HotPotQA runs
Beck LaBash
1 year ago
committed by
elleven11
parent
5269ef4ae0
commit
5942b44c41
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import joblib\n",
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"from react_cls import ReactAgent\n",
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"from mocks import DocStoreExplorerMock, LLMMock"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def summarize_trial(agents):\n",
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" correct = [a for a in agents if a.is_correct()]\n",
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" halted = [a for a in agents if a.is_halted()]\n",
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" incorrect = [a for a in agents if a.is_finished() and not a.is_correct()]\n",
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" return correct, incorrect, halted\n",
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"\n",
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"def log_trial(agents, trial_n):\n",
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" correct, incorrect, halted = summarize_trial(agents)\n",
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"\n",
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" log = f\"\"\"\n",
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"########################################\n",
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"BEGIN TRIAL {trial_n}\n",
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"Trial summary: Correct: {len(correct)}, Incorrect: {len(incorrect)}, Halted: {len(halted)}\n",
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"#######################################\n",
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"\"\"\"\n",
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"\n",
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" log += '------------- BEGIN CORRECT AGENTS -------------\\n\\n'\n",
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" for agent in correct:\n",
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" log += f'Question: {agent.question}{agent.scratchpad}\\nCorrect answer: {agent.key}\\n\\n'\n",
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"\n",
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" log += '------------- BEGIN INCORRECT AGENTS -----------\\n\\n'\n",
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" for agent in incorrect:\n",
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" log += f'Question: {agent.question}{agent.scratchpad}\\nCorrect answer: {agent.key}\\n\\n'\n",
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"\n",
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" log += '------------- BEGIN HALTED AGENTS --------------\\n\\n'\n",
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" for agent in halted:\n",
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" log += f'Question: {agent.question}{agent.scratchpad}\\nCorrect answer: {agent.key}\\n\\n'\n",
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"\n",
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" return log"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"hotpot = joblib.load('data/hotpot-qa-distractor-sample.joblib').reset_index(drop = True)"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"agents = [ReactAgent(row['question'], row['answer']) for _, row in hotpot.iterrows()]"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"trial = 0\n",
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"log = ''"
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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": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"q = 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": 22,
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"metadata": {},
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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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"Trial: 4 (0/66)\n",
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"Trial: 4 (1/66)\n",
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"Trial: 4 (64/66)\n",
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"Trial: 4 (65/66)\n",
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"Finished Trial 5, Correct: 34, Incorrect: 56, Halted: 12\n"
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]
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}
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],
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"source": [
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"agents_to_run = [a for a in agents if not a.is_correct()]\n",
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"\n",
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"while q < len(agents_to_run):\n",
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" print(f'Trial: {trial} ({q}/{len(agents_to_run)})')\n",
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" agents_to_run[q].run()\n",
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" q += 1\n",
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"\n",
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"trial += 1\n",
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"\n",
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"log += log_trial(agents, trial)\n",
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"correct, incorrect, halted = summarize_trial(agents)\n",
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"print(f'Finished Trial {trial}, Correct: {len(correct)}, Incorrect: {len(incorrect)}, Halted: {len(halted)}')"
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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": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('output/base_react/100_questions_5_trials.txt', 'w') as f:\n",
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" f.write(log)"
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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": 26,
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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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"['output/base_react_dicts.joblib']"
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]
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},
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"execution_count": 26,
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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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"dicts = [dict(a.__dict__) for a in agents]\n",
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"for d in dicts:\n",
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" for k, v in d.items():\n",
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" d[k] = str(v)\n",
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"\n",
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"joblib.dump(dicts, 'output/base_react_dicts.joblib')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "env",
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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": "3.8.9"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "e23f799cbd2581634725fbf6ce3480ae26192d78438dfafc8efe944acd6490d5"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,215 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"import joblib\n",
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"from react_cls import ReactReflectAgent, format_reflections\n",
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"from mocks import DocStoreExplorerMock, LLMMock"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"def summarize_trial(agents):\n",
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" correct = [a for a in agents if a.is_correct()]\n",
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" incorrect = [a for a in agents if a.is_finished() and not a.is_correct()]\n",
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" return correct, incorrect\n",
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"\n",
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"def remove_fewshot(prompt: str) -> str:\n",
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" prefix = prompt.split('Here are some examples:')[0]\n",
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" suffix = prompt.split('(END OF EXAMPLES)')[1]\n",
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" return prefix.strip('\\n').strip() +'\\n' + suffix.strip('\\n').strip()\n",
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"\n",
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"def log_trial(agents, trial_n):\n",
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" correct, incorrect = summarize_trial(agents)\n",
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"\n",
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" log = f\"\"\"\n",
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"########################################\n",
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"BEGIN TRIAL {trial_n}\n",
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"Trial summary: Correct: {len(correct)}, Incorrect: {len(incorrect)}\n",
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"#######################################\n",
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"\"\"\"\n",
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"\n",
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" log += '------------- BEGIN CORRECT AGENTS -------------\\n\\n'\n",
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" for agent in correct:\n",
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" log += remove_fewshot(agent._build_agent_prompt()) + f'\\nCorrect answer: {agent.key}\\n\\n'\n",
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"\n",
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" log += '------------- BEGIN INCORRECT AGENTS -----------\\n\\n'\n",
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" for agent in incorrect:\n",
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" log += remove_fewshot(agent._build_agent_prompt()) + f'\\nCorrect answer: {agent.key}\\n\\n'\n",
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"\n",
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" return log\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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"hotpot = joblib.load('data/hotpot-qa-distractor-sample.joblib').reset_index(drop = True)"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"agents = [ReactReflectAgent(row['question'], row['answer']) for _, row in hotpot.iterrows()]"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"trial = 0\n",
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"log = ''\n",
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"last_correct = 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for agent in [a for a in agents if not a.is_correct()]:\n",
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" agent.run(reflect_strategy='last_attempt')\n",
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" print(f'Answer: {agent.key}')\n",
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"trial += 1\n",
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"log += log_trial(agents, trial)\n",
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"correct, incorrect = summarize_trial(agents)\n",
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"print(f'Finished Trial {trial}, Correct: {len(correct)}, Incorrect: {len(incorrect)}')"
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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": 17,
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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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"['output/last_trial_react/react_incorrect_dicts_trial_0.joblib']"
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]
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},
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"execution_count": 17,
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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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"dicts = [dict(a.__dict__) for a in incorrect]\n",
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"for d in dicts:\n",
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" for k, v in d.items():\n",
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" d[k] = str(v)\n",
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"\n",
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"joblib.dump(dicts, 'output/last_trial_react/react_incorrect_dicts_trial_0.joblib')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"while last_correct != correct:\n",
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" last_correct, _ = summarize_trial(agents)\n",
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" for agent in [a for a in agents if not a.is_correct()]:\n",
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" agent.run(reflect_strategy='last_attempt')\n",
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" print(f'Answer: {agent.key}')\n",
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" trial += 1\n",
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" log += log_trial(agents, trial)\n",
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" correct, incorrect = summarize_trial(agents)\n",
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" print(f'Finished Trial {trial}, Correct: {len(correct)}, Incorrect: {len(incorrect)}')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"for agent in [a for a in agents if not a.is_correct()]:\n",
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" agent.run(reflect_strategy='last_attempt + reflexion')\n",
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" print(f'Answer: {agent.key}')\n",
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"trial += 1\n",
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"log += log_trial(agents, trial)\n",
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"correct, incorrect = summarize_trial(agents)\n",
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"print(f'Finished Trial {trial}, Correct: {len(correct)}, Incorrect: {len(incorrect)}')"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"with open('output/last_trial_react/100_questions_5_trials.txt', 'w') as f:\n",
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" f.write(log)"
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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": 18,
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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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"['output/reflect/react_reflect_50_correct_dicts.joblib']"
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]
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},
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"execution_count": 18,
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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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"dicts = [dict(a.__dict__) for a in correct]\n",
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"for d in dicts:\n",
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" for k, v in d.items():\n",
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" d[k] = str(v)\n",
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"\n",
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"joblib.dump(dicts, 'output/reflect/react_reflect_50_correct_dicts.joblib')"
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]
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}
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],
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"metadata": {
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||||
"kernelspec": {
|
||||
"display_name": "env",
|
||||
"language": "python",
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||||
"name": "python3"
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||||
},
|
||||
"language_info": {
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||||
"codemirror_mode": {
|
||||
"name": "ipython",
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||||
"version": 3
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||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.9"
|
||||
},
|
||||
"orig_nbformat": 4,
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "e23f799cbd2581634725fbf6ce3480ae26192d78438dfafc8efe944acd6490d5"
|
||||
}
|
||||
}
|
||||
},
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||||
"nbformat": 4,
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||||
"nbformat_minor": 2
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||||
}
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@ -0,0 +1,172 @@
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import os
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from typing import List
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import dotenv
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||||
|
||||
import gym
|
||||
import tiktoken
|
||||
from langchain import OpenAI
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.prompts import PromptTemplate
|
||||
|
||||
from environment import QAEnv
|
||||
from prompts import reflect_prompt, react_agent_prompt, react_reflect_agent_prompt, REFLECTION_HEADER
|
||||
from fewshots import WEBTHINK_SIMPLE6, REFLECTIONS
|
||||
|
||||
dotenv.load_dotenv()
|
||||
|
||||
class ReactAgent:
|
||||
"""
|
||||
A question answering ReAct Agent.
|
||||
"""
|
||||
def __init__(self,
|
||||
question: str,
|
||||
env: QAEnv,
|
||||
agent_prompt: PromptTemplate = react_agent_prompt,
|
||||
react_llm: BaseLLM = OpenAI(
|
||||
temperature=0,
|
||||
max_tokens=100,
|
||||
model_name="text-davinci-003",
|
||||
model_kwargs={"stop": "\n"},
|
||||
openai_api_key=os.environ['OPENAI_API_KEY']),
|
||||
) -> None:
|
||||
|
||||
self.question = question
|
||||
self.agent_prompt = agent_prompt
|
||||
self.react_examples = WEBTHINK_SIMPLE6
|
||||
|
||||
self.env = env
|
||||
self.env.reset()
|
||||
self.reset()
|
||||
self.truncated, self.reward, self.terminated = False, False, False
|
||||
|
||||
self.llm = react_llm
|
||||
|
||||
self.enc = tiktoken.encoding_for_model("text-davinci-003")
|
||||
|
||||
def run(self, reset = True) -> None:
|
||||
if reset:
|
||||
self.env.reset()
|
||||
self.reset()
|
||||
|
||||
while not (self.is_truncated() or self.is_terminated()):
|
||||
self.step()
|
||||
|
||||
def step(self) -> None:
|
||||
# Think
|
||||
self.scratchpad += f'\nThought {self.curr_step}:'
|
||||
self.scratchpad += ' ' + self.prompt_agent()
|
||||
print(self.scratchpad.split('\n')[-1])
|
||||
|
||||
# Act
|
||||
self.scratchpad += f'\nAction {self.curr_step}:'
|
||||
action = self.prompt_agent()
|
||||
self.scratchpad += ' ' + action
|
||||
print(self.scratchpad.split('\n')[-1])
|
||||
|
||||
# Observe
|
||||
self.scratchpad += f'\nObservation {self.curr_step}: '
|
||||
observation, self.reward, self.terminated, self.truncated, self.curr_step = self.env.step(action)
|
||||
self.scratchpad += observation
|
||||
print(self.scratchpad.split('\n')[-1])
|
||||
|
||||
def prompt_agent(self) -> str:
|
||||
return format_step(self.llm(self._build_agent_prompt()))
|
||||
|
||||
def _build_agent_prompt(self) -> str:
|
||||
return self.agent_prompt.format(
|
||||
examples = self.react_examples,
|
||||
question = self.question,
|
||||
scratchpad = self.scratchpad)
|
||||
|
||||
def is_terminated(self) -> bool:
|
||||
return self.env.is_terminated()
|
||||
|
||||
def is_correct(self) -> bool:
|
||||
return self.env.is_correct()
|
||||
|
||||
def is_truncated(self) -> bool:
|
||||
return self.env.is_truncated() or (len(self.enc.encode(self._build_agent_prompt())) > 3896)
|
||||
|
||||
def reset(self) -> None:
|
||||
self.scratchpad = ''
|
||||
self.curr_step = 1
|
||||
|
||||
|
||||
class ReactReflectAgent(ReactAgent):
|
||||
"""
|
||||
A question answering Self-Reflecting React Agent.
|
||||
"""
|
||||
def __init__(self,
|
||||
question: str,
|
||||
env: QAEnv,
|
||||
agent_prompt: PromptTemplate = react_reflect_agent_prompt,
|
||||
reflect_prompt: PromptTemplate = reflect_prompt,
|
||||
react_llm: BaseLLM = OpenAI(
|
||||
temperature=0,
|
||||
max_tokens=100,
|
||||
model_name="text-davinci-003",
|
||||
model_kwargs={"stop": "\n"},
|
||||
openai_api_key=os.environ['OPENAI_API_KEY']),
|
||||
reflect_llm: BaseLLM = OpenAI(
|
||||
temperature=0,
|
||||
max_tokens=250,
|
||||
model_name="text-davinci-003",
|
||||
openai_api_key=os.environ['OPENAI_API_KEY']),
|
||||
) -> None:
|
||||
|
||||
super().__init__(question, env, agent_prompt, react_llm)
|
||||
self.reflect_llm = reflect_llm
|
||||
self.reflect_prompt = reflect_prompt
|
||||
self.reflect_examples = REFLECTIONS
|
||||
self.reflections = []
|
||||
|
||||
def run(self, reset = True) -> None:
|
||||
if (self.is_terminated() or self.is_truncated()) and not self.is_correct():
|
||||
self.reflect()
|
||||
|
||||
ReactAgent.run(self, reset)
|
||||
|
||||
def reflect(self) -> None:
|
||||
self.reflections.append(self.prompt_reflection())
|
||||
|
||||
def prompt_reflection(self) -> str:
|
||||
return format_step(self.reflect_llm(self._build_reflection_prompt()))
|
||||
|
||||
|
||||
def _build_reflection_prompt(self) -> str:
|
||||
return self.reflect_prompt.format(
|
||||
examples = self.reflect_examples,
|
||||
question = self.question,
|
||||
scratchpad = self._format_scratchpad())
|
||||
|
||||
def _build_agent_prompt(self) -> str:
|
||||
return self.agent_prompt.format(
|
||||
examples = self.react_examples,
|
||||
reflections = format_reflections(self.reflections),
|
||||
question = self.question,
|
||||
scratchpad = self.scratchpad)
|
||||
|
||||
def _format_scratchpad(self) -> str:
|
||||
lines = self.scratchpad.split('\n')
|
||||
lines_by_tokens = sorted(lines, key=lambda x: len(self.enc.encode(x)))
|
||||
while len(self.enc.encode('\n'.join(lines))) > 1600:
|
||||
ind = lines.index(lines_by_tokens.pop(-1))
|
||||
line = lines[ind]
|
||||
lines[ind] = line.split(':')[0] + ': ...'
|
||||
return '\n'.join(lines)
|
||||
|
||||
|
||||
|
||||
### String Operations ###
|
||||
def format_reflections(reflections: List[str]) -> str:
|
||||
if reflections == []:
|
||||
return ''
|
||||
else:
|
||||
header = REFLECTION_HEADER
|
||||
return header + 'Reflections:\n- ' + '\n- '.join([r.strip() for r in reflections])
|
||||
|
||||
def format_step(step: str) -> str:
|
||||
return step.strip('\n').strip().replace('\n', '')
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,84 @@
|
||||
import joblib
|
||||
from react_cls import CoTAgent
|
||||
from mocks import DocStoreExplorerMock, LLMMock
|
||||
import numpy as np
|
||||
|
||||
def summarize_trial(agents):
|
||||
correct = [a for a in agents if a.is_correct()]
|
||||
incorrect = [a for a in agents if a.is_finished() and not a.is_correct()]
|
||||
return correct, incorrect
|
||||
|
||||
def log_trial(agents, trial_n):
|
||||
correct, incorrect = summarize_trial(agents)
|
||||
|
||||
log = f"""
|
||||
########################################
|
||||
BEGIN TRIAL {trial_n}
|
||||
Trial summary: Correct: {len(correct)}, Incorrect: {len(incorrect)}
|
||||
#######################################
|
||||
"""
|
||||
|
||||
log += '------------- BEGIN CORRECT AGENTS -------------\n\n'
|
||||
for agent in correct:
|
||||
log += f'Context: {agent.context} Question: {agent.question}{agent.scratchpad}\nCorrect answer: {agent.key}\n\n'
|
||||
|
||||
log += '------------- BEGIN INCORRECT AGENTS -----------\n\n'
|
||||
for agent in incorrect:
|
||||
log += f'Context: {agent.context} Question: {agent.question}{agent.scratchpad}\nCorrect answer: {agent.key}\n\n'
|
||||
return log
|
||||
|
||||
if __name__ == '__main__':
|
||||
hotpot = joblib.load('data/hotpot-qa-distractor-sample.joblib').reset_index(drop = True)
|
||||
hotpot['supporting_paragraphs'] = None
|
||||
for ind, row in hotpot.iterrows():
|
||||
supporting_articles = row['supporting_facts']['title']
|
||||
articles = row['context']['title']
|
||||
sentences = row['context']['sentences']
|
||||
supporting_paragraphs = []
|
||||
for article in supporting_articles:
|
||||
supporting_paragraph = ''.join(sentences[np.where(articles == article)][0])
|
||||
supporting_paragraphs.append(supporting_paragraph)
|
||||
hotpot.at[ind, 'supporting_paragraphs'] = supporting_paragraphs
|
||||
|
||||
for ind, row in hotpot.iterrows():
|
||||
supporting_paragraphs = row['supporting_paragraphs']
|
||||
supporting_paragraphs = '\n\n'.join(supporting_paragraphs)
|
||||
hotpot.at[ind, 'supporting_paragraphs'] = supporting_paragraphs
|
||||
|
||||
agents = [CoTAgent(row['question'], row['supporting_paragraphs'], row['answer']) for _, row in hotpot.iterrows()]
|
||||
trial = 0
|
||||
log = ''
|
||||
for agent in [a for a in agents if not a.is_correct()]:
|
||||
agent.run(reflect = False)
|
||||
print(f'Answer: {agent.key}')
|
||||
trial += 1
|
||||
|
||||
log += log_trial(agents, trial)
|
||||
correct, incorrect = summarize_trial(agents)
|
||||
print(f'Finished Trial {trial}, Correct: {len(correct)}, Incorrect: {len(incorrect)}')
|
||||
dicts = [dict(a.__dict__) for a in agents]
|
||||
for d in dicts:
|
||||
for k, v in d.items():
|
||||
d[k] = str(v)
|
||||
|
||||
joblib.dump(dicts, 'output/base_cot/cot_reflect_50_correct_dicts-8-trials.joblib')
|
||||
print(log)
|
||||
|
||||
with open('output/base_cot/100_questions_8_trials.txt', 'w') as f:
|
||||
f.write(log)
|
||||
|
||||
trial = 0
|
||||
log = ''
|
||||
q = 0
|
||||
agents_to_run = [a for a in agents if not a.is_correct()]
|
||||
|
||||
while q < len(agents_to_run):
|
||||
print(f'Trial: {trial} ({q}/{len(agents_to_run)})')
|
||||
agents_to_run[q].run()
|
||||
q += 1
|
||||
|
||||
trial += 1
|
||||
|
||||
log += log_trial(agents, trial)
|
||||
correct, incorrect, halted = summarize_trial(agents)
|
||||
print(f'Finished Trial {trial}, Correct: {len(correct)}, Incorrect: {len(incorrect)}, Halted: {len(halted)}')
|
||||
Binary file not shown.
@ -0,0 +1,101 @@
|
||||
import re
|
||||
import string
|
||||
from typing import Tuple
|
||||
|
||||
import gym
|
||||
from langchain import Wikipedia
|
||||
from langchain.agents.react.base import DocstoreExplorer
|
||||
|
||||
class QAEnv(gym.Env):
|
||||
def __init__(self,
|
||||
question: str,
|
||||
key: str,
|
||||
max_steps: int = 6,
|
||||
explorer: DocstoreExplorer = DocstoreExplorer(Wikipedia())):
|
||||
|
||||
self.question = question
|
||||
self.key = key
|
||||
self.max_steps = max_steps
|
||||
self.explorer = explorer
|
||||
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.curr_step = 0
|
||||
self.terminated = False
|
||||
self.answer = ''
|
||||
|
||||
def step(self, action: str) -> Tuple[str, bool, bool, bool, bool]:
|
||||
action_type, argument = parse_action(action)
|
||||
|
||||
if action_type == 'Finish':
|
||||
self.answer = argument
|
||||
if self.is_correct():
|
||||
observation = 'Answer is CORRECT'
|
||||
else:
|
||||
observation = 'Answer is INCORRECT'
|
||||
self.terminated = True
|
||||
|
||||
elif action_type == 'Search':
|
||||
try:
|
||||
observation = self.explorer.search(argument).strip('\n').strip()
|
||||
except Exception as e:
|
||||
print(e)
|
||||
observation = f'Could not find that page, please try again.'
|
||||
|
||||
elif action_type == 'Lookup':
|
||||
try:
|
||||
observation = self.explorer.lookup(argument).strip('\n').strip()
|
||||
except ValueError:
|
||||
observation = f'The last page Searched was not found, so you cannot Lookup a keyword in it. Please try one of the similar pages given.'
|
||||
|
||||
else:
|
||||
observation = 'Invalid Action. Valid Actions are Lookup[<topic>] Search[<topic>] and Finish[<answer>].'
|
||||
|
||||
reward = self.is_correct()
|
||||
terminated = self.is_terminated()
|
||||
truncated = self.is_truncated()
|
||||
|
||||
self.curr_step += 1
|
||||
|
||||
return observation, reward, terminated, truncated, self.curr_step
|
||||
|
||||
def is_correct(self) -> bool:
|
||||
return EM(self.answer, self.key)
|
||||
|
||||
def is_terminated(self) -> bool:
|
||||
return self.terminated
|
||||
|
||||
def is_truncated(self) -> bool:
|
||||
return self.curr_step >= self.max_steps
|
||||
|
||||
def parse_action(string):
|
||||
pattern = r'^(\w+)\[(.+)\]$'
|
||||
match = re.match(pattern, string)
|
||||
|
||||
if match:
|
||||
action_type = match.group(1)
|
||||
argument = match.group(2)
|
||||
return action_type, argument
|
||||
|
||||
else:
|
||||
return None, None
|
||||
|
||||
def normalize_answer(s):
|
||||
def remove_articles(text):
|
||||
return re.sub(r"\b(a|an|the)\b", " ", text)
|
||||
|
||||
def white_space_fix(text):
|
||||
return " ".join(text.split())
|
||||
|
||||
def remove_punc(text):
|
||||
exclude = set(string.punctuation)
|
||||
return "".join(ch for ch in text if ch not in exclude)
|
||||
|
||||
def lower(text):
|
||||
return text.lower()
|
||||
|
||||
return white_space_fix(remove_articles(remove_punc(lower(s))))
|
||||
|
||||
def EM(answer, key) -> bool:
|
||||
return normalize_answer(answer) == normalize_answer(key)
|
||||
@ -0,0 +1,43 @@
|
||||
from langchain.agents.react.base import DocstoreExplorer
|
||||
from langchain.llms.base import BaseLLM
|
||||
|
||||
def reactLLMMock(prompt: str) -> str:
|
||||
last_line = prompt.split('\n')[-1].strip()
|
||||
last_action = last_line.split(' ')[0].lower()
|
||||
if last_action == 'thought':
|
||||
return 'It does not mention the eastern sector. So I need to look up eastern sector.'
|
||||
elif last_action == 'action':
|
||||
return 'Lookup[eastern sector]'
|
||||
else:
|
||||
raise Exception('Invalid action type')
|
||||
|
||||
|
||||
def reflectLLMMock(prompt: str) -> str:
|
||||
return "Last time i should have answered correctly"
|
||||
|
||||
class LLMMock(BaseLLM):
|
||||
def __init__(self):
|
||||
...
|
||||
|
||||
def __call__(self, prompt: str) -> str:
|
||||
if prompt.split('\n')[0].split(' ')[0] == 'Solve':
|
||||
return reactLLMMock(prompt)
|
||||
|
||||
elif prompt.split('\n')[0].split(' ')[0] == 'You':
|
||||
return reflectLLMMock(prompt)
|
||||
else:
|
||||
raise Exception("Invalid LLM prompt")
|
||||
|
||||
def get_num_tokens(self, text: str) -> int:
|
||||
return 0
|
||||
|
||||
class DocStoreExplorerMock(DocstoreExplorer):
|
||||
def __init__(self):
|
||||
self.summary = "The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas."
|
||||
self.body = "(Result 1 / 1) The eastern sector extends into the High Plains and is called the Central Plains orogeny."
|
||||
|
||||
def search(self, search: str, sents: int = 5) -> str:
|
||||
return self.summary
|
||||
|
||||
def lookup(self, term: str) -> str:
|
||||
return self.body
|
||||
@ -0,0 +1,142 @@
|
||||
from langchain.prompts import PromptTemplate
|
||||
|
||||
COT_INSTRUCTION = """Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task. You will be given context that you should use to help you answer the question.
|
||||
Here are some examples:
|
||||
{examples}
|
||||
(END OF EXAMPLES)
|
||||
{reflections}
|
||||
Relevant Context: {context}
|
||||
Question: {question}{scratchpad}"""
|
||||
|
||||
COT_AGENT_REFLECT_INSTRUCTION = """Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task. You will be given context that you should use to help you answer the question.
|
||||
Here are some examples:
|
||||
{examples}
|
||||
(END OF EXAMPLES)
|
||||
|
||||
{reflections}
|
||||
|
||||
Relevant Context: {context}
|
||||
Question: {question}{scratchpad}"""
|
||||
|
||||
COT_REFLECT_INSTRUCTION = """You are an advanced reasoning agent that can improve based on self refection. You will be given a previous reasoning trial in which you were given access to relevant context and a question to answer. You were unsuccessful in answering the question either because you guessed the wrong answer with Finish[<answer>] or there is a phrasing discrepancy with your provided answer and the answer key. In a few sentences, Diagnose a possible reason for failure or phrasing discrepancy and devise a new, concise, high level plan that aims to mitigate the same failure. Use complete sentences.
|
||||
Here are some examples:
|
||||
{examples}
|
||||
(END OF EXAMPLES)
|
||||
|
||||
Previous trial:
|
||||
Relevant Context: {context}
|
||||
Question: {question}{scratchpad}
|
||||
|
||||
Reflection:"""
|
||||
|
||||
cot_agent_prompt = PromptTemplate(
|
||||
input_variables=["examples", "reflections", "context", "question", "scratchpad"],
|
||||
template = COT_INSTRUCTION,
|
||||
)
|
||||
|
||||
cot_reflect_agent_prompt = PromptTemplate(
|
||||
input_variables=["examples", "reflections", "context", "question", "scratchpad"],
|
||||
template = COT_AGENT_REFLECT_INSTRUCTION,
|
||||
)
|
||||
|
||||
cot_reflect_prompt = PromptTemplate(
|
||||
input_variables=["examples", "context", "question", "scratchpad"],
|
||||
template = COT_REFLECT_INSTRUCTION,
|
||||
)
|
||||
|
||||
COT_SIMPLE_INSTRUCTION = """Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task.
|
||||
Here are some examples:
|
||||
{examples}
|
||||
(END OF EXAMPLES)
|
||||
{reflections}
|
||||
{context}
|
||||
Question: {question}{scratchpad}"""
|
||||
|
||||
COT_SIMPLE_AGENT_REFLECT_INSTRUCTION = """Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task.
|
||||
Here are some examples:
|
||||
{examples}
|
||||
(END OF EXAMPLES)
|
||||
{context}
|
||||
{reflections}
|
||||
|
||||
Question: {question}{scratchpad}"""
|
||||
|
||||
COT_SIMPLE_REFLECT_INSTRUCTION = """You are an advanced reasoning agent that can improve based on self refection. You will be given a previous reasoning trial in which you were given a question to answer. You were unsuccessful in answering the question either because you guessed the wrong answer with Finish[<answer>] or there is a phrasing discrepancy with your provided answer and the answer key. In a few sentences, Diagnose a possible reason for failure or phrasing discrepancy and devise a new, concise, high level plan that aims to mitigate the same failure. Use complete sentences.
|
||||
Here are some examples:
|
||||
{examples}
|
||||
(END OF EXAMPLES)
|
||||
{context}
|
||||
Previous trial:
|
||||
Question: {question}{scratchpad}
|
||||
|
||||
Reflection:"""
|
||||
|
||||
cot_simple_agent_prompt = PromptTemplate(
|
||||
input_variables=["examples", "question", "reflections", "context", "scratchpad"],
|
||||
template = COT_SIMPLE_INSTRUCTION,
|
||||
)
|
||||
|
||||
cot_simple_reflect_agent_prompt = PromptTemplate(
|
||||
input_variables=["examples", "context", "reflections", "question", "scratchpad"],
|
||||
template = COT_SIMPLE_AGENT_REFLECT_INSTRUCTION,
|
||||
)
|
||||
|
||||
cot_simple_reflect_prompt = PromptTemplate(
|
||||
input_variables=["examples", "question", "context", "scratchpad"],
|
||||
template = COT_SIMPLE_REFLECT_INSTRUCTION,
|
||||
)
|
||||
|
||||
|
||||
REACT_INSTRUCTION = """Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
|
||||
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
|
||||
(2) Lookup[keyword], which returns the next sentence containing keyword in the last passage successfully found by Search.
|
||||
(3) Finish[answer], which returns the answer and finishes the task.
|
||||
You may take as many steps as necessary.
|
||||
Here are some examples:
|
||||
{examples}
|
||||
(END OF EXAMPLES)
|
||||
Question: {question}{scratchpad}"""
|
||||
|
||||
REACT_REFLECT_INSTRUCTION = """Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
|
||||
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
|
||||
(2) Lookup[keyword], which returns the next sentence containing keyword in the last passage successfully found by Search.
|
||||
(3) Finish[answer], which returns the answer and finishes the task.
|
||||
You may take as many steps as necessary.
|
||||
Here are some examples:
|
||||
{examples}
|
||||
(END OF EXAMPLES)
|
||||
|
||||
{reflections}
|
||||
|
||||
Question: {question}{scratchpad}"""
|
||||
|
||||
REFLECTION_HEADER = 'You have attempted to answer following question before and failed. The following reflection(s) give a plan to avoid failing to answer the question in the same way you did previously. Use them to improve your strategy of correctly answering the given question.\n'
|
||||
REFLECTION_AFTER_LAST_TRIAL_HEADER = 'The following reflection(s) give a plan to avoid failing to answer the question in the same way you did previously. Use them to improve your strategy of correctly answering the given question.\n'
|
||||
LAST_TRIAL_HEADER = 'You have attempted to answer the following question before and failed. Below is the last trial you attempted to answer the question.\n'
|
||||
|
||||
REFLECT_INSTRUCTION = """You are an advanced reasoning agent that can improve based on self refection. You will be given a previous reasoning trial in which you were given access to an Docstore API environment and a question to answer. You were unsuccessful in answering the question either because you guessed the wrong answer with Finish[<answer>], or you used up your set number of reasoning steps. In a few sentences, Diagnose a possible reason for failure and devise a new, concise, high level plan that aims to mitigate the same failure. Use complete sentences.
|
||||
Here are some examples:
|
||||
{examples}
|
||||
|
||||
Previous trial:
|
||||
Question: {question}{scratchpad}
|
||||
|
||||
Reflection:"""
|
||||
|
||||
react_agent_prompt = PromptTemplate(
|
||||
input_variables=["examples", "question", "scratchpad"],
|
||||
template = REACT_INSTRUCTION,
|
||||
)
|
||||
|
||||
react_reflect_agent_prompt = PromptTemplate(
|
||||
input_variables=["examples", "reflections", "question", "scratchpad"],
|
||||
template = REACT_REFLECT_INSTRUCTION,
|
||||
)
|
||||
|
||||
reflect_prompt = PromptTemplate(
|
||||
input_variables=["examples", "question", "scratchpad"],
|
||||
template = REFLECT_INSTRUCTION,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,379 @@
|
||||
import re, string, os
|
||||
from typing import List, Union, Literal
|
||||
|
||||
import tiktoken
|
||||
from langchain import OpenAI, Wikipedia
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.agents.react.base import DocstoreExplorer
|
||||
from langchain.docstore.base import Docstore
|
||||
from langchain.prompts import PromptTemplate
|
||||
from prompts import reflect_prompt, react_agent_prompt, react_reflect_agent_prompt, REFLECTION_HEADER, LAST_TRIAL_HEADER, REFLECTION_AFTER_LAST_TRIAL_HEADER
|
||||
from prompts import cot_agent_prompt, cot_reflect_agent_prompt, cot_reflect_prompt, COT_INSTRUCTION, COT_REFLECT_INSTRUCTION
|
||||
from fewshots import WEBTHINK_SIMPLE6, REFLECTIONS, COT, COT_REFLECT
|
||||
|
||||
class CoTAgent:
|
||||
def __init__(self,
|
||||
question: str,
|
||||
context: str,
|
||||
key: str,
|
||||
agent_prompt: PromptTemplate = cot_reflect_agent_prompt,
|
||||
reflect_prompt: PromptTemplate = cot_reflect_prompt,
|
||||
reflect_header: str = REFLECTION_HEADER,
|
||||
cot_examples: str = COT,
|
||||
reflect_examples: str = COT_REFLECT,
|
||||
self_reflect_llm: BaseLLM = OpenAI(
|
||||
temperature=0,
|
||||
max_tokens=250,
|
||||
model_name="text-davinci-003",
|
||||
model_kwargs={"stop": "\n"},
|
||||
openai_api_key=os.environ['OPENAI_API_KEY']),
|
||||
action_llm: BaseLLM = OpenAI(
|
||||
temperature=0,
|
||||
max_tokens=250,
|
||||
model_name="text-davinci-003",
|
||||
model_kwargs={"stop": "\n"},
|
||||
openai_api_key=os.environ['OPENAI_API_KEY']),
|
||||
) -> None:
|
||||
|
||||
self.question = question
|
||||
self.context = context
|
||||
self.key = key
|
||||
self.agent_prompt = agent_prompt
|
||||
self.reflect_prompt = reflect_prompt
|
||||
self.reflect_header = reflect_header
|
||||
self.cot_examples = cot_examples
|
||||
self.reflect_examples = reflect_examples
|
||||
self.self_reflect_llm = self_reflect_llm
|
||||
self.action_llm = action_llm
|
||||
self.reflections: List[str] = []
|
||||
self.reflections_str = ''
|
||||
self.answer = ''
|
||||
self.step_n: int = 0
|
||||
self.reset()
|
||||
|
||||
def run(self, reflect: bool = True,
|
||||
reflect_strategy: Union[Literal['last_attempt'],
|
||||
Literal['reflexion'],
|
||||
Literal['last_attempt + reflexion']] = 'reflexion') -> None:
|
||||
if self.step_n > 0 and not self.is_correct() and reflect:
|
||||
self.reflect(reflect_strategy)
|
||||
self.reset()
|
||||
self.step()
|
||||
self.step_n += 1
|
||||
|
||||
def step(self) -> None:
|
||||
# Think
|
||||
self.scratchpad += f'\nThought:'
|
||||
self.scratchpad += ' ' + self.prompt_agent()
|
||||
print(self.scratchpad.split('\n')[-1])
|
||||
|
||||
# Act
|
||||
self.scratchpad += f'\nAction:'
|
||||
action = self.prompt_agent()
|
||||
self.scratchpad += ' ' + action
|
||||
action_type, argument = parse_action(action)
|
||||
print(self.scratchpad.split('\n')[-1])
|
||||
|
||||
self.scratchpad += f'\nObservation: '
|
||||
if action_type == 'Finish':
|
||||
self.answer = argument
|
||||
if self.is_correct():
|
||||
self.scratchpad += 'Answer is CORRECT'
|
||||
else:
|
||||
self.scratchpad += 'Answer is INCORRECT'
|
||||
self.finished = True
|
||||
return
|
||||
else:
|
||||
print('Invalid action type, please try again.')
|
||||
|
||||
def reflect(self,
|
||||
strategy: Union[Literal['last_attempt'],
|
||||
Literal['reflexion'],
|
||||
Literal['last_attempt + reflexion']]) -> None:
|
||||
print('Reflecting...')
|
||||
if strategy == 'last_attempt':
|
||||
self.reflections = [self.scratchpad]
|
||||
self.reflections_str = format_last_attempt(self.question , self.reflections[0])
|
||||
elif strategy == 'reflexion':
|
||||
self.reflections += [self.prompt_reflection()]
|
||||
self.reflections_str = format_reflections(self.reflections)
|
||||
elif strategy == 'last_attempt + reflexion':
|
||||
self.reflections_str = format_last_attempt(self.question , self.scratchpad)
|
||||
self.reflections = [self.prompt_reflection()]
|
||||
self.reflections_str += '\n'+ format_reflections(self.reflections, header = REFLECTION_AFTER_LAST_TRIAL_HEADER)
|
||||
else:
|
||||
raise NotImplementedError(f'Unknown reflection strategy: {strategy}')
|
||||
print(self.reflections_str)
|
||||
|
||||
def prompt_reflection(self) -> str:
|
||||
return format_step(self.self_reflect_llm(self._build_reflection_prompt()))
|
||||
|
||||
def reset(self) -> None:
|
||||
|
||||
self.scratchpad: str = ''
|
||||
self.finished = False
|
||||
|
||||
def prompt_agent(self) -> str:
|
||||
return format_step(self.action_llm(self._build_agent_prompt()))
|
||||
|
||||
def _build_agent_prompt(self) -> str:
|
||||
return self.agent_prompt.format(
|
||||
examples = self.cot_examples,
|
||||
reflections = self.reflections_str,
|
||||
context = self.context,
|
||||
question = self.question,
|
||||
scratchpad = self.scratchpad)
|
||||
|
||||
def _build_reflection_prompt(self) -> str:
|
||||
return self.reflect_prompt.format(
|
||||
examples = self.reflect_examples,
|
||||
context = self.context,
|
||||
question = self.question,
|
||||
scratchpad = self.scratchpad)
|
||||
|
||||
def is_finished(self) -> bool:
|
||||
return self.finished
|
||||
|
||||
def is_correct(self) -> bool:
|
||||
return EM(self.answer, self.key)
|
||||
|
||||
class ReactAgent:
|
||||
def __init__(self,
|
||||
question: str,
|
||||
key: str,
|
||||
max_steps: int = 6,
|
||||
agent_prompt: PromptTemplate = react_agent_prompt,
|
||||
docstore: Docstore = Wikipedia(),
|
||||
react_llm: BaseLLM = OpenAI(
|
||||
temperature=0,
|
||||
max_tokens=100,
|
||||
model_name="text-davinci-003",
|
||||
model_kwargs={"stop": "\n"},
|
||||
openai_api_key=os.environ['OPENAI_API_KEY']),
|
||||
) -> None:
|
||||
|
||||
self.question = question
|
||||
self.answer = ''
|
||||
self.key = key
|
||||
self.max_steps = max_steps
|
||||
self.agent_prompt = agent_prompt
|
||||
self.react_examples = WEBTHINK_SIMPLE6
|
||||
|
||||
self.docstore = DocstoreExplorer(docstore) # Search, Lookup
|
||||
self.llm = react_llm
|
||||
|
||||
self.enc = tiktoken.encoding_for_model("text-davinci-003")
|
||||
|
||||
self.__reset_agent()
|
||||
|
||||
def run(self, reset = True) -> None:
|
||||
if reset:
|
||||
self.__reset_agent()
|
||||
|
||||
while not self.is_halted() and not self.is_finished():
|
||||
self.step()
|
||||
|
||||
def step(self) -> None:
|
||||
# Think
|
||||
self.scratchpad += f'\nThought {self.step_n}:'
|
||||
self.scratchpad += ' ' + self.prompt_agent()
|
||||
print(self.scratchpad.split('\n')[-1])
|
||||
|
||||
# Act
|
||||
self.scratchpad += f'\nAction {self.step_n}:'
|
||||
action = self.prompt_agent()
|
||||
self.scratchpad += ' ' + action
|
||||
action_type, argument = parse_action(action)
|
||||
print(self.scratchpad.split('\n')[-1])
|
||||
|
||||
# Observe
|
||||
self.scratchpad += f'\nObservation {self.step_n}: '
|
||||
|
||||
if action_type == 'Finish':
|
||||
self.answer = argument
|
||||
if self.is_correct():
|
||||
self.scratchpad += 'Answer is CORRECT'
|
||||
else:
|
||||
self.scratchpad += 'Answer is INCORRECT'
|
||||
self.finished = True
|
||||
self.step_n += 1
|
||||
return
|
||||
|
||||
if action_type == 'Search':
|
||||
try:
|
||||
self.scratchpad += format_step(self.docstore.search(argument))
|
||||
except Exception as e:
|
||||
print(e)
|
||||
self.scratchpad += f'Could not find that page, please try again.'
|
||||
|
||||
elif action_type == 'Lookup':
|
||||
try:
|
||||
self.scratchpad += format_step(self.docstore.lookup(argument))
|
||||
except ValueError:
|
||||
self.scratchpad += f'The last page Searched was not found, so you cannot Lookup a keyword in it. Please try one of the similar pages given.'
|
||||
|
||||
else:
|
||||
self.scratchpad += 'Invalid Action. Valid Actions are Lookup[<topic>] Search[<topic>] and Finish[<answer>].'
|
||||
|
||||
print(self.scratchpad.split('\n')[-1])
|
||||
|
||||
self.step_n += 1
|
||||
|
||||
def prompt_agent(self) -> str:
|
||||
return format_step(self.llm(self._build_agent_prompt()))
|
||||
|
||||
def _build_agent_prompt(self) -> str:
|
||||
return self.agent_prompt.format(
|
||||
examples = self.react_examples,
|
||||
question = self.question,
|
||||
scratchpad = self.scratchpad)
|
||||
|
||||
def is_finished(self) -> bool:
|
||||
return self.finished
|
||||
|
||||
def is_correct(self) -> bool:
|
||||
return EM(self.answer, self.key)
|
||||
|
||||
def is_halted(self) -> bool:
|
||||
return ((self.step_n > self.max_steps) or (len(self.enc.encode(self._build_agent_prompt())) > 3896)) and not self.finished
|
||||
|
||||
def __reset_agent(self) -> None:
|
||||
self.step_n = 1
|
||||
self.finished = False
|
||||
self.scratchpad: str = ''
|
||||
|
||||
def set_qa(self, question: str, key: str) -> None:
|
||||
self.question = question
|
||||
self.key = key
|
||||
|
||||
class ReactReflectAgent(ReactAgent):
|
||||
def __init__(self,
|
||||
question: str,
|
||||
key: str,
|
||||
max_steps: int = 6,
|
||||
agent_prompt: PromptTemplate = react_reflect_agent_prompt,
|
||||
reflect_prompt: PromptTemplate = reflect_prompt,
|
||||
reflect_header: str = REFLECTION_HEADER,
|
||||
docstore: Docstore = Wikipedia(),
|
||||
react_llm: BaseLLM = OpenAI(
|
||||
temperature=0,
|
||||
max_tokens=100,
|
||||
model_name="text-davinci-003",
|
||||
model_kwargs={"stop": "\n"},
|
||||
openai_api_key=os.environ['OPENAI_API_KEY']),
|
||||
reflect_llm: BaseLLM = OpenAI(
|
||||
temperature=0,
|
||||
max_tokens=250,
|
||||
model_name="text-davinci-003",
|
||||
openai_api_key=os.environ['OPENAI_API_KEY']),
|
||||
) -> None:
|
||||
|
||||
super().__init__(question, key, max_steps, agent_prompt, docstore, react_llm)
|
||||
self.reflect_header = reflect_header
|
||||
self.reflect_llm = reflect_llm
|
||||
self.reflect_prompt = reflect_prompt
|
||||
self.reflect_examples = REFLECTIONS
|
||||
self.reflections: List[str] = []
|
||||
self.reflections_str: str = ''
|
||||
|
||||
def run(self, reset = True, reflect_strategy: Union[Literal['last_attempt'], Literal['reflexion'], Literal['last_attempt + reflexion']] = 'reflexion') -> None:
|
||||
if (self.is_finished() or self.is_halted()) and not self.is_correct():
|
||||
self.reflect(reflect_strategy)
|
||||
|
||||
ReactAgent.run(self, reset)
|
||||
|
||||
def reflect(self,
|
||||
strategy: Union[Literal['last_attempt'], Literal['reflexion'], Literal['last_attempt + reflexion']]) -> None:
|
||||
print('Reflecting...')
|
||||
if strategy == 'last_attempt':
|
||||
self.reflections = [self.scratchpad]
|
||||
self.reflections_str = format_last_attempt(self.question, self.reflections[0])
|
||||
elif strategy == 'reflexion':
|
||||
self.reflections += [self.prompt_reflection()]
|
||||
self.reflections_str = format_reflections(self.reflections)
|
||||
elif strategy == 'last_attempt + reflexion':
|
||||
self.reflections_str = format_last_attempt(self.question, self.scratchpad)
|
||||
self.reflections = [self.prompt_reflection()]
|
||||
self.reflections_str += format_reflections(self.reflections, header = REFLECTION_AFTER_LAST_TRIAL_HEADER)
|
||||
else:
|
||||
raise NotImplementedError(f'Unknown reflection strategy: {strategy}')
|
||||
print(self.reflections_str)
|
||||
|
||||
def prompt_reflection(self) -> str:
|
||||
return format_step(self.reflect_llm(self._build_reflection_prompt()))
|
||||
|
||||
def _build_reflection_prompt(self) -> str:
|
||||
return self.reflect_prompt.format(
|
||||
examples = self.reflect_examples,
|
||||
question = self.question,
|
||||
scratchpad = truncate_scratchpad(self.scratchpad, tokenizer=self.enc))
|
||||
|
||||
def _build_agent_prompt(self) -> str:
|
||||
return self.agent_prompt.format(
|
||||
examples = self.react_examples,
|
||||
reflections = self.reflections_str,
|
||||
question = self.question,
|
||||
scratchpad = self.scratchpad)
|
||||
|
||||
|
||||
### String Stuff ###
|
||||
gpt2_enc = tiktoken.encoding_for_model("text-davinci-003")
|
||||
|
||||
def parse_action(string):
|
||||
pattern = r'^(\w+)\[(.+)\]$'
|
||||
match = re.match(pattern, string)
|
||||
|
||||
if match:
|
||||
action_type = match.group(1)
|
||||
argument = match.group(2)
|
||||
return action_type, argument
|
||||
|
||||
else:
|
||||
return None
|
||||
|
||||
def format_step(step: str) -> str:
|
||||
return step.strip('\n').strip().replace('\n', '')
|
||||
|
||||
def format_reflections(reflections: List[str],
|
||||
header: str = REFLECTION_HEADER) -> str:
|
||||
if reflections == []:
|
||||
return ''
|
||||
else:
|
||||
return header + 'Reflections:\n- ' + '\n- '.join([r.strip() for r in reflections])
|
||||
|
||||
def format_last_attempt(question: str,
|
||||
scratchpad: str,
|
||||
header: str = LAST_TRIAL_HEADER):
|
||||
return header + f'Question: {question}\n' + truncate_scratchpad(scratchpad, tokenizer=gpt2_enc).strip('\n').strip() + '\n(END PREVIOUS TRIAL)\n'
|
||||
|
||||
def truncate_scratchpad(scratchpad: str, n_tokens: int = 1600, tokenizer = gpt2_enc) -> str:
|
||||
lines = scratchpad.split('\n')
|
||||
observations = filter(lambda x: x.startswith('Observation'), lines)
|
||||
observations_by_tokens = sorted(observations, key=lambda x: len(tokenizer.encode(x)))
|
||||
while len(gpt2_enc.encode('\n'.join(lines))) > n_tokens:
|
||||
largest_observation = observations_by_tokens.pop(-1)
|
||||
ind = lines.index(largest_observation)
|
||||
lines[ind] = largest_observation.split(':')[0] + ': [truncated wikipedia excerpt]'
|
||||
return '\n'.join(lines)
|
||||
|
||||
def normalize_answer(s):
|
||||
def remove_articles(text):
|
||||
return re.sub(r"\b(a|an|the)\b", " ", text)
|
||||
|
||||
def white_space_fix(text):
|
||||
return " ".join(text.split())
|
||||
|
||||
def remove_punc(text):
|
||||
exclude = set(string.punctuation)
|
||||
return "".join(ch for ch in text if ch not in exclude)
|
||||
|
||||
def lower(text):
|
||||
return text.lower()
|
||||
|
||||
return white_space_fix(remove_articles(remove_punc(lower(s))))
|
||||
|
||||
def EM(answer, key) -> bool:
|
||||
return normalize_answer(answer) == normalize_answer(key)
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,12 @@
|
||||
EdgeGPT==0.3.6
|
||||
gym==0.26.2
|
||||
joblib==1.2.0
|
||||
langchain==0.0.162
|
||||
numpy==1.24.1
|
||||
openai==0.27.4
|
||||
python-dotenv==1.0.0
|
||||
tenacity==8.2.2
|
||||
tiktoken==0.4.0
|
||||
transformers==4.28.1
|
||||
pandas==1.5.3
|
||||
scikit-learn
|
||||
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