The new context information provided is a list of numerical values, which are likely the embeddings_dict mentioned in the original answer. These values may be used to represent the document in a numerical format for machine learning or natural language processing purposes. However, this information does not provide any additional details about the document store or the specific document mentioned in the original answer. Therefore, the original answer remains the same.

def queryIndex(indexes, query):
    
    catsIndex = Document(text=json.dumps(indexes[0]))
    dogsIndex = Document(text=json.dumps(indexes[1]))
    
    combined = GPTSimpleVectorIndex([catsIndex, dogsIndex])

    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", max_tokens=250))
    return combined.query(query, mode="default", llm_predictor=llm_predictor)