Report generator

orchestrator_agent.py

@@ -57,14 +57,34 @@ def create_agent(csv_url: str, api_key: str) -> Agent:
     csv_metadata = get_csv_basic_info(csv_url)
     
     system_prompt = (
-        "You are a data analyst."
-        "You have all the tools you need to answer any question."
-        "If user asking for multiple answers or charts then break the question into multiple proper questions."
-        "Pass csv_url/path with the questions to the tools to generate the answer."
-        "Explain the answer in a friendly way."
-        "**Format images** in Markdown: `![alt_text](direct-image-url)`"
-        f"Your csv url is {csv_url}"
-        f"Your csv metadata is {csv_metadata}"
+        "You are a data analyst assistant with limited tool capabilities. "
+        "Available tools can only handle simple data queries: "
+        "- Count rows/columns\n- Calculate basic stats (avg, sum, min/max)\n"
+        "- Create simple visualizations (pie charts, bar graphs)\n"
+        "- Show column names/types\n\n"
+        
+        "Query Handling Rules:\n"
+        "1. If query is complex, ambiguous, or exceeds tool capabilities:\n"
+        "   - Break into simpler sub-questions\n"
+        "   - Ask for clarification\n"
+        "   - Rephrase to nearest simple query\n"
+        "2. For 'full report' requests:\n"
+        "   - Outline possible analysis steps\n"
+        "   - Ask user to select one component at a time\n\n"
+        
+        "Examples:\n"
+        "- Bad query: 'Show me everything'\n"
+        "  Response: 'I can show row count (10), columns (5: Name, Age...), "
+        "or a pie chart of categories. Which would you like?'\n"
+        "- Bad query: 'Analyze trends'\n"
+        "  Response: 'For trend analysis, I can show monthly averages or "
+        "year-over-year comparisons. Please specify time period and metric.'\n\n"
+        
+        "Current CSV Context:\n"
+        f"- URL: {csv_url}\n"
+        f"- Metadata: {csv_metadata}\n\n"
+        
+        "Always format images as: ![Chart Description](direct-image-url)"
     )
     return Agent(
         model=initialize_model(api_key),

orchestrator_functions.py

@@ -385,35 +385,76 @@ async def csv_chart(csv_url: str, query: str):
     
     
     
+# async def csv_chat(csv_url: str, query: str):
+#     try:
+#         updated_query = f"{query} and Do not show any charts or graphs."
+        
+#         # Process with langchain_chat first
+#         try:
+#             lang_answer = await asyncio.to_thread(
+#                 langchain_csv_chat, csv_url, query, False
+#             )
+#             if not process_answer(lang_answer):
+#                 return {"answer": jsonable_encoder(lang_answer)}
+#             return {"answer": "Sorry, I couldn't find relevant data..."}
+#         except Exception as langchain_error:
+#             print(f"Langchain error, falling back to Groq: {str(langchain_error)}")
+            
+#             # Process with groq_chat if langchain fails
+#             try:
+#                 groq_answer = await asyncio.to_thread(groq_chat, csv_url, updated_query)
+#                 print("groq_answer:", groq_answer)
+                
+#                 if process_answer(groq_answer) == "Empty response received.":
+#                     return {"answer": "Sorry, I couldn't find relevant data..."}
+                
+#                 if process_answer(groq_answer):
+#                     return {"answer": "error"}
+                
+#                 return {"answer": jsonable_encoder(groq_answer)}
+#             except Exception as groq_error:
+#                 print(f"Groq processing error: {str(groq_error)}")
+#                 return {"answer": "error"}
+                
+#     except Exception as e:
+#         print(f"Error processing request: {str(e)}")
+#         return {"answer": "error"}
+
+
+
+
+
+
 async def csv_chat(csv_url: str, query: str):
     try:
         updated_query = f"{query} and Do not show any charts or graphs."
         
-        # Process with langchain_chat first
+        # Process with Groq first
         try:
-            lang_answer = await asyncio.to_thread(
-                langchain_csv_chat, csv_url, query, False
-            )
-            if not process_answer(lang_answer):
-                return {"answer": jsonable_encoder(lang_answer)}
-            return {"answer": "Sorry, I couldn't find relevant data..."}
-        except Exception as langchain_error:
-            print(f"Langchain error, falling back to Groq: {str(langchain_error)}")
+            groq_answer = await asyncio.to_thread(groq_chat, csv_url, updated_query)
+            print("groq_answer:", groq_answer)
+            
+            if process_answer(groq_answer) == "Empty response received.":
+                return {"answer": "Sorry, I couldn't find relevant data..."}
+            
+            if process_answer(groq_answer):
+                raise Exception("Groq response not usable, falling back to LangChain")
+            
+            return {"answer": jsonable_encoder(groq_answer)}
             
-            # Process with groq_chat if langchain fails
+        except Exception as groq_error:
+            print(f"Groq error, falling back to LangChain: {str(groq_error)}")
+            
+            # Process with LangChain if Groq fails
             try:
-                groq_answer = await asyncio.to_thread(groq_chat, csv_url, updated_query)
-                print("groq_answer:", groq_answer)
-                
-                if process_answer(groq_answer) == "Empty response received.":
-                    return {"answer": "Sorry, I couldn't find relevant data..."}
-                
-                if process_answer(groq_answer):
-                    return {"answer": "error"}
-                
-                return {"answer": jsonable_encoder(groq_answer)}
-            except Exception as groq_error:
-                print(f"Groq processing error: {str(groq_error)}")
+                lang_answer = await asyncio.to_thread(
+                    langchain_csv_chat, csv_url, query, False
+                )
+                if not process_answer(lang_answer):
+                    return {"answer": jsonable_encoder(lang_answer)}
+                return {"answer": "Sorry, I couldn't find relevant data..."}
+            except Exception as langchain_error:
+                print(f"LangChain processing error: {str(langchain_error)}")
                 return {"answer": "error"}
                 
     except Exception as e: