Updated tools.py

my_agent/utils/tools.py

@@ -6,12 +6,13 @@ import os
 import numpy as np
 from langchain_core.tools import tool
 from .data_loader import load_influencer_data
-from .models_loader import  ST
+from .models_loader import  ST , llm
 
 
 os.environ['GROQ_API_KEY']=os.getenv('GROQ_API_KEY')
 
 
+
 class StoryFormatter(BaseModel):
     """Always use this tool to structure your response to the user."""
     story: str=Field(description="How to introduce the scene and set the tone. What is happening in the scene? Describe key visuals and actions")
@@ -29,17 +30,26 @@ class BrainstromTopicFormatter(BaseModel):
   topic4:str=Field(description="Fourth brainstorming topic of the story")
 
 class QueryFormatter(BaseModel):
-    idea:str = Field(description="The video idea which the user wants to create.")
+    idea:str = Field(description="Any idea or query about the business.")
     business_details: str = Field(description="The details of the business of that user.")
 
 @tool("influencer's data-retrieval-tool", args_schema=QueryFormatter, return_direct=False,description="Retrieve influencer-related data for a given query.")
 def retrieve_tool(idea, business_details):
+    
+    # """This tool is responsible for the retrieval of the influencer's data using semantic search by reading any **idea or query about the business** and the **business details of the user.**
+    #  But remember, the idea have to be valid first. Don't retrieve anything if the idea is invalid or it is like General Question Answering or follow up questions.
+    #  If you find the idea as invalid, write the value as "None" in the idea so that i can process it."""
+    
+    """This tool is responsible for the retrieval of the influencer's data using semantic search by reading any **idea or query about the business** and the **business details of the user.**
+     ."""
+    
 
-    """This tool is responsible for the retrieval of the influencer's data using semantic search by reading the video idea and the business details of the user. """
     embedded_query = ST.encode(str(idea)+str(business_details))  # Embed each topic
     data = load_influencer_data()
-    scores, retrieved_examples = data.get_nearest_examples("embeddings", embedded_query, k=1)
+    scores, retrieved_examples = data.get_nearest_examples("embeddings", embedded_query, k=3)
 
     # Construct a list of dictionaries for this topic
     result = [{user: story} for user, story in zip(retrieved_examples['username'], retrieved_examples['agentic_story'])]
-    return result
+    # result = [{u: {"story": s, "likes": l, "comments": c}} for u, s, l, c in zip(retrieved_examples['username'], retrieved_examples['agentic_story'], retrieved_examples['likes'], retrieved_examples['comments'])]
+    print('The tool response:',result)
+    return result