updated .pyc files

brainstroming_agent/utils/tools.py

@@ -17,7 +17,6 @@ import pandas as pd
 from .validators 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.")
 def retrieve_tool(messages, business_details):
     '''
@@ -41,26 +40,10 @@ def retrieve_tool(messages, business_details):
 
     # === Encode the query and search ===
     query_embedding = ST.encode(str(messages)+str(business_details)).reshape(1, -1).astype('float32')
-    top_k=3
+    top_k=10
     distances, indices = index.search(query_embedding, top_k)
 
-    # === Function to extract sections 1 and 6 ===
-    def extract_story_and_branding(full_story):
-        full_story = full_story.replace('**6. Visible Texts or Brandings**', '**6. Visible Texts or Brandings:**')
-        full_story = full_story.replace('**1. Story**', '**1. Story:**')
 
-        pattern = (
-            r"\*\*1\. Story:\*\*(.*?)(?=\*\*\d+\.\s)"
-            r".*?"
-            r"\*\*6\. Visible Texts or Brandings:\*\*(.*?)(?=\*\*\d+\.\s|$)"
-        )
-        match = re.search(pattern, full_story, re.DOTALL)
-        if match:
-            story_section = match.group(1).strip()
-            branding_section = match.group(2).strip()
-            return f"Story:\n{story_section}\n\nVisible Texts or Brandings:\n{branding_section}"
-        else:
-            return "Requested sections not found."
 
     # === Format results ===
     outer_list = []
@@ -68,16 +51,17 @@ def retrieve_tool(messages, business_details):
         res = {
             'rank': i + 1,
             'username': df.iloc[idx]['username'],
-            'agentic_story': df.iloc[idx]['agentic_story'],
+            'story': df.iloc[idx]['story'],
+            'visible_text_or_brandings': df.iloc[idx]['story'],
             'likesCount': df.iloc[idx]['likesCount'],
             'commentCount': df.iloc[idx]['commentCount'],
-            'distance': distances[0][i]
         }
 
         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{extract_story_and_branding(res['agentic_story'])}")
-        inner_list.append(f"Distance: {res['distance']:.4f}")
+        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']}")
+
         outer_list.append(inner_list)
 
     return str(outer_list)

brainstroming_agent/utils/validators.py

@@ -1,22 +0,0 @@
-from pydantic import BaseModel, ConfigDict, Field
-
-
-# Pydantic model for extracted business info
-class DetailsFormatter(BaseModel):
-    business_type: str = Field(description="The type of the business")
-    platform: str = Field(description="The platform used for the 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")
-
-
-class BrainstromTopicFormatter(BaseModel):
-    topic1:str=Field(description="First brainstorming topic of the story")
-    topic2:str=Field(description="Second brainstorming topic of the story")
-    topic3:str=Field(description="Third brainstorming topic of the story")
-    topic4:str=Field(description="Fourth brainstorming topic of the story")
-
-class QueryFormatter(BaseModel):
-    messages:str = Field(description="The user query")
-    business_details: dict = Field(description="The details of the business of that user.")

business_interaction_agent/utils/utils.py

@@ -6,8 +6,11 @@ import numpy as np
 import ast
 
 
-
 def manual_retrieval(messages, business_details):
+    '''
+    Always invoke this tool.
+    Retrieve influencer's data by semantic search of **user messages** and the **business details**.
+    '''
     # === Load CSV ===
     csv_path = 'extracted_data.csv'
     df = pd.read_csv(csv_path)
@@ -25,26 +28,10 @@ def manual_retrieval(messages, business_details):
 
     # === Encode the query and search ===
     query_embedding = ST.encode(str(messages)+str(business_details)).reshape(1, -1).astype('float32')
-    top_k=3
+    top_k=10
     distances, indices = index.search(query_embedding, top_k)
 
-    # === Function to extract sections 1 and 6 ===
-    def extract_story_and_branding(full_story):
-        full_story = full_story.replace('**6. Visible Texts or Brandings**', '**6. Visible Texts or Brandings:**')
-        full_story = full_story.replace('**1. Story**', '**1. Story:**')
 
-        pattern = (
-            r"\*\*1\. Story:\*\*(.*?)(?=\*\*\d+\.\s)"
-            r".*?"
-            r"\*\*6\. Visible Texts or Brandings:\*\*(.*?)(?=\*\*\d+\.\s|$)"
-        )
-        match = re.search(pattern, full_story, re.DOTALL)
-        if match:
-            story_section = match.group(1).strip()
-            branding_section = match.group(2).strip()
-            return f"Story:\n{story_section}\n\nVisible Texts or Brandings:\n{branding_section}"
-        else:
-            return "Requested sections not found."
 
     # === Format results ===
     outer_list = []
@@ -52,16 +39,17 @@ def manual_retrieval(messages, business_details):
         res = {
             'rank': i + 1,
             'username': df.iloc[idx]['username'],
-            'agentic_story': df.iloc[idx]['agentic_story'],
+            'story': df.iloc[idx]['story'],
+            'visible_text_or_brandings': df.iloc[idx]['story'],
             'likesCount': df.iloc[idx]['likesCount'],
             'commentCount': df.iloc[idx]['commentCount'],
-            'distance': distances[0][i]
         }
 
         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{extract_story_and_branding(res['agentic_story'])}")
-        inner_list.append(f"Distance: {res['distance']:.4f}")
+        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']}")
+
         outer_list.append(inner_list)
 
-    return str(outer_list)
+    return str(outer_list).strip()