semvec/pypi-packages huggingface dataset:
zeta-mlx-langchain 0.3.2
LangChain integration for Zeta MLX
Requires Python: <3.13,>=3.10
Keywords: mlx, langchain, llm, apple-silicon

# zeta-mlx-langchain

LangChain integration for Zeta MLX platform.

## Installation

```bash
pip install zeta-mlx-langchain
```

## Features

- **ChatModel**: LangChain BaseChatModel implementation
- **Embeddings**: LangChain Embeddings implementation
- **Streaming**: Async streaming support

## Usage

```python
from zeta_mlx.langchain import ChatZetaMLX

# Create chat model
llm = ChatZetaMLX(model="qwen3-8b")

# Use with LangChain
from langchain_core.messages import HumanMessage

response = llm.invoke([HumanMessage(content="Hello!")])

# Streaming
for chunk in llm.stream([HumanMessage(content="Tell me a story")]):
    print(chunk.content, end="")
```

## Links

- [GitHub](https://github.com/zeta9044/zeta-mlx)
- [Documentation](https://github.com/zeta9044/zeta-mlx#readme)
zeta-mlx-rag 0.3.2
RAG (Retrieval-Augmented Generation) for Zeta MLX
Requires Python: <3.13,>=3.10
Keywords: mlx, rag, retrieval, apple-silicon

# zeta-mlx-rag

RAG (Retrieval-Augmented Generation) pipeline for Zeta MLX.

## Installation

```bash
pip install zeta-mlx-rag
```

## Features

- **Document Processing**: Text chunking and preprocessing
- **Embeddings**: Integration with zeta-mlx-embedding
- **Retrieval**: Vector similarity search
- **Generation**: Context-aware LLM responses

## Usage

```python
from zeta_mlx.rag import RAGPipeline

pipeline = RAGPipeline(
    embedding_model="bge-m3",
    llm_model="qwen3-8b"
)

# Add documents
pipeline.add_documents(["doc1.txt", "doc2.txt"])

# Query
response = pipeline.query("What is MLX?")
```

## Links

- [GitHub](https://github.com/zeta9044/zeta-mlx)
- [Documentation](https://github.com/zeta9044/zeta-mlx#readme)
zeta-mlx-cli 0.3.2
CLI for Zeta MLX - OpenAI-compatible LLM/Embedding on Apple Silicon
Requires Python: <3.13,>=3.10
Keywords: mlx, llm, cli, apple-silicon, openai

# zeta-mlx-cli

CLI for Zeta MLX - OpenAI-compatible LLM/Embedding inference on Apple Silicon.

## Installation

```bash
pip install zeta-mlx-cli
```

## Commands

```bash
# LLM Server (port 9044)
zeta-mlx llm start          # Start server
zeta-mlx llm start --daemon  # Start in background
zeta-mlx llm status         # Check status
zeta-mlx llm stop           # Stop server

# Embedding Server (port 9045)
zeta-mlx embedding start
zeta-mlx embedding status
zeta-mlx embedding stop

# Interactive chat
zeta-mlx chat

# Model management
zeta-mlx models list
```

## API Usage

```bash
# Chat completion
curl -X POST http://localhost:9044/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "qwen3-8b", "messages": [{"role": "user", "content": "Hello!"}]}'

# Tokenize
curl -X POST http://localhost:9044/tokenize \
  -d '{"prompt": "Hello, world!"}'
```

## Links

- [GitHub](https://github.com/zeta9044/zeta-mlx)
- [Documentation](https://github.com/zeta9044/zeta-mlx#readme)
zeta-mlx-embedding 0.3.2
Embedding model serving for Zeta MLX
Requires Python: <3.13,>=3.10
Keywords: mlx, embedding, apple-silicon, bge, e5

# zeta-mlx-embedding

Embedding model serving for Zeta MLX platform.

## Installation

```bash
pip install zeta-mlx-embedding
```

## Features

- **OpenAI Compatible**: `/v1/embeddings` endpoint
- **Multilingual**: BGE-M3, E5-Large support
- **High Dimension**: 1024-dimensional embeddings

## Supported Models

| Model | Dimension | Description |
|-------|-----------|-------------|
| bge-m3 | 1024 | Korean/English optimized |
| multilingual-e5-large | 1024 | Multilingual |
| all-MiniLM-L6-v2 | 384 | Lightweight |

## Usage

```python
from zeta_mlx.embedding import EmbeddingEngine

engine = EmbeddingEngine("BAAI/bge-m3")
embeddings = engine.embed(["Hello", "안녕하세요"])
```

## Links

- [GitHub](https://github.com/zeta9044/zeta-mlx)
- [Documentation](https://github.com/zeta9044/zeta-mlx#readme)
zeta-mlx-inference 0.3.2
MLX inference engine and API for Zeta MLX
Requires Python: <3.13,>=3.10
Keywords: mlx, llm, apple-silicon, inference, openai, vllm

# zeta-mlx-inference

MLX-based LLM inference engine with OpenAI/vLLM compatible API.

## Installation

```bash
pip install zeta-mlx-inference
```

## Features

- **MLX Optimized**: Native Apple Silicon performance
- **OpenAI Compatible**: `/v1/chat/completions` endpoint
- **vLLM Compatible**: `/tokenize`, `/detokenize` endpoints
- **Streaming**: Real-time token streaming support
- **Multi-model**: Support for Qwen, Llama, and more

## Usage

```python
from zeta_mlx.inference import InferenceEngine

# Create engine
engine = InferenceEngine("mlx-community/Qwen3-8B-4bit")

# Generate response
result = engine.generate(messages, params)
```

## API Endpoints

- `POST /v1/chat/completions` - Chat completions
- `POST /tokenize` - Tokenize text
- `POST /detokenize` - Detokenize tokens
- `GET /health` - Health check

## Links

- [GitHub](https://github.com/zeta9044/zeta-mlx)
- [Documentation](https://github.com/zeta9044/zeta-mlx#readme)
zeta-mlx-core 0.3.2
Core types and pure functions for Zeta MLX
Requires Python: <3.13,>=3.10
Keywords: mlx, llm, apple-silicon, inference

# zeta-mlx-core

Core types and pure functions for Zeta MLX platform.

## Installation

```bash
pip install zeta-mlx-core
```

## Features

- **Pure Domain Types**: Message, GenerationParams, ToolDefinition
- **Result Monad**: Railway-oriented programming with `Result[T, E]`
- **Validation**: Pure validation functions
- **Configuration**: YAML-based config management

## Usage

```python
from zeta_mlx.core import (
    Message, GenerationParams, Result, Success, Failure,
    Temperature, TopP, MaxTokens,
)

# Create a message
msg = Message(role="user", content="Hello!")

# Create generation parameters
params = GenerationParams(
    max_tokens=MaxTokens(1024),
    temperature=Temperature(0.7),
    top_p=TopP(0.9),
)

# Use Result for error handling
result: Result[str, str] = Success("Hello!")
```

## Links

- [GitHub](https://github.com/zeta9044/zeta-mlx)
- [Documentation](https://github.com/zeta9044/zeta-mlx#readme)