mirror of
https://github.com/hwchase17/langchain
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281 lines
9.3 KiB
Plaintext
281 lines
9.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "657d2c8c-54b4-42a3-9f02-bdefa0ed6728",
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"metadata": {},
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"source": [
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"# Custom Pairwise Evaluator\n",
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"\n",
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"You can make your own pairwise string evaluators by inheriting from `PairwiseStringEvaluator` class and overwriting the `_evaluate_string_pairs` method (and the `_aevaluate_string_pairs` method if you want to use the evaluator asynchronously).\n",
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"\n",
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"In this example, you will make a simple custom evaluator that just returns whether the first prediction has more whitespace tokenized 'words' than the second.\n",
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"\n",
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"You can check out the reference docs for the [PairwiseStringEvaluator interface](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.PairwiseStringEvaluator.html#langchain.evaluation.schema.PairwiseStringEvaluator) for more info.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "93f3a653-d198-4291-973c-8d1adba338b2",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from typing import Optional, Any\n",
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"from langchain.evaluation import PairwiseStringEvaluator\n",
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"\n",
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"\n",
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"class LengthComparisonPairwiseEvalutor(PairwiseStringEvaluator):\n",
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" \"\"\"\n",
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" Custom evaluator to compare two strings.\n",
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" \"\"\"\n",
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"\n",
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" def _evaluate_string_pairs(\n",
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" self,\n",
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" *,\n",
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" prediction: str,\n",
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" prediction_b: str,\n",
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" reference: Optional[str] = None,\n",
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" input: Optional[str] = None,\n",
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" **kwargs: Any,\n",
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" ) -> dict:\n",
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" score = int(len(prediction.split()) > len(prediction_b.split()))\n",
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" return {\"score\": score}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "7d4a77c3-07a7-4076-8e7f-f9bca0d6c290",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'score': 1}"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"evaluator = LengthComparisonPairwiseEvalutor()\n",
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"\n",
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"evaluator.evaluate_string_pairs(\n",
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" prediction=\"The quick brown fox jumped over the lazy dog.\",\n",
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" prediction_b=\"The quick brown fox jumped over the dog.\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d90f128f-6f49-42a1-b05a-3aea568ee03b",
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"metadata": {},
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"source": [
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"## LLM-Based Example\n",
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"\n",
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"That example was simple to illustrate the API, but it wasn't very useful in practice. Below, use an LLM with some custom instructions to form a simple preference scorer similar to the built-in [PairwiseStringEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain). We will use `ChatAnthropic` for the evaluator chain."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "b4b43098-4d96-417b-a8a9-b3e75779cfe8",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# %pip install anthropic\n",
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"# %env ANTHROPIC_API_KEY=YOUR_API_KEY"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "b6e978ab-48f1-47ff-9506-e13b1a50be6e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from typing import Optional, Any\n",
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"from langchain.evaluation import PairwiseStringEvaluator\n",
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"from langchain.chat_models import ChatAnthropic\n",
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"from langchain.chains import LLMChain\n",
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"\n",
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"\n",
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"class CustomPreferenceEvaluator(PairwiseStringEvaluator):\n",
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" \"\"\"\n",
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" Custom evaluator to compare two strings using a custom LLMChain.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self) -> None:\n",
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" llm = ChatAnthropic(model=\"claude-2\", temperature=0)\n",
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" self.eval_chain = LLMChain.from_string(\n",
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" llm,\n",
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" \"\"\"Which option is preferred? Do not take order into account. Evaluate based on accuracy and helpfulness. If neither is preferred, respond with C. Provide your reasoning, then finish with Preference: A/B/C\n",
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"\n",
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"Input: How do I get the path of the parent directory in python 3.8?\n",
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"Option A: You can use the following code:\n",
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"```python\n",
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"import os\n",
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"\n",
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"os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n",
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"```\n",
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"Option B: You can use the following code:\n",
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"```python\n",
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"from pathlib import Path\n",
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"Path(__file__).absolute().parent\n",
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"```\n",
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"Reasoning: Both options return the same result. However, since option B is more concise and easily understand, it is preferred.\n",
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"Preference: B\n",
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"\n",
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"Which option is preferred? Do not take order into account. Evaluate based on accuracy and helpfulness. If neither is preferred, respond with C. Provide your reasoning, then finish with Preference: A/B/C\n",
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"Input: {input}\n",
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"Option A: {prediction}\n",
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"Option B: {prediction_b}\n",
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"Reasoning:\"\"\",\n",
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" )\n",
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"\n",
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" @property\n",
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" def requires_input(self) -> bool:\n",
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" return True\n",
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"\n",
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" @property\n",
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" def requires_reference(self) -> bool:\n",
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" return False\n",
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"\n",
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" def _evaluate_string_pairs(\n",
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" self,\n",
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" *,\n",
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" prediction: str,\n",
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" prediction_b: str,\n",
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" reference: Optional[str] = None,\n",
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" input: Optional[str] = None,\n",
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" **kwargs: Any,\n",
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" ) -> dict:\n",
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" result = self.eval_chain(\n",
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" {\n",
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" \"input\": input,\n",
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" \"prediction\": prediction,\n",
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" \"prediction_b\": prediction_b,\n",
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" \"stop\": [\"Which option is preferred?\"],\n",
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" },\n",
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" **kwargs,\n",
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" )\n",
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"\n",
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" response_text = result[\"text\"]\n",
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" reasoning, preference = response_text.split(\"Preference:\", maxsplit=1)\n",
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" preference = preference.strip()\n",
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" score = 1.0 if preference == \"A\" else (0.0 if preference == \"B\" else None)\n",
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" return {\"reasoning\": reasoning.strip(), \"value\": preference, \"score\": score}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "5cbd8b1d-2cb0-4f05-b435-a1a00074d94a",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"evaluator = CustomPreferenceEvaluator()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "2c0a7fb7-b976-4443-9f0e-e707a6dfbdf7",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'reasoning': 'Option B is preferred over option A for importing from a relative directory, because it is more straightforward and concise.\\n\\nOption A uses the importlib module, which allows importing a module by specifying the full name as a string. While this works, it is less clear compared to option B.\\n\\nOption B directly imports from the relative path using dot notation, which clearly shows that it is a relative import. This is the recommended way to do relative imports in Python.\\n\\nIn summary, option B is more accurate and helpful as it uses the standard Python relative import syntax.',\n",
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" 'value': 'B',\n",
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" 'score': 0.0}"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"evaluator.evaluate_string_pairs(\n",
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" input=\"How do I import from a relative directory?\",\n",
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" prediction=\"use importlib! importlib.import_module('.my_package', '.')\",\n",
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" prediction_b=\"from .sibling import foo\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "f13a1346-7dbe-451d-b3a3-99e8fc7b753b",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CustomPreferenceEvaluator requires an input string.\n"
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]
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}
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],
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"source": [
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"# Setting requires_input to return True adds additional validation to avoid returning a grade when insufficient data is provided to the chain.\n",
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"\n",
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"try:\n",
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" evaluator.evaluate_string_pairs(\n",
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" prediction=\"use importlib! importlib.import_module('.my_package', '.')\",\n",
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" prediction_b=\"from .sibling import foo\",\n",
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" )\n",
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"except ValueError as e:\n",
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" print(e)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e7829cc3-ebd1-4628-ae97-15166202e9cc",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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