Next commit after refinements

brainstroming_agent/agent.py

@@ -1,6 +1,6 @@
 from langgraph.graph import StateGraph, START, END
 from .utils.state import State
-from .utils.nodes import retrieve, generate_story, generate_brainstroming , route_after_selection, select_preferred_topics,caption_image
+from .utils.nodes import retrieve, generate_story, generate_brainstroming , select_preferred_topics,caption_image
 from langgraph.checkpoint.memory import MemorySaver
 
 memory = MemorySaver()

brainstroming_agent/utils/nodes.py

@@ -54,9 +54,10 @@ def caption_image(state: State) -> State:
 def retrieve(state: State) -> State:
   print('Moving to retrieval process')
   retrievals=[]
+  query_prompt = 'Represent this sentence for searching relevant passages: '
   if len(state.latest_preferred_topics)==0:
       for topic in state.topic:  # Loop through each topic
-          embedded_query = ST.encode(topic)  # Embed each topic
+          embedded_query = ST.encode(topic+query_prompt)  # Embed each topic
           data = load_influencer_data()
           scores, retrieved_examples = data.get_nearest_examples("embeddings", embedded_query, k=1)
 
@@ -70,7 +71,7 @@ def retrieve(state: State) -> State:
       print('The preferred_topics are:',state.latest_preferred_topics)
       state.preferred_topics.append(state.latest_preferred_topics)
       for topic in state.preferred_topics[-1]:  # Loop through each topic
-          embedded_query = ST.encode(topic)  # Embed each topic
+          embedded_query = ST.encode(topic+query_prompt)  # Embed each topic
           data = load_influencer_data()
           scores, retrieved_examples = data.get_nearest_examples("embeddings", embedded_query, k=1)
 
@@ -102,6 +103,7 @@ def generate_story(state:State)-> State:
 
     response = react_agent.invoke({'messages':messages})
     response = response['messages'][-1].content
+    print('The geenrated story: ', response)
     state.stories.append(response)
     # return State(messages="Story generated", topic=state.topic,stories=state.stories)
     return state

brainstroming_agent/utils/prompts.py

@@ -79,23 +79,23 @@ Now, generate the final storyline for the video topic..
 
 def brainstroming_prompt(state):
     return f'''
-You are a creative consultant tasked with enhancing a storyline.
+You are a creative consultant who can diversify the video's story for brainstorming.
+I have a detailed video story. I want to brainstorm creative ways to diversify or expand it into different directions.
 
-Suggest **4 unique and creative enhancements** to the following storyline. Each suggestion must:
-- Be just 1 sentence long”
-- Add something **new or different** (not rephrasing what’s already in the story)
-- Improve the storyline’s **engagement, emotional depth, or audience appeal**
+Please suggest 4 highly creative, diverse, and practical short sentence ideas that show how the story can be visually or narratively diversified. These should not be abstract "what if" scenarios, but specific ways to evolve or branch the storyline — such as adding new scenes, shifting perspectives, integrating emotional moments, or enhancing visual storytelling.
 
-Output format: a numbered list from 1 to 4.
+Each idea should be a small sentence and propose a concrete way the video could take a new direction or include a compelling new element.
 
-Here’s an example for a restaurant promo storyline:
-1. Showcase the chef preparing a signature dish.
-2. Add a sequence of customers sharing their experiences at the restaurant.
-3. Highlight the farm-to-table sourcing of ingredients with a short segment showing local farms.
-4. Include a time-lapse of the restaurant transforming from day to night, capturing its unique ambiance.
+For instance: If the storyline is about creating a promotional video for a restaurant, the brainstorming topics might include:  
+- showcase the chef preparing a signature dish.  
+- add a sequence of customers sharing their experiences at the restaurant.  
+- highlight the farm-to-table sourcing of ingredients with a short segment showing local farms.  
+- include a time-lapse of the restaurant transforming from day to night, capturing its unique ambiance.  
+- feature a quick interview with the owner sharing the story behind the restaurant. 
 
-Now, here is the current storyline you need to enhance:
+**Remember**: Don't include the ideas or topics that are already presented in the video story.
 
+Now, here is the detailed video story:
 "{state.stories[-1]}"
 '''
 
@@ -110,95 +110,3 @@ I want to create a detailed storyline for a video in the given topic. You have t
       **Final Reminder** You have to strongly focus on these topics while creating the storyline: {[item for sublist in final_state['preferred_topics'] for item in sublist]}'''
     )
 
-
-introduction_prompt = '''
-You are a business assistant who collects only valid and relevant data.
-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.
-
-We need these details:
-1. Business Type (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).
-
-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.**'
-
-'''
-business_interaction_prompt = '''
-You are a strict assistant designed to help users **only by using data retrieved from the influencer retrieval tool**. You are not allowed to answer any query using your own knowledge or assumptions — you must respond **exclusively based on the retrieved influencer data** provided by the tool.
-
-For **every user query** — whether it's about business, lifestyle, motivation, strategy, ideas, challenges, or any general topic — you must:
-- **Always invoke the tool**
-- Analyze the influencer data it returns
-- Generate your response **only using that data**
-
-Your response must include:
-- **The names of the influencers**
-- **What those influencers are doing or sharing**
-- **How their content or insights relate to the user's query**
-
-You are strictly forbidden from making up any information, speculating, or answering without the tool’s data. If no relevant data is retrieved from the tool, reply clearly that you cannot generate a response without influencer data.
-Keep the response short and precise. Don't make it long as it will be time consuming to read. Just grab the response of the user query from the tool and give. No more out of the box explanations. 
-'''
-
-
-def details_extract_prompt(interactions):
-    return( f'''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. 
-        I want the response **direct** in the form of python dictionary. The details you have to extract are:\n
-        1. Business Type (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).
-           
-        Now, start doing your work:\n
-        The conversation is:\n{interactions}\n 
-    
-
-        ''')
-
-def business_retrieval_prompt(user_message, retrievals):
-    return f'''
-You are a professional AI assistant helping users understand how influencers can support their business. You will be given:
-
-- A **user message**
-- A list of **retrieved influencer data**
-
-Your job is to:
-1. **First**, analyze the user message and decide if it is actually a business-related question or query that could be answered using influencer content.
-    - If the message is just a casual greeting like “Hi”, “Hello”, “How are you?”, or not business-related (e.g., “Who are you?”, “Tell me a joke”), then **do NOT use the retrievals** and just respond to the user naturally.
-2. **If the message is business-related**, proceed to analyze the influencer data and explain how each influencer might support the business based only on their content.
-
---- USER MESSAGE ---
-{user_message}
-
---- START OF RETRIEVALS ---
-{retrievals}
---- END OF RETRIEVALS ---
-
-Rules:
-- If the user message is **not relevant to influencer or business help**, politely respond in a general helpful way and ignore the retrievals.
-- If the message **is relevant**, then:
-  - Identify which influencer stories seem relevant to the query.
-  - Explain what the influencers are promoting and how it might help the business.
-  - You **must mention influencer usernames** and only use what is in the retrievals.
-- You are not allowed to invent, guess, or add information outside the retrievals.
-
-Keep your response:
-- Context-aware
-- Grounded only in retrievals if relevant
-- Helpful and brief
-'''
-
-
-
-
-
-
-
-

brainstroming_agent/utils/tools.py

@@ -16,7 +16,7 @@ import pandas as pd
 from .state import QueryFormatter
 
 os.environ['GROQ_API_KEY']=os.getenv('GROQ_API_KEY')
-@tool("influencer's data-retrieval-tool", args_schema=QueryFormatter, return_direct=False,description="Retrieve influencer-related data for a given query.")
+@tool("influencers_data_retrieval_tool", args_schema=QueryFormatter, return_direct=False,description="Retrieve influencer-related data for a given query.")
 def retrieve_tool(video_topic):
     '''
     Always invoke this tool.
@@ -39,7 +39,7 @@ def retrieve_tool(video_topic):
 
     # === Encode the query and search ===
     query_embedding = ST.encode(str(video_topic)).reshape(1, -1).astype('float32')
-    top_k=7
+    top_k=3
     distances, indices = index.search(query_embedding, top_k)
 
 
@@ -51,7 +51,7 @@ def retrieve_tool(video_topic):
             'rank': i + 1,
             'username': df.iloc[idx]['username'],
             'story': df.iloc[idx]['story'],
-            'visible_text_or_brandings': df.iloc[idx]['story'],
+            'visible_text_or_brandings': df.iloc[idx]['visible_texts_or_brandings'],
             'likesCount': df.iloc[idx]['likesCount'],
             'commentCount': df.iloc[idx]['commentCount'],
         }

ideation_agent/agent.py

@@ -1,6 +1,8 @@
 from langgraph.graph import StateGraph, START, END
 from .utils.state import State
 from .utils.nodes import ideator , critic , improver , validator1 , validator2 , validator3 , validator4 , route1_after_validation , route2_after_validation , route3_after_validation , route4_after_validation
+from langgraph.checkpoint.memory import MemorySaver
+memory = MemorySaver()
 
 
 def ideation_graph():
@@ -29,4 +31,4 @@ def ideation_graph():
     graph_builder.add_conditional_edges("validator3", route3_after_validation,{False:'improver',True:'validator4'})
     graph_builder.add_conditional_edges("validator4", route4_after_validation,{False:'improver',True:END})
 
-    return graph_builder.compile()
+    return graph_builder.compile(checkpointer=memory)

ideation_agent/utils/nodes.py

@@ -5,138 +5,157 @@ from utils.models_loader import ideator_llm, critic_llm , improver_llm , validat
 from langchain_core.messages import SystemMessage , HumanMessage
 from .prompts import ideator_prompt , critic_prompt , improver_prompt , validator_prompt
 
-def ideator(state:State):
-    tools=[retrieve_tool]
-    react_agent=create_react_agent(
+
+ideator_agent = create_react_agent(
     model=ideator_llm,
-    tools=tools
+    tools=[retrieve_tool]
+    )
+critic_agent = create_react_agent(
+    model=critic_llm,
+    tools=[retrieve_tool]
     )
 
-    template = ideator_prompt(state)
+improver_agent = create_react_agent(
+    model=improver_llm,
+    tools=[]
+    )
 
-    messages = [SystemMessage(content=template),
-                HumanMessage(content=f'''The topic of the video is:\n{state.topic}\n''')]
 
-    response = react_agent.invoke({'messages':messages})
-    response = response['messages'][-1].content
-    print('Ideator Response:',response)
-    state.ideator_response = response
-    print('Ideator Generated the story')
-    return state
+def ideator(state:State):
+    template = ideator_prompt(state)
+    messages = [SystemMessage(content=template),
+                HumanMessage(content=f'''The topic of the video is:\n{state.topic[-1]}\n''')]
+    print('Ideator prompt:', messages)
+    try:
+        response = ideator_agent.invoke({'messages':messages})
+        response = response['messages'][-1].content
+        print('Ideator Response:',response)
+        state.ideator_response.append(response)
+        print('Ideator Generated the story')
+        return state
+
+    except:
+        response = ideator_llm.invoke(messages)
+        print('Ideator backup Response:',response.content)
+        state.ideator_response.append(response.content)
+        return state
 
 def critic(state:State):
-    tools=[retrieve_tool]
-    react_agent=create_react_agent(
-      model=critic_llm,
-      tools=tools
-    )
-
     template = critic_prompt(state)
     messages = [SystemMessage(content=template),
-                    HumanMessage(content=f'''The topic of the video is:\n{state.topic}\n. The business_details is\n{state.business_details}\n''')]
+                    HumanMessage(content=f'''The topic of the video is:\n{state.topic[-1]}\n. The business_details is\n{state.business_details[-1]}\n''')]
+    print('Critic prompt:', messages)
+    
+    try:
+        response = critic_agent.invoke({'messages':messages})
+        response = response['messages'][-1].content
+        print('Critic Response:',response)
+        state.critic_response.append(response)
+        print('Critic Evaluated the story')
+        return state
+    
+    except:
+        response = critic_llm.invoke(messages)
+        print('Critic backup Response:',response.content)
+        state.critic_response.append(response.content)
+        return state
 
-    response = react_agent.invoke({'messages':messages})
-    response = response['messages'][-1].content
-    print('Critic Response:',response)
-
-    state.critic_response = response
-    print('Critic Evaluated the story')
-
-    return state
 
 def improver(state:State):
-    react_agent=create_react_agent(
-    model=improver_llm,
-    tools=[]
-    )
-
     template = improver_prompt(state)
     messages = [SystemMessage(content=template),
-                HumanMessage(content=f'''The topic of the video is:\n{state.topic}\n The business_details is:\n{state.business_details}''')]
-
-    response = react_agent.invoke({'messages':messages})
-    response = response['messages'][-1].content
-    print('Improver Response:',response)
-    state.improver_response = response
-    print('Improver Improved the story')
-    return state
+                HumanMessage(content=f'''The topic of the video is:\n{state.topic[-1]}\n The business_details is:\n{state.business_details[-1]}''')]
+    print('improver prompt:', messages)
+    
+    try:
+        response = improver_agent.invoke({'messages':messages})
+        response = response['messages'][-1].content
+        print('Improver Response:',response)
+        state.improver_response.append(response)
+        print('Improver Improved the story')
+        return state
+
+    except:
+        response = improver_llm.invoke(messages)
+        print('Improver backup Response:',response.content)
+        state.improver_response.append(response.content)
+        return state
 
 def validator1(state:State):
     template = validator_prompt(state)
     messages = [SystemMessage(content=template),
-                HumanMessage(content=f'''The topic of the video is:\n{state.topic}\n The business_details is:\n{state.business_details}''')]
+                HumanMessage(content=f'''The topic of the video is:\n{state.topic[-1]}\n The business_details is:\n{state.business_details[-1]}''')]
 
     response = validator_llm.with_structured_output(ValidationFormatter).invoke(messages)
     print(f'Validator 1 response: {response}')
-    state.validator1_response = response.result
-    print('The state check:',state.validator1_response)
+    state.validator1_response.append(response.result)
+    print('The state check:',state.validator1_response[-1])
     if 'not validated' in response.result:
-        state.disagreement_reason = response.reason
+        state.disagreement_reason.append(response.reason)
     return state
 
 def validator2(state:State):
     template = validator_prompt(state)
     messages = [SystemMessage(content=template),
-                HumanMessage(content=f'''The topic of the video is:\n{state.topic}\n The business_details is:\n{state.business_details}''')]
+                HumanMessage(content=f'''The topic of the video is:\n{state.topic[-1]}\n The business_details is:\n{state.business_details[-1]}''')]
 
     response = ideator_llm.with_structured_output(ValidationFormatter).invoke(messages)
     print(f'Validator 2 response: {response}')
-    state.validator2_response = response.result
-    print('The state check:',state.validator1_response)
+    state.validator2_response.append(response.result)
+    print('The state check:',state.validator2_response[-1])
     if 'not validated' in response.result:
-        state.disagreement_reason = response.reason
+        state.disagreement_reason.append(response.reason)
     return state
 
 def validator3(state:State):
     template = validator_prompt(state)
     messages = [SystemMessage(content=template),
-                HumanMessage(content=f'''The topic of the video is:\n{state.topic}\n The business_details is:\n{state.business_details}''')]
+                HumanMessage(content=f'''The topic of the video is:\n{state.topic[-1]}\n The business_details is:\n{state.business_details[-1]}''')]
 
     response = critic_llm.with_structured_output(ValidationFormatter).invoke(messages)
     print(f'Validator 3 response: {response}')
-    state.validator3_response = response.result
-    print('The state check:',state.validator1_response)
+    state.validator3_response.append(response.result)
+    print('The state check:',state.validator1_response[-1])
     if 'not validated' in response.result:
-        state.disagreement_reason = response.reason
+        state.disagreement_reason.append(response.reason)
     return state
 
 def validator4(state:State):
     template = validator_prompt(state)
     messages = [SystemMessage(content=template),
-                HumanMessage(content=f'''The topic of the video is:\n{state.topic}\n The business_details is:\n{state.business_details}''')]
+                HumanMessage(content=f'''The topic of the video is:\n{state.topic[-1]}\n The business_details is:\n{state.business_details[-1]}''')]
 
     response = improver_llm.with_structured_output(ValidationFormatter).invoke(messages)
     print(f'Validator 4 response: {response}')
-    state.validator4_response = response.result
-    print('The state check:',state.validator1_response)
+    state.validator4_response.append(response.result)
+    print('The state check:',state.validator1_response[-1])
     if 'not validated' in response.result:
-        state.topic=None
-        state.disagreement_reason = response.reason
+        state.disagreement_reason.append(response.reason)
     return state
 
 def route1_after_validation(state:State):
-    if 'not validated' in state.validator1_response:
+    if 'not validated' in state.validator1_response[-1]:
         return False
     else:
         return True
 
 
 def route2_after_validation(state:State):
-    if 'not validated' in state.validator2_response:
+    if 'not validated' in state.validator2_response[-1]:
         return False
     else:
         return True
 
 
 def route3_after_validation(state:State):
-    if 'not validated' in state.validator3_response:
+    if 'not validated' in state.validator3_response[-1]:
         return False
     else:
         return True
 
 
 def route4_after_validation(state:State):
-    if 'not validated' in state.validator4_response:
+    if 'not validated' in state.validator4_response[-1]:
         return False
     else:
         return True

ideation_agent/utils/prompts.py

@@ -12,10 +12,10 @@ def ideator_prompt_old(state):
   ---
 
   **Video Topic**:  
-  {state.topic}
+  {state.topic[-1]}
 
   **Business Details**:  
-  {state.business_details}
+  {state.business_details[-1]}
 
   ---
 
@@ -35,10 +35,10 @@ Use the response of the provided tool (which includes video stories from influen
 ---
 
 **Video Topic**:  
-{state.topic}
+{state.topic[-1]}
 
 **Business Details**:  
-{state.business_details}
+{state.business_details[-1]}
 
 ---
 
@@ -65,13 +65,13 @@ def critic_prompt(state):
   ---
 
   **Video Topic**:  
-  {state.topic}
+  {state.topic[-1]}
 
   **Business Details**:  
-  {state.business_details}
+  {state.business_details[-1]}
 
   **Original Video Idea** (100 words):  
-  {state.ideator_response}
+  {state.ideator_response[-1]}
 
   ---
 
@@ -83,8 +83,8 @@ def critic_prompt(state):
 
 def improver_prompt(state):
     disagreement_note = (
-    f"**Your idea was not validated  before because of this reason:\n{state.disagreement_reason}\n**"
-    if state.disagreement_reason else ""
+    f"**Your idea was not validated  before because of this reason:\n{state.disagreement_reason[-1]}\n**"
+    if len(state.disagreement_reason)>0 else ""
     )
     return f'''
 You are a master-level **video concept developer** known for turning creative ideas into standout, refined gems.
@@ -96,13 +96,13 @@ Focus on making the idea sharper, bolder, and more impactful — while staying a
 ---
 
 **Video Topic**:  
-{state.topic}
+{state.topic[-1]}
 
 **Business Details**:  
-{state.business_details}
+{state.business_details[-1]}
 
 **Critic's Response**:
-{state.critic_response}
+{state.critic_response[-1]}
 
 {disagreement_note}
 
@@ -131,13 +131,13 @@ def validator_prompt(state):
 
 
   **Video Topic**:
-  {state.topic}
+  {state.topic[-1]}
 
   **Business Details**:
-  {state.business_details}
+  {state.business_details[-1]}
 
   **Final idea from Improver**:
-  {state.improver_response}
+  {state.improver_response[-1]}
 
   ---
 
@@ -152,8 +152,8 @@ def validator_prompt(state):
   **Output Instruction**: There are two outputs you have to give.
 
       1.Respond strictly just with one of the following values:
-    - `validated` – if the idea meets all the criteria and is validated.
-    - `not validated` – if it fails to meet any key criteria and needs further improvement.
+    - `validated` - if the idea meets all the criteria and is validated.
+    - `not validated` - if it fails to meet any key criteria and needs further improvement.
 
       2. Give a short reason of 30 words why the idea is validated or not validated.
   '''

ideation_agent/utils/state.py

@@ -4,16 +4,16 @@ import operator
 from typing import Annotated
 
 class State(BaseModel):
-  topic: list
-  business_details: Optional[dict]
-  ideator_response: Optional[str] = None
-  critic_response: Optional[str]=None
-  improver_response: Optional[str]=None
-  validator1_response: Optional[str]=None
-  validator2_response: Optional[str]=None
-  validator3_response: Optional[str]=None
-  validator4_response: Optional[str]=None
-  disagreement_reason: Optional[str]=None
+  topic: Annotated[list[str], operator.add]
+  business_details : Annotated[list[dict],operator.add] = []
+  ideator_response: Annotated[list[str],operator.add] = []
+  critic_response: Annotated[list[str],operator.add] = []
+  improver_response: Annotated[list[str],operator.add] = []
+  validator1_response: Annotated[list[str],operator.add] = []
+  validator2_response: Annotated[list[str],operator.add] = []
+  validator3_response: Annotated[list[str],operator.add] = []
+  validator4_response: Annotated[list[str],operator.add] = []
+  disagreement_reason: Annotated[list[str],operator.add] = []
 
 
 class QueryFormatter(BaseModel):

ideation_agent/utils/tools.py

@@ -7,7 +7,7 @@ import ast
 import faiss
 from utils.models_loader import ST
 
-@tool("influencer's data-retrieval-tool", args_schema=QueryFormatter, return_direct=False,description="Retrieve influencer-related data for a given query.")
+@tool("influencers_data_retrieval_tool", args_schema=QueryFormatter, return_direct=False,description="Retrieve influencer-related data for a given query.")
 def retrieve_tool(video_topic):
     '''
     Always invoke this tool.
@@ -42,16 +42,18 @@ def retrieve_tool(video_topic):
             'rank': i + 1,
             'username': df.iloc[idx]['username'],
             'story': df.iloc[idx]['story'],
-            'visible_text_or_brandings': df.iloc[idx]['story'],
+            'visible_text_or_brandings': df.iloc[idx]['visible_texts_or_brandings'],
             'likesCount': df.iloc[idx]['likesCount'],
             'commentCount': df.iloc[idx]['commentCount'],
+            'product_or_service_details': df.iloc[idx]['product_or_service_details'],
+
         }
 
         inner_list = []
         inner_list.append(f"[{res['rank']}]. The influencer name is: **{res['username']}** — Likes: **{res['likesCount']}**, Comments: **{res['commentCount']}**")
         inner_list.append(f"The story of that particular video is:\n{res['story']}")
         inner_list.append(f"The branding or promotion done is:\n{res['visible_text_or_brandings']}")
-
+        inner_list.append(f"The details of product or service is:\n{res['product_or_service_details']}")
         outer_list.append(inner_list)
 
     return str(outer_list)

main.py

@@ -9,8 +9,8 @@ from business_interaction_agent.agent import BusinessInteractionChatbot
 from context_analysis_agent.utils.utils import save_to_db
 import ast
 from brainstroming_agent.utils.utils import encode_image_to_base64 , generate_final_story, generate_image
-from fastapi import Body, Query
 from ideation_agent.agent import ideation_graph
+from langgraph.errors import GraphRecursionError
 
 import json
 
@@ -23,7 +23,7 @@ idea_graph = ideation_graph()
 brainstrom_graph = brainstroming_graph()
 
 stored_data={}
-stored_data['business_details']={"business_type": "cosmetic", "platform": "instagram", "target_audience": "youths", "business_goals": "to go global", "offerings": "nepali skin care products", "Challenges_faced": "finding new customers, attracting large customers"}
+stored_data['business_details']={"business_type": "restaurant", "platform": "instagram", "target_audience": "youths", "business_goals": "to go global", "offerings": "nepali foods", "Challenges_faced": "finding new customers, attracting large customers"}
 # stored_data['business_details']={}
 class UserMessage(BaseModel):
     message: str
@@ -53,15 +53,23 @@ def business_interaction(interaction: str):
 
 class IdeationRequest(BaseModel):
     topic : List[str]
-    business_details: dict
 
 @app.post("/ideation")
 def ideation_endpoint(request:IdeationRequest):
-    result = idea_graph.invoke({
-        'topic': request.topic,
-        'business_details': stored_data['business_details']
-    })
-    return {'response':result}
+    config={"recursion_limit":10, "configurable": {"thread_id": "ideation_thread123"}}
+    try:
+        result = idea_graph.invoke(
+        {
+            'topic': request.topic,
+            'business_details': [stored_data['business_details']]
+        },
+        config=config,
+        )
+        return {'response':result}
+    except GraphRecursionError:
+        result = idea_graph.get_state({"configurable": {"thread_id": "ideation_thread123"}})
+        return {'response': result[0]}
+    
 
 
 

utils/data_loader.py

@@ -3,7 +3,7 @@ from datasets import load_dataset
 
 print("Loading dataset and indexing FAISS...")  # Optional: for debugging
 
-dataset = load_dataset("subashdvorak/tiktok-formatted-story", revision="embedded")
+dataset = load_dataset("subashdvorak/tiktok-formatted-story-v2", revision="embedded")
 data = dataset['train'].add_faiss_index('embeddings')
 
 def load_influencer_data():

utils/models_loader.py

@@ -63,7 +63,7 @@ ideator_llm = ChatGroq(
 )
 
 critic_llm = ChatGroq(
-    model="gemma2-9b-it",
+    model="llama-3.3-70b-versatile",
     temperature=0.3,
     max_tokens=500,
 
@@ -81,10 +81,8 @@ improver_llm = ChatGroq(
     max_tokens=500,
 
 )
-
-
 validator_llm = ChatGroq(
-    model="gemma2-9b-it",
+    model="llama-3.3-70b-versatile",
     temperature=0.3,
     max_tokens=500,