community[minor], langchain[minor]: refactor output_parsers Rail (#15852)

Moved Rail parser to `community` package.

libs/community/langchain_community/output_parsers/__init__.py

@@ -0,0 +1,14 @@
+"""**OutputParser** classes parse the output of an LLM call.
+
+**Class hierarchy:**
+
+.. code-block::
+
+    BaseLLMOutputParser --> BaseOutputParser --> <name>OutputParser  # GuardrailsOutputParser
+
+**Main helpers:**
+
+.. code-block::
+
+    Serializable, Generation, PromptValue
+"""  # noqa: E501

libs/community/langchain_community/output_parsers/rail_parser.py

@@ -0,0 +1,109 @@
+from __future__ import annotations
+
+from typing import Any, Callable, Dict, Optional
+
+from langchain_core.output_parsers import BaseOutputParser
+
+
+class GuardrailsOutputParser(BaseOutputParser):
+    """Parse the output of an LLM call using Guardrails."""
+
+    guard: Any
+    """The Guardrails object."""
+    api: Optional[Callable]
+    """The LLM API passed to Guardrails during parsing. An example is `openai.completions.create`."""  # noqa: E501
+    args: Any
+    """Positional arguments to pass to the above LLM API callable."""
+    kwargs: Any
+    """Keyword arguments to pass to the above LLM API callable."""
+
+    @property
+    def _type(self) -> str:
+        return "guardrails"
+
+    @classmethod
+    def from_rail(
+        cls,
+        rail_file: str,
+        num_reasks: int = 1,
+        api: Optional[Callable] = None,
+        *args: Any,
+        **kwargs: Any,
+    ) -> GuardrailsOutputParser:
+        """Create a GuardrailsOutputParser from a rail file.
+
+        Args:
+            rail_file: a rail file.
+            num_reasks: number of times to re-ask the question.
+            api: the API to use for the Guardrails object.
+            *args: The arguments to pass to the API
+            **kwargs: The keyword arguments to pass to the API.
+
+        Returns:
+            GuardrailsOutputParser
+        """
+        try:
+            from guardrails import Guard
+        except ImportError:
+            raise ImportError(
+                "guardrails-ai package not installed. "
+                "Install it by running `pip install guardrails-ai`."
+            )
+        return cls(
+            guard=Guard.from_rail(rail_file, num_reasks=num_reasks),
+            api=api,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    @classmethod
+    def from_rail_string(
+        cls,
+        rail_str: str,
+        num_reasks: int = 1,
+        api: Optional[Callable] = None,
+        *args: Any,
+        **kwargs: Any,
+    ) -> GuardrailsOutputParser:
+        try:
+            from guardrails import Guard
+        except ImportError:
+            raise ImportError(
+                "guardrails-ai package not installed. "
+                "Install it by running `pip install guardrails-ai`."
+            )
+        return cls(
+            guard=Guard.from_rail_string(rail_str, num_reasks=num_reasks),
+            api=api,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    @classmethod
+    def from_pydantic(
+        cls,
+        output_class: Any,
+        num_reasks: int = 1,
+        api: Optional[Callable] = None,
+        *args: Any,
+        **kwargs: Any,
+    ) -> GuardrailsOutputParser:
+        try:
+            from guardrails import Guard
+        except ImportError:
+            raise ImportError(
+                "guardrails-ai package not installed. "
+                "Install it by running `pip install guardrails-ai`."
+            )
+        return cls(
+            guard=Guard.from_pydantic(output_class, "", num_reasks=num_reasks),
+            api=api,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    def get_format_instructions(self) -> str:
+        return self.guard.raw_prompt.format_instructions
+
+    def parse(self, text: str) -> Dict:
+        return self.guard.parse(text, llm_api=self.api, *self.args, **self.kwargs)

libs/langchain/langchain/output_parsers/rail_parser.py

@@ -1,109 +1,5 @@
-from __future__ import annotations
+from langchain_community.output_parsers.rail_parser import (
+    GuardrailsOutputParser,
+)
 
-from typing import Any, Callable, Dict, Optional
+__all__ = ["GuardrailsOutputParser"]
-
-from langchain_core.output_parsers import BaseOutputParser
-
-
-class GuardrailsOutputParser(BaseOutputParser):
-    """Parse the output of an LLM call using Guardrails."""
-
-    guard: Any
-    """The Guardrails object."""
-    api: Optional[Callable]
-    """The LLM API passed to Guardrails during parsing. An example is `openai.completions.create`."""  # noqa: E501
-    args: Any
-    """Positional arguments to pass to the above LLM API callable."""
-    kwargs: Any
-    """Keyword arguments to pass to the above LLM API callable."""
-
-    @property
-    def _type(self) -> str:
-        return "guardrails"
-
-    @classmethod
-    def from_rail(
-        cls,
-        rail_file: str,
-        num_reasks: int = 1,
-        api: Optional[Callable] = None,
-        *args: Any,
-        **kwargs: Any,
-    ) -> GuardrailsOutputParser:
-        """Create a GuardrailsOutputParser from a rail file.
-
-        Args:
-            rail_file: a rail file.
-            num_reasks: number of times to re-ask the question.
-            api: the API to use for the Guardrails object.
-            *args: The arguments to pass to the API
-            **kwargs: The keyword arguments to pass to the API.
-
-        Returns:
-            GuardrailsOutputParser
-        """
-        try:
-            from guardrails import Guard
-        except ImportError:
-            raise ImportError(
-                "guardrails-ai package not installed. "
-                "Install it by running `pip install guardrails-ai`."
-            )
-        return cls(
-            guard=Guard.from_rail(rail_file, num_reasks=num_reasks),
-            api=api,
-            args=args,
-            kwargs=kwargs,
-        )
-
-    @classmethod
-    def from_rail_string(
-        cls,
-        rail_str: str,
-        num_reasks: int = 1,
-        api: Optional[Callable] = None,
-        *args: Any,
-        **kwargs: Any,
-    ) -> GuardrailsOutputParser:
-        try:
-            from guardrails import Guard
-        except ImportError:
-            raise ImportError(
-                "guardrails-ai package not installed. "
-                "Install it by running `pip install guardrails-ai`."
-            )
-        return cls(
-            guard=Guard.from_rail_string(rail_str, num_reasks=num_reasks),
-            api=api,
-            args=args,
-            kwargs=kwargs,
-        )
-
-    @classmethod
-    def from_pydantic(
-        cls,
-        output_class: Any,
-        num_reasks: int = 1,
-        api: Optional[Callable] = None,
-        *args: Any,
-        **kwargs: Any,
-    ) -> GuardrailsOutputParser:
-        try:
-            from guardrails import Guard
-        except ImportError:
-            raise ImportError(
-                "guardrails-ai package not installed. "
-                "Install it by running `pip install guardrails-ai`."
-            )
-        return cls(
-            guard=Guard.from_pydantic(output_class, "", num_reasks=num_reasks),
-            api=api,
-            args=args,
-            kwargs=kwargs,
-        )
-
-    def get_format_instructions(self) -> str:
-        return self.guard.raw_prompt.format_instructions
-
-    def parse(self, text: str) -> Dict:
-        return self.guard.parse(text, llm_api=self.api, *self.args, **self.kwargs)