ApeRAG: Production-ready GraphRAG with multi-modal indexing and K8s deployment

[cipherself]

In the list of features, it mentions:

> vision-based search for comprehensive document understanding

but it's not clear to me what this means, is it just vector embeddings for each image in every document via a CLIP-like model?

In addition, I'd be curious what's the rationale behind using the plethora of databases, given the docs on running it in production spins them all up, I assume they're all required, for instance I'd be curious on the trade-offs between using postgres with something like pg_search (for bm25 support, which vanilla postgres FTS doesn't have) vs using both postgres and ElasticSearch.

The docs are also very minimal, I'd have loved to see at least 1 example of usage.

[davidcox143]

Congrats on the launch. How does it compare to HelixDB?

https://github.com/HelixDB/helix-db

[CharlesW]

HelixDB is a database. ApeRAG is an application that uses multiple databases (but that not particular one). Hypothetically, you could fork ApeRAG and modify it to use that database.

[srameshc]

> ApeRAG requires PostgreSQL, Redis, Qdrant, and Elasticsearch. You have two options:

> bash ./02-install-database.sh # Deploys PostgreSQL, Redis, Qdrant, Elasticsearch

Is this built on top of all databases ? I am just trying to understand.

[earayu]

Yes, ApeRAG uses all these databases.

[GloriousMEEPT]

> bash ./02-install-database.sh # Deploys PostgreSQL, Redis, Qdrant, Elasticsearch

geez

sorry but, how much SHIT is it going to take to make AI good?

[earayu]

Maintaining databases is painful, so ApeRAG uses kubeblocks for all these databases.

[cpursley]

What’s funny is Postgres alone can handle this entire workload decently well.

[popalchemist]

This is a very typical, and pretty bare-bones stack. Almost any production grade webapp above a minimal threshold of complexity will have database, cache, and search.