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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() |