# can define filters specific to this vector index (so you can
# reuse pinecone indexes)
metadata_filters = {"title": "paul_graham_essay"}

{
            "index_struct_type": "simple_dict",
            "query_mode": "default",
            "query_kwargs": {
                "similarity_top_k": 3,
            },
        }

    graph = ComposableGraph.from_indices(
        GPTListIndex,
        index_arr,
        index_summaries=summaries,
        service_context=service_context
    )

# set maximum input size
max_input_size = 256
# set number of output tokens
num_outputs = 256
# set maximum chunk overlap
max_chunk_overlap = 20

chunk_size_limit = 512
# define LLM
llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.2, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper, chunk_size_limit=chunk_size_limit)

# Refine Prompt
CHAT_REFINE_PROMPT_TMPL_MSGS = [
    HumanMessagePromptTemplate.from_template("{query_str}"),
    AIMessagePromptTemplate.from_template("{existing_answer}"),
    HumanMessagePromptTemplate.from_template(
        "We have the opportunity to refine the above answer "
        "(only if needed) with some more context below.\n"
        "------------\n"
        "{context_msg}\n"
        "------------\n"
        "Given the new context, refine the original answer to better "
        "answer the question. "
        "If the context isn't useful, output the original answer again.",
    ),
]

# define query configs for graph 
    query_configs = [
        {
            "index_struct_type": "simple_dict",
            "query_mode": "default",
            "query_kwargs": {
                "similarity_top_k": 1,
                "include_summary": True,
                "refine_template": CHAT_REFINE_PROMPT_TMPL_MSGS
            },
            "query_transform": decompose_transform
        },
        {
            "index_struct_type": "list",
            "query_mode": "default",
            "query_kwargs": {
                "response_mode": "tree_summarize",
                "verbose": True,
                "refine_template": CHAT_REFINE_PROMPT_TMPL_MSGS
            }
        },
    ]

line 117, in refine_response_single
    refine_template = self.refine_template.partial_format(
AttributeError: 'list' object has no attribute 'partial_format'

from llama_index.langchain_helpers.agents import LlamaToolkit, create_llama_chat_agent, IndexToolConfig, GraphToolConfig
agent_chain = create_agent(toolkit, llm)
    while True: 
        query = input("What do you want to ask? ")
        print('question: ', query)
        response = agent_chain.run(input=query)
        print(f'Agent: {response}')