generate mdx list and open api

Author: richhuth

scripts/generate_mdx_list.py

@@ -0,0 +1,202 @@
+#!/usr/bin/env python3
+"""
+Python script to generate MDX list from OpenAPI JSON
+"""
+
+import json
+import sys
+from pathlib import Path
+from typing import Dict, Any, List
+
+
+def categorize_evaluators(
+    evaluators: Dict[str, Any],
+) -> Dict[str, Dict[str, Any]]:
+    """Categorize evaluators based on their paths and descriptions."""
+
+    categories = {
+        "Expected Answer Evaluation": {
+            "description": "For when you have the golden answer and want to measure how correct the LLM gets it",
+            "evaluators": [],
+        },
+        "LLM-as-Judge": {
+            "description": "For when you don't have a golden answer, but have a set of rules for another LLM to evaluate quality",
+            "evaluators": [],
+        },
+        "RAG Quality": {
+            "description": "For measuring the quality of your RAG, check for hallucinations with faithfulness and precision/recall",
+            "evaluators": [],
+        },
+        "Quality Aspects Evaluation": {
+            "description": "For when you want to check the language, structure, style and other general quality metrics",
+            "evaluators": [],
+        },
+        "Safety": {
+            "description": "Check for PII, prompt injection attempts and toxic content",
+            "evaluators": [],
+        },
+        "Other": {"description": "Miscellaneous evaluators", "evaluators": []},
+    }
+
+    for path, path_info in evaluators.items():
+        if not path.endswith("/evaluate"):
+            continue
+
+        evaluator_id = path.replace("/evaluate", "")
+        post_info = path_info.get("post", {})
+        summary = post_info.get("summary", evaluator_id)
+        description = post_info.get("description", "")
+
+        # Convert evaluator name to proper endpoint format
+        # Use the evaluator name and convert to kebab-case
+        endpoint_id = summary.lower()
+        # Replace spaces and special characters with hyphens
+        endpoint_id = endpoint_id.replace(" ", "-")
+        endpoint_id = endpoint_id.replace("_", "-")
+        endpoint_id = endpoint_id.replace("/", "-")
+        # Remove any non-alphanumeric characters except hyphens
+        import re
+
+        endpoint_id = re.sub(r"[^a-z0-9\-]", "", endpoint_id)
+        # Remove multiple consecutive hyphens
+        endpoint_id = re.sub(r"-+", "-", endpoint_id)
+        # Remove leading/trailing hyphens
+        endpoint_id = endpoint_id.strip("-")
+
+        evaluator_info = {
+            "id": evaluator_id,
+            "name": summary,
+            "description": description,
+            "endpoint": f"/api-reference/{endpoint_id}",
+        }
+
+        # Categorize based on path and description
+        if any(
+            keyword in evaluator_id.lower()
+            for keyword in [
+                "exact_match",
+                "llm_answer_match",
+                "factual",
+                "sql_query",
+                "rouge",
+                "bleu",
+            ]
+        ):
+            categories["Expected Answer Evaluation"]["evaluators"].append(
+                evaluator_info
+            )
+        elif any(
+            keyword in evaluator_id.lower()
+            for keyword in ["llm_boolean", "llm_score", "llm_category", "rubrics"]
+        ):
+            categories["LLM-as-Judge"]["evaluators"].append(evaluator_info)
+        elif any(
+            keyword in evaluator_id.lower()
+            for keyword in [
+                "faithfulness",
+                "context_precision",
+                "context_recall",
+                "context_f1",
+                "response_relevancy",
+                "response_context",
+            ]
+        ):
+            categories["RAG Quality"]["evaluators"].append(evaluator_info)
+        elif any(
+            keyword in evaluator_id.lower()
+            for keyword in ["language_detection", "valid_format", "summarization"]
+        ):
+            categories["Quality Aspects Evaluation"]["evaluators"].append(
+                evaluator_info
+            )
+        elif any(
+            keyword in evaluator_id.lower()
+            for keyword in [
+                "pii",
+                "jailbreak",
+                "prompt_injection",
+                "content_safety",
+                "moderation",
+                "llama_guard",
+            ]
+        ):
+            categories["Safety"]["evaluators"].append(evaluator_info)
+        else:
+            categories["Other"]["evaluators"].append(evaluator_info)
+
+    return categories
+
+
+def generate_mdx(categories: Dict[str, Any]) -> str:
+    """Generate MDX content."""
+
+    mdx_content = []
+
+    for category_name, category_info in categories.items():
+        if not category_info["evaluators"]:
+            continue
+
+        mdx_content.append(f"## {category_name}")
+        mdx_content.append(f"{category_info['description']}")
+        mdx_content.append("")
+        mdx_content.append("| Evaluator | Description |")
+        mdx_content.append("| --------- | ----------- |")
+
+        for evaluator in category_info["evaluators"]:
+            # Clean description to remove newlines but keep full text
+            desc = evaluator["description"]
+            # Remove newlines and normalize whitespace
+            desc = " ".join(desc.split())
+
+            mdx_content.append(
+                f"| [{evaluator['name']}]({evaluator['endpoint']}) | {desc} |"
+            )
+
+        mdx_content.append("")
+
+    return "\n".join(mdx_content)
+
+
+def main():
+    """Main function to generate MDX list."""
+    try:
+        # Find the openapi.json file
+        script_dir = Path(__file__).parent
+        openapi_file = script_dir / "openapi.json"
+
+        if not openapi_file.exists():
+            print(f"Error: Could not find OpenAPI file at {openapi_file}")
+            sys.exit(1)
+
+        print(f"Reading OpenAPI from: {openapi_file}")
+
+        with open(openapi_file, "r", encoding="utf-8") as f:
+            openapi_data = json.load(f)
+
+        paths = openapi_data.get("paths", {})
+        categories = categorize_evaluators(paths)
+        mdx_content = generate_mdx(categories)
+
+        # Write the MDX content to a file
+        output_path = script_dir / "evaluators-list.mdx"
+        with open(output_path, "w", encoding="utf-8") as f:
+            f.write(mdx_content)
+
+        print(f"MDX list generated successfully at: {output_path}")
+
+        # Print summary
+        total_evaluators = sum(len(cat["evaluators"]) for cat in categories.values())
+        active_categories = len(
+            [cat for cat in categories.values() if cat["evaluators"]]
+        )
+        print(
+            f"Generated {total_evaluators} evaluators across {active_categories} categories"
+        )
+
+    except Exception as error:
+        print(f"Error generating MDX list: {error}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

scripts/generate_openapi.py

@@ -672,7 +672,7 @@ def generate_openapi_schema(evaluators: Dict[str, Any]) -> Dict[str, Any]:
                         {
                             "lang": "python",
                             "label": "Offline Evaluation",
-                            "source": f'import langwatch\n\ndf = langwatch.dataset.get_dataset("dataset-id").to_pandas()\n\nevaluation = langwatch.evaluation.init("my-incredible-evaluation")\n\nfor index, row in evaluation.loop(df.iterrows()):\n\n    # your execution code here\n       \n    evaluation.run(\n        "{evaluator_id}",\n        index=index,\n        data={{\n{chr(10).join(f"            \"{field}\": {field}," if field != "expected_output" else f"            \"{field}\": row[\"{field}\"]," for field in evaluator.get("requiredFields", []) + evaluator.get("optionalFields", []))}\n        }},\n        settings={{}}\n    )\n',
+                            "source": f'import langwatch\n\ndf = langwatch.dataset.get_dataset("dataset-id").to_pandas()\n\nevaluation = langwatch.evaluation.init("my-incredible-evaluation")\n\nfor index, row in evaluation.loop(df.iterrows()):\n    # your execution code here       \n    evaluation.run(\n        "{evaluator_id}",\n        index=index,\n        data={{\n{chr(10).join(f"            \"{field}\": row[\"{field}\"]," if field in ["input", "contexts", "expected_output"] else f"            \"{field}\": {field}," for field in evaluator.get("requiredFields", []) + evaluator.get("optionalFields", []))}\n        }},\n        settings={{}}\n    )\n',
                         },
                         {
                             "lang": "python",
@@ -745,7 +745,7 @@ def generate_openapi_schema(evaluators: Dict[str, Any]) -> Dict[str, Any]:
                         {
                             "lang": "python",
                             "label": "Offline Evaluation",
-                            "source": f'import langwatch\n\ndf = langwatch.dataset.get_dataset("dataset-id").to_pandas()\n\nevaluation = langwatch.evaluation.init("my-incredible-evaluation")\n\nfor index, row in evaluation.loop(df.iterrows()):\n\n    # your execution code here\n       \n    evaluation.run(\n        "{evaluator_id}",\n        index=index,\n        data={{\n{chr(10).join(f"            \"{field}\": {field}," if field != "expected_output" else f"            \"{field}\": row[\"{field}\"]," for field in evaluator.get("requiredFields", []) + evaluator.get("optionalFields", []))}\n        }},\n        settings={{}}\n    )\n',
+                            "source": f'import langwatch\n\ndf = langwatch.dataset.get_dataset("dataset-id").to_pandas()\n\nevaluation = langwatch.evaluation.init("my-incredible-evaluation")\n\nfor index, row in evaluation.loop(df.iterrows()):\n    # your execution code here       \n    evaluation.run(\n        "{evaluator_id}",\n        index=index,\n        data={{\n{chr(10).join(f"            \"{field}\": row[\"{field}\"]," if field in ["input", "contexts", "expected_output"] else f"            \"{field}\": {field}," for field in evaluator.get("requiredFields", []) + evaluator.get("optionalFields", []))}\n        }},\n        settings={{}}\n    )\n',
                         },
                         {
                             "lang": "python",