Community: Update and fix ZenGuardTool docs and add ZenguardTool to init files (#23415)

Thank you for contributing to LangChain!

- [x] **PR title**: "community: update docs and add tool to init.py"

- [x] **PR message**: 
- **Description:** Fixed some errors and comments in the docs and added
our ZenGuardTool and additional classes to init.py for easy access when
importing
- **Question:** when will you update the langchain-community package in
pypi to make our tool available?


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Thank you for review!

---------

Co-authored-by: Baur <baur.krykpayev@gmail.com>

libs/community/langchain_community/tools/zenguard/tool.py

@@ -0,0 +1,104 @@
+import os
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+import requests
+from langchain_core.pydantic_v1 import BaseModel, Field, ValidationError, validator
+from langchain_core.tools import BaseTool
+
+
+class Detector(str, Enum):
+    ALLOWED_TOPICS = "allowed_subjects"
+    BANNED_TOPICS = "banned_subjects"
+    PROMPT_INJECTION = "prompt_injection"
+    KEYWORDS = "keywords"
+    PII = "pii"
+    SECRETS = "secrets"
+    TOXICITY = "toxicity"
+
+
+class DetectorAPI(str, Enum):
+    ALLOWED_TOPICS = "v1/detect/topics/allowed"
+    BANNED_TOPICS = "v1/detect/topics/banned"
+    PROMPT_INJECTION = "v1/detect/prompt_injection"
+    KEYWORDS = "v1/detect/keywords"
+    PII = "v1/detect/pii"
+    SECRETS = "v1/detect/secrets"
+    TOXICITY = "v1/detect/toxicity"
+
+
+class ZenGuardInput(BaseModel):
+    prompts: List[str] = Field(
+        ...,
+        min_items=1,
+        min_length=1,
+        description="Prompt to check",
+    )
+    detectors: List[Detector] = Field(
+        ...,
+        min_items=1,
+        description="List of detectors by which you want to check the prompt",
+    )
+    in_parallel: bool = Field(
+        default=True,
+        description="Run prompt detection by the detector in parallel or sequentially",
+    )
+
+
+class ZenGuardTool(BaseTool):
+    name: str = "ZenGuard"
+    description: str = (
+        "ZenGuard AI integration package. ZenGuard AI - the fastest GenAI guardrails."
+    )
+    args_schema = ZenGuardInput
+    return_direct = True
+
+    zenguard_api_key: Optional[str] = Field(default=None)
+
+    _ZENGUARD_API_URL_ROOT = "https://api.zenguard.ai/"
+    _ZENGUARD_API_KEY_ENV_NAME = "ZENGUARD_API_KEY"
+
+    @validator("zenguard_api_key", pre=True, always=True, check_fields=False)
+    def set_api_key(cls, v: str) -> str:
+        if v is None:
+            v = os.getenv(cls._ZENGUARD_API_KEY_ENV_NAME)
+        if v is None:
+            raise ValidationError(
+                "The zenguard_api_key tool option must be set either "
+                "by passing zenguard_api_key to the tool or by setting "
+                f"the f{cls._ZENGUARD_API_KEY_ENV_NAME} environment variable"
+            )
+        return v
+
+    def _run(
+        self,
+        prompts: List[str],
+        detectors: List[Detector],
+        in_parallel: bool = True,
+    ) -> Dict[str, Any]:
+        try:
+            postfix = None
+            json: Optional[Dict[str, Any]] = None
+            if len(detectors) == 1:
+                postfix = self._convert_detector_to_api(detectors[0])
+                json = {"messages": prompts}
+            else:
+                postfix = "v1/detect"
+                json = {
+                    "messages": prompts,
+                    "in_parallel": in_parallel,
+                    "detectors": detectors,
+                }
+            response = requests.post(
+                self._ZENGUARD_API_URL_ROOT + postfix,
+                json=json,
+                headers={"x-api-key": self.zenguard_api_key},
+                timeout=5,
+            )
+            response.raise_for_status()
+            return response.json()
+        except (requests.HTTPError, requests.Timeout) as e:
+            return {"error": str(e)}
+
+    def _convert_detector_to_api(self, detector: Detector) -> str:
+        return DetectorAPI[detector.name].value