vector-db

What problem does it solve?

Vector databases enable fast, scalable semantic similarity search over large collections of Korean traffic accident case law and precedents by providing a production-ready vector store workflow that supports development (ChromaDB) and production (Pinecone).

Core Features & Use Cases

• Dual-stack architecture: development with ChromaDB for rapid prototyping and testing, production with Pinecone for cloud-scale search and robust metadata filtering.
• Flexible embeddings: supports local Sentence Transformers models as well as API-based embeddings, with options for hybrid setups.
• Ingestion and retrieval: ingest cases, chunk documents, upsert embeddings, and perform top-k similarity search with rich metadata for refined filtering.
• Use cases: find similar fault-ratio cases, filter by court level or accident type, and retrieve concise verdict snippets for legal review.

Quick Start

Set up a local ChromaDB instance, ingest sample Korean traffic case data, and perform a similarity search to validate the end-to-end workflow.