OptimizedPrompt -- k-shot example choice backed by semantic search (#91)

harrison/prompt_examples
Samantha Whitmore 2 years ago committed by GitHub
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e9e2b50b",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains.react.prompt import EXAMPLES, SUFFIX\n",
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.example_generator import generate_example, generate_example_from_dynamic_prompt\n",
"from langchain.llms.openai import OpenAI\n",
"from langchain.prompts.optimized import OptimizedPrompt\n",
"from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
"from langchain.vectorstores.faiss import FAISS"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cb069606",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'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]'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"EXAMPLES[0]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5fda75a4",
"metadata": {},
"outputs": [],
"source": [
"prompt = OptimizedPrompt.from_examples(\n",
" examples=EXAMPLES, \n",
" suffix=SUFFIX, \n",
" input_variables=[\"input\"],\n",
" embeddings=OpenAIEmbeddings(),\n",
" vectorstore_cls=FAISS\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7a601df8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Question: What is the elevation range for the area that the eastern sector of the\n",
"Colorado orogeny extends into?\n",
"Thought 1: I need to search Colorado orogeny, find the area that the eastern sector\n",
"of the Colorado orogeny extends into, then find the elevation range of the\n",
"area.\n",
"Action 1: Search[Colorado orogeny]\n",
"Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in\n",
"Colorado and surrounding areas.\n",
"Thought 2: It does not mention the eastern sector. So I need to look up eastern\n",
"sector.\n",
"Action 2: Lookup[eastern sector]\n",
"Observation 2: (Result 1 / 1) The eastern sector extends into the High Plains and is called\n",
"the Central Plains orogeny.\n",
"Thought 3: The eastern sector of Colorado orogeny extends into the High Plains. So I\n",
"need to search High Plains and find its elevation range.\n",
"Action 3: Search[High Plains]\n",
"Observation 3: High Plains refers to one of two distinct land regions\n",
"Thought 4: I need to instead search High Plains (United States).\n",
"Action 4: Search[High Plains (United States)]\n",
"Observation 4: The High Plains are a subregion of the Great Plains. From east to west, the\n",
"High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130\n",
"m).[3]\n",
"Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer\n",
"is 1,800 to 7,000 ft.\n",
"Action 5: Finish[1,800 to 7,000 ft]\n",
"\n",
"\n",
"\n",
"Question: What is the highest mountain peak in Asia?\n"
]
}
],
"source": [
"print(prompt.format(k=1, input=\"What is the highest mountain peak in Asia?\"))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f7f06820",
"metadata": {},
"outputs": [],
"source": [
"prompt = OptimizedPrompt.from_examples(\n",
" examples=EXAMPLES, \n",
" suffix=SUFFIX, \n",
" input_variables=[\"input\"],\n",
" embeddings=OpenAIEmbeddings(),\n",
" vectorstore_cls=ElasticVectorSearch,\n",
" elasticsearch_url=\"http://localhost:9200\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "bd91f408",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Question: What is the elevation range for the area that the eastern sector of the\n",
"Colorado orogeny extends into?\n",
"Thought 1: I need to search Colorado orogeny, find the area that the eastern sector\n",
"of the Colorado orogeny extends into, then find the elevation range of the\n",
"area.\n",
"Action 1: Search[Colorado orogeny]\n",
"Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in\n",
"Colorado and surrounding areas.\n",
"Thought 2: It does not mention the eastern sector. So I need to look up eastern\n",
"sector.\n",
"Action 2: Lookup[eastern sector]\n",
"Observation 2: (Result 1 / 1) The eastern sector extends into the High Plains and is called\n",
"the Central Plains orogeny.\n",
"Thought 3: The eastern sector of Colorado orogeny extends into the High Plains. So I\n",
"need to search High Plains and find its elevation range.\n",
"Action 3: Search[High Plains]\n",
"Observation 3: High Plains refers to one of two distinct land regions\n",
"Thought 4: I need to instead search High Plains (United States).\n",
"Action 4: Search[High Plains (United States)]\n",
"Observation 4: The High Plains are a subregion of the Great Plains. From east to west, the\n",
"High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130\n",
"m).[3]\n",
"Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer\n",
"is 1,800 to 7,000 ft.\n",
"Action 5: Finish[1,800 to 7,000 ft]\n",
"\n",
"\n",
"\n",
"Question: What is the highest mountain peak in Asia?\n"
]
}
],
"source": [
"print(prompt.format(k=1, input=\"What is the highest mountain peak in Asia?\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "716165c2",
"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",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

@ -0,0 +1,171 @@
"""Optimized prompt schema definition."""
import re
from typing import Any, Callable, Dict, List
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING
from langchain.vectorstores.base import VectorStore
class OptimizedPrompt(BaseModel):
r"""Schema to represent an optimized prompt for an LLM.
Example:
.. code-block:: python
from langchain import DynamicPrompt
vectorstore = FAISS.from_texts(examples, OpenAIEmbeddings()
optimized_prompt = OptimizedPrompt(
examples=["Say hi. Hi", "Say ho. Ho"],
example_separator="\n\n",
prefix="",
suffix="\n\nSay {foo}"
input_variables=["foo"],
max_length=200,
get_text_length=word_count,
vectorstore=vectorstore)
)
"""
examples: List[str]
"""A list of the examples that the prompt template expects."""
example_separator: str = "\n\n"
"""Example separator, e.g. \n\n, for the dynamic prompt creation."""
input_variables: List[str] = []
"""A list of the names of the variables the prompt template expects."""
prefix: str = ""
"""Prefix for the prompt."""
suffix: str = ""
"""Suffix for the prompt."""
template_format: str = "f-string"
"""The format of the prompt template. Options are: 'f-string'."""
get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x))
"""Function to measure prompt length. Defaults to word count."""
max_length: int = 2048
"""Max length for the prompt, beyond which examples are cut."""
vectorstore: VectorStore
"""Vectorstore to use for storing the embeddings."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
extra = Extra.forbid
def template(self, example_list: List[str], **kwargs: Any) -> str:
"""Return template given full example list."""
template = self.example_separator.join(
[self.prefix, *example_list, self.suffix]
)
return DEFAULT_FORMATTER_MAPPING[self.template_format](template, **kwargs)
def format(self, k: int = 4, **kwargs: Any) -> str:
"""Optimize the examples in the prompt for the given inputs.
Args:
k: Number of examples to aim for (may be trimmed by optimizer afterwards)
kwargs: Any arguments to be passed to the prompt template.
Returns:
A formatted string.
Example:
.. code-block:: python
prompt.format(variable1="foo")
"""
query = " ".join([v for k, v in kwargs.items()])
example_docs = self.vectorstore.similarity_search(query, k=k)
curr_examples = [str(e.page_content) for e in example_docs]
template = self.template(curr_examples, **kwargs)
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"]
if len(input_variables) > 1:
raise ValueError("Only one input variable allowed for optimized prompt;")
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
@classmethod
def from_examples(
cls,
examples: List[str],
suffix: str,
input_variables: List[str],
embeddings: Embeddings,
vectorstore_cls: VectorStore,
example_separator: str = "\n\n",
prefix: str = "",
**vectorstore_cls_kwargs: Any,
) -> "OptimizedPrompt":
"""Create k-shot prompt optimizer using example list and embeddings.
Reshuffles examples for the prompt dynamically based on query similarity.
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.
embeddings: An iniialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls: A vector store DB interface class, e.g. FAISS.
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.
vectorstore_cls_kwargs: optional kwargs containing url for vector store
Returns:
The OptimizedPrompt instantiated, backed by a vector store.
"""
vectorstore = vectorstore_cls.from_texts(
examples, embeddings, **vectorstore_cls_kwargs
)
return cls(
examples=examples,
suffix=suffix,
input_variables=input_variables,
example_separator=example_separator,
prefix=prefix,
vectorstore=vectorstore,
)

@ -1,6 +1,4 @@
"""
Test text splitting functionality using NLTK and Spacy based sentence splitters.
"""
"""Test text splitting functionality using NLTK and Spacy based sentence splitters."""
import pytest
from langchain.text_splitter import NLTKTextSplitter, SpacyTextSplitter

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