semvec/pypi-packages huggingface dataset:
moda-ai 0.1.4
Moda SDK - LLM Observability with Automatic Conversation Threading
Requires Python: <4,>=3.10

# moda-ai

Moda's Python SDK for LLM observability with automatic conversation threading. Built on OpenTelemetry.

## Installation

```bash
pip install moda-ai
```

## Quick Start

```python
import moda
from openai import OpenAI

moda.init("YOUR_MODA_API_KEY")

# Set conversation ID for your session (recommended)
moda.conversation_id = "session_" + session_id

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

moda.flush()
```

## Conversation Tracking

### Setting Conversation ID (Recommended)

For production use, explicitly set a conversation ID to group related LLM calls:

```python
# Property-style (recommended)
moda.conversation_id = "support_ticket_123"
client.chat.completions.create(...)
moda.conversation_id = None  # clear when done

# Or use the setter function
moda.set_conversation_id_value("support_ticket_123")
moda.set_conversation_id_value(None)  # clear

# Or use context manager (scoped)
with moda.set_conversation_id("support_ticket_123"):
    client.chat.completions.create(...)
```

### Setting User ID

Associate LLM calls with specific users:

```python
moda.user_id = "user_12345"
client.chat.completions.create(...)
moda.user_id = None  # clear
```

## Automatic Fallback

If you don't set a conversation ID, the SDK automatically computes one from the first user message and system prompt. This works for simple use cases but explicit IDs are recommended for production.

## Full Documentation

See the [main repository README](https://github.com/ModaLabs/moda-python) for complete documentation.
raw-agentws 0.1.0
A powerful and flexible LLM interface library for Gemini and other models.
Requires Python: >=3.14

# RAW Library

A powerful and flexible LLM interface library for Gemini and other models, designed for modern Python applications.

## Features

- **Gemini Integration**: Seamless support for Google's Gemini models (including `gemini-3-flash-preview`).
- **Unified Interface**: Clean, consistent API for chat, completion, and tool usage.
- **Async Support**: Fully asynchronous implementation using `httpx` and `asyncio`.
- **Tool Calling**: Easy-to-use function calling/tool usage.
- **Streaming**: Native support for streaming responses.
- **Vision Support**: Simple API for multimodal (text + image) inputs.

## Installation

```bash
pip install raw
```

or with `uv`:

```bash
uv add raw
```

## Quick Start

### Configuration

Set your Gemini API key as an environment variable:

```bash
export GEMINI_API_KEY="your-api-key-here"
```

### Basic Usage

```python
import asyncio
import os
from RAW.llms.gemini import GeminiLLM

async def main():
    api_key = os.getenv("GEMINI_API_KEY")
    llm = GeminiLLM(api_key=api_key)
    
    response = await llm.generate("Explain quantum computing in one sentence.")
    print(response)

asyncio.run(main())
```

### Chat with Tools

```python
from RAW.llms.gemini import GeminiLLM
from RAW.modals import Message, Tool, ToolParam

# Define a tool
def get_weather(city: str):
    return f"The weather in {city} is sunny."

tool = Tool(
    name="get_weather",
    description="Get weather for a city",
    parameters=[ToolParam(name="city", type="string", required=True)],
    function=get_weather
)

# Chat with tool
messages = [Message(role="user", content="What's the weather in Tokyo?")]
response = await llm.chat(messages, tools=[tool])
```

## License

MIT
bigtime-api-python 0.0.3
bigtime-api-python is an API wrapper for the BigTime API
Requires Python: >=3.13
Keywords: API Wrapper, BigTime

# bigtime-api-python

A BigTime API wrapper for Python

## Docs

[See documentation here](bigtime-api-python/docs/)

## Contributing

If you find any bugs and/or errors, please [submit an issue](https://github.com/N3RM/bigtime-api-python/issues) or fork the repository
and submit a pull request with your updates.

## Examples

Coming soon
toolssql 0.1.2
Small SQL helper utilities with optional external authsql.py config loading.
Requires Python: >=3.9

# toolssql

Small SQL helper utilities (MySQL-focused) with flexible auth configuration.

## Install

```bash
pip install toolssql
```

## Auth config loading

This package will try to load `CONNECTION_CONFIG` and `CONNECTION_CONFIG_DEFAULT`
from an `authsql.py` module/file in this order:

1. `AUTHSQL_MODULE` env var (importable module path, e.g. `myapp.authsql`)
2. Import `authsql` (works when your project root is on `sys.path`)
3. `AUTHSQL_PATH` env var pointing directly to an `authsql.py` file (supports `~` and `$HOME`)
4. Home directory fallbacks:
   - `~/authsql.py`
   - `~/.authsql.py`
   - `~/.config/authsql.py`

Example config:

```python
from typing import Dict, Any
CONNECTION_CONFIG: Dict[str, Any] = {
    'config1':
    {
        'host': 'localhost',
        'user': 'Analyst',
        'password': 'XXXXXXXXXXXXXXXXX',
        'database': 'Playground',
    },
    'config2':
    {
        'host': 'localhost',
        'user': 'Analyst2',
        'password': 'XXXXXXXXXXXXXXXXX',
    },
}

# default connection to be used, if nothing else is specified
CONNECTION_CONFIG_DEFAULT = "config1"
```

If none are found, it falls back to `{}` / `None`.

**Important:** Do not ship secrets inside this PyPI package. Keep `authsql.py` outside git.

## Usage

```python
from toolssql import MysqlConnect

db = MysqlConnect()  # uses loaded defaults if available
df = db.select_df("SELECT 1 AS one")
print(df)
```

Or inject config explicitly:

```python
from toolssql import MysqlConnect

cfg = {
  "prod": {"host": "...", "user": "...", "password": "...", "database": "..."}
}
db = MysqlConnect(connection="prod", connection_config=cfg, connection_default="prod")
```
pangutao 1.0.0
全球首个河图洛书易经螺旋计算引擎
Requires Python: >=3.6


盘古天道引擎 PanguTao Engine
全球首个基于河图洛书、先天八卦、后天八卦、六十四卦、三百八十四爻、易传的螺旋上升式计算引擎。
原创设计者：肖文辉