Semantic Search with TypeScript: Using embed() and embedMany() for Vector Search

The provided code snippets and explanations illustrate how to use NeuroLink's embedding functionalities for building a semantic search engine and integrating it with Retrieval-Augmented Generation (RAG) pipelines. Below is an overview of the key components and steps involved:

Embedding Documents

1. Embedding Individual Texts:

   • Use embed(text: string): Promise<number[]> to generate embeddings for individual pieces of text.

2. Batch Embedding:

   • Use embedMany(texts: string[]): Promise<number[][]> to efficiently embed multiple texts at once.

Building a Semantic Search Engine

1. Indexing Documents:

   • Convert documents into chunks (if necessary).
   • Generate embeddings for each chunk.
   • Store the embeddings along with their metadata in an index using vectorStore.upsert(indexName: string, documentNodes: DocumentNode[]).

2. Querying the Index:

   • For a given query, generate its embedding using embed(query: string).
   • Use this embedding to find similar documents by querying the vector store with vectorStore.query(indexName: string, queryVector: number[], topK: number).

Integration with RAG Pipelines

Read the full article at DEV Community

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