docs(medcat): CU-869b2fcrc Update migration guide (#214)

* CU-869b2fcrc: Update docs with distinction of cogstack-es and medcat-scripts for v2

* CU-869b2fcrc: Add embedding linker to migration guide

* CU-869b2fcrc: Add v2 trainer link

* CU-869b2fcrc: Add trainer and WWC changes to FAQ

* CU-869b2fcrc: Specify scripts-downloader for medcat v2.3.0 and later

* CU-869b2fcrc: Fix optional extra in installation instructions for cogstack-es

* CU-869b2fcrc: Fix typo

Author: mart-r

cogstack-es/ReadMe.md

@@ -6,9 +6,9 @@ This project is responsible for logging in and performing a search for Elasticse
 
 This package is distributed through PyPI and can be installed using one of:
 ```
-pip install cogstack-es[ES9]  # For Elasticsearch 9
-pip install cogstack-es[ES8]  # For Elasticsearch 8
-pip install cogstack-es[OS]  # For Opensearch
+pip install "cogstack-es[ES9]"  # For Elasticsearch 9
+pip install "cogstack-es[ES8]"  # For Elasticsearch 8
+pip install "cogstack-es[OS]"  # For Opensearch
 ```
 
 PS:

medcat-v2/docs/migration_guide_v2.md

@@ -70,6 +70,7 @@ Here's a list of the additional features you can opt for with what they're used
 | Transformer NER     | `deid`       | Enables transformer-based NER, primarily used for de-identification models |
 | Relation Extraction | `rel-cat`    | Adds support for extracting relations between entities                     |
 | Dictionary NER      | `dict-ner`   | Example dictionary NER module (experimental and rarely needed)             |
+| Embedding linker    | `embed-linker` | Linker that uses more sophisiticated context embeddings for linking      |
 
 ## Summary of Changes
 
@@ -122,15 +123,12 @@ The v2 tutorials are available [here](https://github.com/CogStack/cogstack-nlp/t
 ## Updated `working_with_cogstack` scripts
 
 The `working_with_cogstack` scripts have also been upgraded to support v2.
-The changes are currently in [this PR](https://github.com/CogStack/working_with_cogstack/pull/20).
-They have not yet been merged in to the `main` branch but will be in the near future.
-At that point, there will probably be a separate branch to keep track of v1-specific scripts.
+However, they have been split into `cogstack-es` which lives [here](https://github.com/CogStack/cogstack-nlp/tree/main/cogstack-es), but notably also available on PyPI (i.e `pip install "cogstack-es[ES9]"`); and `medcat-scripts` available [here](https://github.com/CogStack/cogstack-nlp/tree/main/medcat-scripts) which can be fetched using `python -m medcat download-scripts` (in v2.3.0 onwards).
 
 ## MedCATtrainer
 
-MedCATtrainer has been modified to work with v2 in [this PR](https://github.com/CogStack/MedCATtrainer/pull/253).
-However, as of writing, this change has not yet been merged in or been released.
-The v2-supporting release will most likely be released as **v3** on the trainer side.
+MedCATtrainer has been modified to work with v2 in [here](https://github.com/CogStack/cogstack-nlp/tree/main/medcat-trainer).
+The v2-supporting releases are those from **v3** on the trainer side.
 
 ## Feedback welcome!
 
@@ -181,12 +179,12 @@ A: Training now uses a dedicated `medcat.trainer.Trainer` class. See tutorials a
 
 **Q: Are v1 `working_with_cogstack` scripts still supported?**
 
-A: No. Many will break due to internal changes. Please refer to the new scripts in the [relevant branch](https://github.com/CogStack/working_with_cogstack/pull/20).
+A: No. You should use [medcat scripts](https://github.com/CogStack/cogstack-nlp/tree/main/medcat-scripts) and [cogstack-es](https://github.com/CogStack/cogstack-nlp/tree/main/cogstack-es) by doing `python -m medcat download-scripts` (in v2.3.0 onwards) and `pip install "cogstack-es[ES9]"`.
 
 
 **Q: Does MedCATtrainer work out of the box for v2?**
 
-A: No. While the [changes have been ported](https://github.com/CogStack/MedCATtrainer/pull/253), there is currently no release for these changes and it is unlikely to already be spun up yet. But it will be soon.
+A: Yes. Trainer versions from v3 onwards will work natively with v2.
 
 
 **Q: Does `medcat-service` work for serving a model?**