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import gradio as gr
from langchain_community.chat_models import HuggingFaceHub
from langchain_community.vectorstores import Chroma
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.output_parsers import StrOutputParser
from langchain_huggingface import HuggingFaceEmbeddings
from langchain import hub
from rerankers import Reranker
import os
# Configuraci贸n del token de acceso a Hugging Face (si usas modelo privado)
os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN", "")
# Cargar PDF
loader = PyPDFLoader("80dias.pdf")
documents = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
splits = splitter.split_documents(documents)
# Crear embeddings
embedding_model = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
vectordb = Chroma.from_documents(splits, embedding=embeddings)
# Modelo LLM desde HuggingFace (usa uno disponible en Spaces)
llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.1", model_kwargs={"temperature": 0.5, "max_new_tokens": 500})
chain = llm | StrOutputParser()
# Reranker
ranker = Reranker("answerdotai/answerai-colbert-small-v1", model_type="colbert")
# Funci贸n RAG
def rag_chat(query):
results = vectordb.similarity_search_with_score(query)
context = []
for doc, score in results:
if score < 7:
context.append(doc.page_content)
if not context:
return "No tengo informaci贸n para responder a esa pregunta."
ranking = ranker.rank(query=query, docs=context)
best_context = ranking[0].text
prompt = hub.pull("rlm/rag-prompt")
rag_chain = prompt | llm | StrOutputParser()
result = rag_chain.invoke({"context": best_context, "question": query})
return result
# Interfaz Gradio
iface = gr.ChatInterface(fn=rag_chat, title="Chat Julio Verne - RAG", description="Pregunta lo que quieras sobre *La vuelta al mundo en 80 d铆as* de Julio Verne.")
iface.launch()
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