Add inheritance to CompletionFn and CompletionResult subclasses (open…

…ai#635)

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evals/completion_fns/cot.py

@@ -1,6 +1,7 @@
 """
 Extending Completion Functions with Chain-of-Thought
 """
+from evals.api import CompletionFn, CompletionResult
 from evals.prompt.base import ChatCompletionPrompt
 from evals.record import record_sampling
 from evals.registry import Registry
@@ -11,15 +12,15 @@
 )
 
 
-class ChainOfThoughtCompletionResult:
+class ChainOfThoughtCompletionResult(CompletionResult):
  def __init__(self, response) -> None:
  self.response = response
 
  def get_completions(self) -> list[str]:
  return [self.response.strip()]
 
 
-class ChainOfThoughtCompletionFn:
+class ChainOfThoughtCompletionFn(CompletionFn):
  def __init__(
  self,
  cot_template: str = DEFAULT_COT_TEMPLATE,

evals/completion_fns/langchain_llm.py

@@ -1,21 +1,22 @@
 import importlib
 from typing import Optional
+from evals.api import CompletionFn, CompletionResult
 
 from langchain.llms import BaseLLM
 
 from evals.prompt.base import CompletionPrompt
 from evals.record import record_sampling
 
 
-class LangChainLLMCompletionResult:
+class LangChainLLMCompletionResult(CompletionResult):
  def __init__(self, response) -> None:
  self.response = response
 
  def get_completions(self) -> list[str]:
  return [self.response.strip()]
 
 
-class LangChainLLMCompletionFn:
+class LangChainLLMCompletionFn(CompletionFn):
  def __init__(self, llm: str, llm_kwargs: Optional[dict] = {}, **kwargs) -> None:
  # Import and resolve self.llm to an instance of llm argument here, assuming it's always a subclass of BaseLLM
  module = importlib.import_module("langchain.llms")

evals/completion_fns/langchain_math.py

@@ -1,27 +1,30 @@
 import importlib
 from typing import Optional
 
+from openai import Completion
+from evals.api import CompletionResult
+
 from langchain import OpenAI, LLMMathChain
 
 from evals.prompt.base import CompletionPrompt
 from evals.record import record_sampling
 
 
-class LangChainCompletionResult:
+class LangChainCompletionResult(CompletionResult):
  def __init__(self, response) -> None:
  self.response = response
 
  def get_completions(self) -> list[str]:
  return [self.response.strip()]
 
 
-class LangChainMathChainCompletionFn:
+class LangChainMathChainCompletionFn(Completion):
  def __init__(self, **kwargs) -> None:
  llm = OpenAI(temperature=0)
  self.llm_math = LLMMathChain(llm=llm)
 
  def __call__(self, prompt, **kwargs) -> LangChainCompletionResult:
- 
+
  prompt = CompletionPrompt(prompt).to_formatted_prompt()
  response = self.llm_math.run(prompt)
  # The LangChain response comes with `Answer: ` ahead of this, let's strip it out

evals/completion_fns/openai.py

@@ -1,4 +1,6 @@
 from typing import Any, Optional, Union
+from evals.api import CompletionFn, CompletionResult
+from evals.base import CompletionFnSpec
 
 from evals.prompt.base import (
  ChatCompletionPrompt,
@@ -14,7 +16,7 @@
 )
 
 
-class OpenAIBaseCompletionResult:
+class OpenAIBaseCompletionResult(CompletionResult):
  def __init__(self, raw_data: Any, prompt: Any):
  self.raw_data = raw_data
  self.prompt = prompt
@@ -43,7 +45,7 @@ def get_completions(self) -> list[str]:
  return completions
 
 
-class OpenAICompletionFn:
+class OpenAICompletionFn(CompletionFn):
  def __init__(
  self,
  model: Optional[str] = None,
@@ -90,7 +92,7 @@ def __call__(
  return result
 
 
-class OpenAIChatCompletionFn:
+class OpenAIChatCompletionFn(CompletionFnSpec):
  def __init__(
  self,
  model: Optional[str] = None,