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Having written some genetic algorithms and stored the chromosomes, which can be a representation of vectors in any type of database, I find this interesting and

Having written some genetic algorithms and stored the chromosomes, which can be a representation of vectors in any type of database, I find this interesting and will some of them a try. Please correct me if I am wrong, with genetic algorithm, DL, etc. The vector embedding happen on the software layer (inside the network). I imagine it would be somewhat tricky to separate the vector embedding and put it inside a vector database. It could also be a performance hit during the training process.

dmitrykan 5y ago
Such a great question! I think vector search is designed to solve a specific task, like finding K nearest neighbors for your given vector embedding. If you have a few hundred thousand, a million, it might work fine with a linear scan. Again, this depends on your use case. But if you have tens of millions, hundreds of millions or even a billion of vectors, you will need something more robust to store, index and search over this data. An example in NLP world is BERT-like NNs, that allow you to embed your text into a dense vector representation.
- svcrunch 5y ago
  > An example in NLP world is BERT-like NNs, that allow you to embed your text into a dense vector representation. I might say transformer-based NNs instead. The problem with cross-attentional models like BERT is that they won't scale to large datasets. They are more often used in reranking results within an IR pipeline. However, even for that use they require distillation.
dmitrykan 5y ago
btw, if would be cool to see some example of a genetic algorithm input and resulting vector? And how do you use the vectors in down-stream tasks?
- ryohkyo 5y ago
  Thank you for the reply. Most of my repos are currently in private, so it's difficult for me to point you to the code I wrote. However, I can list a simple example here. Genetic algorithm usually deals with chromosomes. I have used binary and hexadecimal chromosomes in the past and found that the binary chromosomes are more flexible, especially with bitwise operations. Let's say a chromosome with four chunks of 4-digit binary, with each digit as dimension of true/false value; we end up with something like 1011 1100 0010 0101. Then I stored these four chunks in four documents in a NoSQL database. Each document then also has the records of other 16-digit chromosomes, so that I can refer to those 16-digit chromosomes contain an exact/similar 4-digit chunk. This was the fastest method I could come up with the last time I worked on it; I am sure that there are more efficient methods out there. Hopefully, this can shed some light on how the genetic algorithm works.
  - dmitrykan 5y ago
    Thanks for more detail, this sounds quite interesting. So basically the 16-digit chromosome records you added to each document would work as nearest neighbors? Did this satisfy all your search needs for an algorithm?
    - ryohkyo 5y ago
      In concept each 16-digit chromosome would work as the nearest neighbors, and finding “a range” of the nearest neighbors would be very fast. In a data scientist’s perspective, this may seem to be a hack. I would be very appreciated to learn more from you about this. I can tell you that the weakness on this method is the multiple writes to the database. I assume vector database can gets this implemented with less writes. In the past, I have used this for supervised training and yielded very good results. However, I think this would be inefficient in large scale networks. I am planning to use Go + Clickhouse to improve the performance in the next project.
      - dmitrykan 5y ago
        Yes, for larger datasets of vectors, having the search do a linear scan will likely be slow. So you could take a look at KNN/ANN (K nearest / Approximate nearest neighbors), like https://faiss.ai/ https://faiss.ai/ But if you prefer to offload this complexity to a database, you can pick and evaluate one from my blog post. You have 4 out of 6 open source DBs, and 2 commercial ones give you the managed service. All 6 can scale to quite large numbers of vectors. My goal is to systematically study each DB through the lens of a specific search task, which will not be (only) text based. If you think we could collaborate in some way on your dataset, that would be fantastic and probably a learning experience to both sides.

dmitrykan 5y ago

Such a great question! I think vector search is designed to solve a specific task, like finding K nearest neighbors for your given vector embedding. If you have a few hundred thousand, a million, it might work fine with a linear scan. Again, this depends on your use case. But if you have tens of millions, hundreds of millions or even a billion of vectors, you will need something more robust to store, index and search over this data. An example in NLP world is BERT-like NNs, that allow you to embed your text into a dense vector representation.

svcrunch 5y ago
> An example in NLP world is BERT-like NNs, that allow you to embed your text into a dense vector representation. I might say transformer-based NNs instead. The problem with cross-attentional models like BERT is that they won't scale to large datasets. They are more often used in reranking results within an IR pipeline. However, even for that use they require distillation.

dmitrykan 5y ago

btw, if would be cool to see some example of a genetic algorithm input and resulting vector? And how do you use the vectors in down-stream tasks?

ryohkyo 5y ago
Thank you for the reply. Most of my repos are currently in private, so it's difficult for me to point you to the code I wrote. However, I can list a simple example here. Genetic algorithm usually deals with chromosomes. I have used binary and hexadecimal chromosomes in the past and found that the binary chromosomes are more flexible, especially with bitwise operations. Let's say a chromosome with four chunks of 4-digit binary, with each digit as dimension of true/false value; we end up with something like 1011 1100 0010 0101. Then I stored these four chunks in four documents in a NoSQL database. Each document then also has the records of other 16-digit chromosomes, so that I can refer to those 16-digit chromosomes contain an exact/similar 4-digit chunk. This was the fastest method I could come up with the last time I worked on it; I am sure that there are more efficient methods out there. Hopefully, this can shed some light on how the genetic algorithm works.
- dmitrykan 5y ago
  Thanks for more detail, this sounds quite interesting. So basically the 16-digit chromosome records you added to each document would work as nearest neighbors? Did this satisfy all your search needs for an algorithm?
  - ryohkyo 5y ago
    In concept each 16-digit chromosome would work as the nearest neighbors, and finding “a range” of the nearest neighbors would be very fast. In a data scientist’s perspective, this may seem to be a hack. I would be very appreciated to learn more from you about this. I can tell you that the weakness on this method is the multiple writes to the database. I assume vector database can gets this implemented with less writes. In the past, I have used this for supervised training and yielded very good results. However, I think this would be inefficient in large scale networks. I am planning to use Go + Clickhouse to improve the performance in the next project.
    - dmitrykan 5y ago
      Yes, for larger datasets of vectors, having the search do a linear scan will likely be slow. So you could take a look at KNN/ANN (K nearest / Approximate nearest neighbors), like https://faiss.ai/ https://faiss.ai/ But if you prefer to offload this complexity to a database, you can pick and evaluate one from my blog post. You have 4 out of 6 open source DBs, and 2 commercial ones give you the managed service. All 6 can scale to quite large numbers of vectors. My goal is to systematically study each DB through the lens of a specific search task, which will not be (only) text based. If you think we could collaborate in some way on your dataset, that would be fantastic and probably a learning experience to both sides.