init

app.py

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+import pandas as pd
+import numpy as np
+import faiss
+import pickle
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from llama_cpp import Llama
+import gradio as gr
+
+# =========================
+# STEP 1: 載入 Hugging Face Dataset
+# =========================
+dataset = load_dataset("pcreem/37", split="train")
+df = dataset.to_pandas()
+df.columns = df.columns.str.strip()  # 清理欄位空白
+
+def make_passage(row):
+    return f"""藥品名稱：{row['中文品名']}
+英文品名：{row['英文品名']}
+主成分：{row['主成分略述']}
+劑型：{row['劑型']}
+適應症：{row['適應症']}
+用法用量：{row['用法用量']}
+申請商：{row['申請商名稱']}
+製造商：{row['製造商名稱']}
+製造廠地址：{row['製造廠廠址']}
+包裝：{row['包裝']}
+有效日期：{row['有效日期']}
+許可證字號：{row['許可證字號']}"""
+
+df["retrieval_passage"] = df.apply(make_passage, axis=1)
+passages = df["retrieval_passage"].tolist()
+
+# =========================
+# STEP 2: 建立 FAISS 檢索
+# =========================
+embedding_model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
+embeddings = embedding_model.encode(passages, show_progress_bar=True)
+dimension = embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(np.array(embeddings).astype("float32"))
+
+# =========================
+# STEP 3: 載入 Llama 模型
+# =========================
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(
+    repo_id="chienweichang/Llama-3-Taiwan-8B-Instruct-GGUF",
+    filename="llama-3-taiwan-8B-instruct-q5_1.gguf"
+)
+
+llm = Llama(
+    model_path=model_path,
+    n_gpu_layers=35,
+    n_ctx=2048,
+    seed=42,
+    verbose=False,
+)
+
+# =========================
+# STEP 4: 定義查詢函式
+# =========================
+def rag_qa(query, k=3):
+    query_embedding = embedding_model.encode([query])
+    D, I = index.search(np.array(query_embedding).astype("float32"), k=k)
+    top_passages = [passages[idx] for idx in I[0]]
+
+    context = "\n\n---\n\n".join(top_passages)
+    system_prompt = "你是一位專業藥師，根據以下藥品資料，回答使用者的問題，請用簡潔中文說明並避免虛構資訊。\n"
+    user_prompt = f"{system_prompt}\n以下是參考資料：\n\n{context}\n\n使用者問題：{query}"
+    chat_prompt = f"<|start_header_id|>user<|end_header_id|>\n{user_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n"
+
+    output = llm(chat_prompt, max_tokens=512, temperature=0.7, top_p=0.9, stop=["<|eot_id|>"])
+    answer = output["choices"][0]["text"]
+    return answer.strip()
+
+# =========================
+# STEP 5: Gradio 介面
+# =========================
+gr.Interface(
+    fn=rag_qa,
+    inputs=gr.Textbox(label="請輸入問題", placeholder="例如：感冒藥有什麼選擇？"),
+    outputs=gr.Textbox(label="藥師回答"),
+    title="台灣藥品問答系統",
+    description="輸入藥品相關問題，我會根據台灣合法藥品資料庫回答你！"
+).launch()

requirements.txt

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+sentence-transformers
+faiss-cpu
+llama-cpp-python
+datasets
+gradio
+huggingface_hub