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https://github.com/JasonYANG170/CodeGeeX4.git
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55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
"""
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References: https://python.langchain.com/v0.2/docs/tutorials/rag/
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"""
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import argparse
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import gradio as gr
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.runnables import RunnablePassthrough
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from models.codegeex import CodegeexChatModel
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from utils.prompts import CUSTOM_RAG_PROMPT
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from utils.vector import load_vector_store
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument('--vector_path', type=str, help="path to load the vectors", default='vectors')
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parser.add_argument('--model_name_or_path', type=str, default='THUDM/codegeex4-all-9b')
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parser.add_argument('--device', type=str, help="cpu or cuda", default="cpu")
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parser.add_argument('--temperature', type=float, help="model's temperature", default=0.2)
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return parser.parse_args()
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def format_docs(docs):
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return "\n\n".join(
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[f"[[citation:{i + 1}]]\n```markdown\n{doc.page_content}\n```" for i, doc in enumerate(docs)]
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)
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def chat(query, history):
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retrieve_chain = ({"context": retriever | format_docs, "question": RunnablePassthrough()} | CUSTOM_RAG_PROMPT)
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retrieve_output = retrieve_chain.invoke(query)
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ans = retrieve_output.text
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yield ans
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ans += "模型回复".center(150, '-') + '\n'
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yield ans
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parse_chain = (llm | StrOutputParser())
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ans += parse_chain.invoke(retrieve_output)
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yield ans
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if __name__ == '__main__':
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args = parse_arguments()
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llm = CodegeexChatModel(args)
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try:
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retriever = load_vector_store(args.vector_path).as_retriever()
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except Exception as e:
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print(f"Fail to load vectors，caused by {e}")
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exit()
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demo = gr.ChatInterface(chat).queue()
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demo.launch(server_name="127.0.0.1", server_port=8080)
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