semvec/pypi-packages huggingface dataset:
cltrier_promptClassify 0.0.1
Description needed
Requires Python: >=3.10

# CLTrier PromptClassify

## Usage

### Python Module

```python
from cltrier_promptClassify import Pipeline

# init pipeline object
pipeline = Pipeline({
    # pipeline objects
    'do_classification': True,
    'do_evaluation': True,
    'do_export': True,
    # path to export dir (only if do_export)
    'export_path': './path/dir/',
    # dataset configuration
    'dataset': {
        # path to data file (.csv)
        'path': './path/file.csv',
        # column containing src text
        'text_column': 'text',
        # column containing gold label (only if do_evaluation)
        'gold_column': 'gold',
        # (optional) batch size used during classification
        'batch_size': 16,
    },
    # classifier configuration
    'classifiers': [
        # label for export, slug/url from hugging face hub
        ['model_label', 'model_huggingface_slug'],
        # ...
    ],
    'templates': [
        # {classes}, {text} are dynamically replaced during runtime
        ['template_label', 'prompt_template (must include {classes} and {text})'],
        # ...
    ],
    # list of classes to use
    'classes': ['class_1', 'class_2']
})

# call pipeline object
pipeline()
```

### Terminal Script

```bash
python3 -m cltrier_promptClassify ./path/to/config.toml
```

```toml
# pipeline objects
do_classification = true
do_evaluation = true
do_export = true
# path to export dir
export_path = './path/dir/'

# dataset configuration
[dataset]
# path to data file (.csv)
path = './path/file.csv'
# column containing src text
text_column = 'text'
# column containing gold label (only if do_evaluation)
gold_column = 'gold'
# (optional) batch size used during classification
batch_size = 16

# classifier configuration
[classify]
# label for export, slug/url from hugging face hub
models = [
    ['model_label', 'model_huggingface_slug'],
    # ...
]
# {classes}, {text} are dynamically replaced during runtime
templates = [
    ['template_label', 'prompt_template (must include {classes} and {text})'],
]
# list of classes to use
classes = ['class_1', 'class_2']
```
bravo-python-sdk 1.1.0
Bravo SDK Library for Python
Requires Python: >=3.6
Keywords: privateid,cryptonets,face identification

## Bravo Python SDK : White Labeled

## Project description

This distribution only supports the Bravo Organization. This package provides an interface to CryptoNets™ 1:N fully homomorphically encrypted (FHE) face recognition, Ultrapass™
ID and human age estimation.

## Getting Started

Sign up on the waitlist on https://private.id to obtain your apiKey.

## Requirements

- Python >= 3.6

## Notices

Licensing Restriction: This SDK is not licensed for use in regulated gaming. To license CryptoNets in this market,
please contact CentralAMS.

## Installation

Installation of the Bravo Python SDK and its dependencies is provided by `pip`.
If pip is not installed, see the [pip user guide](https://pip.pypa.io/en/stable/installing/ "pip User Guide") to install
pip.

To ensure smooth installation, it’s recommended to use:

- `pip: 9.0.2` or greater

The safest way to install the SDK is to use pip in a virtualenv:

```
python -m pip install bravo_python_sdk
```

If you have the bravo_python_sdk package installed and want to upgrade to the latest version, you can run:

```
python -m pip install --upgrade bravo_python_sdk
```

This will install the bravo_python_sdk package as well as all dependencies.
See the [installation](https://privid-sdk.s3.us-east-2.amazonaws.com/cryptonets-python-sdk/installation.html) section of
the SDK homepage Guide for more information.

## Usage

You will receive the Server URL and apiKey when you subscribe.

### Import Face factor

`from bravo_python_sdk.factor import FaceFactor`

`from bravo_python_sdk.settings.loggingLevel import LoggingLevel`

### Initialize and use factor

```
server_url = URL of the server

api_key = APIKEY issued on successful signup

server = FaceFactor(url=server_url, api_key=api_key,
        logging_level=LoggingLevel.full)
```

## License

[Private Identity License](https://github.com/openinfer/PrivateIdentity/blob/e19cb4870048f14e04a6be99d3cab78f4d8c6360/images/AWS%20EULA%20Template%20(2020.11.20)%20(Private%20Identity).pdf)
Copyright (c) 2020-present, Private Identity All rights reserved.

## More Resources:

[Getting Started](https://privid-sdk.s3.us-east-2.amazonaws.com/cryptonets-python-sdk/index.html#getting-started)

[Installation](https://privid-sdk.s3.us-east-2.amazonaws.com/cryptonets-python-sdk/installation.html)

[Usage](https://privid-sdk.s3.us-east-2.amazonaws.com/cryptonets-python-sdk/usage.html)

[SDK-Docs](https://privid-sdk.s3.us-east-2.amazonaws.com/cryptonets-python-sdk/Factor/Face.html#cryptonets_python_sdk.factor.FaceFactor)
vecsim 0.0.62
Vector Similarity Search Engine
Keywords: vector-similarity,faiss,hnsw,redis,matching,ranking,elasticsearch,search,embedding

# VecSim - A unified interface for similarity servers
A standard, light-weight interface to all popular similarity servers.

## The problems we are trying to solve:
1. **Standard API** - Different vector similarity servers have different APIs - so switching is not trivial.
1. **Identifiers** - Some vector similarity servers support string IDs, some do not - we keep track of the mapping.
1. **Partitions** - In most cases, pre-filtering is needed prior to querying, we abstract this concept away.
1. **Aggregations** - In some cases, one item is being indexed to multiple vectors.

## Supported engines:
1. Scikit-learn, via [NearestNeighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html)
1. [RediSearch](https://redis.io/docs/stack/search/reference/vectors/)
1. [Faiss](https://github.com/facebookresearch/faiss)
1. [ElasticSearch](https://www.elastic.co)
1. [Pinecone](https://www.pinecone.io)


## QuickStart example
```python
import numpy as np
# Import a similarity server of your choice:
# SKlearn (best for small datasets or testing)
from vecsim import SciKitIndex
sim = SciKitIndex(metric='cosine', dim=32)

user_ids = ["user_"+str(1+i) for i in range(100)]
user_data = np.random.random((100,32))
item_ids=["item_"+str(101+i) for i in range(100)]
item_data = np.random.random((100,32))
sim.add_items(user_data, user_ids, partition="users")
sim.add_items(item_data, item_ids, partition="items")
# Index the data
sim.init()
# Run nearest neighbor vector search
query = np.random.random(32)
dists, items = sim.search(query, k=10) # returns a list of users and items
dists, items = sim.search(query, k=10, partition="users") # returns a list of users only
```

For more examples, please read our [documentation](https://vecsim.readthedocs.io/)