# LangChain Zunno Integration
A LangChain integration for Zunno LLM and Embeddings, providing easy-to-use wrappers for text generation and embeddings.
## Installation
```bash
pip install langchain-zunno
```
## Quick Start
### Text Generation (LLM)
```python
from langchain_zunno import ZunnoLLM
# Create an LLM instance
llm = ZunnoLLM(model_name="mistral:latest")
# Generate text
response = llm.invoke("Hello, how are you?")
print(response)
```
### Embeddings
```python
from langchain_zunno import ZunnoLLMEmbeddings
# Create an embeddings instance
embeddings = ZunnoLLMEmbeddings(model_name="mistral:latest")
# Get embeddings for a single text
embedding = embeddings.embed_query("Hello, how are you?")
print(f"Embedding dimension: {len(embedding)}")
# Get embeddings for multiple texts
texts = ["Hello world", "How are you?", "Good morning"]
embeddings_list = embeddings.embed_documents(texts)
print(f"Number of embeddings: {len(embeddings_list)}")
```
### Async Usage
```python
import asyncio
from langchain_zunno import ZunnoLLM, ZunnoLLMEmbeddings
async def main():
# Async LLM
llm = ZunnoLLM(model_name="mistral:latest")
response = await llm.ainvoke("Hello, how are you?")
print(response)
# Async embeddings
embeddings = ZunnoLLMEmbeddings(model_name="mistral:latest")
embedding = await embeddings.aembed_query("Hello, how are you?")
print(f"Embedding dimension: {len(embedding)}")
asyncio.run(main())
```
## Factory Functions
For convenience, you can use factory functions to create instances:
```python
from langchain_zunno import create_zunno_llm, create_zunno_embeddings
# Create LLM
llm = create_zunno_llm(
model_name="mistral:latest",
temperature=0.7,
max_tokens=100
)
# Create embeddings
embeddings = create_zunno_embeddings(
model_name="mistral:latest"
)
```
## Configuration
### LLM Configuration
- `model_name`: The name of the model to use
- `base_url`: API endpoint (default: "http://15.206.124.44/v1/prompt-response")
- `temperature`: Controls randomness in generation (default: 0.7)
- `max_tokens`: Maximum number of tokens to generate (optional)
- `timeout`: Request timeout in seconds (default: 300)
### Embeddings Configuration
- `model_name`: The name of the embedding model to use
- `base_url`: API endpoint (default: "http://15.206.124.44/v1/text-embeddings")
- `timeout`: Request timeout in seconds (default: 300)
## API Endpoints
The package connects to the following Zunno API endpoints:
- **Text Generation**: `http://15.206.124.44/v1/prompt-response`
- **Embeddings**: `http://15.206.124.44/v1/text-embeddings`
## Error Handling
The package includes comprehensive error handling:
```python
try:
response = llm.invoke("Hello")
except Exception as e:
print(f"Error: {e}")
```
## Development
### Installation for Development
```bash
git clone https://github.com/zunno/langchain-zunno.git
cd langchain-zunno
pip install -e ".[dev]"
```
### Running Tests
```bash
pytest
```
### Code Formatting
```bash
black .
isort .
```
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request
## Support
For support, please open an issue on GitHub or contact us at support@zunno.ai.
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