Building a Content-Based Search Engine II: Extracting Feature Vectors
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Re: Building a Content-Based Search Engine II: Extracting Feature Vectors
#2This is a good summary.
I've build an interesting NN-based model, and I've been thinking about using some of the early layers as feature for a search engine.
The obvious thing to do is to dump the feature vectors into Elastic or Solr or something.
Ideally what I want is to:
1) Put 1024 dimensional vectors of floats into the index
2) Use plain cosine distance as the distance metric
2b) Customize the distance metric (or preferably use a preexisting and optimized earth-mover-distance implementation).
My initial Googling indicated that 1 and 2 are harder than I expected - it seems both Elastic and Solr don't have good representations for vectors, and assume you want BM25 or TF-IDF for your ranking.
Surely I'm missing something? Not super-keen on having to drop back to using Lucene.
Re: Building a Content-Based Search Engine II: Extracting Feature Vectors
#3[deleted]
Re: Building a Content-Based Search Engine II: Extracting Feature Vectors
#4This is a good summary. I've build an interesting NN-based model, and I've been thinking about using some of the early layers as feature for a search engine. The obvious thing to do is to dump the feature vectors into Elastic or Solr or something. Ideally what I want is to: 1) Put 1024 dimensional vectors of floats into the index 2) Use plain cosine distance as the distance metric 2b) Customize the distance metric (o…
Facebook's FAISS might be of interest to you:
https://code.facebook.com/posts/1373769912645926/faiss-a-lib...
Re: Building a Content-Based Search Engine II: Extracting Feature Vectors
#5This is a good summary. I've build an interesting NN-based model, and I've been thinking about using some of the early layers as feature for a search engine. The obvious thing to do is to dump the feature vectors into Elastic or Solr or something. Ideally what I want is to: 1) Put 1024 dimensional vectors of floats into the index 2) Use plain cosine distance as the distance metric 2b) Customize the distance metric (o…
I don't think you're missing anything. Elastic (I'm not very familiar with Solr) is built on assumptions from TFIDF etc., and a big one is that you reduce the size of your potential result set by looking up words in an index. Obviously that doesn't hold if your documents are represented as word vectors.
You can use something like annoy for searching vector space. It's not made specifically for text search, so you'll have to roll your own normalization and so on, but that's usually the less complicated part anyway.
Re: Building a Content-Based Search Engine II: Extracting Feature Vectors
#6This is a good summary. I've build an interesting NN-based model, and I've been thinking about using some of the early layers as feature for a search engine. The obvious thing to do is to dump the feature vectors into Elastic or Solr or something. Ideally what I want is to: 1) Put 1024 dimensional vectors of floats into the index 2) Use plain cosine distance as the distance metric 2b) Customize the distance metric (o…
Facebook's FAISS might be of interest to you: https://code.facebook.com/posts/1373769912645926/faiss-a-lib...
Yeah I know about FAISS, and I saw the recent change they made where it lets you spill to disk.
Also https://erikbern.com/2018/02/15/new-benchmarks-for-approxima...
Re: Building a Content-Based Search Engine II: Extracting Feature Vectors
#7This is a good summary. I've build an interesting NN-based model, and I've been thinking about using some of the early layers as feature for a search engine. The obvious thing to do is to dump the feature vectors into Elastic or Solr or something. Ideally what I want is to: 1) Put 1024 dimensional vectors of floats into the index 2) Use plain cosine distance as the distance metric 2b) Customize the distance metric (o…
I don't think you're missing anything. Elastic (I'm not very familiar with Solr) is built on assumptions from TFIDF etc., and a big one is that you reduce the size of your potential result set by looking up words in an index. Obviously that doesn't hold if your documents are represented as word vectors. You can use something like annoy for searching vector space. It's not made specifically for text search, so you'll…
Yeah, Annoy (and the other reply which pointed to FAISS) are interesting.
I read https://erikbern.com/2018/02/15/new-benchmarks-for-approxima... (by the author of Annoy) with great interest.
He points to HNSW which I've know nothing about, but seems very fast!
Re: Building a Content-Based Search Engine II: Extracting Feature Vectors
#8This is a good summary. I've build an interesting NN-based model, and I've been thinking about using some of the early layers as feature for a search engine. The obvious thing to do is to dump the feature vectors into Elastic or Solr or something. Ideally what I want is to: 1) Put 1024 dimensional vectors of floats into the index 2) Use plain cosine distance as the distance metric 2b) Customize the distance metric (o…
I am building Deep Video Analytics which aims to become Solr/Postgres of visual data. Currently it uses LOPQ but I am working on integrating FAISS.
Re: Building a Content-Based Search Engine II: Extracting Feature Vectors
#9You can setup a feature based image search engine or image object search engine with Open Source libraries, a few additional lines of Python, and a couple of pre-trained models, see https://github.com/beniz/deepdetect/tree/master/demo/imgsear... and https://github.com/beniz/deepdetect/tree/master/demo/objsear...
Re: Building a Content-Based Search Engine II: Extracting Feature Vectors
#10This is a good summary. I've build an interesting NN-based model, and I've been thinking about using some of the early layers as feature for a search engine. The obvious thing to do is to dump the feature vectors into Elastic or Solr or something. Ideally what I want is to: 1) Put 1024 dimensional vectors of floats into the index 2) Use plain cosine distance as the distance metric 2b) Customize the distance metric (o…
I am building Deep Video Analytics which aims to become Solr/Postgres of visual data. Currently it uses LOPQ but I am working on integrating FAISS. https://www.deepvideoanalytics.com
Interesting - looks like you've done quite a lot of work on that.
What does querying it look like?