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app.py
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import gradio as gr
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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="You are a friendly Chatbot.", 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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)
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if __name__ == "__main__":
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import gradio as gr
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import faiss
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import numpy as np
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from rank_bm25 import BM25Okapi
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from transformers import AutoTokenizer, AutoModel
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from litellm import completion
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import os
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import torch
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from sentence_transformers import CrossEncoder
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# --- 1. Завантаження документів ---
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def load_documents(file_paths):
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documents = []
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for path in file_paths:
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with open(path, 'r', encoding='utf-8') as file:
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documents.append(file.read().strip())
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return documents
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# --- 2. Індексування документів ---
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class DocumentIndexer:
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def __init__(self, documents):
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self.documents = documents
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self.bm25 = BM25Okapi([doc.split() for doc in documents])
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self.tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
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self.model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
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self.index = self.create_faiss_index()
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def create_faiss_index(self):
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embeddings = self.embed_documents(self.documents)
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dimension = embeddings.shape[1]
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index = faiss.IndexFlatL2(dimension)
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index.add(embeddings)
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return index
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def embed_documents(self, docs):
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tokens = self.tokenizer(docs, padding=True, truncation=True, return_tensors="pt")
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with torch.no_grad():
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embeddings = self.model(**tokens).last_hidden_state.mean(dim=1).numpy()
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return embeddings
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def search_bm25(self, query, top_k=5):
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query_terms = query.split()
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scores = self.bm25.get_scores(query_terms)
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top_indices = np.argsort(scores)[::-1][:top_k]
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return [self.documents[i] for i in top_indices]
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def search_semantic(self, query, top_k=5):
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query_embedding = self.embed_documents([query])
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distances, indices = self.index.search(query_embedding, top_k)
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return [self.documents[i] for i in indices[0]]
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# --- 3. Ререйкер ---
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class Reranker:
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def __init__(self, model_name="cross-encoder/ms-marco-TinyBERT-L-6"):
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self.tokenizer = AutoTokenizer.from_pretrained(model_name)
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self.model = CrossEncoder(model_name)
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def rank(self, query, documents):
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pairs = [(query, doc) for doc in documents]
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scores = self.model.predict(pairs)
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ranked_docs = [documents[i] for i in np.argsort(scores)[::-1]]
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return ranked_docs
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# --- 4. Генерація відповіді ---
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class QAChatbot:
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def __init__(self, indexer, reranker):
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self.indexer = indexer
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self.reranker = reranker
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def generate_answer(self, query):
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# 1. Шукаємо релевантні документи
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bm25_results = self.indexer.search_bm25(query)
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semantic_results = self.indexer.search_semantic(query)
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combined_results = list(set(bm25_results + semantic_results))
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# 2. Ранжуємо документи
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ranked_docs = self.reranker.rank(query, combined_results)
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# 3. Генеруємо відповідь
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context = "\n".join(ranked_docs[:3]) # Використовуємо топ-3 документи
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response = completion(
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model="groq/llama3-8b-8192",
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messages=[
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{
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"role": "system",
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"content": PROMPT
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},
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{
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"role": "user",
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"content": f"Context: {context}\n\nQuestion: {query}\nAnswer:",
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}
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],
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)
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return response
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# --- 5. Створення Gradio інтерфейсу ---
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def chatbot_interface(query):
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file_paths = ["company.txt", "Base.txt"] # Вкажіть ваші файли
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documents = load_documents(file_paths)
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# Налаштовуємо індексер та ререйкер
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indexer = DocumentIndexer(documents)
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reranker = Reranker()
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# Запускаємо чат-бота
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chatbot = QAChatbot(indexer, reranker)
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answer = chatbot.generate_answer(query)
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return answer["choices"][0]["message"]["content"]
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# Створення інтерфейсу Gradio
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iface = gr.Interface(fn=chatbot_interface, inputs="text", outputs="text",
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live=True, title="Чат-бот для ритейл-компанії",
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description="Запитуйте мене про товари і я допоможу вам вибрати найкраще!")
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# Запуск інтерфейсу
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if __name__ == "__main__":
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iface.launch()
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