Vector Ingestion Overview
Vector Ingestion (VNG) is the layer that turns your raw data—files, text, web pages, logs, images, or any content stream—into clean, structured, and index-ready vectors.
If your vector database is “where vectors live and get searched”, VNG is how vectors are produced correctly, continuously, and at scale.
Why Vector Ingestion exists
Most teams underestimate ingestion.
It’s not just “embed and insert”. Real ingestion needs to handle:
- Many input types (PDF, HTML, docs, images, code, etc.)
- Chunking (split content into meaningful pieces)
- Metadata extraction (titles, timestamps, author, source URL, page numbers, etc.)
- Consistency (same rules today and tomorrow)
- Reprocessing when data changes
- Scale + reliability (jobs, retries, progress, failures, and recovery)
VNG exists to make ingestion repeatable, observable, and production-safe.
What Vector Ingestion does
Vector Ingestion provides a pipeline that typically includes:
Ingest sources
Bring data from files, URLs, object storage, cloud drives or streams.
Normalize + extract
Detect content type, extract text/structure, and preserve important metadata.
Chunk
Split content into chunks that are:
- small enough for embeddings
- meaningful enough for retrieval
Embed
Convert chunks into vectors using one or more internal embedding models.
Write outputs
Send results to your vector database (and optionally a blob store / dataset store) with:
- vectors
- chunk text
- metadata
- spans (where this chunk came from)
What you get as a user
When you use VNG, you get:
- Faster time to production: ingestion is already designed as a workflow.
- Better retrieval quality: good chunking + metadata improves search results.
- Reproducibility: re-run ingestion with the same configuration and get consistent outputs.
- Observability: jobs, progress tracking, failures, retries.
- Scalability: parallel workers and distributed processing.