Refactored context analysis

.gitignore

@@ -1,2 +1,4 @@
 .env
 myenv
+*.pyc
+__pycache__/

api/routers/context_analysis.py

@@ -4,62 +4,37 @@ import logging
 from fastapi import APIRouter
 from fastapi.responses import StreamingResponse
 from api.stored_data import stored_data
-from src.genai.context_analysis_agent.agent import IntroductionChatbot
+from src.genai.context_analysis_agent.agent import ContextAnalysisAgent
+from src.genai.context_analysis_agent.utils.utils import DetailsExtractorNode , SaveToDB
 from api.schemas.context_analysis import UserMessage
 
 app_logger = logging.getLogger("app_logger")
 error_logger = logging.getLogger("error_logger")
 
 router = APIRouter()
-context_analysis_graph = IntroductionChatbot()   
-
+agent=ContextAnalysisAgent()
+context_graph = agent.context_analysis_graph()   
 @router.post("/context-analysis")
 def context_analysis(msg: UserMessage):
-    def event_generator():
-        try:
-            accumulated_response = ""
-            for chunk in context_analysis_graph.chat(msg.message):
-                accumulated_response += chunk
-                payload = {
-                    "streamed_response": chunk,
-                }
-                yield json.dumps(payload) + "\n"  
-            app_logger.info('Executed context analysis agent')
+    stored_data['context_analysis_interactions'].append({'role':'human', 'content':msg.message})
+    config={"configurable": {"thread_id": "context_analysis_1"}}
+    try:
+        result=context_graph.invoke({'messages':msg.message},config=config)
+        stored_data['context_analysis_interactions'].append({'role':'ai', 'content':result['messages'][-1].content})
+        if result['completion']==True:
+           formatted_details=DetailsExtractorNode(stored_data['context_analysis_interactions']).run()
+           app_logger.info('Context Analysis Completed.')
 
-            last_response = context_analysis_graph.messages[-1]["content"]
-            print('Last-Response:', last_response)
+           return {'response':result['messages'][-1].content , 'complete':True , 'business_details': formatted_details}
+            
+        else:
+            return {'response':result['messages'][-1].content , 'complete':False}
+        
 
-            if context_analysis_graph.is_complete(last_response) == 'complete':
-                app_logger.info('Context analysis completed.')
-                try:
-                    details = context_analysis_graph.extract_details()
-                    app_logger.info('Details extracted after context analysis.')
-                except Exception as e:
-                    error_logger.error(f'Unable to extract details: {e}')
+    except Exception as e:
+       error_logger.error('Failed Executing context analysis:', e)
+       raise 
 
-                if type(details) != dict:details = details.model_dump()
-                if isinstance(details, str):details = ast.literal_eval(details)
-                    
-                stored_data["business_details"] = details
-                try:
-                    context_analysis_graph.reset()
-                    app_logger.info('Context analysis memory reset after details extraction.')
-                except Exception as e:
-                    error_logger.error('Unable to reset memory:', e)
 
-                final_payload = {
-                    "response": accumulated_response,
-                    "complete": True,
-                    "business_details": details
-                }
-            else:
-                final_payload = {
-                    "response": accumulated_response,
-                    "complete": False
-                }
 
-            yield json.dumps(final_payload) + "\n"
-        except Exception as e:
-            error_logger.error('Unable to run context analysis agent:', e)
 
-    return StreamingResponse(event_generator(), media_type="text/event-stream")

api/stored_data.py

@@ -8,4 +8,5 @@ stored_data['business_details'] = {
     "Challenges_faced": "attracting loyal members, standing out in a competitive market, and promoting consistent engagement"
 }
 stored_data['human_ideation_interactions'] = []
-stored_data['brainstorming_response']={}
+stored_data['brainstorming_response']={}
+stored_data['context_analysis_interactions']= []

logs/access.log

@@ -482,3 +482,62 @@
 2025-08-22 16:40:10,680 | INFO | access_logger | api\main.py:21 | Response status: 200
 2025-08-22 16:45:45,350 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
 2025-08-22 16:45:46,127 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-25 11:46:36,225 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-08-25 11:46:36,225 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 11:46:36,824 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-25 11:46:36,827 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 11:46:49,089 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 11:49:19,613 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:04:01,090 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:04:04,849 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:04:30,095 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:04:32,379 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:04:41,276 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:04:43,228 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:04:52,951 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:04:54,492 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:05:57,204 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:05:59,251 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:06:08,161 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:06:09,967 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:06:20,187 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:06:22,149 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:06:33,957 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:06:36,278 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:06:51,855 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:09:41,824 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:09:45,123 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:09:55,885 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:09:56,982 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:10:01,302 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:10:03,720 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:10:11,125 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:10:12,957 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:10:27,560 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:10:28,712 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:10:37,201 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:10:38,967 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:10:47,639 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:10:49,416 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:10:58,887 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:11:00,409 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:11:21,585 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:11:23,105 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:15:18,358 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:15:21,822 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:15:30,647 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:15:32,245 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:15:37,780 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:15:38,744 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:15:45,022 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:15:46,481 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:15:56,436 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:15:58,052 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:16:09,272 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:16:12,900 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:16:21,623 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:16:22,893 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:16:32,047 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:16:33,281 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-08-25 12:16:43,062 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-25 12:16:46,807 | INFO | access_logger | api/main.py:21 | Response status: 200

src/genai/context_analysis_agent/agent.py

@@ -1,55 +1,22 @@
-import logging
-from langchain_core.messages import SystemMessage , HumanMessage
-from langgraph.graph import StateGraph, MessagesState, START, END
+from langgraph.graph import StateGraph, START, END
 from langgraph.checkpoint.memory import MemorySaver
-from .utils.state import State, CompletionFormatter
+from .utils.state import State
 from .utils.nodes import IntroductionNode
-from .utils.utils import DetailsExtractor
-from src.genai.utils.models_loader import llm_gpt
-from .utils.prompts import completion_check_prompt
 
 
-business_state = State()
-
-class IntroductionChatbot:
+class ContextAnalysisAgent:
     def __init__(self):
         self.memory = MemorySaver()
-        self.llm = llm_gpt
-        self.workflow = self._initialize_workflow()
-        self.interact_agent = self.workflow.compile(checkpointer=self.memory)
-        self.messages = []
-
-
-    def _initialize_workflow(self):
-        workflow = StateGraph(MessagesState)
-        workflow.add_node("chatbot", lambda state: IntroductionNode().run(state, self.llm))
-        workflow.add_edge(START, "chatbot")
-        workflow.add_edge("chatbot", END)
-        return workflow
-
-    def chat(self, user_input: str):
-        self.messages.append({"role": "user", "content": user_input})
-        config = {"configurable": {"thread_id": "1"}}
-        for message_chunk, metadata in self.interact_agent.stream(
-            {"messages": [user_input]},
-            config=config,
-            stream_mode="messages"
-        ):
-            yield message_chunk.content
-
-
-    def is_complete(self, latest_response: str) -> bool:
-        messages = [SystemMessage(content=completion_check_prompt()),HumanMessage(content=f'''The response of assistant is: {latest_response}''')]
-        response = llm_gpt.with_structured_output(CompletionFormatter).invoke(messages)
-        print('Completion response:', response.completion)
-        return response.completion
-
-    def extract_details(self):
-        response = DetailsExtractor(business_state.interactions).run()
-        return response
-    
-    def reset(self):
-        self.memory= MemorySaver()
-        self.interact_agent = self.workflow.compile(checkpointer=self.memory) 
+
+    def context_analysis_graph(self):
+        graph_builder= StateGraph(State)
+        graph_builder.add_node("details_collector", IntroductionNode().run)
+        graph_builder.add_edge(START, "details_collector")
+        graph_builder.add_edge("details_collector", END)
+
+
+
+        return graph_builder.compile(checkpointer=self.memory)
+
 
 

src/genai/context_analysis_agent/utils/nodes.py

@@ -1,25 +1,25 @@
 from langchain_core.messages import SystemMessage
 from src.genai.utils.models_loader import llm_gpt
 from .prompts import introduction_prompt
-from .state import ConversationFormatter
+from .state import ConversationFormatter, State
 
 
 class IntroductionNode:
     def __init__(self):
         self.llm = llm_gpt
 
-    def run(self, state, llm):
+    def run(self, state:State):
         template = introduction_prompt
         messages = [SystemMessage(content=template)] + state["messages"]
-        response = llm.with_structured_output(ConversationFormatter).invoke(messages)
-        print('The response:', response)
-        print('Type of response:', type(response))
-        if 'True' in response.complete:
-            return {'messages':['completed']}
-        else:
-            return {"messages": [response.response]}
+        result = self.llm.with_structured_output(ConversationFormatter).invoke(messages)
+        print('Response:', result)
+        return {
+        "messages": [{"role": "assistant", "content": result.response}],
+        "response": result.response,
+        "completion": result.completion,
+    }
+
+
 
 
 
-    
-    

src/genai/context_analysis_agent/utils/prompts.py

@@ -36,12 +36,10 @@ Output Format:
 Just give the output in one word. Either **completed** or **not completed**.
 '''
 
-def details_extract_prompt(interactions):
+def details_extract_prompt():
     return( f''' You're provided with the messages of business interactions between the business and AI assistant.
         Extract the following details of the business from the conversation as it is. Don't trim or simplify any of the business details. You must not  have to lose any information.
         No problem if the details are long, but give the business details as it is from the conversation.
-
-        Now, start doing your work:\n
-        The conversation is:\n{interactions}\n 
+        The interactions will be provided to you through HumanMessage.
         ''')
 

src/genai/context_analysis_agent/utils/state.py

@@ -1,9 +1,14 @@
 from pydantic import BaseModel, ConfigDict , Field
-from typing import Optional
+from typing import  Annotated , TypedDict
+from langgraph.graph.message import add_messages
 
-class State(BaseModel):
-    interactions: Optional[list] = []
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+    response: str
+    completion: bool
+    interactions: list
+    formatted_details: dict
 
 # Pydantic model for extracted business info
 class DetailsFormatter(BaseModel):
@@ -19,10 +24,8 @@ class DetailsFormatter(BaseModel):
     Challenges_faced: str = Field(description="The challenges faced by the business")
     additional_informations: str = Field(description="Additional queries or details regarding the video or idea creation.")
 
-class CompletionFormatter(BaseModel):
-    completion: str = Field(description="Just the one word result: **completed** or **not completed**")
 
 class ConversationFormatter(BaseModel):
-    response: str = Field(description="The entire response of the assistant.")
-    complete: str = Field(description= "Return 'True' if the details extraction is completed. Return 'False' if not completed.")
+    response: str= Field(description="The entire response of the assistant.")
+    completion: bool=Field(description=''''Return 'True' if the details extraction is completed. Return 'False' if not completed.''')
 

src/genai/context_analysis_agent/utils/utils.py

@@ -2,19 +2,20 @@ import pandas as pd
 from src.genai.utils.data_loader import caption_df
 from src.genai.utils.models_loader import llm_gpt
 from .prompts import details_extract_prompt
-from langchain_core.messages import SystemMessage
+from langchain_core.messages import SystemMessage, HumanMessage
 from .state import DetailsFormatter
 
-class DetailsExtractor:
+class DetailsExtractorNode:
     def __init__(self, interactions):
         self.llm = llm_gpt
         self.interactions = interactions
 
     def run(self):
-        template = details_extract_prompt(self.interactions)
-        messages = [SystemMessage(content=template)]
+        template = details_extract_prompt()
+        messages = [SystemMessage(content=template), HumanMessage(content=str(self.interactions))]
         response=llm_gpt.with_structured_output(DetailsFormatter).invoke(messages)
-        return response
+        return response.model_dump()
+
 
 class SaveToDB:
     def __init__(self, caption_df):