Restauração do Echo Bot.

app.py

@@ -1,136 +1,24 @@
 import streamlit as st
-import multiprocessing
-from langchain.docstore.document import Document as LangChainDocument
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from huggingface_hub import login
-from loguru import logger
-import os
-from dotenv import load_dotenv
 
-multiprocessing.freeze_support()
-load_dotenv()
-access_token = os.getenv("ACCESS_TOKEN")
-login(token=access_token)
-logger.info('Login realizado com sucesso.')
-
-logger.info('Carregando arquivo no qual será baseado o RAG.')
-with open('train.txt', 'r') as f:
-    data = f.read()
-
-logger.info('Representando o documento utilizando o LangChainDocument.')
-raw_database = LangChainDocument(page_content=data)
-
-MARKDOWN_SEPARATORS = [
-    "\n#{1,6} ",
-    "```\n",
-    "\n\\*\\*\\*+\n",
-    "\n---+\n",
-    "\n___+\n",
-    "\n\n",
-    "\n",
-    " ",
-    "",
-]
-
-logger.info('Quebrando o documento para a criação dos chunks.')
-splitter = RecursiveCharacterTextSplitter(separators=MARKDOWN_SEPARATORS, chunk_size=1000, chunk_overlap=100)
-process_data = splitter.split_documents([raw_database])
-process_data = process_data[:5]  # TODO: REMOVER DEPOIS
-
-embedding_model_name = "thenlper/gte-small"
-logger.info(f'Definição do modelo de embeddings: {embedding_model_name}.')
-embedding_model = HuggingFaceEmbeddings(
-    model_name=embedding_model_name,
-    multi_process=True,
-    model_kwargs={"device": "cuda"},
-    encode_kwargs={"normalize_embeddings": True},  # Set `True` for cosine similarity
-)
-
-logger.info('Criação da base de dados vetorial (em memória).')
-vectors = FAISS.from_documents(process_data, embedding_model)
-
-from transformers import pipeline
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-
-# model_name = "meta-llama/Llama-3.2-1B"
-model_name = "HuggingFaceH4/zephyr-7b-beta"
-# model_name = "mistralai/Mistral-7B-Instruct-v0.3"
-# model_name = "meta-llama/Llama-3.2-3B-Instruct"
-logger.info(f'Carregamento do modelo de linguagem principal: {model_name}')
-
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16,
-)
-model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-llm_model = pipeline(
-    model=model,
-    tokenizer=tokenizer,
-    task="text-generation",
-    do_sample=True,
-    temperature=0.4,
-    repetition_penalty=1.1,
-    return_full_text=False,
-    max_new_tokens=500
-)
-logger.info(f'Modelo {model_name} carregado com sucesso.')
-
-prompt = """
-<|system|>
-You are a helpful assistant that answers on medical questions based on the real information provided from different sources and in the context.
-Give the rational and well written response. If you don't have proper info in the context, answer "I don't know"
-Respond only to the question asked.
-
-<|user|>
-Context:
-{}
----
-Here is the question you need to answer.
-
-Question: {}
----
-<|assistant|>
-"""
-
-st.title("Echo Bot")
+st.title('Echo Bot')
 
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
 for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
+    with st.chat_message(message['role']):
+        st.markdown(message['content'])
 
-question = st.chat_input("How can I help you?")
-if question:
-    with st.chat_message("user"):
+prompt = st.chat_input('How can I help you?')
+if prompt:
+    with st.chat_message('user'):
         st.markdown(prompt)
 
-    st.session_state.messages.append({"role": "user", "content": prompt})
-
-    search_results = vectors.similarity_search(question, k=3)
-
-    logger.info('Contexto: ')
-    for i, search_result in enumerate(search_results):
-        logger.info(f"{i + 1}) {search_result.page_content}")
-
-    context = " ".join([search_result.page_content for search_result in search_results])
-    final_prompt = prompt.format(context, question)
-    logger.info(f'\n{final_prompt}\n')
-
-    answer = llm_model(final_prompt)
-    text_answer = answer[0]['generated_text']
+    st.session_state.messages.append({'role': 'user', 'content': prompt})
 
-    logger.info("AI response: ", text_answer)
+    response = f'**Echo**: {prompt}'
 
-    with st.chat_message("assistant"):
-        st.markdown(text_answer)
+    with st.chat_message('assistant'):
+        st.markdown(response)
 
-    st.session_state.messages.append({"role": "assistant", "content": text_answer})
+    st.session_state.messages.append({'role': 'assistant', 'content': response})

app_bkp.py

@@ -0,0 +1,136 @@
+import streamlit as st
+import multiprocessing
+from langchain.docstore.document import Document as LangChainDocument
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from huggingface_hub import login
+from loguru import logger
+import os
+from dotenv import load_dotenv
+
+multiprocessing.freeze_support()
+load_dotenv()
+access_token = os.getenv("ACCESS_TOKEN")
+login(token=access_token)
+logger.info('Login realizado com sucesso.')
+
+logger.info('Carregando arquivo no qual será baseado o RAG.')
+with open('train.txt', 'r') as f:
+    data = f.read()
+
+logger.info('Representando o documento utilizando o LangChainDocument.')
+raw_database = LangChainDocument(page_content=data)
+
+MARKDOWN_SEPARATORS = [
+    "\n#{1,6} ",
+    "```\n",
+    "\n\\*\\*\\*+\n",
+    "\n---+\n",
+    "\n___+\n",
+    "\n\n",
+    "\n",
+    " ",
+    "",
+]
+
+logger.info('Quebrando o documento para a criação dos chunks.')
+splitter = RecursiveCharacterTextSplitter(separators=MARKDOWN_SEPARATORS, chunk_size=1000, chunk_overlap=100)
+process_data = splitter.split_documents([raw_database])
+process_data = process_data[:5]  # TODO: REMOVER DEPOIS
+
+embedding_model_name = "thenlper/gte-small"
+logger.info(f'Definição do modelo de embeddings: {embedding_model_name}.')
+embedding_model = HuggingFaceEmbeddings(
+    model_name=embedding_model_name,
+    multi_process=True,
+    model_kwargs={"device": "cuda"},
+    encode_kwargs={"normalize_embeddings": True},  # Set `True` for cosine similarity
+)
+
+logger.info('Criação da base de dados vetorial (em memória).')
+vectors = FAISS.from_documents(process_data, embedding_model)
+
+from transformers import pipeline
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+# model_name = "meta-llama/Llama-3.2-1B"
+model_name = "HuggingFaceH4/zephyr-7b-beta"
+# model_name = "mistralai/Mistral-7B-Instruct-v0.3"
+# model_name = "meta-llama/Llama-3.2-3B-Instruct"
+logger.info(f'Carregamento do modelo de linguagem principal: {model_name}')
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+llm_model = pipeline(
+    model=model,
+    tokenizer=tokenizer,
+    task="text-generation",
+    do_sample=True,
+    temperature=0.4,
+    repetition_penalty=1.1,
+    return_full_text=False,
+    max_new_tokens=500
+)
+logger.info(f'Modelo {model_name} carregado com sucesso.')
+
+prompt = """
+<|system|>
+You are a helpful assistant that answers on medical questions based on the real information provided from different sources and in the context.
+Give the rational and well written response. If you don't have proper info in the context, answer "I don't know"
+Respond only to the question asked.
+
+<|user|>
+Context:
+{}
+---
+Here is the question you need to answer.
+
+Question: {}
+---
+<|assistant|>
+"""
+
+st.title("Echo Bot")
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+question = st.chat_input("How can I help you?")
+if question:
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    search_results = vectors.similarity_search(question, k=3)
+
+    logger.info('Contexto: ')
+    for i, search_result in enumerate(search_results):
+        logger.info(f"{i + 1}) {search_result.page_content}")
+
+    context = " ".join([search_result.page_content for search_result in search_results])
+    final_prompt = prompt.format(context, question)
+    logger.info(f'\n{final_prompt}\n')
+
+    answer = llm_model(final_prompt)
+    text_answer = answer[0]['generated_text']
+
+    logger.info("AI response: ", text_answer)
+
+    with st.chat_message("assistant"):
+        st.markdown(text_answer)
+
+    st.session_state.messages.append({"role": "assistant", "content": text_answer})