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!

Thanks! Fixed that up!

The one area I keep seeing knowledge graphs come up are for: Product Knowledge Graphs (PKGs), which are a centralized, semantic, and highly interconnected data structure that brings together information about products, customers, and their interactions into a single, comprehensive "360-degree" view. Basically, it's the idea of combing through all the data (CRMs, codebases, Ticketing System, Churn Management System, sales calls, ...) that the company has digitally about their customers, and building one giant knowledge graph that they can use to determine a bunch of business intelligence use cases, or using it to power how to create new features. Then you slap an answer bar or semantic search on top of it, and you have a powerful way of getting insights or doing gap analysis on your product versus your customer needs.

Anyway, that's just one example of why you might want to use a knowledge graph. I'm sure there are literally hundreds, of more examples.

I can't speak for everyone, knowledge graphs are the "new hotness" of the ai space (RAG and MCP are seeing a lull in their hype cycles I guess). But I've used graphs professionally for a long time to connect relationships that SQL normal forms have trouble expressing non-recursively. E.g. I used graphs to define identity relationships between data sources hierarchically, and then had a another graph relationship on top of that to define connections between those identities, user at one level and organizations at the next. Graphs as indexes allow you to express arbitrary relationships between data to allow for more efficient lookups by a database. Some folks use it to express conceptual relationship between data for AI now, so if I have a bunch of images stored in google drive, I might want to abstract the concept of pets and pets have relationship with a human etc. then my database queries for looking up all pictures related to the dog-pets owned by some human becomes a tractable search instead of a scan of the corpus!

I've added a specific example for that using the go-sdk https://github.com/antflydb/antfly/pull/5 here!

There's some examples in the quickstart on the website but I'll add an explicit e2e example case for that too. Otherwise the tests for that are a little lower level in the code! I'll add the RSF (merging of the two lists) example for that too!! Thanks for the feedback.

I built this for myself because I hated running a large ElasticSearch instance at work and wanted something that would autoscale and something that allowed for reindexing data. I also had a lot of experience running a large BigTable/Elasticsearch custom graph database I thought could be unified into a single database to cut costs. Started adding an embedding index for fun based on some Google papers and now here we are!

Definitely open to working with you on supporting even better tooling for this as I imagine many different "styles" of migration will be necessary.

The number 1 supported migration path for users though is one of my personal favorite features of antfly which is the linear merge api, which allows you to incrementally reconcile an external pageable datasource with antfly at the pace you want while also getting the benefit of batching! We support index templates just like ES and the ability to change you schema and antfly manages the full-text reindex for you. If you're looking at migrating your embeddings in Elastic or another vectordb we can also support that! Let us know :)