Inicio

app.py

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+Aquí tienes el código Python sin comentarios:
+
+import streamlit as st
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_openai import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain
+import tempfile
+import os
+
+st.set_page_config(layout="wide")
+
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+if 'vector_store' not in st.session_state:
+    st.session_state.vector_store = None
+if 'selected_llm' not in st.session_state:
+    st.session_state.selected_llm = None
+if 'api_key' not in st.session_state:
+    st.session_state.api_key = ""
+
+def process_pdf(pdf_file):
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        tmp_file.write(pdf_file.getvalue())
+        tmp_file_path = tmp_file.name
+
+    loader = PyPDFLoader(tmp_file_path)
+    pages = loader.load_and_split()
+    
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    chunks = text_splitter.split_documents(pages)
+    
+    os.unlink(tmp_file_path)
+    return chunks
+
+def setup_rag(chunks, selected_llm, api_key):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_store = Chroma.from_documents(chunks, embeddings)
+    
+    if selected_llm == "Gemini 1.5 Pro":
+        llm = ChatGoogleGenerativeAI(model_name="gemini-1.5-pro-latest", temperature=0, google_api_key=api_key)
+    else:
+        llm = ChatOpenAI(model_name="gpt-4", temperature=0, api_key=api_key)
+    
+    retriever = vector_store.as_retriever(search_kwargs={"k": 3})
+    chain = ConversationalRetrievalChain.from_llm(llm, retriever=retriever)
+    
+    return chain
+
+col1, col2 = st.columns([7, 3])
+
+with col2:
+    st.header("Configuración")
+    
+    st.session_state.api_key = st.text_input("API Key", type="password", value=st.session_state.api_key)
+    st.session_state.selected_llm = st.selectbox("Seleccionar LLM", ["Gemini 1.5 Pro", "GPT-4"], index=0 if st.session_state.selected_llm == "Gemini 1.5 Pro" else 1)
+    
+    st.header("Cargar PDFs")
+    uploaded_files = st.file_uploader("Selecciona los archivos PDF", accept_multiple_files=True, type=['pdf'])
+    
+    if uploaded_files and st.session_state.api_key and st.session_state.selected_llm:
+        if st.button("Procesar PDFs"):
+            all_chunks = []
+            for pdf_file in uploaded_files:
+                chunks = process_pdf(pdf_file)
+                all_chunks.extend(chunks)
+            
+            st.session_state.vector_store = setup_rag(all_chunks, st.session_state.selected_llm, st.session_state.api_key)
+            st.success(f"Se han procesado {len(uploaded_files)} archivos PDF.")
+    else:
+        st.warning("Por favor, asegúrate de proporcionar la API Key, seleccionar un LLM y cargar al menos un archivo PDF.")
+
+with col1:
+    st.header("Chat")
+    
+    for message in st.session_state.chat_history:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+    
+    if query := st.chat_input("Haz una pregunta sobre los documentos"):
+        st.session_state.chat_history.append({"role": "user", "content": query})
+        with st.chat_message("user"):
+            st.markdown(query)
+        
+        if st.session_state.vector_store:
+            with st.chat_message("assistant"):
+                response = st.session_state.vector_store({"question": query, "chat_history": [(msg["role"], msg["content"]) for msg in st.session_state.chat_history]})
+                st.write(f"Respuesta de {st.session_state.selected_llm}:")
+                st.write(response['answer'])
+            
+            st.session_state.chat_history.append({"role": "assistant", "content": response['answer']})
+        else:
+            with st.chat_message("assistant"):
+                st.write("Por favor, carga y procesa algunos archivos PDF primero.")
+            st.session_state.chat_history.append({"role": "assistant", "content": "Por favor, carga y procesa algunos archivos PDF primero."})
+