How do you ingest historical case law or statute collections into a vector DB?

To ingest historical case law or statute collections into a vector database, you need to follow a structured process involving data preparation, embedding generation, and database integration. First, the raw legal texts must be cleaned, formatted, and split into manageable chunks. Next, you generate numerical representations (embeddings) of these chunks using a language model. Finally, you store these embeddings alongside metadata in a vector database to enable efficient similarity searches. Here’s how to approach each step.

Data Preparation and Chunking Start by gathering the raw legal documents, which could be in formats like PDFs, scanned images, or plain text. If the data isn’t digitized (e.g., scanned images), use OCR tools like Tesseract or cloud services like AWS Textract to extract text. Clean the text by removing irrelevant elements such as headers, footers, or page numbers. Legal texts often have long, complex paragraphs, so split them into smaller chunks (e.g., 500-1,000 tokens) using libraries like LangChain’s text splitter or custom rules based on section headings (e.g., “Article 1,” “Section 2”). For example, a statute might be divided into clauses, each addressing a specific legal condition. Metadata like jurisdiction, year, or case citation should be preserved and linked to each chunk for later filtering.

Generating Embeddings Choose an embedding model suited to legal language, such as BERT-based models (e.g., LegalBERT) or OpenAI’s text-embedding models. Use a library like Sentence Transformers to convert text chunks into vectors. For instance, you could run model.encode(text_chunk) to generate a 768-dimensional vector for each chunk. Batch processing is recommended for efficiency—process hundreds of chunks at once using GPU acceleration if available. Validate the embeddings by testing similarity between related texts (e.g., two sections of the same statute should have higher cosine similarity than unrelated texts). Fine-tuning the model on legal corpora can improve relevance but requires labeled data and compute resources.

Storing in a Vector Database Load the embeddings into a vector database like Pinecone, FAISS, or Chroma. For example, with Chroma, create a collection, add embeddings, and attach metadata:

import chromadb
client = chromadb.Client()
collection = client.create_collection("statutes")
collection.add(
 embeddings=[[0.1, 0.2, ...], ...], # Your embedding arrays
 documents=["Section 1: ...", "Section 2: ..."], # Text chunks
 metadatas=[{"year": 1990, "jurisdiction": "US"}, ...] # Metadata
)

Index the embeddings for fast retrieval—most databases handle this automatically. Ensure metadata is indexed separately to support hybrid searches (e.g., filtering by jurisdiction before comparing vectors). Test queries to verify results align with expected legal relationships, and optimize parameters like distance metrics (cosine vs. Euclidean) based on your use case.