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https://github.com/lennartpollvogt/ollama-instructor
Python library for the instruction and reliable validation of structured outputs (JSON) of Large Language Models (LLMs) with Ollama and Pydantic. -> Deterministic work with LLMs.
https://github.com/lennartpollvogt/ollama-instructor
instructor json json-schema llm local-llm ollama prompting pydantic validation
Last synced: 5 days ago
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Python library for the instruction and reliable validation of structured outputs (JSON) of Large Language Models (LLMs) with Ollama and Pydantic. -> Deterministic work with LLMs.
- Host: GitHub
- URL: https://github.com/lennartpollvogt/ollama-instructor
- Owner: lennartpollvogt
- License: mit
- Created: 2024-05-27T18:43:46.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-12-21T12:37:26.000Z (about 1 month ago)
- Last Synced: 2025-01-09T02:33:37.196Z (12 days ago)
- Topics: instructor, json, json-schema, llm, local-llm, ollama, prompting, pydantic, validation
- Language: Python
- Homepage:
- Size: 417 KB
- Stars: 70
- Watchers: 5
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ollama-instructor
`ollama-instructor` is a lightweight Python library that provides a convenient wrapper around the Ollama Client, extending it with validation features for obtaining valid JSON responses from Large Language Models (LLMs). Utilizing Pydantic, `ollama-instructor` ensures that responses from LLMs adhere to defined schemas.
[](https://pepy.tech/project/ollama-instructor)
> **Note**: This library depends on having [Ollama](https://ollama.com) installed and running. For more information, please refer to the official website of Ollama.
## Breaking Changes in Version 1.0.0
Version 1.0.0 introduces significant changes from version 0.5.2:
- Complete refactoring to directly inherit from Ollama's official Client classes
- Simplified API that aligns more closely with Ollama's native interface
- Improved logging system using Python's built-in logging module
- Streamlined validation process using Pydantic
- Removal of partial validation features to focus on core functionality
- New method names: `chat_completion` and `chat_stream` (previously `chat_completion_with_stream`)
## Features
- **Direct Integration**: Inherits directly from Ollama's official client for seamless integration
- **Schema Validation**: Uses Pydantic BaseModel to ensure valid JSON responses
- **Retry Mechanism**: Automatically retries failed validations with configurable attempts
- **Logging**: Comprehensive logging system with configurable levels
- **Async Support**: Full async/await support through `OllamaInstructorAsync`
## Installation
```bash
pip install ollama-instructor
```
## Quick Start
For streaming examples click [here](examples/)
### Synchronous Usage:
```python
from pydantic import BaseModel
from ollama_instructor import OllamaInstructor
class FriendInfo(BaseModel):
name: str
age: int
is_available: bool
class FriendList(BaseModel):
friends: list[FriendInfo]
# Create client with logging enabled
client = OllamaInstructor(enable_logging=True, log_level='DEBUG')
# Chat completion
response = client.chat_completion(
format=FriendList,
model='llama2:latest',
messages=[
{
'role': 'user',
'content': 'I have two friends: John (25, available) and Mary (30, busy)'
}
]
)
```
### Asynchronous Usage:
```python
import asyncio
from pydantic import BaseModel
from ollama_instructor import OllamaInstructorAsync
class FriendInfo(BaseModel):
name: str
age: int
is_available: bool
async def main():
client = OllamaInstructorAsync(enable_logging=True)
response = await client.chat_completion(
format=FriendInfo,
model='llama2:latest',
messages=[
{
'role': 'user',
'content': 'John is 25 years old and available to hang out'
}
]
)
if __name__ == "__main__":
asyncio.run(main())
```
## Logging
The library includes comprehensive logging capabilities. You can enable and configure logging when initializing the client:
```python
client = OllamaInstructor(
enable_logging=True,
log_level="DEBUG", # Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
log_format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
```
## Support and Community
If you need help or want to discuss `ollama-instructor`, feel free to:
- Open an issue on GitHub
- Start a discussion in the GitHub repository
- Contact via email: [email protected]
Contributions and feedback are always welcome! 😊
## License
`ollama-instructor` is released under the MIT License. See the [LICENSE](LICENSE) file for more details.