Chroma

How to use Chroma in a web scraping pipeline

Chroma stores documents, identifiers, metadata and embeddings for similarity retrieval. It can make an already collected website corpus searchable by meaning after the crawler has produced clean text.

A practical workflow

1. Use a separate crawler and parser to extract article text, then split it into chunks with stable page and chunk identifiers.
2. Add the chunks to a Chroma collection with source URLs and capture metadata, using a configured embedding function or embeddings you supply.
3. Query the collection and return matching chunks with their original URLs to a search or retrieval application.

When it fits

Use this for semantic discovery over your own collected documentation, articles or product descriptions. Source metadata gives the application a way to show where a match came from instead of presenting isolated generated text.

Boundaries

Chroma does not crawl the public web or verify a document's truth. Embedding configuration, chunking and update/delete policies affect retrieval quality. Keep a canonical copy of the content and remove or replace obsolete chunks when the underlying page changes.