0b542a9706
This PR introduces a new module, `elasticsearch_embeddings.py`, which provides a wrapper around Elasticsearch embedding models. The new ElasticsearchEmbeddings class allows users to generate embeddings for documents and query texts using a [model deployed in an Elasticsearch cluster](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-model-ref.html#ml-nlp-model-ref-text-embedding). ### Main features: 1. The ElasticsearchEmbeddings class initializes with an Elasticsearch connection object and a model_id, providing an interface to interact with the Elasticsearch ML client through [infer_trained_model](https://elasticsearch-py.readthedocs.io/en/v8.7.0/api.html?highlight=trained%20model%20infer#elasticsearch.client.MlClient.infer_trained_model) . 2. The `embed_documents()` method generates embeddings for a list of documents, and the `embed_query()` method generates an embedding for a single query text. 3. The class supports custom input text field names in case the deployed model expects a different field name than the default `text_field`. 4. The implementation is compatible with any model deployed in Elasticsearch that generates embeddings as output. ### Benefits: 1. Simplifies the process of generating embeddings using Elasticsearch models. 2. Provides a clean and intuitive interface to interact with the Elasticsearch ML client. 3. Allows users to easily integrate Elasticsearch-generated embeddings. Related issue https://github.com/hwchase17/langchain/issues/3400 --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com> |
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| __init__.py | ||
| data.py | ||
| README.md | ||
Readme tests(draft)
Integrations Tests
Prepare
This repository contains functional tests for several search engines and databases. The tests aim to verify the correct behavior of the engines and databases according to their specifications and requirements.
To run some integration tests, such as tests located in
tests/integration_tests/vectorstores/, you will need to install the following
software:
- Docker
- Python 3.8.1 or later
We have optional group test_integration in the pyproject.toml file. This group
should contain dependencies for the integration tests and can be installed using the
command:
poetry install --with test_integration
Any new dependencies should be added by running:
# add package and install it after adding:
poetry add tiktoken@latest --group "test_integration" && poetry install --with test_integration
Before running any tests, you should start a specific Docker container that has all the
necessary dependencies installed. For instance, we use the elasticsearch.yml container
for test_elasticsearch.py:
cd tests/integration_tests/vectorstores/docker-compose
docker-compose -f elasticsearch.yml up
Prepare environment variables for local testing:
- copy
tests/.env.exampletotests/.env - set variables in
tests/.envfile, e.gOPENAI_API_KEY
Additionally, it's important to note that some integration tests may require certain
environment variables to be set, such as OPENAI_API_KEY. Be sure to set any required
environment variables before running the tests to ensure they run correctly.
Recording HTTP interactions with pytest-vcr
Some of the integration tests in this repository involve making HTTP requests to external services. To prevent these requests from being made every time the tests are run, we use pytest-vcr to record and replay HTTP interactions.
When running tests in a CI/CD pipeline, you may not want to modify the existing cassettes. You can use the --vcr-record=none command-line option to disable recording new cassettes. Here's an example:
pytest --log-cli-level=10 tests/integration_tests/vectorstores/test_pinecone.py --vcr-record=none
pytest tests/integration_tests/vectorstores/test_elasticsearch.py --vcr-record=none
Run some tests with coverage:
pytest tests/integration_tests/vectorstores/test_elasticsearch.py --cov=langchain --cov-report=html
start "" htmlcov/index.html || open htmlcov/index.html