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Refactor prompts into module, add example generation utils (#64)

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Samantha Whitmore 2022-11-06 15:40:33 -08:00 committed by GitHub
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20 changed files with 414 additions and 254 deletions
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4
README.md
View File

@ -107,7 +107,7 @@ but full API docs can be found [here](https://langchain.readthedocs.io/en/latest
## 🤖 Developer Guide
To begin developing on this project, first clone to the repo locally.
To install requirements, run `pip install -r requirments.txt`.
To install requirements, run `pip install -r requirements.txt`.
This will install all requirements for running the package, examples, linting, formatting, and tests.
Formatting for this project is a combination of [Black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/).
@ -125,6 +125,8 @@ Integration tests cover logic that requires making calls to outside APIs (often
To run integration tests, run `make integration_tests`.
If you add support for a new external API, please add a new integration test.
If you are adding a Jupyter notebook example, you can run `pip install -e .` to build the langchain package from your local changes, so your new logic can be imported into the notebook.
Docs are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code.
For that reason, we ask that you add good documentation to all classes and methods.
Similar to linting, we recognize documentation can be annoying - if you do not want to do it, please contact a project maintainer and they can help you with it. We do not want this to be a blocker for good code getting contributed.

121
examples/generate_examples.ipynb Normal file
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@ -0,0 +1,121 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "1685fa2f",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains.react.prompt import EXAMPLES\n",
"from langchain.llms.openai import OpenAI\n",
"from langchain.example_generator import generate_example, generate_example_from_dynamic_prompt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "334ef4f7",
"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": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# print initial example for visibility\n",
"EXAMPLES[0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a7bd36bc",
"metadata": {},
"outputs": [],
"source": [
"new_example = generate_example(EXAMPLES, OpenAI())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e1efb008",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['',\n",
" '',\n",
" 'Question: Is the Mount Everest taller than the Mount Kilimanjaro?',\n",
" '',\n",
" 'Thought 1: I need to search Mount Everest and Mount Kilimanjaro, find their',\n",
" 'heights, then compare them.',\n",
" '',\n",
" 'Action 1: Search[Mount Everest]',\n",
" '',\n",
" \"Observation 1: Mount Everest, at 8,848 metres (29,029 ft), is the world's highest mountain\",\n",
" 'and a particularly popular goal for mountaineers.',\n",
" '',\n",
" 'Thought 2: Mount Everest is 8,848 metres tall. I need to search Mount Kilimanjaro',\n",
" 'next.',\n",
" '',\n",
" 'Action 2: Search[Mount Kilimanjaro]',\n",
" '',\n",
" 'Observation 2: Mount Kilimanjaro, with its three volcanic cones, Kibo, Mawenzi, and',\n",
" 'Shira, is a freestanding mountain in Tanzania. It is the highest mountain in',\n",
" 'Africa, and rises approximately 4,900 metres (16,100 ft) from its base to 5,895',\n",
" 'metres (19,341 ft) above sea level.',\n",
" '',\n",
" 'Thought 3: Mount Kilimanjaro is 5,895 metres tall. 8,848 metres (Mount Everest) >',\n",
" '5,895 metres (Mount Kil']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_example.split('\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d8843d7b",
"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
}

2
langchain/__init__.py
View File

@ -18,7 +18,7 @@ from langchain.chains import (
from langchain.docstore import Wikipedia
from langchain.faiss import FAISS
from langchain.llms import Cohere, HuggingFaceHub, OpenAI
from langchain.prompt import BasePrompt, DynamicPrompt, Prompt
from langchain.prompts import BasePrompt, DynamicPrompt, Prompt
from langchain.sql_database import SQLDatabase
__all__ = [

2
langchain/chains/llm.py
View File

@ -5,7 +5,7 @@ from pydantic import BaseModel, Extra
from langchain.chains.base import Chain
from langchain.llms.base import LLM
from langchain.prompt import BasePrompt
from langchain.prompts.base import BasePrompt
class LLMChain(Chain, BaseModel):

2
langchain/chains/llm_math/prompt.py
View File

@ -1,5 +1,5 @@
# flake8: noqa
from langchain.prompt import Prompt
from langchain.prompts.prompt import Prompt
_PROMPT_TEMPLATE = """You are GPT-3, and you can't do math.

2
langchain/chains/mapreduce.py
View File

@ -11,7 +11,7 @@ from pydantic import BaseModel, Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.llms.base import LLM
from langchain.prompt import BasePrompt
from langchain.prompts.base import BasePrompt
from langchain.text_splitter import TextSplitter

14
langchain/chains/natbot/prompt.py
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@ -1,5 +1,5 @@
# flake8: noqa
from langchain.prompt import Prompt
from langchain.prompts.prompt import Prompt
_PROMPT_TEMPLATE = """
You are an agent controlling a browser. You are given:
@ -30,7 +30,7 @@ Based on your given objective, issue whatever command you believe will get you c
You always start on Google; you should submit a search query to Google that will take you to the best page for
achieving your objective. And then interact with that page to achieve your objective.
If you find yourself on Google and there are no search results displayed yet, you should probably issue a command
If you find yourself on Google and there are no search results displayed yet, you should probably issue a command
like "TYPESUBMIT 7 "search query"" to get to a more useful page.
Then, if you find yourself on a Google search results page, you might issue the command "CLICK 24" to click
@ -66,7 +66,7 @@ CURRENT BROWSER CONTENT:
------------------
OBJECTIVE: Find a 2 bedroom house for sale in Anchorage AK for under $750k
CURRENT URL: https://www.google.com/
YOUR COMMAND:
YOUR COMMAND:
TYPESUBMIT 8 "anchorage redfin"
==================================================
@ -95,7 +95,7 @@ CURRENT BROWSER CONTENT:
------------------
OBJECTIVE: Make a reservation for 4 at Dorsia at 8pm
CURRENT URL: https://www.google.com/
YOUR COMMAND:
YOUR COMMAND:
TYPESUBMIT 8 "dorsia nyc opentable"
==================================================
@ -114,15 +114,15 @@ CURRENT BROWSER CONTENT:
<text id=9>Sep 28, 2022</text>
<text id=10>7:00 PM</text>
<text id=11>2 people</text>
<input id=12 alt="Location, Restaurant, or Cuisine"></input>
<input id=12 alt="Location, Restaurant, or Cuisine"></input>
<button id=13>Let’s go</button>
<text id=14>It looks like you're in Peninsula. Not correct?</text>
<text id=14>It looks like you're in Peninsula. Not correct?</text>
<button id=15>Get current location</button>
<button id=16>Next</button>
------------------
OBJECTIVE: Make a reservation for 4 for dinner at Dorsia in New York City at 8pm
CURRENT URL: https://www.opentable.com/
YOUR COMMAND:
YOUR COMMAND:
TYPESUBMIT 12 "dorsia new york city"
==================================================

2
langchain/chains/react/prompt.py
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@ -1,5 +1,5 @@
# flake8: noqa
from langchain.prompt import Prompt
from langchain.prompts.prompt import Prompt
EXAMPLES = [
"""Question: What is the elevation range for the area that the eastern sector of the

2
langchain/chains/self_ask_with_search/prompt.py
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@ -1,5 +1,5 @@
# flake8: noqa
from langchain.prompt import Prompt
from langchain.prompts.prompt import Prompt
_DEFAULT_TEMPLATE = """Question: Who lived longer, Muhammad Ali or Alan Turing?
Are follow up questions needed here: Yes.

4
langchain/chains/sql_database/prompt.py
View File

@ -1,7 +1,7 @@
# flake8: noqa
from langchain.prompt import Prompt
from langchain.prompts.prompt import Prompt
_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
Use the following format:
Question: "Question here"

20
langchain/example_generator.py Normal file
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@ -0,0 +1,20 @@
"""Utility functions for working with prompts."""
from typing import List
from langchain.chains.llm import LLMChain
from langchain.llms.base import LLM
from langchain.prompts.dynamic import DynamicPrompt
TEST_GEN_TEMPLATE_SUFFIX = "Add another example."
def generate_example(examples: List[str], llm: LLM) -> str:
"""Return another example given a list of examples for a prompt."""
prompt = DynamicPrompt(examples=examples, suffix=TEST_GEN_TEMPLATE_SUFFIX)
chain = LLMChain(llm=llm, prompt=prompt)
return chain.predict()
def generate_example_from_dynamic_prompt(prompt: DynamicPrompt, llm: LLM) -> str:
"""Return another example given a DynamicPrompt object."""
return generate_example(prompt.examples, llm)

234
langchain/prompt.py
View File

@ -1,234 +0,0 @@
"""Prompt schema definition."""
import re
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List
from pydantic import BaseModel, Extra, root_validator
from langchain.formatting import formatter
_FORMATTER_MAPPING = {
"f-string": formatter.format,
}
class BasePrompt(ABC):
"""Base prompt should expose the format method, returning a prompt."""
input_variables: List[str]
"""A list of the names of the variables the prompt template expects."""
@abstractmethod
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")
"""
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 _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 _FORMATTER_MAPPING:
valid_formats = list(_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 = _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)
class DynamicPrompt(BaseModel, BasePrompt):
r"""Schema to represent a dynamic prompt for an LLM.
Example:
.. code-block:: python
from langchain import DynamicPrompt
dynamic_prompt = DynamicPrompt(
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
)
"""
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."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def template(self, example_list: List[str], **kwargs: Any) -> str:
"""Return template given example list."""
template = self.example_separator.join(
[self.prefix, *example_list, self.suffix]
)
return _FORMATTER_MAPPING[self.template_format](template, **kwargs)
def format(self, **kwargs: Any) -> str:
"""Dynamically 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")
"""
curr_examples = self.examples
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"]
suffix = values["suffix"]
template_format = values["template_format"]
if template_format not in _FORMATTER_MAPPING:
valid_formats = list(_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}
# TODO variables could be in prefix or suffix
try:
formatter_func = _FORMATTER_MAPPING[template_format]
formatter_func(suffix, **dummy_inputs)
except KeyError:
raise ValueError("Invalid prompt schema.")
return values

6
langchain/prompts/__init__.py Normal file
View File

@ -0,0 +1,6 @@
"""Prompt template classes."""
from langchain.prompts.base import BasePrompt
from langchain.prompts.dynamic import DynamicPrompt
from langchain.prompts.prompt import Prompt
__all__ = ["BasePrompt", "Prompt", "DynamicPrompt"]

33
langchain/prompts/base.py Normal file
View File

@ -0,0 +1,33 @@
"""BasePrompt schema definition."""
from abc import ABC, abstractmethod
from typing import Any, List
from langchain.formatting import formatter
DEFAULT_FORMATTER_MAPPING = {
"f-string": formatter.format,
}
class BasePrompt(ABC):
"""Base prompt should expose the format method, returning a prompt."""
input_variables: List[str]
"""A list of the names of the variables the prompt template expects."""
@abstractmethod
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")
"""

112
langchain/prompts/dynamic.py Normal file
View File

@ -0,0 +1,112 @@
"""Dynamic prompt schema definition."""
import re
from typing import Any, Callable, Dict, List
from pydantic import BaseModel, Extra, root_validator
from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, BasePrompt
class DynamicPrompt(BaseModel, BasePrompt):
r"""Schema to represent a dynamic prompt for an LLM.
Example:
.. code-block:: python
from langchain import DynamicPrompt
dynamic_prompt = DynamicPrompt(
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
)
"""
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."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def template(self, example_list: List[str], **kwargs: Any) -> str:
"""Return template given 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, **kwargs: Any) -> str:
"""Dynamically 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")
"""
curr_examples = self.examples
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"]
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

99
langchain/prompts/prompt.py Normal file
View File

@ -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)

2
tests/unit_tests/chains/test_llm.py
View File

@ -2,7 +2,7 @@
import pytest
from langchain.chains.llm import LLMChain
from langchain.prompt import Prompt
from langchain.prompts.prompt import Prompt
from tests.unit_tests.llms.fake_llm import FakeLLM

2
tests/unit_tests/chains/test_react.py
View File

@ -9,7 +9,7 @@ from langchain.chains.react.base import ReActChain, predict_until_observation
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
from langchain.llms.base import LLM
from langchain.prompt import Prompt
from langchain.prompts.prompt import Prompt
_PAGE_CONTENT = """This is a page about LangChain.

3
tests/unit_tests/test_dynamic_prompt.py
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@ -1,5 +1,6 @@
"""Test functionality related to dynamic prompts."""
from langchain.prompt import DynamicPrompt, Prompt
from langchain.prompts.dynamic import DynamicPrompt
from langchain.prompts.prompt import Prompt
# FULL TEMPLATES
LONGER_TEMPLATE = """Test Prompt:

2
tests/unit_tests/test_prompt.py
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@ -1,7 +1,7 @@
"""Test functionality related to prompts."""
import pytest
from langchain.prompt import Prompt
from langchain.prompts.prompt import Prompt
def test_prompt_valid() -> None: