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* pgvector requirements * first pass on pgvector notebook and sql alchemy file * created code for loading vectors into db * added query for embedding distance * updates to pgvector notebook * update function with time decay * update pgvector notebook to use example code * remove old create table script * add readme for pgvector * update example to use get_date()
Loading unstructured outputs into Postgres with pgvector
The following example shows how to load unstructured output into Postgres with the
pgvector extension installed. Combining the similarity search functionality of
pgvector with the traditional RDBMS capabilities of Postgres allow users to performing
similarity searches that are conditioned on metadata or biased toward more recent documents.
Use cases include document discovery and more sophisticated retrieval augmented generation
for LLMs.
The langchain docs have more information
about retrieval augmented generation.