Add BasePrompt as abstract base class (#60)

langchain/__init__.py

@@ -17,7 +17,7 @@ from langchain.chains import (
 from langchain.docstore import Wikipedia
 from langchain.faiss import FAISS
 from langchain.llms import Cohere, HuggingFaceHub, OpenAI
-from langchain.prompt import Prompt
+from langchain.prompt import BasePrompt, Prompt
 from langchain.sql_database import SQLDatabase
 
 __all__ = [
@@ -28,6 +28,7 @@ __all__ = [
     "SerpAPIChain",
     "Cohere",
     "OpenAI",
+    "BasePrompt",
     "Prompt",
     "ReActChain",
     "Wikipedia",

langchain/chains/llm.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel, Extra
 
 from langchain.chains.base import Chain
 from langchain.llms.base import LLM
-from langchain.prompt import Prompt
+from langchain.prompt import BasePrompt
 
 
 class LLMChain(Chain, BaseModel):
@@ -20,7 +20,7 @@ class LLMChain(Chain, BaseModel):
             llm = LLMChain(llm=OpenAI(), prompt=prompt)
     """
 
-    prompt: Prompt
+    prompt: BasePrompt
     """Prompt object to use."""
     llm: LLM
     """LLM wrapper to use."""

langchain/chains/mapreduce.py

@@ -11,7 +11,7 @@ from pydantic import BaseModel, Extra
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.llms.base import LLM
-from langchain.prompt import Prompt
+from langchain.prompt import BasePrompt
 from langchain.text_splitter import TextSplitter
 
 
@@ -29,7 +29,7 @@ class MapReduceChain(Chain, BaseModel):
 
     @classmethod
     def from_params(
-        cls, llm: LLM, prompt: Prompt, text_splitter: TextSplitter
+        cls, llm: LLM, prompt: BasePrompt, text_splitter: TextSplitter
     ) -> "MapReduceChain":
         """Construct a map-reduce chain that uses the chain for map and reduce."""
         llm_chain = LLMChain(llm=llm, prompt=prompt)

langchain/prompt.py

@@ -1,4 +1,5 @@
 """Prompt schema definition."""
+from abc import ABC, abstractmethod
 from typing import Any, Dict, List
 
 from pydantic import BaseModel, Extra, root_validator
@@ -10,7 +11,31 @@ _FORMATTER_MAPPING = {
 }
 
 
-class Prompt(BaseModel):
+class BasePrompt(ABC):
+    """Base prompt should expose the format method, returning a prompt."""
+
+    input_variables: List[str]
+    """A list of the names of the variables the prompt template expects."""
+
+    @abstractmethod
+    def format(self, **kwargs: Any) -> str:
+        """Format the prompt with the inputs.
+
+        Args:
+            kwargs: Any arguments to be passed to the prompt template.
+
+        Returns:
+            A formatted string.
+
+        Example:
+
+        .. code-block:: python
+
+            prompt.format(variable1="foo")
+        """
+
+
+class Prompt(BaseModel, BasePrompt):
     """Schema to represent a prompt for an LLM.
 
     Example: