Kubernetes Troubleshooting AI Assistant. This project utilizes Large Language Models (LLM) to automatically analyze issues in Kubernetes clusters and provide troubleshooting and optimization suggestions. It generates reliable diagnostic reports by reading the cluster's status data and configurations.

Data Framework for Large Language Models. This project is a data framework designed specifically for Large Language Model (LLM) applications, helping developers easily integrate private data with LLMs. It offers data connectors that support the creation of indexes from various data sources such as APIs, PDFs, documents, and SQL databases, simplifying the data import and query operations to the point where beginners can enhance the context of LLMs with just a few lines of code.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("Some question about the data should go here")
print(response)

Retrieval-Augmented Generation (RAG) Tutorial Collection. This project offers more than 20 advanced tutorials on RAG techniques, including implementation guides and sample codes, with regular updates. It covers a variety of RAG technologies such as retrieval query, context augmentation, fusion retrieval, hierarchical indexing, context compression, and knowledge graph integration.

Lossless Video and Image Upscaling Tool. This project integrates various super-resolution algorithms (such as Waifu2x, Anime4K, and Real-ESRGAN), effectively enhancing the resolution of videos and images, and offers usage methods like Graphical User Interface (GUI), Docker, and Command Line Interface (CLI).

Minimalist GPT-4o Client. This project is a client for GPT-4o that is compatible with Windows, macOS, and Ubuntu. It features a minimalist user interface and supports various tasks, including reading the screen, opening applications, system audio, and text input.

Python Library for Enhancing Context Continuity in Large Language Models. This project provides a memory layer for various mainstream large language models, supporting the saving of user interactions and context during conversations with LLMs, and is capable of dynamic real-time updates and adjustments, thereby enhancing the personalization of AI. It is suitable for personalized LLM applications that require long-term memory, such as learning assistants, medical assistants, and virtual companions.

from mem0 import Memory
m = Memory()
# Add
result = m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
# Search
related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
# Update
result = m.update(memory_id="m1", data="Likes to play tennis on weekends")

Plug-and-Play AI Voice Changer. This project is a voice conversion framework based on VITS, which requires only a small amount of voice data and a regular graphics card to quickly train a high-quality voice conversion model. It offers a simple and user-friendly web and GUI interface, supporting real-time voice change, voice and accompaniment separation, and other features.

Large Language Model API Aggregation Gateway. This project provides a unified and fast API, allowing developers to easily access over 200 LLMs, and also supports multi-modality, automatic retry, and load balancing features.

One-Click AI Tool for Generating Short Videos. This project is an AI video generation tool based on large-scale model services. It can automatically generate high-definition short videos just by providing a topic or keyword. It features an easy-to-use web interface that supports batch generation, setting video duration, and screen orientation (portrait/landscape) among other functionalities.

Minimalist Prompt-word Mind-mapping Tool. This project leverages the programming capabilities of Large Language Models (LLM) to help users automatically complete the prompt words for mind mapping. It can generate high-quality images based on brief user-provided prompt words and supports features like localized image modification, such as turning a dragon into a dinosaur within an image, significantly reducing the barrier to creating satisfying images without the need for complex prompt words.