streamlit-ollama-agent 0.1.1

A reusable Streamlit-based chat interface for Ollama models using PydanticAI

Streamlit Ollama Agent

A reusable Python package that provides a Streamlit-based chat interface for Ollama models using PydanticAI. This package makes it easy to create chat applications with streaming responses and conversation history.

Features

• 🚀 Easy-to-use Streamlit chat interface
• 📝 Streaming responses with real-time updates
• 💬 Conversation history support
• 🔄 Compatible with any Ollama model
• 🛠 Built on PydanticAI for robust type safety
• ⚡ Dual streaming modes: OpenAI compatibility or direct Ollama

Installation

# From PyPI (once published)
pip install streamlit-ollama-agent

Quick Start

1. Make sure you have Ollama running locally:

ollama run llama2  # or your preferred model

2. Create a new Python file (e.g., chat_app.py):

import streamlit as st
from streamlit_ollama_agent import OllamaAgent

# Initialize the agent (using OpenAI compatibility mode)
agent = OllamaAgent(
    model_name="llama2",  # or your preferred model
    base_url="http://localhost:11434/v1"
)

# Or use direct Ollama streaming
agent = OllamaAgent(
    model_name="llama2",
    use_direct_streaming=True  # Bypass OpenAI compatibility for direct Ollama streaming
)

# Create your chat interface
st.title("Chat with Ollama")

# Use the built-in Streamlit app
from streamlit_ollama_agent import create_chat_app
create_chat_app(agent)

3. Run your app:

streamlit run chat_app.py

Advanced Usage

Streaming Modes

The package supports two streaming modes:

1. OpenAI Compatibility Mode (default):
   • Uses OpenAI's API format
   • Compatible with other OpenAI-like APIs
   • More standardized approach

agent = OllamaAgent(
    model_name="llama2",
    base_url="http://localhost:11434/v1",  # Ollama's OpenAI-compatible endpoint
    api_key="ollama"  # Not required for Ollama
)

2. Direct Ollama Streaming:
   • Connects directly to Ollama's native API
   • Potentially faster
   • Access to Ollama-specific features

agent = OllamaAgent(
    model_name="llama2",
    use_direct_streaming=True
)

Custom Chat Interface

You can create your own custom chat interface using the OllamaAgent directly:

import asyncio
from streamlit_ollama_agent import OllamaAgent

agent = OllamaAgent()

async def get_response(prompt, history=None):
    async for chunk in agent.stream_response(prompt, history):
        # Handle each chunk of the response
        yield chunk

Configuration Options

agent = OllamaAgent(
    model_name="your-model",      # Choose your Ollama model
    base_url="your-url",          # Custom Ollama API URL (OpenAI mode)
    api_key="your-key",           # If needed (OpenAI mode)
    use_direct_streaming=False    # Choose streaming mode
)

Project Structure

streamlit-ollama-agent/
├── streamlit_ollama_agent/
│   ├── __init__.py
│   ├── agent.py          # OllamaAgent implementation
│   └── app.py           # Streamlit app implementation
├── setup.py
├── README.md
└── requirements.txt

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.