Experimental Prompting DSL (#25)

openai_function_call/function_calls.py

@@ -0,0 +1,111 @@
+# MIT License
+#
+# Copyright (c) 2023 Jason Liu
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import json
+from functools import wraps
+from typing import Any, Callable
+from pydantic import validate_arguments, BaseModel
+
+
+def _remove_a_key(d, remove_key) -> None:
+    """Remove a key from a dictionary recursively"""
+    if isinstance(d, dict):
+        for key in list(d.keys()):
+            if key == remove_key:
+                del d[key]
+            else:
+                _remove_a_key(d[key], remove_key)
+
+
+class openai_function:
+    def __init__(self, func: Callable) -> None:
+        self.func = func
+        self.validate_func = validate_arguments(func)
+        parameters = self.validate_func.model.schema()
+        parameters["properties"] = {
+            k: v
+            for k, v in parameters["properties"].items()
+            if k not in ("v__duplicate_kwargs", "args", "kwargs")
+        }
+        parameters["required"] = sorted(
+            parameters["properties"]
+        )  # bug workaround see lc
+        _remove_a_key(parameters, "title")
+        _remove_a_key(parameters, "additionalProperties")
+        self.openai_schema = {
+            "name": self.func.__name__,
+            "description": self.func.__doc__,
+            "parameters": parameters,
+        }
+        self.model = self.validate_func.model
+
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        @wraps(self.func)
+        def wrapper(*args, **kwargs):
+            return self.validate_func(*args, **kwargs)
+
+        return wrapper(*args, **kwargs)
+
+    def from_response(self, completion, throw_error=True):
+        """Execute the function from the response of an openai chat completion"""
+        message = completion.choices[0].message
+
+        if throw_error:
+            assert "function_call" in message, "No function call detected"
+            assert (
+                message["function_call"]["name"] == self.openai_schema["name"]
+            ), "Function name does not match"
+
+        function_call = message["function_call"]
+        arguments = json.loads(function_call["arguments"])
+        return self.validate_func(**arguments)
+
+
+class OpenAISchema(BaseModel):
+    @classmethod
+    @property
+    def openai_schema(cls):
+        schema = cls.schema()
+        parameters = {
+            k: v for k, v in schema.items() if k not in ("title", "description")
+        }
+        parameters["required"] = sorted(parameters["properties"])
+        _remove_a_key(parameters, "title")
+        return {
+            "name": schema["title"],
+            "description": schema["description"],
+            "parameters": parameters,
+        }
+
+    @classmethod
+    def from_response(cls, completion, throw_error=True):
+        message = completion.choices[0].message
+
+        if throw_error:
+            assert "function_call" in message, "No function call detected"
+            assert (
+                message["function_call"]["name"] == cls.openai_schema["name"]
+            ), "Function name does not match"
+
+        function_call = message["function_call"]
+        arguments = json.loads(function_call["arguments"])
+        return cls(**arguments)