docs: Document and test PydanticOutputFunctionsParser (langchain-ai#1…

…5759)

This PR adds documentation and testing to
`PydanticOutputFunctionsParser(OutputFunctionsParser)`.

libs/langchain/langchain/output_parsers/openai_functions.py

@@ -136,10 +136,52 @@ def parse_result(self, result: List[Generation], *, partial: bool = False) -> An
 
 
 class PydanticOutputFunctionsParser(OutputFunctionsParser):
-    """Parse an output as a pydantic object."""
+    """Parse an output as a pydantic object.
+
+    This parser is used to parse the output of a ChatModel that uses
+    OpenAI function format to invoke functions.
+
+    The parser extracts the function call invocation and matches
+    them to the pydantic schema provided.
+
+    An exception will be raised if the function call does not match
+    the provided schema.
+
+    Example:
+
+        ... code-block:: python
+
+            message = AIMessage(
+                content="This is a test message",
+                additional_kwargs={
+                    "function_call": {
+                        "name": "cookie",
+                        "arguments": json.dumps({"name": "value", "age": 10}),
+                    }
+                },
+            )
+            chat_generation = ChatGeneration(message=message)
+
+            class Cookie(BaseModel):
+                name: str
+                age: int
+
+            class Dog(BaseModel):
+                species: str
+
+            # Full output
+            parser = PydanticOutputFunctionsParser(
+                pydantic_schema={"cookie": Cookie, "dog": Dog}
+            )
+            result = parser.parse_result([chat_generation])
+    """
 
     pydantic_schema: Union[Type[BaseModel], Dict[str, Type[BaseModel]]]
-    """The pydantic schema to parse the output with."""
+    """The pydantic schema to parse the output with.
+    
+    If multiple schemas are provided, then the function name will be used to
+    determine which schema to use.
+    """
 
     @root_validator(pre=True)
     def validate_schema(cls, values: Dict) -> Dict:

libs/langchain/tests/unit_tests/output_parsers/test_openai_functions.py

@@ -1,3 +1,4 @@
+import json
 from typing import Any, Dict
 
 import pytest
@@ -7,7 +8,9 @@
 
 from langchain.output_parsers.openai_functions import (
     JsonOutputFunctionsParser,
+    PydanticOutputFunctionsParser,
 )
+from langchain.pydantic_v1 import BaseModel
 
 
 def test_json_output_function_parser() -> None:
@@ -134,3 +137,61 @@ def test_exceptions_raised_while_parsing(bad_message: BaseMessage) -> None:
 
     with pytest.raises(OutputParserException):
         JsonOutputFunctionsParser().parse_result([chat_generation])
+
+
+def test_pydantic_output_functions_parser() -> None:
+    """Test pydantic output functions parser."""
+    message = AIMessage(
+        content="This is a test message",
+        additional_kwargs={
+            "function_call": {
+                "name": "function_name",
+                "arguments": json.dumps({"name": "value", "age": 10}),
+            }
+        },
+    )
+    chat_generation = ChatGeneration(message=message)
+
+    class Model(BaseModel):
+        """Test model."""
+
+        name: str
+        age: int
+
+    # Full output
+    parser = PydanticOutputFunctionsParser(pydantic_schema=Model)
+    result = parser.parse_result([chat_generation])
+    assert result == Model(name="value", age=10)
+
+
+def test_pydantic_output_functions_parser_multiple_schemas() -> None:
+    """Test that the parser works if providing multiple pydantic schemas."""
+
+    message = AIMessage(
+        content="This is a test message",
+        additional_kwargs={
+            "function_call": {
+                "name": "cookie",
+                "arguments": json.dumps({"name": "value", "age": 10}),
+            }
+        },
+    )
+    chat_generation = ChatGeneration(message=message)
+
+    class Cookie(BaseModel):
+        """Test model."""
+
+        name: str
+        age: int
+
+    class Dog(BaseModel):
+        """Test model."""
+
+        species: str
+
+    # Full output
+    parser = PydanticOutputFunctionsParser(
+        pydantic_schema={"cookie": Cookie, "dog": Dog}
+    )
+    result = parser.parse_result([chat_generation])
+    assert result == Cookie(name="value", age=10)