diff --git a/billion-scale-image-search/src/main/bash/download_models.sh b/billion-scale-image-search/src/main/bash/download_models.sh index 6f5b1a854..7efc41553 100755 --- a/billion-scale-image-search/src/main/bash/download_models.sh +++ b/billion-scale-image-search/src/main/bash/download_models.sh @@ -9,8 +9,8 @@ if [ -f "$FILE" ]; then echo "$FILE exists." else echo "$FILE does not exist." - echo "Downloading model https://data.vespa.oath.cloud/sample-apps-data/clip_text_transformer.onnx" + echo "Downloading model https://data.vespa-cloud.com/sample-apps-data/clip_text_transformer.onnx" curl -L -o $DIR/text_transformer.onnx \ - https://data.vespa.oath.cloud/sample-apps-data/clip_text_transformer.onnx + https://data.vespa-cloud.com/sample-apps-data/clip_text_transformer.onnx fi diff --git a/billion-scale-vector-search/README.md b/billion-scale-vector-search/README.md index 5b1a24948..86db7c7b9 100644 --- a/billion-scale-vector-search/README.md +++ b/billion-scale-vector-search/README.md @@ -85,7 +85,7 @@ It uses the first 10M vectors of the 100M slice sample. This sample file is about 1GB (10M vectors): 
 $ curl -L -o spacev10m_base.i8bin \
-  https://data.vespa.oath.cloud/sample-apps-data/spacev10m_base.i8bin
+  https://data.vespa-cloud.com/sample-apps-data/spacev10m_base.i8bin
Generate the feed file for the first 10M vectors from the 100M sample. @@ -141,7 +141,7 @@ Download the query vectors and the ground truth for the 10M first vectors: $ curl -L -o query.i8bin \ https://github.com/microsoft/SPTAG/raw/main/datasets/SPACEV1B/query.bin $ curl -L -o spacev10m_gt100.i8bin \ - https://data.vespa.oath.cloud/sample-apps-data/spacev10m_gt100.i8bin + https://data.vespa-cloud.com/sample-apps-data/spacev10m_gt100.i8bin Note, initially, the routine above used the query file from https://comp21storage.blob.core.windows.net/publiccontainer/comp21/spacev1b/query.i8bin diff --git a/commerce-product-ranking/README.md b/commerce-product-ranking/README.md index 1609e3a22..1ecba0bdf 100644 --- a/commerce-product-ranking/README.md +++ b/commerce-product-ranking/README.md @@ -89,7 +89,7 @@ $ vespa clone commerce-product-ranking my-app && cd my-app Download cross-encoder model: 
 $ curl -L -o application/models/title_ranker.onnx \
-    https://data.vespa.oath.cloud/sample-apps-data/title_ranker.onnx
+    https://data.vespa-cloud.com/sample-apps-data/title_ranker.onnx
See [scripts/export-bi-encoder.py](scripts/export-bi-encoder.py) and @@ -181,7 +181,7 @@ This run file can then be evaluated using the [trec_eval](https://github.com/usn Download a pre-processed query-product relevance judgments in TREC format: 
 $  curl -L -o test.qrels \
-    https://data.vespa.oath.cloud/sample-apps-data/test.qrels
+    https://data.vespa-cloud.com/sample-apps-data/test.qrels
Install `trec_eval` (your mileage may vary): @@ -237,7 +237,7 @@ Download a pre-processed feed file with all (1,215,854) products: 
 $  curl -L -o product-search-products.jsonl.zstd \
-    https://data.vespa.oath.cloud/sample-apps-data/product-search-products.jsonl.zstd
+    https://data.vespa-cloud.com/sample-apps-data/product-search-products.jsonl.zstd
This step is resource intensive as the semantic embedding model encodes diff --git a/commerce-product-ranking/application/services.xml b/commerce-product-ranking/application/services.xml index 92fc1889e..545b8a306 100644 --- a/commerce-product-ranking/application/services.xml +++ b/commerce-product-ranking/application/services.xml @@ -10,12 +10,12 @@ - + output_0 - + output_0 diff --git a/commerce-product-ranking/notebooks/Train-lightgbm.ipynb b/commerce-product-ranking/notebooks/Train-lightgbm.ipynb index 1a3ea13eb..a0e05b5d1 100644 --- a/commerce-product-ranking/notebooks/Train-lightgbm.ipynb +++ b/commerce-product-ranking/notebooks/Train-lightgbm.ipynb @@ -83,7 +83,7 @@ "metadata": {}, "outputs": [], "source": [ - "train = pandas.read_parquet('https://data.vespa.oath.cloud/sample-apps-data/product_ranking_train.parquet')" + "train = pandas.read_parquet('https://data.vespa-cloud.com/sample-apps-data/product_ranking_train.parquet')" ] }, { @@ -387,8 +387,8 @@ "metadata": {}, "outputs": [], "source": [ - "train_queries = pandas.read_parquet('https://data.vespa.oath.cloud/sample-apps-data/train_query_ids.parquet')['query_id'].unique()\n", - "dev_queries = pandas.read_parquet('https://data.vespa.oath.cloud/sample-apps-data/dev_query_ids.parquet')['query_id'].unique()" + "train_queries = pandas.read_parquet('https://data.vespa-cloud.com/sample-apps-data/train_query_ids.parquet')['query_id'].unique()\n", + "dev_queries = pandas.read_parquet('https://data.vespa-cloud.com/sample-apps-data/dev_query_ids.parquet')['query_id'].unique()" ] }, { diff --git a/commerce-product-ranking/notebooks/Train-xgboost.ipynb b/commerce-product-ranking/notebooks/Train-xgboost.ipynb index 2d77ed522..83444850e 100644 --- a/commerce-product-ranking/notebooks/Train-xgboost.ipynb +++ b/commerce-product-ranking/notebooks/Train-xgboost.ipynb @@ -79,7 +79,7 @@ "metadata": {}, "outputs": [], "source": [ - "train = pandas.read_parquet('https://data.vespa.oath.cloud/sample-apps-data/product_ranking_train.parquet')" + "train = pandas.read_parquet('https://data.vespa-cloud.com/sample-apps-data/product_ranking_train.parquet')" ] }, { @@ -123,8 +123,8 @@ "metadata": {}, "outputs": [], "source": [ - "train_queries = pandas.read_parquet('https://data.vespa.oath.cloud/sample-apps-data/train_query_ids.parquet')['query_id'].unique()\n", - "dev_queries = pandas.read_parquet('https://data.vespa.oath.cloud/sample-apps-data/dev_query_ids.parquet')['query_id'].unique()" + "train_queries = pandas.read_parquet('https://data.vespa-cloud.com/sample-apps-data/train_query_ids.parquet')['query_id'].unique()\n", + "dev_queries = pandas.read_parquet('https://data.vespa-cloud.com/sample-apps-data/dev_query_ids.parquet')['query_id'].unique()" ] }, { diff --git a/commerce-product-ranking/notebooks/train_neural.ipynb b/commerce-product-ranking/notebooks/train_neural.ipynb index 46e07c1ec..d750939de 100644 --- a/commerce-product-ranking/notebooks/train_neural.ipynb +++ b/commerce-product-ranking/notebooks/train_neural.ipynb @@ -200,7 +200,7 @@ }, "outputs": [], "source": [ - "train_queries = pd.read_parquet(\"https://data.vespa.oath.cloud/sample-apps-data/train_query_ids.parquet\")['query_id'].unique()" + "train_queries = pd.read_parquet(\"https://data.vespa-cloud.com/sample-apps-data/train_query_ids.parquet\")['query_id'].unique()" ] }, { diff --git a/examples/embedding-service/README.md b/examples/embedding-service/README.md index 24243fde2..41c6b2ca9 100644 --- a/examples/embedding-service/README.md +++ b/examples/embedding-service/README.md @@ -24,9 +24,9 @@ which takes a string as an input and returns a vector embedding of that string. ``` mkdir -p src/main/application/embedder-models/e5-small-v2 curl -o src/main/application/embedder-models/e5-small-v2/model.onnx \ - https://data.vespa.oath.cloud/onnx_models/e5-small-v2/model.onnx + https://data.vespa-cloud.com/onnx_models/e5-small-v2/model.onnx curl -o src/main/application/embedder-models/e5-small-v2/tokenizer.json \ - https://data.vespa.oath.cloud/onnx_models/e5-small-v2/tokenizer.json + https://data.vespa-cloud.com/onnx_models/e5-small-v2/tokenizer.json ``` 4. Add a public certificate: ``vespa auth cert`` 5. Compile and deploy the application: ``mvn install && vespa deploy --wait 600`` @@ -44,8 +44,8 @@ Check out ``services.xml`` for more information. 3. Download the models: ``` mkdir -p src/main/application/embedder-models/e5-small-v2 -curl -o src/main/application/embedder-models/e5-small-v2/model.onnx https://data.vespa.oath.cloud/onnx_models/e5-small-v2/model.onnx -curl -o src/main/application/embedder-models/e5-small-v2/tokenizer.json https://data.vespa.oath.cloud/onnx_models/e5-small-v2/tokenizer.json +curl -o src/main/application/embedder-models/e5-small-v2/model.onnx https://data.vespa-cloud.com/onnx_models/e5-small-v2/model.onnx +curl -o src/main/application/embedder-models/e5-small-v2/tokenizer.json https://data.vespa-cloud.com/onnx_models/e5-small-v2/tokenizer.json ``` 4. Compile and deploy the application: ``mvn install && vespa deploy --wait 300`` diff --git a/incremental-search/search-as-you-type/README.md b/incremental-search/search-as-you-type/README.md index c15c2f271..5c54f0b07 100644 --- a/incremental-search/search-as-you-type/README.md +++ b/incremental-search/search-as-you-type/README.md @@ -61,7 +61,7 @@ $ mvn clean package -U Download feed file: 
 $ curl -L -o search-as-you-type-index.jsonl \
-  https://data.vespa.oath.cloud/sample-apps-data/search-as-you-type-index.jsonl 
+  https://data.vespa-cloud.com/sample-apps-data/search-as-you-type-index.jsonl
Verify that configuration service (deploy api) is ready: diff --git a/multilingual-search/services.xml b/multilingual-search/services.xml index 5972d3555..6d3187dcb 100644 --- a/multilingual-search/services.xml +++ b/multilingual-search/services.xml @@ -7,8 +7,8 @@ - - + + query: diff --git a/retrieval-augmented-generation/services.xml b/retrieval-augmented-generation/services.xml index 85fa76ec2..d117f59ad 100644 --- a/retrieval-augmented-generation/services.xml +++ b/retrieval-augmented-generation/services.xml @@ -13,7 +13,7 @@ - + 4096 1 diff --git a/text-image-search/README.md b/text-image-search/README.md index 7ad8b214f..5b21bf49a 100644 --- a/text-image-search/README.md +++ b/text-image-search/README.md @@ -39,7 +39,7 @@ which is suitable for exploration and analysis. The other is a stand-alone Vespa application, which is more suitable for production (below). The Python sample app includes a streamlit user interface: -[Animation](https://data.vespa.oath.cloud/sample-apps-data/image_demo.gif) +[Animation](https://data.vespa-cloud.com/sample-apps-data/image_demo.gif) The application takes a textual description and returns the file names of the images that best match the description. The main difference between this app @@ -144,7 +144,7 @@ $ python3 src/python/clip_feed.py Alternatively, instead of computing the embeddings, use the pre-computed embeddings: 
 $ curl -L -o flickr-8k-clip-embeddings.jsonl.zst \
-    https://data.vespa.oath.cloud/sample-apps-data/flickr-8k-clip-embeddings.jsonl.zst 
+    https://data.vespa-cloud.com/sample-apps-data/flickr-8k-clip-embeddings.jsonl.zst 
 
diff --git a/text-image-search/src/python/README.md b/text-image-search/src/python/README.md
index 331660784..7bc30a527 100644
--- a/text-image-search/src/python/README.md
+++ b/text-image-search/src/python/README.md
@@ -51,4 +51,4 @@ Run the app:
 streamlit run app.py
 ```
 
-[Animation](https://data.vespa.oath.cloud/sample-apps-data/image_demo.gif)
+[Animation](https://data.vespa-cloud.com/sample-apps-data/image_demo.gif)
diff --git a/text-image-search/src/sh/download_onnx_model.sh b/text-image-search/src/sh/download_onnx_model.sh
index f53b6d2d6..697eac29c 100755
--- a/text-image-search/src/sh/download_onnx_model.sh
+++ b/text-image-search/src/sh/download_onnx_model.sh
@@ -5,6 +5,6 @@ echo "[INFO] Downloading model into $DIR"
 
 mkdir -p $DIR
 
-echo "Downloading https://data.vespa.oath.cloud/onnx_models/clip_transformer.onnx"
+echo "Downloading https://data.vespa-cloud.com/onnx_models/clip_transformer.onnx"
 curl -L -o $DIR/transformer.onnx \
-https://data.vespa.oath.cloud/onnx_models/clip_transformer.onnx
+https://data.vespa-cloud.com/onnx_models/clip_transformer.onnx
diff --git a/text-video-search/README.md b/text-video-search/README.md
index 300511478..c98963e3d 100644
--- a/text-video-search/README.md
+++ b/text-video-search/README.md
@@ -9,7 +9,7 @@
 
 Build a text-video search from scratch based on CLIP models with Vespa python API.
 
-[See Animation](https://data.vespa.oath.cloud/sample-apps-data/video_demo.gif)
+[See Animation](https://data.vespa-cloud.com/sample-apps-data/video_demo.gif)
 
 ## Create the application from scratch in a Jupyter Notebook
 
diff --git a/text-video-search/src/python/app.py b/text-video-search/src/python/app.py
index 4a3e4cc86..0f23fe4fc 100644
--- a/text-video-search/src/python/app.py
+++ b/text-video-search/src/python/app.py
@@ -32,7 +32,7 @@ def get_video(video_file_name, video_dir):
 
 def get_predefined_queries():
     return get(
-        "https://data.vespa.oath.cloud/blog/ucf101/predefined_queries.txt"
+        "https://data.vespa-cloud.com/blog/ucf101/predefined_queries.txt"
     ).text.split("\n")[:-1]
 
 
diff --git a/use-case-shopping/README.md b/use-case-shopping/README.md
index 249234975..c04685d9f 100644
--- a/use-case-shopping/README.md
+++ b/use-case-shopping/README.md
@@ -90,13 +90,13 @@ $ vespa test src/test/application/tests/system-test/product-search-test.json
 
 First, create data feed for products:
 
-$ curl -L -o meta_sports_20k_sample.json.zst https://data.vespa.oath.cloud/sample-apps-data/meta_sports_20k_sample.json.zst 
+$ curl -L -o meta_sports_20k_sample.json.zst https://data.vespa-cloud.com/sample-apps-data/meta_sports_20k_sample.json.zst 
 $ zstdcat meta_sports_20k_sample.json.zst | ./convert_meta.py > feed_items.json
 

 
 Next, data feed for reviews:
 
-$ curl -L -o reviews_sports_24k_sample.json.zst https://data.vespa.oath.cloud/sample-apps-data/reviews_sports_24k_sample.json.zst
+$ curl -L -o reviews_sports_24k_sample.json.zst https://data.vespa-cloud.com/sample-apps-data/reviews_sports_24k_sample.json.zst
 $ zstdcat reviews_sports_24k_sample.json.zst | ./convert_reviews.py > feed_reviews.json