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Right now, search for document chunks is performed in a "semantic" way using vector similarity. An alternative approach might be to use full text search (full text is already built into Postgres).
Changes required:
rag/load.py: store the ts_vector along the text and embedding vectors
rag/librag.py, function search() perform full text search (with stemming and stop words)
One could make full text search optional and if enabled the ranking is done by adding in a weighted way full text search rank and vector similarit search rank.
-- Chris Mair
The text was updated successfully, but these errors were encountered:
Right now, search for document chunks is performed in a "semantic" way using vector similarity. An alternative approach might be to use full text search (full text is already built into Postgres).
Changes required:
rag/load.py: store the ts_vector along the text and embedding vectors
rag/librag.py, function search() perform full text search (with stemming and stop words)
One could make full text search optional and if enabled the ranking is done by adding in a weighted way full text search rank and vector similarit search rank.
-- Chris Mair
The text was updated successfully, but these errors were encountered: