Fix links to Data Visualizer / Upload Files content

kilfoyle · kilfoyle · commit f2158e6e5fd4 · 2025-02-11T16:59:11.000-05:00
diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md b/explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md
@@ -113,7 +113,7 @@ Using the example text "Elastic is headquartered in Mountain View, California.",
 
 ## Add the NER model to an {{infer}} ingest pipeline [ex-ner-ingest]
 
-You can perform bulk {{infer}} on documents as they are ingested by using an [{{infer}} processor](https://www.elastic.co/guide/en/elasticsearch/reference/current/inference-processor.html) in your ingest pipeline. The novel *Les Misérables* by Victor Hugo is used as an example for {{infer}} in the following example. [Download](https://github.com/elastic/stack-docs/blob/8.5/docs/en/stack/ml/nlp/data/les-miserables-nd.json) the novel text split by paragraph as a JSON file, then upload it by using the [Data Visualizer](../../../manage-data/ingest.md#upload-data-kibana). Give the new index the name `les-miserables` when uploading the file.
+You can perform bulk {{infer}} on documents as they are ingested by using an [{{infer}} processor](https://www.elastic.co/guide/en/elasticsearch/reference/current/inference-processor.html) in your ingest pipeline. The novel *Les Misérables* by Victor Hugo is used as an example for {{infer}} in the following example. [Download](https://github.com/elastic/stack-docs/blob/8.5/docs/en/stack/ml/nlp/data/les-miserables-nd.json) the novel text split by paragraph as a JSON file, then upload it by using the [Data Visualizer](../../../manage-data/ingest/tools/upload-data-files.md). Give the new index the name `les-miserables` when uploading the file.
 
 Now create an ingest pipeline either in the [Stack management UI](ml-nlp-inference.md#ml-nlp-inference-processor) or by using the API:
 
diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md b/explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md
@@ -103,7 +103,7 @@ In this step, you load the data that you later use in an ingest pipeline to get
 
 The data set `msmarco-passagetest2019-top1000` is a subset of the MS MARCO Passage Ranking data set used in the testing stage of the 2019 TREC Deep Learning Track. It contains 200 queries and for each query a list of relevant text passages extracted by a simple information retrieval (IR) system. From that data set, all unique passages with their IDs have been extracted and put into a [tsv file](https://github.com/elastic/stack-docs/blob/8.5/docs/en/stack/ml/nlp/data/msmarco-passagetest2019-unique.tsv), totaling 182469 passages. In the following, this file is used as the example data set.
 
-Upload the file by using the [Data Visualizer](../../../manage-data/ingest.md#upload-data-kibana). Name the first column `id` and the second one `text`. The index name is `collection`. After the upload is done, you can see an index named `collection` with 182469 documents.
+Upload the file by using the [Data Visualizer](../../../manage-data/ingest/tools/upload-data-files.md). Name the first column `id` and the second one `text`. The index name is `collection`. After the upload is done, you can see an index named `collection` with 182469 documents.
 
 :::{image} ../../../images/machine-learning-ml-nlp-text-emb-data.png
 :alt: Importing the data
diff --git a/raw-migrated-files/elasticsearch/elasticsearch-reference/semantic-search-inference.md b/raw-migrated-files/elasticsearch/elasticsearch-reference/semantic-search-inference.md
@@ -824,7 +824,7 @@ In this step, you load the data that you later use in the {{infer}} ingest pipel
 
 Use the `msmarco-passagetest2019-top1000` data set, which is a subset of the MS MARCO Passage Ranking data set. It consists of 200 queries, each accompanied by a list of relevant text passages. All unique passages, along with their IDs, have been extracted from that data set and compiled into a [tsv file](https://github.com/elastic/stack-docs/blob/main/docs/en/stack/ml/nlp/data/msmarco-passagetest2019-unique.tsv).
 
-Download the file and upload it to your cluster using the [Data Visualizer](../../../manage-data/ingest.md#upload-data-kibana) in the {{ml-app}} UI. After your data is analyzed, click **Override settings**. Under **Edit field names***, assign `id` to the first column and `content` to the second. Click ***Apply***, then ***Import**. Name the index `test-data`, and click **Import**. After the upload is complete, you will see an index named `test-data` with 182,469 documents.
+Download the file and upload it to your cluster using the [Data Visualizer](../../../manage-data/ingest/tools/upload-data-files.md) in the {{ml-app}} UI. After your data is analyzed, click **Override settings**. Under **Edit field names***, assign `id` to the first column and `content` to the second. Click ***Apply***, then ***Import**. Name the index `test-data`, and click **Import**. After the upload is complete, you will see an index named `test-data` with 182,469 documents.
 
 
 ## Ingest the data through the {{infer}} ingest pipeline [reindexing-data-infer]
diff --git a/solutions/search/hybrid-semantic-text.md b/solutions/search/hybrid-semantic-text.md
@@ -53,7 +53,7 @@ In this step, you load the data that you later use to create embeddings from.
 
 Use the `msmarco-passagetest2019-top1000` data set, which is a subset of the MS MARCO Passage Ranking data set. It consists of 200 queries, each accompanied by a list of relevant text passages. All unique passages, along with their IDs, have been extracted from that data set and compiled into a [tsv file](https://github.com/elastic/stack-docs/blob/main/docs/en/stack/ml/nlp/data/msmarco-passagetest2019-unique.tsv).
 
-Download the file and upload it to your cluster using the [Data Visualizer](../../manage-data/ingest.md#upload-data-kibana) in the {{ml-app}} UI. After your data is analyzed, click **Override settings**. Under **Edit field names***, assign `id` to the first column and `content` to the second. Click ***Apply***, then ***Import**. Name the index `test-data`, and click **Import**. After the upload is complete, you will see an index named `test-data` with 182,469 documents.
+Download the file and upload it to your cluster using the [Data Visualizer](../../manage-data/iingest/tools/upload-data-files.md) in the {{ml-app}} UI. After your data is analyzed, click **Override settings**. Under **Edit field names***, assign `id` to the first column and `content` to the second. Click ***Apply***, then ***Import**. Name the index `test-data`, and click **Import**. After the upload is complete, you will see an index named `test-data` with 182,469 documents.
 
 
 ## Reindex the data for hybrid search [hybrid-search-reindex-data]
diff --git a/solutions/search/semantic-search/semantic-search-elser-ingest-pipelines.md b/solutions/search/semantic-search/semantic-search-elser-ingest-pipelines.md
@@ -99,7 +99,7 @@ The `msmarco-passagetest2019-top1000` dataset was not utilized to train the mode
 ::::
 
 
-Download the file and upload it to your cluster using the [File Uploader](../../../manage-data/ingest.md#upload-data-kibana) in the UI. After your data is analyzed, click **Override settings**. Under **Edit field names***, assign `id` to the first column and `content` to the second. Click ***Apply***, then ***Import**. Name the index `test-data`, and click **Import**. After the upload is complete, you will see an index named `test-data` with 182,469 documents.
+Download the file and upload it to your cluster using the [File Uploader](../../../manage-data/ingest/tools/upload-data-files.md) in the UI. After your data is analyzed, click **Override settings**. Under **Edit field names***, assign `id` to the first column and `content` to the second. Click ***Apply***, then ***Import**. Name the index `test-data`, and click **Import**. After the upload is complete, you will see an index named `test-data` with 182,469 documents.
 
 
 ### Ingest the data through the {{infer}} ingest pipeline [reindexing-data-elser]
diff --git a/solutions/search/semantic-search/semantic-search-inference.md b/solutions/search/semantic-search/semantic-search-inference.md
@@ -826,7 +826,7 @@ In this step, you load the data that you later use in the {{infer}} ingest pipel
 
 Use the `msmarco-passagetest2019-top1000` data set, which is a subset of the MS MARCO Passage Ranking data set. It consists of 200 queries, each accompanied by a list of relevant text passages. All unique passages, along with their IDs, have been extracted from that data set and compiled into a [tsv file](https://github.com/elastic/stack-docs/blob/main/docs/en/stack/ml/nlp/data/msmarco-passagetest2019-unique.tsv).
 
-Download the file and upload it to your cluster using the [Data Visualizer](../../../manage-data/ingest.md#upload-data-kibana) in the {{ml-app}} UI. After your data is analyzed, click **Override settings**. Under **Edit field names***, assign `id` to the first column and `content` to the second. Click ***Apply***, then ***Import**. Name the index `test-data`, and click **Import**. After the upload is complete, you will see an index named `test-data` with 182,469 documents.
+Download the file and upload it to your cluster using the [Data Visualizer](../../../manage-data/ingest/tools/upload-data-files.md) in the {{ml-app}} UI. After your data is analyzed, click **Override settings**. Under **Edit field names***, assign `id` to the first column and `content` to the second. Click ***Apply***, then ***Import**. Name the index `test-data`, and click **Import**. After the upload is complete, you will see an index named `test-data` with 182,469 documents.
 
 
 ## Ingest the data through the {{infer}} ingest pipeline [reindexing-data-infer]
diff --git a/solutions/search/semantic-search/semantic-search-semantic-text.md b/solutions/search/semantic-search/semantic-search-semantic-text.md
@@ -60,7 +60,7 @@ In this step, you load the data that you later use to create embeddings from it.
 
 Use the `msmarco-passagetest2019-top1000` data set, which is a subset of the MS MARCO Passage Ranking data set. It consists of 200 queries, each accompanied by a list of relevant text passages. All unique passages, along with their IDs, have been extracted from that data set and compiled into a [tsv file](https://github.com/elastic/stack-docs/blob/main/docs/en/stack/ml/nlp/data/msmarco-passagetest2019-unique.tsv).
 
-Download the file and upload it to your cluster using the [Data Visualizer](../../../manage-data/ingest.md#upload-data-kibana) in the {{ml-app}} UI. After your data is analyzed, click **Override settings**. Under **Edit field names***, assign `id` to the first column and `content` to the second. Click ***Apply***, then ***Import**. Name the index `test-data`, and click **Import**. After the upload is complete, you will see an index named `test-data` with 182,469 documents.
+Download the file and upload it to your cluster using the [Data Visualizer](../../../manage-data/ingest/tools/upload-data-files.md) in the {{ml-app}} UI. After your data is analyzed, click **Override settings**. Under **Edit field names***, assign `id` to the first column and `content` to the second. Click ***Apply***, then ***Import**. Name the index `test-data`, and click **Import**. After the upload is complete, you will see an index named `test-data` with 182,469 documents.
 
 
 ## Reindex the data [semantic-text-reindex-data]
