apollo-agent 0.2.0

A custom AI agent that implements various functions for code assistance

ApolloAgent

ApolloAgent is a custom AI agent that implements various functions for code assistance.

"ApolloAgent is a versatile PoC showcasing how AI-driven tools can simplify coding tasks and enhance productivity."

ApolloAgent provides the following functionality:

• Web Search: Get info from duck duck.
• Wiki Search: Get info from Wikipedia.
• Grep Search: Perform fast, text-based regex searches within files or directories.
• File Search: Locate files quickly using fuzzy matching on file paths.
• File Operations: Delete and edit files directly through the agent.
• Session: Each session is stored in a separate file inside the chat_sessions folder

Installation

Ensure you have Python 3.10+ installed.

# Clone the repository
git clone https://github.com/albertobarrago/ApolloAgent.git

# Navigate to the project directory
cd ApolloAgent

# Install dependencies
pip install -r requirements.txt

If no requirements.txt is included, install dependencies manually as needed.

Usage

To start ApolloAgent, simply run:

python main.py

You can:

• Search for a file: search {file_name}
• Search on web: Search on web {query}
• Search on wiki: Search on wiki {argument}
• Create a New file: Create a new file called {file_name} with this content: {content}

Docker (docker-compose)

Pull the LLM model into Ollama: Ensure the required LLM model (e.g., llama3.1) is available in your Ollama container before running ApolloAgent. * First, start just the Ollama service: bash docker compose up -d ollama * Then, execute the pull command inside the running Ollama container: bash docker exec -it ollama ollama pull llama3.1 * Wait for the download to complete.

1. Start all services: From your project root (where docker-compose.yml is located), run:

   docker compose up -d
   

   This command builds your apolloagent image, creates the Docker network, and starts both Ollama and ApolloAgent in detached mode.

2. Interact with ApolloAgent: To access the interactive chat terminal of ApolloAgent:

   docker attach apollo-agent
   

   You can detach from the terminal by pressing Ctrl+C.

3. Stop and Clean Up: To stop and remove all services defined in your docker-compose.yml file:

   docker compose down
   

Tool: codebase_search

The codebase_search tool is designed to help you find relevant code snippets within the project's codebase based on a natural language query. It's particularly useful when you're looking for code related to a specific concept or functionality but don't know the exact file names or precise syntax.

How it Works

Internally, codebase_search takes your natural language query and processes it to extract significant keywords. It then searches through the files in the workspace (respecting specified included_extensions like .py, .js, .md, etc.) to find files that contain all of these extracted keywords. This approach aims to provide more relevant results than a simple substring match of the entire query.

When to Use

Use codebase_search when:

• You want to understand how a particular feature is implemented.
• You're looking for code related to a general concept (e.g., "error handling," "user authentication").
• You remember what a piece of code does but not where it is or its exact variable/function names.

Parameters

When the ApolloAgent decides to use this tool, it will invoke it with the following parameter:

• query (string): Your natural language search query. For example:
  • "how are database connections managed"
  • "find the main configuration settings"
  • "show me code related to payment processing"

Return Value

The codebase_search tool returns a JSON object. This object will always contain query, results, and error keys.

• query (string): The original natural language query you provided.
• results (array of objects): A list of found items.
  • If matches are found, each object in the array represents a distinct match and includes the following fields:
    • file_path (string): The path to the file where the keywords were found, relative to the workspace root.
    • content_snippet (string): A preview of the file's content (up to the first 500 characters).
    • relevance_score (number): A score indicating the relevance. (Note: This is currently a fixed value for keyword matches but is designed for future semantic enhancements).
  • If no matches are found (but no error occurred), this will be an empty list ([]).
• error (string | null):
  • If the search operation encounters an issue (e.g., an invalid workspace path, permission errors), this field will contain a descriptive error message (string). In such cases, the results list will typically be empty.
  • If the search completes successfully (even if no items are found), this field will be null.

Example JSON Response (Success with results):

License

ApolloAgent is licensed under the BSD 3-Clause License. See the LICENSE file for more details.

Contributing

We welcome contributions to ApolloAgent! If you'd like to help:

• Report bugs or suggest new features via GitHub Issues.
• Submit pull requests for enhancements or changes.

Getting Started with Contributions

1. Pick an Area: Choose one of the areas above that interests you.
2. Create an Issue: Describe what you plan to implement or improve.
3. Fork and Clone: Fork the repository and clone it locally.
4. Implement Changes: Make your changes following the project's coding style.
5. Add Tests: Write tests for your new functionality.
6. Submit a PR: Create a pull request with a clear description of your changes.

We're particularly interested in contributions that make ApolloAgent more robust, user-friendly, and versatile as a coding assistant.