LangChain LLM7 Integration

Official LangChain compatibility layer for LLM7 API services.

Installation

pip install langchain-llm7

Features

• 🚀 Native integration with LangChain's BaseChatModel interface
• ⚡ Support for both streaming and non-streaming responses
• 🔧 Customizable model parameters (temperature, max_tokens, stop sequences)
• 📊 Token usage metadata tracking
• 🛠 Robust error handling and retry mechanisms

Usage

Basic Implementation

from langchain_llm7 import ChatLLM7
from langchain_core.messages import HumanMessage

# Initialize with default parameters
llm = ChatLLM7()

# Basic invocation
response = llm.invoke([HumanMessage(content="Hello!")])
print(response.content)

Streaming Responses

# Enable streaming
llm = ChatLLM7(streaming=True)

for chunk in llm.stream([HumanMessage(content="Tell me about quantum computing")]):
    print(chunk.content, end="", flush=True)

Advanced Configuration

# Custom model configuration
llm = ChatLLM7(
    model="llama-3.3-70b-instruct-fp8-fast",
    temperature=0.7,
    max_tokens=500,
    stop=["\n", "Observation:"],
    timeout=45
)

Parameters

Error Handling

The library provides detailed error messages for:

• API communication failures
• Invalid message formats
• Unsupported message types
• Response parsing errors

try:
    llm.invoke([{"invalid": "message"}])
except ValueError as e:
    print(f"Error: {e}")

Testing

To run the test suite:

pip install pytest
pytest tests/

Documentation

For complete documentation see:

• LangChain Core Documentation
• LLM7 API Reference

Contributing

Contributions are welcome! Please open an issue or submit a PR:

• GitHub Repository

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Contact

Eugene Evstafev

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