Figma
Figma 是一款用于界面设计的协作式 Web 应用程序。
此笔记本介绍如何将数据从 Figma REST API 加载到 LangChain 可以摄取的格式中，以及代码生成的使用示例。
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 参考：VectorstoreIndexCreator | FigmaFileLoader | ChatPromptTemplate | HumanMessagePromptTemplate | SystemMessagePromptTemplate | ChatOpenAI
Figma API 需要访问令牌、节点 ID 和文件密钥。
文件密钥可以从 URL 中提取。 https://www.figma.com/file/\{filekey\}/sampleFilename
节点 ID 也可在 URL 中找到。点击任何内容并查找 '?node-id={node_id}' 参数。
访问令牌说明在 Figma 帮助中心文章中： 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://python.langchain.ac.cn/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://python.langchain.ac.cn/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 中返回以下内容
<!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>