Discover how to build a robust legal case semantic search application using Pinecone, LangChain, and Voyage's domain-specific embeddings model. This app allows users to search legal cases stored as PDFs through natural language queries and showcases how to create a custom knowledge base using vector databases.
Inspiration:
I was searching for a tutorial to help me understand Pinecone but found this example app on Pinecone's official website. Although it was informative, I found it challenging to follow and fully grasp. This inspired me to create a video to make it easier for you to understand, so you don’t have to struggle. Here’s the link to the official project: https://docs.pinecone.io/examples/sam...
🎓 What You'll Learn:
-Next.js fundamentals for building full-stack applications
-Integrating LangChain for parsing and chunking PDF documents
-Utilizing Pinecone for vector database creation and management
-Embedding models using Voyage Embeddings tailored for legal texts
-Building responsive UI designs with Tailwind CSS
-Implementing efficient server-side processing with Node.js (version 20+)
📚 Project Highlights:
-Programmatic bootstrapping of knowledge bases with PDF documents
-Semantic search functionality designed for legal text retrieval
-Dynamic suggested search interface for improved user experience
-Automated index creation and data embedding for fast onboarding
👨💻 Step-by-Step Guide:
-Setting up and initializing the Next.js project structure
-Installing dependencies and configuring the environment variables
-Implementing backend logic for document chunking and metadata handling
-Creating a Pinecone vector database and embedding document chunks
-Developing a user-friendly search interface with real-time feedback
-Running and deploying your application for local testing and use
⭐ Key Technologies:
-Pinecone Vector Database
-LangChain for Document Processing
-Voyage Embeddings
-Next.js Framework
-Tailwind CSS
-Node.js (20+)
📚 Materials/References:
Pinecone API Key Setup: https://app.pinecone.io/
Voyage AI Dashboard: https://dash.voyageai.com/
Next.js Official Docs: https://nextjs.org/
LangChain Documentation: https://www.langchain.com/
GitHub Repository Starter file (Give it a star ⭐): https://github.com/mendsalbert/legal-...
Complete Code: https://github.com/mendsalbert/legal-...
Dev Environment Tools: Node.js, npm
👋 Social Media:
/ mendsalbert
/ mendsalbert_
/ mends-albert
https://t.me/albertmends
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Timestamps
00:00:00 - Intro
00:00:49 - pinecone intro
00:05:43 - starter file
00:06:54 - app overview
00:09:39 - bootstrapping
00:22:05 - pinecone api key
00:23:26 - bootstrap + ingest + embedding + search api
00:50:11 - home page
01:02:50 - testing & conclusion
💼 Business inquiries: albert.k.mends@gmail.com
👨🏽💻 Tools and Tech Stack: Next.js, TailwindCSS, Node.js, LangChain, Pinecone, Voyage Embeddings, JavaScript, TypeScript
Whether you're a seasoned developer or just starting with semantic search projects, this tutorial will guide you through building and deploying a full-stack application for powerful legal text search.
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#NextJS #SemanticSearch #LangChain #AIinLegal #Pinecone #FullStackDevelopment #TailwindCSS #NodeJS #VoyageAI #CodingTutorial
