Data / RAG
Vector DBs, embeddings, document parsing, data pipelines
189
listed
7 of 7
categories in use
97%
carrying a data signal
What is in Data / RAG
01Retrieval and search52
02ETL and ingestion44
03Labelling and annotation38
04Vector databases27
05Synthetic data20
06Document parsing19
07Data versioning4
Retrieval and searchOPEN52
The search layer a question actually goes through: hybrid search, reranking, query planning and the plumbing around a corpus. A store that holds the vectors is a vector database; several products are honestly both and are filed in both.
Ordered by attributes answered — the column we hold for most of them. Blank where we hold none.
LanceDB8 ATTRIBUTES ANSWERED
Qdrant7 ATTRIBUTES ANSWERED
Chroma6 ATTRIBUTES ANSWERED
Jina AI6 ATTRIBUTES ANSWERED
paradedb6 ATTRIBUTES ANSWERED
socraticode5 ATTRIBUTES ANSWERED
Vespa5 ATTRIBUTES ANSWERED
Elasticsearch4 ATTRIBUTES ANSWERED
meilisearch4 ATTRIBUTES ANSWERED
redisearch4 ATTRIBUTES ANSWERED
txtai4 ATTRIBUTES ANSWERED
agentset3 ATTRIBUTES ANSWEREDSHOWING 12 OF 52All 52 Retrieval and search →
A CATEGORY OPENS AT 12 LISTINGS · AN EMPTY ONE KEEPS ITS ROW RATHER THAN BEING HIDDEN, BECAUSE THE GAP IS INFORMATION TOO
Everything in Data / RAG
SHOWING THE FIRST 40 OF 189 · THE CATEGORY PAGES HOLD THE REST









































