lurndatascience / legal-online-comp

legal-online-comp

Legal Online Bot

Overview

This project, legal-online-comp, is designed to create a legal-related application with both online and offline components. It integrates an LLM (Large Language Model) with a Vector Database (VectorDB) to process and manage data efficiently. The current implementation uses Ollama for the LLM and PGVector as the VectorDB.

Components

1. Online Component: Handles interactions with the LLM and serves the application.
2. Offline Component: Responsible for ingesting data into the VectorDB.
3. VectorDB: Stores and retrieves vectorized data for efficient querying.
4. Integrated LLM: Utilizes Ollama for natural language processing. You can also use alternatives like LM Studio or a Docker container with llama-cpp-python.

Configuration

The configurations for both the LLM and the VectorDB are defined within their respective components. Ensure that the configuration settings align with the specific requirements of the LLM bindings.

Offline Component

The offline component is used to ingest data into the VectorDB. You can choose from different VectorDBs, such as PGVector, FAISSDB, or ChromaDB. This example uses PGVector.

Getting Started

Prerequisites

• Docker installed on your machine.

Building the Docker Image

To build the Docker image for this project, run the following command in your terminal:

docker build -t legal-online-bot .
docker run -d -p 4000:80 legal-online-bot
docker ps

This command will list all running Docker containers.

Future Scope

This project can be further scaled by introducing Kubernetes for container orchestration, allowing for better scalability and management of the application.

License

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

Acknowledgments

Ollama for the LLM integration. PGVector for the vector database.