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{
"cells": [
{
"cell_type": "markdown",
"id": "32e022a2",
"metadata": {},
"source": [
"# PAL\n",
"\n",
"Implements Program-Aided Language Models, as in https://arxiv.org/pdf/2211.10435.pdf.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1370e40f",
"metadata": {},
"outputs": [],
"source": [
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"from langchain.chains import PALChain\n",
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"from langchain import OpenAI"
]
},
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{
"cell_type": "code",
"execution_count": null,
"id": "9a58e15e",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)"
]
},
{
"cell_type": "markdown",
"id": "095adc76",
"metadata": {},
"source": [
"## Math Prompt"
]
},
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{
"cell_type": "code",
"execution_count": 2,
"id": "beddcac7",
"metadata": {},
"outputs": [],
"source": [
"pal_chain = PALChain.from_math_prompt(llm, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e2eab9d4",
"metadata": {},
"outputs": [],
"source": [
"question = \"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3ef64b27",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mdef solution():\n",
" \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
" cindy_pets = 4\n",
" marcia_pets = cindy_pets + 2\n",
" jan_pets = marcia_pets * 3\n",
" total_pets = cindy_pets + marcia_pets + jan_pets\n",
" result = total_pets\n",
" return result\u001b[0m\n",
"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
},
{
"data": {
"text/plain": [
"'28'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pal_chain.run(question)"
]
},
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{
"cell_type": "markdown",
"id": "0269d20a",
"metadata": {},
"source": [
"## Colored Objects"
]
},
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{
"cell_type": "code",
"execution_count": 5,
"id": "e524f81f",
"metadata": {},
"outputs": [],
"source": [
"pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "03a237b8",
"metadata": {},
"outputs": [],
"source": [
"question = \"On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a84a4352",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
"objects = []\n",
"objects += [('booklet', 'blue')] * 2\n",
"objects += [('booklet', 'purple')] * 2\n",
"objects += [('sunglasses', 'yellow')] * 2\n",
"\n",
"# Remove all pairs of sunglasses\n",
"objects = [object for object in objects if object[0] != 'sunglasses']\n",
"\n",
"# Count number of purple objects\n",
"num_purple = len([object for object in objects if object[1] == 'purple'])\n",
"answer = num_purple\u001b[0m\n",
"\n",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"\u001b[1m> Finished PALChain chain.\u001b[0m\n"
2022-11-29 05:38:34 +00:00
]
},
{
"data": {
"text/plain": [
"'2'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pal_chain.run(question)"
]
},
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{
"cell_type": "markdown",
"id": "fc3d7f10",
"metadata": {},
"source": [
"## Intermediate Steps\n",
"You can also use the intermediate steps flag to return the code executed that generates the answer."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9d2d9c61",
"metadata": {},
"outputs": [],
"source": [
"pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True, return_intermediate_steps=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b29b971b",
"metadata": {},
"outputs": [],
"source": [
"question = \"On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a2c40c28",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
"objects = []\n",
"objects += [('booklet', 'blue')] * 2\n",
"objects += [('booklet', 'purple')] * 2\n",
"objects += [('sunglasses', 'yellow')] * 2\n",
"\n",
"# Remove all pairs of sunglasses\n",
"objects = [object for object in objects if object[0] != 'sunglasses']\n",
"\n",
"# Count number of purple objects\n",
"num_purple = len([object for object in objects if object[1] == 'purple'])\n",
"answer = num_purple\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
}
],
"source": [
"result = pal_chain({\"question\": question})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "efddd033",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"# Put objects into a list to record ordering\\nobjects = []\\nobjects += [('booklet', 'blue')] * 2\\nobjects += [('booklet', 'purple')] * 2\\nobjects += [('sunglasses', 'yellow')] * 2\\n\\n# Remove all pairs of sunglasses\\nobjects = [object for object in objects if object[0] != 'sunglasses']\\n\\n# Count number of purple objects\\nnum_purple = len([object for object in objects if object[1] == 'purple'])\\nanswer = num_purple\""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result['intermediate_steps']"
]
},
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{
"cell_type": "code",
"execution_count": null,
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"id": "dfd88594",
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"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"version": "3.10.9"
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}
},
"nbformat": 4,
"nbformat_minor": 5
}