intersearch

Semantic search across internal and external data is performed by generating vector representations of text with sentence-transformers and querying a persistent vector store alongside Exa web search results. This unifies internal knowledge with up-to-date web information.

Text-to-vector conversion uses sentence-transformers to generate vector embeddings, which are then stored in a vector database to facilitate efficient similarity searches across a large corpus of documents or code snippets.

The best way to find semantically similar code snippets is to generate sentence embeddings for your corpus and persistently store them in a vector database, enabling efficient similarity queries when matching against new text inputs.

Yes, you can use sentence-transformers to generate text embeddings and store them persistently in a vector store, ensuring your data remains available for efficient similarity searches and information retrieval across sessions.

Yes, Exa web search integration supports combining internal knowledge with external data by querying both your persistent vector store and the web, streamlining research and information retrieval workflows.

Limitations of using embeddings for information retrieval include the need for robust text-to-vector conversion handling and efficient vector database querying, as performance depends on the quality of the sentence-transformer models and the scale of your data corpus.