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import os
import gradio as gr
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFacePipeline
from transformers import pipeline
EMBEDDINGS_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
LLM_MODEL_NAME = "google/flan-t5-small"
def load_and_split_docs(list_file_path):
if not list_file_path:
return [], "Fehler: Keine Dokumente gefunden!"
loaders = [PyPDFLoader(x) for x in list_file_path]
documents = []
for loader in loaders:
documents.extend(loader.load())
text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=32)
return text_splitter.split_documents(documents)
def create_db(docs):
embeddings = HuggingFaceEmbeddings(model_name=EMBEDDINGS_MODEL_NAME)
return FAISS.from_documents(docs, embeddings)
def initialize_llm_chain(llm_model, temperature, max_tokens, vector_db):
local_pipeline = pipeline(
"text2text-generation",
model=llm_model,
max_length=max_tokens,
temperature=temperature
)
llm = HuggingFacePipeline(pipeline=local_pipeline)
memory = ConversationBufferMemory(memory_key="chat_history")
retriever = vector_db.as_retriever()
return ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
memory=memory,
return_source_documents=True
)
def demo():
with gr.Blocks() as demo:
vector_db = gr.State()
qa_chain = gr.State()
gr.HTML("<center><h1>RAG Chatbot mit FAISS und lokalen Modellen</h1></center>")
with gr.Row():
with gr.Column():
document = gr.Files(file_types=[".pdf"], label="PDF hochladen")
db_btn = gr.Button("Erstelle Vektordatenbank")
db_status = gr.Textbox(value="Status: Nicht initialisiert", show_label=False)
slider_temperature = gr.Slider(0.01, 1.0, value=0.5, label="Temperature")
slider_max_tokens = gr.Slider(64, 512, value=256, label="Max Tokens")
qachain_btn = gr.Button("Initialisiere QA-Chatbot")
with gr.Column():
chatbot = gr.Chatbot(type='messages', height=400)
msg = gr.Textbox(placeholder="Frage eingeben...")
submit_btn = gr.Button("Absenden")
db_btn.click(initialize_database, [document], [vector_db, db_status])
qachain_btn.click(initialize_llm_chain_wrapper, [slider_temperature, slider_max_tokens, vector_db], [qa_chain])
submit_btn.click(conversation, [qa_chain, msg, []], [qa_chain, "message", "history"])
demo.launch(debug=True, enable_queue=True)
if __name__ == "__main__":
demo()
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