Figma
Figma 是一款用於介面設計的協作式網路應用程式。
本筆記本涵蓋如何將 Figma REST API 的資料載入到可以導入 LangChain 的格式，以及用於程式碼產生的範例用法。
import os
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders.figma import FigmaFileLoader
from langchain_core.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain_openai import ChatOpenAI
API 參考：VectorstoreIndexCreator | FigmaFileLoader | ChatPromptTemplate | HumanMessagePromptTemplate | SystemMessagePromptTemplate | ChatOpenAI
Figma API 需要存取權杖、node_ids 和檔案金鑰。
檔案金鑰可以從 URL 中提取。 https://www.figma.com/file/\{filekey\}/sampleFilename
Node ID 也可在 URL 中取得。點擊任何內容並尋找 '?node-id={node_id}' 參數。
存取權杖說明位於 Figma 說明中心文章中：https://help.figma.com/hc/en-us/articles/8085703771159-Manage-personal-access-tokens
figma_loader = FigmaFileLoader(
os.environ.get("ACCESS_TOKEN"),
os.environ.get("NODE_IDS"),
os.environ.get("FILE_KEY"),
)
# see https://langchain-python.dev.org.tw/en/latest/modules/data_connection/getting_started.html for more details
index = 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://langchain-python.dev.org.tw/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 create idiomatic HTML/CSS code as possible based on the user request.
Everything must be inline in one file and your response must be directly renderable by the browser.
Figma file nodes and metadata: {context}"""
human_prompt_template = "Code the {text}. Ensure it's mobile responsive"
system_message_prompt = SystemMessagePromptTemplate.from_template(
system_prompt_template
)
human_message_prompt = HumanMessagePromptTemplate.from_template(
human_prompt_template
)
# delete the gpt-4 model_name to use the default gpt-3.5 turbo for faster results
gpt_4 = ChatOpenAI(temperature=0.02, model_name="gpt-4")
# Use the retriever's 'get_relevant_documents' method if needed to filter down longer docs
relevant_nodes = figma_doc_retriever.invoke(human_input)
conversation = [system_message_prompt, human_message_prompt]
chat_prompt = ChatPromptTemplate.from_messages(conversation)
response = gpt_4(
chat_prompt.format_prompt(
context=relevant_nodes, text=human_input
).to_messages()
)
return response
response = generate_code("page top header")
在 response.content 中傳回以下內容
<!DOCTYPE html>\n<html lang="en">\n<head>\n <meta charset="UTF-8">\n <meta name="viewport" content="width=device-width, initial-scale=1.0">\n <style>\n @import url(\'https://fonts.googleapis.com/css2?family=DM+Sans:wght@500;700&family=Inter:wght@600&display=swap\');\n\n body {\n margin: 0;\n font-family: \'DM Sans\', sans-serif;\n }\n\n .header {\n display: flex;\n justify-content: space-between;\n align-items: center;\n padding: 20px;\n background-color: #fff;\n box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);\n }\n\n .header h1 {\n font-size: 16px;\n font-weight: 700;\n margin: 0;\n }\n\n .header nav {\n display: flex;\n align-items: center;\n }\n\n .header nav a {\n font-size: 14px;\n font-weight: 500;\n text-decoration: none;\n color: #000;\n margin-left: 20px;\n }\n\n @media (max-width: 768px) {\n .header nav {\n display: none;\n }\n }\n </style>\n</head>\n<body>\n <header class="header">\n <h1>Company Contact</h1>\n <nav>\n <a href="#">Lorem Ipsum</a>\n <a href="#">Lorem Ipsum</a>\n <a href="#">Lorem Ipsum</a>\n </nav>\n </header>\n</body>\n</html>