Create app.py

app.py

@@ -0,0 +1,88 @@
+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()