deprioritize duckduckgo

agents.py

@@ -104,7 +104,7 @@ def salesAgent(instruction):
         print("panda> " + output)
     except Exception as e:
         logger.error(e)
-        output = f"Please rephrase and try again for company sales data {e}"
+        output = f"Rephrasing your prompt could get better results {e}"
     return output
 
 def chinookAgent(instruction, model_name):
@@ -115,5 +115,5 @@ def chinookAgent(instruction, model_name):
         print("chinook> " + output)
     except Exception as e:
         logger.error(e)
-        output = "Please rephrase and try again for digital media data"
+        output = "Rephrasing your prompt could get better db results {e}"
     return output

app.py

@@ -68,6 +68,7 @@ if question_text and len(question_text) > 1:
     height = min(2*len(output), 280)
     st.text_area(label="In response ...", 
                  value=output, height=height)
+    ## st.code(output, language="markdown")
 
 ##############################################################################
 

models.py

@@ -72,14 +72,14 @@ from langchain.tools import DuckDuckGoSearchRun, GoogleSearchRun
 from langchain.utilities import GoogleSearchAPIWrapper
 def load_chained_agent(verbose=True, model_name="text-davinci-003"):
     llm = createLLM(model_name)
-    toolkit = [DuckDuckGoSearchRun()]
-    toolkit += load_tools(["serpapi", "open-meteo-api", "news-api", 
+    toolkit = load_tools(["serpapi", "open-meteo-api", "news-api", 
                            "python_repl", "wolfram-alpha"], 
                             llm=llm, 
                             serpapi_api_key=os.getenv('SERPAPI_API_KEY'),
                             news_api_key=os.getenv('NEWS_API_KEY'),
                             tmdb_bearer_token=os.getenv('TMDB_BEARER_TOKEN')
                             )
+    toolkit += [DuckDuckGoSearchRun()]
 
     agent = initialize_agent(toolkit, 
                              llm,