mirror of
https://github.com/hwchase17/langchain
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3179ee3a56
Still don't have good "how to's", and the guides / examples section could be further pruned and improved, but this PR adds a couple examples for each of the common evaluator interfaces. - [x] Example docs for each implemented evaluator - [x] "how to make a custom evalutor" notebook for each low level APIs (comparison, string, agent) - [x] Move docs to modules area - [x] Link to reference docs for more information - [X] Still need to finish the evaluation index page - ~[ ] Don't have good data generation section~ - ~[ ] Don't have good how to section for other common scenarios / FAQs like regression testing, testing over similar inputs to measure sensitivity, etc.~
119 lines
2.6 KiB
Plaintext
119 lines
2.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ee2a3a21",
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"metadata": {},
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"source": [
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"# QA Generation\n",
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"This notebook shows how to use the `QAGenerationChain` to come up with question-answer pairs over a specific document.\n",
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"This is important because often times you may not have data to evaluate your question-answer system over, so this is a cheap and lightweight way to generate it!"
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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": "33d3f0b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader"
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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": "2029a29c",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = TextLoader(\"../../modules/state_of_the_union.txt\")"
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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": "87edb84c",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc = loader.load()[0]"
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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": "04125b6d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains import QAGenerationChain\n",
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"\n",
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"chain = QAGenerationChain.from_llm(ChatOpenAI(temperature=0))"
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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": 8,
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"id": "4f1593e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"qa = chain.run(doc.page_content)"
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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": 10,
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"id": "ee831f92",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'question': 'What is the U.S. Department of Justice doing to combat the crimes of Russian oligarchs?',\n",
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" 'answer': 'The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.'}"
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]
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},
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"execution_count": 10,
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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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"qa[1]"
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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": "7028754e",
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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": "3.9.1"
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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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