working...

app.py

@@ -48,7 +48,7 @@ if "messages" not in st.session_state:
 if "thread_id" not in st.session_state:
     st.session_state.thread_id = "streamlit_thread"
 
-config = {"configurable": {"thread_id": st.session_state.thread_id}}
+config = {"recursion_limit": 15, "configurable": {"thread_id": st.session_state.thread_id}}
 
 # Display chat messages from history on app rerun
 for message in st.session_state.messages:
@@ -71,9 +71,11 @@ if prompt := st.chat_input("What is your question?"):
         full_response = "⚠️ **_Note: Information provided may be inaccurate._** \n\n\n"
         for char in response_content:
             full_response += char
+            
             time.sleep(0.03)
-            message_placeholder.markdown(full_response + "▌")
+            message_placeholder.markdown(full_response + "|")
         message_placeholder.markdown(full_response)
+        print(full_response)
     
     # Add assistant response to chat history
     st.session_state.messages.append({"role": "assistant", "content": full_response})

src/buildgraph.py

@@ -2,6 +2,7 @@ from src.graph import *
 from langgraph.graph import END, StateGraph, START
 import sys
 from langgraph.checkpoint.memory import MemorySaver
+from langgraph.errors import GraphRecursionError
 
 memory = MemorySaver()
 
@@ -20,6 +21,8 @@ try:
     workflow.add_node("grade_documents", grade_documents)
     workflow.add_node("generate", generate)
     workflow.add_node("transform_query", transform_query)
+    workflow.add_node("grade_generation", grade_generation_v_documents_and_question)
+
     print("Nodes added successfully.")
 
     print("Building graph edges...")
@@ -36,36 +39,60 @@ try:
 
     workflow.add_edge("greeting", END)
     workflow.add_edge("off_topic", END)
-
-    workflow.add_edge("retrieve", "grade_documents")
+    
     workflow.add_conditional_edges(
-        "grade_documents",
-        decide_to_generate,
+        "route_question",
+        lambda x: x["route_question"],
         {
-            "transform_query": "transform_query",
-            "generate": "generate",
-        },
+            "web_search": "web_search",
+            "vectorstore": "retrieve",
+        }   
     )
-    workflow.add_edge("transform_query", "retrieve")
+    
+    workflow.add_conditional_edges(
+        "retrieve",
+        check_recursion_limit,
+        {
+            "web_search": "web_search",
+            "continue": "grade_documents",
+        }
+    )
+    
     workflow.add_conditional_edges(
         "generate",
-        grade_generation_v_documents_and_question,
+        check_recursion_limit,
+        {
+            "web_search": "web_search",
+            "continue": "grade_generation",
+        }
+    )
+    
+    workflow.add_conditional_edges(
+        "grade_generation",
+        lambda x: x["grade_generation"],
         {
             "not supported": "generate",
             "useful": END,
             "not useful": "transform_query",
-        },
+        }
     )
+    
+    workflow.add_edge("transform_query", "route_question")
+
     workflow.add_conditional_edges(
-        "route_question",
-        lambda x: x["route_question"],
+        "grade_documents",
+        decide_to_generate,
         {
-            "web_search": "web_search",
-            "vectorstore": "retrieve",
-        }
+            "transform_query": "transform_query",
+            "generate": "generate",
+        },
     )
+    
+    workflow.add_edge("web_search", "generate")
+
     print("Graph edges built successfully.")
 
+
     print("Compiling the workflow...")
     app = workflow.compile(checkpointer=memory)
     print("Workflow compiled successfully.")
@@ -102,6 +129,9 @@ try:
         print(f"Error handling graph visualization: {e}")
         print("Graph visualization skipped.")
     
+except GraphRecursionError:
+    print("Graph recursion limit reached during compilation.")
+    # Handle the error as needed
 except Exception as e:
     print(f"Error building the graph: {e}")
     sys.exit(1)
@@ -120,12 +150,23 @@ def run_workflow(question, config):
         }
         
         final_output = None
-        for output in app.stream(input_state, config):
-            for key, value in output.items():
-                print(f"Node '{key}'")
-                # print(f"Value: {json.dumps(value, default=str)}")  # Debug print
-                if key in ["generate", "off_topic", "greeting"]:
-                    final_output = value
+        use_web_search = False
+        
+        try:
+            for output in app.stream(input_state, config):
+                for key, value in output.items():
+                    print(f"Node '{key}'")
+                    if key in ["grade_generation", "off_topic", "greeting", "web_search"]:
+                        final_output = value
+        except GraphRecursionError:
+            print("Graph recursion limit reached, switching to web search")
+            use_web_search = True
+        
+        if use_web_search:
+            # Force the use of web_search
+            web_search_result = web_search(input_state)
+            generate_result = generate(web_search_result)
+            final_output = generate_result
         
         if final_output is None:
             return {"generation": "I'm sorry, I couldn't generate a response. Could you please rephrase your question?"}

src/graph.py

@@ -3,6 +3,7 @@ from typing_extensions import TypedDict
 from src.websearch import *
 from src.llm import *
 from langchain.schema import Document, AIMessage
+from langgraph.errors import GraphRecursionError
 
 class GraphState(TypedDict):
     question: str
@@ -65,6 +66,8 @@ def generate(state):
 
     generation_prompt = f"""
     As LegalAlly, an AI assistant specializing in the Indian Penal Code, provide a helpful and informative response to the following question. Use the given context and chat history for reference.
+    Responses are concise and answer user's queries directly. They are not verbose. The answer feels natural and not robotic.
+    Make sure the answer is grounded in the documents and is not hallucination.
 
     Context:
     {context}
@@ -79,7 +82,7 @@ def generate(state):
     
     generation = llm.invoke(generation_prompt)
     generation = generation.content if hasattr(generation, 'content') else str(generation)
-
+    
     return {
         "documents": documents,
         "question": question,
@@ -185,6 +188,7 @@ def grade_generation_v_documents_and_question(state):
     question = state["question"]
     documents = state["documents"]
     generation = state["generation"]
+    chat_history = state.get("chat_history", [])
 
     score = hallucination_grader.invoke(
         {"documents": documents, "generation": generation}
@@ -197,14 +201,32 @@ def grade_generation_v_documents_and_question(state):
         grade = score.binary_score
         if grade == "yes":
             print("---DECISION: GENERATION ADDRESSES QUESTION---")
-            return "useful"
+            return {
+                "grade_generation": "useful",
+                "question": question,
+                "generation": generation,
+                "documents": documents,
+                "chat_history": chat_history
+            }
         else:
             print("---DECISION: GENERATION DOES NOT ADDRESS QUESTION---")
-            return "not useful"
+            return {
+                "grade_generation": "not useful",
+                "question": question,
+                "generation": generation,
+                "documents": documents,
+                "chat_history": chat_history
+            }
     else:
         print("---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---")
-        return "not supported"
-    
+        return {
+            "grade_generation": "not supported",
+            "question": question,
+            "generation": generation,
+            "documents": documents,
+            "chat_history": chat_history
+        }
+
 def greeting(state):
     print("---GREETING---")
     return {
@@ -216,3 +238,9 @@ def off_topic(state):
     return {
         "generation": "I apologize, but I specialize in matters related to the Indian Penal Code. Could you please ask a question about Indian law or legal matters?"
     }
+
+# conditional edges for recursion limit
+def check_recursion_limit(state):
+    # LangGraph will automatically raise GraphRecursionError if the limit is reached
+    # We don't need to check for it explicitly
+    return "continue"

src/llm.py

@@ -27,8 +27,11 @@ structured_llm_router = llm.with_structured_output(RouteQuery)
 # Prompt
 system = """You are an expert at routing a user question to a vectorstore or web search.
 The vectorstore contains documents related to Indian Penal Code and The Indian Constitution. 
-It can answer most questions related to IPC and the Constitution.
-Use web-search if the answer is not in the vectorstore."""
+It can answer questions related to Indian Law, IPC and the Constitution.
+Use vectorstore if the question is a legal query within the scope of IPC, Indian Law and the Indian Constitution.
+Use web-search if the question is a legal query outside the scope of IPC, Indian Law and the Indian Constitution.
+Use web-search and your own knowledge if the question requires general legal help.
+Use web-search if the questions is a legal query that requires latest information."""
 route_prompt = ChatPromptTemplate.from_messages(
     [
         ("system", system),

vectordb/65ba2328-ffa1-497d-b641-c6b84db7f0e1/length.bin

@@ -1,3 +1,3 @@
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+oid sha256:8e2f111e5a36cc65e03a6865c3705bf32a17689ff8658620547a6164df6dff14
 size 4000