langchain-custom-models 0.1.3

Custom Langchain chat models for various large model service providers

Langchain Custom Models

This repository provides a collection of custom ChatModel integrations for Langchain, enabling support for various Large Language Model (LLM) service providers. The goal is to offer a unified interface for models that are not yet officially supported by Langchain.

Currently, the following provider is supported:

• Volcengine Ark

Features

• Seamless Integration: Drop-in replacement for any Langchain ChatModel.
• Volcengine Ark Support: Full support for ChatVolcEngine to interact with Volcengine's Ark API.
• Standard Interface: Works with standard Langchain message types (SystemMessage, HumanMessage, AIMessage, ToolMessage).
• Tool Binding Support: Full support for bind_tools() method for function calling capabilities.

Installation

You can install the package directly from this repository:

pip install git+https://github.com/Hellozaq/langchain-custom-models.git

Additionally, ensure you have the Volcengine Ark SDK installed:

pip install volcengine-python-sdk[ark]

Usage

ChatVolcEngine

To use the ChatVolcEngine model, you need to provide your Volcengine Ark API key. The recommended approach is to use a .env file to manage your credentials securely.

1. Create a .env file

In your project's root directory, create a file named .env and add your API key:

VOLCANO_API_KEY="your-ark-api-key"

2. Load Credentials and Use the Model

Now, you can use dotenv to load the API key from the .env file into your environment.

Here is a basic example:

from dotenv import load_dotenv
from langchain_custom_models import ChatVolcEngine
from langchain_core.messages import HumanMessage, SystemMessage

# Load environment variables from .env file
load_dotenv()

# Initialize the chat model
# The API key is automatically read from the VOLCANO_API_KEY environment variable.
# Replace 'your-model-id' with the actual model ID from Volcengine Ark, e.g., 'deepseek-v3-250324'
llm = ChatVolcEngine(model="your-model-id")

# Prepare messages
messages = [
    SystemMessage(content="You are a helpful assistant."),
    HumanMessage(content="Hello, who are you?"),
]

# Get a response
response = llm.invoke(messages)

print(response.content)

Parameters

• model (str): Required. The model ID from Volcengine Ark (e.g., deepseek-v3-250324).
• ark_api_key (Optional[str]): Your Volcengine Ark API key. If not provided, it will be read from the VOLCANO_API_KEY environment variable.
• max_tokens (int): The maximum number of tokens to generate. Defaults to 4096.
• temperature (float): Controls the randomness of the output. Defaults to 0.7.
• top_p (float): Nucleus sampling parameter. Defaults to 1.0.

Contributing

Contributions are welcome! If you would like to add support for a new LLM provider or improve existing integrations, please feel free to open a pull request.

License

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