Updated prompts in business interaction

my_agent/utils/initial_interaction.py

@@ -6,7 +6,7 @@ from langchain_core.messages import SystemMessage
 from pydantic import BaseModel, ConfigDict, Field
 from typing import Optional, List
 from .models_loader import llm
-from .prompts import business_interaction_prompt
+from .prompts import business_interaction_prompt , details_extract_prompt
 
 # Pydantic model for extracted business info
 class DetailsFormatter(BaseModel):
@@ -47,25 +47,6 @@ class BusinessInteractionChatbot:
         response = self.llm.invoke(messages)
         return {"messages": [response]}
 
-    def _get_prompt_template(self):
-        return (
-            '''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.**'
-'''
-        )
-
     def chat(self, user_input: str):
         self.messages.append({"role": "user", "content": user_input})
         config = {"configurable": {"thread_id": "1"}}
@@ -78,14 +59,7 @@ VERY IMPORTANT: Once all valid details are received, say: '**Thanks for providin
         return "Thanks for providing all your required business details" in latest_response
 
     def extract_details(self):
-        template = f'''Extract the following details of the business from the conversation.
-        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).
-        The conversation is:\n{business_state.interactions}'''
+        template = details_extract_prompt(business_state.interactions)
 
         messages = [SystemMessage(content=template)]
         response = self.llm.bind_tools([DetailsFormatter]).invoke(messages)

my_agent/utils/prompts.py

@@ -76,4 +76,14 @@ We need these 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.**'
-'''
+'''
+
+def details_extract_prompt(interactions):
+    return( f'''Extract the following details of the business from the conversation.
+        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).
+        The conversation is:\n{interactions}''')

start.sh

@@ -1,2 +0,0 @@
-#!/bin/bash
-uvicorn main:app --host 0.0.0.0 --port 10000

vercel.json

@@ -1,15 +0,0 @@
-{
-    "builds": [
-      {
-        "src": "main.py",
-        "use": "@vercel/python"
-      }
-    ],
-    "routes": [
-      {
-        "src": "/(.*)",
-        "dest": "main.py"
-      }
-    ]
-    
-  }