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Great point! (Disclaimer: I work for Elastic) Elasticsearch has recently added a data type called semantic_text, which automatically chunks text, calculates e

Great point! (Disclaimer: I work for Elastic) Elasticsearch has recently added a data type called semantic_text, which automatically chunks text, calculates embeddings, and stores the chunks with sensible defaults. Queries are similarly simplified, where vectors are calculated and compared internally, which makes a lot less I/O and a lot simpler client code. https://www.elastic.co/search-labs/blog/semantic-search-simplified-semantic-text https://www.elastic.co/search-labs/blog/semantic-search-simp...

jdthedisciple 2y ago
How does their embedding model compare in terms of retrieval accuracy to, say `text-embedding-3-small` and `text-embedding-3-large`?
- binarymax 2y ago
  It’s impossible to answer that question without knowing what content/query domain you are embedding. Checkout MTEB leaderboard, dig into the retrieval benchmark, and look for analogous datasets.
  - 3abiton 2y ago
    So we're talking maximizing embedding model per use case? Medical dats would require differnet model than say sales data? Sounds very fragmented approach.
    - ekianjo 2y ago
      The answer lies with a validation dataset that you create for testing.
- splike 2y ago
  You can use openai embeddings in elastic if you don't want to use their elser sparse embeddings
pjot 2y ago
I made something similar, but used duckDB as the vector store (and query engine)! It’s impressively fast https://github.com/patricktrainer/duckdb-embedding-search https://github.com/patricktrainer/duckdb-embedding-search
- ekianjo 2y ago
  There is vector type data available in duckdb now?
  - wild_egg 2y ago
    They call it a fixed size array type but, yes. It was added earlier this year. Works really great https://duckdb.org/2024/05/03/vector-similarity-search-vss.html https://duckdb.org/2024/05/03/vector-similarity-search-vss.h...
  - pjot 2y ago
    Yep! It was added in v0.10.0 - which was released a month or two after I made this. This is using v0.9.1
- barrenko 2y ago
  Amy specific reason to use dDB? I've got a crapload of json q & a formatted discussions on a topic, and am trying to figure out if I just store it somewhere and query it, or do I also do vector embeddings, kinda lost with all the possible options.
  - pjot 2y ago
    Embeddings are what encode the “meaning” of a given text. Similarity search works by computing the angle between your query vector and the rest of the vectors already stored. DuckDB (and columnar stores in general) is great at aggregation. It’s particularly well suited because DuckDB is a single file. There’s no server to muck with.
- jackbravo 2y ago
  I love duckdb, but their concurrency model is very limiting: DuckDB has two configurable options for concurrency: 1. One process can both read and write to the database. 2. Multiple processes can read from the database, but no processes can write (access_mode = 'READ_ONLY'). https://duckdb.org/docs/connect/concurrency.html https://duckdb.org/docs/connect/concurrency.html

jdthedisciple 2y ago

How does their embedding model compare in terms of retrieval accuracy to, say `text-embedding-3-small` and `text-embedding-3-large`?

binarymax 2y ago
It’s impossible to answer that question without knowing what content/query domain you are embedding. Checkout MTEB leaderboard, dig into the retrieval benchmark, and look for analogous datasets.
- 3abiton 2y ago
  So we're talking maximizing embedding model per use case? Medical dats would require differnet model than say sales data? Sounds very fragmented approach.
  - ekianjo 2y ago
    The answer lies with a validation dataset that you create for testing.
splike 2y ago
You can use openai embeddings in elastic if you don't want to use their elser sparse embeddings

pjot 2y ago

I made something similar, but used duckDB as the vector store (and query engine)! It’s impressively fast https://github.com/patricktrainer/duckdb-embedding-search https://github.com/patricktrainer/duckdb-embedding-search

ekianjo 2y ago
There is vector type data available in duckdb now?
- wild_egg 2y ago
  They call it a fixed size array type but, yes. It was added earlier this year. Works really great https://duckdb.org/2024/05/03/vector-similarity-search-vss.html https://duckdb.org/2024/05/03/vector-similarity-search-vss.h...
- pjot 2y ago
  Yep! It was added in v0.10.0 - which was released a month or two after I made this. This is using v0.9.1
barrenko 2y ago
Amy specific reason to use dDB? I've got a crapload of json q & a formatted discussions on a topic, and am trying to figure out if I just store it somewhere and query it, or do I also do vector embeddings, kinda lost with all the possible options.
- pjot 2y ago
  Embeddings are what encode the “meaning” of a given text. Similarity search works by computing the angle between your query vector and the rest of the vectors already stored. DuckDB (and columnar stores in general) is great at aggregation. It’s particularly well suited because DuckDB is a single file. There’s no server to muck with.
jackbravo 2y ago
I love duckdb, but their concurrency model is very limiting: DuckDB has two configurable options for concurrency: 1. One process can both read and write to the database. 2. Multiple processes can read from the database, but no processes can write (access_mode = 'READ_ONLY'). https://duckdb.org/docs/connect/concurrency.html https://duckdb.org/docs/connect/concurrency.html