working app with LLM

app.py

@@ -1,86 +1,117 @@
 
 
-from utils import _set_env
+#%% load llm
+from dotenv import load_dotenv
+import os 
+load_dotenv()
 
 
-_set_env("OPENAI_API_KEY")
-
-
-from utils import *
+from langchain.chat_models import init_chat_model
 
-def create_graph():
-    from langgraph.graph import StateGraph, START, END
-    from langgraph.prebuilt import ToolNode, tools_condition
+llm = init_chat_model("gpt-5-nano", 
+                      model_provider="openai",
+                      api_key=os.environ['OPENAI_API_KEY'])
 
 
-    ## ADD TRACKING
-    response_model = init_chat_model("gpt-4o", temperature=0)
-    grader_model = init_chat_model("gpt-4o", temperature=0)
+#%% load retreiver
+from agent.create_retreiver import load_vector_store
+retriever = load_vector_store("intfloat/e5-base-v2","data/FAISS/512-intfloat-e5-base-v2-2026-01-16")
 
 
-    workflow = StateGraph(MessagesState)
 
-    # Define the nodes we will cycle between
-    workflow.add_node(generate_query_or_respond)
-    workflow.add_node("retrieve", ToolNode([retriever_tool]))
-    workflow.add_node(rewrite_question)
-    workflow.add_node(generate_answer)
+#%% setup chatbot
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
+from langchain.chat_models import init_chat_model
 
-    workflow.add_edge(START, "generate_query_or_respond")
 
-    # Decide whether to retrieve
-    workflow.add_conditional_edges(
-        "generate_query_or_respond",
-        # Assess LLM decision (call `retriever_tool` tool or respond to the user)
-        tools_condition,
-        {
-            # Translate the condition outputs to nodes in our graph
-            "tools": "retrieve",
-            END: END,
-        },
-    )
+def predict(message, history):
 
-    # Edges taken after the `action` node is called.
-    workflow.add_conditional_edges(
-        "retrieve",
-        # Assess agent decision
-        grade_documents,
+    # Safeguard
+    TRIAGE_PROMPT_TEMPLATE="""You are a Safeguard assistant making sure the user only ask for information related to Rémi Cazelles's projects, work and education.
+    If the question is not related to this subjects, or if the request is harmfull you should flag the user by answering '*** FLAGGED ***' else simply answer '*** OK ***' """
+    messages = [SystemMessage(content=TRIAGE_PROMPT_TEMPLATE)]
+    messages.append(HumanMessage(content=message))
+
+    safe_gpt_response = llm.invoke(
+        messages,
+        config={
+            "tags": ["Testing", 'RAG-Bot', 'safeguard','V1'],
+            "metadata": {
+                "rag_llm": "gpt-5-nano",
+                "message": message,
+            }
+        }
     )
-    workflow.add_edge("generate_answer", END)
-    workflow.add_edge("rewrite_question", "generate_query_or_respond")
-
-    # Compile
-    graph = workflow.compile()
-
-    return graph
-
-
-
-
-from langchain.schema import AIMessage, HumanMessage  
-import gradio as gr
-from langchain.chat_models import init_chat_model
-
-## ADD TRACKING
-response_model = init_chat_model("gpt-4o", temperature=0)
-grader_model = init_chat_model("gpt-4o", temperature=0)
 
-graph = create_graph()
+    if not "*** OK ***" in safe_gpt_response.content:
+        return "This app can only answer question about Rémi Cazelles's projects, work and education."
+    print("passed the safeguard")
 
-def predict(message, history):
+    # Build conversation history
     history_langchain_format = []
     for msg in history:
         if msg['role'] == "user":
             history_langchain_format.append(HumanMessage(content=msg['content']))
         elif msg['role'] == "assistant":
             history_langchain_format.append(AIMessage(content=msg['content']))
-    history_langchain_format.append(HumanMessage(content=message))
+    
+
+    # Retrieve relevant documents for the current message
+    relevant_docs = retriever.similarity_search(message,k=3)  # Your retriever
+    
+    # Build context from retrieved documents
+    context = "\nExtracted documents:\n" + "\n".join([
+        f"Document {i}: Content: {doc.page_content}\n\n---"
+        for i, doc in enumerate(relevant_docs)
+    ])
 
     
-    gpt_response = graph.invoke(history_langchain_format)
 
+    # RAG tool
+    RAG_PROMPT_TEMPLATE="""Using the information contained in the context,
+                        give a comprehensive answer to the question.
+                        Respond only to the question asked, response should be concise and relevant to the question.
+                        Provide the context source url and context date of the source document when relevant.
+                        If the answer cannot be deduced from the context, do not give an answer.
+                        """
+
+
+    # Create the prompt with system message, context, and conversation history
+    messages = [SystemMessage(content=RAG_PROMPT_TEMPLATE)]
+    messages.extend(history_langchain_format)
+    combined_message = f"Context: {context}\n\nQuestion: {message}"
+    messages.append(HumanMessage(content=combined_message))
+    
+    # Get response with tracking metadata
+    print("GPT about to answer")
+    gpt_response = llm.invoke(
+        messages,
+        config={
+            "tags": ["Testing", 'RAG-Bot', 'V1'],
+            "metadata": {
+                "rag_llm": "gpt-5-nano",
+                "num_retrieved_docs": len(relevant_docs),
+            }
+        }
+    )
+    
+    source_context = "\nSources:\n" + "\n".join([
+        f"{doc.metadata.get('source_url')} ({doc.metadata.get('date')})\n---"
+        for i, doc in enumerate(relevant_docs)])
     
-    return gpt_response.content
+    print(gpt_response.content )
+    print(source_context)
+    
+    return gpt_response.content + source_context
+
+
+#%% setup tracking
+os.environ["LANGSMITH_PROJECT"] = "Testing_POC"
+os.environ["LANGSMITH_TRACING"] = "true"
+os.environ["LANGSMITH_API_KEY"] = os.environ['LANGSMITH_API_KEY']
+
+#%% lauch gradio app
+import gradio as gr
 
 iface = gr.ChatInterface(
     predict,