For guides, tutorials on how to use this package, visit https://docs.relevance.ai/docs.
🔥 Features
Features of the library include:
- Quick vector search with free dashboard to preview results
- Vector clustering with support with built-in easy customisation
- Multi-vector search with filtering, facets, weighting
- Hybrid search (weighting exact text matching and vector search together) ... and more!
You can
🧠 Documentation
There are two main ways of documentations to take a look at:
| API type | Link |
|---|---|
| Guides | Documentation |
| Python Reference | Documentation |
🛠️ Installation
pip install -U relevanceai
Or you can install it via conda to:
conda install pip
pip install -c relevanceai
You can also install on conda (only available on Linux environments at the moment): conda install -c relevance relevanceai.
⏩ Quickstart
Login into your project space
from relevanceai import Client
client = relevanceai.Client(<project_name>, <api_key>)
This is a data example in the right format to be uploaded to relevanceai. Every document you upload should:
- Be a list of dictionaries
- Every dictionary has a field called _id
- Vector fields end in vector
docs = [
{"_id": "1", "example_vector_": [0.1, 0.1, 0.1], "data": "Documentation"},
{"_id": "2", "example_vector_": [0.2, 0.2, 0.2], "data": "Best document!"},
{"_id": "3", "example_vector_": [0.3, 0.3, 0.3], "data": "document example"},
{"_id": "4", "example_vector_": [0.4, 0.4, 0.4], "data": "this is another doc"},
{"_id": "5", "example_vector_": [0.5, 0.5, 0.5], "data": "this is a doc"},
]
Upload data into a new dataset
The documents will be uploaded into a new dataset that you can name in whichever way you want. If the dataset name does not exist yet, it will be created automatically. If the dataset already exist, the uploaded _id will be replacing the old data.
client.insert_documents(dataset_id="quickstart", docs=docs)
Perform a vector search
client.services.search.vector(
dataset_id="quickstart",
multivector_query=[
{"vector": [0.2, 0.2, 0.2], "fields": ["example_vector_"]},
],
page_size=3,
query="sample search" # Stored on the dashboard but not required
🚧 Development
Getting Started
To get started with development, ensure you have pytest and mypy installed. These will help ensure typechecking and testing.
python -m pip install pytest mypy
Then run testing using:
Make sure to set your test credentials!
export TEST_PROJECT = xxx
export TEST_API_KEY = xxx
python -m pytest
mypy relevanceai
🧰 Config
The config contains the adjustable global settings for the SDK. For a description of all the settings, see here.
To view setting options, run the following:
client.config.options
The syntax for selecting an option is section.key. For example, to disable logging, run the following to modify logging.enable_logging:
client.config.set_option('logging.enable_logging', False)
To restore all options to their default, run the following:
Changing the base URL
You can change the base URL as such:
client.base_url = "https://.../latest"
You can also update the ingest base URL:
client.ingest_base_url = "https://.../latest
RelevanceAI
For guides, tutorials on how to use this package, visit https://docs.relevance.ai/docs.
If you are looking for an SDK reference, you can find that here.
Built mainly for data scientists/engineers looking to experiment with vectors/embeddings.
Installation
The easiest way is to install this package is to run pip install --upgrade relevanceai.
You can also install on conda (only available on Linux environments at the moment): conda install -c relevance relevanceai.
How to use the RelevanceAI client
For example:
## To instantiate the client
from relevanceai import Client
client = Client()
Development
Getting Started
To get started with development, ensure you have pytest and mypy installed. These will help ensure typechecking and testing.
python -m pip install pytest mypy
Then run testing using:
Make sure to set your test credentials!
export TEST_PROJECT = xxx
export TEST_API_KEY = xxx
python -m pytest
mypy relevanceai
Config
The config contains the adjustable global settings for the SDK. For a description of all the settings, see here.
To view setting options, run the following:
client.config.options
The syntax for selecting an option is section.key. For example, to disable logging, run the following to modify logging.enable_logging:
client.config.set_option('logging.enable_logging', False)
To restore all options to their default, run the following:
client.config.reset_to_default()
Changing Base URL
You can change the base URL as such:
client.base_url = "https://.../latest"
You can also update the ingest base URL:
client.ingest_base_url = "https://.../latest
1 maintainer
Metadata
1 maintainer
Release files for RelevanceAI-dev 0.26.6.2022.1.9.10.9.49.737708
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| RelevanceAI-dev-0.26.6.2022.1.9.10.9.49.737708.tar.gz | 73.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| RelevanceAI_dev-0.26.6.2022.1.9.10.9.49.737708-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 170.4 kB
Release files / RelevanceAI-dev-0.26.6.2022.1.9.10.9.49.737708.tar.gz
| Download URL | RelevanceAI-dev-0.26.6.2022.1.9.10.9.49.737708.tar.gz |
|---|---|
| Size | 73.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
689a697fc24f44a3b421134e317245b3df1c6a9d4769f4788b4e78ac3b56c1f6
|
|
BLAKE2b-256 checksum How to use checksums |
d08365dc26c83b38f1309413035ac98be1959cf5cfc80d37726ce0601da35145
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1
|
Release files / RelevanceAI_dev-0.26.6.2022.1.9.10.9.49.737708-py3-none-any.whl
| Download URL | RelevanceAI_dev-0.26.6.2022.1.9.10.9.49.737708-py3-none-any.whl |
|---|---|
| Size | 96.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
49876d0c7910dd1e3c9c261a837ff3fb271414847c48190baf3bc91a72c0ed74
|
|
BLAKE2b-256 checksum How to use checksums |
e623169e043816d3901bdbf74a22be96354bbd710b0f4379f5fc2dea6d74f1d9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1
|
