Merge pull request #18 from StacklokLabs/add-sys-msg

Make system message optional

Author: Luke Hinds

README.md

@@ -26,6 +26,7 @@ just open an issue).
 - **YAML Configuration**: Define your generation tasks using YAML configuration files
 - **Command Line Interface**: Run generation tasks directly from the command line
 - **Push to Hugging Face**: Push the generated dataset to Hugging Face Hub with automatic dataset cards and tags
+- **System Message Control**: Choose whether to include system messages in the generated dataset
 
 ## Getting Started
 
@@ -95,6 +96,7 @@ dataset:
     num_steps: 5
     batch_size: 1
     model_name: "ollama/mistral:latest"
+    sys_msg: true  # Include system message in dataset (default: true)
   save_as: "basic_prompt_dataset.jsonl"
 
 # Optional Hugging Face Hub configuration
@@ -128,6 +130,7 @@ promptwright start config.yaml \
   --tree-depth 3 \
   --num-steps 10 \
   --batch-size 2 \
+  --sys-msg true \  # Control system message inclusion (default: true)
   --hf-repo username/dataset-name \
   --hf-token your-token \
   --hf-tags tag1 --hf-tags tag2
@@ -185,6 +188,7 @@ engine = DataEngine(
         model_name="ollama/llama3",
         temperature=0.9,
         max_retries=2,
+        sys_msg=True,  # Include system message in dataset (default: true)
     )
 )
 ```
@@ -218,6 +222,7 @@ make all
 
 ### Prompt Output Examples
 
+With sys_msg=true (default):
 ```json
 {
   "messages": [
@@ -237,6 +242,22 @@ make all
 }
 ```
 
+With sys_msg=false:
+```json
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "Create a descriptive passage about a character discovering their hidden talents."
+    },
+    {
+      "role": "assistant",
+      "content": "As she stared at the canvas, Emma's fingers hovered above the paintbrushes, as if hesitant to unleash the colors that had been locked within her. The strokes began with bold abandon, swirling blues and greens merging into a mesmerizing dance of light and shadow. With each passing moment, she felt herself becoming the art – her very essence seeping onto the canvas like watercolors in a spring storm. The world around her melted away, leaving only the vibrant symphony of color and creation."
+    }
+  ]
+}
+```
+
 ## Model Compatibility
 
 The library should work with most LLM models. It has been tested with the

examples/example_basic_prompt.py

@@ -29,6 +29,7 @@
         model_name="ollama/mistral-nemo:latest",  # Model name
         temperature=0.9,  # Higher temperature for more creative variations
         max_retries=2,  # Retry failed prompts up to 2 times
+        sys_msg=True,  # Include system message in dataset (default: true)
     )
 )
 

examples/example_basic_prompt.yaml

@@ -27,4 +27,5 @@ dataset:
     batch_size: 1
     provider: "ollama"  # LLM provider
     model: "mistral-nemo:latest"  # Model name
+    sys_msg: true  # Include system message in dataset (default: true)
   save_as: "basic_prompt_dataset.jsonl"

examples/example_culinary_database.yaml

@@ -1,13 +1,14 @@
 # Example YAML configuration for basic prompt generation
-system_prompt: "You are a helpful assistant. You provide clear and concise answers to user questions."
+system_prompt: |
+  You are a culinary expert who documents recipes and cooking techniques.
+  Your entries should be detailed, precise, and include both traditional and modern cooking methods.
 
 topic_tree:
   args:
-    root_prompt: "You are a culinary expert who documents recipes and cooking techniques.
-Your entries should be detailed, precise, and include both traditional and modern cooking methods."
+    root_prompt: "Global Cuisine and Cooking Techniques"
     model_system_prompt: "<system_prompt_placeholder>"  # Will be replaced with system_prompt
-    tree_degree: 5  # Different continents
-    tree_depth: 3  # Deeper tree for more specific topics
+    tree_degree: 5  # Different cuisine types
+    tree_depth: 3  # Specific dishes and techniques
     temperature: 0.7  # Higher temperature for more creative variations
     provider: "ollama"  # LLM provider
     model: "mistral-nemo:latest"  # Model name
@@ -35,4 +36,5 @@ dataset:
     batch_size: 1
     provider: "ollama"  # LLM provider
     model: "mistral-nemo:latest"  # Model name
+    sys_msg: true  # Include system message in dataset (default: true)
   save_as: "culinary_database.jsonl"

examples/example_historic_figures.yaml

@@ -5,11 +5,11 @@ system_prompt: |
 
 topic_tree:
   args:
-    root_prompt: "Capital Cities of the World."
+    root_prompt: "Notable Historical Figures Across Different Eras and Fields"
     model_system_prompt: "<system_prompt_placeholder>"  # Will be replaced with system_prompt
-    tree_degree: 3  # Different continents
-    tree_depth: 2  # Deeper tree for more specific topics
-    temperature: 0.7  # Higher temperature for more creative variations
+    tree_degree: 4  # Different categories
+    tree_depth: 3  # Deeper tree for more specific figures
+    temperature: 0.6  # Balanced temperature for creativity and accuracy
     provider: "ollama"  # LLM provider
     model: "mistral-nemo:latest"  # Model name
   save_as: "historical_figures_tree.jsonl"
@@ -23,7 +23,7 @@ data_engine:
     system_prompt: "<system_prompt_placeholder>"  # Will be replaced with system_prompt
     provider: "ollama"  # LLM provider
     model: "mistral-nemo:latest"  # Model name
-    temperature: 0.9  # Higher temperature for more creative variations
+    temperature: 0.7  # Balance between creativity and accuracy
     max_retries: 2  # Retry failed prompts up to 2 times
 
 dataset:
@@ -32,4 +32,5 @@ dataset:
     batch_size: 1
     provider: "ollama"  # LLM provider
     model: "mistral-nemo:latest"  # Model name
-  save_as: "basic_prompt_dataset.jsonl"
+    sys_msg: true  # Include system message in dataset (default: true)
+  save_as: "historical_figures_database.jsonl"

examples/example_programming_challenges.py.yaml

@@ -7,12 +7,12 @@ topic_tree:
   args:
     root_prompt: "Programming Challenges Across Different Difficulty Levels and Concepts"
     model_system_prompt: "<system_prompt_placeholder>"  # Will be replaced with system_prompt
-    tree_degree: 3  # Different continents
-    tree_depth: 2  # Deeper tree for more specific topics
-    temperature: 0.7  # Higher temperature for more creative variations
+    tree_degree: 4  # Different programming concepts
+    tree_depth: 2  # Various difficulty levels
+    temperature: 0.7  # Higher temperature for creative problem scenarios
     provider: "ollama"  # LLM provider
     model: "mistral-nemo:latest"  # Model name
-  save_as: "basic_prompt_topictree.jsonl"
+  save_as: "programming_challenges_tree.jsonl"
 
 data_engine:
   args:
@@ -27,7 +27,7 @@ data_engine:
     system_prompt: "<system_prompt_placeholder>"  # Will be replaced with system_prompt
     provider: "ollama"  # LLM provider
     model: "mistral-nemo:latest"  # Model name
-    temperature: 0.9  # Higher temperature for more creative variations
+    temperature: 0.8  # Higher temperature for creative problem scenarios
     max_retries: 2  # Retry failed prompts up to 2 times
 
 dataset:
@@ -36,4 +36,5 @@ dataset:
     batch_size: 1
     provider: "ollama"  # LLM provider
     model: "mistral-nemo:latest"  # Model name
-  save_as: "basic_prompt_dataset.jsonl"
+    sys_msg: true  # Include system message in dataset (default: true)
+  save_as: "programming_challenges.jsonl"

examples/example_with_hf.yaml

@@ -23,6 +23,7 @@ dataset:
     num_steps: 5
     batch_size: 1
     model_name: "ollama/mistral:latest"
+    sys_msg: true  # Include system message in dataset (default: true)
   save_as: "basic_prompt_dataset.jsonl"
 
 # Hugging Face Hub configuration (optional)

promptwright/cli.py

@@ -47,6 +47,11 @@ def cli():
     multiple=True,
     help="Additional tags for the dataset (can be specified multiple times)",
 )
+@click.option(
+    "--sys-msg",
+    type=bool,
+    help="Include system message in dataset (default: true)",
+)
 def start(  # noqa: PLR0912
     config_file: str,
     topic_tree_save_as: str | None = None,
@@ -61,6 +66,7 @@ def start(  # noqa: PLR0912
     hf_repo: str | None = None,
     hf_token: str | None = None,
     hf_tags: list[str] | None = None,
+    sys_msg: bool | None = None,
 ) -> None:
     """Generate training data from a YAML configuration file."""
     try:
@@ -150,6 +156,7 @@ def start(  # noqa: PLR0912
                 batch_size=batch_size or dataset_params.get("batch_size", 1),
                 topic_tree=tree,
                 model_name=model_name,
+                sys_msg=sys_msg,  # Pass sys_msg to create_data
             )
         except Exception as e:
             handle_error(

promptwright/config.py

@@ -86,12 +86,17 @@ def get_engine_args(self, **overrides) -> EngineArguments:
         # Construct full model string
         args["model_name"] = construct_model_string(provider, model)
 
+        # Get sys_msg from dataset config, defaulting to True
+        dataset_config = self.get_dataset_config()
+        sys_msg = dataset_config.get("creation", {}).get("sys_msg", True)
+
         return EngineArguments(
             instructions=args.get("instructions", ""),
             system_prompt=args.get("system_prompt", ""),
             model_name=args["model_name"],
             temperature=args.get("temperature", 0.9),
             max_retries=args.get("max_retries", 2),
+            sys_msg=sys_msg,
         )
 
     def get_dataset_config(self) -> dict:

promptwright/engine.py

@@ -54,6 +54,7 @@ class EngineArguments:
     default_batch_size: int = 5
     default_num_examples: int = 3
     request_timeout: int = 30
+    sys_msg: bool = True  # Default to True for including system message
 
 
 class DataEngine:
@@ -81,7 +82,10 @@ def __init__(self, args: EngineArguments):
             "malformed_responses": [],
             "other_errors": [],
         }
-        self.args.system_prompt = ENGINE_JSON_INSTRUCTIONS + self.args.system_prompt
+        # Store original system prompt for dataset inclusion
+        self.original_system_prompt = args.system_prompt
+        # Use ENGINE_JSON_INSTRUCTIONS only for generation prompt
+        self.generation_system_prompt = ENGINE_JSON_INSTRUCTIONS + args.system_prompt
 
     def analyze_failure(self, response_content: str, error: Exception = None) -> str:
         """Analyze the failure reason for a sample."""
@@ -134,6 +138,7 @@ def create_data(  # noqa: PLR0912
         batch_size: int = 10,
         topic_tree: TopicTree = None,
         model_name: str = None,
+        sys_msg: bool = None,  # Allow overriding sys_msg from args
     ):
         if num_steps is None:
             raise ValueError("num_steps must be specified")  # noqa: TRY003
@@ -144,6 +149,9 @@ def create_data(  # noqa: PLR0912
         if not self.model_name:
             raise ValueError("No valid model_name provided")  # noqa: TRY003
 
+        # Use provided sys_msg or fall back to args.sys_msg
+        include_sys_msg = sys_msg if sys_msg is not None else self.args.sys_msg
+
         data_creation_prompt = SAMPLE_GENERATION_PROMPT
 
         tree_paths = None
@@ -204,6 +212,17 @@ def create_data(  # noqa: PLR0912
                                 response_content = r.choices[0].message.content
                                 parsed_json = validate_json_response(response_content)
 
+                                if parsed_json and include_sys_msg:  # noqa: SIM102
+                                    # Add system message at the start if sys_msg is True
+                                    if "messages" in parsed_json:
+                                        parsed_json["messages"].insert(
+                                            0,
+                                            {
+                                                "role": "system",
+                                                "content": self.original_system_prompt,
+                                            },
+                                        )
+
                                 if parsed_json:
                                     samples.append(parsed_json)
                                 else:
@@ -284,7 +303,7 @@ def build_prompt(
         subtopics_list: list[str] = None,
     ) -> str:
         prompt = data_creation_prompt.replace(
-            "{{{{system_prompt}}}}", self.build_system_prompt()
+            "{{{{system_prompt}}}}", self.generation_system_prompt
         )
         prompt = prompt.replace(
             "{{{{instructions}}}}", self.build_custom_instructions_text()
@@ -297,7 +316,8 @@ def build_prompt(
         )
 
     def build_system_prompt(self):
-        return self.args.system_prompt
+        """Return the original system prompt for dataset inclusion."""
+        return self.original_system_prompt
 
     def build_custom_instructions_text(self) -> str:
         if self.args.instructions is None:

promptwright/hf_hub.py

@@ -48,8 +48,8 @@ def update_dataset_card(self, repo_id: str, tags: list[str] | None = None):
         try:
             card = DatasetCard.load(repo_id)
 
-            # Initialize tags if not a list
-            if not isinstance(card.data.tags, list):
+            # Initialize tags if not present
+            if not hasattr(card.data, "tags") or not isinstance(card.data.tags, list):
                 card.data.tags = []
 
             # Add default promptwright tags

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "promptwright"
-version = "1.1.1"
+version = "1.2.1"
 description = "LLM based Synthetic Data Generation"
 authors = ["Luke Hinds <[email protected]>"]
 readme = "README.md"