LangChain Model Action

JIVAS action wrapper around LangChain library for abstracted LLM interfacing.

LangChain Model Action

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JIVAS action wrapper around the LangChain library for abstracted LLM interfacing.

Package Information

Name: jivas/langchain_model_action
Author: V75 Inc.
Architype: LangChainModelAction
Version: 0.0.1

Meta Information

Title: LangChain Model Action
Group: core
Type: action

Configuration

Singleton: true

Dependencies

Jivas: ^2.0.0
Pip:
- openai: 1.51.0
- langchain: 0.3.21
- langchain-community: 0.3.20
- langchain-core: 0.3.47
- langchain-experimental: 0.3.4
- langchain-openai: 0.3.9

This package, developed by V75 Inc., provides a JIVAS action wrapper for the LangChain library, facilitating abstracted interfacing with large language models (LLM). As a core action, it simplifies and streamlines interactions with LLMs. The package is a singleton and requires the Jivas library version 2.0.0. It also depends on various Python packages for its functionality, including openai and different components of the LangChain ecosystem.

How to Use

Below is detailed guidance on how to configure and use the LangChain Model Action.

Overview

The LangChain Model Action provides an abstraction layer for interacting with large language models (LLMs). It supports multiple configurations for various use cases, including:

Custom prompts for specific tasks.
Integration with OpenAI and other LLM providers.
Pipeline management for chaining multiple LLM calls.

Configuration Structure

The configuration consists of the following components:

`model_settings`

Defines the settings for the LLM model, such as provider, API keys, and parameters.

model_settings = {
    "provider": "openai",       # Example: "openai", "huggingface"
    "api_key": "your_api_key",  # API key for the provider
    "parameters": {             # Model-specific parameters
        "temperature": 0.7,
        "max_tokens": 150
    }
}

Example Configurations

Basic Configuration for OpenAI

model_settings = {
    "provider": "openai",
    "api_key": "your_openai_api_key",
    "parameters": {
        "temperature": 0.5,
        "max_tokens": 100
    }
}

Best Practices

Validate your API keys and model parameters before deployment.
Test pipelines in a staging environment before production use.

🔰 Contributing

🐛 Report Issues: Submit bugs found or log feature requests for the langchain_model_action project.
💡 Submit Pull Requests: Review open PRs, and submit your own PRs.

Contributing Guidelines

Fork the Repository: Start by forking the project repository to your GitHub account.
Clone Locally: Clone the forked repository to your local machine using a git client.
```
git clone https://github.com/TrueSelph/langchain_model_action
```
Create a New Branch: Always work on a new branch, giving it a descriptive name.
```
git checkout -b new-feature-x
```
Make Your Changes: Develop and test your changes locally.
Commit Your Changes: Commit with a clear message describing your updates.
```
git commit -m 'Implemented new feature x.'
```
Push to GitHub: Push the changes to your forked repository.
```
git push origin new-feature-x
```
Submit a Pull Request: Create a PR against the original project repository. Clearly describe the changes and their motivations.
Review: Once your PR is reviewed and approved, it will be merged into the main branch. Congratulations on your contribution!