semvec/pypi-packages huggingface dataset:
unisql 1.1.0
SQL Database Wrapper
Requires Python: >=3.8

# UniSQL: SQL Database Wrapper

UniSQL is a software package that offers a unified interface for connecting to and interacting with various database engines, including SQLite, MySQL, and PostgreSQL. This package simplifies database management by abstracting the underlying complexities of each database engine, providing a consistent and user-friendly API for developers.

## Installation

```
Dependencies:

- Python 3.8, 3.9, 3.11, 3.12, 3.13

Installation:

$ pip install unisql
```

## Synchronous Connections

For synchronous connections, use the following code:

```python
from unisql import connect

# SQLite
db = connect.sqlite("database.db")

# MySQL
db = connect.mysql(database="mydatabase", host="localhost", user="myuser", password="mypassword", port=3306)

# PostgreSQL
db = connect.postgresql(database="mydatabase", host="localhost", user="myuser", password="mypassword", port=5432)
```

## Asynchronous Connections (with asyncio)

For asynchronous connections using asyncio, use the following code:

```python
from unisql.asyncio import connect

# SQLite
db = await connect.sqlite("database.db")

# PostgreSQL
db = await connect.postgresql(database="mydatabase", host="localhost", user="myuser", password="mypassword", port=5432)
```

> [!IMPORTANT]
> Asynchronous connections are currently not supported for MySQL in this package.

## Executing Queries

After establishing a connection, you can execute queries using the query and value properties, and then call the fetch or execute methods.

```python
# Set the query and values
query = "SELECT * FROM users WHERE name = ? AND email = ?;"
value = ("John Doe", "john@example.com")

# Fetch a single row
result = db.fetch(query, value, multiple=False)

# Fetch all rows
results = db.fetch(query, value, multiple=True)

# Execute an INSERT or UPDATE query at once
query = "INSERT INTO users (name, email) VALUES (?, ?);"
value = ("John Doe", "john@example.com")
db.execute(query, value)

# Execute an INSERT or UPDATE query at many
value = [("Sam Smith", "sam@example.com"), ("Adam Page", "adam@example.com")]
db.execute(query, value)
```

To close the connection:

```python
db.close()
```
mcp-lance-db 0.1.6
mcp-lance-db
Requires Python: >=3.12

# mcp-lance-db: A LanceDB MCP server

> The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.

This repository is an example of how to create a MCP server for [LanceDB](https://lancedb.com/), an embedded vector database.

## Overview

A basic Model Context Protocol server for storing and retrieving memories in the LanceDB vector database.
It acts as a semantic memory layer that allows storing text with vector embeddings for later retrieval.

## Components

### Tools

The server implements two tools:
- add-memory: Adds a new memory to the vector database
  - Takes "content" as a required string argument
  - Stores the text with vector embeddings for later retrieval
  
- search-memories: Retrieves semantically similar memories
  - Takes "query" as a required string argument
  - Optional "limit" parameter to control number of results (default: 5)
  - Returns memories ranked by semantic similarity to the query
  - Updates server state and notifies clients of resource changes

## Configuration

The server uses the following configuration:
- Database path: "./lancedb"
- Collection name: "memories"
- Embedding provider: "sentence-transformers"
- Model: "BAAI/bge-small-en-v1.5"
- Device: "cpu"
- Similarity threshold: 0.7 (upper bound for distance range)

## Quickstart

#### Claude Desktop

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

```
{
  "lancedb": {
    "command": "uvx",
    "args": [
      "mcp-lance-db"
    ]
  }
}
```

## Development

### Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:
```bash
uv sync
```

2. Build package distributions:
```bash
uv build
```

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI:
```bash
uv publish
```

Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token: `--token` or `UV_PUBLISH_TOKEN`
- Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

### Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector).


You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:

```bash
npx @modelcontextprotocol/inspector uv --directory $(PWD) run mcp-lance-db
```

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.