diff --git a/.gitignore b/.gitignore
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+++ b/.gitignore
@@ -5,6 +5,7 @@ build/
!**/src/test/**/build/
### IntelliJ IDEA ###
+.idea/workspace.xml
.idea/modules.xml
.idea/jarRepositories.xml
.idea/compiler.xml
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
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- "RunOnceActivity.OpenProjectViewOnStart": "true",
- "RunOnceActivity.ShowReadmeOnStart": "true",
- "SHARE_PROJECT_CONFIGURATION_FILES": "true",
- "TODO_SCOPE": "All Places",
- "WebServerToolWindowFactoryState": "false",
- "git-widget-placeholder": "main",
- "jdk.selected.JAVA_MODULE": "openjdk-20",
- "last_opened_file_path": "/Users/javier/projects/eLLMental",
- "node.js.detected.package.eslint": "true",
- "node.js.detected.package.tslint": "true",
- "node.js.selected.package.eslint": "(autodetect)",
- "node.js.selected.package.tslint": "(autodetect)",
- "nodejs_package_manager_path": "npm",
- "settings.editor.selected.configurable": "preferences.pluginManager",
- "vue.rearranger.settings.migration": "true"
- }
-}
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diff --git a/docs-site/docs/03_components/02_embeddings_space.md b/docs-site/docs/03_components/02_embeddings_space.md
index 285b6e2..94ab11a 100644
--- a/docs-site/docs/03_components/02_embeddings_space.md
+++ b/docs-site/docs/03_components/02_embeddings_space.md
@@ -96,85 +96,6 @@ List closestNeighbors = embeddingsSpace.mostSimilarEmbeddings(embeddi
// closestNeighbors will contain the embeddings for "Hello, eLLMental!" and "Hello, world!"
```
-## `calculateRelationshipVector`
-
-Computes a relationship vector for provided text pairs (To be used with the `translateEmbedding` method)
-
-- **Parameters**:
- - `textPairs`: Array of text pairs.
-
-For instance, with the following list of text pairs:
-
-| Text 1 | Text 2 |
-|--------|--------|
-| Man | Woman |
-| Boy | Girl |
-| King | Queen |
-| Prince | Princess |
-| Father | Mother |
-
-The relationship vector for this group represents a translation in the embeddings space that, given a word that matches the ones in the left column, provides the location of a word that would likely appear in the right column for the given word. See the documentation for [`translateEmbedding`](#translateEmbedding) for more details.
-
-The `RelationshipVector` class is defined as follows:
-
-```java
-public class RelationshipVector {
- public final String label;
- public final float[] vector;
-}
-```
-
-And it can be calculated like this:
-
-```java
-String[][] textPairs = [["Man", "Woman"], ["Boy", "Girl"], ["King", "Queen"], ["Prince", "Princess"], ["Father", "Mother"]];
-RelationshipVector relationshipVector = embeddingsSpace.calculateRelationshipVector(textPairs);
-```
-
-## `storeNamedRelationshipVector`
-
-Stores a relationship vector in the embeddings store and assigns it a label for later use.
-
-- **Parameters**:
- - `label`: The label to assign to the relationship vector.
- - `relationshipVector`: The relationship vector to store.
-
-
-```java
-// First we calculate a relationship vector
-RelationshipVector relationshipVector = embeddingsSpace.calculateRelationshipVector(textPairs);
-
-// Then we store it in the embeddings store for future use
-embeddingsSpace.storeNamedRelationshipVector("feminize", relationshipVector);
-```
-
-## `translateEmbedding`
-
-Shifts a reference text embedding in the embeddings space to find the location of the text that would meet the relationship represented by the vector.
-
-- **Parameters**:
- - `referenceText`: The primary embedding.
- - `vector`: The vector determining translation.
-
-This is useful if you want to search for embeddings that are similar to a given one, but in a different context. For instance, let's say we have the following embedding:
-
-And a relationship vector calculated with the `calculateRelationshipVector` method as follows:
-
-```java
-String[][] textPairs = [["Man", "Woman"], ["Boy", "Girl"], ["King", "Queen"], ["Prince", "Princess"], ["Father", "Mother"]];
-RelationshipVector relationshipVector = embeddingsSpace.calculateRelationshipVector(textPairs);
-```
-
-We can use the relationship vector to find the location of the words that would be similar to "Cow" instead of "Bull". Notice that embeddings cannot be reversed, and we can't really know if this embedding represents a cow, but it will give us a good approximation that can be used to refine search results later.
-
-```java
-// This will create an estimated embedding of the word "Cow"
-Embedding likelyACowEmbedding = embeddingsSpace.translateEmbedding("Bull", relationshipVector);
-
-// We use it as any other embedding to find stored texts that are similar to "Cow"
-List similarToCowEmbeddings = embeddingsSpace.mostSimilarEmbeddings(likelyACowEmbedding, 5);
-```
-
## `get`
Retrieves an embedding from the embeddings store using its ID.