experimental[patch] Update prompt injection model (#13930)

- **Description:** Existing model used for Prompt Injection is quite
outdated but we fine-tuned and open-source a new model based on the same
model deberta-v3-base from Microsoft -
[laiyer/deberta-v3-base-prompt-injection](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection).
It supports more up-to-date injections and less prone to
false-positives.
  - **Dependencies:** No
  - **Tag maintainer:** -
  - **Twitter handle:** @alex_yaremchuk

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py

@@ -1,16 +1,18 @@
 """Tool for the identification of prompt injection attacks."""
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
-from langchain.pydantic_v1 import Field
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.tools.base import BaseTool
 
 if TYPE_CHECKING:
     from transformers import Pipeline
 
 
-def _model_default_factory() -> Pipeline:
+def _model_default_factory(
+    model_name: str = "deepset/deberta-v3-base-injection"
+) -> Pipeline:
     try:
         from transformers import pipeline
     except ImportError as e:
@@ -18,11 +20,11 @@ def _model_default_factory() -> Pipeline:
             "Cannot import transformers, please install with "
             "`pip install transformers`."
         ) from e
-    return pipeline("text-classification", model="deepset/deberta-v3-base-injection")
+    return pipeline("text-classification", model=model_name)
 
 
 class HuggingFaceInjectionIdentifier(BaseTool):
-    """Tool that uses deberta-v3-base-injection to detect prompt injection attacks."""
+    """Tool that uses HF model to detect prompt injection attacks."""
 
     name: str = "hugging_face_injection_identifier"
     description: str = (
@@ -30,7 +32,19 @@ class HuggingFaceInjectionIdentifier(BaseTool):
         "Useful for when you need to ensure that prompt is free of injection attacks. "
         "Input should be any message from the user."
     )
-    model: Pipeline = Field(default_factory=_model_default_factory)
+    model: Any = Field(default_factory=_model_default_factory)
+    """Model to use for prompt injection detection. 
+    
+    Can be specified as transformers Pipeline or string. String should correspond to the
+        model name of a text-classification transformers model. Defaults to 
+        ``deepset/deberta-v3-base-injection`` model.
+    """
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: dict) -> dict:
+        if isinstance(values.get("model"), str):
+            values["model"] = _model_default_factory(model_name=values["model"])
+        return values
 
     def _run(self, query: str) -> str:
         """Use the tool."""