change dockerfile permissions

Dockerfile

@@ -8,12 +8,12 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
     PATH="/home/appuser/.local/bin:$PATH"
 
 RUN pip install --user --no-cache-dir --upgrade pip
-COPY ./requirements.txt /home/appuser/requirements.txt
+COPY --chown=appuser ./requirements.txt /home/appuser/requirements.txt
 RUN pip install --user --no-cache-dir  --upgrade -r /home/appuser/requirements.txt
 
-COPY ./langchain-streamlit-demo/ /home/appuser/langchain-streamlit-demo/
+COPY --chown=appuser ./langchain-streamlit-demo/ /home/appuser/langchain-streamlit-demo/
 
 WORKDIR /home/appuser/langchain-streamlit-demo
 EXPOSE 7860
 
-CMD ["streamlit", "run", "/home/appuser/langchain-streamlit-demo/app.py", "--server.port", "7860", "--server.address", "0.0.0.0"]
+CMD ["streamlit", "run", "/home/appuser/langchain-streamlit-demo/app.py", "--server.port", "7860", "--server.address", "0.0.0.0", "--server.enableXsrfProtection", "false"]

langchain-streamlit-demo/.streamlit/config.toml

@@ -4,3 +4,7 @@ backgroundColor="#FFFFFF"
 secondaryBackgroundColor="#F0F2F6"
 textColor="#262730"
 font="sans serif"
+
+[server]
+enableCORS = false
+enableXsrfProtection = false

langchain-streamlit-demo/app.py

@@ -27,8 +27,9 @@ from llm_resources import (
     get_runnable,
     get_texts_and_multiretriever,
 )
-from python_coder import get_agent as get_python_agent
-from research_assistant.chain import get_chain as get_research_assistant_chain
+
+# from python_coder import get_agent as get_python_agent
+# from research_assistant.chain import get_chain as get_research_assistant_chain
 from streamlit_feedback import streamlit_feedback
 
 __version__ = "2.1.4"
@@ -248,12 +249,16 @@ with sidebar:
                 "refine",
                 "map_reduce",
                 "map_rerank",
-                "Q&A Generation",
-                "Summarization",
+                #    "Q&A Generation",
+                #    "Summarization",
             ],
             index=0,
             help=chain_type_help,
         )
+        question = st.selectbox(
+            "Choose a test question", ["--"] + default_values.TEST_QUESTIONS
+        )
+
         # use_azure = st.toggle(
         #     label="Use Azure OpenAI",
         #     value=st.session_state.AZURE_EMB_AVAILABLE,
@@ -417,6 +422,8 @@ if st.session_state.llm:
 
     # --- Chat Input ---
     prompt = st.chat_input(placeholder="Ask me a question!")
+    if question and question != "--":
+        prompt = question
     if prompt:
         st.chat_message("user").write(prompt)
         feedback_update = None
@@ -458,38 +465,39 @@ if st.session_state.llm:
             default_tools += load_tools(["requests_get"])
             default_tools += load_tools(["llm-math"], llm=st.session_state.llm)
             if st.session_state.provider in ("Azure OpenAI", "OpenAI"):
-                research_assistant_chain = get_research_assistant_chain(
-                    search_llm=get_llm(**get_llm_args_temp_zero),  # type: ignore
-                    writer_llm=get_llm(**get_llm_args_temp_zero),  # type: ignore
-                )
+                # research_assistant_chain = get_research_assistant_chain(
+                #     search_llm=get_llm(**get_llm_args_temp_zero),  # type: ignore
+                #     writer_llm=get_llm(**get_llm_args_temp_zero),  # type: ignore
+                # )
                 st_callback = StreamlitCallbackHandler(st.container())
                 callbacks.append(st_callback)
 
-                @tool("web-research-assistant")
-                def research_assistant_tool(question: str, callbacks: Callbacks = None):
-                    """This assistant returns a comprehensive report based on web research.
-                    It's slow and relatively expensive, so use it sparingly.
-                    Consider using a different tool for quick facts or web queries.
-                    """
-                    return research_assistant_chain.invoke(
-                        dict(question=question),
-                        config=get_config(callbacks),
-                    )
-
-                python_coder_agent = get_python_agent(st.session_state.llm)
-
-                @tool("python-coder-assistant")
-                def python_coder_tool(input_str: str, callbacks: Callbacks = None):
-                    """This assistant writes PYTHON code.
-                    Give it clear instructions and requirements.
-                    Do not use it for tasks other than Python.
-                    """
-                    return python_coder_agent.invoke(
-                        dict(input=input_str),
-                        config=get_config(callbacks),
-                    )
-
-                TOOLS = [research_assistant_tool, python_coder_tool] + default_tools
+                # @tool("web-research-assistant")
+                # def research_assistant_tool(question: str, callbacks: Callbacks = None):
+                #     """This assistant returns a comprehensive report based on web research.
+                #     It's slow and relatively expensive, so use it sparingly.
+                #     Consider using a different tool for quick facts or web queries.
+                #     """
+                #     return research_assistant_chain.invoke(
+                #         dict(question=question),
+                #         config=get_config(callbacks),
+                #     )
+
+                # python_coder_agent = get_python_agent(st.session_state.llm)
+
+                # @tool("python-coder-assistant")
+                # def python_coder_tool(input_str: str, callbacks: Callbacks = None):
+                #     """This assistant writes PYTHON code.
+                #     Give it clear instructions and requirements.
+                #     Do not use it for tasks other than Python.
+                #     """
+                #     return python_coder_agent.invoke(
+                #         dict(input=input_str),
+                #         config=get_config(callbacks),
+                #     )
+
+                TOOLS = default_tools
+                # [research_assistant_tool, python_coder_tool] + default_tools
 
                 if use_document_chat:
                     st.session_state.doc_chain = get_runnable(

langchain-streamlit-demo/defaults.py

@@ -25,7 +25,13 @@ DEFAULT_MODEL = os.environ.get("DEFAULT_MODEL", "gpt-4-turbo-preview")
 
 DEFAULT_SYSTEM_PROMPT = os.environ.get(
     "DEFAULT_SYSTEM_PROMPT",
-    "You are a helpful chatbot. Do not rush. Always plan, think, and act in a step-by-step manner.",
+    # "You are a helpful chatbot. Do not rush. Always plan, think, and act in a step-by-step manner.",
+    """
+    Comportati come un operatore di call center. 
+    Hai accesso a tutte le informazioni necessarie per rispondere alle domande dei clienti tramite il vectorstore.
+    Se non sai rispondere ad una domanda, rispondi che non hai la risposta e offri di connettere il cliente con un operatore umano.
+    Rispondi in italiano, usando uno stile amichevole ma formale.
+    """,
 )
 
 MIN_TEMP = float(os.environ.get("MIN_TEMPERATURE", 0.0))
@@ -38,6 +44,21 @@ DEFAULT_MAX_TOKENS = int(os.environ.get("DEFAULT_MAX_TOKENS", 1000))
 
 DEFAULT_LANGSMITH_PROJECT = os.environ.get("LANGCHAIN_PROJECT")
 
+TEST_QUESTIONS = [
+    "non ho ricevuto le credenziali di accesso all'area riservata: dove posso trovarle?",
+    "vorrei informazioni relativamente alle prestazioni presenti nel checkup",
+    "la risonanza magnetica è coperta dalla polizza?",
+    "le visite odontoiatriche sono coperte dalla polizza?",
+    "vorrei informazioni su come richiedere il checkup",
+    "vorrei informazioni su come trovare struttura convenzionata che faccia le risonanze magnetiche",
+    "come chiede autorizzazione per fare una risonanza magnetica?",
+    "come chiedere rimborso per una risonanza magnetica?",
+    "quali prestazioni sono incluse nel pacchetto maternità?",
+    "come chiedere autorizzazione per prestazione del pacchetto maternità?",
+    "come devo procedere per fare l'estensione della copertura al nucleo familiare?",
+    "Altro",
+]
+
 # AZURE_VARS = [
 #     "AZURE_OPENAI_BASE_URL",
 #     "AZURE_OPENAI_API_VERSION",
@@ -102,6 +123,7 @@ DEFAULT_VALUES = namedtuple(
         "DEFAULT_RETRIEVER_K",
         "SHOW_LANGSMITH_OPTIONS",
         "SHOW_AZURE_OPTIONS",
+        "TEST_QUESTIONS",
     ],
 )
 
@@ -131,4 +153,5 @@ default_values = DEFAULT_VALUES(
     DEFAULT_RETRIEVER_K,
     SHOW_LANGSMITH_OPTIONS,
     SHOW_AZURE_OPTIONS,
+    TEST_QUESTIONS,
 )

langchain-streamlit-demo/llm_resources.py

@@ -29,8 +29,9 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.tools.base import BaseTool
 from langchain.vectorstores import FAISS
 from langchain_core.messages import SystemMessage
-from qagen import get_rag_qa_gen_chain
-from summarize import get_rag_summarization_chain
+
+# from qagen import get_rag_qa_gen_chain
+# from summarize import get_rag_summarization_chain
 
 
 def get_agent(
@@ -138,17 +139,17 @@ def get_runnable(
             memory=memory,
         ) | (lambda output: output["text"])
 
-    if document_chat_chain_type == "Q&A Generation":
-        return get_rag_qa_gen_chain(
-            retriever,
-            llm,
-        )
-    elif document_chat_chain_type == "Summarization":
-        return get_rag_summarization_chain(
-            summarization_prompt,
-            retriever,
-            llm,
-        )
+    # if document_chat_chain_type == "Q&A Generation":
+    #     return get_rag_qa_gen_chain(
+    #         retriever,
+    #         llm,
+    #     )
+    # elif document_chat_chain_type == "Summarization":
+    #     return get_rag_summarization_chain(
+    #         summarization_prompt,
+    #         retriever,
+    #         llm,
+    #     )
     else:
         return RetrievalQA.from_chain_type(
             llm=llm,