Project Overview

this project is a ai agent graph

  nodes/           # Directory for defining tools and their configurations
  data/            # Directory for storing raw data files
  edges/           # Directory for managing interactions and connections between agents
  env/             # Directory for environment-specific configurations (e.g., .env files)
  .git             # Git version control folder
  .gitignore       # Specifies files and directories ignored by Git
  llm/             # Directory containing configurations and utilities for working with large language models
  logs/            # Directory for storing log files
  main.py          # Entry point for running the application
  Readme.md        # Documentation of the project
  requirements.txt # Dependencies and libraries required for the project
  settings.py      # Application-level settings and configurations
  state.py         # Handles the application's state management
  utils.py         # Helper functions and utilities
  vectorstore.py   # Implements vector storage and retrieval for the application
  workflows/       # Directory for defining and managing workflows

File/Folder Details

nodes/

This folder contains all tools and nodes.

data/

Used to store raw data files or processed datasets required by the application or agents.

edges/

This folder manages interactions between agents, allowing for modular connections and defining agent-to-agent communication logic.

env/

Contains environment-specific configuration files, such as .env files, that define sensitive information like API keys or database URLs.

.git

Git directory for version control.

.gitignore

Lists files and directories to be ignored by Git, such as logs, environment files, and build artifacts.

llm/

Houses configurations, prompt templates, and utilities related to large language models (LLMs). This includes code for LLM initialization and prompt engineering.

logs/

Stores application logs, including error logs, debug logs, and execution traces.

main.py

The main entry point of the application. This script initializes the app, sets up workflows, and runs the primary logic.

Readme.md

This documentation file provides an overview of the project, its structure, and usage instructions.

requirements.txt

Lists all Python dependencies required for the project, including libraries like langchain, openai, and numpy. Install these using:

pip install -r requirements.txt

settings.py

Contains application-wide configurations, including constants and environment-specific settings such as:

• API keys
• Vector store configurations
• Workflow parameters

state.py

Manages and persists application state, such as:

• Agent states
• Workflow progress
• Vector store references

utils.py

Includes helper functions and utilities that support the main functionality, such as:

• Data preprocessing
• Common utility methods for workflows and agents

vectorstore.py

Implements the logic for setting up and querying vector stores, used for similarity searches and LLM-powered context retrieval.

workflows/

Defines workflows as modular components. Each workflow is represented as a YAML/JSON file or Python script. These workflows specify sequences of actions and tool invocations.

How to Run the Application

1. Install Dependencies:

   pip install -r requirements.txt

2. Set Up Environment: Place your .env file in the env/ directory with required keys like API keys.

3. Run the Application:

   python main.py

Logs and Debugging

• Logs are stored in the logs/ folder. Check the logs for error messages or debugging information.

Contribution

Feel free to contribute by opening issues or submitting pull requests. Ensure you follow the structure and guidelines provided in this documentation.