next commit

README.md

@@ -1,6 +1,6 @@
 
 ---
-title: trygithubaction
+title: rt-genai-fastapi
 sdk: docker
 emoji: 🚀
 colorFrom: blue

api/routers/analytics_chatbot.py

@@ -10,17 +10,16 @@ from api.stored_data import stored_data
 from src.genai.analytics_chatbot.agent import ChatbotAgent
 from src.genai.analytics_chatbot.utils.utils import process_query
 from api.schemas.analytics_chatbot import UserMessage
+from config.redis_config import redis_client
+
 
 app_logger = logging.getLogger("app_logger")
 error_logger = logging.getLogger("error_logger")
-password=os.environ['REDIS_PASSWORD']
-host = os.environ['REDIS_HOST']
-port = os.environ['REDIS_PORT']
+
 
 router = APIRouter()
 agent=ChatbotAgent()
 graph = agent.chatbot_graph() 
-redis_client = Redis(host=host, port=port, db=0, decode_responses=True, username="default",password=password) 
 
 @router.get("/analytics-chatbot")
 def get_analytics(msg:str):
@@ -45,6 +44,5 @@ def get_analytics(msg:str):
         }
 
     # Save to Redis with TTL (e.g., 1 hour)
-    redis_client.set(cache_key, json.dumps(response_to_cache), ex=60)  # ex=seconds
-
+    redis_client.set(cache_key, json.dumps(response_to_cache), ex=1)  # ex=seconds
     return response_to_cache

app.py

@@ -1,5 +1,5 @@
 from fastapi import FastAPI, Request
-from logger_config import setup_loggers
+from config.logger_config import setup_loggers
 import logging
 from api.routers import orchestration, context_analysis, ideation , brainstorm , generate_final_story , generate_image, show_analytics, analytics_chatbot
 

config/logger_config.py

@@ -0,0 +1,58 @@
+# logger_config.py
+import logging
+import os
+
+class RelativePathFormatter(logging.Formatter):
+    def format(self, record):
+        record.relpath = os.path.relpath(record.pathname, os.getcwd())
+        return super().format(record)
+    
+def setup_loggers():
+    os.makedirs("logs", exist_ok=True)
+
+    # === Format ===
+    # formatter = logging.Formatter("%(asctime)s | %(levelname)s | %(name)s | %(message)s")
+    formatter = RelativePathFormatter("%(asctime)s | %(levelname)s | %(name)s | %(relpath)s:%(lineno)d | %(message)s")
+
+    # === App Logger ===
+    app_logger = logging.getLogger("app_logger")
+    app_handler = logging.FileHandler("logs/app.log")
+    app_handler.setLevel(logging.INFO)
+    app_handler.setFormatter(formatter)
+    app_logger.setLevel(logging.INFO)
+    app_logger.addHandler(app_handler)
+
+    # === Error Logger ===
+    error_logger = logging.getLogger("error_logger")
+    error_handler = logging.FileHandler("logs/errors.log")
+    error_handler.setLevel(logging.ERROR)
+    error_handler.setFormatter(formatter)
+    error_logger.setLevel(logging.ERROR)
+    error_logger.addHandler(error_handler)
+
+    # === Access Logger === (optional for request logs)
+    access_logger = logging.getLogger("access_logger")
+    access_handler = logging.FileHandler("logs/access.log")
+    access_handler.setLevel(logging.INFO)
+    access_handler.setFormatter(formatter)
+    access_logger.setLevel(logging.INFO)
+    access_logger.addHandler(access_handler)
+
+
+    # === Warning Logger ===
+    warning_logger = logging.getLogger("warning_logger")
+    warning_handler = logging.FileHandler("logs/warnings.log")
+    warning_handler.setLevel(logging.WARNING)  # WARNING, ERROR, CRITICAL
+    warning_handler.setFormatter(formatter)
+    warning_logger.setLevel(logging.WARNING)
+    warning_logger.addHandler(warning_handler)
+
+
+    # Optional: also log to console
+    console_handler = logging.StreamHandler()
+    console_handler.setFormatter(formatter)
+    app_logger.addHandler(console_handler)
+    error_logger.addHandler(console_handler)
+    access_logger.addHandler(console_handler)
+    warning_logger.addHandler(console_handler)
+

config/redis_config.py

@@ -0,0 +1,17 @@
+from redis import Redis
+import os
+
+# Load Redis credentials from environment
+password = os.environ['REDIS_PASSWORD']
+host = os.environ['REDIS_HOST']
+port = os.environ.get('REDIS_PORT', 6379)  # default fallback
+
+# Initialize Redis client
+redis_client = Redis(
+    host=host,
+    port=port,
+    db=0,
+    decode_responses=True,
+    username="default",
+    password=password
+)

logger_config.py

@@ -1,58 +1,58 @@
-# logger_config.py
-import logging
-import os
-
-class RelativePathFormatter(logging.Formatter):
-    def format(self, record):
-        record.relpath = os.path.relpath(record.pathname, os.getcwd())
-        return super().format(record)
+# # logger_config.py
+# import logging
+# import os
+
+# class RelativePathFormatter(logging.Formatter):
+#     def format(self, record):
+#         record.relpath = os.path.relpath(record.pathname, os.getcwd())
+#         return super().format(record)
     
-def setup_loggers():
-    os.makedirs("logs", exist_ok=True)
-
-    # === Format ===
-    # formatter = logging.Formatter("%(asctime)s | %(levelname)s | %(name)s | %(message)s")
-    formatter = RelativePathFormatter("%(asctime)s | %(levelname)s | %(name)s | %(relpath)s:%(lineno)d | %(message)s")
-
-    # === App Logger ===
-    app_logger = logging.getLogger("app_logger")
-    app_handler = logging.FileHandler("logs/app.log")
-    app_handler.setLevel(logging.INFO)
-    app_handler.setFormatter(formatter)
-    app_logger.setLevel(logging.INFO)
-    app_logger.addHandler(app_handler)
-
-    # === Error Logger ===
-    error_logger = logging.getLogger("error_logger")
-    error_handler = logging.FileHandler("logs/errors.log")
-    error_handler.setLevel(logging.ERROR)
-    error_handler.setFormatter(formatter)
-    error_logger.setLevel(logging.ERROR)
-    error_logger.addHandler(error_handler)
-
-    # === Access Logger === (optional for request logs)
-    access_logger = logging.getLogger("access_logger")
-    access_handler = logging.FileHandler("logs/access.log")
-    access_handler.setLevel(logging.INFO)
-    access_handler.setFormatter(formatter)
-    access_logger.setLevel(logging.INFO)
-    access_logger.addHandler(access_handler)
-
-
-    # === Warning Logger ===
-    warning_logger = logging.getLogger("warning_logger")
-    warning_handler = logging.FileHandler("logs/warnings.log")
-    warning_handler.setLevel(logging.WARNING)  # WARNING, ERROR, CRITICAL
-    warning_handler.setFormatter(formatter)
-    warning_logger.setLevel(logging.WARNING)
-    warning_logger.addHandler(warning_handler)
-
-
-    # Optional: also log to console
-    console_handler = logging.StreamHandler()
-    console_handler.setFormatter(formatter)
-    app_logger.addHandler(console_handler)
-    error_logger.addHandler(console_handler)
-    access_logger.addHandler(console_handler)
-    warning_logger.addHandler(console_handler)
+# def setup_loggers():
+#     os.makedirs("logs", exist_ok=True)
+
+#     # === Format ===
+#     # formatter = logging.Formatter("%(asctime)s | %(levelname)s | %(name)s | %(message)s")
+#     formatter = RelativePathFormatter("%(asctime)s | %(levelname)s | %(name)s | %(relpath)s:%(lineno)d | %(message)s")
+
+#     # === App Logger ===
+#     app_logger = logging.getLogger("app_logger")
+#     app_handler = logging.FileHandler("logs/app.log")
+#     app_handler.setLevel(logging.INFO)
+#     app_handler.setFormatter(formatter)
+#     app_logger.setLevel(logging.INFO)
+#     app_logger.addHandler(app_handler)
+
+#     # === Error Logger ===
+#     error_logger = logging.getLogger("error_logger")
+#     error_handler = logging.FileHandler("logs/errors.log")
+#     error_handler.setLevel(logging.ERROR)
+#     error_handler.setFormatter(formatter)
+#     error_logger.setLevel(logging.ERROR)
+#     error_logger.addHandler(error_handler)
+
+#     # === Access Logger === (optional for request logs)
+#     access_logger = logging.getLogger("access_logger")
+#     access_handler = logging.FileHandler("logs/access.log")
+#     access_handler.setLevel(logging.INFO)
+#     access_handler.setFormatter(formatter)
+#     access_logger.setLevel(logging.INFO)
+#     access_logger.addHandler(access_handler)
+
+
+#     # === Warning Logger ===
+#     warning_logger = logging.getLogger("warning_logger")
+#     warning_handler = logging.FileHandler("logs/warnings.log")
+#     warning_handler.setLevel(logging.WARNING)  # WARNING, ERROR, CRITICAL
+#     warning_handler.setFormatter(formatter)
+#     warning_logger.setLevel(logging.WARNING)
+#     warning_logger.addHandler(warning_handler)
+
+
+#     # Optional: also log to console
+#     console_handler = logging.StreamHandler()
+#     console_handler.setFormatter(formatter)
+#     app_logger.addHandler(console_handler)
+#     error_logger.addHandler(console_handler)
+#     access_logger.addHandler(console_handler)
+#     warning_logger.addHandler(console_handler)
 

logs/access.log

@@ -1537,3 +1537,57 @@
 2025-10-26 15:34:26,248 | INFO | access_logger | app.py:20 | Response status: 200
 2025-10-26 15:41:29,547 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=I%20want%20sentiment%20distribution%20of%20divya%20dhakal
 2025-10-26 15:41:32,818 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 12:15:41,325 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/
+2025-10-27 12:15:41,326 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 12:15:41,805 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/favicon.ico
+2025-10-27 12:15:41,807 | INFO | access_logger | app.py:20 | Response status: 404
+2025-10-27 12:15:44,388 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/docs
+2025-10-27 12:15:44,389 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 12:15:44,535 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-10-27 12:15:44,543 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 15:05:10,775 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=is%20the%20analytics%20of%20divya%20dhakal%20good%20or%20bad%3F
+2025-10-27 15:05:27,085 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 15:07:29,247 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=is%20the%20analytics%20of%20divya%20dhakal%20good%20or%20bad%3F
+2025-10-27 15:07:31,099 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 15:08:27,971 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=is%20the%20analytics%20of%20divya%20dhakal%20good%20or%20bad%3F
+2025-10-27 15:08:29,879 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 15:09:01,555 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=is%20the%20overall%20analytics%20of%20divya%20dhakal%20good%20or%20bad%3F
+2025-10-27 15:09:08,484 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 15:10:29,900 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=Which%20influencer%20is%20more%20close%20to%20the%20audience%3F
+2025-10-27 15:10:35,983 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 15:17:11,285 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=is%20the%20analytics%20of%20divya%20dhakal%20good%20or%20bad%3F
+2025-10-27 15:17:11,613 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 15:17:49,986 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=which%20influencer%20is%20more%20close%20to%20the%20audience%3F
+2025-10-27 15:18:00,905 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-27 15:39:42,259 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=which%20influencer%20is%20more%20close%20to%20the%20audience%3F
+2025-10-27 15:39:56,557 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-28 13:44:52,420 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/
+2025-10-28 13:44:52,420 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-28 13:44:52,918 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/favicon.ico
+2025-10-28 13:44:52,919 | INFO | access_logger | app.py:20 | Response status: 404
+2025-10-28 13:44:55,371 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/docs
+2025-10-28 13:44:55,372 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-28 13:44:55,491 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-10-28 13:44:55,497 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-28 13:45:19,086 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=Which%20influencer%20has%20got%20more%20audience%3F
+2025-10-28 13:45:52,641 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-28 13:54:45,938 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=Which%20influencer%20has%20got%20more%20audience%3F
+2025-10-28 13:54:54,946 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-28 14:04:53,103 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=Which%20influencer%20has%20got%20more%20audience%3F
+2025-10-28 14:05:07,615 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-28 14:06:20,111 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=Which%20influencer%20has%20got%20more%20audience%3F
+2025-10-28 14:06:29,195 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-28 14:11:15,115 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=Which%20influencer%20is%20good%20overall%3F
+2025-10-28 14:11:27,010 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-28 14:15:32,566 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=I%20want%20to%20compare%20the%20analytics%20of%20muna%20and%20divya
+2025-10-28 15:04:15,698 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=I%20want%20to%20compare%20the%20analytics%20of%20muna%20and%20divya
+2025-10-28 15:04:29,863 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-29 11:44:41,699 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/
+2025-10-29 11:44:41,700 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-29 11:44:42,220 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/favicon.ico
+2025-10-29 11:44:42,221 | INFO | access_logger | app.py:20 | Response status: 404
+2025-10-29 11:44:44,605 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/docs
+2025-10-29 11:44:44,606 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-29 11:44:44,718 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-10-29 11:44:44,726 | INFO | access_logger | app.py:20 | Response status: 200
+2025-10-29 11:46:57,249 | INFO | access_logger | app.py:18 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=which%20influencer%20has%20the%20largest%20audeince%3F

src/genai/analytics_chatbot/agent.py

@@ -1,7 +1,7 @@
 from langgraph.graph import StateGraph, START, END
 from langgraph.checkpoint.memory import MemorySaver
 from .utils.state import State
-from .utils.nodes import FetchDataNode , FetchLastMessage , RetrievePossibleEndpoints , FetchParametersNode , RetrieveExactEndpoint, BackupRetrievalNode, RoutingNode
+from .utils.nodes import FetchDataNode , FetchLastMessage , RetrievePossibleEndpoints , FetchParametersNode , RetrieveExactEndpoint, BackupRetrievalNode, BackupRoutingNode, QueryCheckNode
 
 fetch_last_message_node = FetchLastMessage()
 retrieve_api_endpoints_node = RetrievePossibleEndpoints()
@@ -9,6 +9,7 @@ retrieve_exact_endpoint_node = RetrieveExactEndpoint()
 fetch_parameters_node = FetchParametersNode()
 fetch_data_node = FetchDataNode()
 backup_response_node = BackupRetrievalNode()
+query_check_node = QueryCheckNode()
 
 class ChatbotAgent:
     def __init__(self):
@@ -19,19 +20,23 @@ class ChatbotAgent:
         graph_builder.add_node("fetch_last_message", fetch_last_message_node.run)
         graph_builder.add_node("retrieve_api_endpoints", retrieve_api_endpoints_node.run)
         graph_builder.add_node("retrieve_exact_endpoint", retrieve_exact_endpoint_node.run)
+        graph_builder.add_node("check_query_type", query_check_node.run)
+
         graph_builder.add_node("fetch_parameters", fetch_parameters_node.run)
         graph_builder.add_node("fetch_data", fetch_data_node.run)
         graph_builder.add_node("backup_response", backup_response_node.run)
+
         
         graph_builder.add_edge(START, "fetch_last_message")
         graph_builder.add_edge("fetch_last_message", 'retrieve_api_endpoints')
         graph_builder.add_edge("retrieve_api_endpoints", 'retrieve_exact_endpoint')
-        graph_builder.add_edge("retrieve_exact_endpoint", 'fetch_parameters')
+        graph_builder.add_edge("retrieve_exact_endpoint", 'check_query_type')
+        graph_builder.add_edge("check_query_type", 'fetch_parameters')
         graph_builder.add_edge("fetch_parameters", 'fetch_data')
         graph_builder.add_edge("fetch_data", END)
         graph_builder.add_edge("backup_response", END)
 
-        graph_builder.add_conditional_edges("fetch_parameters", RoutingNode().run,{'execute_backup':'backup_response', 'go_on':"fetch_data"})
-        graph_builder.add_conditional_edges("fetch_data", RoutingNode().run,{'execute_backup':'backup_response', 'go_on':END})
+        graph_builder.add_conditional_edges("fetch_parameters", BackupRoutingNode().run,{'execute_backup':'backup_response', 'go_on':"fetch_data"})
+        graph_builder.add_conditional_edges("fetch_data", BackupRoutingNode().run,{'execute_backup':'backup_response', 'go_on':END})
 
         return graph_builder.compile(checkpointer=self.memory)

src/genai/analytics_chatbot/utils/nodes.py

@@ -3,7 +3,7 @@ from langchain_core.messages import SystemMessage , HumanMessage , FunctionMessa
 from .state import State
 from .tools import RetrieverBackup
 from .schemas import ResponseFormatter , CompareBodyFormatter, ParameterFormatter, EndpointFormatter
-from .prompts import chatbot_prompt , get_body_prompt , fetch_last_message_prompt , fetch_parameters_prompt, fetch_endpoint_prompt, backup_retrieval_prompt
+from .prompts import query_check_prompt , get_body_prompt , fetch_last_message_prompt , fetch_parameters_prompt, fetch_endpoint_prompt, backup_retrieval_prompt
 from .utils import generate_api_knowledge , process_query, get_endpoint_info
 from src.genai.utils.models_loader import llm_gpt
 import numpy as np
@@ -37,7 +37,7 @@ class RetrievePossibleEndpoints:
     def run(self,state:State):
         print('Gone to retrieve possible endpoints')
         query_embedding = np.array(embedding_model.embed_query(state['latest_message'])).reshape(1, -1).astype('float32')
-        distances, indices = self.index.search(query_embedding, 3)
+        distances, indices = self.index.search(query_embedding, 5)
         for idx in indices[0]:
             row = self.df.iloc[idx]
             print('Endpoint:',row['endpoint'])
@@ -69,7 +69,24 @@ class RetrieveExactEndpoint:
             "needed_parameters": endpoint_info["parameters"]
         }
         
-     
+class QueryCheckNode:
+    def __init__(self):
+        self.llm = llm_gpt
+
+    def run(self, state:State):
+        try:
+            print('Entered to query checking')
+            messages = [SystemMessage(content=query_check_prompt),
+            HumanMessage(content=f'''The user query is: {state['latest_message']}''')]
+            result = self.llm.invoke(messages)
+            print(result.content)
+            return{'query_type': result.content}
+        
+        except Exception as e:
+            print('Error occoured:', e)
+            return {'error_message': str(e)}
+
+
 class FetchParametersNode:
     def __init__(self):
         self.llm = llm_gpt
@@ -78,25 +95,25 @@ class FetchParametersNode:
         try:
             print('Entered to fetch parameters')
             print(state['method'])
-            if state['method'] == 'GET':
-                print('Condition satisfied')
+
+            if state['method']=='GET':
                 template = fetch_parameters_prompt
-                messages=[SystemMessage(content=template),
-                        HumanMessage(content=f'''The query is: {state['latest_message']}\n. The needed parameters: {str(state['needed_parameters'])}''')
-                        ]
+                messages=[SystemMessage(content=template),HumanMessage(content=f'''The query is: {state['latest_message']}\n. The needed parameters: {str(state['needed_parameters'])}''')]
                 print('messages:', messages)
                 result = self.llm.with_structured_output(ParameterFormatter, method='function_calling').invoke(messages)
-                # parameters_values={key: process_query(value) for key, value in result.parameters_values.items()}
                 parameters_values = {k: (process_query(v) if isinstance(v, str) else v) for k, v in result.parameters_values.items()}
-
-                print('The parameter values:', parameters_values)
-                return {
-                    'parameters_values':parameters_values
-                }
-            else:
-                return{
-                    'parameters_values': {}
-                }
+                            
+                if 'single_influencer_query' in state['query_type']:
+                    print('The parameter values:', parameters_values)
+                    return {
+                        'parameters_values':parameters_values
+                    }
+                elif 'aggregate_query' in state['query_type']:
+                    parameters_values['influencer_username'] = ['divyadhakal_','munachiya','mydarlingfood','_its.me.muskan_']
+                    print('The parameter values:', parameters_values)
+                    return{
+                        'parameters_values': parameters_values
+                    }
         except Exception as e:
             print('Error occoured:', e)
             return {'error_message': str(e)}
@@ -116,9 +133,8 @@ class FetchDataNode:
         try:
             print('Entered to fetch data')
             url = f'''{self.base_url}{state['endpoint']}'''
-            if state['method'] == 'GET':
-                response = requests.get(url, params=state['parameters_values'],headers=self.headers)
-            elif state['endpoint'] == '/api/v1/compare/':
+
+            if state['endpoint'] == '/api/v1/compare/':
                 print('Condition satisfied')
                 messages = [SystemMessage(content=get_body_prompt()), 
                 HumanMessage(content=str(state['messages']))]
@@ -136,8 +152,29 @@ class FetchDataNode:
                 }
 
                 response = requests.post(url, json=payload, headers=headers)
+                return {'response': response.json()}
+            
+            elif 'single_influencer_query' in  state['query_type']:
+                response = requests.get(url, params=state['parameters_values'],headers=self.headers)
+                return {'response':response.json()}
+            
+            elif 'aggregate_query' in state['query_type']:
+                print('Entered to aggregrated query execution')
+                print(state['parameters_values'])
+                params = state["parameters_values"]
+                if "influencer_username" in params and isinstance(params["influencer_username"], list):
+                    results = {}
+
+                    # Iterate through each influencer username
+                    for username in params["influencer_username"]:
+                        current_params = params.copy()
+                        current_params["influencer_username"] = username
+
+                        response = requests.get(url, params=current_params, headers=self.headers)
+                        results[username] = response.json()  # Store influencer-wise response
+
+                    return {"response": results}
 
-            return {'response':response.json()}
         except Exception as e:
             print('Error occoured:', e)
             return {'error_message': str(e)}
@@ -151,7 +188,9 @@ class BackupRetrievalNode:
         retrieval=RetrieverBackup().retrieve(state['latest_message'])
         return {'backup_data': retrieval}
 
-class RoutingNode:
+
+
+class BackupRoutingNode:
     def __init__(self):
         pass
     

src/genai/analytics_chatbot/utils/prompts.py

@@ -54,15 +54,16 @@ Return the result strictly in this JSON format:
   "names": ["<influencer_1>", "<influencer_2>", ...],
   "frequency": "<frequency_value>"
 }
+If the frequency is not provided to you, use 'weekly' as default.
 
 Example:
-If the query is :"I want to compare the analytics of divyadhakal_ and munachiya in weekly basis", then 
-
-Then the expected output is:
+If the query is :"I want to compare the analytics of divyadhakal_ and munachiya in monthly basis", then 
+output: 
 {
   "names": ["divyadhakal_", "munachiya"],
-  "frequency": "weekly"
+  "frequency": "monthly"
 }
+
 '''
 
 fetch_last_message_prompt = '''
@@ -110,4 +111,14 @@ endpoint: /api/v1/analytics/engagement
 backup_retrieval_prompt = '''
 You are provided with the retrieved data as a function message and the user query.
 Respond to the user query only through the context of retrieved data. Don't give hallucinated responses.
+'''
+
+query_check_prompt = '''
+You are an intent classification assistant.  
+Given a user query about influencer analytics, classify it as one of the following types:
+
+1. single_influencer_query — if the query refers to one specific influencer (e.g., "Show Muna's engagement rate"). Simply if the query includes the name of influencer.
+2. aggregate_query — if the query involves comparing multiple influencers, rankings, or overall statistics (e.g., "Who has the highest engagement?").  
+
+Return only one label: "single_influencer_query" or "aggregate_query".
 '''

src/genai/analytics_chatbot/utils/state.py

@@ -12,4 +12,5 @@ class State(TypedDict):
     error_message:str
     latest_message:str
     parameters_values:str
-    backup_data:str
+    backup_data:str
+    query_type: str

src/genai/analytics_chatbot/utils/tools.py

@@ -35,34 +35,6 @@ class RetrieverBackup:
         else:
             return self.df
 
-
-    def retrieve_old(self, query):
-        query_embedding = np.array(embedding_model.embed_query(str(query))).reshape(1, -1).astype('float32')
-        print('Embeddings Generated')
-        faiss.normalize_L2(query_embedding)
-        print('Query embedded')
-        filtered_df = self._filter_dataset(query)
-        distances, indices = self.index.search(query_embedding, len(filtered_df))
-        similarity_threshold = 0.35
-        selected = [(idx, sim) for idx, sim in zip(indices[0], distances[0]) if sim >= similarity_threshold]
-        if not selected:
-            return "No influencers found."
-
-        outer_list = []
-        for rank, (idx, sim) in enumerate(selected, 1):
-            row = filtered_df.iloc[idx]
-            inner_list = [
-                f"[{rank}]. The influencer name is: **{row['username']}** — Likes: **{row['likesCount']}**, Comments: **{row['commentCount']}**",
-                f"The branding or promotion done is:\n{row['visible_texts_or_brandings']}",
-                f"The details of product or service is:\n{row['product_or_service_details']}"
-            ]
-            outer_list.append(inner_list)
-
-        cleaned_response = clean_text(str(outer_list))
-        print('response cleaned')
-        tokens = encoding_model.encode(cleaned_response)[:500]
-        print('tokens got')
-        return encoding_model.decode(tokens)
     
     def retrieve(self, query):
         query_embedding = np.array(embedding_model.embed_query(str(query))).reshape(1, -1).astype('float32')
@@ -70,7 +42,7 @@ class RetrieverBackup:
 
         # Search on full dataset (index is built on full df)
         distances, indices = self.index.search(query_embedding, len(self.df))
-        similarity_threshold = 0.35
+        similarity_threshold = 0.1
 
         # Prepare matched usernames
         usernames = self.df["username"].dropna().unique()

src/genai/analytics_chatbot/utils/utils.py

@@ -103,3 +103,6 @@ def process_query(user_query: str) -> str:
 
 
 
+
+
+

src/genai/utils/models_loader.py

@@ -24,6 +24,7 @@ llm_gpt_small = ChatOpenAI(model="gpt-3.5-turbo",temperature=0.3)
 llm_gpt = ChatOpenAI(model="gpt-3.5-turbo",temperature=0.3)
 llm_gpt_high = ChatOpenAI(model="gpt-5-nano",temperature=0.5)
 encoding_model = tiktoken.encoding_for_model('gpt-4o-mini')
+# encoding_model = 'encoding_model'
 
 
 captioning_model = "meta-llama/llama-4-scout-17b-16e-instruct"