chore: initial public snapshot for github upload

llm-gateway-competitors/litellm-wheel-src/litellm/proxy/management_endpoints/policy_endpoints/__init__.py

@@ -0,0 +1,20 @@
+"""
+Policy endpoints package.
+
+Re-exports everything from endpoints module so existing imports
+like `from litellm.proxy.management_endpoints.policy_endpoints import router`
+continue to work. Patch targets also resolve correctly since names
+are imported directly into this namespace.
+"""
+
+from litellm.proxy.management_endpoints.policy_endpoints.endpoints import *  # noqa: F401, F403
+from litellm.proxy.management_endpoints.policy_endpoints.endpoints import (  # noqa: F401
+    _build_all_names_per_competitor,
+    _build_comparison_blocked_words,
+    _build_competitor_guardrail_definitions,
+    _build_name_blocked_words,
+    _build_recommendation_blocked_words,
+    _build_refinement_prompt,
+    _clean_competitor_line,
+    _parse_variations_response,
+)

llm-gateway-competitors/litellm-wheel-src/litellm/proxy/management_endpoints/policy_endpoints/ai_policy_suggester.py

@@ -0,0 +1,131 @@
+"""
+AI Policy Suggester - uses LLM tool calling to suggest policy templates
+based on user-provided attack examples and descriptions.
+"""
+
+import json
+from typing import List, Optional
+
+import litellm
+from litellm._logging import verbose_proxy_logger
+from litellm.constants import DEFAULT_COMPETITOR_DISCOVERY_MODEL
+
+SUGGEST_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "select_policy_templates",
+        "description": "Select one or more policy templates that best match the user's security requirements",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "selected_templates": {
+                    "type": "array",
+                    "items": {
+                        "type": "object",
+                        "properties": {
+                            "template_id": {
+                                "type": "string",
+                                "description": "The ID of the selected template",
+                            },
+                            "reason": {
+                                "type": "string",
+                                "description": "Brief reason why this template matches",
+                            },
+                        },
+                        "required": ["template_id", "reason"],
+                    },
+                    "description": "List of templates that match the user's requirements",
+                },
+                "explanation": {
+                    "type": "string",
+                    "description": "Overall explanation of why these templates were suggested",
+                },
+            },
+            "required": ["selected_templates", "explanation"],
+        },
+    },
+}
+
+
+class AiPolicySuggester:
+    """Suggests policy templates using LLM tool calling."""
+
+    async def suggest(
+        self,
+        templates: list,
+        attack_examples: List[str],
+        description: str,
+        model: Optional[str] = None,
+    ) -> dict:
+        system_prompt = self._build_system_prompt(templates)
+        user_prompt = self._build_user_prompt(attack_examples, description)
+        model = model or DEFAULT_COMPETITOR_DISCOVERY_MODEL
+
+        try:
+            response = await litellm.acompletion(
+                model=model,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt},
+                ],
+                tools=[SUGGEST_TOOL],
+                tool_choice={
+                    "type": "function",
+                    "function": {"name": "select_policy_templates"},
+                },
+                temperature=0.2,
+            )
+
+            tool_calls = response.choices[0].message.tool_calls  # type: ignore
+            if not tool_calls:
+                return {
+                    "selected_templates": [],
+                    "explanation": "No templates could be matched to your requirements.",
+                }
+
+            result = json.loads(tool_calls[0].function.arguments)
+
+            valid_ids = {t["id"] for t in templates}
+            result["selected_templates"] = [
+                s
+                for s in result.get("selected_templates", [])
+                if s.get("template_id") in valid_ids
+            ]
+
+            return result
+        except Exception as e:
+            verbose_proxy_logger.error("AI policy suggestion failed: %s", e)
+            raise
+
+    def _build_system_prompt(self, templates: list) -> str:
+        template_descriptions = []
+        for t in templates:
+            examples = t.get("example_sentences", [])
+            examples_str = ", ".join(f'"{e}"' for e in examples) if examples else "none"
+            entry = (
+                f"- ID: {t['id']}\n"
+                f"  Title: {t['title']}\n"
+                f"  Description: {t['description']}\n"
+                f"  Example attacks it protects against: {examples_str}"
+            )
+            template_descriptions.append(entry)
+
+        return (
+            "You are a security policy advisor. The user will describe attacks or content "
+            "they want to block. Your job is to select the most relevant policy templates "
+            "from the available set. Use the select_policy_templates tool to return your "
+            "selections. Only select templates that are clearly relevant to what the user "
+            "wants to block.\n\n"
+            "Available templates:\n\n" + "\n\n".join(template_descriptions)
+        )
+
+    def _build_user_prompt(self, attack_examples: List[str], description: str) -> str:
+        parts = []
+        filtered_examples = [e for e in attack_examples if e.strip()]
+        if filtered_examples:
+            parts.append("Example attack prompts I want to block:")
+            for i, ex in enumerate(filtered_examples, 1):
+                parts.append(f"  {i}. {ex}")
+        if description.strip():
+            parts.append(f"\nDescription of what I want to block: {description}")
+        return "\n".join(parts)