semvec/pypi-packages huggingface dataset:
pyrabbitmq 0.0.5
Simple Python client library for RabbitMQ

# pyrabbitmq

### Scenarios
The library is not proposed with same functions as 'pika'.
It helps to initialize a rabbitmq instance via its native API in following cases:
- Many rabbitmq instances to be kicked via scripts but not its management UI, or
- For some reason you need reset rabbitmq instances more often.

Good Luck.

### Usage

```python
from pyrabbitmq import connection

# rmq = connection.Instance(
#     rabbitmq_host="127.0.0.1",
#     rabbitmq_port="5673",
#     rabbitmq_admin_user="guest",
#     rabbitmq_admin_password="guest",
#     rabbitmq_tls_enabled=False
# )

# It reads the rabbitmq os $env values. 
# If no $env variables find, default values used..
rmq = connection.Instance()

# Virtual Hosts
v = rmq.VirtualHost
v.create('a-vhost-name')
v.delete('a-vhost-name')

# Users
u = rmq.User
u.create('test-user','test-user-password')
u.delete('test-user')

# Queues
q = rmq.Queue
q.create(vhost='vhost01', queue='queue01')
q.delete(vhost='vhost01', queue='queue01')

# Bindings
b = rmq.Binding
b.create(vhost='vhost01',exchange='exchange01', queue='queue01')
b.delete(vhost='vhost01',exchange='exchange01',queue='queue01',routing_key='routing_key-queue01')


```
amitbest 0.0.1
India Papa Love
Keywords: experts,begginers,deep learning,ai ultimate,ayush agrawal,automate steps of deep learning


    * This project helps the begginers to generate ai models with 1 line of code.
    * It is also useful for experts as it will automate repetative task and experts can focus upon main model
    * It works right now only for image classification but there may be updates in future
amit-papa 0.0.1
India Papa Love
Keywords: experts,begginers,deep learning,ai ultimate,ayush agrawal,automate steps of deep learning


    * This project helps the begginers to generate ai models with 1 line of code.
    * It is also useful for experts as it will automate repetative task and experts can focus upon main model
    * It works right now only for image classification but there may be updates in future
ragas-once 0.0.1
A one-step Ragas cli tool to evaluate RAG apps
Requires Python: >=3.8

# Ragas CLI

A one-step Ragas cli tool to evaluate QCAG testsets generated by RAG apps. (Q = Question, C = Contexts, A = Answer, G = Ground_truth)

## Install with pip

```bash
pip install ragas langchain==0.0.354
```

then:

```bash
pip install ragas_once
```

## Arguments

- `--model`: Specifies the model to use for evaluation.
    - Default value is "gpt-3.5-turbo". Langchain compatible.
- `--api_base`: Specifies the base URL for the API.
    - Default value is "https://api.openai.com/v1".
- `--api_key`: Specifies the API key to authenticate requests. 
    - Not required if using psuedo-openai API server, e.g. vLLM, Fastchat, etc.
- `--embeddings`: Specifies the Huggingface embeddings model to use for evaluation. 
    - Embeddings will run **locally**.
    - Will use OpenAI embeddings if not set.
    - Better set if using psuedo-openai API server.
- `--metrics`: Specifies the metrics to use for evaluation.
    - Will use Ragas default metrics if not set.
    - Default metrics: `["answer_relevancy", "context_precision", "faithfulness", "context_recall", "context_relevancy"]`
    - Other metrics: `"answer_similarity", "answer_correctness"`
- `--dataset`: Specifies the path to the dataset for evaluation.    
    - Dataset format must meet RAGAS requirements.
    - Will use fiqa dataset as demo if not set.

## Usage

### Fiqa dataset demo:

```bash
python3 -m ragas_once.cli --api_key "YOUR_OPENAI_API_KEY"
```

### Evaluate with GPT-4 and `BAAI/bge-small-en` embeddings

The huggingface embeddings will run locally, so **Make sure your machine works and have [sentence-transformers](https://pypi.org/project/sentence-transformers/) installed:**

```bash
pip install sentence-transformers
```
Then run:

```bash
python3 -m ragas_once.cli --model "gpt-4" --api_key "YOUR_OPENAI_API_KEY" --embeddings "BAAI/bge-small-en" --dataset "path/to/dataset.csv"
```

### Prepare Dataset

See [**Ragas documentation**](https://docs.ragas.io/en/stable/howtos/applications/data_preparation.html)
amit-ayush-aditi 0.0.1
Ayush
Keywords: experts,begginers,deep learning,ai ultimate,ayush agrawal,automate steps of deep learning


    * This project helps the begginers to generate ai models with 1 line of code.
    * It is also useful for experts as it will automate repetative task and experts can focus upon main model
    * It works right now only for image classification but there may be updates in future