Marco Gorelli
July 12, 2024
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Large Language Models (LLMs) have been around for several years but recent advances have revolutionized the field of natural language processing (NLP) and artificial intelligence (AI), opening up a world of possibilities across various domains. OpenAI’s ChatGPT has taken the world by storm and shown remarkable ability to generate human-like responses to a wide range of queries.
These models require training on vast amounts of data and in some sense can be thought of as a way to summarize and expose the information in the training dataset. It is important to remember, however, that the information returned in response to a query is a statistical answer that does not result from the same logical reasoning steps humans are capable of. This leads to the so called hallucination effect that is now being talked about, i.e., when a confidently written but factually wrong response is given to a set of queries.
Between discussion sites like StackOverflow and code repositories like GitHub and GitLab, there is a tremendous amount of source code available online for potential training. Efforts like Github Copilot, show the power of AI assisted coding to boost productivity. Our testing has shown, however, that while Copilot can greatly assist a developer, it is also prone to hallucinations inventing new methods that don’t exist or sometimes using outdated APIs that no longer exist in recent versions of the code.
We decided to experiment to see if we could add a feedback loop into the process of code generation to reduce model hallucinations and iterate towards a working solution faster. We also experimented with adding conversational feedback to the results rather than the auto-complete mechanism that Copilot currently implements (Note: Copilot just announced a preview release of a conversational chat based mode).
As an experiment we developed a Python package pseudocode which allows developers to use an LLM chat session to produce correct and tested code simply by providing code annotations and tests. We believe that pseudocode is a “higher level language” for writing Python code. Below you will find an example. We emphasize that the interface is via type annotations and function docstrings and provides easy ways to include automated tests.
Below we walk through a demonstration of this process.
Let’s use a concrete example of a slightly non-trivial task of getting GitHub issues.
import typing
import datetime
from pseudocode import pseudo_function
@pseudo_function(review=True)
def get_issues(repository: str) -> typing.List[typing.Tuple[str, int, datetime.datetime]]:
"""A function to fetch all issues created by
A function that does the following:
- assume you have a github token environment variable GITHUB_TOKEN
- assume that repository is of the form organization/repo
- use the requests library
- fetch all github issues from repository in last 10 days
- only show issue numbers which are odd
- return a tuple with issue titles, number, and date created
Examples
--------
>>> all([_[1] % 2 == 1 for _ in get_issues("conda/conda")])
True
"""
pass
print('github issues', get_issues("conda/conda"))
We feel this is a good example because it is hard to remember how to use the GitHub API exactly and inevitably requires some back and forth using the PyGithub Python library, API requests, and viewing API documentation. LLMs show promise for these applications since there is a large amount of surrounding documentation but no one example to do exactly what you need. pseudocode enforces the practice of creating an interface specification which:
def get_issues(repository: str) -> typing.List[typing.Tuple[str, int, datetime.datetime]]docstring to specify what the function should be doingpseudocode to provide feedback to the LLM of the quality and correctness of the code generatedThe specification is essential for helping the LLM avoid hallucinating, and we have found that it is highly effective for the examples we’ve experimented with. When you run the example above, you will see the following.
╭────────────────────────────────────────────────────── Function Specification ────────────────────────────────────╮
│ │
│ The following is a short description of what this function should do "A function to fetch all issues created by".│
│ A longer more detailed description of what this function should do is as follows: │
│ A function that does the following: │
│ - assume you have a github token environment variable GITHUB_TOKEN │
│ - assume that repository is of the form organization/repo │
│ - use the requests library │
│ - fetch all github issues from repository in last 10 days │
│ - only show issue numbers which are odd │
│ - return a tuple with issue titles, number, and date created │
│ The function takes the following arguments: │
│ - variable "respository" of python type "<class 'str'>" │
│ This function must return a result of python type "typing.List[typing.Tuple]". │
│ Output code that will satisfy the given requirements. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────────────────── Code Review ─────────────────────────────────────────╮
│ │
│ import os │
│ import datetime │
│ import requests │
│ │
│ def run(repository: str) -> list: │
│ │
│ headers = { │
│ 'Authorization': f"Bearer {os.environ['GITHUB_TOKEN']}", │
│ 'Accept': 'application/vnd.github.v3+json' │
│ } │
│ │
│ url = f"https://api.github.com/repos/{repository}/issues" │
│ │
│ now = datetime.datetime.now() │
│ ten_days_ago = now - datetime.timedelta(days=10) │
│ │
│ response = requests.get(url, headers=headers, params={"since": ten_days_ago}) │
│ │
│ issues = response.json() │
│ odd_issue_numbers = [f"{issue['number']}" for issue in issues if issue['number'] % 2 != 0] │
│ │
│ result = [] │
│ for issue in issues: │
│ if str(issue['number']) in odd_issue_numbers: │
│ result.append((issue['title'], issue['number'], issue['created_at'])) │
│ │
│ return result │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
What code feedback would you like to provide? (leave empty for approval):
pseudocode generates appropriate prompts to the LLM asking it to provide code in response to the user’s requirements. In this case we specified review=True in the decorator which prompted pseudocode to ask for user feedback on the generated code.
What code feedback would you like to provide? (leave empty for approval): this code is messy can you simplify it?
╭─────────────────────────────────────────────────────────── LLM Feedback ─────────────────────────╮
│ this code is messy can you simplify it? │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────────────────── Code Review ─────────────────────────╮
│ │
│ import os │
│ import datetime │
│ import requests │
│ │
│ def run(repository: str) -> list: │
│ headers = {'Authorization': f"Bearer {os.environ['GITHUB_TOKEN']}"} │
│ url = f"https://api.github.com/repos/{repository}/issues" │
│ since = datetime.datetime.now() - datetime.timedelta(days=10) │
│ params = {'since': since.isoformat()} │
│ response = requests.get(url, headers=headers, params=params) │
│ issues = response.json() │
│ return [(i['title'], i['number'], i['created_at']) for i in issues if i['number'] % 2 != 0] │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
What code feedback would you like to provide? (leave empty for approval):
Already after some quick initial feedback we can see that the LLM takes into account the users needs. After user provided feedback the automated tests are run. If those tests fail or run into exceptions they will be automatically passed back to the LLM for corrections.
pass result = str(all([_[1] % 2 == 1 for _ in run("conda/conda")]))
github issues [
('Use appropriate `defaults` value for Windows is use when subdir=win-*', 12555, '2023-03-30T17:44:55Z'),
...
('Document steps to debug tests with vscode', 12459, '2023-03-07T18:20:08Z')
]
The final generated code after passing all tests.
import os
import datetime
import requests
def run(repository: str) -> list:
headers = {'Authorization': f"Bearer {os.environ['GITHUB_TOKEN']}"}
url = f"https://api.github.com/repos/{repository}/issues"
since = datetime.datetime.now() - datetime.timedelta(days=10)
params = {'since': since.isoformat()}
response = requests.get(url, headers=headers, params=params)
issues = response.json()
return [(i['title'], i['number'], i['created_at']) for i in issues if i['number'] % 2 != 0]
There are several key ideas here we want to highlight about why we think the approaches taken in pseudocode are unique.
Autogenerated code needs guidance. Feedback is generated from the user and the rest is guided by automated testing and exception reporting back to the LLM.
Interface design is naturally a high level task which is the key to composable code. Declaring interfaces for LLMs to operate within is important.
Separation of code declarations and generated code. Similar to .py and .pyc files we should separate the interfaces from the autogenerated code.
We mentioned earlier that this was an experiment of the interaction between automated testing, user feedback, and LLMs. We want to explore this more and already have additional ideas like applying formatting and linting to generated LLM code via black, ruff, and isort, and caching of generated LLM code to avoid repetitive API calls and store a database of reusable code. Similar to pseudocode.pseudo_function(...) shown below, it would be nice to have an equivalent for arbitrary files. This would provide a feel somewhat like cookiecutter on steroids.
def test_dockercompose(contents):
pass
pseudocode.pseudo_file(
'docker-compose.yaml,
'A docker-compose file to generate a running redis service on port 5000 with no password',
[test_dockercompose]
)
To see the full example, have a look at this video.
Check out the project at Quansight/pseudocode.
