main run

graphdoc/graphdoc/__init__.py

@@ -22,7 +22,6 @@
     setup_logging,
 )
 from graphdoc.eval import DocGeneratorEvaluator
-from graphdoc.main import GraphDoc
 from graphdoc.modules import DocGeneratorModule
 from graphdoc.prompts import (
     BadDocGeneratorSignature,
@@ -45,7 +44,6 @@
 )
 
 __all__ = [
-    "GraphDoc",
     "DocGeneratorModule",
     "DocGeneratorEvaluator",
     "DocGeneratorTrainer",

graphdoc/graphdoc/main.py

@@ -1,9 +1,12 @@
 # Copyright 2025-, Semiotic AI, Inc.
 # SPDX-License-Identifier: Apache-2.0
 
-# system packages
+import argparse
 import logging
 import random
+
+# system packages
+import sys
 from pathlib import Path
 from typing import List, Literal, Optional, Union
 
@@ -633,3 +636,147 @@ def doc_generator_eval_from_yaml(
             evaluator_prediction_field=evaluator_prediction_field,
             readable_value=readable_value,
         )
+
+
+#######################
+# Main Entry Point    #
+#######################
+"""Run GraphDoc as a command-line application.
+
+This module can be run directly to train models, generate documentation,
+or evaluate documentation quality.
+
+Usage:
+    python -m graphdoc.main --config CONFIG_FILE [--log-level LEVEL] COMMAND [ARGS]
+
+Global Arguments:
+    --config PATH          Path to YAML configuration file with GraphDoc
+                           and language model settings
+    --log-level LEVEL      Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
+                           Default: INFO
+
+Commands:
+    train                  Train a prompt using a dataset
+        --trainer-config PATH    Path to trainer YAML configuration
+
+    generate               Generate documentation for schema files
+        --module-config PATH     Path to module YAML configuration
+        --input PATH             Path to input schema file or directory
+        --output PATH            Path to output file
+
+    evaluate               Evaluate documentation quality
+        --eval-config PATH       Path to evaluator YAML configuration
+
+Examples:
+    # Train a documentation quality model
+    python -m graphdoc.main \
+        --config config.yaml \
+        train \
+        --trainer-config trainer_config.yaml
+
+    # Generate documentation for schemas
+    python -m graphdoc.main \
+        --config config.yaml \
+        generate \
+        --module-config module_config.yaml \
+        --input schema.graphql \
+        --output documented_schema.graphql
+
+    # Evaluate documentation quality
+    python -m graphdoc.main \
+        --config config.yaml \
+        evaluate \
+        --eval-config eval_config.yaml
+
+Configuration:
+    See example YAML files in the documentation for format details.
+"""  # noqa: B950
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description="GraphDoc - Documentation Generator")
+    parser.add_argument("--config", type=str, help="Path to YAML configuration file")
+    parser.add_argument(
+        "--log-level",
+        type=str,
+        default="INFO",
+        choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
+        help="Logging level",
+    )
+    subparsers = parser.add_subparsers(dest="command", help="Command to execute")
+
+    ###################
+    # train           #
+    ###################
+    train_parser = subparsers.add_parser("train", help="Train a prompt")
+    train_parser.add_argument(
+        "--trainer-config",
+        type=str,
+        required=True,
+        help="Path to trainer YAML configuration",
+    )
+
+    ###################
+    # generate        #
+    ###################
+    generate_parser = subparsers.add_parser("generate", help="Generate documentation")
+    generate_parser.add_argument(
+        "--module-config",
+        type=str,
+        required=True,
+        help="Path to module YAML configuration",
+    )
+    generate_parser.add_argument(
+        "--input", type=str, required=True, help="Path to input schema file"
+    )
+    generate_parser.add_argument(
+        "--output", type=str, required=True, help="Path to output schema file"
+    )
+
+    ###################
+    # evaluate        #
+    ###################
+    eval_parser = subparsers.add_parser(
+        "evaluate", help="Evaluate documentation quality"
+    )
+    eval_parser.add_argument(
+        "--eval-config",
+        type=str,
+        required=True,
+        help="Path to evaluator YAML configuration",
+    )
+
+    args = parser.parse_args()
+    if not args.config:
+        parser.print_help()
+        sys.exit(1)
+
+    graphdoc = GraphDoc.from_yaml(args.config)
+
+    if args.command == "train":
+        trainer = graphdoc.single_trainer_from_yaml(args.trainer_config)
+        trained_prompt = trainer.train()
+        print(
+            f"Training complete. Saved to MLflow with name: {trainer.mlflow_model_name}"
+        )
+
+    elif args.command == "generate":
+        module = graphdoc.doc_generator_module_from_yaml(args.module_config)
+
+        with open(args.input, "r") as f:
+            schema = f.read()
+
+        documented_schema = module.document_full_schema(schema)
+
+        with open(args.output, "w") as f:
+            f.write(documented_schema.documented_schema)
+        print(f"Generation complete. Documentation saved to {args.output}")
+
+    elif args.command == "evaluate":
+        evaluator = graphdoc.doc_generator_eval_from_yaml(args.eval_config)
+        results = evaluator.evaluate()
+        print(
+            "Evaluation complete. Results saved to MLflow experiment: "
+            f"{evaluator.mlflow_experiment_name}"
+        )
+    else:
+        parser.print_help()

graphdoc/graphdoc/modules/doc_generator_module.py

@@ -64,6 +64,45 @@ def __init__(
             )
             self.prompt.prompt_metric.prompt_metric = "rating"
 
+    #######################
+    # MLFLOW TRACING      #
+    #######################
+    # TODO: we will break this out into a separate class later
+    # when we have need for it elsewhere
+    def _start_trace(
+        self,
+        client: mlflow.MlflowClient,
+        expirement_name: str,
+        trace_name: str,
+        inputs: dict,
+        attributes: dict,
+    ):
+        # set the experiment name so that everything is logged to the same experiment
+        mlflow.set_experiment(expirement_name)
+
+        # start the trace
+        trace = client.start_trace(
+            name=trace_name,
+            inputs=inputs,
+            attributes=attributes,
+            # experiment_id=expirement_name,
+        )
+
+        return trace
+
+    def _end_trace(
+        self,
+        client: mlflow.MlflowClient,
+        trace: Any,  # TODO: trace: mlflow.Span,
+        # E   AttributeError: module 'mlflow' has no attribute 'Span'
+        outputs: dict,
+        status: Literal["OK", "ERROR"],
+    ):
+        client.end_trace(request_id=trace.request_id, outputs=outputs, status=status)
+
+    #######################
+    # MODULE FUNCTIONS    #
+    #######################
     def _retry_by_rating(self, database_schema: str) -> str:
         """Retry the generation if the quality check fails. Rating threshold is
         determined at initialization.
@@ -211,42 +250,6 @@ def forward(self, database_schema: str) -> dspy.Prediction:
         else:
             return self._predict(database_schema=database_schema)
 
-    #######################
-    # MLFLOW TRACING      #
-    #######################
-    # TODO: we will break this out into a separate class later
-    # when we have need for it elsewhere
-    def _start_trace(
-        self,
-        client: mlflow.MlflowClient,
-        expirement_name: str,
-        trace_name: str,
-        inputs: dict,
-        attributes: dict,
-    ):
-        # set the experiment name so that everything is logged to the same experiment
-        mlflow.set_experiment(expirement_name)
-
-        # start the trace
-        trace = client.start_trace(
-            name=trace_name,
-            inputs=inputs,
-            attributes=attributes,
-            # experiment_id=expirement_name,
-        )
-
-        return trace
-
-    def _end_trace(
-        self,
-        client: mlflow.MlflowClient,
-        trace: Any,  # TODO: trace: mlflow.Span,
-        # E   AttributeError: module 'mlflow' has no attribute 'Span'
-        outputs: dict,
-        status: Literal["OK", "ERROR"],
-    ):
-        client.end_trace(request_id=trace.request_id, outputs=outputs, status=status)
-
     def document_full_schema(
         self,
         database_schema: str,

graphdoc/runners/eval/eval_doc_generator_module.py

@@ -11,7 +11,7 @@
 from dotenv import load_dotenv
 
 # internal packages
-from graphdoc import GraphDoc
+from graphdoc.main import GraphDoc
 
 # logging
 log = logging.getLogger(__name__)

graphdoc/runners/train/single_prompt_trainer.py

@@ -14,7 +14,7 @@
 import mlflow
 from dotenv import load_dotenv
 
-from graphdoc import GraphDoc, load_yaml_config
+from graphdoc.main import GraphDoc, load_yaml_config
 
 # logging
 log = logging.getLogger(__name__)

graphdoc/tests/conftest.py

@@ -17,10 +17,10 @@
 from graphdoc import (
     DocGeneratorPrompt,
     DocQualityPrompt,
-    GraphDoc,
     LocalDataHelper,
     Parser,
 )
+from graphdoc.main import GraphDoc
 
 # logging
 log = logging.getLogger(__name__)

graphdoc/tests/test_confest.py

@@ -7,10 +7,10 @@
 from graphdoc import (
     DocGeneratorPrompt,
     DocQualityPrompt,
-    GraphDoc,
     LocalDataHelper,
     Parser,
 )
+from graphdoc.main import GraphDoc
 
 from .conftest import (
     OverwriteSchemaCategory,

graphdoc/tests/test_graphdoc.py

@@ -16,10 +16,10 @@
     DocGeneratorTrainer,
     DocQualityPrompt,
     DocQualityTrainer,
-    GraphDoc,
     SinglePromptTrainer,
     load_yaml_config,
 )
+from graphdoc.main import GraphDoc
 
 # logging
 log = logging.getLogger(__name__)