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app.py

@@ -86,8 +86,8 @@ llm = ChatOpenAI(
 # This initializes the Chat OpenAI model with the provided endpoint, API key, deployment name, and a temperature setting of 0 (to control response variability).
 
 # set the LLM and embedding model in the LlamaIndex settings.
-Settings.llm = llm # _____ # Complete the code to define the LLM model
-Settings.embedding = embedding_model # _____ # Complete the code to define the embedding model
+Settings.llm = llm # Complete the code to define the LLM model
+Settings.embedding = embedding_model # Complete the code to define the embedding model
 
 #================================Creating Langgraph agent======================#
 
@@ -167,7 +167,7 @@ def retrieve_context(state):
         Dict: The updated state with the retrieved context.
     """
     print("---------retrieve_context---------")
-    query = state['query'] # state['_____']  # Complete the code to define the key for the expanded query
+    query = state['query'] # Complete the code to define the key for the expanded query
     #print("Query used for retrieval:", query)  # Debugging: Print the query
 
     # Retrieve documents from the vector store
@@ -182,7 +182,7 @@ def retrieve_context(state):
         }
         for doc in docs
     ]
-    state['context'] = context # state['_____'] = context  # Complete the code to define the key for storing the context
+    state['context'] = context # Complete the code to define the key for storing the context
     print("Extracted context with metadata:", context)  # Debugging: Print the extracted context
     #print(f"Groundedness loop count: {state['groundedness_loop_count']}")
     return state
@@ -228,7 +228,7 @@ def craft_response(state: Dict) -> Dict:
     response = chain.invoke({
         "query": state['query'],
         "context": "\n".join([doc["content"] for doc in state['context']]),
-        "feedback": state["feedback"] # ________________ # add feedback to the prompt
+        "feedback": state["feedback"] # add feedback to the prompt
     })
     state['response'] = response
     print("intermediate response: ", response)
@@ -277,7 +277,7 @@ def score_groundedness(state: Dict) -> Dict:
     chain = groundedness_prompt | llm | StrOutputParser()
     groundedness_score = float(chain.invoke({
         "context": "\n".join([doc["content"] for doc in state['context']]),
-        "response": state['response'] # __________ # Complete the code to define the response
+        "response": state['response'] # Complete the code to define the response
     }))
     print("groundedness_score: ", groundedness_score)
     state['groundedness_loop_count'] += 1
@@ -311,10 +311,10 @@ def check_precision(state: Dict) -> Dict:
         ("user", "Query: {query}\nResponse: {response}\n\nPrecision score:")
     ])
 
-    chain = precision_prompt | llm | StrOutputParser() # _____________ | llm | StrOutputParser() # Complete the code to define the chain of processing
+    chain = precision_prompt | llm | StrOutputParser() # Complete the code to define the chain of processing
     precision_score = float(chain.invoke({
         "query": state['query'],
-        "response": state['response'] # ______________ # Complete the code to access the response from the state
+        "response": state['response'] # Complete the code to access the response from the state
     }))
     state['precision_score'] = precision_score
     print("precision_score:", precision_score)
@@ -397,7 +397,7 @@ def should_continue_groundedness(state):
   """Decides if groundedness is sufficient or needs improvement."""
   print("---------should_continue_groundedness---------")
   print("groundedness loop count: ", state['groundedness_loop_count'])
-  if state['groundedness_score'] >= 0.8 # _____:  # Complete the code to define the threshold for groundedness
+  if state['groundedness_score'] >= 0.8 # Complete the code to define the threshold for groundedness
       print("Moving to precision")
       return "check_precision"
   else:
@@ -412,14 +412,14 @@ def should_continue_precision(state: Dict) -> str:
     """Decides if precision is sufficient or needs improvement."""
     print("---------should_continue_precision---------")
     print("precision loop count: ", state["precision_loop_count"])
-    if state['precision_score'] > 0.8: # ___________:  # Threshold for precision
+    if state['precision_score'] > 0.8: # Threshold for precision
         return "pass"  # Complete the workflow
     else:
-        if state["precision_loop_count"] >= state['loop_max_iter']: # ___________:  # Maximum allowed loops
+        if state["precision_loop_count"] >= state['loop_max_iter']: # Maximum allowed loops
             return "max_iterations_reached"
         else:
             print(f"---------Precision Score Threshold Not met. Refining Query-----------")  # Debugging
-            return "refine_query" # ____________  # Refine the query
+            return "refine_query" # Refine the query
 
 
 
@@ -438,17 +438,17 @@ from langgraph.graph import END, StateGraph, START
 
 def create_workflow() -> StateGraph:
     """Creates the updated workflow for the AI nutrition agent."""
-    workflow = StateGraph(State) # StateGraph(_____ )  # Complete the code to define the initial state of the agent
+    workflow = StateGraph(START) # Complete the code to define the initial state of the agent
 
     # Add processing nodes
-    workflow.add_node("expand_query", expand_query) # _____ )         # Step 1: Expand user query. Complete with the function to expand the query
-    workflow.add_node("retrieve_context", retrieve_context) #_____ ) # Step 2: Retrieve relevant documents. Complete with the function to retrieve context
-    workflow.add_node("craft_response", craft_response) # _____ )     # Step 3: Generate a response based on retrieved data. Complete with the function to craft a response
-    workflow.add_node("score_groundedness", score_groundedness) # _____ )  # Step 4: Evaluate response grounding. Complete with the function to score groundedness
-    workflow.add_node("refine_response", refine_response) # _____ )   # Step 5: Improve response if it's weakly grounded. Complete with the function to refine the response
-    workflow.add_node("check_precision", check_precision) # _____ )   # Step 6: Evaluate response precision. Complete with the function to check precision
-    workflow.add_node("refine_query", refine_query) # _____ )         # Step 7: Improve query if response lacks precision. Complete with the function to refine the query
-    workflow.add_node("max_iterations_reached", max_iterations_reached) # _____ )  # Step 8: Handle max iterations. Complete with the function to handle max iterations
+    workflow.add_node("expand_query", expand_query) # Step 1: Expand user query. Complete with the function to expand the query
+    workflow.add_node("retrieve_context", retrieve_context) # Step 2: Retrieve relevant documents. Complete with the function to retrieve context
+    workflow.add_node("craft_response", craft_response) # Step 3: Generate a response based on retrieved data. Complete with the function to craft a response
+    workflow.add_node("score_groundedness", score_groundedness) # Step 4: Evaluate response grounding. Complete with the function to score groundedness
+    workflow.add_node("refine_response", refine_response) # Step 5: Improve response if it's weakly grounded. Complete with the function to refine the response
+    workflow.add_node("check_precision", check_precision) # Step 6: Evaluate response precision. Complete with the function to check precision
+    workflow.add_node("refine_query", refine_query) # Step 7: Improve query if response lacks precision. Complete with the function to refine the query
+    workflow.add_node("max_iterations_reached", max_iterations_reached) # Step 8: Handle max iterations. Complete with the function to handle max iterations
 
     # Main flow edges
     workflow.add_edge(START, "expand_query")
@@ -459,28 +459,28 @@ def create_workflow() -> StateGraph:
     # Conditional edges based on groundedness check
     workflow.add_conditional_edges(
         "score_groundedness",
-        should_continue_groundedness, # ___________,  # Use the conditional function
+        should_continue_groundedness, # Use the conditional function
         {
-            "check_precision": "check_precision", # ___________,  # If well-grounded, proceed to precision check.
-            "refine_response": "refine_response", # ___________,  # If not, refine the response.
-            "max_iterations_reached": max_iterations_reached # ___________  # If max loops reached, exit.
+            "check_precision": "check_precision", # If well-grounded, proceed to precision check.
+            "refine_response": "refine_response", # If not, refine the response.
+            "max_iterations_reached": max_iterations_reached # If max loops reached, exit.
         }
     )
 
-    workflow.add_edge("refine_response", "craft_response") # __________, ___________)  # Refined responses are reprocessed.
+    workflow.add_edge("refine_response", "craft_response") # Refined responses are reprocessed.
 
     # Conditional edges based on precision check
     workflow.add_conditional_edges(
         "check_precision",
-        should_continue_precision, # ___________,  # Use the conditional function
+        should_continue_precision, # Use the conditional function
         {
-            "pass": END, # ___________,              # If precise, complete the workflow.
-            "refine_query": "refine_query", # ___________,  # If imprecise, refine the query.
-            "max_iterations_reached": "max_iterations_reached" # ___________  # If max loops reached, exit.
+            "pass": END, # If precise, complete the workflow.
+            "refine_query": "refine_query", # If imprecise, refine the query.
+            "max_iterations_reached": "max_iterations_reached" # If max loops reached, exit.
         }
     )
 
-    workflow.add_edge("refine_query", "expand_query") # __________, ___________)  # Refined queries go through expansion again.
+    workflow.add_edge("refine_query", "expand_query") # Refined queries go through expansion again.
 
     workflow.add_edge("max_iterations_reached", END)
 
@@ -505,16 +505,16 @@ def agentic_rag(query: str):
     # Initialize state with necessary parameters
     inputs = {
         "query": query,  # Current user query
-        "expanded_query": "", # "_____",  # Complete the code to define the expanded version of the query
+        "expanded_query": "", # Complete the code to define the expanded version of the query
         "context": [],  # Retrieved documents (initially empty)
-        "response": "", # "_____",  # Complete the code to define the AI-generated response
-        "precision_score": 0.0, # _____,  # Complete the code to define the precision score of the response
-        "groundedness_score": 0.0, # _____,  # Complete the code to define the groundedness score of the response
-        "groundedness_loop_count": 0, # _____,  # Complete the code to define the counter for groundedness loops
-        "precision_loop_count": 0, # _____,  # Complete the code to define the counter for precision loops
-        "feedback": "", # "_____",  # Complete the code to define the feedback
-        "query_feedback": "", # "_____",  # Complete the code to define the query feedback
-        "loop_max_iter": 3 # _____  # Complete the code to define the maximum number of iterations for loops
+        "response": "", # Complete the code to define the AI-generated response
+        "precision_score": 0.0, # Complete the code to define the precision score of the response
+        "groundedness_score": 0.0, # Complete the code to define the groundedness score of the response
+        "groundedness_loop_count": 0, # Complete the code to define the counter for groundedness loops
+        "precision_loop_count": 0, # Complete the code to define the counter for precision loops
+        "feedback": "", # Complete the code to define the feedback
+        "query_feedback": "", # Complete the code to define the query feedback
+        "loop_max_iter": 3 # Complete the code to define the maximum number of iterations for loops
     }
 
     output = WORKFLOW_APP.invoke(inputs)
@@ -645,7 +645,7 @@ class NutritionBot:
         return self.memory.search(
             query=query,  # Search for interactions related to the query
             user_id=user_id,  # Restrict search to the specific user
-            limit=_____  # Complete the code to define the limit for retrieved interactions
+            limit=5  # Complete the code to define the limit for retrieved interactions
         )
 
 
@@ -736,7 +736,7 @@ def nutrition_disorder_streamlit():
                 st.write(message["content"])
 
         # Chat input with custom placeholder text
-        user_query = st.chat_input("Type your question here (or 'exit' to end)...") # __________)  # Blank #1: Fill in the chat input prompt (e.g., "Type your question here (or 'exit' to end)...")
+        user_query = st.chat_input("Type your question here (or 'exit' to end)...") # Blank #1: Fill in the chat input prompt (e.g., "Type your question here (or 'exit' to end)...")
         if user_query:
             if user_query.lower() == "exit":
                 st.session_state.chat_history.append({"role": "user", "content": "exit"})
@@ -755,15 +755,15 @@ def nutrition_disorder_streamlit():
                 st.write(user_query)
 
             # Filter input using Llama Guard
-            filtered_result = filter_input_with_llama_guard(user_query) # __________(user_query)  # Blank #2: Fill in with the function name for filtering input (e.g., filter_input_with_llama_guard)
+            filtered_result = filter_input_with_llama_guard(user_query) # Blank #2: Fill in with the function name for filtering input (e.g., filter_input_with_llama_guard)
             filtered_result = filtered_result.replace("\n", " ")  # Normalize the result
 
             # Check if input is safe based on allowed statuses
-            if filtered_result in ["safe", "safe S7", "safe S6"]: # __________, __________, __________]:  # Blanks #3, #4, #5: Fill in with allowed safe statuses (e.g., "safe", "unsafe S7", "unsafe S6")
+            if filtered_result in ["safe", "safe S7", "safe S6"]: # Blanks #3, #4, #5: Fill in with allowed safe statuses (e.g., "safe", "unsafe S7", "unsafe S6")
                 try:
                     if 'chatbot' not in st.session_state:
-                        st.session_state.chatbot = NutritionBot() # __________()  # Blank #6: Fill in with the chatbot class initialization (e.g., NutritionBot)
-                    response = st.session_state.chatbot.handle_customer_query(st.session_state.user_id, user_query) # __________(st.session_state.user_id, user_query)
+                        st.session_state.chatbot = NutritionBot() # Blank #6: Fill in with the chatbot class initialization (e.g., NutritionBot)
+                    response = st.session_state.chatbot.handle_customer_query(st.session_state.user_id, user_query) 
                     # Blank #7: Fill in with the method to handle queries (e.g., handle_customer_query)
                     st.write(response)
                     st.session_state.chat_history.append({"role": "assistant", "content": response})