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Fix typos and minor text improvements (#4663) 

Signed-off-by: Christian Adamini <christian.adamini@invision.de>
Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com>
Co-authored-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com>
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_search-plugins/neural-search.md
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@ -25,7 +25,7 @@ In order to ingest vectorized documents, you need to create a Neural Search inge
PUT _ingest/pipeline/<pipeline_name>
```
In the pipeline request body, The `text_embedding` processor, the only processor supported by Neural Search, converts a document's text to vector embeddings. `text_embedding` uses `field_map`s to determine what fields from which to generate vector embeddings and also which field to store the embedding.
In the pipeline request body, the `text_embedding` processor, the only processor supported by Neural Search, converts a document's text to vector embeddings. `text_embedding` uses `field_map`s to determine what fields from which to generate vector embeddings and also which field to store the embedding.
### Path parameter
@ -78,7 +78,8 @@ In order to use the text embedding processor defined in your pipelines, create a
### Example request
The following example request creates an index that attaches to a Neural Search ingest pipeline. Because the index maps to k-NN vector fields, the index setting field `index-knn` is set to `true`. Furthermore, `mapping` settings use [k-NN method definitions]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-index/#method-definitions) to match the maps defined in the Neural Search ingest pipeline.
The following example request creates an index that attaches to a Neural Search pipeline. Because the index maps to k-NN vector fields, the index setting field `index-knn` is set to `true`. To match the maps defined in the Neural Search pipeline, `mapping` settings use [k-NN method definitions]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-index/#method-definitions).
```json
PUT /my-nlp-index-1
@ -121,7 +122,7 @@ OpenSearch responds with information about your new index:
## Ingest documents into Neural Search
Document ingestion is managed by OpenSearch's [Ingest API]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/index/), similarly to other OpenSearch indexes. For example, you can ingest a document that contains the `passage_text: "Hello world"` with a simple POST method:
OpenSearch's [Ingest API]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/index/) manages document ingestion, similar to other OpenSearch indexes. For example, you can ingest a document that contains the `passage_text: "Hello world"` with a simple POST method:
```json
POST /my-nlp-index-1/_doc
@ -134,9 +135,7 @@ With the text_embedding processor in place through a Neural Search ingest pipeli
## Search a neural index
If you want to use a language model to convert a text query into a k-NN vector query, use the `neural` query fields in your query. The neural query request fields can be used in both the [k-NN plugin API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api/#search-model) and [Query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/index/). Furthermore, you can use a [k-NN search filter]({{site.url}}{{site.baseurl}}/search-plugins/knn/filter-search-knn/) to refine your neural search query.
To convert a text query into a k-NN vector query by using a language model, use the `neural` query fields in your query. The neural query request fields can be used in both the [k-NN plugin API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api/#search-model) and [Query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/index/). Furthermore, you can use a [k-NN search filter]({{site.url}}{{site.baseurl}}/search-plugins/knn/filter-search-knn/) to refine your neural search query.
### Neural request fields
@ -149,7 +148,6 @@ query_text | string | The query text from which to produce queries.
model_id | string | The ID of the model that will be used in the embedding interface. The model must be indexed in OpenSearch before it can be used in Neural Search.
k | integer | The number of results the k-NN search returns.
### Example request
The following example request uses a search query that returns vectors for the "Hello World" query text: