app.py updated

app.py

@@ -1,70 +1,79 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    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
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+import os
+from openai import OpenAI
+from retriever import (
+    load_collection, load_encoder, encode_query, retrieve_docs,
+    query_rerank, expand_with_neighbors, dedup_by_chapter_event
+)
+from sentence_transformers import CrossEncoder
 
-    messages = [{"role": "system", "content": system_message}]
+api_key = os.getenv("OPENAI_API_KEY")
+client = OpenAI(api_key=api_key)
 
-    messages.extend(history)
+collection = load_collection()
+encoder = load_encoder()
+reranker = CrossEncoder("BAAI/bge-reranker-large")
 
-    messages.append({"role": "user", "content": message})
+def build_rag_prompt(query, context):
+    prompt = f"""已知资料如下：
+{context}
 
-    response = ""
+用户提问：{query}
+请参考所有已知资料, 并结合资料内容，简明、准确地回答问题。如果有多个符合的答案, 可以根据你是否确定而决定是否分别陈述这些答案.如果不能确定答案，请如实说明理由，不要凭空编造。"""
+    return prompt
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
 
-        response += token
-        yield response
+def answer_fn(query, history=None):
+    query_vec = encode_query(encoder, query)
+    results = retrieve_docs(collection, query_vec, top_k=30)
+    reranked = query_rerank(reranker, query, results, top_n=10)
+    deduped = dedup_by_chapter_event(reranked, max_per_group=1)
+    expanded_results = expand_with_neighbors(deduped[:3], collection)
 
+    context = expanded_results[0][0] if expanded_results else ""
+    rag_prompt = build_rag_prompt(query, context)
+    system_prompt = "你是BangDream知识问答助手, 也就是邦学家. 只能基于提供的资料内容作答。"
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    response = client.chat.completions.create(
+        model="gpt-4o",
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": rag_prompt}
+        ],
+        temperature=0.2,
+        max_tokens=512,
+    )
+    answer = response.choices[0].message.content.strip()
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+    references = ""
+    for idx, (doc, score, meta) in enumerate(expanded_results, 1):
+        chapter = meta.get("chapterTitle", "UnknownChapter")
+        event = meta.get("eventName", "UnknownEvent")
+        references += f"\n--- reference: {idx} (chapter: {chapter}, event: {event}, score={score:.4f}) ---\n"
+        references += doc[:300] + "...\n"
 
+    return answer, references
 
-if __name__ == "__main__":
-    demo.launch()
+# Gradio UI
+with gr.Blocks(title="Dr-Bang RAG QA") as demo:
+    gr.Markdown("# Dr-Bang RAG QA\n\n基于BangDream知识库的RAG问答系统。")
+    with gr.Row():
+        chatbot = gr.ChatInterface(
+            fn=answer_fn,
+            title="Dr-Bang RAG Chat",
+            description="输入你有关BangDream的问题，邦学家会基于资料库为你检索并作答。",
+            additional_inputs=[],
+            retry_btn=None,
+            undo_btn=None,
+            clear_btn="clear",
+            examples=[
+                ["乐奈为什么喜欢吉他？"],
+                ["LOCK和CHU²第一次见面是什么情节？"],
+                ["谁是RAS的初代成员？"],
+            ],
+            outputs=[
+                gr.Textbox(label="Answer", lines=6, interactive=False),
+                gr.Textbox(label="Reference", lines=8, interactive=False)
+            ]
+        )
+demo.launch()