https://github.com/laavanjan/simple-llm-application-using-lcel
This repository provides a simple API server for translating text using LangChain, LCEL (LangChain Expression Language), and the Groq Gemma2-9b-It LLM.
https://github.com/laavanjan/simple-llm-application-using-lcel
Last synced: 7 months ago
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This repository provides a simple API server for translating text using LangChain, LCEL (LangChain Expression Language), and the Groq Gemma2-9b-It LLM.
- Host: GitHub
- URL: https://github.com/laavanjan/simple-llm-application-using-lcel
- Owner: laavanjan
- License: gpl-3.0
- Created: 2025-02-12T14:23:25.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-12T14:44:51.000Z (8 months ago)
- Last Synced: 2025-02-12T15:48:41.966Z (8 months ago)
- Language: Python
- Size: 21.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🌐 LangChain Translation API 💬
This repository provides a simple API server for translating text using LangChain, LCEL (LangChain Expression Language), and the Groq Gemma2-9b-It LLM. It leverages FastAPI for creating the API and LangServe for easy deployment and interaction. This guide focuses specifically on Windows environments using Conda.
## ✨ Features ✨
* **Text Translation:** 📝 Translates input text to a specified language.
* **LangChain Integration:** 🔗 Utilizes LangChain's powerful LCEL for defining and executing the translation chain.
* **Groq LLM:** 🤖 Employs the Groq Gemma2-9b-It large language model for high-quality translations.
* **FastAPI Framework:** 🚀 Built with FastAPI for robust and efficient API handling.
* **LangServe Deployment:** ☁️ Integrates with LangServe for streamlined deployment and a user-friendly interface.
* **Environment Variables:** 🔑 Securely manages API keys using `.env` files.
## 🛠️ Technologies Used 🛠️
* **LangChain:** A framework for building applications with LLMs. Specifically, we use LCEL for defining the chain.
* **Groq Gemma2-9b-It:** A large language model from Groq used for text translation.
* **FastAPI:** A modern, fast (high-performance) web framework for building APIs with Python.
* **LangServe:** A tool for serving LangChain runnables, making them accessible via API.
* **Uvicorn:** An ASGI server, typically used with FastAPI for running the API.
* **Python:** The programming language used for the project.
* **Conda:** For environment management.
* **.env:** A format for loading environment variables from a file.
## 🚀 Getting Started (Windows/Conda) 🚀
### ⚙️ Prerequisites ⚙️
* Python 3.9+ (Install via Anaconda/Miniconda)
* A Groq API key (obtainable from the Groq platform)
### 📥 Installation 📥
1. **Fork the repository:**
Click the "Fork" button in the top right corner of the repository on GitHub ((https://github.com/laavanjan/LLM-application-using-LCEL)). This creates a copy of the repository in your GitHub account.
2. **Clone your forked repository:**
```bash
git clone [https://github.com/your-github-username/LLM-application-using-LCEL.git](https://www.google.com/search?q=https://github.com/your-github-username/LLM-application-using-LCEL.git) # Replace with your username
cd LLM-application-using-LCEL
```
3. **Create and activate a Conda environment:**
```bash
conda create -n langchain-env python=3.9 # Or your preferred Python version
conda activate langchain-env
```
4. **Install dependencies:**
```bash
conda install -f requirements.txt
```
### 🏃 Running the API 🏃
1. **Activate the Conda environment (if you haven't already):**
```bash
conda activate langchain-env
```
2. **Create a `.env` file and add your Groq API key:**
```bash
type nul > .env # Create an empty file (Windows)
echo GROQ_API_KEY=your_actual_groq_api_key >> .env # Add API key to .env
```
3. **Run the FastAPI application using Uvicorn:**
```bash
uvicorn main:app --reload # Replace 'main' with the name of your main Python file if it's different
```
This will start the server at `http://127.0.0.1:8000`.
### 💻 Using the API 💻
You can interact with the API through the LangServe interface, which is typically available at `http://127.0.0.1:8000/docs` after running the server. This provides a user-friendly way to test the API endpoints.
Alternatively, you can use tools like `curl` or Postman to make requests directly. For example:
```bash
curl -X POST -H "Content-Type: application/json" -d '{"text": "Hello, world!", "language": "French"}' [http://127.0.0.1:8000/chain](http://127.0.0.1:8000/chain)
```
This will send a POST request to the `/chain` endpoint with the text "Hello, world!" and the target language "French".
## 📂 Project Structure 📂
```
LLM-application-using-LCEL/
├── main.py # Main application file
├── requirements.txt # Project dependencies
└── .env # Environment variables (API keys)
```
## 🚀 Deployment 🚀
This application can be deployed using various platforms. LangServe provides documentation and tools to assist with deployment. Refer to the LangServe documentation for more information.
## 🙌 Contributing 🙌
Contributions are welcome! Please open an issue or submit a pull request to your forked repository.
## 📝 License 📝
This project is licensed under the GNU General Public License v3.0. See the [LICENSE](LICENSE) file for details. (You'll need to create a LICENSE file in your repository)
Key changes:
* **Removed separate ".env" file creation section:** The creation and population of the `.env` file are now done directly within the "Running the API" section. This streamlines the process.
* **Added `echo` command for populating `.env`:** The `echo` command is used to add the `GROQ_API_KEY` to the `.env` file. This is a more concise way to create and populate the file in Windows. The user still needs to replace `your_actual_groq_api_key` with their key.
This version is more concise and easier to follow, especially for Windows users. The API key setup is now integrated directly into the running instructions.