forked from Archives/langchain
Refactor prompts into module, add example generation utils (#64)
Samantha Whitmore
2 years ago
committed by
GitHub
parent
dce26dfcec
commit
a5b61d59e1
@ -0,0 +1,121 @@
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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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"id": "1685fa2f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains.react.prompt import EXAMPLES\n",
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"from langchain.llms.openai import OpenAI\n",
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"from langchain.example_generator import generate_example, generate_example_from_dynamic_prompt"
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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": "334ef4f7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Question: What is the elevation range for the area that the eastern sector of the\\nColorado orogeny extends into?\\nThought 1: I need to search Colorado orogeny, find the area that the eastern sector\\nof the Colorado orogeny extends into, then find the elevation range of the\\narea.\\nAction 1: Search[Colorado orogeny]\\nObservation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in\\nColorado and surrounding areas.\\nThought 2: It does not mention the eastern sector. So I need to look up eastern\\nsector.\\nAction 2: Lookup[eastern sector]\\nObservation 2: (Result 1 / 1) The eastern sector extends into the High Plains and is called\\nthe Central Plains orogeny.\\nThought 3: The eastern sector of Colorado orogeny extends into the High Plains. So I\\nneed to search High Plains and find its elevation range.\\nAction 3: Search[High Plains]\\nObservation 3: High Plains refers to one of two distinct land regions\\nThought 4: I need to instead search High Plains (United States).\\nAction 4: Search[High Plains (United States)]\\nObservation 4: The High Plains are a subregion of the Great Plains. From east to west, the\\nHigh Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130\\nm).[3]\\nThought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer\\nis 1,800 to 7,000 ft.\\nAction 5: Finish[1,800 to 7,000 ft]'"
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]
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},
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"execution_count": 3,
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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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"# print initial example for visibility\n",
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"EXAMPLES[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": 6,
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"id": "a7bd36bc",
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"metadata": {},
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"outputs": [],
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"source": [
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"new_example = generate_example(EXAMPLES, OpenAI())"
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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": "e1efb008",
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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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"['',\n",
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" '',\n",
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" 'Question: Is the Mount Everest taller than the Mount Kilimanjaro?',\n",
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" '',\n",
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" 'Thought 1: I need to search Mount Everest and Mount Kilimanjaro, find their',\n",
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" 'heights, then compare them.',\n",
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" '',\n",
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" 'Action 1: Search[Mount Everest]',\n",
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" '',\n",
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" \"Observation 1: Mount Everest, at 8,848 metres (29,029 ft), is the world's highest mountain\",\n",
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" 'and a particularly popular goal for mountaineers.',\n",
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" '',\n",
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" 'Thought 2: Mount Everest is 8,848 metres tall. I need to search Mount Kilimanjaro',\n",
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" 'next.',\n",
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" '',\n",
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" 'Action 2: Search[Mount Kilimanjaro]',\n",
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" '',\n",
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" 'Observation 2: Mount Kilimanjaro, with its three volcanic cones, Kibo, Mawenzi, and',\n",
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" 'Shira, is a freestanding mountain in Tanzania. It is the highest mountain in',\n",
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" 'Africa, and rises approximately 4,900 metres (16,100 ft) from its base to 5,895',\n",
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" 'metres (19,341 ft) above sea level.',\n",
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" '',\n",
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" 'Thought 3: Mount Kilimanjaro is 5,895 metres tall. 8,848 metres (Mount Everest) >',\n",
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" '5,895 metres (Mount Kil']"
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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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"new_example.split('\\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": null,
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"id": "d8843d7b",
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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": "3.10.4"
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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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@ -0,0 +1,20 @@
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"""Utility functions for working with prompts."""
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from typing import List
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from langchain.chains.llm import LLMChain
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from langchain.llms.base import LLM
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from langchain.prompts.dynamic import DynamicPrompt
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TEST_GEN_TEMPLATE_SUFFIX = "Add another example."
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def generate_example(examples: List[str], llm: LLM) -> str:
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"""Return another example given a list of examples for a prompt."""
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prompt = DynamicPrompt(examples=examples, suffix=TEST_GEN_TEMPLATE_SUFFIX)
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chain = LLMChain(llm=llm, prompt=prompt)
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return chain.predict()
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def generate_example_from_dynamic_prompt(prompt: DynamicPrompt, llm: LLM) -> str:
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"""Return another example given a DynamicPrompt object."""
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return generate_example(prompt.examples, llm)
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@ -1,234 +0,0 @@
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"""Prompt schema definition."""
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import re
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Dict, List
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from pydantic import BaseModel, Extra, root_validator
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from langchain.formatting import formatter
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_FORMATTER_MAPPING = {
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"f-string": formatter.format,
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}
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class BasePrompt(ABC):
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"""Base prompt should expose the format method, returning a prompt."""
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input_variables: List[str]
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"""A list of the names of the variables the prompt template expects."""
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@abstractmethod
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def format(self, **kwargs: Any) -> str:
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"""Format the prompt with the inputs.
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Args:
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kwargs: Any arguments to be passed to the prompt template.
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Returns:
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A formatted string.
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Example:
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.. code-block:: python
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prompt.format(variable1="foo")
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"""
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class Prompt(BaseModel, BasePrompt):
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"""Schema to represent a prompt for an LLM.
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Example:
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.. code-block:: python
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from langchain import Prompt
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prompt = Prompt(input_variables=["foo"], template="Say {foo}")
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"""
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input_variables: List[str]
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"""A list of the names of the variables the prompt template expects."""
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template: str
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"""The prompt template."""
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template_format: str = "f-string"
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"""The format of the prompt template. Options are: 'f-string'."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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def format(self, **kwargs: Any) -> str:
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"""Format the prompt with the inputs.
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Args:
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kwargs: Any arguments to be passed to the prompt template.
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Returns:
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A formatted string.
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Example:
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.. code-block:: python
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prompt.format(variable1="foo")
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"""
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return _FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
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@root_validator()
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def template_is_valid(cls, values: Dict) -> Dict:
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"""Check that template and input variables are consistent."""
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input_variables = values["input_variables"]
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template = values["template"]
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template_format = values["template_format"]
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if template_format not in _FORMATTER_MAPPING:
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valid_formats = list(_FORMATTER_MAPPING)
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raise ValueError(
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f"Invalid template format. Got `{template_format}`;"
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f" should be one of {valid_formats}"
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)
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dummy_inputs = {input_variable: "foo" for input_variable in input_variables}
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try:
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formatter_func = _FORMATTER_MAPPING[template_format]
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formatter_func(template, **dummy_inputs)
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except KeyError:
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raise ValueError("Invalid prompt schema.")
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return values
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@classmethod
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def from_examples(
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cls,
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examples: List[str],
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suffix: str,
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input_variables: List[str],
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example_separator: str = "\n\n",
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prefix: str = "",
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) -> "Prompt":
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"""Take examples in list format with prefix and suffix to create a prompt.
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Intended be used as a way to dynamically create a prompt from examples.
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Args:
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examples: List of examples to use in the prompt.
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suffix: String to go after the list of examples. Should generally
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set up the user's input.
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input_variables: A list of variable names the final prompt template
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will expect.
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example_separator: The seperator to use in between examples. Defaults
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to two new line characters.
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prefix: String that should go before any examples. Generally includes
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examples. Default to an empty string.
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Returns:
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The final prompt generated.
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"""
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example_str = example_separator.join(examples)
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template = prefix + example_str + suffix
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return cls(input_variables=input_variables, template=template)
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class DynamicPrompt(BaseModel, BasePrompt):
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r"""Schema to represent a dynamic prompt for an LLM.
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Example:
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.. code-block:: python
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from langchain import DynamicPrompt
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dynamic_prompt = DynamicPrompt(
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examples=["Say hi. Hi", "Say ho. Ho"],
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example_separator="\n\n",
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prefix="",
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suffix="\n\nSay {foo}"
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input_variables=["foo"],
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max_length=200,
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get_text_length=word_count
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)
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"""
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examples: List[str]
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"""A list of the examples that the prompt template expects."""
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example_separator: str = "\n\n"
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"""Example separator, e.g. \n\n, for the dynamic prompt creation."""
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input_variables: List[str]
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"""A list of the names of the variables the prompt template expects."""
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prefix: str
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"""Prefix for the prompt."""
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suffix: str
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"""Suffix for the prompt."""
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template_format: str = "f-string"
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"""The format of the prompt template. Options are: 'f-string'."""
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get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x))
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"""Function to measure prompt length. Defaults to word count."""
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max_length: int = 2048
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"""Max length for the prompt, beyond which examples are cut."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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def template(self, example_list: List[str], **kwargs: Any) -> str:
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"""Return template given example list."""
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template = self.example_separator.join(
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[self.prefix, *example_list, self.suffix]
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)
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return _FORMATTER_MAPPING[self.template_format](template, **kwargs)
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def format(self, **kwargs: Any) -> str:
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"""Dynamically format the prompt with the inputs.
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Args:
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kwargs: Any arguments to be passed to the prompt template.
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Returns:
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A formatted string.
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Example:
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.. code-block:: python
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prompt.format(variable1="foo")
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"""
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curr_examples = self.examples
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template = self.template(curr_examples, **kwargs)
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while self.get_text_length(template) > self.max_length and curr_examples:
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curr_examples = curr_examples[:-1]
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template = self.template(curr_examples, **kwargs)
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return template
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@root_validator()
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def template_is_valid(cls, values: Dict) -> Dict:
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"""Check that prefix, suffix and input variables are consistent."""
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input_variables = values["input_variables"]
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suffix = values["suffix"]
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template_format = values["template_format"]
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if template_format not in _FORMATTER_MAPPING:
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valid_formats = list(_FORMATTER_MAPPING)
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raise ValueError(
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f"Invalid template format. Got `{template_format}`;"
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f" should be one of {valid_formats}"
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)
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try:
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result = values["get_text_length"]("foo")
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assert isinstance(result, int)
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except AssertionError:
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raise ValueError(
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"Invalid text length callable, must take string & return int;"
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)
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dummy_inputs = {input_variable: "foo" for input_variable in input_variables}
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# TODO variables could be in prefix or suffix
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try:
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formatter_func = _FORMATTER_MAPPING[template_format]
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formatter_func(suffix, **dummy_inputs)
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except KeyError:
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raise ValueError("Invalid prompt schema.")
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return values
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@ -0,0 +1,6 @@
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"""Prompt template classes."""
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from langchain.prompts.base import BasePrompt
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from langchain.prompts.dynamic import DynamicPrompt
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from langchain.prompts.prompt import Prompt
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__all__ = ["BasePrompt", "Prompt", "DynamicPrompt"]
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@ -0,0 +1,33 @@
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"""BasePrompt schema definition."""
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from abc import ABC, abstractmethod
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from typing import Any, List
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from langchain.formatting import formatter
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DEFAULT_FORMATTER_MAPPING = {
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"f-string": formatter.format,
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}
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class BasePrompt(ABC):
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"""Base prompt should expose the format method, returning a prompt."""
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input_variables: List[str]
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"""A list of the names of the variables the prompt template expects."""
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@abstractmethod
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def format(self, **kwargs: Any) -> str:
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"""Format the prompt with the inputs.
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Args:
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kwargs: Any arguments to be passed to the prompt template.
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Returns:
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A formatted string.
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Example:
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.. code-block:: python
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prompt.format(variable1="foo")
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"""
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@ -0,0 +1,112 @@
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"""Dynamic prompt schema definition."""
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import re
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from typing import Any, Callable, Dict, List
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from pydantic import BaseModel, Extra, root_validator
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from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, BasePrompt
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class DynamicPrompt(BaseModel, BasePrompt):
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r"""Schema to represent a dynamic prompt for an LLM.
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Example:
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.. code-block:: python
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from langchain import DynamicPrompt
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dynamic_prompt = DynamicPrompt(
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examples=["Say hi. Hi", "Say ho. Ho"],
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example_separator="\n\n",
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prefix="",
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suffix="\n\nSay {foo}"
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input_variables=["foo"],
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max_length=200,
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get_text_length=word_count
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)
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"""
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examples: List[str]
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"""A list of the examples that the prompt template expects."""
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example_separator: str = "\n\n"
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"""Example separator, e.g. \n\n, for the dynamic prompt creation."""
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input_variables: List[str] = []
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"""A list of the names of the variables the prompt template expects."""
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prefix: str = ""
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"""Prefix for the prompt."""
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suffix: str = ""
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"""Suffix for the prompt."""
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template_format: str = "f-string"
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"""The format of the prompt template. Options are: 'f-string'."""
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get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x))
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"""Function to measure prompt length. Defaults to word count."""
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max_length: int = 2048
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"""Max length for the prompt, beyond which examples are cut."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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def template(self, example_list: List[str], **kwargs: Any) -> str:
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"""Return template given example list."""
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template = self.example_separator.join(
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[self.prefix, *example_list, self.suffix]
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)
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return DEFAULT_FORMATTER_MAPPING[self.template_format](template, **kwargs)
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def format(self, **kwargs: Any) -> str:
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"""Dynamically format the prompt with the inputs.
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Args:
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kwargs: Any arguments to be passed to the prompt template.
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Returns:
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A formatted string.
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Example:
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.. code-block:: python
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prompt.format(variable1="foo")
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"""
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curr_examples = self.examples
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template = self.template(curr_examples, **kwargs)
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while self.get_text_length(template) > self.max_length and curr_examples:
|
||||
curr_examples = curr_examples[:-1]
|
||||
template = self.template(curr_examples, **kwargs)
|
||||
return template
|
||||
|
||||
@root_validator()
|
||||
def template_is_valid(cls, values: Dict) -> Dict:
|
||||
"""Check that prefix, suffix and input variables are consistent."""
|
||||
input_variables = values["input_variables"]
|
||||
prefix = values["prefix"]
|
||||
suffix = values["suffix"]
|
||||
template_format = values["template_format"]
|
||||
if template_format not in DEFAULT_FORMATTER_MAPPING:
|
||||
valid_formats = list(DEFAULT_FORMATTER_MAPPING)
|
||||
raise ValueError(
|
||||
f"Invalid template format. Got `{template_format}`;"
|
||||
f" should be one of {valid_formats}"
|
||||
)
|
||||
try:
|
||||
result = values["get_text_length"]("foo")
|
||||
assert isinstance(result, int)
|
||||
except AssertionError:
|
||||
raise ValueError(
|
||||
"Invalid text length callable, must take string & return int;"
|
||||
)
|
||||
dummy_inputs = {input_variable: "foo" for input_variable in input_variables}
|
||||
try:
|
||||
formatter_func = DEFAULT_FORMATTER_MAPPING[template_format]
|
||||
formatter_func(prefix + suffix, **dummy_inputs)
|
||||
except KeyError:
|
||||
raise ValueError("Invalid prompt schema.")
|
||||
return values
|
||||
@ -0,0 +1,99 @@
|
||||
"""Prompt schema definition."""
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from pydantic import BaseModel, Extra, root_validator
|
||||
|
||||
from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, BasePrompt
|
||||
|
||||
|
||||
class Prompt(BaseModel, BasePrompt):
|
||||
"""Schema to represent a prompt for an LLM.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain import Prompt
|
||||
prompt = Prompt(input_variables=["foo"], template="Say {foo}")
|
||||
"""
|
||||
|
||||
input_variables: List[str]
|
||||
"""A list of the names of the variables the prompt template expects."""
|
||||
|
||||
template: str
|
||||
"""The prompt template."""
|
||||
|
||||
template_format: str = "f-string"
|
||||
"""The format of the prompt template. Options are: 'f-string'."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
def format(self, **kwargs: Any) -> str:
|
||||
"""Format the prompt with the inputs.
|
||||
|
||||
Args:
|
||||
kwargs: Any arguments to be passed to the prompt template.
|
||||
|
||||
Returns:
|
||||
A formatted string.
|
||||
|
||||
Example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
prompt.format(variable1="foo")
|
||||
"""
|
||||
return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
|
||||
|
||||
@root_validator()
|
||||
def template_is_valid(cls, values: Dict) -> Dict:
|
||||
"""Check that template and input variables are consistent."""
|
||||
input_variables = values["input_variables"]
|
||||
template = values["template"]
|
||||
template_format = values["template_format"]
|
||||
if template_format not in DEFAULT_FORMATTER_MAPPING:
|
||||
valid_formats = list(DEFAULT_FORMATTER_MAPPING)
|
||||
raise ValueError(
|
||||
f"Invalid template format. Got `{template_format}`;"
|
||||
f" should be one of {valid_formats}"
|
||||
)
|
||||
dummy_inputs = {input_variable: "foo" for input_variable in input_variables}
|
||||
try:
|
||||
formatter_func = DEFAULT_FORMATTER_MAPPING[template_format]
|
||||
formatter_func(template, **dummy_inputs)
|
||||
except KeyError:
|
||||
raise ValueError("Invalid prompt schema.")
|
||||
return values
|
||||
|
||||
@classmethod
|
||||
def from_examples(
|
||||
cls,
|
||||
examples: List[str],
|
||||
suffix: str,
|
||||
input_variables: List[str],
|
||||
example_separator: str = "\n\n",
|
||||
prefix: str = "",
|
||||
) -> "Prompt":
|
||||
"""Take examples in list format with prefix and suffix to create a prompt.
|
||||
|
||||
Intended be used as a way to dynamically create a prompt from examples.
|
||||
|
||||
Args:
|
||||
examples: List of examples to use in the prompt.
|
||||
suffix: String to go after the list of examples. Should generally
|
||||
set up the user's input.
|
||||
input_variables: A list of variable names the final prompt template
|
||||
will expect.
|
||||
example_separator: The seperator to use in between examples. Defaults
|
||||
to two new line characters.
|
||||
prefix: String that should go before any examples. Generally includes
|
||||
examples. Default to an empty string.
|
||||
|
||||
Returns:
|
||||
The final prompt generated.
|
||||
"""
|
||||
example_str = example_separator.join(examples)
|
||||
template = prefix + example_str + suffix
|
||||
return cls(input_variables=input_variables, template=template)
|
||||
Loading…
Reference in New Issue