Github Link : Legal-AI-For-Bankruptcy-Cases
The Problem That Hooked Me
Bankruptcy law isnât just about numbers on a balance sheetâitâs a maze of case law, filings, motions, and obscure legal jargon.
When I first started exploring this domain, I noticed how time-consuming it was for legal professionals to manually sift through lengthy documents just to extract relevant precedents. Unlike other industries, legal text is unforgivingâif you miss context, you risk making the wrong judgment.
Thatâs when the idea hit me:
đ What if I could build an AI that retrieves the right legal passages and generates answers like a seasoned paralegalâfast, reliable, and context-aware?
From Frustration to Framework
At first, I naively thought: âThrow GPT at it, and weâre done.â
But reality humbled me. A generic LLM struggledâit hallucinated, mixed up precedents, and ignored nuanced legal definitions. In law, âclose enoughâ is the same as wrong.
So, I turned to Retrieval-Augmented Generation (RAG).
Instead of relying on a modelâs memory, I designed a system where the AI could:
Search legal documents (via Pinecone đ˛ for vector search)
Rank passages by relevance (thanks to Cohere đ)
Generate nuanced responses (with Cohere, Ollama, and Gemini â¨)
Orchestrate workflows (using LangGraph 𧊠for state management)
What I Actually Built
The result is what I call Legal AI for Bankruptcy Cases.
Itâs not just another chatbotâitâs a multi-component system:
Streamlit UI for an intuitive interface.
Poetry-managed dependencies so devs can spin it up cleanly.
Pinecone + Cohere + Ollama + Gemini powering retrieval and generation.
LangGraph to manage complex legal workflow states.
XML handling for parsing structured legal files.
Why This Matters
Bankruptcy law affects everything from small businesses to global corporations. A system like this isnât replacing lawyersâbut itâs amplifying their ability to find relevant information instantly. Think of it as a turbocharged research assistant that never sleeps.
Lessons Learned (the hard way)
Legal data is messyâparsing and indexing documents took longer than I expected.
RAG is powerful, but state management (via LangGraph) was the real unlock to making workflows consistent.
AI isnât about replacing expertiseâitâs about scaling it.
Whatâs Next
I see this project as a foundation. The legal AI ecosystem is still in its infancy, and the potential is massive. Bankruptcy is just one domain; imagine extending this to contracts, compliance, or litigation prep.
If youâre a legal professional, ML engineer, or just curious about where AI meets lawâyouâll want to keep an eye on this space.
đ Full repo here: Legal-AI-For-Bankruptcy-Cases
đŹ Got thoughts? Hit reply or drop me a message. Iâd love to hear how you see AI reshaping law.