3 ms·
Yes, I use retrieval for Endless Academy [1] , and it works well. Some tips: - Most vector search is basically kNN under the hood, with some kind of compres
by vikp 3y ago
Yes, I use retrieval for Endless Academy [1] , and it works well.
Some tips:
- Most vector search is basically kNN under the hood, with some kind of compression. If you have too many embeddings in your DB, this starts to pull up irrelevant text very quickly. The key is to segment the DB using other data before doing the embedding search. Postgres extensions are good for this.
- The quality of the data you put into your embedding DB matters a lot.
- How you chunk text matters. Chunking by paragraph is much better than naive chunking, for example.
- This is a good benchmark for embedding models [2]
[1] https://www.endless.academy https://www.endless.academy
[2] https://huggingface.co/blog/mteb https://huggingface.co/blog/mteb
- raoufchebri 3y agoAdding https://ann-benchmarks.com/ https://ann-benchmarks.com/
- chrisrickard 3y ago> The key is to segment the DB using other data before doing the embedding search By segmenting data, is this as simple as adding a further condition to the SQL? E.g. if your embeddings belonged to a "client" you would start with WHERE client_id - x? > How you chunk text matters. Chunking by paragraph is much better than naive chunking Would love some more info on this. SO if you had a 400 word slab of text, it would be better to create m3 or 4 embeddings from that?