updated

agent.py

@@ -4,13 +4,44 @@ from pydantic import Field, BaseModel
 from omegaconf import OmegaConf
 
 from vectara_agentic.agent import Agent
-from vectara_agentic.tools import VectaraToolFactory, ToolsFactory
+from vectara_agentic.tools import VectaraToolFactory
 
 from dotenv import load_dotenv
 load_dotenv(override=True)
 
 initial_prompt = "How can I help you today?"
 
+prompt = """
+[
+  {"role": "system", "content": 
+  "Think step by step, analyze the search results provided, and craft a clear and accurate response to diagnostics and troubleshooting questions from the user."
+  },
+  {"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.
+    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
+    Think carefully step by step, analyze the search results provided, and craft a clear and accurate response to *** $vectaraQuery *** using information and facts in the search results provided. 
+    Give a slight preference to search results that appear earlier in the list.
+    Your goal is to help with customer support and diagnostics questions about hardware (SuperMicro products).
+    Only cite relevant search results in your answer following these specific instructions: $vectaraCitationInstructions
+    If the search results are irrelevant to the query, respond with ***I do not have enough information to answer this question.***. 
+    Respond always in the $vectaraLangName language, and only in that language."}
+]
+"""
+
 def create_assistant_tools(cfg):
 
     class QueryTIProducts(BaseModel):
@@ -33,46 +64,55 @@ def create_assistant_tools(cfg):
         returns a response to a user question about Supermicro servers.
         """,
         tool_args_schema = QueryTIProducts,
-        reranker = "slingshot", rerank_k = 100, rerank_cutoff = 0.5,
-        n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.005,
+        reranker = "slingshot", rerank_k = 100, rerank_cutoff = 0.1,
+        # reranker = "chain", rerank_k = 100,
+        #     rerank_chain = [
+        #         {
+        #             "type": "slingshot",
+        #             "cutoff": 0.2
+        #         },
+        #         {
+        #             "type": "mmr",
+        #             "diversity_bias": 0.1
+        #         },
+        #     ],
+        n_sentences_before = 2, n_sentences_after = 6, lambda_val = 0.02,
         vectara_summarizer = summarizer,
-        summary_num_results = 20,
+        vectara_prompt_text = prompt,
+        summary_num_results = 15,
         include_citations = True,
         verbose = True,
         save_history = True
     )
 
-    search_supermicro = vec_factory.create_search_tool(
-        tool_name = "search_supermicro",
-        tool_description = """
-        Given a user query, 
-        returns a list of Supermicro documents matching the query, along with metadata.
-        """,
-        tool_args_schema = QueryTIProducts,
-        reranker = "slingshot", rerank_k = 100, rerank_cutoff = 0.5,
-    )
+    # search_supermicro = vec_factory.create_search_tool(
+    #     tool_name = "search_supermicro",
+    #     tool_description = """
+    #     Given a user query, 
+    #     returns a list of Supermicro documents matching the query, along with metadata.
+    #     """,
+    #     tool_args_schema = QueryTIProducts,
+    #     reranker = "slingshot", rerank_k = 100, rerank_cutoff = 0.5,
+    # )
 
-    return [ask_supermicro, search_supermicro]
+    return [ask_supermicro] #, search_supermicro]
 
 def initialize_agent(_cfg, agent_progress_callback=None):
-    ti_bot_instructions = """
-    - You are a helpful assistant, with expertise in products from SuperMicro.
+    bot_instructions = """
+    - You are a helpful assistant, with expertise in diagnosing customer issues related to SuperMicro products.
+      You can help diagnose, troubleshoot and understand hardware issues.
     - Always use the 'ask_supermicro' tool to get the information you need to respond to the user query, 
       and never use your internal knoweldge.
-    - When the 'ask_supermicro' tool does not provide an adequare response, 
-      plan a sequence of queries that will help you get the information you need. 
-      Run each query in sequence, and use the results to refine the next query. 
-      Then use all this information to form a response.
-    - If the user does not specify a server type, ask the user to specify it before attempting a response.
+    - WHen using the 'ask_supermicro' tool, always rephrase the query to the tool as a diagnostics question.
+      If the tool does not return relevant information, try to rephrase the query in a different way, and call the tool again.
     """
-    # can try "When filtering by the device_name, if the tool cannot provide an answer or does not have enough information
-    # try calling the tool again with the device_family and include the device_name in the query string itself."
 
     agent = Agent(
         tools=create_assistant_tools(_cfg),
-        topic="Supermicro and its products",
-        custom_instructions=ti_bot_instructions,
+        topic="troubleshooting Supermicro products",
+        custom_instructions=bot_instructions,
         agent_progress_callback=agent_progress_callback,
+        use_structured_planning=False,
     )
     agent.report()
     return agent

requirements.txt

@@ -6,4 +6,4 @@ streamlit_feedback==0.1.3
 uuid==1.30
 langdetect==1.0.9
 langcodes==3.4.0
-vectara-agentic==0.2.1
+vectara-agentic==0.2.5