Update agent.py

creating the prompt for ProA

agent.py

@@ -5,39 +5,6 @@ from vectara_agentic.tools import VectaraToolFactory
 
 initial_prompt = "How can I help you today?"
 
-prompt_old = """
-[
-  {"role": "system", "content": "You are an AI assistant that forms a detailed and comprehensive answer to a user query based on search results that are provided to you." },
-  {"role": "user", "content": " 
-    [INSTRUCTIONS]
-    If the search results are irrelevant to the question respond with *** I do not have enough information to answer this question.***
-    Search results may include tables in a markdown format. 
-    When answering a question using a table be careful about which rows and columns contain the answer and include all relevant information from the relevant rows and columns that the query is asking about.
-    Do not base your response on information or knowledge that is not in the search results.
-    Make sure your response is answering the query asked. If the query is related to an entity (such as a person or place), make sure you use search results related to that entity.
-    Consider that each search result is a partial segment from a bigger text, and may be incomplete.
-    Your output should always be in a single language - the $vectaraLangName language. Check spelling and grammar for the $vectaraLangName language.
-    Search results for the query *** $vectaraQuery***, are listed below, some are text, some MAY be tables in markdown format.
-    #foreach ($qResult in $vectaraQueryResultsDeduped)
-      [$esc.java($foreach.index + 1)]
-      #if($qResult.hasTable())
-        Table Title: $qResult.getTable().title() || Table Description: $qResult.getTable().description() || Table Data:
-        $qResult.getTable().markdown()
-      #else
-        $qResult.getText()
-      #end
-    #end
-    Generate a comprehensive response to the query *** $vectaraQuery *** using information and facts in the search results provided. 
-    Give a slight preference to search results that appear earlier in the list.
-    Include statistical and numerical evidence to support and contextualize your response.
-    Your response should include all relevant information and values from the search results. Do not omit anything relevant.
-    Prioritize a long, detailed, thorough and comprehensive response over a short one.
-    Cite relevant search results in your answer following these specific instructions: $vectaraCitationInstructions
-    Respond always in the $vectaraLangName language, and only in that language."}
-]
-"""
-
-
 prompt = """
 [
   {"role": "system", "content": "
@@ -130,7 +97,7 @@ def create_assistant_tools(cfg):
     )
 
 def initialize_agent(_cfg, agent_progress_callback=None):
-    menarini_bot_instructions = """
+    proa_capital_bot_instructions = """
     - You are an expert in clinical trial and statistical data analysis with extensive experience in designing, analyzing, and interpreting clinical research data.
     - Your task is to answer user question, using the tools you have available.
     - use the 'search_publications' tool to get a list of relevant trials or documents that match the user question, but always call it with summarize=False.
@@ -158,8 +125,8 @@ def initialize_agent(_cfg, agent_progress_callback=None):
 
     agent = Agent(
         tools=create_assistant_tools(_cfg),
-        topic="Drug trials publications",
-        custom_instructions=menarini_bot_instructions,
+        topic="Market Analysis",
+        custom_instructions=proa_capital_bot_instructions,
         agent_progress_callback=agent_progress_callback,
     )
     agent.report()