update

src/interface.jl

@@ -320,21 +320,57 @@ function reflector()
   """
   You are an advanced reasoning agent that can improve based on self refection. 
   You will be given the information related to the previous help you've done for a user.
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-  You were unsuccessful in helping the user either because you guessed the wrong answer with Finish[<answer>], or you used up your set number of reasoning steps.
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+  You were unsuccessful in helping the user either because you guessed the wrong answer with Finish[answer], or you used up your set number of reasoning steps.
   In a few sentences, Diagnose a possible reason for failure and devise a new, concise, high level plan that aims to mitigate the same failure. 
   Use complete sentences.  
   
   Here are some examples:
   Previous Trial:
+  {
+    "question": "Hello, I would like a get a bottle of wine", 
+    "thought_1": "A customer wants to buy a bottle of wine. Before making a recommendation, I need to know more about their preferences.", 
+    "action_1": {"name": "chatbox", "input": "What is the occasion for which you're buying this wine?"},
+    "observation_1": "We are holding a wedding party",
+
+    "thought_2": "A wedding party, that's a great occasion! The customer might be looking for a celebratory drink. Let me ask some more questions to narrow down the options.",
+    "action_2": {"name": "chatbox", "input": "What type of food will you be serving at the wedding?"},
+    "observation_2": "It will be Thai dishes.", 
+
+    "thought_3": "With Thai food, I should recommend a wine that complements its spicy and savory flavors. And since it's a celebratory occasion, the customer might prefer a full-bodied wine.",
+    "action_3": {"name": "chatbox", "input": "What is your budget for this bottle of wine?"},
+    "observation_3": "I would spend up to 50 bucks.",
+
+    "thought_4": "Now that I have some more information, it's time to narrow down the options.", 
+    "action_4": {"name": "winestock", "input": "red wine with full body, pairs well with spicy food, budget \$50"},
+    "observation_4": "I found the following wines in our stock: \n{\n  1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", 
+
+    "thought_5": "Now that I have a list of potential wines, I need to know more about the customer's taste preferences.",
+    "action_5": {"name": "chatbox", "input": "What type of wine characteristics are you looking for? (e.g. t.e.g. tannin level, sweetness, intensity, acidity)"},
+    "observation_5": "I like full-bodied Red wine with low tannin.", 
+
+    "thought_6": "Now that I have more information about the customer's preferences, it's time to make a recommendation.",
+    "action_6": {"name": "recommendbox", "input": "El Enemigo Cabernet Franc 2019"},
+    "observation_6": "I don't like the one you recommend. I want dry wine."
+  }
+  Reflection: I searched one of the prime ministers involved in the signing, then attemted to answer right away. I should have searched each of the prime ministers, then looked up 'death' on each of their pages in order to get more information before answering.
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+
   Question: The Rome Protocols were signed by three Prime Ministers one of which was assassinated as part of what?
   Thought 1: I need to search Rome Protocols, find the three Prime Ministers, then find what they were assassinated as part of.
   Action 1: Search[Rome Protocols]
@@ -386,6 +422,52 @@ function reflector()
   Previous trial:
   {trajectory}Reflection:
   """
+
+  # apply LLM specific instruct format
+  externalService = a.config[:externalservice][:text2textinstruct]
+  llminfo = externalService[:llminfo]
+  prompt = 
+  if llminfo[:name] == "llama3instruct"
+    formatLLMtext_llama3instruct("system", _prompt)
+  else
+    error("llm model name is not defied yet $(@__LINE__)")
+  end
+
+  msgMeta = GeneralUtils.generate_msgMeta(
+    a.config[:externalservice][:text2textinstruct][:mqtttopic],
+    senderName= "progressValueEstimator",
+    senderId= a.id,
+    receiverName= "text2textinstruct",
+    mqttBroker= a.config[:mqttServerInfo][:broker],
+    mqttBrokerPort= a.config[:mqttServerInfo][:port],
+  )
+
+  outgoingMsg = Dict(
+    :msgMeta=> msgMeta,
+    :payload=> Dict(
+      :text=> prompt,
+      :kwargs=> Dict(
+        :max_tokens=> 512,
+        :stop=> ["<|eot_id|>"],
+        )
+      )
+    )
+
+  _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
+  _thoughtJsonStr = _response[:response][:text]
+
+  # expectedJsonExample =
+  # """
+  # Here is an expected JSON format:
+  # {"evaluation": "...", "score": "..."}
+  # """
+  # resultJsonStr = jsoncorrection(a, _thoughtJsonStr, expectedJsonExample)
+  # resultDict = copy(JSON3.read(resultJsonStr)) 
+
+  # return resultDict[:evaluation], resultDict[:score]
+
+
+  return _thoughtJsonStr
 end