While semantic search shine at various tasks including advanced search engine and recommender systems, it did not shine that much on coding task so far appart from benchmarks available in papers. We think standard semantic code search tools lack of advanced filtering features. A key element to code search is to be able to reduce "search space", i.e reduce the set of likely candidates given the intent of the user or the coding agent. We think semantic search alone is not enough and needs to be paired with advanced filters such as GREP filters to succeed.
Blog introducing late-interaction models with max-similarity scoring function visualization. pylate-rs is a rust crate providing python and wasm bindings.
Very interesting work! Starred the project. Would love to see such features integrated into the compiler itself
Anyway, fully agree with you on the complementarity of ML and Rust
I created my personnal search engine to index and retrieve content I like. I like the idea of keeping track of content you like without too much effort.
Author here, I like the idea to have a public and personal search-engine. I'd love to deep dive in some people bookmarks such as expert in NLP field in order to get clue / a point of view on problem I want to solve but those informations are not available most of the time
It upload content some of my friends like (github stars) and they have very good taste, so when I'm looking for a document / information on a new topic in machine learning or databases I like to give it a chance
You could recommend content based on user query, tag content produced by the user, use colbert as part of a ChatBot to show evidences to the user questions
Yes, ColBERT is fast because you can pre-compute most embeddings. It's important to compute documents embeddings only once. neural-cherche do not compute embeddings on the fly and the retrieve method ask for queries and documents embeddings rather than queries and documents texts.
Documents and queries embeddings can be obtained using .encode_documents and .encode_queries methods
I save most of my embeddings (python dictionnary with documents id as key and embeddings as values) using joblib in a Bucket in the cloud. I don't really know if it's a good pratice but it does scale fine to few millions documents for offline (no real-time) applications.
It's because of the loss of the model. I ask the model to produce a higher similarity between the query and the positive document rather than between the query and the negative document. I'll add more losses soon so there are more choices