diff --git a/src/llmfunction.jl b/src/llmfunction.jl index c5640f2..26326e9 100644 --- a/src/llmfunction.jl +++ b/src/llmfunction.jl @@ -829,111 +829,115 @@ function compareState(query, highValueStateList) - The user's attempted actions and their corresponding results - Selected_response: the number of the most accurate and relevant response - Rationale: a brief explanation of why you selected this response + Comparison: a comparison of the results from each attempt + Rationale: a brief explanation of why the selected response is the most accurate and relevant + Selected_response_number: the number the selected response in the list of results + - - Selected_response: ... - - Rationale: ... + Comparison: ... + Rationale: ... + Selected_response_number: ... User's question: "How many German wines do you have?" Attempt 1: - Action: SELECT * FROM wines WHERE country = 'Germany' + Action: SELECT COUNT(*) FROM wines WHERE country = 'Germany' Result: 100 wines Attempt 2: - Action: SELECT * FROM wines WHERE country = 'Germany' AND type = 'Red' + Action: SELECT COUNT(*) FROM wines WHERE country = 'Germany' AND type = 'Red' Result: 50 red wines - Selected_response: 1 - Rationale: The question is about German wines, so the most accurate response is the one that includes all German wines. + Comparison: The second attempt counts only German red wines while the first attempt includes all German wines. + Rationale: The user is asking for the number of German wines without specifying a type, so the most accurate response is the first attempt because it includes all German wines. + Selected_response_number: 1 Let's begin! """ - thoughthistory = "" - for (k, v) in state[:thoughtHistory] - thoughthistory *= "$k: $v\n" - end + # thoughthistory = "" + # for (k, v) in state[:thoughtHistory] + # thoughthistory *= "$k: $v\n" + # end - errornote = "" + # errornote = "" - for attempt in 1:10 - errorFlag = false + # for attempt in 1:10 + # errorFlag = false - usermsg = - """ - Trajectory: $thoughthistory - Error_note: $errornote - """ + # usermsg = + # """ + # Trajectory: $thoughthistory + # Error_note: $errornote + # """ - _prompt = - [ - Dict(:name=> "system", :text=> systemmsg), - Dict(:name=> "user", :text=> usermsg) - ] + # _prompt = + # [ + # Dict(:name=> "system", :text=> systemmsg), + # Dict(:name=> "user", :text=> usermsg) + # ] - # put in model format - prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen") + # # put in model format + # prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen") - header = ["Trajectory_evaluation:", "Answer_evaluation:", "Accepted_as_answer:", "Score:", "Suggestion:"] - dictkey = ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"] + # header = ["Trajectory_evaluation:", "Answer_evaluation:", "Accepted_as_answer:", "Score:", "Suggestion:"] + # dictkey = ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"] - response = text2textInstructLLM(prompt) + # response = text2textInstructLLM(prompt) - # sometime LLM output something like **Comprehension**: which is not expected - response = replace(response, "**"=>"") - response = replace(response, "***"=>"") + # # sometime LLM output something like **Comprehension**: which is not expected + # response = replace(response, "**"=>"") + # response = replace(response, "***"=>"") - # make sure every header is in the response - for i in header - detected = GeneralUtils.detect_keyword(i, response) - if detected === nothing - errornote = "Your previous response didn't provide $i" - errorFlag = true - end - end - if errorFlag - continue # skip to the next iteration - end + # # make sure every header is in the response + # for i in header + # detected = GeneralUtils.detect_keyword(i, response) + # if detected === nothing + # errornote = "Your previous response didn't provide $i" + # errorFlag = true + # end + # end + # if errorFlag + # continue # skip to the next iteration + # end - responsedict = GeneralUtils.textToDict(response, header; - dictKey=dictkey, symbolkey=true) + # responsedict = GeneralUtils.textToDict(response, header; + # dictKey=dictkey, symbolkey=true) - responsedict[:score] = responsedict[:score][1] # some time "6\nThe trajectories are incomplete" is generated but I only need the number. - try - responsedict[:score] = parse(Int, responsedict[:score]) # convert string "5" into integer 5 - catch - continue - end + # responsedict[:score] = responsedict[:score][1] # some time "6\nThe trajectories are incomplete" is generated but I only need the number. + # try + # responsedict[:score] = parse(Int, responsedict[:score]) # convert string "5" into integer 5 + # catch + # continue + # end - accepted_as_answer::AbstractString = responsedict[:accepted_as_answer] + # accepted_as_answer::AbstractString = responsedict[:accepted_as_answer] - if accepted_as_answer ∉ ["Yes", "No"] # [PENDING] add errornote into the prompt - error("generated accepted_as_answer has wrong format") - end + # if accepted_as_answer ∉ ["Yes", "No"] # [PENDING] add errornote into the prompt + # error("generated accepted_as_answer has wrong format") + # end - # add to state here instead to in transition() because the latter causes julia extension crash (a bug in julia extension) - state[:evaluation] = "$(responsedict[:trajectory_evaluation]) $(responsedict[:answer_evaluation])" - state[:evaluationscore] = responsedict[:score] - state[:accepted_as_answer] = responsedict[:accepted_as_answer] - state[:suggestion] = responsedict[:suggestion] + # # add to state here instead to in transition() because the latter causes julia extension crash (a bug in julia extension) + # state[:evaluation] = "$(responsedict[:trajectory_evaluation]) $(responsedict[:answer_evaluation])" + # state[:evaluationscore] = responsedict[:score] + # state[:accepted_as_answer] = responsedict[:accepted_as_answer] + # state[:suggestion] = responsedict[:suggestion] - # mark as terminal state when the answer is achieved - if accepted_as_answer == "Yes" + # # mark as terminal state when the answer is achieved + # if accepted_as_answer == "Yes" - # mark the state as terminal state because the evaluation say so. - state[:isterminal] = true + # # mark the state as terminal state because the evaluation say so. + # state[:isterminal] = true - # evaluation score as reward because different answers hold different value for the user. - state[:reward] = responsedict[:score] - end - println("\n~~~ Evaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())") - pprintln(Dict(responsedict)) + # # evaluation score as reward because different answers hold different value for the user. + # state[:reward] = responsedict[:score] + # end + # println("\n~~~ Evaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())") + # pprintln(Dict(responsedict)) - return responsedict[:score] - end - error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>") + # return responsedict[:score] + # end + # error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>") end diff --git a/system_prompt_template.jl b/system_prompt_template.jl index a3a4153..4112aef 100644 --- a/system_prompt_template.jl +++ b/system_prompt_template.jl @@ -85,12 +85,12 @@ Example: - Manage stakeholder relationships - Drive fundraising and business development - - - User's specific caregiving challenges - - Context and severity of the situation - - Feedback from family caregivers - - Potential solutions based on immediate and long-term impact - + + Challenges: user's specific caregiving challenges + Context: context and severity of the situation + Feedback: comments from family caregivers + Solutions: potential solution based on immediate and long-term impact + - Always prioritize patient safety and well-being - Maintain empathy and understanding in all interactions @@ -117,39 +117,34 @@ Example: Example 1: - User: "My mother has been bedridden for 3 months, and I'm struggling to monitor her condition while working from home. I'm worried I might miss important signs of deterioration." - - Comprehension: The user is facing challenges balancing work-from-home responsibilities with monitoring their bedridden mother's health condition. There's anxiety about potentially missing critical health changes. - - Plan: - 1. Assess current monitoring needs and gaps - 2. Propose smart monitoring system setup - 3. Implement remote alerts and notifications - 4. Provide training on using the system - 5. Set up regular check-ins for system optimization - + Challenges: "My mother needs constant monitoring at night, but I'm exhausted from lack of sleep." + Context: Elderly patient with dementia, requires 24/7 supervision + Feedback: "Need urgent solution for night monitoring" + Solutions: Smart monitoring system with motion sensors and alerts + + Comprehension: The caregiver is experiencing severe sleep deprivation due to nighttime monitoring requirements + Plan: + 1. Assess current monitoring needs + 2. Propose smart monitoring system installation + 3. Set up emergency alert system + 4. Train family on system usage Action_name: CHATBOX - Action_input: Gather specific information about mother's condition, current monitoring methods, and work schedule to tailor the smart monitoring solution effectively. + Action_input: Discuss specific nighttime behaviors and incidents to determine optimal sensor placement and alert thresholds Example 2: - User: "I need to implement a fall detection system for my father who is partially mobile but at high risk of falls. What solutions do you recommend?" - - Comprehension: The user requires a reliable fall detection system for a partially mobile patient with high fall risk. Safety is the primary concern while maintaining the patient's limited mobility. + Challenges: "Managing medication schedules is becoming overwhelming" + Context: Patient on multiple medications with complex timing requirements + Feedback: "Need help with medication management" + Solutions: Automated medication reminder and tracking system + Comprehension: Caregiver struggling with complex medication management tasks Plan: - 1. Review available fall detection technologies - 2. Check compatibility with existing infrastructure - 3. Evaluate cost and implementation requirements - 4. Propose specific solution options - 5. Create implementation timeline - - Action_name: CHECKRESOURCES - Action_input: Query database for available fall detection systems, focusing on: - - Motion sensors - - Wearable devices - - Computer vision systems - - Integration capabilities - - Cost ranges + 1. Review current medication schedule + 2. Implement automated reminder system + 3. Set up medication tracking log + 4. Connect with pharmacy for refill automation + Action_name: IMPLEMENT + Action_input: Deploy medication management module with smart alerts and compliance tracking Let's begin!