ape example

examples/ape.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "081420ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "words = [\"sane\", \"direct\", \"informally\", \"unpopular\", \"subtractive\", \"nonresidential\",\n",
+    "         \"inexact\", \"uptown\", \"incomparable\", \"powerful\", \"gaseous\", \"evenly\", \"formality\",\n",
+    "         \"deliberately\", \"off\"]\n",
+    "antonyms = [\"insane\", \"indirect\", \"formally\", \"popular\", \"additive\", \"residential\",\n",
+    "            \"exact\", \"downtown\", \"comparable\", \"powerless\", \"solid\", \"unevenly\", \"informality\",\n",
+    "            \"accidentally\", \"on\"]\n",
+    "data = (words, antonyms)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6c7c00b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "examples = [f\"INPUT: {i}\\nOUTPUT: {j}\" for i, j in zip(words, antonyms)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "70d4ea31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.ape.base import APEChain\n",
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f64829c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0663e721",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = APEChain(llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4d65f1ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' reverse the input.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.ape(examples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bd0bd06",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

langchain/chains/ape/base.py

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+from typing import Dict, List
+
+from langchain.chains.base import Chain
+from pydantic import BaseModel, Extra
+from langchain.llms.base import LLM
+from langchain.chains.llm import LLMChain
+from langchain.chains.ape.prompt import PROMPT
+
+
+class APEChain(Chain, BaseModel):
+
+    llm: LLM
+
+    input_key: str = "code"  #: :meta private:
+    output_key: str = "output"  #: :meta private:
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Expect input key.
+
+        :meta private:
+        """
+        return [self.input_key]
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Return output key.
+
+        :meta private:
+        """
+        return [self.output_key]
+
+    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
+        chain = LLMChain(llm=self.llm, prompt=PROMPT)
+        output = chain.run(inputs[self.input_key])
+        return {self.output_key: output}
+
+    def ape(self, examples: List[str]) -> str:
+        combined_examples = "\n\n".join(examples)
+        return self.run(combined_examples)
+

langchain/chains/ape/prompt.py

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+# flake8: noqa
+from langchain.prompts.prompt import Prompt
+_TEMPLATE = """I gave a friend an instruction. Based on the instruction they produced the following input-output pairs:
+
+{examples}
+
+The instruction was to"""
+
+PROMPT = Prompt(input_variables=["examples"], template=_TEMPLATE)