develop-vector-db

Build a vector database for legal RAG by initializing a persistent ChromaDB client to index statutes, precedents, and policies. The store embeds contextual chunks of legal texts to provide searchable evidence for analysis.

Chunk legal documents by preserving structural boundaries like articles, sections, and rulings into 500–1000 character segments. This contextual chunking includes 100–200 character overlaps to maintain continuity for accurate information retrieval.

Yes, ChromaDB provides persistent storage to ingest and search statutes and precedents. It preserves source metadata, handles duplicate upserts, and filters search results by a defined similarity threshold for legal data.

The best way to ingest legal texts is through a pipeline that fetches data, cleans HTML, chunks it contextually, and upserts it into the correct collection. This process ensures deduplication and attaches source metadata.

The vector store handles duplicate entries during upsert and supports periodic data synchronization. Sync and maintenance features keep statutes, precedents, and store policies up to date for reliable retrieval.

Query the ChromaDB vector store by searching across stored chunks to retrieve relevant context. Results are filtered by a defined similarity threshold to ensure only appropriate legal evidence is returned for analysis.