<ul><li>This blog focuses on building an index with text embeddings and querying it with natural language in a minimalistic approach.</li><li>The indexing flow involves reading text files, chunking documents, embedding text with a model, and storing embeddings in a vector database.</li><li>The process includes ingesting files, chunking and embedding text, and exporting the embeddings to a Postgres table using CocoIndex functions.</li><li>Queries can be performed by writing SQL or utilizing the query engine of the target storage, with an example of a search function provided for querying the index interactively.</li></ul>

How to build index with text embeddings

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