Update agent.py

agent.py

@@ -1,63 +1,115 @@
-import pandas as pd
-import requests
 from pydantic import Field, BaseModel
 
-from omegaconf import OmegaConf
-
 from vectara_agentic.agent import Agent
-from vectara_agentic.tools import ToolsFactory, VectaraToolFactory
+from vectara_agentic.tools import VectaraToolFactory
+
+initial_prompt = "How can I help you today?"
+
+prompt = """
+[
+  {"role": "system", "content": "
+      You are an AI assistant that forms a detailed and comprehensive answer to a user question based solely on the search results provided. 
+      You are an expert in market analysis, financial evaluation, and strategic competitor research with extensive experience in evaluating mutual funds, private equity strategies, and overall market trends.
+      When analyzing financial performance and market dynamics, include as many relevant metrics and key performance indicators as possible, such as net asset value (NAV), expense ratios, P/E ratios, revenue growth, and M&A transaction details.
+      Your response should detail company descriptions, competitor activities, M&A activity, exit strategies, and any relevant financial evidence and analysis.
+      If the question is vague or ambiguous, ask for clarification.
+      Your response should incorporate all relevant information and values from the provided search results and should not include any information not present in the search results.
+      Be precise, data-driven, and comprehensive in your analysis."},
+      
+  {"role": "user", "content": "
+    [INSTRUCTIONS]
+    - Generate a highly detailed and comprehensive response to the question *** $vectaraQuery *** using the search results provided.
+    - Your answer should include an in-depth market analysis, a detailed financial evaluation, and an analysis of competitor strategies – including what other Private Equity houses and competitors are currently doing in the space such as recent M&A transactions, exit strategies, and key financial trends.
+    - If the search results do not provide sufficient relevant information to fully answer the query, respond with *** I do not have enough information to answer this question.***
+    - Do not include any information or analysis that is not explicitly supported by the search results.
+    - Ensure that you focus on detailed descriptions including metrics such as revenue growth, NAV, expense ratios, and any statistical financial indicators present.
+    - Follow all instructions in the search results and always prioritize results that appear earlier in the list.
+    - Only cite the relevant search results by following these specific instructions: $vectaraCitationInstructions.
+    - The search results provided may include text segments and tables in markdown format. Consider that each search result might be a partial excerpt from a larger document.
+    - Respond exclusively in the $vectaraLangName language, ensuring correct spelling and grammar for that language.
+    
+    Search results for the question *** $vectaraQuery*** are listed below, including text excerpts and tables:
+    
+    #foreach ($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
+    Respond always in the $vectaraLangName language, and only in that language.
+  "}
+]
+"""
 
 def create_assistant_tools(cfg):
-    class QueryDocsArgs(BaseModel):
-        query: str = Field(..., description="The user query, always in the form of a question", 
-                           examples=["Based on uploaded documents, what are the top four challenges of the Fintech sector in Saudi Arabia? list them in bullet points."])        
+
+    class QueryPublicationsArgs(BaseModel):
+        query: str = Field(..., description="The user query, always in the form of a question?"),
+        name: str = Field(..., description="The name of the memo use for research")
         
     vec_factory = VectaraToolFactory(vectara_api_key=cfg.api_key,
                                      vectara_corpus_key=cfg.corpus_key)
-    summarizer = 'mockingbird-1.0-2024-07-16'
-    ask_docs = vec_factory.create_rag_tool(
-        tool_name = "ask_docs",
+    summarizer = 'vectara-summary-table-md-query-ext-jan-2025-gpt-4o'
+    ask_publications = vec_factory.create_rag_tool(
+        tool_name = "ask_publications",
         tool_description = """
-        Responds to an user question about a particular analysis, based on the documentation provide.
+        Responds to an user question about investment opportunity, focusing on a specific information and data.
         """,
-        tool_args_schema = QueryDocsArgs,
-        reranker = "chain", rerank_k = 100, 
-        rerank_chain = [
-            {
-                "type": "multilingual_reranker_v1",
-#                "cutoff": 0.2
-            },
-            {
-                "type": "mmr",
-                "diversity_bias": 0.2,
-                "limit": 50
-            }
-        ],
-        n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.005,
+        tool_args_schema = QueryPublicationsArgs,
+        reranker = "slingshot", rerank_k = 100, rerank_cutoff = 0.1,
+        n_sentences_before = 1, n_sentences_after = 1, lambda_val = 0.1,
         summary_num_results = 15,
+        max_response_chars = 8192, max_tokens = 4096,
         vectara_summarizer = summarizer,
         include_citations = True,
-        #vectara_prompt_text=prompt,
+        vectara_prompt_text = prompt,
+        save_history = True,
+        verbose = False
+    )
+
+    class SearchPublicationsArgs(BaseModel):
+        query: str = Field(..., description="The user query, always in the form of a question?"),
+    search_publications = vec_factory.create_search_tool(
+        tool_name = "search_publications",
+        tool_description = """
+        Responds with a list of relevant publications that match the user query
+        Use a high value for top_k (3 times what you think is needed) to make sure to get all relevant results.
+        """,
+        tool_args_schema = SearchPublicationsArgs,
+        reranker = "mmr", rerank_k = 100, mmr_diversity_bias = 0.5,
+        n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.3,
         save_history = True,
-        verbose=False
+        verbose = False
     )
-    tools_factory = ToolsFactory()
+
+
     return (
-            tools_factory.standard_tools() +
-            [ask_docs]
+        [ask_publications, search_publications]
     )
 
 def initialize_agent(_cfg, agent_progress_callback=None):
-    stc_bank_bot_instructions = """
-    - Call the the ask_docs tool to retrieve the information to answer the user query.
-    - If the question has an 'Excel' or 'excel' word only fetch for the documents with 'type_file' equals to 'excel'.
-    - Always print the title of the References
+    proa_capital_bot_instructions = """
+    - You are an expert in market analysis, financial evaluation, and strategic competitor research with extensive experience in the mutual fund and private equity sectors.
+    - Your task is to answer user questions regarding market trends, detailed company profiles, competitor strategies, M&A activity, exit scenarios, and comprehensive financial analysis.
+    - Use the 'search_market_data' tool to retrieve up-to-date market trends, competitor performance, and data on recent M&A deals, exits, and overall industry activity. Always request detailed data to ensure accuracy.
+    - Call the 'search_company_data' tool to gather in-depth information on specific mutual funds and private equity houses, including company profiles, financial performance metrics, key management information, and market positioning.
+    - When querying tools, frame your questions clearly with specific requests such as "what are the current market share trends in the mutual fund sector?", "what are the most recent M&A transactions in this space?", or "what are the key financial ratios and performance metrics for the leading funds?" 
+    - If a tool indicates that there is not enough information to answer your query, refine your request by being more explicit and retry up to 10 times to obtain the necessary data.
+    - Your analysis should be data-driven and presented with advanced financial terminology and rigorous evidence. Include metrics like NAV, expense ratios, P/E ratios, and other relevant financial indicators.
+    - Ensure that your responses include detailed company descriptions, competitor comparisons, and strategic insights, highlighting what other Private Equity houses and market competitors are currently doing.
+    - Provide precise, comprehensive, and evidence-based answers that are accessible to an audience familiar with sophisticated financial analysis and market research.
+    - Include sources and citations in your response, directly referencing the data obtained through the tools.
+    - Your final deliverable should be thorough, clear, and actionable for stakeholders seeking insights on mutual fund market dynamics and competitor strategies.
     """
 
+
     agent = Agent(
         tools=create_assistant_tools(_cfg),
-        topic="STC Bank questions",
-        custom_instructions=stc_bank_bot_instructions,
+        topic="Market Analysis",
+        custom_instructions=proa_capital_bot_instructions,
         agent_progress_callback=agent_progress_callback,
     )
     agent.report()