the flag of {country}?")model_parser = model | StrOutputParser()color_generator = {"attribute": RunnablePassthrough()} | prompt1 | {"color": model_parser}color_to_fruit = prompt2 | model_parsercolor_to_country = prompt3 | model_parserquestion_generator = color_generator | {"fruit": color_to_fruit, "country": color_to_country} | prompt4question_generator.invoke("warm")    ChatPromptValue(messages=[HumanMessage(content='What is the color of strawberry and the flag of China?', additional_kwargs={}, example=False)])prompt = question_generator.invoke("warm")model.invoke(prompt)    AIMessage(content='The color of an apple is typically red or green. The flag of China is predominantly red with a large yellow star in the upper left corner and four smaller yellow stars surrounding it.', additional_kwargs={}, example=False)Branching and Merging‚ÄãYou may want the output of one component to be processed by 2 or more other components. RunnableMaps let you split or fork the chain so multiple components can process the input in parallel. Later, other components can join or merge the results to synthesize a final response. This type of chain creates a computation graph that looks like the following:     Input      / \     /   \ Branch1 Branch2     \   /      \ /      Combineplanner = (    ChatPromptTemplate.from_template(        "Generate an argument about: {input}"    )    | ChatOpenAI()    | StrOutputParser()    | {"base_response": RunnablePassthrough()})arguments_for = (    ChatPromptTemplate.from_template(        "List the pros or positive aspects of {base_response}"    )    | ChatOpenAI()    | StrOutputParser())arguments_against =  (    ChatPromptTemplate.from_template(        "List the cons or negative aspects of {base_response}"    )    | ChatOpenAI()    | StrOutputParser())final_responder = (    ChatPromptTemplate.from_messages(        [            ("ai", "{original_response}"),            ("human", "Pros:\n{results_1}\n\nCons:\n{results_2}"),            ("system",
Runnables can easily be used to string together multiple Chains
Runnables can easily be used to string together multiple Chains ->: the flag of {country}?")model_parser = model | StrOutputParser()color_generator = {"attribute": RunnablePassthrough()} | prompt1 | {"color": model_parser}color_to_fruit = prompt2 | model_parsercolor_to_country = prompt3 | model_parserquestion_generator = color_generator | {"fruit": color_to_fruit, "country": color_to_country} | prompt4question_generator.invoke("warm")    ChatPromptValue(messages=[HumanMessage(content='What is the color of strawberry and the flag of China?', additional_kwargs={}, example=False)])prompt = question_generator.invoke("warm")model.invoke(prompt)    AIMessage(content='The color of an apple is typically red or green. The flag of China is predominantly red with a large yellow star in the upper left corner and four smaller yellow stars surrounding it.', additional_kwargs={}, example=False)Branching and Merging‚ÄãYou may want the output of one component to be processed by 2 or more other components. RunnableMaps let you split or fork the chain so multiple components can process the input in parallel. Later, other components can join or merge the results to synthesize a final response. This type of chain creates a computation graph that looks like the following:     Input      / \     /   \ Branch1 Branch2     \   /      \ /      Combineplanner = (    ChatPromptTemplate.from_template(        "Generate an argument about: {input}"    )    | ChatOpenAI()    | StrOutputParser()    | {"base_response": RunnablePassthrough()})arguments_for = (    ChatPromptTemplate.from_template(        "List the pros or positive aspects of {base_response}"    )    | ChatOpenAI()    | StrOutputParser())arguments_against =  (    ChatPromptTemplate.from_template(        "List the cons or negative aspects of {base_response}"    )    | ChatOpenAI()    | StrOutputParser())final_responder = (    ChatPromptTemplate.from_messages(        [            ("ai", "{original_response}"),            ("human", "Pros:\n{results_1}\n\nCons:\n{results_2}"),            ("system",