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import gradio as gr
import requests
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.document_loaders import PyPDFLoader
from rerankers import Reranker
from langchain_community.chat_models import ChatOllama
from langchain import hub
from langchain_core.output_parsers import StrOutputParser
# LLM local
local_llm = "llama3.2:1b"
llm = ChatOllama(model=local_llm, temperature=0, top_k=50, top_p=0.95)
chain = llm | StrOutputParser()
# Descargar y cargar PDF
URL = "https://rua.ua.es/dspace/bitstream/10045/118060/1/Isurus_01_02.pdf"
response = requests.get(URL)
open("Isurus_01_02.pdf", "wb").write(response.content)
loader = PyPDFLoader("Isurus_01_02.pdf")
documents = loader.load()
# Divisi贸n en chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
all_splits = text_splitter.split_documents(documents)
# Embeddings con device CPU
model_name = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
model_kwargs = {"device": "cpu"}
embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
# Vector store
vectordb = Chroma.from_documents(documents=all_splits, embedding=embeddings, persist_directory="chroma_db")
# Funci贸n para test RAG
def test_rag(query):
docs = vectordb.similarity_search_with_score(query)
prompt = hub.pull("rlm/rag-prompt")
rag_chain = prompt | llm | StrOutputParser()
context = []
for doc, score in docs:
if score < 7:
doc_details = doc.to_json()['kwargs']
context.append(doc_details['page_content'])
if context:
generation = rag_chain.invoke({"context": "\n".join(context), "question": query})
return generation
else:
return "No tengo informaci贸n para responder a esta pregunta."
# Interfaz de Gradio
def chat_function(message, history):
return test_rag(message)
iface = gr.ChatInterface(
fn=chat_function,
chatbot=gr.Chatbot(),
title="RAG Bot",
description="Hazme preguntas sobre el contenido del PDF.",
)
iface.launch()
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