mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-01 19:12:42 +00:00
add custom prompt notebooks (#198)
This commit is contained in:
Harrison Chase
GitHub
176
docs/examples/prompts/custom_example_selector.ipynb
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176
docs/examples/prompts/custom_example_selector.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f897c784",
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"metadata": {},
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"source": [
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"# Custom ExampleSelector\n",
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"\n",
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"This notebook goes over how to implement a custom ExampleSelector. ExampleSelectors are used to select examples to use in few shot prompts.\n",
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"\n",
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"An ExampleSelector must implement two methods:\n",
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"\n",
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"1. An `add_example` method which takes in an example and adds it into the ExampleSelector\n",
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"2. A `select_examples` method which takes in input variables (which are meant to be user input) and returns a list of examples to use in the few shot prompt.\n",
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"\n",
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"\n",
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"Let's implement a custom ExampleSelector that just selects two examples at random."
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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": "1a945da1",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts.example_selector.base import BaseExampleSelector\n",
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"from typing import Dict, List\n",
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"import numpy as np"
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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": "62cf0ad7",
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"metadata": {},
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"outputs": [],
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"source": [
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"class CustomExampleSelector(BaseExampleSelector):\n",
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" \n",
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" def __init__(self, examples: List[Dict[str, str]]):\n",
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" self.examples = examples\n",
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" \n",
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" def add_example(self, example: Dict[str, str]) -> None:\n",
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" \"\"\"Add new example to store for a key.\"\"\"\n",
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" self.examples.append(example)\n",
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"\n",
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" def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:\n",
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" \"\"\"Select which examples to use based on the inputs.\"\"\"\n",
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" return np.random.choice(self.examples, size=2, replace=False)"
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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": "242d3213",
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"metadata": {},
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"outputs": [],
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"source": [
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"examples = [{\"foo\": \"1\"}, {\"foo\": \"2\"}, {\"foo\": \"3\"}]\n",
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"example_selector = CustomExampleSelector(examples)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a038065",
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"metadata": {},
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"source": [
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"Let's now try it out! We can select some examples and try adding examples."
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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": "74fbbef5",
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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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"array([{'foo': '2'}, {'foo': '3'}], dtype=object)"
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]
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},
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"execution_count": 4,
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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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"example_selector.select_examples({\"foo\": \"foo\"})"
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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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"id": "9bbb5421",
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"metadata": {},
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"outputs": [],
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"source": [
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"example_selector.add_example({\"foo\": \"4\"})"
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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": "c0eb9f22",
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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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"[{'foo': '1'}, {'foo': '2'}, {'foo': '3'}, {'foo': '4'}]"
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]
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},
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"execution_count": 6,
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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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"example_selector.examples"
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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": "cc39b1e3",
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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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"array([{'foo': '1'}, {'foo': '4'}], dtype=object)"
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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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"example_selector.select_examples({\"foo\": \"foo\"})"
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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": "1739dd96",
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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": "3.7.6"
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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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153
docs/examples/prompts/custom_llm.ipynb
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153
docs/examples/prompts/custom_llm.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9e9b7651",
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"metadata": {},
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"source": [
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"# Custom LLM\n",
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"\n",
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"This notebook goes over how to create a custom LLM wrapper, in case you want to use your own LLM or a different wrapper than one that is supported in LangChain.\n",
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"\n",
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"There is only one required thing that a custom LLM needs to implement:\n",
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"\n",
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"1. A `__call__` method that takes in a string, some optional stop words, and returns a string\n",
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"\n",
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"There is a second optional thing it can implement:\n",
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"\n",
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"1. An `_identifying_params` property that is used to help with printing of this class. Should return a dictionary.\n",
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"\n",
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"Let's implement a very simple custom LLM that just returns the first N characters of the input."
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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": "a65696a0",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms.base import LLM\n",
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"from typing import Optional, List, Mapping, Any"
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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": "d5ceff02",
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"metadata": {},
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"outputs": [],
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"source": [
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"class CustomLLM(LLM):\n",
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" \n",
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" def __init__(self, n: int):\n",
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" self.n = n\n",
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" \n",
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" def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str:\n",
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" if stop is not None:\n",
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" raise ValueError(\"stop kwargs are not permitted.\")\n",
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" return prompt[:self.n]\n",
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" \n",
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" @property\n",
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" def _identifying_params(self) -> Mapping[str, Any]:\n",
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" \"\"\"Get the identifying parameters.\"\"\"\n",
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" return {\"n\": self.n}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "714dede0",
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"metadata": {},
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"source": [
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"We can now use this as an any other LLM."
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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": "10e5ece6",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = CustomLLM(n=10)"
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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": "8cd49199",
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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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"'This is a '"
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]
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},
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"execution_count": 4,
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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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"llm(\"This is a foobar thing\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bbfebea1",
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"metadata": {},
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"source": [
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"We can also print the LLM and see its custom print."
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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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"id": "9c33fa19",
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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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"\u001b[1mCustomLLM\u001b[0m\n",
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"Params: {'n': 10}\n"
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]
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}
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],
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"source": [
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"print(llm)"
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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": "6dac3f47",
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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": "3.7.6"
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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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116
docs/examples/prompts/custom_prompt_template.ipynb
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116
docs/examples/prompts/custom_prompt_template.ipynb
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@@ -0,0 +1,116 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a37d9694",
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"metadata": {},
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"source": [
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"# Custom Prompt Template\n",
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"\n",
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"This notebook goes over how to create a custom prompt template, in case you want to create your own methodology for creating prompts.\n",
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"\n",
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"The only two requirements for all prompt templates are:\n",
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"\n",
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"1. They have a `input_variables` attribute that exposes what input variables this prompt template expects.\n",
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"2. They expose a `format` method which takes in keyword arguments corresponding to the expected `input_variables` and returns the formatted prompt.\n",
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"\n",
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"Let's imagine that we want to create a prompt template that takes in input variables and formats them into the template AFTER capitalizing them. "
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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": "26f796e5",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import BasePromptTemplate\n",
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"from pydantic import BaseModel"
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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": "27919e96",
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"metadata": {},
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"outputs": [],
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"source": [
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"class CustomPromptTemplate(BasePromptTemplate, BaseModel):\n",
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" template: str\n",
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" \n",
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" def format(self, **kwargs) -> str:\n",
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" capitalized_kwargs = {k: v.upper() for k, v in kwargs.items()}\n",
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" return self.template.format(**capitalized_kwargs)\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": "76d1d84d",
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"metadata": {},
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"source": [
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"We can now see that when we use this, the input variables get formatted."
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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": "eed1ff28",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt = CustomPromptTemplate(input_variables=[\"foo\"], template=\"Capitalized: {foo}\")"
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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": 9,
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"id": "94892a3c",
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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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"'Capitalized: LOWERCASE'"
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]
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},
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"execution_count": 9,
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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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"prompt.format(foo=\"lowercase\")"
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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": "d3d9a7c7",
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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": "3.7.6"
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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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@@ -602,7 +602,7 @@
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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": "3.10.4"
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"version": "3.7.6"
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}
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},
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"nbformat": 4,
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