From efb8dcaee35f94612e3fa2d4a8f9aced6f8494f1 Mon Sep 17 00:00:00 2001 From: Markus Blomqvist Date: Tue, 10 Sep 2024 20:56:13 +0300 Subject: [PATCH] Add initial implementation --- .editorconfig | 9 + .flake8 | 4 + .github/workflows/lint.yaml | 22 + .gitignore | 164 ++++++ .pre-commit-config.yaml | 14 + LICENSE | 21 + README.md | 77 +++ main.py | 413 +++++++++++++++ poetry.lock | 970 ++++++++++++++++++++++++++++++++++++ pyproject.toml | 47 ++ requirements.txt | 325 ++++++++++++ 11 files changed, 2066 insertions(+) create mode 100644 .editorconfig create mode 100644 .flake8 create mode 100644 .github/workflows/lint.yaml create mode 100644 .gitignore create mode 100644 .pre-commit-config.yaml create mode 100644 LICENSE create mode 100644 README.md create mode 100644 main.py create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 requirements.txt diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..95a4d5a --- /dev/null +++ b/.editorconfig @@ -0,0 +1,9 @@ +root = true + +[*.py] +indent_style = space +indent_size = 4 +end_of_line = lf +charset = utf-8 +trim_trailing_whitespace = true +insert_final_newline = true diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..8558e59 --- /dev/null +++ b/.flake8 @@ -0,0 +1,4 @@ +[flake8] +max-line-length = 88 +ignore = E203, W503 +exclude = .git,__pycache__,env,.venv,venv diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml new file mode 100644 index 0000000..206c174 --- /dev/null +++ b/.github/workflows/lint.yaml @@ -0,0 +1,22 @@ +name: Lint + +on: [push, pull_request] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.9" + - name: Install dependencies + run: | + pip install black isort flake8 + - name: Run Black + run: black --check . + - name: Run isort + run: isort --check-only . + - name: Run Flake8 + run: flake8 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..59b6f98 --- /dev/null +++ b/.gitignore @@ -0,0 +1,164 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +embeddings_cache/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..7cea622 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,14 @@ +repos: + - repo: https://github.com/psf/black + rev: 24.8.0 + hooks: + - id: black + - repo: https://github.com/PyCQA/isort + rev: 5.13.2 + hooks: + - id: isort + - repo: https://github.com/PyCQA/flake8 + rev: 7.1.1 + hooks: + - id: flake8 + args: [--max-line-length=88, "--ignore=E203,W503"] diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..be09e16 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Markus Blomqvist + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..95c238e --- /dev/null +++ b/README.md @@ -0,0 +1,77 @@ +# Git Vector + +## Overview + +Git Vector is a Python tool that enhances the capabilities of OpenAI models by providing them with context from Git repositories. This allows for more informed and relevant interactions with the models, leveraging the rich history and content of your codebase. + +## Features + +- **Contextual Prompting**: Integrate Git repository context into prompts for OpenAI models. +- **Embeddings Cache**: Efficiently cache embeddings to speed up repeated queries. +- **Tracked File Retrieval**: Automatically retrieve tracked files from a Git repository. +- **Text File Detection**: Identify text files to ensure only relevant content is processed. + +## Installation + +To install Git Vector, you'll need to have Python 3.12 or higher. You can install the project using Poetry: + +```bash +poetry install +``` + +Alternatively, you can clone the repository and install the dependencies manually: + +```bash +git clone git@github.com:blomqma/git-vector.git +cd git-vector +pip install -r requirements.txt +``` + +## Usage + +1. **Set Up Environment Variables**: Create a `.env` file in the root directory of your project and add your OpenAI API key: + + ``` + OPENAI_API_KEY=your_api_key_here + ``` + +2. **Run the Application**: Use the command line interface to interact with the tool. For example: + + ```bash + python main.py + ``` + + You can specify the directory of your Git repository as an argument. + +## Development + +To contribute to the development of Git Vector, you can set up your development environment using the following commands: + +```bash +# Install development dependencies +poetry install --with dev + +# Format code +poetry run black . + +# Check code style +poetry run flake8 . + +# Run tests +poetry run pytest +``` + +## License + +This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. + +## Author + +- Markus Blomqvist - [blomqvist_markus@hotmail.com](mailto:blomqvist_markus@hotmail.com) + +## Acknowledgments + +- [OpenAI](https://openai.com) for providing the API that powers this tool. +- [GitPython](https://gitpython.readthedocs.io/en/stable/) for interacting with Git repositories. +- [NumPy](https://numpy.org/) and [scikit-learn](https://scikit-learn.org/) for numerical and machine learning + 2024-09-25 00:12:15,639 [INFO] Response provided to the user. diff --git a/main.py b/main.py new file mode 100644 index 0000000..108fc42 --- /dev/null +++ b/main.py @@ -0,0 +1,413 @@ +import hashlib +import logging +import mimetypes +import os +import pickle +import time +from typing import Any, Dict, List, Tuple + +import click +import git +import numpy as np +from dotenv import load_dotenv +from openai import OpenAI, RateLimitError +from sklearn.metrics.pairwise import cosine_similarity +from tqdm import tqdm + +load_dotenv() +client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(message)s", + handlers=[logging.FileHandler("codebase_assistant.log"), logging.StreamHandler()], +) + +logger = logging.getLogger(__name__) + + +def get_repo_hash(repo_dir: str) -> str: + """Generate a unique hash for the repository directory path.""" + repo_path_bytes = repo_dir.encode("utf-8") + return hashlib.md5(repo_path_bytes).hexdigest() + + +def load_embeddings_cache(cache_file: str) -> Dict[str, Any]: + """Load the embeddings cache from a file or initialize an empty cache.""" + if os.path.exists(cache_file): + with open(cache_file, "rb") as f: + cache = pickle.load(f) + logger.info("Embeddings cache loaded.") + else: + cache = {} + logger.info("Initialized empty embeddings cache.") + return cache + + +def save_embeddings_cache(cache: Dict[str, Any], cache_file: str) -> None: + """Save the embeddings cache to a file.""" + with open(cache_file, "wb") as f: + pickle.dump(cache, f) + logger.info("Embeddings cache saved.") + + +def get_tracked_files(repo_dir: str) -> List[str]: + """Get a list of all tracked files in the Git repository using GitPython.""" + try: + repo = git.Repo(repo_dir) + tracked_files = [ + os.path.join(repo_dir, item.path) for item in repo.tree().traverse() + ] + logger.info(f"Found {len(tracked_files)} tracked files using GitPython.") + return tracked_files + except git.exc.InvalidGitRepositoryError: + logger.error("Invalid Git repository.") + click.echo("The specified directory is not a valid Git repository.") + return [] + except Exception as e: + logger.error(f"Error accessing Git repository: {e}") + click.echo("An error occurred while accessing the Git repository.") + return [] + + +def is_text_file(file_path: str) -> bool: + """Check if a file is a text file.""" + mime_type, _ = mimetypes.guess_type(file_path) + if mime_type and "text" in mime_type: + return True + else: + # Fallback for files without a MIME type + try: + with open(file_path, "r", encoding="utf-8") as f: + f.read(1024) + return True + except Exception: + return False + + +def collect_code_files(repo_dir: str) -> List[str]: + """Collect all text-based code files in the repository.""" + code_files = [] + tracked_files = get_tracked_files(repo_dir) + + if not tracked_files: + return [] + + logger.info("Filtering text files...") + + for file_path in tqdm(tracked_files, desc="Filtering files"): + if is_text_file(file_path): + code_files.append(file_path) + + logger.info(f"Collected {len(code_files)} text files for processing.") + return code_files + + +def split_into_chunks(text: str, max_tokens: int = 500) -> List[str]: + """Split text into chunks of approximately max_tokens tokens.""" + tokens = text.split() + chunks = [ + " ".join(tokens[i : i + max_tokens]) for i in range(0, len(tokens), max_tokens) + ] + return chunks + + +def generate_embeddings( + text_list: List[str], model_name: str, batch_size: int = 100 +) -> List[List[float]]: + """Generate embeddings for a list of texts using the specified model.""" + embeddings = [] + logger.info(f"Generating embeddings with model: {model_name}") + + for i in tqdm(range(0, len(text_list), batch_size), desc="Embedding chunks"): + batch = text_list[i : i + batch_size] + max_retries = 5 + retry_count = 0 + while retry_count < max_retries: + try: + response = client.embeddings.create(input=batch, model=model_name) + batch_embeddings = [data.embedding for data in response.data] + embeddings.extend(batch_embeddings) + break # Exit the retry loop on success + except RateLimitError: + retry_count += 1 + wait_time = 2**retry_count # Exponential backoff + logger.warning( + f"Rate limit exceeded. Retrying in {wait_time} seconds..." + ) + time.sleep(wait_time) + except Exception as e: + logger.error(f"Error generating embeddings: {e}") + break # Exit the loop on non-rate-limit errors + else: + logger.error(f"Max retries exceeded for batch starting at index {i}") + raise Exception("Max retries exceeded") + + return embeddings + + +def chunk_and_embed_files( + file_paths: List[str], cache: Dict[str, Any], cache_file: str, model_name: str +) -> Tuple[List[Dict[str, str]], List[List[float]]]: + """Chunk the code files and generate embeddings, using cache.""" + code_chunks = [] + embeddings = [] + logger.info("Processing files and generating embeddings...") + + for file_path in tqdm(file_paths, desc="Processing files"): + try: + last_modified = os.path.getmtime(file_path) + cache_entry = cache.get(file_path) + + if cache_entry and cache_entry["last_modified"] == last_modified: + code_chunks.extend(cache_entry["chunks"]) + embeddings.extend(cache_entry["embeddings"]) + logger.debug(f"Loaded cached embeddings for {file_path}") + else: + with open(file_path, "r", encoding="utf-8", errors="ignore") as f: + content = f.read() + + if not content.strip(): + continue + + chunks = split_into_chunks(content) + chunk_embeddings = generate_embeddings(chunks, model_name) + + for chunk, embedding in zip(chunks, chunk_embeddings): + code_chunks.append({"content": chunk, "file_path": file_path}) + embeddings.append(embedding) + + cache[file_path] = { + "last_modified": last_modified, + "chunks": [ + {"content": chunk, "file_path": file_path} for chunk in chunks + ], + "embeddings": chunk_embeddings, + } + except Exception as e: + logger.warning(f"Failed to process {file_path}: {e}") + + save_embeddings_cache(cache, cache_file) + + if not code_chunks: + logger.warning("No code chunks to process.") + + return code_chunks, embeddings + + +def get_top_k_similar_chunks( + query: str, + code_chunks: List[Dict[str, str]], + embeddings: List[List[float]], + embedding_model: str, + k: int = 5, +) -> List[Dict[str, str]]: + """Retrieve the top k code chunks most similar to the query.""" + response = client.embeddings.create(input=[query], model=embedding_model) + + query_embedding = response.data[0].embedding + similarities = cosine_similarity([query_embedding], embeddings)[0] + top_k_indices = similarities.argsort()[-k:][::-1] + top_k_chunks = [code_chunks[i] for i in top_k_indices] + return top_k_chunks + + +def construct_prompt( + question: str, code_contexts: List[Dict[str, str]], max_prompt_tokens: int +) -> str: + """Construct the prompt for the OpenAI API.""" + context_texts = [] + + for context in code_contexts: + context_text = f"File: {context['file_path']}\nContent:\n{context['content']}\n" + context_texts.append(context_text) + + context_combined = "\n".join(context_texts) + prompt = f""" +You are an AI assistant helping a developer understand their codebase. + +Code Context: +{context_combined} + +Question: +{question} + +Answer: +""" + + # Handle token limits + prompt_tokens_estimate = len(prompt) // 4 # Approximate token count + if prompt_tokens_estimate > max_prompt_tokens: + # Truncate context to fit within token limit + allowed_context_tokens = ( + max_prompt_tokens - len(question) // 4 - 100 + ) # Reserve tokens + # Truncate context_combined + context_combined = context_combined[: allowed_context_tokens * 4] + prompt = f""" +You are an AI assistant helping a developer understand their codebase. + +Code Context: +{context_combined} + +Question: +{question} + +Answer: +""" + return prompt + + +def interactive_cli( + code_chunks: List[Dict[str, str]], + embeddings: List[List[float]], + chat_model: str, + embedding_model: str, + max_prompt_tokens: int, + max_response_tokens: int, +) -> None: + """Start the interactive CLI for the user to chat with the codebase.""" + click.echo( + "Welcome to the Codebase Assistant. Type your questions below (type 'exit' to quit)." # noqa: E501 + ) + while True: + question = click.prompt("\nYour question", type=str) + if question.lower() == "exit": + click.echo("Goodbye!") + break + logger.info(f"User question: {question}") + try: + top_chunks = get_top_k_similar_chunks( + query=question, + code_chunks=code_chunks, + embeddings=embeddings, + embedding_model=embedding_model, + ) + prompt = construct_prompt( + question=question, + code_contexts=top_chunks, + max_prompt_tokens=max_prompt_tokens, + ) + response = None + max_retries = 5 + retry_count = 0 + while retry_count < max_retries: + try: + response = client.chat.completions.create( + model=chat_model, + messages=[{"role": "user", "content": prompt}], + max_tokens=max_response_tokens, + temperature=0.2, + ) + break # Exit the retry loop on success + except RateLimitError: + retry_count += 1 + wait_time = 2**retry_count # Exponential backoff + logger.warning( + f"Rate limit exceeded. Retrying in {wait_time} seconds..." + ) + time.sleep(wait_time) + except Exception as e: + logger.error(f"Error during API call: {e}") + click.echo("An error occurred while processing your request.") + break # Exit the loop on non-rate-limit errors + if response: + answer = response.choices[0].message.content + click.echo(f"\nAssistant:\n{answer}") + logger.info("Response provided to the user.") + else: + logger.error("Failed to get a response from the API.") + click.echo("Failed to get a response from the API.") + except Exception as e: + logger.error(f"Error processing your question: {e}") + click.echo("An error occurred while processing your question.") + + +@click.command() +@click.option( + "--repo-dir", prompt="Repository path", help="The path to the Git repository." +) +@click.option( + "--embedding-model", + default="text-embedding-3-small", + show_default=True, + help="The OpenAI model to use for embeddings.", +) +@click.option( + "--chat-model", + default="gpt-4o-mini", + show_default=True, + help="The OpenAI model to use for chat completions.", +) +@click.option( + "--max-prompt-tokens", + default=2000, + show_default=True, + help="Maximum number of tokens for the prompt.", +) +@click.option( + "--max-response-tokens", + default=500, + show_default=True, + help="Maximum number of tokens for the response.", +) +def main( + repo_dir: str, + embedding_model: str, + chat_model: str, + max_prompt_tokens: int, + max_response_tokens: int, +) -> None: + """Main function to run the CLI tool.""" + logger.info("Starting Codebase Assistant...") + + cli_dir = os.path.dirname(os.path.abspath(__file__)) + cache_dir = os.path.join(cli_dir, "embeddings_cache") + os.makedirs(cache_dir, exist_ok=True) + + repo_dir = os.path.abspath(repo_dir) + repo_hash = get_repo_hash(repo_dir) + + cache_file = os.path.join(cache_dir, f"embeddings_cache_{repo_hash}.pkl") + logger.info(f"Using cache file: {cache_file}") + + if not os.path.isdir(repo_dir): + logger.error("Invalid repository path.") + click.echo("The specified repository path is invalid.") + return + + cache = load_embeddings_cache(cache_file) + click.echo("Collecting code files...") + code_files = collect_code_files(repo_dir) + + if not code_files: + logger.error("No code files found.") + click.echo( + "No code files found in the repository or failed to access the repository." + ) + return + + code_chunks, embeddings = chunk_and_embed_files( + file_paths=code_files, + cache=cache, + cache_file=cache_file, + model_name=embedding_model, + ) + if not code_chunks or not embeddings: + logger.error("Failed to process code files for embeddings.") + click.echo("Failed to process code files.") + return + + embeddings_array = np.array(embeddings) + interactive_cli( + code_chunks=code_chunks, + embeddings=embeddings_array, + chat_model=chat_model, + embedding_model=embedding_model, + max_prompt_tokens=max_prompt_tokens, + max_response_tokens=max_response_tokens, + ) + + +if __name__ == "__main__": + 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