Find answers from the community

Updated 2 years ago

does llama-index work with Spotify's `annoy` library?

At a glance

The post asks if the llama-index library works with Spotify's annoy library. The community members discuss the potential integration, with one member noting that they do not have any integration with annoy. They mention that annoy is an Approximate Nearest Neighbors (ANN) library, and discuss the potential benefits of using ANN over cosine similarity for retrieving nodes using embeddings, especially for small chunks of text. However, there is no definitive answer provided in the comments.

Useful resources
@kapa.ai does llama-index work with Spotify's annoy library?
1
k
r
L
13 comments
Please stand by @ryanglambert as I search the knowledge base. We are experiencing especially high traffic right now and might take longer than usual. :wave:
----------------------
Hmm, I don't know enough to give you a confident answer yet. The provided extracts do not mention any integration with Spotify's annoy library. You can refer to the LlamaIndex documentation for more information: https://gpt-index.readthedocs.io/en/latest/index.html
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
Should I thumbs down based on model's ability to get the answer? or more on the UX?
I think the answer is fine. We don't have a integration with that
(not sure what that is haha)
annoy is an ANN library
Interesting. So it could be used to retrieve nodes using embeddings right, instead of raw cosine similarity?
This sounds very interesting ... are there papers that compare ANN and cosine similarity efficiency ?
with small chunks, embeddings are not always very efficient ... wondering if ANN works better ...
That's definitely a good point. But I think with small chunks, the problem is probably more with embeddings themselves? Well, at least that's my impression (the cosine similarity starts to become very similar for small chunks)
Cosine similarity will be the upperbound of performance of ANN on accurate retrieval.

simply put, ANN is always 'trying' to be as accurate as cosine similarity while being wildly faster
ANN/KNN/etc. will always have more efficient retrieval than cosine against all documents' vectors.
Add a reply
Sign up and join the conversation on Discord