Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -4,14 +4,33 @@ from typing import List, Dict, Any, Optional
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import hashlib
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import json
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from datetime import datetime
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# PDF ์ฒ๋ฆฌ ๋ผ์ด๋ธ๋ฌ๋ฆฌ
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import pymupdf # PyMuPDF
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import chromadb
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from chromadb.utils import embedding_functions
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from sentence_transformers import SentenceTransformer
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import numpy as np
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# Custom CSS (๊ธฐ์กด CSS + ์ถ๊ฐ ์คํ์ผ)
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custom_css = """
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border: 1px solid rgba(251, 191, 36, 0.5);
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color: #f59e0b;
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}
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.document-card {
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padding: 12px;
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margin: 8px 0;
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border-radius: 8px;
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background: rgba(255, 255, 255, 0.1);
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border: 1px solid rgba(255, 255, 255, 0.2);
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cursor: pointer;
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transition: all 0.3s ease;
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}
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.document-card:hover {
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background: rgba(255, 255, 255, 0.2);
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transform: translateX(5px);
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}
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"""
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class
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"""
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def __init__(self):
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self.documents = {}
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self.embedder = None
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)
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self.initialize_vector_store()
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def initialize_vector_store(self):
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"""๋ฒกํฐ ์ ์ฅ์ ์ด๊ธฐํ"""
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try:
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# Sentence Transformer ๋ชจ๋ธ ๋ก๋
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self.embedder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
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# ChromaDB ํด๋ผ์ด์ธํธ ์ด๊ธฐํ
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self.chroma_client = chromadb.Client()
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self.collection = self.chroma_client.create_collection(
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name="pdf_documents",
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metadata={"hnsw:space": "cosine"}
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)
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except Exception as e:
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print(f"Vector store initialization error: {e}")
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def extract_text_from_pdf(self, pdf_path: str) -> Dict[str, Any]:
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"""PDF์์ ํ
์คํธ ์ถ์ถ"""
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try:
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doc =
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text_content = []
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metadata = {
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"title": doc.metadata.get("title", "Untitled"),
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"author": doc.metadata.get("author", "Unknown"),
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"pages": len(doc),
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"creation_date": doc.metadata.get("creationDate", ""),
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"file_name": os.path.basename(pdf_path)
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}
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for page_num, page in enumerate(doc):
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text = page.get_text()
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if text.strip():
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text_content.append(
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"page": page_num + 1,
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"content": text
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})
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doc.close()
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return {
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"metadata": metadata,
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"
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"full_text": "\n\n".join([p["content"] for p in text_content])
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}
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except Exception as e:
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raise Exception(f"PDF ์ฒ๋ฆฌ ์ค๋ฅ: {str(e)}")
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def
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"""PDF ์ฒ๋ฆฌ ๋ฐ
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try:
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# PDF ํ
์คํธ ์ถ์ถ
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pdf_data = self.extract_text_from_pdf(pdf_path)
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# ํ
์คํธ๋ฅผ ์ฒญํฌ๋ก ๋ถํ
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chunks = self.text_splitter.split_text(pdf_data["full_text"])
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#
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# ChromaDB์ ์ ์ฅ
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ids = [f"{doc_id}_{i}" for i in range(len(chunks))]
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metadatas = [
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{
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"doc_id": doc_id,
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"chunk_index": i,
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"source": pdf_data["metadata"]["file_name"],
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"page_count": pdf_data["metadata"]["pages"]
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}
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for i in range(len(chunks))
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]
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embeddings=
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metadatas=metadatas
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)
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# ๋ฌธ์ ์ ๋ณด ์ ์ฅ
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self.documents[doc_id] = {
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"metadata": pdf_data["metadata"],
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"chunk_count": len(chunks),
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"upload_time": datetime.now().isoformat()
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}
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return {
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"error": str(e)
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}
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def search_relevant_chunks(self, query: str, top_k: int = 5) -> List[Dict]:
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"""์ฟผ๋ฆฌ์ ๊ด๋ จ๋ ์ฒญํฌ ๊ฒ์"""
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results = self.collection.query(
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query_embeddings=query_embedding.tolist(),
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n_results=top_k
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)
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chunks.
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def
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"""
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]
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{context}
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return
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# RAG ์์คํ
์ธ์คํด์ค ์์ฑ
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rag_system =
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# State variables
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current_model = gr.State("openai/gpt-oss-120b")
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uploaded_documents = gr.State({})
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rag_enabled = gr.State(False)
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def upload_pdf(file):
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doc_id = f"doc_{file_hash}"
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# PDF ์ฒ๋ฆฌ ๋ฐ
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result = rag_system.
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if result["success"]:
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status_html = f"""
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def clear_documents():
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"""์
๋ก๋๋ ๋ฌธ์ ์ด๊ธฐํ"""
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try:
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# ChromaDB ์ปฌ๋ ์
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rag_system.chroma_client.delete_collection("pdf_documents")
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rag_system.collection = rag_system.chroma_client.create_collection(
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name="pdf_documents",
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metadata={"hnsw:space": "cosine"}
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rag_system.documents = {}
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return gr.update(value="<div class='pdf-status pdf-success'>โ
๋ชจ๋ ๋ฌธ์๊ฐ ์ญ์ ๋์์ต๋๋ค</div>"), gr.update(choices=[], value=[]), gr.update(value=False)
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except Exception as e:
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return gr.update(value=f"<div class='pdf-status pdf-error'>โ ์ญ์ ์คํจ: {str(e)}</div>"), gr.update(), gr.update()
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def
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"""
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if
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return
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# ๊ด๋ จ ์ฒญํฌ ๊ฒ์
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relevant_chunks = rag_system.search_relevant_chunks(message, top_k
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if relevant_chunks:
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#
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filtered_chunks = [
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chunk for chunk in relevant_chunks
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if chunk['metadata']['doc_id'] in selected_doc_ids
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]
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rag_prompt = rag_system.generate_rag_prompt(message, filtered_chunks[:top_k])
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return rag_prompt
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return message
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except Exception as e:
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print(f"RAG processing error: {e}")
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return message
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# Gradio ์ธํฐํ์ด์ค
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with gr.Blocks(fill_height=True, theme="Nymbo/Nymbo_Theme", css=custom_css) as demo:
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info="๋ต๋ณ ์์ฑ์ ์ฐธ๊ณ ํ ๋ฌธ์ ์ฒญํฌ์ ๊ฐ์"
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)
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# ๊ณ ๊ธ ์ต์
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with gr.Accordion("โ๏ธ ๋ชจ๋ธ ์ค์ ", open=False):
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# ๋ชจ๋ธ ์ธํฐํ์ด์ค ์ปจํ
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with gr.Column(visible=True) as model_120b_container:
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gr.Markdown("### Model: openai/gpt-oss-120b")
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# ์ค์ ๋ชจ๋ธ ๋ก๋๋ gr.load()๋ก ์ฒ๋ฆฌ
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chatbot_120b = gr.Chatbot(height=400)
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msg_box_120b = gr.Textbox(
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label="๋ฉ์์ง ์
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outputs=[]
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)
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# ์ฑํ
๊ธฐ๋ฅ (RAG ํตํฉ)
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def chat_with_rag(message, history, enable_rag, selected_docs, top_k):
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"""RAG๋ฅผ ํ์ฉํ ์ฑํ
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# RAG ์ฒ๋ฆฌ
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processed_message = process_with_rag(message, enable_rag, selected_docs, top_k)
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# ์ฌ๊ธฐ์ ์ค์ ๋ชจ๋ธ API ํธ์ถ ์ฝ๋๊ฐ ๋ค์ด๊ฐ์ผ ํจ
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# ํ์ฌ๋ ์์ ์๋ต
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if enable_rag and selected_docs:
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response = f"[RAG ํ์ฑํ] ์ ํ๋ {len(selected_docs)}๊ฐ ๋ฌธ์๋ฅผ ์ฐธ๊ณ ํ์ฌ ๋ต๋ณํฉ๋๋ค:\n\n{processed_message[:200]}..."
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else:
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response = f"[์ผ๋ฐ ๋ชจ๋] {message}์ ๋ํ ๋ต๋ณ์
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history.append((message, response))
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return "", history
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# 120b ๋ชจ๋ธ ์ฑํ
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msg_box_120b.submit(
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fn=
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inputs=[msg_box_120b, chatbot_120b, enable_rag, document_list, top_k_chunks],
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outputs=[msg_box_120b, chatbot_120b]
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)
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send_btn_120b.click(
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fn=
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inputs=[msg_box_120b, chatbot_120b, enable_rag, document_list, top_k_chunks],
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outputs=[msg_box_120b, chatbot_120b]
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)
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# 20b ๋ชจ๋ธ ์ฑํ
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msg_box_20b.submit(
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fn=
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inputs=[msg_box_20b, chatbot_20b, enable_rag, document_list, top_k_chunks],
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outputs=[msg_box_20b, chatbot_20b]
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)
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send_btn_20b.click(
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fn=
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inputs=[msg_box_20b, chatbot_20b, enable_rag, document_list, top_k_chunks],
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outputs=[msg_box_20b, chatbot_20b]
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)
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import hashlib
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import json
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from datetime import datetime
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import tempfile
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# PDF ์ฒ๋ฆฌ ๋ผ์ด๋ธ๋ฌ๋ฆฌ (์ค์น ํ์ํ ๊ฒฝ์ฐ๋ฅผ ์ํ ๋์ฒด ๊ตฌํ ํฌํจ)
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try:
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import fitz # PyMuPDF
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PDF_AVAILABLE = True
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except ImportError:
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PDF_AVAILABLE = False
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print("PyMuPDF not installed. Install with: pip install pymupdf")
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try:
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import chromadb
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from chromadb.utils import embedding_functions
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CHROMA_AVAILABLE = True
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except ImportError:
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CHROMA_AVAILABLE = False
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print("ChromaDB not installed. Install with: pip install chromadb")
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try:
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from sentence_transformers import SentenceTransformer
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ST_AVAILABLE = True
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except ImportError:
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ST_AVAILABLE = False
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print("Sentence Transformers not installed. Install with: pip install sentence-transformers")
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import numpy as np
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from typing import Tuple
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# Custom CSS (๊ธฐ์กด CSS + ์ถ๊ฐ ์คํ์ผ)
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custom_css = """
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border: 1px solid rgba(251, 191, 36, 0.5);
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color: #f59e0b;
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}
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"""
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class SimpleTextSplitter:
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"""๊ฐ๋จํ ํ
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def __init__(self, chunk_size=1000, chunk_overlap=200):
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self.chunk_size = chunk_size
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self.chunk_overlap = chunk_overlap
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def split_text(self, text: str) -> List[str]:
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+
"""ํ
์คํธ๋ฅผ ์ฒญํฌ๋ก ๋ถํ """
|
97 |
+
chunks = []
|
98 |
+
start = 0
|
99 |
+
text_len = len(text)
|
100 |
+
|
101 |
+
while start < text_len:
|
102 |
+
end = start + self.chunk_size
|
103 |
+
|
104 |
+
# ๋ฌธ์ฅ ๋์์ ์๋ฅด๊ธฐ ์ํด ๋ง์นจํ ์ฐพ๊ธฐ
|
105 |
+
if end < text_len:
|
106 |
+
last_period = text.rfind('.', start, end)
|
107 |
+
if last_period != -1 and last_period > start:
|
108 |
+
end = last_period + 1
|
109 |
+
|
110 |
+
chunk = text[start:end].strip()
|
111 |
+
if chunk:
|
112 |
+
chunks.append(chunk)
|
113 |
+
|
114 |
+
start = end - self.chunk_overlap
|
115 |
+
if start < 0:
|
116 |
+
start = 0
|
117 |
+
|
118 |
+
return chunks
|
119 |
+
|
120 |
+
class SimplePDFRAGSystem:
|
121 |
+
"""๊ฐ๋จํ PDF ๊ธฐ๋ฐ RAG ์์คํ
"""
|
122 |
|
123 |
def __init__(self):
|
124 |
self.documents = {}
|
125 |
+
self.document_chunks = {}
|
126 |
+
self.embeddings_store = {}
|
127 |
+
self.text_splitter = SimpleTextSplitter(chunk_size=1000, chunk_overlap=200)
|
128 |
+
|
129 |
+
# ์๋ฒ ๋ฉ ๋ชจ๋ธ ์ด๊ธฐํ (๊ฐ๋ฅํ ๊ฒฝ์ฐ)
|
130 |
self.embedder = None
|
131 |
+
if ST_AVAILABLE:
|
132 |
+
try:
|
133 |
+
self.embedder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
|
134 |
+
print("Embedding model loaded successfully")
|
135 |
+
except Exception as e:
|
136 |
+
print(f"Failed to load embedding model: {e}")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
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|
|
|
|
137 |
|
138 |
def extract_text_from_pdf(self, pdf_path: str) -> Dict[str, Any]:
|
139 |
"""PDF์์ ํ
์คํธ ์ถ์ถ"""
|
140 |
+
if not PDF_AVAILABLE:
|
141 |
+
# PyMuPDF๊ฐ ์๋ ๊ฒฝ์ฐ ๋์ฒด ๋ฐฉ๋ฒ
|
142 |
+
return {
|
143 |
+
"metadata": {
|
144 |
+
"title": "PDF Reader Not Available",
|
145 |
+
"file_name": os.path.basename(pdf_path),
|
146 |
+
"pages": 0
|
147 |
+
},
|
148 |
+
"full_text": "PDF ์ฒ๋ฆฌ ๋ผ์ด๋ธ๋ฌ๋ฆฌ๊ฐ ์ค์น๋์ง ์์์ต๋๋ค. 'pip install pymupdf'๋ฅผ ์คํํด์ฃผ์ธ์."
|
149 |
+
}
|
150 |
+
|
151 |
try:
|
152 |
+
doc = fitz.open(pdf_path)
|
153 |
text_content = []
|
154 |
metadata = {
|
155 |
"title": doc.metadata.get("title", "Untitled"),
|
156 |
"author": doc.metadata.get("author", "Unknown"),
|
157 |
"pages": len(doc),
|
|
|
158 |
"file_name": os.path.basename(pdf_path)
|
159 |
}
|
160 |
|
161 |
for page_num, page in enumerate(doc):
|
162 |
text = page.get_text()
|
163 |
if text.strip():
|
164 |
+
text_content.append(text)
|
|
|
|
|
|
|
165 |
|
166 |
doc.close()
|
167 |
|
168 |
return {
|
169 |
"metadata": metadata,
|
170 |
+
"full_text": "\n\n".join(text_content)
|
|
|
171 |
}
|
172 |
except Exception as e:
|
173 |
raise Exception(f"PDF ์ฒ๋ฆฌ ์ค๋ฅ: {str(e)}")
|
174 |
|
175 |
+
def process_and_store_pdf(self, pdf_path: str, doc_id: str) -> Dict[str, Any]:
|
176 |
+
"""PDF ์ฒ๋ฆฌ ๋ฐ ์ ์ฅ"""
|
177 |
try:
|
178 |
# PDF ํ
์คํธ ์ถ์ถ
|
179 |
pdf_data = self.extract_text_from_pdf(pdf_path)
|
|
|
181 |
# ํ
์คํธ๋ฅผ ์ฒญํฌ๋ก ๋ถํ
|
182 |
chunks = self.text_splitter.split_text(pdf_data["full_text"])
|
183 |
|
184 |
+
# ์ฒญํฌ ์ ์ฅ
|
185 |
+
self.document_chunks[doc_id] = chunks
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
186 |
|
187 |
+
# ์๋ฒ ๋ฉ ์์ฑ (๊ฐ๋ฅํ ๊ฒฝ์ฐ)
|
188 |
+
if self.embedder:
|
189 |
+
embeddings = self.embedder.encode(chunks)
|
190 |
+
self.embeddings_store[doc_id] = embeddings
|
|
|
|
|
191 |
|
192 |
# ๋ฌธ์ ์ ๋ณด ์ ์ฅ
|
193 |
self.documents[doc_id] = {
|
194 |
"metadata": pdf_data["metadata"],
|
195 |
"chunk_count": len(chunks),
|
196 |
+
"upload_time": datetime.now().isoformat(),
|
197 |
+
"full_text": pdf_data["full_text"][:500] # ์ฒ์ 500์ ์ ์ฅ
|
198 |
}
|
199 |
|
200 |
return {
|
|
|
211 |
"error": str(e)
|
212 |
}
|
213 |
|
214 |
+
def search_relevant_chunks(self, query: str, doc_ids: List[str], top_k: int = 5) -> List[Dict]:
|
215 |
"""์ฟผ๋ฆฌ์ ๊ด๋ จ๋ ์ฒญํฌ ๊ฒ์"""
|
216 |
+
all_relevant_chunks = []
|
217 |
+
|
218 |
+
if self.embedder and self.embeddings_store:
|
219 |
+
# ์๋ฒ ๋ฉ ๊ธฐ๋ฐ ๊ฒ์
|
220 |
+
query_embedding = self.embedder.encode([query])[0]
|
|
|
|
|
|
|
|
|
221 |
|
222 |
+
for doc_id in doc_ids:
|
223 |
+
if doc_id in self.embeddings_store and doc_id in self.document_chunks:
|
224 |
+
doc_embeddings = self.embeddings_store[doc_id]
|
225 |
+
chunks = self.document_chunks[doc_id]
|
226 |
+
|
227 |
+
# ์ฝ์ฌ์ธ ์ ์ฌ๋ ๊ณ์ฐ
|
228 |
+
similarities = []
|
229 |
+
for emb in doc_embeddings:
|
230 |
+
sim = np.dot(query_embedding, emb) / (np.linalg.norm(query_embedding) * np.linalg.norm(emb))
|
231 |
+
similarities.append(sim)
|
232 |
+
|
233 |
+
# ์์ k๊ฐ ์ ํ
|
234 |
+
top_indices = np.argsort(similarities)[-top_k:][::-1]
|
235 |
+
|
236 |
+
for idx in top_indices:
|
237 |
+
all_relevant_chunks.append({
|
238 |
+
"content": chunks[idx],
|
239 |
+
"doc_id": doc_id,
|
240 |
+
"doc_name": self.documents[doc_id]["metadata"]["file_name"],
|
241 |
+
"chunk_index": idx,
|
242 |
+
"similarity": similarities[idx]
|
243 |
+
})
|
244 |
+
else:
|
245 |
+
# ์๋ฒ ๋ฉ์ด ์๋ ๊ฒฝ์ฐ ํค์๋ ๊ธฐ๋ฐ ๊ฐ๋จํ ๊ฒ์
|
246 |
+
query_lower = query.lower()
|
247 |
+
query_words = set(query_lower.split())
|
248 |
|
249 |
+
for doc_id in doc_ids:
|
250 |
+
if doc_id in self.document_chunks:
|
251 |
+
chunks = self.document_chunks[doc_id]
|
252 |
+
for idx, chunk in enumerate(chunks):
|
253 |
+
chunk_lower = chunk.lower()
|
254 |
+
# ์ฟผ๋ฆฌ ๋จ์ด๊ฐ ์ฒญํฌ์ ํฌํจ๋์ด ์๋์ง ํ์ธ
|
255 |
+
matching_words = sum(1 for word in query_words if word in chunk_lower)
|
256 |
+
if matching_words > 0:
|
257 |
+
all_relevant_chunks.append({
|
258 |
+
"content": chunk,
|
259 |
+
"doc_id": doc_id,
|
260 |
+
"doc_name": self.documents[doc_id]["metadata"]["file_name"],
|
261 |
+
"chunk_index": idx,
|
262 |
+
"similarity": matching_words / len(query_words)
|
263 |
+
})
|
264 |
+
|
265 |
+
# ์ ์ฌ๋ ์์ผ๋ก ์ ๋ ฌํ๊ณ ์์ k๊ฐ ๋ฐํ
|
266 |
+
all_relevant_chunks.sort(key=lambda x: x.get('similarity', 0), reverse=True)
|
267 |
+
return all_relevant_chunks[:top_k]
|
268 |
|
269 |
+
def generate_context_prompt(self, query: str, chunks: List[Dict]) -> str:
|
270 |
+
"""์ปจํ
์คํธ๋ฅผ ํฌํจํ ํ๋กฌํํธ ์์ฑ"""
|
271 |
+
if not chunks:
|
272 |
+
return query
|
273 |
+
|
274 |
+
context_parts = []
|
275 |
+
for i, chunk in enumerate(chunks, 1):
|
276 |
+
context_parts.append(
|
277 |
+
f"[๋ฌธ์: {chunk['doc_name']}, ์น์
{chunk['chunk_index']+1}]\n{chunk['content']}\n"
|
278 |
+
)
|
279 |
|
280 |
+
context = "\n---\n".join(context_parts)
|
281 |
+
|
282 |
+
enhanced_prompt = f"""๋ค์ ๋ฌธ์ ๋ด์ฉ์ ์ฐธ๊ณ ํ์ฌ ์ง๋ฌธ์ ๋ต๋ณํด์ฃผ์ธ์.
|
283 |
|
284 |
+
## ์ฐธ๊ณ ๋ฌธ์:
|
285 |
{context}
|
286 |
|
287 |
+
## ์ง๋ฌธ:
|
288 |
+
{query}
|
289 |
|
290 |
+
## ๋ต๋ณ:
|
291 |
+
์ ๋ฌธ์ ๋ด์ฉ์ ๋ฐํ์ผ๋ก ์ง๋ฌธ์ ๋ํด ์์ธํ๊ณ ์ ํํ๊ฒ ๋ต๋ณํ๊ฒ ์ต๋๋ค."""
|
292 |
|
293 |
+
return enhanced_prompt
|
294 |
|
295 |
# RAG ์์คํ
์ธ์คํด์ค ์์ฑ
|
296 |
+
rag_system = SimplePDFRAGSystem()
|
297 |
|
298 |
# State variables
|
299 |
current_model = gr.State("openai/gpt-oss-120b")
|
|
|
300 |
rag_enabled = gr.State(False)
|
301 |
|
302 |
def upload_pdf(file):
|
|
|
311 |
|
312 |
doc_id = f"doc_{file_hash}"
|
313 |
|
314 |
+
# PDF ์ฒ๋ฆฌ ๋ฐ ์ ์ฅ
|
315 |
+
result = rag_system.process_and_store_pdf(file.name, doc_id)
|
316 |
|
317 |
if result["success"]:
|
318 |
status_html = f"""
|
|
|
351 |
def clear_documents():
|
352 |
"""์
๋ก๋๋ ๋ฌธ์ ์ด๊ธฐํ"""
|
353 |
try:
|
|
|
|
|
|
|
|
|
|
|
|
|
354 |
rag_system.documents = {}
|
355 |
+
rag_system.document_chunks = {}
|
356 |
+
rag_system.embeddings_store = {}
|
357 |
|
358 |
return gr.update(value="<div class='pdf-status pdf-success'>โ
๋ชจ๋ ๋ฌธ์๊ฐ ์ญ์ ๋์์ต๋๋ค</div>"), gr.update(choices=[], value=[]), gr.update(value=False)
|
359 |
except Exception as e:
|
360 |
return gr.update(value=f"<div class='pdf-status pdf-error'>โ ์ญ์ ์คํจ: {str(e)}</div>"), gr.update(), gr.update()
|
361 |
|
362 |
+
def switch_model(model_choice):
|
363 |
+
"""๋ชจ๋ธ ์ ํ ํจ์"""
|
364 |
+
if model_choice == "openai/gpt-oss-120b":
|
365 |
+
return gr.update(visible=True), gr.update(visible=False), model_choice
|
366 |
+
else:
|
367 |
+
return gr.update(visible=False), gr.update(visible=True), model_choice
|
368 |
+
|
369 |
+
def chat_with_model(message: str, history: List[Tuple[str, str]], enable_rag: bool, selected_docs: List[str], top_k: int, model: str):
|
370 |
+
"""๋ชจ๋ธ๊ณผ ๋ํ (RAG ํฌํจ)"""
|
371 |
|
372 |
+
# RAG๊ฐ ํ์ฑํ๋๊ณ ๋ฌธ์๊ฐ ์ ํ๋ ๊ฒฝ์ฐ
|
373 |
+
if enable_rag and selected_docs:
|
374 |
+
# ์ ํ๋ ๋ฌธ์ ID ์ถ์ถ
|
375 |
+
doc_ids = [doc.split(":")[0] for doc in selected_docs]
|
376 |
+
|
377 |
# ๊ด๋ จ ์ฒญํฌ ๊ฒ์
|
378 |
+
relevant_chunks = rag_system.search_relevant_chunks(message, doc_ids, top_k)
|
379 |
|
380 |
if relevant_chunks:
|
381 |
+
# ์ปจํ
์คํธ๋ฅผ ํฌํจํ ํ๋กฌํํธ ์์ฑ
|
382 |
+
enhanced_message = rag_system.generate_context_prompt(message, relevant_chunks)
|
|
|
|
|
|
|
|
|
383 |
|
384 |
+
# ๋๋ฒ๊ทธ ์ ๋ณด ํฌํจ ์๋ต (์ค์ ๊ตฌํ์ ๋ชจ๋ธ API ํธ์ถ๋ก ๋์ฒด)
|
385 |
+
response = f"""๐ RAG ๊ธฐ๋ฐ ๋ต๋ณ (๋ชจ๋ธ: {model})
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
386 |
|
387 |
+
์ฐพ์ ๊ด๋ จ ๋ฌธ์ ์น์
: {len(relevant_chunks)}๊ฐ
|
388 |
+
|
389 |
+
์ง๋ฌธ: {message}
|
390 |
+
|
391 |
+
๋ต๋ณ:
|
392 |
+
{enhanced_message[:2000]}...
|
393 |
+
|
394 |
+
[์ฐธ๊ณ : ์ค์ ๊ตฌํ์ ์ฌ๊ธฐ์ ๋ชจ๋ธ API๋ฅผ ํธ์ถํ์ฌ enhanced_message๋ฅผ ์ ์กํ๊ณ ์๋ต์ ๋ฐ์์ผ ํฉ๋๋ค]
|
395 |
+
|
396 |
+
๊ด๋ จ ๋ฌธ์ ์น์
์์ฝ:
|
397 |
+
"""
|
398 |
+
for i, chunk in enumerate(relevant_chunks[:3], 1):
|
399 |
+
response += f"\n{i}. {chunk['doc_name']} - ์น์
{chunk['chunk_index']+1} (์ ์ฌ๋: {chunk['similarity']:.2f})"
|
400 |
+
response += f"\n ๋ด์ฉ: {chunk['content'][:200]}...\n"
|
401 |
+
else:
|
402 |
+
response = f"โ ๏ธ ์ ํ๋ ๋ฌธ์์์ '{message}'์ ๊ด๋ จ๋ ๋ด์ฉ์ ์ฐพ์ ์ ์์ต๋๋ค. ๋ค๋ฅธ ์ง๋ฌธ์ ์๋ํด๋ณด์ธ์."
|
403 |
+
else:
|
404 |
+
# RAG ๋นํ์ฑํ ์ํ
|
405 |
+
response = f"""์ผ๋ฐ ๋ต๋ณ ๋ชจ๋ (๋ชจ๋ธ: {model})
|
406 |
+
|
407 |
+
์ง๋ฌธ: {message}
|
408 |
+
|
409 |
+
[์ฐธ๊ณ : ์ค์ ๊ตฌํ์ ์ฌ๊ธฐ์ ๋ชจ๋ธ API๋ฅผ ํธ์ถํ์ฌ message๋ฅผ ์ ์กํ๊ณ ์๋ต์ ๋ฐ์์ผ ํฉ๋๋ค]
|
410 |
+
|
411 |
+
PDF ๋ฌธ์๋ฅผ ์
๋ก๋ํ๊ณ RAG๋ฅผ ํ์ฑํํ๋ฉด ๋ฌธ์ ๊ธฐ๋ฐ ๋ต๋ณ์ ๋ฐ์ ์ ์์ต๋๋ค."""
|
412 |
+
|
413 |
+
history.append((message, response))
|
414 |
+
return "", history
|
415 |
|
416 |
# Gradio ์ธํฐํ์ด์ค
|
417 |
with gr.Blocks(fill_height=True, theme="Nymbo/Nymbo_Theme", css=custom_css) as demo:
|
|
|
472 |
info="๋ต๋ณ ์์ฑ์ ์ฐธ๊ณ ํ ๋ฌธ์ ์ฒญํฌ์ ๊ฐ์"
|
473 |
)
|
474 |
|
475 |
+
gr.Markdown("""
|
476 |
+
### ๐ RAG ์ฌ์ฉ ํ:
|
477 |
+
1. PDF ํ์ผ์ ์
๋ก๋ํ์ธ์
|
478 |
+
2. ์
๋ก๋๋ ๋ฌธ์๋ฅผ ์ ํํ์ธ์
|
479 |
+
3. RAG๋ฅผ ํ์ฑํํ์ธ์
|
480 |
+
4. ๋ฌธ์ ๋ด์ฉ์ ๋ํด ์ง๋ฌธํ์ธ์
|
481 |
+
|
482 |
+
์์ ์ง๋ฌธ:
|
483 |
+
- "๋ฌธ์์ ์ฃผ์ ๋ด์ฉ์ ์์ฝํด์ฃผ์ธ์"
|
484 |
+
- "์ด ๋ฌธ์์์ ์ธ๊ธ๋ ๋ ์ง๋ ์ธ์ ์ธ๊ฐ์?"
|
485 |
+
- "์ฐธ๊ฐ ์๊ฒฉ ์กฐ๊ฑด์ ๋ฌด์์ธ๊ฐ์?"
|
486 |
+
""")
|
487 |
|
488 |
# ๊ณ ๊ธ ์ต์
|
489 |
with gr.Accordion("โ๏ธ ๋ชจ๋ธ ์ค์ ", open=False):
|
|
|
516 |
# ๋ชจ๋ธ ์ธํฐํ์ด์ค ์ปจํ
์ด๋
|
517 |
with gr.Column(visible=True) as model_120b_container:
|
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gr.Markdown("### Model: openai/gpt-oss-120b")
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chatbot_120b = gr.Chatbot(height=400)
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msg_box_120b = gr.Textbox(
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label="๋ฉ์์ง ์
๋ ฅ",
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outputs=[]
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)
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# 120b ๋ชจ๋ธ ์ฑํ
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msg_box_120b.submit(
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+
fn=lambda msg, hist, rag, docs, k: chat_with_model(msg, hist, rag, docs, k, "openai/gpt-oss-120b"),
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inputs=[msg_box_120b, chatbot_120b, enable_rag, document_list, top_k_chunks],
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outputs=[msg_box_120b, chatbot_120b]
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)
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send_btn_120b.click(
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+
fn=lambda msg, hist, rag, docs, k: chat_with_model(msg, hist, rag, docs, k, "openai/gpt-oss-120b"),
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inputs=[msg_box_120b, chatbot_120b, enable_rag, document_list, top_k_chunks],
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outputs=[msg_box_120b, chatbot_120b]
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)
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# 20b ๋ชจ๋ธ ์ฑํ
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msg_box_20b.submit(
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+
fn=lambda msg, hist, rag, docs, k: chat_with_model(msg, hist, rag, docs, k, "openai/gpt-oss-20b"),
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inputs=[msg_box_20b, chatbot_20b, enable_rag, document_list, top_k_chunks],
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outputs=[msg_box_20b, chatbot_20b]
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)
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send_btn_20b.click(
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+
fn=lambda msg, hist, rag, docs, k: chat_with_model(msg, hist, rag, docs, k, "openai/gpt-oss-20b"),
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inputs=[msg_box_20b, chatbot_20b, enable_rag, document_list, top_k_chunks],
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outputs=[msg_box_20b, chatbot_20b]
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)
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