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Use the “create embeddings” API to create embedding vectors for documents and to search for semantically similar documents.

The idea is to compare embedding vectors of given documents with the embedding vectors of a search query - as a distance measure a simple cosine similarity works well.

Use the “create embeddings” API to createembedding vectors for documents and tosearch for semantically similar documents.The idea is to compare embedding vectors ofgiven documents with the embedding vectorsof a search query - as a distance measure asimple cosine similarity works well.More details about the API is available here:https://platform.openai.com/docs/api-reference/embeddings/create Node 20TODO:Enter your personal API key hereNode 34Node 35Node 36Node 37Node 38Node 40Node 41Node 42Node 43Node 50Node 55Example queriesNode 59Node 60Node 61Node 62Node 63Node 64Node 65Node 66Node 67Node 68Node 69Node 70Node 71Node 72 JSON Path API Key Chunk Loop Start ConstantValue Column Table to JSON Table Rowto Variable Create Embedding Java Snippet ConstantValue Column Column Filter Loop End Split CollectionColumn Numeric Distances Table Creator Create Embedding Java Snippet JSON Path Column Filter ConstantValue Column Loop End ConstantValue Column Chunk Loop Start Table Rowto Variable Table to JSON Column Filter Similarity Search Split CollectionColumn Get example documentsfrom Wikipedia Use the “create embeddings” API to createembedding vectors for documents and tosearch for semantically similar documents.The idea is to compare embedding vectors ofgiven documents with the embedding vectorsof a search query - as a distance measure asimple cosine similarity works well.More details about the API is available here:https://platform.openai.com/docs/api-reference/embeddings/create Node 20TODO:Enter your personal API key hereNode 34Node 35Node 36Node 37Node 38Node 40Node 41Node 42Node 43Node 50Node 55Example queriesNode 59Node 60Node 61Node 62Node 63Node 64Node 65Node 66Node 67Node 68Node 69Node 70Node 71Node 72JSON Path API Key Chunk Loop Start ConstantValue Column Table to JSON Table Rowto Variable Create Embedding Java Snippet ConstantValue Column Column Filter Loop End Split CollectionColumn Numeric Distances Table Creator Create Embedding Java Snippet JSON Path Column Filter ConstantValue Column Loop End ConstantValue Column Chunk Loop Start Table Rowto Variable Table to JSON Column Filter Similarity Search Split CollectionColumn Get example documentsfrom Wikipedia

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Created by: pk
Created at: 2023-06-15
On NodePit since: 2023-06-15
Last update: 2026-03-27
Created with KNIME version: v4.7.4
Tags: openaiembeddingsdocumentsvectorsimilaritynodepit
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