Initial Commit of Local files

src/app.py

@@ -0,0 +1,67 @@
+import streamlit as st
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from src.rag_core import get_retriever, get_llm
+
+st.set_page_config(page_title="RAG Chat", layout="wide")
+st.title("Paperless-NGX RAG Assistant")
+
+retriever, vectorstore, _ = get_retriever()
+llm = get_llm()
+
+# Prompt Template
+system_prompt = (
+    "Du bist ein hilfreicher Assistent. Nutze den folgenden Kontext, um die Frage zu beantworten. "
+    "Wenn du die Antwort nicht weißt, sage einfach, dass du sie nicht weißt.\n\n"
+    "{context}"
+)
+prompt = ChatPromptTemplate.from_messages([
+    ("system", system_prompt),
+    ("human", "{input}"),
+])
+
+question_answer_chain = create_stuff_documents_chain(llm, prompt)
+
+# Optionale Filter-UI
+st.sidebar.header("Filter")
+filter_id = st.sidebar.text_input("Nur in Document ID suchen (optional):")
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for msg in st.session_state.messages:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+
+if prompt_input := st.chat_input("Stelle eine Frage zu deinen Dokumenten..."):
+    st.session_state.messages.append({"role": "user", "content": prompt_input})
+    with st.chat_message("user"):
+        st.markdown(prompt_input)
+
+    with st.chat_message("assistant"):
+        # Dynamischer Retriever mit Metadaten-Filter
+        search_kwargs = {"k": 3}
+        if filter_id:
+            search_kwargs["filter"] = {"paperless_id": int(filter_id)}
+        
+        # Override search_kwargs temporär
+        retriever.search_kwargs = search_kwargs
+        
+        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+        
+        with st.spinner("Denke nach..."):
+            response = rag_chain.invoke({"input": prompt_input})
+            answer = response["answer"]
+            sources = response.get("context", [])
+            
+            st.markdown(answer)
+            
+            if sources:
+                st.write("---")
+                st.write("**Quellen:**")
+                unique_sources = {doc.metadata.get("source") for doc in sources if doc.metadata.get("source")}
+                for s in unique_sources:
+                    st.write(f"- {s}")
+
+    st.session_state.messages.append({"role": "assistant", "content": answer})