Skip to main content

Useful classes and methods for researching code-generation by LLMs.

Project description

llm-codegen-research

lint code workflow test code workflow release workflow test coverage

about

A collection of methods and classes I repeatedly use when conducting research on LLM code-generation. Covers both prompting various LLMs, and analysing the markdown responses.

installation

Install directly from PyPI, using pip:

pip install llm-codegen-research

This installs the core package, which includes code parsing and analysis utilities. To also install the LLM API client libraries, use the api extra:

pip install llm-codegen-research[api]

usage

First configure environment vairables for the APIs you want to use:

export OPENAI_API_KEY=...
export ANTHROPIC_API_KEY=...
export TOGETHER_API_KEY=...
export MISTRAL_API_KEY=...
export DEEPSEEK_API_KEY=...
export NSCALE_API_KEY=...

You can get a quick response from an LLM:

from llm_cgr import generate, Markdown

response = generate("Write python code to generate the nth fibonacci number.")

markdown = Markdown(text=response)

Or define a client to generate multiple repsonses, or have a chat interaction:

from llm_cgr import get_llm

# create the llm
llm = get_llm(
    model="gpt-4.1-mini",
    system="You're a really funny comedian.",
)

# get multiple responses and see the difference
responses = llm.generate(
    user="Tell me a joke I haven't heard before!",
    samples=3,
)
print(responses)

# or have a multi-prompt chat interaction
llm.chat(user="Tell me a knock knock joke?")
llm.chat(user="Wait, I'm meant to say who's there!")
print(llm.history)

development

Clone the repository code:

git clone https://github.com/itsluketwist/llm-codegen-research.git

We use uv for project management. Once cloned, create a virtual environment and install uv and the project:

python -m venv .venv

. .venv/bin/activate

pip install uv

uv sync --extra api

Use make commands to lint and test:

make lint

make test

Use uv to add new dependencies into the project and uv.lock:

uv add openai

Or to upgrade dependencies:

uv sync --upgrade

Check typings with ty:

ty check

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

llm_codegen_research-2.14.tar.gz (27.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

llm_codegen_research-2.14-py3-none-any.whl (32.8 kB view details)

Uploaded Python 3

File details

Details for the file llm_codegen_research-2.14.tar.gz.

File metadata

  • Download URL: llm_codegen_research-2.14.tar.gz
  • Upload date:
  • Size: 27.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.11.8 {"installer":{"name":"uv","version":"0.11.8","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for llm_codegen_research-2.14.tar.gz
Algorithm Hash digest
SHA256 eff8bee0e4d2df9f579a170e1c204837b9f837347e8ed05739f2fa4645035ad2
MD5 a022af5ca3b58406a59dad609c7847e2
BLAKE2b-256 4ef2a54d5ca122b343dbbd688c181ce4926a466fccda25eb2df4e3271e1e5a3f

See more details on using hashes here.

File details

Details for the file llm_codegen_research-2.14-py3-none-any.whl.

File metadata

  • Download URL: llm_codegen_research-2.14-py3-none-any.whl
  • Upload date:
  • Size: 32.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.11.8 {"installer":{"name":"uv","version":"0.11.8","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for llm_codegen_research-2.14-py3-none-any.whl
Algorithm Hash digest
SHA256 a1b7b9b8299d65a1a21e6b16f9a5607d795d352efdf6b183f9cf6ef3122e8093
MD5 fdcdd60aa0cd374ee068cb93854d94c9
BLAKE2b-256 80f7c15eebcaeec1131af97d02248948cb057d8cd60fc72c6d0453bb23e2ce1a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page