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Saba Sturua 427551eabf
DocArray as a Retriever (#6031) 
## DocArray as a Retriever

[DocArray](https://github.com/docarray/docarray) is an open-source tool
for managing your multi-modal data. It offers flexibility to store and
search through your data using various document index backends. This PR
introduces `DocArrayRetriever` - which works with any available backend
and serves as a retriever for Langchain apps.

Also, I added 2 notebooks:
DocArray Backends - intro to all 5 currently supported backends, how to
initialize, index, and use them as a retriever
DocArray Usage - showcasing what additional search parameters you can
pass to create versatile retrievers

Example:
```python
from docarray.index import InMemoryExactNNIndex
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.retrievers import DocArrayRetriever


# define document schema
class MyDoc(BaseDoc):
    description: str
    description_embedding: NdArray[1536]


embeddings = OpenAIEmbeddings()
# create documents
descriptions = ["description 1", "description 2"]
desc_embeddings = embeddings.embed_documents(texts=descriptions)
docs = DocList[MyDoc](
    [
        MyDoc(description=desc, description_embedding=embedding)
        for desc, embedding in zip(descriptions, desc_embeddings)
    ]
)

# initialize document index with data
db = InMemoryExactNNIndex[MyDoc](docs)

# create a retriever
retriever = DocArrayRetriever(
    index=db,
    embeddings=embeddings,
    search_field="description_embedding",
    content_field="description",
)

# find the relevant document
doc = retriever.get_relevant_documents("action movies")
print(doc)
```

#### Who can review?

@dev2049

---------

Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
2023-06-17 09:09:33 -07:00
..
integration_tests DocArray as a Retriever (#6031) 2023-06-17 09:09:33 -07:00
mock_servers Add a mock server (#2443) 2023-04-05 10:35:46 -07:00
unit_tests Doc refactor (#6300) 2023-06-16 11:52:56 -07:00
__init__.py initial commit 2022-10-24 14:51:15 -07:00
data.py Add workflow for testing with all deps (#4410) 2023-05-10 09:35:07 -04:00
README.md feat: improve pinecone tests (#2806) 2023-04-13 21:49:31 -07:00

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.example to tests/.env
  • set variables in tests/.env file, e.g OPENAI_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