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Comment by thefogman | Hacker News Reader

Interesting project.

I’ve got a project right now, separate vector DB, Elasticsearch, graph store, all for an agent system.

When you say Antfly combines all three, what does that actually look like at query time? Can I write one query that does semantic similarity + full-text + graph traversal together, or is it more like three separate indexes that happen to live in the same binary?

Does it ship with a CLI that's actually good? I’m pivoting away from MCP. Like can I pipe stuff in, run queries, manage indexes from the terminal without needing to write a client? That matters more to me than the MCP server honestly.

And re: Termite + single binary, is the idea that I can just run `antfly swarm`, throw docs and images at it, and have a working local RAG setup with no API keys? If so, that might save me a lot of docker-compose work.

Who's actually running this distributed vs. single-node? Curious what the typical user experience looks like.

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Thanks for the awesome questions!!

Exactly the use case I built it for! I wanted a world where you could build your indexes and the query planner could just be smart enough to use them in a single query. I've not quite nailed down the agentic query planner side 100% (it's getting there), but the JSON query DSL allows you to pipeline, join, fuse all the full-text, semantic, graph, reranking, pruning (score/token pruning) all in one query.

The CLI is my primary development tool with antfly, I am definitely looking for feedback on what people would like to see there, it's a little chonky with the flags --pruner e.g. requires writing the JSON for the config because I didn't want users to have to memorize 1000 subflags. It's definitely a first class citizen.

With respect to "Termite + single binary" that's exactly right, Termite handles chunking, multimodal chunking, embeddings (sparse + dense), reranking, fused chunking/embedding models, and we're excitedly getting more support for a variety of onnx based llms/ner models to help with data extraction use cases (functiongemma/gliner2/etc) so you don't have to setup 10 different services for testing vs deployment.

We run Antfly ourselves for our https://platform.searchaf.com (cheeky search AntFly) Algolia style search product in a distributed setup, and some users run Antfly in single node with large instances (more at the Postgres size datasets with millions of documents vs. large multitenant depoys). But we really wanted to build something with a more seamless experience of going back and forth between a distributed vs single node instance than elasticsearch or postgres can offer.

Hope that helps! Let me know if I can help you with anything!

A quick note, on platform.searchaf.com The account creation process hits a snag with verify-email links received on email giving a 404. hope it helps.

On a parallel note, It would be nice to put an architecture diagram in the github repo. Are there particular aspects of the current implementation which you want to actively improve/rearchitect/change?

I agree with the goals set out for the project and can testify that elasticsearch's DX is pretty annoying. Having said that, distributed indexing with pluggable ingestion/query custom indexes may be a good goal to aim for. - Finite State Transducers (FST) or Finite state automata based memory efficient indexes for specific data mimetypes - adding hashing based search semantic search indexes.

And even changing the indexer/reranker implementation would help make things super hackable.

Oh thanks for the 404 on the verify link (I abstracted out the auth OIDC for cross domain login and must have missed a path).

Yes good call, I tried to start that on the website with a react-flows based architectural flow chart a little bit but it's a bit high level, and not consumable directly in github markdown files but I'll work on that!

That's exactly the direction I've been working on, the reranking, embedders and chunkers are all plugable and the schema design (using jsonschema for our "schema-ish" approach allows for fine-grained index backend hints for individual data types etc.) I'll work on getting a good architecture doc up today and tomorrow!

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