Resolve more links

Author: wajihaparvez

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/tools/upload-data-files.md). 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/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:
 

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/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.
+Upload the file by using the [Data Visualizer](../../../manage-data/ingest/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

manage-data/ingest.md

@@ -28,7 +28,7 @@ Elastic offer tools designed to ingest specific types of general content. The co
 * To send **application data** directly to {{es}}, use an [{{es}} language client](https://www.elastic.co/guide/en/elasticsearch/client/index.html).
 * To index **web page content**, use the Elastic [web crawler](https://www.elastic.co/web-crawler).
 * To sync **data from third-party sources**, use [connectors](https://www.elastic.co/guide/en/elasticsearch/reference/current/es-connectors.html). A connector syncs content from an original data source to an {{es}} index. Using connectors you can create *searchable*, read-only replicas of your data sources.
-* To index **single files** for testing in a non-production environment, use the {{kib}} [file uploader](ingest/tools/upload-data-files.md).
+* To index **single files** for testing in a non-production environment, use the {{kib}} [file uploader](ingest/upload-data-files.md).
 
 If you would like to try things out before you add your own data, try using our [sample data](ingest/sample-data.md).
 

manage-data/ingest/tools.md

@@ -38,7 +38,7 @@ Depending on the type of data you want to ingest, you have a number of methods a
 
 | Tools   | Usage           | Links to more information |
 | ------- | --------------- | ------------------------- |
-| File upload | Upload data from a file and inspect it before importing it into {{es}}. | [Upload data files](/manage-data/ingest/tools/upload-data-files.md) |
+| File upload | Upload data from a file and inspect it before importing it into {{es}}. | [Upload data files](/manage-data/ingest/upload-data-files.md) |
 | APIs  | Ingest data through code by using the APIs of one of the language clients or the {{es}} HTTP APIs. | [Document APIs](https://www.elastic.co/guide/en/elasticsearch/reference/current/docs.html) |
 | OpenTelemetry | Collect and send your telemetry data to Elastic Observability | [Elastic Distributions of OpenTelemetry](https://github.com/elastic/opentelemetry?tab=readme-ov-file#elastic-distributions-of-opentelemetry) |
 | Fleet and Elastic Agent | Add monitoring for logs, metrics, and other types of data to a host using Elastic Agent, and centrally manage it using Fleet. | [Fleet and {{agent}} overview](https://www.elastic.co/guide/en/fleet/current/fleet-overview.html) <br> [{{fleet}} and {{agent}} restrictions (Serverless)](https://www.elastic.co/guide/en/fleet/current/fleet-agent-serverless-restrictions.html) <br> [{{beats}} and {{agent}} capabilities](https://www.elastic.co/guide/en/fleet/current/beats-agent-comparison.html)||

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/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.
+Download the file and upload it to your cluster using the [Data Visualizer](../../../manage-data/ingest/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]

solutions/search/hybrid-semantic-text.md

@@ -56,7 +56,7 @@ 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/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.
+Download the file and upload it to your cluster using the [Data Visualizer](../../manage-data/ingest/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]

solutions/search/ingest-for-search.md

@@ -41,7 +41,7 @@ You can use these specialized tools to add general content to {{es}} indices.
 |--------|-------------|-------|
 | [**Web crawler**](https://github.com/elastic/crawler) | Programmatically discover and index content from websites and knowledge bases | Crawl public-facing web content or internal sites accessible via HTTP proxy |
 | [**Search connectors**](https://github.com/elastic/connectors) | Third-party integrations to popular content sources like databases, cloud storage, and business applications | Choose from a range of Elastic-built connectors or build your own in Python using the Elastic connector framework|
-| [**File upload**](/manage-data/ingest/tools/upload-data-files.md)| One-off manual uploads through the UI | Useful for testing or very small-scale use cases, but not recommended for production workflows |
+| [**File upload**](/manage-data/ingest/upload-data-files.md)| One-off manual uploads through the UI | Useful for testing or very small-scale use cases, but not recommended for production workflows |
 
 ### Process data at ingest time
 

solutions/search/semantic-search/semantic-search-elser-ingest-pipelines.md

@@ -102,7 +102,7 @@ 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/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.
+Download the file and upload it to your cluster using the [File Uploader](../../../manage-data/ingest/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]

solutions/search/semantic-search/semantic-search-inference.md

@@ -829,7 +829,7 @@ 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/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.
+Download the file and upload it to your cluster using the [Data Visualizer](../../../manage-data/ingest/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]

solutions/search/semantic-search/semantic-search-semantic-text.md

@@ -63,7 +63,7 @@ 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/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.
+Download the file and upload it to your cluster using the [Data Visualizer](../../../manage-data/ingest/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]