semvec/pypi-packages huggingface dataset:
valueapiconnector 0.1.0
A python value api connector
Requires Python: >=3.13

# Value API - Python Connector
A Python client for the [Value API Server](https://github.com/ValueAPI/Server).

## Install 
```
pip install valueapiconnector
```


## Usage
```python
from valueapiconnector import ValueApiConnector, ValueDataType, ABCValue
connector = ValueAPIConnector("https://values.my-server.com/")
# The auth token is optional
context = connector.get_context("test",
        auth_token="<my_secret_token>",
    )
context.update_auth_token("<my_secret_token>")
x = context.get_value("my_int_list", ValueDataType.INTEGER_LIST)
print(x.pull().unwrap()) # []
x.push([1, 2, 3])
print(x.pull().unwrap()) # [1, 2, 3]
x.delete()
print(x.pull().unwrap()) # []

class TestValues(ABCValue):
    def __init__(self, identifier):
        super().__init__(context, identifier)

    def __str__(self) -> str:
        return str(self.__dict__)

test_obj = TestValues(1)
print(test_obj) # {}
test_obj.x = 24
test_obj.y = [4, 5]
print(test_obj) # {x: 24, y=[4,5]}
test_obj = TestValues(1)
print(test_obj) # {x: 24, y=[4,5]}
test_obj = TestValues(2)
print(test_obj) # {}
```
mcp-standalone 0.1.0
A standalone MCP client without external dependencies
Requires Python: >=3.8

# MCP Standalone

A standalone implementation of the MCP client without external dependencies. This package provides a command-line interface for interacting with MCP servers, with no dependency on the external `mcp` package.

## Features

- Connect to any MCP server using the SSE protocol
- Ask questions to MCP servers with automatic tool detection
- Manage server configurations with an easy-to-use CLI
- Support for servers with predefined questions
- Automatic question matching for servers that require specific questions

## Installation

You can install the package directly from the source:

```bash
cd /path/to/mcp-standalone
pip install -e .
```

Or build and install the package:

```bash
cd /path/to/mcp-standalone
pip install build
python -m build
pip install dist/mcp_standalone-0.1.0-py3-none-any.whl
```

## Usage

### Ask a question to an MCP server

```bash
# Ask a question to the default server
mcp-standalone ask "What is the weather today?"

# Ask a question to a specific server
mcp-standalone ask "What is the weather today?" --server trilogy-mcp

# Ask a question to a custom URL
mcp-standalone ask "What is the weather today?" --url https://mcp-server.example.com/sse

# List available predefined questions for a server
mcp-standalone ask --list-questions --server trilogy-mcp
```

### Manage server configurations

```bash
# List all configured servers
mcp-standalone servers list

# Add a new server
mcp-standalone servers add my-server https://mcp-server.example.com/sse

# Remove a server
mcp-standalone servers remove my-server

# Set the default server
mcp-standalone servers set-default trilogy-mcp

# Export server configurations
mcp-standalone servers export --file servers.json

# Import server configurations
mcp-standalone servers import servers.json
```

## Configuration

The package stores server configurations in `~/.config/mcp_standalone/config.json`. This file is created automatically with default servers when you first run the tool.

## Requirements

- Python 3.8 or higher
- httpx
- httpx-sse
- anyio
- pydantic
- pydantic-settings
pymilvus.model 0.3.2
Model components for PyMilvus, the Python SDK for Milvus
Requires Python: >=3.8

# Milvus Model Lib

The `milvus-model` library provides the integration with common embedding and reranker models for Milvus, a high performance open-source vector database built for AI applications.  `milvus-model` lib is included as a dependency in `pymilvus`, the Python SDK of Milvus.

`milvus-model` supports embedding and reranker models from service providers like OpenAI, Voyage AI, Cohere, and open-source models through SentenceTransformers or Hugging Face [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) .

`milvus-model` supports Python 3.8 and above.

## Installation

If you use `pymilvus`, you can install `milvus-model` through its alias `pymilvus[model]`:
```bash
pip install pymilvus[model] 
# or pip install "pymilvus[model]" for zsh.
```

You can also install it directly:
```bash
pip install pymilvus.model
```

To upgrade milvus-model to the latest version, use:
```
pip install pymilvus.model --upgrade
```
If milvus-model was initially installed as part of the PyMilvus optional components, you should also upgrade PyMilvus to ensure compatibility. This can be done with:
```
pip install pymilvus[model] --upgrade
```
If you need to install a specific version of milvus-model, specify the version number:
```bash
pip install pymilvus.model==0.3.0
```
This command installs version 0.3.0 of milvus-model.