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RelevanceAI-dev-3.2.9.2022.9.19.14.3.23.717862
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مشاهده بیشتر
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مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
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مشاهده بیشترتوضیحات
-
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | RelevanceAI-dev-3.2.9.2022.9.19.14.3.23.717862 |
| نام | RelevanceAI-dev |
| نسخه کتابخانه | 3.2.9.2022.9.19.14.3.23.717862 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Relevance AI |
| ایمیل نویسنده | dev@relevance.ai |
| آدرس صفحه اصلی | https://relevance.ai/ |
| آدرس اینترنتی | https://pypi.org/project/RelevanceAI-dev/ |
| مجوز | - |
## Relevance AI - The ML Platform for Unstructured Data Analysis
[](https://relevanceai.readthedocs.io/en/latest/?badge=latest)
[](https://img.shields.io/pypi/l/relevanceai)
🌎 80% of data in the world is unstructured in the form of text, image, audio, videos, and more.
🔥 Use Relevance to unlock the value of your unstructured data:
- ⚡ Quickly analyze unstructured data with pre-trained machine learning models in a few lines of code.
- ✨ Visualize your unstructured data. Text highlights from Named entity recognition, Word cloud from keywords, Bounding box from images.
- 📊 Create charts for both structured and unstructured.
- 🔎 Drilldown with filters and similarity search to explore and find insights.
- 🚀 Share data apps with your team.
[Sign up for a free account ->](https://hubs.ly/Q017CkXK0)
Relevance AI also acts as a platform for:
- 🔑 Vectors, storing and querying vectors with flexible vector similarity search, that can be combined with multiple vectors, aggregates and filters.
- 🔮 ML Dataset Evaluation, for debugging dataset labels, model outputs and surfacing edge cases.
## 🧠 Documentation
| Type | Link |
| ------------- | ----------- |
| Python API | [Documentation](https://sdk.relevance.ai/) |
| Python Reference | [Documentation](https://relevanceai.readthedocs.io/en/latest/) |
| Cloud Dashboard | [Documentation](https://docs.relevance.ai/) |
## 🛠️ Installation
Using pip:
```{bash}
pip install -U relevanceai
```
Using conda:
```{bash}
conda install -c relevance relevanceai
```
## ⏩ Quickstart
[](https://colab.research.google.com/github/RelevanceAI/RelevanceAI/blob/development/guides/quickstart_guide.ipynb)
Login to `relevanceai`:
```{python}
from relevanceai import Client
client = Client()
```
Prepare your documents for insertion by following the below format:
- Each document should be a dictionary
- Include a field `_id` as a primary key, otherwise it's automatically generated
- Suffix vector fields with `_vector_`
```{python}
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"},
]
```
### Insert data into a dataset
Create a dataset object with the name of the dataset you'd like to use. If it doesn't exist, it'll be created for you.
```{python}
ds = client.Dataset("quickstart")
ds.insert_documents(docs)
```
> Quick tip! Our Dataset object is compatible with common dataframes methods like `.head()`, `.shape()` and `.info()`.
### Perform vector search
```{python}
query = [
{"vector": [0.2, 0.2, 0.2], "field": "example_vector_"}
]
results = ds.search(
vector_search_query=query,
page_size=3,
)
```
[Learn more about how to flexibly configure your vector search ->](https://sdk.relevance.ai/docs/search)
### Perform clustering
Generate clusters
```{python}
clusterop = ds.cluster(vector_fields=["example_vector_"])
clusterop.list_closest()
```
Generate clusters with sklearn
```{python}
from sklearn.cluster import AgglomerativeClustering
cluster_model = AgglomerativeClustering()
clusterop = ds.cluster(vector_fields=["example_vector_"], model=cluster_model, alias="agglomerative")
clusterop.list_closest()
```
[Learn more about how to flexibly configure your clustering ->](https://sdk.relevance.ai/docs/search)
## 🧰 Config
The config object contains the adjustable global settings for the SDK. For a description of all the settings, see [here](https://github.com/RelevanceAI/RelevanceAI/blob/development/relevanceai/constants/config.ini).
To view setting options, run the following:
```{python}
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:
```{python}
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:
```{python}
client.base_url = "https://.../latest"
```
## 🚧 Development
### Getting Started
To get started with development, ensure you have pytest and mypy installed. These will help ensure typechecking and testing.
```{bash}
python -m pip install pytest mypy
```
Then run testing using:
> Don't forget to set your test credentials!
```{bash}
export TEST_PROJECT = xxx
export TEST_API_KEY = xxx
python -m pytest
mypy relevanceai
```
Set up precommit
```{bash}
pip install precommit
pre-commit install
```
نیازمندی
| مقدار | نام |
|---|---|
| - | autopep8 |
| >=4.49.0 | tqdm |
| >=1.0.0 | pandas |
| >=0.5.3 | loguru |
| >=1.7.1 | document-utils |
| >=2.0.0 | requests |
| >=1.19.0 | numpy |
| <1.2.0 | joblib |
| >=0.20.0 | scikit-learn |
| >=3.0 | typing-extensions |
| ~=1.4.0 | analytics-python |
| >=3.8.1 | aiohttp |
| >=1.4.4 | appdirs |
| >=3.6.7 | orjson |
| >=5.9.1 | psutil |
| - | pylint |
| - | jupyter |
| - | pre-commit |
| - | black |
| - | mypy |
| - | xenon |
| - | pytest |
| - | pytest-dotenv |
| - | pytest-xdist |
| - | pytest-cov |
| - | pytest-mock |
| - | types-requests |
| - | pytest-sugar |
| - | pytest-rerunfailures |
| >=4.49.0 | tqdm |
| >=1.0.0 | pandas |
| >=0.5.3 | loguru |
| >=1.7.1 | document-utils |
| >=2.0.0 | requests |
| >=1.19.0 | numpy |
| <1.2.0 | joblib |
| >=0.20.0 | scikit-learn |
| >=3.0 | typing-extensions |
| ~=1.4.0 | analytics-python |
| >=3.8.1 | aiohttp |
| >=1.4.4 | appdirs |
| >=3.6.7 | orjson |
| >=5.9.1 | psutil |
| >=3.0.9 | openpyxl |
| >=2021.10.1 | fsspec |
| >=0.5.2 | umap-learn |
| >=0.5.0 | sphinx-rtd-theme |
| ==0.8.1 | pydata-sphinx-theme |
| ==1.8.4 | sphinx-autoapi |
| ==1.12.0 | sphinx-autodoc-typehints |
| - | autopep8 |
| - | pylint |
| - | jupyter |
| - | pytest |
| - | pytest-dotenv |
| - | pytest-xdist |
| - | pytest-cov |
| - | pytest-mock |
| - | mypy |
| - | types-requests |
| - | pytest-sugar |
| - | pytest-rerunfailures |
| >=4.49.0 | tqdm |
| >=1.0.0 | pandas |
| >=0.5.3 | loguru |
| >=1.7.1 | document-utils |
| >=2.0.0 | requests |
| >=1.19.0 | numpy |
| <1.2.0 | joblib |
| >=0.20.0 | scikit-learn |
| >=3.0 | typing-extensions |
| ~=1.4.0 | analytics-python |
| >=3.8.1 | aiohttp |
| >=1.4.4 | appdirs |
| >=3.6.7 | orjson |
| >=5.9.1 | psutil |
| >=3.0.9 | openpyxl |
| >=2021.10.1 | fsspec |
| >=0.5.2 | umap-learn |
| >=0.5.0 | sphinx-rtd-theme |
| ==0.8.1 | pydata-sphinx-theme |
| ==1.8.4 | sphinx-autoapi |
| ==1.12.0 | sphinx-autodoc-typehints |
| - | autopep8 |
| - | pylint |
| - | jupyter |
| - | pytest |
| - | pytest-dotenv |
| - | pytest-xdist |
| - | pytest-cov |
| - | pytest-mock |
| - | mypy |
| - | types-requests |
| - | pytest-sugar |
| - | pytest-rerunfailures |
| >=4.49.0 | tqdm |
| >=1.0.0 | pandas |
| >=0.5.3 | loguru |
| >=1.7.1 | document-utils |
| >=2.0.0 | requests |
| >=1.19.0 | numpy |
| <1.2.0 | joblib |
| >=0.20.0 | scikit-learn |
| >=3.0 | typing-extensions |
| ~=1.4.0 | analytics-python |
| >=3.8.1 | aiohttp |
| >=1.4.4 | appdirs |
| >=3.6.7 | orjson |
| >=5.9.1 | psutil |
| >=3.0.9 | openpyxl |
| >=2021.10.1 | fsspec |
| >=0.5.2 | umap-learn |
| >=0.5.0 | sphinx-rtd-theme |
| ==0.8.1 | pydata-sphinx-theme |
| ==1.8.4 | sphinx-autoapi |
| ==1.12.0 | sphinx-autodoc-typehints |
| >=0.5.0 | sphinx-rtd-theme |
| ==0.8.1 | pydata-sphinx-theme |
| ==1.8.4 | sphinx-autoapi |
| ==1.12.0 | sphinx-autodoc-typehints |
| >=4.49.0 | tqdm |
| >=1.0.0 | pandas |
| >=0.5.3 | loguru |
| >=1.7.1 | document-utils |
| >=2.0.0 | requests |
| >=1.19.0 | numpy |
| <1.2.0 | joblib |
| >=0.20.0 | scikit-learn |
| >=3.0 | typing-extensions |
| ~=1.4.0 | analytics-python |
| >=3.8.1 | aiohttp |
| >=1.4.4 | appdirs |
| >=3.6.7 | orjson |
| >=5.9.1 | psutil |
| >=3.0.9 | openpyxl |
| >=2021.10.1 | fsspec |
| >=0.8.27 | hdbscan |
| >=0.2.0 | scikit-learn-extra |
| >=4.49.0 | tqdm |
| >=1.0.0 | pandas |
| >=0.5.3 | loguru |
| >=1.7.1 | document-utils |
| >=2.0.0 | requests |
| >=1.19.0 | numpy |
| <1.2.0 | joblib |
| >=0.20.0 | scikit-learn |
| >=3.0 | typing-extensions |
| ~=1.4.0 | analytics-python |
| >=3.8.1 | aiohttp |
| >=1.4.4 | appdirs |
| >=3.6.7 | orjson |
| >=5.9.1 | psutil |
| - | sentence-transformers |
| - | jsonshower |
| >=4.49.0 | tqdm |
| >=1.0.0 | pandas |
| >=0.5.3 | loguru |
| >=1.7.1 | document-utils |
| >=2.0.0 | requests |
| >=1.19.0 | numpy |
| <1.2.0 | joblib |
| >=0.20.0 | scikit-learn |
| >=3.0 | typing-extensions |
| ~=1.4.0 | analytics-python |
| >=3.8.1 | aiohttp |
| >=1.4.4 | appdirs |
| >=3.6.7 | orjson |
| >=5.9.1 | psutil |
| - | pytest |
| - | pytest-dotenv |
| - | pytest-xdist |
| - | pytest-cov |
| - | pytest-mock |
| - | mypy |
| - | types-requests |
| - | pytest-sugar |
| - | pytest-rerunfailures |
| >=4.49.0 | tqdm |
| >=1.0.0 | pandas |
| >=0.5.3 | loguru |
| >=1.7.1 | document-utils |
| >=2.0.0 | requests |
| >=1.19.0 | numpy |
| <1.2.0 | joblib |
| >=0.20.0 | scikit-learn |
| >=3.0 | typing-extensions |
| ~=1.4.0 | analytics-python |
| >=3.8.1 | aiohttp |
| >=1.4.4 | appdirs |
| >=3.6.7 | orjson |
| >=5.9.1 | psutil |
| >=3.0.9 | openpyxl |
| >=2021.10.1 | fsspec |
| >=0.5.2 | umap-learn |
| >=0.5.2 | umap-learn |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl RelevanceAI-dev-3.2.9.2022.9.19.14.3.23.717862:
pip install RelevanceAI-dev-3.2.9.2022.9.19.14.3.23.717862.whl
نصب پکیج tar.gz RelevanceAI-dev-3.2.9.2022.9.19.14.3.23.717862:
pip install RelevanceAI-dev-3.2.9.2022.9.19.14.3.23.717862.tar.gz