start click, fixtures, types, challenge creation, mock run -stable (#37)

This commit is contained in:
Silen Naihin
2023-06-21 11:43:18 -04:00
committed by GitHub
parent 04536e92a5
commit b7deb984f7
47 changed files with 1057 additions and 222 deletions

167
README.md
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@@ -1,15 +1,8 @@
# agbenchmark
# Auto-GPT Benchmark
A repo built for the purpose of benchmarking the performance of agents far and wide, regardless of how they are set up and how they work
Simple boilerplate code that spins up a webserver to plug their agent into. We call multiple tasks by invoking different pytest commands on folders and once the agent stops or reaches 50 loops (which they will have to define). We handle the deletion of files after a run loop ends. Then we call call the POST request for the next task. Then we will spit out a combined benchmark once all tests run
- Agent adds tests by adding to our repo
- Agent abstracted from benchmark
- Scalable (parallel servers running tests)
- Better standardization
##### Diagrams (out of date, cloud oriented): https://whimsical.com/agbenchmark-5n4hXBq1ZGzBwRsK4TVY7x
##### Diagrams: https://whimsical.com/agbenchmark-5n4hXBq1ZGzBwRsK4TVY7x
## Contributing
@@ -19,7 +12,7 @@ Simple boilerplate code that spins up a webserver to plug their agent into. We c
- To add requirements `poetry add requirement`.
- To run in venv `poetry run python script.py`
Feel free to merge with `main` at will (but also to ask for review) - if you can't send msg in R&D chat for access.
Feel free to create prs to merge with `main` at will (but also feel free to ask for review) - if you can't send msg in R&D chat for access.
If you push at any point and break things - it'll happen to everyone - fix it asap. Step 1 is to revert `main` to last working commit
@@ -27,63 +20,90 @@ Let people know what beautiful code you write does, document everything well
Share your progress :)
## How this works
1. `pip install auto-gpt-benchmarks`
2. Add boilerplate code to start webserver to your agent (run loop and stop condition)
3. `agbenchmark start --challenge challenge_category` remove challenge flag to run all tests. specify config of hostname, port, and workspace directory
4. We call the server to run the agent for each test
5. Show pass rate of tests, logs, and any other metrics
### To run the basic existing mock (June 21)
1. clone the repo `auto-gpt-benchmarks`
2. `pip install poetry`
3. `poetry shell`
4. `poetry install`
5. `agbenchmark start`
Keep config the same and watch the logs :)
#### Bonuses
- You can adds tests by git cloning auto-gpt-benchmarks to your repo
- Agent is abstracted from benchmark, don't need to do any extra setup other then starting the server
- Simple, easy to use
- Don't have to deal with cloud or parallelization yet
### Pytest
to create a test:
```
@pytest.mark.parametrize(
"server_response",
["VARIABLE"], # VARIABLE = the query/goal you provide to the model
indirect=True,
)
@pytest.mark.(VARIABLE) # VARIABLE = category of the test
def test_file_in_workspace(workspace): # VARIABLE = the actual test that asserts
assert os.path.exists(os.path.join(workspace, "file_to_check.txt"))
```
## Api
FastAPI with REST, import requests
```
POST hostname:8080/challenges
{
"test_name": ""
"challenge": "memory" - optional
}
```
## Auth:
get preSignedUrl from API
```
POST preSignedUrl
{
"artifacts": [{}]
}
```
FastAPI with REST, import requests to call in auto-gpt-benchmarks. Boilerplate code given to agent project to start server
## Workspace
Kubernetes with AWS3 or GCP
## Challenges
Defined by the user on config
#### Dataset
Manually created, existing challenges within Auto-Gpt, https://osu-nlp-group.github.io/Mind2Web/
#### Simple challenge creation through a DSL (domain specific language)
## Repo
```
Challenge TicTacToeCoding
Description "The agent should implement a basic tic-tac-toe game in Python."
Artifacts {
Code "tictactoe.py"
}
Tasks {
Code "Write a function to initialize the game board."
Code "Write a function to handle a player's turn."
Code "Write a function to check for a winning move."
Test "Write tests for the blog post model, serializer, and view."
Command "Run Django's test suite to ensure everything is working as expected."
}
SuccessCriteria {
Correctness "The game should correctly alternate between two players."
Correctness "The game should correctly identify a winning move."
Efficiency "The game should not use unnecessary computational resources."
Design "The solution should follow good practices for Django and Django Rest Framework."
}
EndChallenge
|-- auto-gpt-benchmarks/ **main project directory**
| |-- metrics.py **combining scores, metrics, final evaluation**
| |-- start_benchmark.py **entry point from cli**
| |-- conftest.py **shared fixtures across all tests**
| |-- Challenge.py **easy challenge creation class?**
| |-- config.json **hostname, port, workspace folder**
| |-- challenges/ **challenges across different domains**
| | |-- adaptability/
| | |-- basic_abilities/
| | |-- code/
| | |-- memory/
| | |-- retrieval/
| | |-- web_navigation/
| | |-- writing/
| |-- tests/ **challenges across different metrics**
| | |-- basic_abilities/
| | |-- interface/
| |-- workspace/ **workspace related func**
| | |-- **init**.py
| | |-- workspace_manager.py **creation, deletion**
```
### Easy Challenge Creation
tbd, but potentially shared Challenge class that challenges instantiate as challenges need different utils/metrics for eval
#### Written Challenges
For code, writing we can create a reference text and use metrics like METEOR, BERTScore, BARTScore
#### Validators
Designed to handle specific types of output (e.g., text, code, structured data)
@@ -92,45 +112,4 @@ Designed to handle specific types of output (e.g., text, code, structured data)
Log different requests coming in - write file, change file, etc. Maybe a db in the future for metrics, logs, etc
#### Written Challenges
For code, writing we can create a reference text and use metrics like METEOR, BERTScore, BARTScore
## Repo
```
|-- agbenchmark/ **main project directory**
| |-- **init**.py
| |-- server/
| | |-- **init**.py
| | |-- api.py **opens server on host and exposes urls**
| | |-- utils.py
| |-- benchmark/
| | |-- **init**.py
| | |-- benchmark.py **combining scores, metrics, final evaluation**
| | |-- run.py **entry point. sets everything up**
| | |-- challenges/ **challenges across different metrics**
| | | |-- **init**.py
| | | |-- Challenge.py **easy challenge creation through Challenge class. potentially how DSL is defined. may need to inherit challenge class like Adaptability(Challenge)**
| | | |-- utils.py
| | | |-- adaptability.py
| | | |-- basic_abilities.py
| | | |-- code.py
| | | |-- memory.py
| | | |-- retrieval.py
| | | |-- web_navigation.py
| | | |-- writing.py
| |-- workspace/ **workspace related func**
| | |-- **init**.py
| | |-- workspace_manager.py **creation, deletion, preSignedUrl generation**
| | |-- cloud_services/
| | | |-- **init**.py
| | | |-- aws.py **not finalized, but write, read, and del files**
|-- tests/ **test func of agbenchmark**
| |-- **init**.py
| |-- test_api.py
| |-- test_benchmark.py
| |-- test_workspace_manager.py
```
Later: GitHub Actions integration, OpenAPI?, good versioning and backward compatibility

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agbenchmark/Challenge.py Normal file
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import os
from typing import Optional
class Challenge:
"""The parent class to all specific challenges classes.
Defines helper methods for running a challenge"""
@staticmethod
def open_file(workspace: str, filename: str):
script_dir = os.path.abspath(workspace)
workspace_dir = os.path.join(script_dir, filename)
with open(workspace_dir, "r") as f:
return f.read()
@staticmethod
def write_to_file(workspace: str, filename: str, content: str):
script_dir = os.path.abspath(workspace)
print("Writing file at", script_dir)
workspace_dir = os.path.join(script_dir, filename)
# Open the file in write mode.
with open(workspace_dir, "w") as f:
# Write the content to the file.
f.write(content)
def get_filenames_in_workspace(self, workspace: str):
return [
filename
for filename in os.listdir(workspace)
if os.path.isfile(os.path.join(workspace, filename))
]

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# how well the agent did on the challenges, the metrics calculation

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import json
class Challenge(object):
def __init__(self, json_data):
self.json_data = json_data
@classmethod
def from_json_file(cls, json_file):
with open(json_file) as f:
json_data = json.load(f)
return cls(json_data)

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from ..Challenge import Challenge
class RetrievelChallenge(Challenge):
""" Chanllenge for information-retrieval """
def __init__(self, json_data):
self.json_data = json_data
assert self.json_data["category"] == "information-retrieval"
@property
def agent_input(self):
return self.json_data["query"]
def scoring(self, content):
for should_contain_word in self.json_data["ground"]["should_contain"]:
if should_contain_word not in content:
return 0.
for should_not_contain_word in self.json_data["ground"]["should_not_contain"]:
if should_not_contain_word in content:
return 0.
return 1.
def run(self, output_file):
output = open(output_file).read().strip()
score = self.scoring(output)
return score

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# running all of the different challenges

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# Challenges Data Schema of Benchmark
## General challenges
Input:
- **category** (str): information-retrieval
- **difficulty**(str): the difficulty of this query. choices from
## Information-retrieval challenges
Input:
- **category** (str): information-retrieval
- **task** (str): the question the agent needs to be solve.
- **ground** (dict): The ground truth.
- **answer** (str): The raw text of ground truth answer
- **should_contain** (list): the exact strings that is required in the final answer
- **should_not_contain** (list): the exact strings that should not be in the final answer
- **files**: files that the are used for retrieval
- **difficulty**(str): the difficulty of this query. choices from
Example:
```python
{
"category": "retrieval",
"task": "What is the capital of America?",
"ground": {
"answer": "Washington",
"should_contain": ["Washington"],
"should_not_contain": ["New York", "Los Angeles", "San Francisco"],
"files": ["file_to_check.txt"]
},
"difficulty": "easy"
}
```
Output:
- **score** (float): scores range from [0, 1]

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from pydantic import BaseModel
from typing import List, Optional
import json
import os
class Ground(BaseModel):
answer: str
should_contain: Optional[List[str]]
should_not_contain: Optional[List[str]]
files: List[str]
class Challenge(BaseModel):
category: str
task: str
ground: Ground
difficulty: str
def serialize(self, path: str) -> None:
with open(path, "w") as file:
file.write(self.json())
@staticmethod
def deserialize(path: str) -> "Challenge":
print("Deserializing", path)
with open(path, "r") as file:
data = json.load(file)
return Challenge(**data)

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from agbenchmark.Challenge import Challenge
from agbenchmark.challenges.define_task_types import Ground
class RetrievalChallenge(Challenge):
"""Challenge for information-retrieval"""
def scoring(self, content: str, ground: Ground):
if ground.should_contain:
for should_contain_word in ground.should_contain:
if should_contain_word not in content:
return 0.0
else:
print(
f"Word that should exist: {should_contain_word} exists in the content"
)
if ground.should_not_contain:
for should_not_contain_word in ground.should_not_contain:
if should_not_contain_word in content:
return 0.0
else:
print(
f"Word that should not exist: {should_not_contain_word} does not exist in the content"
)
return 1.0

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{
"category": "retrieval",
"task": "What is the capital of America?",
"ground": {
"answer": "Washington",
"should_contain": ["Washington"],
"should_not_contain": ["New York", "Los Angeles", "San Francisco"],
"files": ["file_to_check.txt"]
},
"difficulty": "easy"
}

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import pytest
from agbenchmark.challenges.retrieval.Retrieval import RetrievalChallenge
from agbenchmark.challenges.define_task_types import Challenge, Ground
import os
data = Challenge.deserialize(os.path.join(os.path.dirname(__file__), "r1_data.json"))
class TestRetrieval1(RetrievalChallenge):
"""The first information-retrieval challenge"""
@pytest.mark.parametrize(
"server_response",
[data.task],
indirect=True,
)
@pytest.mark.retrieval
def test_retrieval(self, workspace):
file = self.open_file(workspace, data.ground.files[0])
score = self.scoring(file, data.ground)
print("You score is:", score)
assert score

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agbenchmark/config.json Normal file
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{
"hostname": "localhost",
"port": 8080,
"workspace": "agbenchmark/mocks/workspace"
}

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agbenchmark/conftest.py Normal file
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import json
import os
import pytest
import shutil
from agbenchmark.mocks.tests.retrieval_manual import mock_retrieval
import requests
@pytest.fixture(scope="module")
def config():
config_file = os.path.abspath("agbenchmark/config.json")
print(f"Config file: {config_file}")
with open(config_file, "r") as f:
config = json.load(f)
return config
@pytest.fixture
def workspace(config):
yield config["workspace"]
# teardown after test function completes
for filename in os.listdir(config["workspace"]):
file_path = os.path.join(config["workspace"], filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print(f"Failed to delete {file_path}. Reason: {e}")
@pytest.fixture(autouse=True)
def server_response(request, config):
task = request.param # The task is passed in indirectly
print(f"Server starting at {request.module}")
# response = requests.post(
# f"{config['hostname']}:{config['port']}", data={"task": task}
# )
# assert (
# response.status_code == 200
# ), f"Request failed with status code {response.status_code}"
mock_retrieval(task, config["workspace"])

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agbenchmark/metrics.py Normal file
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# how well the agent did on the challenges, the metrics calculation for the future if we're tracking specific tests
# POTENTIAL METRICS
# pass/fail - in the future could have a % metric of challenge completed, milestones achieved
# convergence - how long it took to get the result
# difficulty of the task - defined by previous comparing to runs against other agents
# consistency
# time passed
# budget used
# divergence (distractions not related to task at hand)

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import json
import openai
def basic_gpt_agent(query) -> str:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo-0613", messages=[{"role": "user", "content": query}]
)
answer = response["choices"][0]["message"]["content"] # type: ignore
print("QUERY : ", query)
print("AGENT ANSWER: ", answer)
return answer
if __name__ == "__main__":
# server boilerplate example here
basic_gpt_agent("")

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from ..basic_gpt_agent import basic_gpt_agent
from agbenchmark.Challenge import Challenge
def mock_retrieval(task: str, workspace: str):
# Call the basic_gpt_agent to get a response.
response = basic_gpt_agent(task)
# Open the file in write mode.
Challenge.write_to_file(workspace, "file_to_check.txt", response)

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import click
import pytest
import json
import os
@click.group()
def cli():
pass
@cli.command()
@click.option("--challenge", default=None, help="Specific challenge to run")
def start(challenge):
"""Start the benchmark tests. If a challenge flag is is provided, run the challenges with that mark."""
with open("agbenchmark/config.json", "r") as f:
config = json.load(f)
print("Current configuration:")
for key, value in config.items():
print(f"{key}: {value}")
update_config = click.confirm(
"\nDo you want to update these parameters?", default=False
)
if update_config:
config["hostname"] = click.prompt(
"\nPlease enter a new hostname", default=config["hostname"]
)
config["port"] = click.prompt("Please enter a new port", default=config["port"])
config["workspace"] = click.prompt(
"Please enter a new workspace path", default=config["workspace"]
)
with open("agbenchmark/config.json", "w") as f:
json.dump(config, f)
print("Starting benchmark tests...", challenge)
if challenge:
print(f"Running {challenge} challenges")
pytest.main(["agbenchmark", "-m", challenge, "-vs"])
else:
print("Running all challenges")
pytest.main(["agbenchmark", "-vs"])
if __name__ == "__main__":
start()

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agbenchmark/utils.py Normal file
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# radio charts, logs, helper functions for tests, anything else relevant.

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# Manages the workspaces including creation, deletion, etc

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# Challenges Data Schema of Benchmark
## General challenges
Input:
- **category** (str): information-retrieval
- **difficulty_level**(str): the difficulty of this query. choices from ["easy", "medium", "hard"]
## Information-retrieval challenges
Input:
- **category** (str): information-retrieval
- **query** (str): the question need to be solve.
- **ground** (dict): The ground truth.
- **answer** (str): The raw text of ground truth answer
- **should_contain** (list): the exact strings that is required in the final answer
- **should_not_contain** (list): the exact strings that should not be in the final answer
- **difficulty_level**(str): the difficulty of this query. choices from ["easy", "medium", "hard"]
Example:
```python
{
"category": "information-retrieval",
"query": "what is the capital of America",
"ground": {
"answer": "Washington",
"should_contain": ["Washington"],
"should_not_contain": ["New York", "Los Angeles", "San Francisco"]
},
"difficulty_level": "easy"
}
```
Output:
- **score** (float): scores range from [0, 1]

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{
"category": "information-retrieval",
"query": "what is the capital of America",
"ground": {
"answer": "Washington",
"should_contain": ["Washington"],
"should_not_contain": ["New York", "Los Angeles", "San Francisco"]
},
"difficulty_level": "easy"
}

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@@ -1,10 +0,0 @@
{
"category": "information-retrieval",
"query": "The Nobel Prize in Literature 2012",
"ground": {
"answer": "Mo Yan",
"should_contain": ["Mo Yan"],
"should_not_contain": ["Murakami Haruki"]
},
"difficulty_level": "easy"
}

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import json
import openai
from agbenchmark.benchmark.challenges.retrieval.r1_test import RetrievelChallenge
def basic_gpt_agent(challenge_file):
challenge = RetrievelChallenge.from_json_file(challenge_file)
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo-0613",
messages=[{"role": "user", "content": challenge.agent_input}])
answer = response["choices"][0]["message"]["content"]
output_file = "./basic_gpt_agent_retrieval_results.txt"
with open(output_file, "w") as f:
f.write(answer)
print("QUERY : ", challenge.agent_input)
print("AGENT ANSWER: ", answer)
score = challenge.run(output_file)
print("AGENT SCORE : ", score)
if __name__ == "__main__":
basic_gpt_agent("./data/retrieval/r1_test_data_1.json")

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file_to_check.txt Normal file
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The capital of America is Washington, D.C.

669
poetry.lock generated
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# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
[[package]]
name = "aiohttp"
version = "3.8.4"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.6"
files = [
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test = ["pytest (>=6)"]
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idna = ">=2.0"
multidict = ">=4.0"
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lock-version = "2.0"
python-versions = "^3.9"
content-hash = "c5b989915c413ab901c39dd0c4f3b0fe203558c2879952a2460a52bda4f3e857"
content-hash = "a13e69f2bd9e511e1af92ed02b155a90dec38a9b8d983a711e1b67931b467d38"

View File

@@ -5,10 +5,15 @@ description = "Benchmarking the performance of agents far and wide, regardless o
authors = ["Silen Naihin <silen.naihin@gmail.com>"]
license = "MIT"
readme = "README.md"
packages = [{include = "agbenchmark"}]
[tool.poetry.dependencies]
python = "^3.9"
pytest = "^7.3.2"
click = "^8.1.3"
requests = "^2.31.0"
openai = "^0.27.8"
pydantic = "^1.10.9"
[build-system]
@@ -19,5 +24,11 @@ build-backend = "poetry.core.masonry.api"
minversion = "6.0"
addopts = "-ra -q"
testpaths = [
"tests", "benchmark/challenges",
"tests", "agbenchmark",
]
markers = [
"retrieval",
]
[tool.poetry.scripts]
agbenchmark = "agbenchmark.start_benchmark:cli"

View File

View File