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Update Langchain4j documentation with examples
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‎modules/genai-ecosystem/pages/langchain4j.adoc‎

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@@ -40,6 +40,181 @@ An example is avalable at: https://github.com/langchain4j/langchain4j-examples/t
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include::https://github.com/langchain4j/langchain4j-examples/raw/main/neo4j-example/src/main/java/Neo4jEmbeddingStoreExample.java[]
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----
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=== Usage Examples
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To create a Neo4jEmbeddingStore, we can write:
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[source,java]
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----
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Neo4jEmbeddingStore embeddingStore = Neo4jEmbeddingStore.builder().<builderParameters>.build();
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// where <builderParameters> can be the following:
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.dimension(384)
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.label(LABEL_TO_SANITIZE)
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.build();
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// we can use with `withBasicAuth`
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.withBasicAuth(neo4jContainer.getBoltUrl(), USERNAME, ADMIN_PASSWORD)
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// as alternative to driver:
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Driver driver = GraphDatabase.driver(neo4jContainer.getBoltUrl(), AuthTokens.basic(USERNAME, ADMIN_PASSWORD));
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----
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To add embeddings, execute:
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[source,java]
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----
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Embedding embedding = embeddingModel.embed("embedText").content();
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String id = embeddingStore.add(embedding);
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// output: id of the embedding
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----
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To add embeddings with id:
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[source,java]
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----
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String id = randomUUID();
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Embedding embedding = embeddingModel.embed("embedText").content();
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embeddingStore.add(id, embedding);
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// output: id of the embedding
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----
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To add embeddings with segment:
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[source,java]
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----
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TextSegment segment = TextSegment.from(randomUUID());
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Embedding embedding = embeddingModel.embed(segment.text()).content();
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String id = embeddingStore.add(embedding, segment);
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// output: id of the embedding
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----
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Too add embeddings with segment and metadata:
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[source,java]
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----
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TextSegment segment = TextSegment.from(randomUUID(), Metadata.from(METADATA_KEY, "test-value"));
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Embedding embedding = embeddingModel.embed(segment.text()).content();
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String id = embeddingStore.add(embedding, segment);
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// output: id of the embedding
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----
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Search embeddings:
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[source,java]
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----
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Embedding embedding = embeddingModel.embed("embedText").content();
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String id = embeddingStore.add(embedding);
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final EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
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.queryEmbedding(embedding)
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.maxResults(10)
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.build();
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final List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.search(request).matches();
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// output: list of embeddings
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----
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Search embeddings with minScore:
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[source,java]
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----
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Embedding embedding = embeddingModel.embed("embedText").content();
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String id = embeddingStore.add(embedding);
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final EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
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.queryEmbedding(embedding)
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.maxResults(10)
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.minScore(0.15)
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.build();
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final List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.search(request).matches();
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// output: list of embeddings
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----
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Search embeddings with segment with custom metadata prefix:
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[source,java]
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----
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String metadataPrefix = "metadata.";
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String labelName = "CustomLabelName";
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embeddingStore = Neo4jEmbeddingStore.builder()
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.withBasicAuth(neo4jContainer.getBoltUrl(), USERNAME, ADMIN_PASSWORD)
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.dimension(384)
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.metadataPrefix(metadataPrefix)
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.label(labelName)
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.indexName("customIdxName")
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.build();
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String metadataCompleteKey = metadataPrefix + METADATA_KEY;
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TextSegment segment = TextSegment.from(randomUUID(), Metadata.from(METADATA_KEY, "test-value"));
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Embedding embedding = embeddingModel.embed(segment.text()).content();
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String id = embeddingStore.add(embedding, segment);
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// output: id of the embedding
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final EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
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.queryEmbedding(embedding)
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.maxResults(10)
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.build();
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final List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.search(request).matches();
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// output: list of embeddings
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----
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Search embeddings with segment with metadata and custom id prop:
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[source,java]
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----
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String metadataPrefix = "metadata.";
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String customIdProp = "customId ` & Prop ` To Sanitize";
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embeddingStore = Neo4jEmbeddingStore.builder()
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.withBasicAuth(neo4jContainer.getBoltUrl(), USERNAME, ADMIN_PASSWORD)
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.dimension(384)
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.metadataPrefix(metadataPrefix)
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.label("CustomLabelName")
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.indexName("customIdxName")
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.idProperty(customIdProp)
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.build();
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String metadataCompleteKey = metadataPrefix + METADATA_KEY;
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TextSegment segment = TextSegment.from(randomUUID(), Metadata.from(METADATA_KEY, "test-value"));
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Embedding embedding = embeddingModel.embed(segment.text()).content();
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String id = embeddingStore.add(embedding, segment);
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// output: id of the embedding
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final EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
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.queryEmbedding(embedding)
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.maxResults(10)
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.build();
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final List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.search(request).matches();
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// output: list of embeddings
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----
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Add multiple embeddings
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[source,java]
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----
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Embedding firstEmbedding = embeddingModel.embed("firstEmbedText").content();
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Embedding secondEmbedding = embeddingModel.embed("secondEmbedText").content();
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List<String> ids = embeddingStore.addAll(asList(firstEmbedding, secondEmbedding));
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// output: list of the embedding ids
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final EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
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.queryEmbedding(firstEmbedding)
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.maxResults(10)
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.build();
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final List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.search(request).matches();
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// output: list of embeddings
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----
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Add multiple embeddings with segments
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[source,java]
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----
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TextSegment firstSegment = TextSegment.from("firstText");
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Embedding firstEmbedding = embeddingModel.embed(firstSegment.text()).content();
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TextSegment secondSegment = TextSegment.from("secondText");
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Embedding secondEmbedding = embeddingModel.embed(secondSegment.text()).content();
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List<String> ids = embeddingStore.addAll(
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asList(firstEmbedding, secondEmbedding),
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asList(firstSegment, secondSegment)
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); // output: list of the embedding ids
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final EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
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.queryEmbedding(firstEmbedding)
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.maxResults(10)
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.build();
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final List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.search(request).matches();
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// output: list of embeddings
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----
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== Relevant Links
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[cols="1,4"]
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|===

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