bagelML 0.0.19

BagelML is a library for Bagel's finetuning API.

Bagel Python Client 🥯

Welcome to the Bagel Python Client! Bagel is your platform for peer-to-peer machine learning, fine-tuning open-source models like Llama or Mistral, and using retrieval augmented generation (RAG).

One of the perks? No need to manage complex embeddings or model integrations yourself! The Bagel client handles these processes, saving you time and money. 🥯

Table of Contents

1. Prerequisites
2. Installation
3. Import the necessary modules
4. Define the Bagel server settings
5. Create the Bagel client
6. Ping the Bagel server
7. Get the Bagel server version
8. Create an Asset
9. Delete an Asset
10. Download Model Files
11. Query Asset
12. Update Asset
13. Download file
14. File Upload
15. Fine-tune
16. Get all assets
17. Get all assets by Id
18. Get finetuned model
19. Get job by job id
20. Get job
21. List Job
22. Add data to asset

Prerequisites

• Python 3.6+
• pip package manager
• Asset size limit 500MB (*Create a new issue if you want to increase the limit)

Installation

To install the Bagel Python client, run the following command in your terminal:

pip install bagelML

Import the necessary modules

import uuid
import bagel
from bagel.config import Settings

This snippet imports the required modules for using Bagel.

Define the Bagel server settings

server_settings = Settings(
    bagel_api_impl="rest",
    bagel_server_host="api.bageldb.ai"
)

Here, we define the settings for connecting to the Bagel server.

Create the Bagel client

client = bagel.Client(server_settings)

Create an instance of the Bagel client using the previously defined server settings.

Ping the Bagel server

print(client.ping())

This checks the connectivity to the Bagel server.

Get the Bagel server version

print(client.get_version())

Retrieves and prints the version of the Bagel server.

Create an Asset

Assets in Bagel serve as powerful containers for large datasets, encapsulating embeddings — high-dimensional vectors that represent various data forms, such as text, images, or audio. These Assets enable efficient similarity searches, which are fundamental to a wide range of applications, from recommendation systems and search engines to data analytics tools.

api_key = 'insert api key'
payload = {
    "dataset_type": "RAW",
    "title": "",
    "category": "",
    "details": "Testing",
    "tags": ["AI", "DEMO", "TEST"],
    "user_id": 'insert user id'
}

client.create_asset(payload, api_key)

Delete an Asset

api_key = 'insert api key'
dataset_id = 'insert dataset/asset id'
client.delete_asset(dataset_id, api_key)

This method deletes a specific Asset.

Download Model Files

api_key = 'insert api key'
asset_id = 'insert dataset/asset id'
file_name = "insert file .txt"

client.download_file(asset_id, file_name, api_key)

Downloads a file associated with a specific Asset.

Query Asset

api_key = ""
asset_id = ""

payload = {
    "where": {
        # "category": "Cat2",
    },
    "where_document": {
        # "is_published": True,
    },
    # "query_embeddings": [em],
    "n_results": 1,
    "include": ["metadatas", "documents", "distances"],
    "query_texts": ["insert query text"],
    "padding": False,
}

client.query_asset(asset_id, payload, api_key)

Queries a specific Asset with detailed parameters.

Update Asset

import bagel
from bagel.config import Settings

api_key = ""
asset_id = ""

payload = {
   "title": "Updated dataset title",
    "category": "Updated category",
    "details": "Updated dataset description.",
    "tags": ["Updated", "Tags"]
}

server_settings = Settings(
    bagel_api_impl="rest",
    bagel_server_host="api.bageldb.ai",
    bagel_server_http_port="80",
)
client = bagel.Client(server_settings)

client.update_asset(asset_id, payload, api_key)

Updates the details of an existing Asset.

Download file

api_key = ""
asset_id = ""
file_name = ""

client.download_file(asset_id, file_name, api_key)

Downloads a specific file from an Asset.

File Upload

api_key = ""
dataset_id = ""
file_path = ""

client.file_upload(file_path, dataset_id, api_key)

Uploads a file to a specific Asset.

Fine-tune

# Define the URL for the fine-tune function
apiKey = ""
# Define the payload for the fine-tune function
payload = {
  "dataset_type": 'RAW',
  "title": 'what!',
  "category": '',
  "details": '',
  "tags": [],
  "user_id": '',
  "fine_tune_payload": {
    "asset_id": '', # Move asset_id here
    "model_name": '', # Same as the title
    "base_model": '',
    "file_name": 'catch.txt',
    "user_id": '',
  }
}

client.fine_tune(payload, apiKey)

Fine-tunes a model using a specific Asset and provided parameters.

Get all assets

user_Id = ""
api_key = ""

client.get_all_asset(user_Id, api_key)

Retrieves all assets for a specific user.

Get all assets by Id

asset_id = ""
api_key = ""

client.get_asset_by_id(asset_id, api_key)

Retrieves a specific asset by its ID.

Get finetuned model

# Replace these values with actual ones
api_key = ""
asset_id = ""
file_name = "train.txt"

# Call the function
client.download_file_by_asset_and_name(asset_id, file_name)

Downloads a fine-tuned model by asset ID and file name.

Get job by job id

job_id = ""  # Replace with the actual job ID
api_key = ""  # Replace with the actual API key

client.get_job(job_id, api_key)

Retrieves the status of a specific job by job ID.

Get job

job_id = ""  # Replace with the actual job ID
api_key = ""  # Replace with the actual API key

client.get_job(job_id, api_key)

Retrieves details of a job.

List Job

# Replace "your_api_key_here" with the provided API key
api_key = ""
user_id = ""

# Call the function
client.list_jobs(user_id, api_key)

Lists all jobs for a specific user.

Add data to asset

asset_id = ""
api_key = ""

payload = {
  "metadatas": [{ "source": "testing" }],
  "documents": ["Hi man"],
  "ids": ["xxxx-xxxx-xxxx-xxxx--xxxxx"], #manually generated by you
}

client.add_data_to_asset(asset_id, payload, api_key)

Adds data to an existing asset.

Download Finetuned Model

api_key = "insert api key"
asset_id = "insert asset id"

response = client.download_model(asset_id, api_key)

Buy Asset

api_key = "insert api key"
asset_id = "insert asset id"
user_id = "insert userid"

client.buy_asset(asset_id, user_id, api_key)