google/generativeai/types/retriever_types.py [522:540]:
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        response = type(response).to_dict(response)

        # Create a RelevantChunk object for each chunk listed in response['relevant_chunks']
        relevant_chunks = []
        for c in response["relevant_chunks"]:
            rc = RelevantChunk(
                chunk_relevance_score=c["chunk_relevance_score"], chunk=Chunk(**c["chunk"])
            )
            relevant_chunks.append(rc)

        return relevant_chunks

    async def query_async(
        self,
        query: str,
        metadata_filters: Iterable[MetadataFilter] | None = None,
        results_count: int | None = None,
        client: glm.RetrieverServiceAsyncClient | None = None,
        request_options: helper_types.RequestOptionsType | None = None,
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google/generativeai/types/retriever_types.py [1096:1114]:
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        response = type(response).to_dict(response)

        # Create a RelevantChunk object for each chunk listed in response['relevant_chunks']
        relevant_chunks = []
        for c in response["relevant_chunks"]:
            rc = RelevantChunk(
                chunk_relevance_score=c["chunk_relevance_score"], chunk=Chunk(**c["chunk"])
            )
            relevant_chunks.append(rc)

        return relevant_chunks

    async def query_async(
        self,
        query: str,
        metadata_filters: Iterable[MetadataFilter] | None = None,
        results_count: int | None = None,
        client: glm.RetrieverServiceAsyncClient | None = None,
        request_options: helper_types.RequestOptionsType | None = None,
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