Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from sentence_transformers import SentenceTransformer
+import faiss
+import os
+
+# 📌 1. Загружаем LLaMA 3
+MODEL_NAME = "meta-llama/Meta-Llama-3-8B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
+
+# 📌 2. Загружаем Sentence Transformer для эмбеддингов
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# 📌 3. Загружаем свою базу знаний
+def load_documents():
+    knowledge_base = []
+    for file_name in os.listdir("files"):
+        file_path = os.path.join("files", file_name)
+        with open(file_path, "r", encoding="utf-8") as file:
+            text = file.read()
+            knowledge_base.append(text)
+    return knowledge_base
+
+documents = load_documents()
+document_embeddings = embedder.encode(documents, convert_to_tensor=True)
+
+# 📌 4. Создаем FAISS-индекс
+index = faiss.IndexFlatL2(document_embeddings.shape[1])
+index.add(document_embeddings.cpu().numpy())
+
+# 📌 5. Функция поиска релевантной информации
+def retrieve_relevant_info(query, top_k=2):
+    query_embedding = embedder.encode([query], convert_to_tensor=True)
+    query_embedding = query_embedding.cpu().numpy()
+    distances, indices = index.search(query_embedding, top_k)
+    retrieved_docs = [documents[idx] for idx in indices[0]]
+    return " ".join(retrieved_docs)
+
+# 📌 6. Функция генерации ответа
+def generate_response(query):
+    relevant_info = retrieve_relevant_info(query)
+    input_text = f"Context: {relevant_info}\nQuestion: {query}\nAnswer:"
+    inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+    outputs = model.generate(**inputs, max_length=200)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# 📌 7. Gradio-интерфейс
+interface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(lines=2, placeholder="Введите ваш вопрос..."),
+    outputs=gr.Textbox(),
+    title="RAG с LLaMA 3",
+    description="Этот чатбот использует RAG (Retrieval-Augmented Generation) с LLaMA 3 и вашими документами."
+)
+
+interface.launch()