https://github.com/mehdox-b/langchain-ops

🤖🔗 Langchain-Ops: AI content generation with LangChain, Streamlit UI, and Langserve integration. Explore LLM capabilities effortlessly.
https://github.com/mehdox-b/langchain-ops

Last synced: 11 days ago
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🤖🔗 Langchain-Ops: AI content generation with LangChain, Streamlit UI, and Langserve integration. Explore LLM capabilities effortlessly.

• Host: GitHub
• URL: https://github.com/mehdox-b/langchain-ops
• Owner: Mehdox-B
• License: mit
• Created: 2024-04-29T12:33:46.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-09-10T13:56:42.000Z (2 months ago)
• Last Synced: 2024-09-10T15:31:28.109Z (2 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 2.48 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Langchain-Ops project

🤖⚙️🔗

## Overview

Langchain-Ops is a project that explores the power of the LangChain framework in generating AI-generated content using large language models (LLMs) such as GPT-based models. The project integrates various tools and technologies including LangChain, Streamlit for UI, Lanserve, Langsmith, and more.

## Features

- **LangChain Framework**: Utilizes LangChain for generating AI-generated content based on large language models.

- **Streamlit UI**: Provides a user-friendly interface powered by Streamlit for interacting with and exploring the generated content.

- **Lanserve Integration**: Integrates Lanserve for serving the generated content to clients.

- **Langsmith Compatibility**: Compatible with Langsmith for additional processing and analysis of the generated content.

- **Extendable Architecture**: Modular and extendable architecture allows for easy integration of additional tools and technologies.

## Setup

To set up the project locally, follow these steps:

1. Clone the repository:

   ```bash

   git clone https://github.com/your_username/Langchain-Ops.git

2. Install the dependencies:

   ```bash

   cd Langchain-Ops

   pip install -r requirements.txt

3. Run the streamlit app:

   

   ```bash

   streamlit run client.py