Minimize readme and possibly fix release

.github/workflows/release.yml

@@ -34,19 +34,19 @@ jobs:
       with:
         java-version: 12.0.2
 
-    # Release docs
-    - name: Setup Docs
-      if: github.event_name == 'push' && env.GET_RELEASE == '404'
-      run: |
-        cp docs/Gemfile .
-
-    # Specify the Jekyll source location as a parameter
-    - uses: helaili/jekyll-action@2.0.0
-      if: github.event_name == 'push' && env.GET_RELEASE == '404'
-      env:
-        JEKYLL_PAT: ${{ secrets.JEKYLL_PAT }}
-      with:
-        jekyll_src: 'docs'
+#    # Release docs
+#    - name: Setup Docs
+#      if: github.event_name == 'push' && env.GET_RELEASE == '404'
+#      run: |
+#        cp docs/Gemfile .
+#
+#    # Specify the Jekyll source location as a parameter
+#    - uses: helaili/jekyll-action@2.0.0
+#      if: github.event_name == 'push' && env.GET_RELEASE == '404'
+#      env:
+#        JEKYLL_PAT: ${{ secrets.JEKYLL_PAT }}
+#      with:
+#        jekyll_src: 'docs'
 
     # Release software
     - name: Release software artifacts

readme.md

@@ -2,6 +2,8 @@
 
 An Elasticsearch plugin for exact and approximate K-nearest-neighbors search in high-dimensional vector spaces.
 
+**[Documentation](http://alexklibisz.github.io/elastiknn)**
+
 ## Builds and Releases
 
 |Item|Status|
@@ -14,119 +16,6 @@ An Elasticsearch plugin for exact and approximate K-nearest-neighbors search in
 |Scala 2.12 Client, Release| [![Scala Client Release Status][Badge-Scala-Release]][Link-Scala-Release]|
 |Scala 2.12 Client, Snapshot| [![Scala Client Snapshot Status][Badge-Scala-Snapshot]][Link-Scala-Snapshot]|
 
-## Features
-
-### Completed
-
-**Exact KNN search for five distance functions: L1, L2, Angular, Hamming, and Jaccard.**
-
-This is fairly thoroughly profiled and optimized, but it's still an n^2 algorithm so it should only be used for testing 
-and on relatively small datasets.
-
-**Approximate KNN using Locality Sensitive Hashing; currently only works for Jaccard similarity**
-
-This should scale much better for large datasets. I'm working on implementations for the other similarity functions as
-well as a multiprobe-LSH variant where possible.
-
-**Pipeline Processors for ingesting vectors**
-
-The plugin uses [pipeline processors](https://www.elastic.co/guide/en/elasticsearch/reference/current/pipeline-processor.html) 
-to validate, pre-process, and index vectors. This means you can just pass in the vectors as JSON documents.
-
-**Integrates KNN queries seamlessly with existing Elasticsearch queries**
-
-The exact and approximate KNN searches are implemented as Elasticsearch queries. This means you can store your vectors
-inside of existing documents and mix your queries into existing queries. The semantics resemble 
-[GeoShape queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-geo-shape-query.html).
-Under the hood, the queries are implemented using standard Elasticsearch constructs, specifically 
-[Function Score Queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html)
-and [Boolean Queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-bool-query.html).
-
-### Forthcoming
-
-**Approximate KNN for L1, L2, Angular and Hamming similarities**
-
-The API will be the same as it is for Jaccard. It's just a matter of implementing and optimizing the internals.
-
-**Multiprobe LSH Queries**
-
-Multiprobe LSH has been shown to improve performance. I'm planning to implement this for the similarity functions where
-it makes sense. The API should be a simple extension of the existing approximate queries.
-
-**Fixed radius KNN queries**
-
-Instead of returning the nearest vectors, this query will return the vectors that fall within some radius of a given
-query vector.
-
-## Usage
-
-### Install ElastiKnn on an ElasticSearch cluster
-
-TODO
-
-### Run a Docker container with ElastiKnn already installed
-
-TODO
-
-### Exact search using the Elasticsearch REST API
-
-TODO
-
-### Python Client
-
-TODO
-
-### Scala Client
-
-TODO
-
-## Performance
-
-### Ann-Benchmarks
-
-Currently working on this in a fork of the [Ann-Benchmarks repo here](https://github.com/alexklibisz/ann-benchmarks).
-Planning to submit a PR when all of the approximate similarities are implemented and the Docker image can be built with
-a release elastiknn zip file. 
-
-### Million-Scale
-
-TODO
-
-Planning to implement this using one of the various word vector datasets.
-
-### Billion-Scale
-
-TODO
-
-Not super sure of the feasability of this yet. There are some notes in benchmarks/billion.
-
-## Development
-
-### Builds and Releases
-
-There are three main artifacts produced by this project:
-
-1. The actual plugin, which is a zip file published to Github releases.
-2. The python client library, which gets published to PyPi.
-3. The scala client library, which gets published to Sonatype.
-
-All three artifacts are built and published as "snapshots" on every PR commit and every push/merge to master. All three
-artifacts are released on every push/merge to master in which the `version` file has changed. We detect a change in the
-version file by checking if a release tag exists with the same name as the version.
-
-All of this is handled by Github Workflows with all steps defined in the yaml files in `.github/workflows`.
-
-## References
-
-In no particular order:
-
-- [Alex Reelsen](https://github.com/spinscale) has several open-source plugins which were useful examples for the general structure of a plugin project: 
-- [Mining of Massive Datasets (MMDS) by Leskovec, et. al](http://www.mmds.org/), particularly chapter 3, is a great reference for approximate similarity search.
-- [The Read Only Rest Plugin](https://github.com/sscarduzio/elasticsearch-readonlyrest-plugin) served as an example for much of the Gradle and testing setup.
-- [The Scalable Data Science Lectures on Youtube](https://www.youtube.com/playlist?list=PLbRMhDVUMngekIHyLt8b_3jQR7C0KUCul) were helpful for better understanding LSH. I think much of that content is also based on the MMDS book.
-- [_Build Your Own Custom Lucene Query and Scorer_, Doug Turnball](https://opensourceconnections.com/blog/2014/01/20/build-your-own-custom-lucene-query-and-scorer/) was extremely helpful
-for implementing custom Lucene indexing and queries.
-
 <!-- Links -->
 
 [Link-Github-CI]: https://github.com/alexklibisz/elastiknn/actions?query=workflow%3ACI