Merge pull request #10 from JuliaConstraints/feature

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README.md

@@ -37,19 +37,14 @@ Finally, we can start playing with the package. Below, an example for translatin
 ```julia
 using ConstraintsTranslator
 
-llm = GoogleLLM("gemini-1.5-pro")
+llm = GoogleLLM("gemini-1.5-pro-latest")
 
 description = """
 We need to determine the shortest possible route for a salesman who must visit a set of cities exactly once and return to the starting city.
 The objective is to minimize the total travel distance while ensuring that each city is visited exactly once.
 
 Example input data:
-1. cities.csv
-city_id,city_name
-1,CityA
-2,CityB
-
-2. distances.csv
+1. distances.csv
 from,to,distance
 CityA,CityB,10
 CityA,CityC,8

src/ConstraintsTranslator.jl

@@ -4,6 +4,7 @@ module ConstraintsTranslator
 import Constraints: USUAL_CONSTRAINTS
 import HTTP
 import InteractiveUtils
+import InteractiveUtils: clipboard
 import JSONSchema
 import JSON3
 import REPL

src/llm.jl

@@ -16,7 +16,7 @@ struct GroqLLM <: OpenAILLM
     model_id::String
     url::String
 
-    function GroqLLM(model_id::String = "llama3-70b-8192", url = GROQ_URL)
+    function GroqLLM(model_id::String = "llama-3.1-70b-versatile", url = GROQ_URL)
         api_key = get(ENV, "GROQ_API_KEY", "")
         if isempty(api_key)
             error("Environment variable GROQ_API_KEY is not set")
@@ -37,7 +37,7 @@ struct GoogleLLM <: AbstractLLM
     model_id::String
     url::String
 
-    function GoogleLLM(model_id::String = "gemini-1.5-flash")
+    function GoogleLLM(model_id::String = "gemini-1.5-flash-latest")
         api_key = get(ENV, "GOOGLE_API_KEY", "")
         if isempty(api_key)
             error("Environment variable GOOGLE_API_KEY is not set")
@@ -192,6 +192,7 @@ function stream_completion(llm::GoogleLLM, prompt::Prompt)
             chunk = String(readavailable(io))
             for line in eachmatch(r"(?<=data: ).*", chunk)
                 if isnothing(line)
+                    print("\n")
                     continue
                 end
                 message = JSON3.read(line.match)

src/translate.jl

@@ -22,6 +22,7 @@ function extract_structure(
     if interactive
         options = [
             "Accept the response",
+            "Copy to clipboard",
             "Edit the response",
             "Try again with a different prompt",
             "Try again with the same prompt",
@@ -33,13 +34,16 @@ function extract_structure(
             if choice == 1
                 break
             elseif choice == 2
+                clipboard(response)
+                println("Response copied to the system's clipboard!")
+            elseif choice == 3
                 response = edit_in_editor(response)
                 println(response)
-            elseif choice == 3
+            elseif choice == 4
                 description = edit_in_editor(description)
                 prompt = format_template(prompt_template; description, constraints)
                 response = stream_completion(model, prompt)
-            elseif choice == 4
+            elseif choice == 5
                 response = stream_completion(model, prompt)
             elseif choice == -1
                 InterruptException()
@@ -77,6 +81,7 @@ function jumpify_model(
 
             options = [
                 "Accept the response",
+                "Copy to clipboard",
                 "Edit the response",
                 "Try again with a different prompt",
                 "Try again with the same prompt",
@@ -85,21 +90,24 @@ function jumpify_model(
                 @warn "The generated Julia code has one or more syntax errors!"
                 push!(options, "Fix syntax errors")
             end
-            menu = RadioMenu(options; pagesize = 5)
+            menu = RadioMenu(options; pagesize = 6)
 
             choice = request("What do you want to do?", menu)
             if choice == 1
                 break
             elseif choice == 2
+                clipboard(parse_code(response)["julia"])
+                println("Response copied to the system's clipboard!")
+            elseif choice == 3
                 response = edit_in_editor(response)
                 println(response)
-            elseif choice == 3
+            elseif choice == 4
                 description = edit_in_editor(description)
                 prompt = format_template(template; description, examples)
                 response = stream_completion(model, prompt)
-            elseif choice == 4
-                response = stream_completion(model, prompt)
             elseif choice == 5
+                response = stream_completion(model, prompt)
+            elseif choice == 6
                 response = fix_syntax_errors(model, code, error_message)
             elseif choice == -1
                 InterruptException()

templates/ExtractStructure.json

@@ -7,7 +7,7 @@
         "_type": "metadatamessage"
     },
     {
-        "content": "You are an AI assistant specialized in modeling Constraint Programming (CP) problems. You have extensive knowledge of the XCSP3 Constraints and of the most used modeling patterns in Constraint Programming.\nYour task is to examine a given problem description and extract key structural information. Provide your analysis in the following format:\n\n1. Problem Description:\n- Summarize the problem statement and all of its specifications.\n\n2. Input data. Describe the format of the input data of the optimization problem. If no format is specified by the user, make sensible assumptions about one or multiple .csv files representing the problem inputs, and very concisely describe their headers.\n3. Parameter Sets:\n- Identify sets of known quantities given in the problem description. These are fixed inputs to the problem, not determined by the optimization process.\n- For each set of parameters:\n* Provide a descriptive name for the set.\n\n*Define a symbolic notation for the set using subscripts (e.g., a_ijk), specifying the meaning and the range of each index.\n\n3. Decision Variables:\n- Identify the key sets of decisions that need to be made. For each set of decision variables:\n* Provide a descriptive name for the set.\n* Specify the domain (possible values) for elements in this set, which can be either binary, integer or continuous.\n*Define a notation for the set using subscripts (e.g., x_ijk), specifying the meaning and the range of each index.\n\n4. Problem Type: determine whether the problem is a satisfaction or an optimization problem. If it is an optimization problem, provide: - a description of the objective function; - a symbolic Expression, consistently with the notation already defined. Otherwise, if the problem is a satisfaction problem, concisely state this fact.\n\n5. Constraints. Express the problem's constraint using user-provided Core Constraints. You must prefer using CP-oriented global constraints when possible. For each constraint:\n* Write a short description\n*Write the name (only the name) of Core Constraint(s) enforcing the constraint.\n*Write the scope of the constraint, that is, the indexes of the variables appearing in the constraint.\n\nList of core constraints:\n{{constraints}}\n\nIMPORTANT: - Prioritize Constraint Programming formulations over MIP formulations.\n-You must use as few variables and constraints as possible: you must avoid useless or redundant constraints.\n-You must not refer to constraints outside the Core Constraints list.\n-You must make sure that the Core Constraints are used with the appropriate arguments.\n-You must output the requested information only.",
+        "content": "You are an AI assistant specialized in modeling Constraint Programming (CP) problems. You have extensive knowledge of the XCSP3 Constraints and of the most used modeling patterns in Constraint Programming.\nYour task is to examine a given problem description and extract key structural information. Provide your analysis in a MarkDown document containing the following sections:\n\n# 1. Problem Description\n- Summarize the problem statement and all of its specifications.\n\n# 2. Input data\n Describe the format of the input data of the optimization problem. Use MarkDown tables where appropriate. If no format is specified by the user, make sensible assumptions about one or multiple .csv files representing the problem inputs, and very concisely describe their headers.\n\n# 3. Parameter Sets\n- Identify sets of known quantities given in the problem description. These are fixed inputs to the problem, not determined by the optimization process.\n- For each set of parameters:\n* Provide a descriptive name for the set.\n\n*Define a mathematical notation for the set in LaTeX (e.g., $a_{ijk}$), specifying the meaning and the range of each index\n\n# 4. Decision Variables\n- Identify the key sets of decisions that need to be made. For each set of decision variables:\n* Provide a descriptive name for the set.\n* Specify the domain (possible values) for elements in this set, which can be either binary, integer or continuous.\n*Define a mathematical notation for the set using LaTeX (e.g., $x_{ijk}$)\n\n# 5. Problem Type\nDetermine whether the problem is a satisfaction or an optimization problem. If it is an optimization problem, provide:\n- A description of the objective function\n- A mathematical expression using LaTeX, consistently with the LaTeX notation already defined. Otherwise, if the problem is a satisfaction problem, concisely state this fact.\n\n6. Constraints\nExpress the problem's constraint using user-provided Core Constraints. You must prefer using CP-oriented global constraints when possible. For each constraint:\n* Write a short description\n*Write the name (only the name) of Core Constraint(s) enforcing the constraint.\n*Write the scope of the constraint, that is, the indexes of the variables appearing in the constraint.\n\nList of core constraints:\n{{constraints}}\n\nIMPORTANT:\n- Prioritize Constraint Programming formulations over MIP formulations.\n-You must use as few variables and constraints as possible: you must avoid useless or redundant constraints.\n-You must not refer to constraints outside the Core Constraints list.\n-You must make sure that the Core Constraints are used with the appropriate arguments.\n-You must output the requested information only.",
         "variables": [
             "constraints"
         ],