Was versteht man unter natürlicher Sprachverarbeitung (NLP)?
Natürliche Sprachverarbeitung (Natural Language Processing, NLP) ist eine Art von künstlicher Intelligenz (KI), die sich damit befasst, wie Computer und ...
ELSER (Elastic Learned Sparse Encoder)
Unspezifische und zu allgemeine Daten führen dazu, dass die NLP die Bedeutung von Texten weniger genau verstehen und vermitteln kann. Für spezifische Themenbereiche sind mehr Daten erforderlich als die meisten NLP-Systeme zur Verfügung haben, um zielsichere Aussagen zu treffen. Dies gilt insbesondere für Fachbereiche, die von aktuellen und sehr spezifischen Informationen abhängen. Neue Forschungsarbeiten wie etwa der <anchor>ELSER (Elastic Learned Sparse Encoder)</anchor> versuchen, dieses Problem zu beheben und relevantere Ergebnisse zu liefern.
ELSER – Elastic Learned Sparse EncodeR | Machine Learning in the Elastic Stack [8.15] | Elastic
# ELSER – Elastic Learned Sparse EncodeR

## ELSER – Elastic Learned Sparse EncodeR

Elastic Learned Sparse EncodeR - or ELSER - is a retrieval model trained by
Elastic that enables you to perform
semantic search to retrieve more relevant
search results. This search type provides you search results based on contextual
meaning and user intent, rather than exact keyword matches.

ELSER is an out-of-domain model which means it does not require fine-tuning on
your own data, making it adaptable for various use cases out of the box.

This model is recommended for English language documents and queries. If you
want to perform semantic search on non-English language documents, use the
E5 model.

While ELSER V2 is generally available, ELSER V1 is in
[preview]

This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

and will remain in technical preview.

#### Tokens - not synonyms

ELSER expands the indexed and searched passages into collections of terms that
are learned to co-occur frequently within a diverse set of training data. The
terms that the text is expanded into by the model are not synonyms for the
search terms; they are learned associations capturing relevance. These expanded
terms are weighted as some of them are more significant than others. Then the
Elasticsearch sparse vector
(or rank features) field type is used to store the
terms and weights at index time, and to search against later.

This approach provides a more understandable search experience compared to
vector embeddings. However, attempting to directly interpret the tokens and
weights can be misleading, as the expansion essentially results in a vector in a
very high-dimensional space. Consequently, certain tokens, especially those with
low weight, contain information that is intertwined with other low-weight tokens
in the representation. In this regard, they function similarly to a dense vector
representation, making it challenging to separate their individual
contributions. This complexity can potentially lead to misinterpretations if not
carefully considered during analysis.

#### Requirements

To use ELSER, you must have the appropriate subscription level
for semantic search or the trial period activated.

The minimum dedicated ML node size for deploying and using the ELSER model
is 4 GB in Elasticsearch Service if
deployment autoscaling is turned off. Turning on
autoscaling is recommended because it allows your deployment to dynamically
adjust resources based on demand. Better performance can be achieved by using
more allocations or more threads per allocation, which requires bigger ML nodes.
Autoscaling provides bigger nodes when required. If autoscaling is turned off,
you must provide suitably sized nodes yourself.

#### ELSER v2

Compared to the initial version of the model, ELSER v2 offers improved retrieval
accuracy and more efficient indexing. This enhancement is attributed to the
extension of the training data set, which includes high-quality question and
answer pairs and the improved FLOPS regularizer which reduces the cost of
computing the similarity between a query and a document.

ELSER v2 has two versions: one cross-platform version which runs on any hardware
and one version which is optimized for Intel® silicon. The
Model Management > Trained Models page shows you which version of ELSER
v2 is recommended to deploy based on your cluster’s hardware. However, the
recommended way to use ELSER is through the
inference API as a service which makes it easier
to download and deploy the model and you don’t need to select from different
versions.

If you want to learn more about the ELSER V2 improvements, refer to
this blog post.

##### Upgrading to ELSER v2

ELSER v2 is not backward compatible. If you indexed your data with ELSER v1, you
need to reindex it with an ingest pipeline referencing ELSER v2 to be able to
use v2 for search. This tutorial shows you how
to create an ingest pipeline with an inference processor that uses ELSER v2, and
how to reindex your data through the pipeline.

Additionally, the `elasticearch-labs` GitHub repository contains an interactive
Python notebook
that walks through upgrading an index to ELSER V2.

#### Download and deploy ELSER

The easiest and recommended way to download and deploy ELSER is to use the inference API.

1. In Kibana, navigate to the Dev Console.

2. Create an inference endpoint with the ELSER service by running the following API request:

The API request automatically initiates the model download and then deploy the model.

After you created the ELSER inference endpoint, it’s ready to be used for semantic search.
The easiest way to perform semantic search in the Elastic Stack is to follow the `semantic_text` workflow.

##### Alternative methods to download and deploy ELSER

You can also download and deploy ELSER either from Machine Learning > Trained Models, from Search > Indices, or by using the trained models API in Dev Console.

- For most cases, the preferred version is the Intel and Linux optimized
  model, it is recommended to download and deploy that version.

- You can deploy the model multiple times by assigning a unique deployment ID
  when starting the deployment. It enables you to have dedicated deployments for
  different purposes, such as search and ingest. By doing so, you ensure that the
  search speed remains unaffected by ingest workloads, and vice versa. Having
  separate deployments for search and ingest mitigates performance issues
  resulting from interactions between the two, which can be hard to diagnose.

Using the Trained Models page

###### Using the Trained Models page

1. In Kibana, navigate to Machine Learning > Trained Models. ELSER can be found
   in the list of trained models. There are two versions available: one portable
   version which runs on any hardware and one version which is optimized for Intel®
   silicon. You can see which model is recommended to use based on your hardware
   configuration.

2. Click the Add trained model button. Select the ELSER model version you
   want to use in the opening modal window. The model that is recommended for you
   based on your hardware configuration is highlighted. Click Download. You can
   check the download status on the Notifications page.

Downloading ELSER

Alternatively, click the Download model button under Actions in the
trained model list.

3. After the download is finished, start the deployment by clicking the
   Start deployment button.

4. Provide a deployment ID, select the priority, and set the number of
   allocations and threads per allocation values.

Deploying ELSER

5. Click Start.

Using the search indices UI

###### Using the search indices UI

Alternatively, you can download and deploy ELSER to an inference pipeline using
the search indices UI.

1. In Kibana, navigate to Search > Indices.

2. Select the index from the list that has an inference pipeline in which you want
   to use ELSER.

3. Navigate to the Pipelines tab.

4. Under Machine Learning Inference Pipelines, click the Deploy button to
   begin downloading the ELSER model. This may take a few minutes depending on your
   network.

Deploying ELSER in Elasticsearch

5. Once the model is downloaded, click the Start single-threaded button to
   start the model with basic configuration or select the Fine-tune performance
   option to navigate to the Trained Models page where you can configure the
   model deployment.

Start ELSER in Elasticsearch

Using the traned models API in Dev Console

###### Using the trained models API in Dev Console

1. In Kibana, navigate to the Dev Console.

2. Create the ELSER model configuration by running the following API call:

The API call automatically initiates the model download if the model is not
downloaded yet.

You can deploy the model multiple times with different deployment IDs.

#### Deploy ELSER in an air-gapped environment

If you want to deploy ELSER in a restricted or closed network, you have two
options:

- create your own HTTP/HTTPS endpoint with the model artifacts on it,

- put the model artifacts into a directory inside the config directory on all
  master-eligible nodes.

##### Model artifact files

For the cross-platform verison, you need the following files in your system:

For the optimized version, you need the following files in your system:

##### Using an HTTP server

INFO: If you use an existing HTTP server, note that the model downloader only
supports passwordless HTTP servers.

You can use any HTTP service to deploy ELSER. This example uses the official
Nginx Docker image to set a new HTTP download service up.

1. Download the model artifact files.

2. Put the files into a subdirectory of your choice.

3. Run the following commands:

   ```
   export ELASTIC_ML_MODELS="/path/to/models"
   docker run --rm -d -p 8080:80 --name ml-models -v ${ELASTIC_ML_MODELS}:/usr/share/nginx/html nginx
   ```

   Don’t forget to change `/path/to/models` to the path of the subdirectory where
   the model artifact files are located.

   These commands start a local Docker image with an Nginx server with the
   subdirectory containing the model files. As the Docker image has to be
   downloaded and built, the first start might take a longer period of time.
   Subsequent runs start quicker.

4. Verify that Nginx runs properly by visiting the following URL in your
   browser:

   ```
   http://{IP_ADDRESS_OR_HOSTNAME}:8080/elser_model_2.metadata.json
   ```

   If Nginx runs properly, you see the content of the metdata file of the model.

5. Point your Elasticsearch deployment to the model artifacts on the HTTP server
   by adding the following line to the `config/elasticsearch.yml` file:

   ```
   xpack.ml.model_repository: http://{IP_ADDRESS_OR_HOSTNAME}:8080
   ```

   If you use your own HTTP or HTTPS server, change the address accordingly. It is
   important to specificy the protocol ("http://" or "https://"). Ensure that all
   master-eligible nodes can reach the server you specify.

6. Repeat step 5 on all master-eligible nodes.

7. Restart the
   master-eligible nodes one by one.

8. Navigate to the Trained Models page in Kibana, ELSER can be found in the
   list of trained models.

9. Click the Add trained model button, select the ELSER model version you
   downloaded in step 1 and want to deploy, and click Download. The selected
   model will be downloaded from the HTTP/HTTPS server you configured.

10. After the download is finished, start the deployment by clicking the
    Start deployment button.

11. Provide a deployment ID, select the priority, and set the number of
    allocations and threads per allocation values.

12. Click Start.

The HTTP server is only required for downloading the model. After the download
has finished, you can stop and delete the service. You can stop the Docker image
used in this example by running the following command:

##### Using file-based access

For a file-based access, follow these steps:

1. Download the model artifact files.

2. Put the files into a `models` subdirectory inside the `config` directory of
   your Elasticsearch deployment.

3. Point your Elasticsearch deployment to the model directory by adding the
   following line to the `config/elasticsearch.yml` file:

   ```
   xpack.ml.model_repository: file://${path.home}/config/models/`
   ```

4. Repeat step 2 and step 3 on all master-eligible nodes.

5. Restart the
   master-eligible nodes one by one.

6. Navigate to the Trained Models page in Kibana, ELSER can be found in the
   list of trained models.

7. Click the Add trained model button, select the ELSER model version you
   downloaded in step 1 and want to deploy and click Download. The selected
   model will be downloaded from the model directory where you put in step 2.

8. After the download is finished, start the deployment by clicking the
   Start deployment button.

9. Provide a deployment ID, select the priority, and set the number of
   allocations and threads per allocation values.

10. Click Start.

#### Testing ELSER

You can test the deployed model in Kibana. Navigate to Model Management >
Trained Models, locate the deployed ELSER model in the list of trained
models, then select Test model from the Actions menu.

You can use data from an existing index to test the model. Select the index,
then a field of the index you want to test ELSER on. Provide a search query and
click Test. Evaluating model recall is simpler when using a query related to
the documents.

The results contain a list of ten random values for the selected field along
with a score showing how relevant each document is to the query. The higher the
score, the more relevant the document is. You can reload example documents by
clicking Reload examples.

Testing ELSER

#### Performance considerations

- ELSER works best on small-to-medium sized fields that contain natural
  language. For connector or web crawler use cases, this aligns best with fields
  like title, description, summary, or abstract. As ELSER encodes the
  first 512 tokens of a field, it may not provide as relevant of results for large
  fields. For example, `body_content` on web crawler documents, or body fields
  resulting from extracting text from office documents with connectors. For larger
  fields like these, consider "chunking" the content into multiple values, where
  each chunk can be under 512 tokens.

- Larger documents take longer at ingestion time, and inference time per
  document also increases the more fields in a document that need to be processed.

- The more fields your pipeline has to perform inference on, the longer it takes
  per document to ingest.

#### Further reading

#### Benchmark information

The recommended way to use ELSER is through the inference API as a service.

The following sections provide information about how ELSER performs on different
hardwares and compares the model performance to Elasticsearch BM25 and other strong
baselines.

##### Version overview

ELSER V2 has a optimized version that is designed to run only on Linux with
an x86-64 CPU architecture and a cross-platform version that can be run on
any platform.

###### ELSER V2

Besides the performance improvements, the biggest change in ELSER V2 is the
introduction of the first platform specific ELSER model - that is, a model
optimized to run only on Linux with an x86-64 CPU architecture. The optimized
model is designed to work best on newer Intel CPUs, but it works on AMD CPUs as
well. It is recommended to use the new optimized Linux-x86-64 model for all new
users of ELSER as it is significantly faster than the cross-platform model which
can be run on any platform. ELSER V2 produces significantly higher quality
embeddings than ELSER V1. Regardless of which ELSER V2 model you use (optimized
or cross-platform), the particular embeddings produced are the same.

##### Qualitative benchmarks

The metric that is used to evaluate ELSER’s ranking ability is the Normalized
Discounted Cumulative Gain (NDCG) which can handle multiple relevant documents
and fine-grained document ratings. The metric is applied to a fixed-sized list
of retrieved documents which, in this case, is the top 10 documents (NDCG@10).

The table below shows the performance of ELSER V2 compared to BM 25. ELSER V2
has 10 wins, 1 draw, 1 loss and an average improvement in NDCG@10 of 18%.

ELSER V2 benchmarks compared to BM25

NDCG@10 for BEIR data sets for BM25 and ELSER V2 - higher values are better)

##### Hardware benchmarks

While the goal is to create a model that is as performant as
possible, retrieval accuracy always take precedence over speed, this is one of
the design principles of ELSER. Consult with the tables below to learn more
about the expected model performance. The values refer to operations performed
on two data sets and different hardware configurations. Your data set has an
impact on the model performance. Run tests on your own data to have a more
realistic view on the model performance for your use case.

###### ELSER V2

Overall the optimized V2 model ingested at a max rate of 26 docs/s, compared
with the ELSER V1 max rate of 14 docs/s from the ELSER V1 benchamrk, resulting
in a 90% increase in throughput.

The performance of virtual cores (that is, when the number of allocations is
greater than half of the vCPUs) has increased. Previously, the increase in
performance between 8 and 16 allocations was around 7%. It has increased to 17%
(ELSER V1 on 8.11) and 20% (for ELSER V2 optimized). These tests were performed
on a 16vCPU machine, with all documents containing exactly 256 tokens.

The length of the documents in your particular dataset will have a
significant impact on your throughput numbers.

Refer to
this blog post
to learn more about ELSER V2 improved performance.

Summary of ELSER V1 and V2 benchmark reports

The optimized model results show a nearly linear growth up until 8
allocations, after which performance improvements become smaller. In this case,
the performance at 8 allocations was 22 docs/s, while the performance of 16
allocations was 26 docs/s, indicating a 20% performance increase due to virtual
cores.

ELSER V2 optimized benchmarks

The cross-platform model performance of 8 and 16 allocations are
respectively 14 docs/s and 16 docs/s, indicating a performance improvement due
to virtual cores of 12%.

ELSER V2 cross-platform benchmarks

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