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RAG & Knowledge Systems

AI Lawyer Rag With Deepseek

AI Lawyer Rag With Deepseek is a Python legal-document chatbot with a Streamlit interface. It processes uploaded PDFs, indexes their text with FAISS, retrieves relevant passages for questions, and uses DeepSeek R1 to generate responses. The project also describes document summaries, case-law retrieval and downloadable AI-generated PDF reports.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

AI Lawyer Rag With Deepseek — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to query or summarize legal PDF material through a document-grounded chatbot, or examine the repository as a Python RAG application. The README documents local environment setup and a Streamlit Cloud deployment path.

Who it is for

  • People who want to ask questions about legal PDFs.
  • Users seeking a document-retrieval and summarization aid for legal research.
  • Developers exploring a Python RAG application built with Streamlit, FAISS and language-model components.

Documented uses

  • Upload a legal PDF and ask questions about its contents.
  • Retrieve relevant text from an indexed document to inform responses.
  • Summarize legal documents or contracts.
  • Generate a downloadable AI-produced report.
Before you choose

Requirements & limits

  • The documented document-upload workflow centers on PDFs; DOCX and TXT are listed as future improvements.
  • The repository is public and identifies an MIT license.
  • The README provides local setup steps and a Streamlit Cloud deployment path that asks for GROQ_API_KEY in Streamlit Secrets.
  • The project names both Ollama and Groq in relation to model use; the README's feature list specifically describes answering questions with DeepSeek R1 and Groq.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the Python project environment

Prepare the repository and install its listed Python dependencies.

Before you start

  • Python
  • The project repository

Steps

  1. Clone the repository and change into its directory.

  2. Create a virtual environment with python -m venv venv.

  3. Activate the environment using the documented macOS/Linux or Windows command.

  4. Install dependencies with pip install -r requirements.txt.

Expected outcome

The repository's Python dependencies are installed in the virtual environment.

Guide limitations

  • These steps cover setup only; they do not establish that the application runs successfully.
  • The README does not provide a model configuration walkthrough in this setup sequence.
Documentation 1 ↗
Source-based practical guide

Deploy the app through Streamlit Cloud

Follow the repository's described deployment steps and provide its Groq API key through Streamlit Secrets.

Before you start

  • The project repository
  • A GROQ_API_KEY value for Streamlit Secrets

Steps

  1. Push the project code to GitHub.

  2. In Streamlit Cloud, choose the option to deploy a new app.

  3. Set GROQ_API_KEY in Streamlit Secrets.

  4. Start the deployment.

Expected outcome

The documented deployment process is submitted to Streamlit Cloud.

Guide limitations

  • The README does not specify deployment resource requirements or guarantee successful deployment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
AbhaySingh71/AI-Lawyer-RAG-with-Deepseek · Official homepage title · retained in its source language
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