Structured the prompts in a separate module

my_agent/utils/nodes.py

@@ -7,6 +7,7 @@ from .models_loader import llm , ST
 from .data_loader import load_influencer_data
 from groq import Groq
 import os
+from .prompts import image_captioning_prompt , initial_story_prompt , refined_story_prompt , brainstroming_prompt , final_story_prompt
 
 
 def caption_image(state: State) -> State:
@@ -19,7 +20,7 @@ def caption_image(state: State) -> State:
               {
                   "role": "user",
                   "content": [
-                      {"type": "text", "text": "What's in this image?"},
+                      {"type": "text", "text": image_captioning_prompt},
                       {
                           "type": "image_url",
                           "image_url": {
@@ -39,10 +40,6 @@ def caption_image(state: State) -> State:
        state.image_captions.append(None)
        return state
     
-    # elif state.images[-1]==None:
-    #   state.image_captions.append(None)
-
-    
 
 
 def retrieve(state: State) -> State:
@@ -92,17 +89,9 @@ def generate_story(state:State)-> State:
   retrieval = " ".join(agentic_stories)
 
   if len(state.preferred_topics)==0:
-    template = f'''I want to create a detailed storyline for a video in any domain. You have to provide me that storyline what to include in the video.
-    Now, i am giving you the topic of the video. But the need is to generate the story focusing on the format that i'll provide to you.
-    You can use this format for the reference purpose, not for the exact similar generation. Th format is:\n{retrieval}.
-    \n\n Now let's start creating the storyline for my topic. The topic of the video is: \n\n{state.topic}'''
+    template = initial_story_prompt(retrieval , state)
   else:
-    template = f'''I want to create a detailed storyline for a video in the given topic. You have to provide me that storyline what to include in the video.
-  Now, i am giving you the topic of the video. But the need is to generate the story focusing on the format that i'll provide to you.
-  You can use this format for the reference purpose, not for the exact similar generation. The format is:\n{retrieval}.
-  \n\n Now let's start creating the storyline for my topic. The topic of the video is: \n\n{state.topic}\n\n
-
-  **Final Reminder** You have to strongly focus on these topics while creating the storyline: {state.preferred_topics[-1]}'''
+    template = refined_story_prompt(retrieval , state)
 
 # and {state.image_captions[-1]}
 
@@ -122,17 +111,8 @@ def generate_story(state:State)-> State:
 
 
 def generate_brainstroming(state:State)-> State:
-  story=state.stories[-1]
-
-  template= f'''I want to brainstorm ways to diversify or improve a storyline in exactly 4 sentences.
-The goal is to generate creative and actionable ideas that are not on the storyline on how the storyline can be expanded or modified for better engagement.
-For example: If the storyline is about creating a promotional video for a restaurant, the new suggestions might include:
-- I want to showcase the chef preparing a signature dish.
-- I want to add a sequence of customers sharing their experiences at the restaurant.
-- I want to highlight the farm-to-table sourcing of ingredients with a short segment showing local farms.
-- I want to include a time-lapse of the restaurant transforming from day to night, capturing its unique ambiance.
-- I want to feature a quick interview with the owner sharing the story behind the restaurant.
-Now, I will provide you with the storyline. The storyline is:\n{story}'''
+
+  template= brainstroming_prompt(state)
 
   messages = [SystemMessage(content=template)]
   response = llm.bind_tools([BrainstromTopicFormatter]).invoke(messages)
@@ -193,14 +173,9 @@ def route_after_selection(state:State):
   elif len(state.latest_preferred_topics)>0:
     return True
 
-def generate_final_story(query):
-    if len(query['preferred_topics'])>0:
-        template = f'''I want to create a detailed storyline for a video in the given topic. You have to provide me that storyline what to include in the video.
-      Now, i am giving you the topic of the video. But the need is to generate the story focusing on the format that i'll provide to you.
-      You can use this format for the reference purpose, not for the exact similar generation. The format is:\n{query['retrievals'][-1]}.
-      \n\n Now let's start creating the storyline for my topic. The topic of the video is: \n\n{query['topic']}\n\n
-
-      **Final Reminder** You have to strongly focus on these topics while creating the storyline: {[item for sublist in query['preferred_topics'] for item in sublist]}'''
+def generate_final_story(final_state):
+    if len(final_state['preferred_topics'])>0:
+        template = final_story_prompt(final_state)
         messages = [SystemMessage(content=template)]
         response = llm.bind_tools([StoryFormatter]).invoke(messages)
         print('The final response is:',response)
@@ -215,7 +190,7 @@ def generate_final_story(query):
         return response
     
     else:
-        return query['stories'][-1]
+        return final_state['stories'][-1]
 
 
 

my_agent/utils/prompts.py

@@ -8,4 +8,54 @@ Example input:
 
 Desired output:
 "A cinematic street scene reveals the charming exterior of 'Vegetarian Delights,' then glides into a warmly lit, spice-scented interior where skilled chefs craft vibrant vegetarian dishes and a delighted diner savors each flavorful bite in glowing ambiance."
-'''
+'''
+
+image_captioning_prompt = '''
+Analyze the image deeply and give the story of the image what it wants to say?
+'''
+
+
+def initial_story_prompt(retrieval , state):
+    return (
+        f'''
+I want to create a detailed storyline for a video in any domain. You have to provide me that storyline what to include in the video.
+    Now, i am giving you the topic of the video. But the need is to generate the story focusing on the format that i'll provide to you.
+    You can use this format for the reference purpose, not for the exact similar generation. The format is:\n{retrieval}.
+    \n\n Now let's start creating the storyline for my topic. The topic of the video is: \n\n{state.topic}'''
+    )
+
+def refined_story_prompt(retrieval , state):
+    return(
+        f'''
+I want to create a detailed storyline for a video in the given topic. You have to provide me that storyline what to include in the video.
+  Now, i am giving you the topic of the video. But the need is to generate the story focusing on the format that i'll provide to you.
+  You can use this format for the reference purpose, not for the exact similar generation. The format is:\n{retrieval}.
+  \n\n Now let's start creating the storyline for my topic. The topic of the video is: \n\n{state.topic}\n\n
+
+  **Final Reminder** You have to strongly focus on these topics while creating the storyline: {state.preferred_topics[-1]} and {state.image_captions[-1]}'''
+    )
+
+def brainstroming_prompt(state):
+    return(
+        f'''
+I want to brainstorm ways to diversify or improve a storyline in exactly 4 sentences.
+The goal is to generate creative and actionable ideas that are not on the storyline on how the storyline can be expanded or modified for better engagement.
+For example: If the storyline is about creating a promotional video for a restaurant, the new suggestions might include:
+- I want to showcase the chef preparing a signature dish.
+- I want to add a sequence of customers sharing their experiences at the restaurant.
+- I want to highlight the farm-to-table sourcing of ingredients with a short segment showing local farms.
+- I want to include a time-lapse of the restaurant transforming from day to night, capturing its unique ambiance.
+- I want to feature a quick interview with the owner sharing the story behind the restaurant.
+Now, I will provide you with the storyline. The storyline is:\n{state.stories[-1]}'''
+    )
+
+def final_story_prompt(final_state):
+    return(
+        f'''
+I want to create a detailed storyline for a video in the given topic. You have to provide me that storyline what to include in the video.
+      Now, i am giving you the topic of the video. But the need is to generate the story focusing on the format that i'll provide to you.
+      You can use this format for the reference purpose, not for the exact similar generation. The format is:\n{final_state['retrievals'][-1]}.
+      \n\n Now let's start creating the storyline for my topic. The topic of the video is: \n\n{final_state['topic']}\n\n
+
+      **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]}'''
+    )