next commit

api/routers/context_analysis.py

@@ -27,8 +27,9 @@ def context_analysis(msg: UserMessage):
             app_logger.info('Executed context analysis agent')
 
             last_response = context_analysis_graph.messages[-1]["content"]
+            print('Last-Response:', last_response)
 
-            if context_analysis_graph.is_complete(last_response):
+            if context_analysis_graph.is_complete(last_response) == 'complete':
                 app_logger.info('Context analysis completed.')
                 try:
                     details = context_analysis_graph.extract_details()

api/routers/ideation.py

@@ -14,7 +14,7 @@ idea_graph = agent.ideation_graph()
 
 @router.post("/ideation")
 def ideation_endpoint():
-    config={"recursion_limit":15, "configurable": {"thread_id": "ideation_thread123"}}
+    config={"recursion_limit":25, "configurable": {"thread_id": "ideation_thread123"}}
     try:
         result = idea_graph.invoke(
         {
@@ -23,11 +23,11 @@ def ideation_endpoint():
         },
         config=config)
 
-        stored_data['final_ideation'] = result['formatted_response'][-1]
-        stored_data['final_ideation']=ast.literal_eval(stored_data['final_ideation'])
+        stored_data['final_ideation'] = result['top_4_ideas'][-1]
+        # stored_data['final_ideation']=ast.literal_eval(stored_data['final_ideation'])
         app_logger.info('Executed the ideation pipeline.')
         
-        return {'response':ast.literal_eval(result['formatted_response'][-1])}
+        return {'response':result['top_4_ideas'][-1]}
     
     except GraphRecursionError:
         error_logger.error('Ideation loop ran more than specified.')

check.py

@@ -0,0 +1,13 @@
+from src.genai.utils.models_loader import llm_gpt
+from src.genai.ideation_agent.utils.prompts import ideator_prompt_v3
+from src.genai.ideation_agent.utils.schemas import ideation_json_schema
+from api.stored_data import stored_data
+from langchain_core.messages import HumanMessage , SystemMessage
+
+template =ideator_prompt_v3(stored_data['business_details'])
+messages = [SystemMessage(content=template),
+                    HumanMessage(content=f'''The business_details is\n{stored_data['business_details']}\n 
+                    The information of the image is:\n''')]
+
+response = llm_gpt.with_structured_output(ideation_json_schema).invoke(messages)
+print(response)

logs/access.log

@@ -381,3 +381,104 @@
 2025-08-17 15:22:52,619 | INFO | access_logger | api\main.py:21 | Response status: 200
 2025-08-17 15:23:25,844 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
 2025-08-17 15:24:08,120 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-19 16:02:02,820 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-08-19 16:02:02,847 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-19 16:02:03,185 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-19 16:02:03,669 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-19 16:02:14,935 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-19 16:12:43,018 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-19 16:30:36,700 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-19 16:39:11,277 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-19 17:15:35,457 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-20 12:26:51,641 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-08-20 12:26:51,650 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-20 12:26:52,199 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-20 12:26:52,324 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-20 12:26:58,811 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-20 12:49:37,673 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-20 14:02:48,043 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-20 14:02:48,353 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-20 14:04:00,398 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-20 14:18:25,067 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-20 14:19:26,780 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-20 14:22:59,676 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-08-20 14:22:59,678 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-20 14:23:02,148 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-20 14:23:02,477 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-20 14:34:06,588 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-20 14:41:22,386 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-20 14:42:13,807 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-20 15:48:39,672 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-20 15:48:40,001 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-20 15:48:46,301 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-20 15:49:43,697 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 15:13:40,245 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-08-22 15:13:40,339 | INFO | access_logger | api\main.py:21 | Response status: 200
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+2025-08-22 15:14:26,928 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 15:14:46,025 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
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+2025-08-22 15:15:05,845 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 15:15:05,847 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 15:15:35,529 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 15:15:35,531 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 15:15:57,354 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 15:15:57,361 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 15:16:10,105 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 15:16:10,108 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 15:16:58,064 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
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+2025-08-22 16:04:11,077 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:04:11,080 | INFO | access_logger | api\main.py:21 | Response status: 200
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+2025-08-22 16:04:37,207 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:04:37,284 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 16:04:52,517 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
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+2025-08-22 16:32:47,773 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:32:47,775 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 16:32:59,712 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:32:59,714 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 16:33:23,642 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:33:23,644 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 16:33:34,480 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:33:34,482 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 16:33:45,378 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:33:45,380 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 16:34:02,049 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:34:02,051 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 16:34:16,163 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:34:16,165 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 16:34:30,074 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:34:30,076 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 16:34:49,371 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-22 16:34:49,373 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-22 16:40:10,586 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+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

logs/app.log

@@ -33,3 +33,36 @@
 2025-08-17 14:13:45,517 | INFO | app_logger | api\routers\show_analytics.py:14 | Influencer Analytics returned by orchestrator.
 2025-08-17 15:22:52,597 | INFO | app_logger | api\routers\show_analytics.py:14 | Influencer Analytics returned by orchestrator.
 2025-08-17 15:24:08,119 | INFO | app_logger | api\routers\ideation.py:28 | Executed the ideation pipeline.
+2025-08-20 14:42:13,597 | INFO | app_logger | api\routers\ideation.py:28 | Executed the ideation pipeline.
+2025-08-20 15:49:43,187 | INFO | app_logger | api\routers\ideation.py:28 | Executed the ideation pipeline.
+2025-08-22 15:14:30,556 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 15:14:47,390 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 15:15:08,142 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 15:15:37,181 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 15:15:58,251 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 15:16:11,455 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 15:16:59,158 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 15:17:26,486 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 15:18:06,522 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 15:18:30,038 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 15:20:54,775 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:03:31,947 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:03:45,991 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:03:59,045 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:04:12,079 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:04:24,354 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:04:38,884 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:04:54,600 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:05:24,378 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:05:46,373 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:28:13,344 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:32:37,212 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:32:48,691 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:33:01,635 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:33:24,859 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:33:35,725 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:33:46,403 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:34:03,128 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:34:17,796 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:34:33,195 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-22 16:34:51,470 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent

logs/errors.log

@@ -1 +1,2 @@
 2025-08-13 12:14:46,509 | ERROR | error_logger | Error while showing analytics: retrieve_data_for_analytics() missing 1 required positional argument: 'business_details'
+2025-08-20 14:19:20,731 | ERROR | error_logger | api\routers\ideation.py:33 | Ideation loop ran more than specified.

requirements.txt

@@ -2,7 +2,7 @@
 langgraph
 langsmith 
 langchain_groq
-pydantic 
+pydantic==2.11.7
 datasets 
 faiss-cpu
 dotenv

src/genai/context_analysis_agent/agent.py

@@ -1,10 +1,12 @@
 import logging
+from langchain_core.messages import SystemMessage , HumanMessage
 from langgraph.graph import StateGraph, MessagesState, START, END
 from langgraph.checkpoint.memory import MemorySaver
-from .utils.state import State 
+from .utils.state import State, CompletionFormatter
 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()
@@ -37,7 +39,10 @@ class IntroductionChatbot:
 
 
     def is_complete(self, latest_response: str) -> bool:
-        return "Thanks for providing all your required business details" in latest_response
+        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()

src/genai/context_analysis_agent/utils/nodes.py

@@ -1,6 +1,7 @@
 from langchain_core.messages import SystemMessage
 from src.genai.utils.models_loader import llm_gpt
 from .prompts import introduction_prompt
+from .state import ConversationFormatter
 
 
 class IntroductionNode:
@@ -10,8 +11,13 @@ class IntroductionNode:
     def run(self, state, llm):
         template = introduction_prompt
         messages = [SystemMessage(content=template)] + state["messages"]
-        response = llm.invoke(messages)
-        return {"messages": [response]}
+        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]}
 
 
 

src/genai/context_analysis_agent/utils/prompts.py

@@ -1,38 +1,45 @@
 introduction_prompt = '''
-You are a business assistant who collects only valid and relevant data.
+You are a business assistant who collects only valid and relevant data of the brands.
+Those brands connects with influencers to grow their business.
 Your job is to gather details from business owners in a friendly and conversational manner to understand their business better. Ask in very easy and short way.
 No matter what the user asks, you have to say to user that we have to collect these details first and only you can move forward.
 If user asks you for some other queries related to influencers, marketing, video ideas etc or anything. Don't say i'm not here to help you. Just say, First i will collect your all the details and only can help you analyzing your details.
 You have to say user to be patient until al the details are collected.
 
 We need these details:
-1. Business Type (e.g., e-commerce, SaaS, consulting),
+1. Business Type and Name (e.g., e-commerce, SaaS, consulting),
 2. Platform(s) used (e.g., website, app, Instagram),
 3. Target Audience (who are their customers or clients),
 4. Business Goals (short-term or long-term objectives),
 5. Offerings (products or services they provide),
-6. Challenges faced (any current business problems or limitations).
+6. Devices used to create the videos.(smart phones , camera , high end gadgets etc.)
+7. Challenges faced (any current business problems or limitations).
+8. Lastly, ask about any queries or details that the business wants to say regarding their video idea and content creation. This is kind of additional details.
 
 Keep interacting until all valid details are collected.
 
 VERY IMPORTANT: Once all valid details are received, say: '**Thanks for providing all your required business details.**'
 
+Give the output in structured format like this:
+response: Your complete response here for details collection
+complete: True or False. If the details extraction is completed, return True. If not completed return False.
 '''
 
+def completion_check_prompt():
+    return f'''
+You are a perfect checker who checks whether the assistant has ended the conversation with user or not by analyzing it's response. 
+If the assistant has ended the conversation , it will say the message: **Thanks for providing your required business details** or something thank you message like this.
+The assistant will not further ask any questions if the conversation is completed. If the assistant is asking any question , it means the conversation is not completed.
+So you are working as an indicator here who gives the signal whether the conversation is **completed** or **not completed**.
+
+Output Format:
+Just give the output in one word. Either **completed** or **not completed**.
+'''
 
 def details_extract_prompt(interactions):
-    return( f''' You're provided with the messages of business interactions between the user and assistant.
-        Extract the following details of the business from the conversation in the form of dictionary. Don't give any further explanations either in the beginning or ending of the response. 
-        The details you have to extract are:\n
-           
-        ----------********---------------
-        "business_type": "...",
-        "platform": "...",
-        "target_audience": "...",
-        "business_goals": "...",
-        "offerings": "...",
-        "Challenges_faced": "..."
-        ----------********---------------
+    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 

src/genai/context_analysis_agent/utils/state.py

@@ -10,9 +10,19 @@ class DetailsFormatter(BaseModel):
     '''
     Format the details from the business interaction of User and Assistant
     '''
-    business_type: str = Field(description="The type of the business")
-    platform: str = Field(description="The platform used for the business")
+    business_type_or_name: str = Field(description="The type or name of the business.")
+    platform: str = Field(description="The platform used for the promotion of business")
     target_audience: str = Field(description="The target audience of the business")
     business_goals: str = Field(description="The business goals of the business")
     offerings: str = Field(description="The offerings of the business")
-    Challenges_faced: str = Field(description="The challenges faced by the business")
+    devices_used_to_create_videos: str = Field(description="The devices used to create videos.")
+    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.")
+