retriever = VectorIndexRetriever(
    index=index,
    similarity_top_k=3
  )
  node_postprocessors = [
        SimilarityPostprocessor(similarity_cutoff=0.5),
        MetadataReplacementPostProcessor(target_metadata_key="window"),
        # KeywordNodePostprocessor(
        # required_keywords=["news"],
        # exclude_keywords=["playground"])
    ]
  response_synthesizer = get_response_synthesizer(
    response_mode="compact",
    # text_qa_template=text_qa_template,
    # refine_template=refine_template
  )
  vector_db_query_engine = RetrieverQueryEngine(
    retriever=retriever,
    # node_postprocessors=node_postprocessors,
    # response_synthesizer=response_synthesizer
  )
  vector_query_engine_tool = QueryEngineTool(
    query_engine=vector_db_query_engine,
    metadata=ToolMetadata(
      # FIXME: Inconsistent naming, should be `article_database` instead
      name="news_database",
      description="Get news articles and information about political entities and events. Use a detailed plain text question as input to the tool.",
    )
  )
  return OpenAIAgent.from_tools(
    [latest_articles_tool, vector_query_engine_tool],
    llm=OpenAI(model="gpt-3.5-turbo-1106"),
    verbose=True,
    # system_prompt=system_prompt,
    temperature=0,
    seed=1234
  )

documents.append(Document(
        id_=str(s['id']),
        text=s['bodytext'],
        metadata={
          'id': s['id'],
          'headline': s['headline'],
          'published_time': s['date'],
...