semvec/pypi-packages huggingface dataset:
bifrost-ai 1.0.0
Unified Python SDK for multiple AI providers with automatic conversation management
Requires Python: >=3.8

# BifrostAI Python SDK

Official Python SDK for BifrostAPI — one API for various AI models (ChatGPT, Claude, Gemini).

## Get Your API Key

Subscribe to BifrostAPI on one of these platforms:
- [RapidAPI](https://rapidapi.com/bifrost/api/bifrostapi)

## Installation

```bash
pip install bifrost-ai
```

## Quick Start

```python
from bifrost import BifrostAI

client = BifrostAI(
    api_key="bfr-xxxxx",
    openai_key="sk-xxxxx",
    storage="memory"
)

response = client.chat("conv1", "Hello!", model="gpt-4")
print(response.content)
```

## Storage Options

The `storage` parameter is required when using methods with `conversation_id` (automatic history management).

| Method | Storage Required |
|--------|------------------|
| `client.chat()` | ✅ Yes |
| `client.chat_with_files()` | ✅ Yes |
| `client.chat_stream()` | ✅ Yes |
| `client.create_chat_completion()` | ❌ No |

**Storage backends:**

| Type | Format | Persistence |
|------|--------|-------------|
| Memory | `"memory"` | ❌ Lost on restart |
| File | `"file://path.json"` | ✅ Saved to disk |
| Redis | `"redis://localhost:6379"` | ✅ Saved to Redis |
| PostgreSQL | `"postgresql://user:pass@host/db"` | ✅ Saved to DB |

### High-level API (requires storage)
```python
client = BifrostAI(api_key="...", openai_key="...", storage="memory")

# SDK manages conversation history automatically
response = client.chat("conv1", "My name is Farid", model="gpt-4")
response = client.chat("conv1", "What is my name?", model="gpt-4")  # Remembers!
```

### Low-level API (no storage needed)
```python
client = BifrostAI(api_key="...", openai_key="...")

# You manage messages yourself
response = client.create_chat_completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)
```

## Streaming

```python
for chunk in client.chat_stream("conv1", "Count 1 to 5", model="gpt-4"):
    print(chunk.content, end="", flush=True)
```

## File Upload

```python
# Simple - just pass file paths
response = client.chat_with_files(
    "conv1",
    "Summarize this document",
    files=["document.pdf"],  # Same folder
    model="gpt-4-turbo"
)
print(response.content)

# Full path
response = client.chat_with_files(
    "conv1",
    "What is in this file?",
    files=[r"C:\Users\You\Documents\report.pdf"],
    model="gpt-4-turbo"
)

# Multiple files
response = client.chat_with_files(
    "conv1",
    "Compare these documents",
    files=["doc1.pdf", "doc2.pdf"],
    model="gpt-4-turbo"
)
```

## License

MIT
process-bigraph 1.0.5
protocol and execution for compositional systems biology
Requires Python: >=3.11

# Process-Bigraph

[![PyPI](https://img.shields.io/pypi/v/process-bigraph.svg)](https://pypi.org/project/process-bigraph/)
[![GitHub Pages](https://img.shields.io/badge/GitHub%20Pages-Tutorial-brightgreen)](https://vivarium-collective.github.io/process-bigraph/notebooks/process-bigraphs.html)

**Process-Bigraph** is an extension of the [bigraph-schema](https://github.com/vivarium-collective/bigraph-schema) 
library, offering a computational framework that integrates process modules into bigraphs. This allows for the 
representation of complex, multiscale systems, that combine the structural capabilities of bigraphs with modular dynamic
processes. It serves as a tool for creating, simulating, and analyzing intricate and dynamic models, 
fostering a more comprehensive understanding of complex systems. 

<p align="center">
    <img src="https://github.com/vivarium-collective/process-bigraph/blob/main/doc/_static/process-bigraph.png?raw=true" width="800" alt="Process Bigraph">
</p>

## What are Process Bigraphs?

Process Bigraphs are based on a mathematical formalism introduced by Robin Milner, which was expanded in Vivarium with 
the addition of Processes, and standardized with the introduction of the Schema format. Bigraphs provide a powerful 
framework for compositional modeling due to their ability to represent complex systems through hierarchical structures 
and flexible reconfigurations, thus enabling the seamless composition and decomposition of system components. Variables 
are contained in Stores (circles), which can be embedded in the place graph hierarchy, represented by the dark edges. 
Instead of hyperedges, CBS employs Processes (the rectangles) which have ports (solid black dots) connect via wires 
(dashed edges) to variables within the Stores. Processes are functions that read and write to variables through their 
ports. They can be used to rewrite the bigraph by adding new structure and new processes.

## Getting Started

### Installation

You can install `process-bigraph` using pip:

```console
pip install process-bigraph
```

## Tutorial

To get started with Bigraph-viz, explore our resources: 
* [Process Bigraphs Intro](https://vivarium-collective.github.io/process-bigraph/notebooks/process-bigraphs.html).
* [Bigraph Schema Basics Tutorial](https://vivarium-collective.github.io/bigraph-viz/notebooks/basics.html). For an introduction to the basic elements of process-bigraph schema.
* [Formatting Tutorial](https://vivarium-collective.github.io/bigraph-viz/notebooks/format.html) for examples
about how to adjust the final look of your bigraph figure.
* [Ecoli](https://raw.githubusercontent.com/vivarium-collective/bigraph-viz/main/doc/_static/ecoli.png) for the wiring
diagraph of a whole-cell E. coli model.

## License

process-bigraph is open-source software released under the [Apache 2 License](https://github.com/vivarium-collective/process-bigraph/blob/main/LICENSE).
omniai-lakshya 2.0.0
OmniAI - Complete AutoML Pipeline with 52+ algorithms for medical, financial, and text data
Requires Python: >=3.7

# 🚀 OmniAI - Universal AI with Medical Specialization

**One library for ALL your AI needs. Medical, Business, Research - Zero configuration needed.**

[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI version](https://badge.fury.io/py/omniai.svg)](https://pypi.org/project/omniai/)

## ✨ Features

- **Auto Algorithm Selection** - 20+ algorithms (Neural Networks, SVM, XGBoost, etc.)
- **One-Click Training** - Upload data → Get trained model
- **Medical Specialization** - FDA-acceptable algorithms for healthcare
- **Business Analytics** - Customer churn, sales prediction, risk assessment
- **100GB+ Ready** - Dask integration for massive datasets
- **Web Interface** - No-code UI (Streamlit)
- **API Ready** - Production REST API

## 🚀 Quick Start

```bash
# Install
pip install omniai-lakshya