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import gradio as gr |
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from huggingface_hub import InferenceClient, login |
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from langchain_community.vectorstores import FAISS |
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from langchain_huggingface import HuggingFaceEmbeddings |
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import os |
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""" |
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For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co./docs/huggingface_hub/v0.22.2/en/guides/inference |
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""" |
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login(token=os.getenv('TOKEN')) |
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client = InferenceClient("meta-llama/Llama-3.2-1B-Instruct") |
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folder = snapshot_download(repo_id="umaiku/faiss_index", repo_type="dataset", local_dir=os.getcwd()) |
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embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-small") |
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vector_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True) |
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retriever = vector_db.as_retriever() |
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def respond( |
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message, |
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history: list[tuple[str, str]], |
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system_message, |
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max_tokens, |
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temperature, |
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top_p, |
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): |
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messages = [{"role": "system", "content": system_message}] |
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for val in history: |
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if val[0]: |
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messages.append({"role": "user", "content": val[0]}) |
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if val[1]: |
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messages.append({"role": "assistant", "content": val[1]}) |
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messages.append({"role": "user", "content": message}) |
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response = "" |
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for message in client.chat_completion( |
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messages, |
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max_tokens=max_tokens, |
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stream=True, |
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temperature=temperature, |
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top_p=top_p, |
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): |
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token = message.choices[0].delta.content |
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response += token |
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yield response |
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""" |
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface |
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""" |
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demo = gr.ChatInterface( |
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respond, |
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additional_inputs=[ |
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gr.Textbox(value="An Expert in Legal advice.", label="System message"), |
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider( |
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minimum=0.1, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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label="Top-p (nucleus sampling)", |
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), |
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], |
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description="# 📜 Legal AI RAG Chatbot", |
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) |
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if __name__ == "__main__": |
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demo.launch(debug=True) |
