EXTRACT_TEMPLATE_STR = """\
Here is the content of a section:
----------------
{context_str}
----------------
Given the contextual information, extract out a {class_name} object.\
"""

openai_program_summary = OpenAIPydanticProgram.from_defaults(
    llm=get_llm(model=MODEL_BASIC),
    output_cls=NodeSummaryMetadata,
    prompt_template_str="You must answer in the same language as the context given. {input}",
    extract_template_str=EXTRACT_TEMPLATE_STR,
)

openai_program_keywords = OpenAIPydanticProgram.from_defaults(
    llm=get_llm(model=MODEL_BASIC),
    output_cls=NodeKeywordsMetadata,
    prompt_template_str="You must answer in the same language as the context given. {input}",
    extract_template_str=EXTRACT_TEMPLATE_STR,
)

summary_extractor = PydanticProgramExtractor(program=openai_program_summary, input_key="input", num_workers=12)
keywords_extractor = PydanticProgramExtractor(program=openai_program_keywords, input_key="input", num_workers=12)

@app.route("/history")
def history():
    # Load data
    documents = SimpleDirectoryReader("./src/data/paul_graham").load_data()

    # create index
    index: VectorStoreIndex = VectorStoreIndex.from_documents(documents)

    custom_prompt = PromptTemplate(
        """\
    Given a conversation (between Human and Assistant) and a follow up message from Human, \
    rewrite the message to be a standalone question that captures all relevant context \
    from the conversation.

    <Chat History>
    {chat_history}

    <Follow Up Message>
    {question}

    <Standalone question>
    """
    )

    # list of `ChatMessage` objects
    custom_chat_history = [
        ChatMessage(
            role=MessageRole.USER,
            content="Remember that John Doe is wearing a blue shirt.",
        ),
        ChatMessage(role=MessageRole.ASSISTANT,
                    content=(
                        "Certainly, I'll remember that John Doe is wearing a blue shirt."
                    )
                    ),
    ]

    query_engine = index.as_query_engine()

    chat_engine = CondenseQuestionChatEngine.from_defaults(
        query_engine=query_engine,
        condense_question_prompt=custom_prompt,
        chat_history=custom_chat_history,
        verbose=True,
    )

    chat_response: AgentChatResponse = chat_engine.chat(
        "What color shirt is John Doe wearing?",
        # tool_choice="query_engine_tool"
    )

    pprint("answer:")
    pprint(chat_response.response)

    return "Done."

I'm sorry, but I cannot answer that question based on the given context information.

Expecting value: line 1 column 1 (char 0) (type=value_error.jsondecode; msg=Expecting value; doc=Empty Response; pos=0; lineno=1; colno=1)