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+Amazon Automotive Dataset For Filtered ANN
+
+Model details:
+
 This dataset contains around 2M vectors for amazon products. 
 The embeddings are generated using cohere-english-light model (https://huggingface.co/Cohere/Cohere-embed-english-light-v3.0)
 The base text used for generating embeddings is title + description of products
 The queries are modifications of randomly sampled products from the base: after sampling, we prompt GPT-3.5 to output a simple query phrase for which the product is a suitable result, and embed that phrase using the cohere model.
-We also choose brands from the appropriate category of the query and provide them as OR filters. The item price of the sampled item is used as indicative for a PRICE range filter.
+We also choose appropriate category of the query and provide them as filter. The desired rating is also used as a filter.
+
+
+Dataset details:
+
+The base vector file contains 2000640 vectors in 384 dimensions, fp32 values.
+The query vector file contains 7119 query vectors.
+
+The base label file contains 2000640 rows, one row per vector. The row contains all the labels associated with the corresponding vector as "KEY=VALUE" entries in a comma-separated list.
+For example, the first vector has the following attribute meta-data based on which one can filter:
+BRAND=Caltric,CAT=Automotive,CAT=MotorcyclePowersports,CAT=Parts,CAT=Filters,CAT=OilFilters,RATING=5
+
+Here there are 3 keys, BRAND, CAT (for category), and RATING.
+The product has 1 brand, many categories, and one rating value.
+
+The query label file has one row per query, specifying the filtering predicate. We write it as an AND of ORs.
+CAT=ExteriorAccessories
+CAT=ExteriorAccessories&&RATING=4||RATING=5
+CAT=ExteriorAccessories&&RATING=5
+CAT=BumperStickersDecalsMagnets
+
+In the second example above, we want top K vectors among all those which have CAT=ExteriorAccessories and the RATING must be 4 or 5.
+
+The files are here:
+base vectors: https://comp21storage.blob.core.windows.net/$web/amazon-automotive/amazon_base.fbin
+base labels: https://comp21storage.blob.core.windows.net/$web/amazon-automotive/amazon_base_labels.txt
+query vectors: https://comp21storage.blob.core.windows.net/$web/amazon-automotive/amazon_query.fbin
+query labels: https://comp21storage.blob.core.windows.net/$web/amazon-automotive/amazon_query_labels.txt