text: notebook covers how to load data from the Figma REST API into a format that can be ingested into LangChain, along with example usage for code generation.import osfrom langchain.document_loaders.figma import FigmaFileLoaderfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.chat_models import ChatOpenAIfrom langchain.indexes import VectorstoreIndexCreatorfrom langchain.chains import ConversationChain, LLMChainfrom langchain.memory import ConversationBufferWindowMemoryfrom langchain.prompts.chat import (    ChatPromptTemplate,    SystemMessagePromptTemplate,    AIMessagePromptTemplate,    HumanMessagePromptTemplate,)The Figma API Requires an access token, node_ids, and a file key.The file key can be pulled from the URL.  https://www.figma.com/file/{filekey}/sampleFilenameNode IDs are also available in the URL. Click on anything and look for the '?node-id={node_id}' param.Access token instructions are in the Figma help center article: https://help.figma.com/hc/en-us/articles/8085703771159-Manage-personal-access-tokensfigma_loader = FigmaFileLoader(    os.environ.get("ACCESS_TOKEN"),    os.environ.get("NODE_IDS"),    os.environ.get("FILE_KEY"),)# see https://python.langchain.com/en/latest/modules/data_connection/getting_started.html for more detailsindex = VectorstoreIndexCreator().from_loaders([figma_loader])figma_doc_retriever = index.vectorstore.as_retriever()def generate_code(human_input):    # I have no idea if the Jon Carmack thing makes for better code. YMMV.    # See https://python.langchain.com/en/latest/modules/models/chat/getting_started.html for chat info    system_prompt_template = """You are expert coder Jon Carmack. Use the provided design context to