Legal ChatBot 👩‍⚖️

Legal ChatBot is an innovative project designed to assist users in navigating the complex world of legal documents.

Utilizing a combination of RAG (Retrieval-Augmented Generation) technology and a deep knowledge base of law articles, this bot can intelligently reference relevant legal texts during interactions. It offers an interactive platform for querying legal information, making it a valuable tool for professionals, students, and anyone needing quick insights into legal matters.

Setup involves Poetry for dependency management, Qdrant for vector database functionality, and Langfuse for enhancing chatbot performance, ensuring a robust and efficient user experience.

Setting Up the Project

Step 1: Installing Poetry

We use Poetry for dependency management. First, check if Poetry is installed by running:

poetry -V

If it shows a version like Poetry (version x.y.z), you're set. If not, follow the official guidelines to install Poetry.

📎 Note: Though a requirements.txt is provided, Poetry is recommended for efficient dependency management.

Step 2: Environment Setup

Configuring Poetry

To maintain organization, configure Poetry to create a virtual environment in the project's directory:

poetry config virtualenvs.in-project true

Step 3: Installing Dependencies

Install all the project's dependencies with:

poetry install --no-root

📎 Note: Use the --no-root option to skip installing the project package itself.

Step 4: Activating the Virtual Environment

Post-installation, activate the virtual environment located in the .venv directory.

For macOS/Linux:

source .venv/bin/activate

For Windows:

.venv\Scripts\activate

Step 5: Initializing Qdrant:

Qdrant is a sophisticated vector database and vector similarity search engine that operates as an API service. It allows for the searching of nearest high-dimensional vectors, transforming embeddings or neural network encoders into comprehensive applications suitable for matching, searching, recommending, among other functionalities.

For setup, you will require two crucial pieces of information: QDRANT_CLUSTER_URL and QDRANT_API_KEY.

To begin, create a free account with Qdrant by signing up here. Following account creation, proceed to set up a cluster for your vector database; this is where you'll obtain your QDRANT_CLUSTER_URL. Lastly, generate your QDRANT_API_KEY by navigating to the "Data Access Control" section within your Qdrant dashboard.

Step 6: Integrate Langfuse

Langfuse plays a vital role in enhancing the functionality and performance of chatbots by offering observability, analytics, prompt management, and integration support. It is a valuable tool for developers looking to build and optimize chatbot applications powered by Large Language Models.

Here is an overview of Langfuse documentation. To begin, create a free account on Langfuse by signing up here. Create a project, generate the necessary keys, and place them in .env file.

Step 7: Environment variables:

For the project to work you need to create a .env file in the project root.

The file should look like this:

QDRANT_CLUSTER_URL=ADD_YOUR_QDRANT_CLUSTER_URL
QDRANT_API_KEY=ADD_YOUR_QDRANT_API_KEY

OPENAI_API_KEY=ADD_YOUR_OPENAI_API_KEY

LANGFUSE_SECRET_KEY=ADD_YOUR_LANGFUSE_SECRET_KEY
LANGFUSE_PUBLIC_KEY=ADD_YOUR_LANGFUSE_PUBLIC_KEY
LANGFUSE_HOST=ADD_YOUR_LANGFUSE_HOST

Run the Demo:

You can run the demo locally simply by executing this command in your terminal:

streamlit run app.py  

And UI will be available in your browser on the URL:

http://localhost:8501