app.py

import gradio as gr
from langchain.chains import RagChain
from langchain.vectorstores import Chroma
from transformers import RagTokenizer, RagSequenceForGeneration
from sentence_transformers import SentenceTransformer

# Initialisierung des Sentence-BERT Modells für die Embeddings
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

# Initialisierung von Tokenizer und RAG Modell
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq")

# Verbindung zur Chroma DB und Laden der Dokumente
chroma_db = Chroma(embedding_model=embedding_model, persist_directory = PATH_WORK + CHROMA_DIR)

# Erstellen eines eigenen Retrievers mit Chroma DB und Embeddings
retriever = chroma_db.as_retriever()

# Erstellung der RAG-Kette mit dem benutzerdefinierten Retriever
rag_chain = RagChain(model=model, retriever=retriever, tokenizer=tokenizer, vectorstore=chroma_db)
#############################################


def document_retrieval_chroma2(): 
    #HF embeddings -----------------------------------
    #Alternative Embedding - für Vektorstore, um Ähnlichkeitsvektoren zu erzeugen - die ...InstructEmbedding ist sehr rechenaufwendig
    embeddings = HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"})
    #etwas weniger rechenaufwendig:
    #embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cpu"}, encode_kwargs={'normalize_embeddings': False})
    #oder einfach ohne Langchain:
    #embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")

    #ChromaDb um die embedings zu speichern
    db = Chroma(embedding_function = embeddings, persist_directory = PATH_WORK + CHROMA_DIR)
    print ("Chroma DB bereit ...................")
    
    return db 
	
	
	
def get_rag_response(prompt):
	global rag_chain
	#rag-chain nutzen, um Antwort zu generieren
	result = rag_chain({"Frage: " : prompt})
	
	#relevante Dokumente extrahieren
	docs = result['docs']
	passages = [doc['text'] for doc in docs]
	links = doc['url'] for doc in docs
	
	#Antwort generieren
	answer = result['output']
	response = {
			"answer" : answer,
			"documents" : [{"link" : link, "passage" : passage} for link, passage in zip(links, passages)]
			}
	return response
	
	
def chatbot_response (user_input, chat_history=[]):
	response = get_rag_response(user_input)
	answer = response['answer']
	documents = response['documents']
	doc_links = "\n\n".join([f"Link: {doc['link']} \nAuszüge der Dokumente: {doc['passage']}" for doc in documents])
	bot_response = f"{answer} \n\nRelevante Dokumente: \n{doc_links}"
	
	chat_history.append((user_inptu, bot_response))
	
	return chat_history, chat_history
	
	
#############################
#GUI.........
def user (user_input, history):
	return "", history + [[user_input, None]]

with gr.Blocks() as chatbot:
	chat_interface = gr.Chatbot()
	msg = gr.Textbox()
	clear = gr.Button("Löschen")
	
	#Buttons listener
	msg.submit(user, [msg, chat_interface], [msg, chat_interface], queue = False). then(chatbot_response, [msg, chat_interface], [chat_interface, chat_interface])
	
	clear.click(lambda: None, None, chat_interface, queue=False)
	
chatbot.launch()