memexllm 0.0.4

A library for managing LLM conversation history

MemexLLM

Overview

MemexLLM is a Python library for managing and storing LLM conversations. It provides a flexible and extensible framework for history management, storage, and retrieval of conversations.

Installation

Choose the installation option that best suits your needs:

Basic Installation

pip install memexllm

OpenAI Installation

pip install memexllm[openai]

Development Installation

pip install memexllm[dev]

Quick Usage

from memexllm.storage import MemoryStorage
from memexllm.algorithms import FIFOAlgorithm
from memexllm.core import HistoryManager

# Initialize components
storage = MemoryStorage()
algorithm = FIFOAlgorithm(max_messages=100)
history_manager = HistoryManager(storage=storage, algorithm=algorithm)

# Create a conversation thread
thread = history_manager.create_thread()

# Add messages
history_manager.add_message(
    thread_id=thread.id,
    content="Hello, how can I help you today?",
    role="assistant"
)

# Retrieve conversation
thread = history_manager.get_thread(thread.id)
for msg in thread.messages:
    print(f"{msg.role}: {msg.content}")

For more examples check out the examples directory.

Feature Roadmap

Here are the planned features and improvements for MemexLLM:

Short-term Goals

• OpenAI integration
• Memory storage backend
• FIFO algorithm
• Anthropic integration
• LiteLLM integration
• MongoDB storage backend support
• SQLite storage backend support
• Redis storage backend support
• PostgreSQL storage backend support
• Conversation summarization algorithm
• Other algorithms
• Export conversations to various formats (JSON, CSV, PDF)

Medium-term Goals

• Advanced conversation analytics
• Integration with popular LLM providers
• Conversation branching and versioning

Long-term Goals

• Distributed storage support
• Multi-modal conversation support
• Advanced privacy and security features
• API Gateway integration
• Enterprise-grade features

Contributing

We welcome contributions from the community! Here's how you can help:

Ways to Contribute

1. Code Contributions

   • Fork the repository
   • Create a feature branch
   • Write clean, documented, and tested code
   • Submit a pull request

2. Bug Reports

   • Use the GitHub issue tracker
   • Include detailed steps to reproduce
   • Provide system information and context

3. Feature Requests

   • Open a GitHub issue with the "enhancement" label
   • Describe the feature and its use cases
   • Discuss with the community

4. Documentation

   • Help improve documentation
   • Write tutorials and examples
   • Fix typos and clarify explanations

Development Setup

1. Clone the repository:

   git clone https://github.com/eyenpi/memexllm.git
   cd memexllm
   

2. Install development dependencies:

   pip install -e ".[dev]"
   

3. Install pre-commit hooks:

   pip install pre-commit
   pre-commit install
   

Running Tests

pytest tests/

Code Style

• We follow PEP 8 guidelines
• Use Black for code formatting
• Use isort for import sorting
• Write meaningful commit messages

Review Process

1. All code changes require tests
2. CI must pass
3. Code review by maintainers
4. Documentation updates if needed