

Does the function vectorstore.as_retriever first filter metadata using the keyword and then retrieve documents based on similarity? Is it possible to avoid limiting the returned top_k?
If not, are there any methods to acheive this requirement?

System Info

pip freeze | grep langchain"
platform windows
python=3.9.12