next commit

.gitignore

@@ -2,7 +2,8 @@
 myenv
 *.pyc
 __pycache__/
-logs
+logs/
+research/
 delete_pycache.py
 docker_file_for_actions.txt
 vercel.json

api/main.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 .routers import orchestration, context_analysis, ideation , human_idea_refining , brainstorm , generate_final_story , generate_image, show_analytics, analytics_chatbot
 

api/routers/analytics_chatbot.py

@@ -26,38 +26,69 @@ agent=ChatbotAgent()
 graph = agent.chatbot_graph() 
 
 
+# @router.post("/analytics-chatbot")
+# def get_analytics(request: AnalyticsChatRequest):
+#     user_query = process_query(request.msg)
+#     print('Processed user query:', user_query)
+    
+#     cache_key = f"analytics:{hashlib.md5(user_query.encode()).hexdigest()}"
+#     cached_response = redis_client.get(cache_key)
+#     print('cached-response:', cached_response)
+
+#     if cached_response:
+#         response_to_cache = json.loads(cached_response)
+#     else:
+#         response_to_cache = {}
+
+#     if not response_to_cache.get('response') or not response_to_cache.get('endpoint'):
+#         config = {"configurable": {"thread_id": "analytics-chatbot-thread"},
+#                   "run_name": "analytics-chatbot"}
+#         result = graph.invoke({'messages': user_query}, config=config)
+
+#         if result.get('backup_data') is not None:
+#             response_to_cache['backup_response'] = result['backup_data']
+#         else:
+#             response_to_cache['response'] = result['response']
+#             response_to_cache['endpoint'] = result['endpoint']
+
+#     if request.image_base64 and not response_to_cache.get('description'):
+#         description = generate_analytics_description(user_query, request.image_base64)
+#         if description is not None:
+#             response_to_cache['description'] = description
+
+#     redis_client.set(cache_key, json.dumps(response_to_cache), ex=3000)
+#     return response_to_cache
+
 @router.post("/analytics-chatbot")
 def get_analytics(request: AnalyticsChatRequest):
     user_query = process_query(request.msg)
-    print('Processed user query:', user_query)
-    
-    cache_key = f"analytics:{hashlib.md5(user_query.encode()).hexdigest()}"
-    cached_response = redis_client.get(cache_key)
-    print('cached-response:', cached_response)
+    print("Processed user query:", user_query)
+
+    response_to_return = {}
+
+    # Always invoke the graph (no caching)
+    config = {
+        "configurable": {"thread_id": "analytics-chatbot-thread"},
+        "run_name": "analytics-chatbot"
+    }
+
+    result = graph.invoke({'messages': user_query}, config=config)
 
-    if cached_response:
-        response_to_cache = json.loads(cached_response)
+    # Handle primary vs backup response
+    if result.get('backup_data') is not None:
+        response_to_return['backup_response'] = result['backup_data']
     else:
-        response_to_cache = {}
-
-    if not response_to_cache.get('response') or not response_to_cache.get('endpoint'):
-        config = {"configurable": {"thread_id": "analytics-chatbot-thread"},
-                  "run_name": "analytics-chatbot"}
-        result = graph.invoke({'messages': user_query}, config=config)
-
-        if result.get('backup_data') is not None:
-            response_to_cache['backup_response'] = result['backup_data']
-        else:
-            response_to_cache['response'] = result['response']
-            response_to_cache['endpoint'] = result['endpoint']
-
-    if request.image_base64 and not response_to_cache.get('description'):
-        description = generate_analytics_description(user_query, request.image_base64)
-        if description is not None:
-            response_to_cache['description'] = description
-
-    redis_client.set(cache_key, json.dumps(response_to_cache), ex=3000)
-    return response_to_cache
+        response_to_return['response'] = result.get('response')
+        # response_to_return['endpoint'] = result.get('endpoint')
+
+    # # Handle image description if image is provided
+    # if request.image_base64:
+    #     description = generate_analytics_description(user_query, request.image_base64)
+    #     if description is not None:
+    #         response_to_return['description'] = description
+
+    return response_to_return
+
 
     
 

api/schemas/analytics_chatbot.py

@@ -2,5 +2,4 @@ from pydantic import BaseModel
 from typing import Optional
 
 class AnalyticsChatRequest(BaseModel):
-    msg: str
-    image_base64:Optional[str] = None
+    msg: str

logs/access.log

@@ -1847,3 +1847,67 @@
 2025-11-18 13:36:20,855 | INFO | access_logger | app.py:20 | Response status: 200
 2025-11-18 13:36:37,986 | INFO | access_logger | app.py:18 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
 2025-11-18 13:36:43,906 | INFO | access_logger | app.py:20 | Response status: 200
+2026-01-27 11:48:27,640 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/
+2026-01-27 11:48:27,641 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-27 11:48:27,650 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/favicon.ico
+2026-01-27 11:48:27,650 | INFO | access_logger | api/main.py:21 | Response status: 404
+2026-01-27 11:48:31,040 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/api/docs/
+2026-01-27 11:48:31,040 | INFO | access_logger | api/main.py:21 | Response status: 404
+2026-01-27 11:48:31,638 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2026-01-27 11:48:31,638 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-27 11:48:32,047 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2026-01-27 11:48:32,055 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-27 11:51:41,870 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2026-01-27 11:51:41,871 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-27 11:51:41,915 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2026-01-27 11:51:41,922 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-27 11:52:25,526 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-27 11:55:51,806 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-27 11:57:12,540 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-27 11:57:18,353 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-27 11:59:38,797 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-27 11:59:43,649 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:36:17,287 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/
+2026-01-28 12:36:17,288 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:36:20,280 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/api/docs/
+2026-01-28 12:36:20,280 | INFO | access_logger | api/main.py:21 | Response status: 404
+2026-01-28 12:36:21,692 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/api/docs/
+2026-01-28 12:36:21,693 | INFO | access_logger | api/main.py:21 | Response status: 404
+2026-01-28 12:36:29,727 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs/
+2026-01-28 12:36:29,728 | INFO | access_logger | api/main.py:21 | Response status: 307
+2026-01-28 12:36:29,959 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2026-01-28 12:36:29,960 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:36:30,354 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2026-01-28 12:36:30,363 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:39:19,755 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-28 12:39:26,554 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:42:13,063 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-28 12:42:18,846 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:43:13,014 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-28 12:43:18,591 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:44:46,720 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-28 12:44:51,546 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:46:30,957 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-28 12:46:38,936 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:47:27,890 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-28 12:47:32,087 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:48:28,043 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/api/docs/
+2026-01-28 12:48:28,044 | INFO | access_logger | api/main.py:21 | Response status: 404
+2026-01-28 12:48:31,941 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2026-01-28 12:48:31,942 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:48:32,004 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2026-01-28 12:48:32,011 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:49:32,758 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-28 12:49:38,290 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:50:13,517 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/api/docs/
+2026-01-28 12:50:13,519 | INFO | access_logger | api/main.py:21 | Response status: 404
+2026-01-28 12:50:16,334 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2026-01-28 12:50:16,334 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:50:16,379 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2026-01-28 12:50:16,380 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 12:50:46,245 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
+2026-01-28 12:50:49,854 | INFO | access_logger | api/main.py:21 | Response status: 200
+2026-01-28 13:03:32,192 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/api/docs/
+2026-01-28 13:03:32,194 | INFO | access_logger | api/main.py:21 | Response status: 404
+2026-01-28 13:03:33,251 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/api/docs/
+2026-01-28 13:03:33,252 | INFO | access_logger | api/main.py:21 | Response status: 404

requirements.txt

@@ -1,6 +1,7 @@
 langgraph==0.6.5
 langsmith==0.4.16
-langchain_groq==0.3.7
+langchain-openai
+langchain-groq
 pydantic==2.11.7
 datasets==4.0.0
 faiss-cpu==1.12.0
@@ -22,6 +23,6 @@ tiktoken==0.11.0
 langchain-anthropic==0.3.19
 pytest==8.4.1
 langchain_google_genai==2.1.9
-mangum
+mangum==0.19.0
 redis==7.0.0
-google-genai
+google-genai==1.50.1

src/genai/analytics_chatbot/utils/endpoint_graphs.json

@@ -0,0 +1,15 @@
+[
+    {
+        "endpoint": "/api/v1/overview/buzz_trend",
+        "graph": "vertical_bar_chart"
+    },
+    {
+        "endpoint": "/api/v1/overview/basic_info",
+        "graph": "kpi_cards"
+    },
+    {
+        "endpoint": "/api/v1/overview/sentiment_trend",
+        "graph": "line_chart"
+    }
+]
+

src/genai/analytics_chatbot/utils/nodes.py

@@ -77,22 +77,7 @@ 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:
@@ -111,17 +96,6 @@ class FetchParametersNode:
                 result = self.llm.with_structured_output(ParameterFormatter, method='function_calling').invoke(messages)
                 parameters_values = {k: (process_query(v) if isinstance(v, str) else v) for k, v in result.parameters_values.items()}
                             
-                # 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
-                #     }
                 print('The parameter values:', parameters_values)
                 return {'parameters_values': parameters_values}
         except Exception as e:
@@ -159,32 +133,17 @@ class FetchDataNode:
                 print('Entered to handler.')
                 handler = self.endpoint_handlers[state['endpoint']]
                 response = handler(state, llm_gpt, url)
-                print('Returned by handler.')
+                print('Returned by handler:', response.json())
+                
                 return {'response':response.json()}
 
 
             elif 'single_influencer_query' in  state['query_type']:
                 response = requests.get(url, params=state['parameters_values'],headers=self.headers)
-                print('Data from api:', response)
+                print('Data from api:', response.json())
                 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
-            #         print('Data from api:', response)
-            #         return {"response": results}
-            
+
 
         except Exception as e:
             print('Error occoured:', e)