update docs with l1

supabase · Oct 9, 2024 · 03ee252 · 03ee252
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diff --git a/docs/api.md b/docs/api.md
@@ -89,6 +89,7 @@ Available options for distance `measure` are:
 
 - `vecs.IndexMeasure.cosine_distance`
 - `vecs.IndexMeasure.l2_distance`
+- `vecs.IndexMeasure.l1_distance`
 - `vecs.IndexMeasure.max_inner_product`
 
 which correspond to different methods for comparing query vectors to the vectors in the database.

diff --git a/docs/concepts_indexes.md b/docs/concepts_indexes.md
@@ -26,6 +26,7 @@ Available options for distance `measure` are:
 
 - `vecs.IndexMeasure.cosine_distance`
 - `vecs.IndexMeasure.l2_distance`
+- `vecs.IndexMeasure.l1_distance`
 - `vecs.IndexMeasure.max_inner_product`
 
 which correspond to different methods for comparing query vectors to the vectors in the database.

diff --git a/docs/index.md b/docs/index.md
@@ -30,7 +30,7 @@ Vecs is a Python client library for managing and querying vector stores in Postg
 ## Overview
 
 - Vector Management: create collections to persist and update vectors in a PostgreSQL database.
-- Querying: Query vectors efficiently using measures such as cosine distance, l2 distance, or max inner product.
+- Querying: Query vectors efficiently using measures such as cosine distance, l2 distance, l1 distance, or max inner product.
 - Metadata: Each vector can have associated metadata, which can also be used as filters during queries.
 - Hybrid Data: vecs creates its own schema and can coexist with your existing relational data