satori-client 0.1.7

Python client for Satori database

📚 Satori Python SDK

This library allows you to easily and efficiently interact with the Satori database via WebSockets, supporting CRUD operations, real-time notifications, and advanced queries.

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✨ Main Features

• Ultra-fast CRUD operations ⚡
• Advanced queries using field_array 🔍
• Real-time notifications 📢
• Graph-like relations (vertices and references) 🕸️
• Data encryption and decryption 🔐

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🚀 Installation

pip install satori-client

Or, if you use the repository directly:

pip install websockets uuid

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🏁 Basic Usage

import asyncio
from satori import Satori

async def main():
    client = Satori(
        username='user',
        password='password',
        host='ws://localhost:8000'
    )
    await client.connect()

asyncio.run(main())

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🗃️ CRUD Operations

Create Data

await client.set({
    'key': 'user:123',
    'data': { 'name': 'John', 'email': '[e-mail]' },
    'type': 'user'
})

Read Data

user = await client.get({ 'key': 'user:123' })

Modify a Field

await client.put({
    'key': 'user:123',
    'replace_field': 'name',
    'replace_value': 'Peter'
})

Delete Data

await client.delete({ 'key': 'user:123' })

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🧩 Advanced Queries with field_array 🔍

You can perform operations on multiple objects that meet certain conditions using the field_array field:

await client.get({
    'field_array': [
        { 'field': 'email', 'value': '[e-mail]' }
    ]
})

• field_array is an array of conditions { field, value }.
• You can combine it with one: True to get only the first matching result.

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🔔 Real-time Notifications

Receive automatic updates when an object changes:

async def on_update(data):
    print('User updated!', data)

await client.notify('user:123', on_update)

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🕸️ Relations and Graphs

You can create relationships between objects (vertices):

await client.set_vertex({
    'key': 'user:123',
    'vertex': 'friend:456',
    'relation': 'friend',
    'encryption_key': 'secret'
})

Traverse the graph with DFS:

await client.dfs({ 'node': 'user:123', 'encryption_key': 'secret' })

Get all neighbors of an object:

await client.get_vertex({
    'key': 'user:123',
    'encryption_key': 'secret',
    'relation': 'friends'
})

Delete a specific neighbor:

await client.delete_vertex({
    'key': 'user:123',
    'vertex': 'user:512',
    'encryption_key': 'secret'
})

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🔐 Encryption and Security

Easily encrypt and decrypt data:

await client.encrypt({ 'key': 'user:123', 'encryption_key': 'secret' })
await client.decrypt({ 'key': 'user:123', 'encryption_key': 'secret' })

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📦 Array Manipulation Methods

Below are the available methods to manipulate arrays in the Satori database using the Python client:

🔹 push

Adds a value to an existing array in an object.

await client.push({ 'key': 'user:123', 'array': 'friends', 'value': 'user:456' })

• key: Object key.
• array: Name of the array.
• value: Value to add.

🔹 pop

Removes the last element from an array in an object.

await client.pop({ 'key': 'user:123', 'array': 'friends' })

• key: Object key.
• array: Name of the array.

🔹 splice

Modifies an array in an object (for example, to cut or replace elements).

await client.splice({ 'key': 'user:123', 'array': 'friends' })

• key: Object key.
• array: Name of the array.

🔹 remove

Removes a specific value from an array in an object.

await client.remove({ 'key': 'user:123', 'array': 'friends', 'value': 'user:456' })

• key: Object key.
• array: Name of the array.
• value: Value to remove.

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🤖 AI Methods

Satori has AI features integrated that boost developers productivity. By example you can train an embedding model with your data and use it wherever you want to. You can train your embedding model manually whenever you want to but Satori will automatically fine-tune your model with any new updates and use this updated model for all emebedding operations.

🔹 train

Train an embedding model with your data. The model will be at the root of your db in the satori_semantic_model folder

await client.train();

🔹 ann

Perform an Aproximate Nearest Neighbors search

await client.ann({'key' : 'user:123', 'top_k' : '5'});

• key: Source object key.
• top_k: Number of nearest neighbors to return

🔹 query

Make querys in natural language

await client.query({'query' : 'Insert the value 5 into the grades array of user:123', 'backend' : 'openai:gpt-4o-mini'|);

• query: Your query in natural language.
• ref: The LLM backend. Must be openai:model-name or ollama:model-name, if not specified openai:gpt-4o-mini will be used as default. If you're using OpenAI as your backend you must specify the OPENAI_API_KEY env variable.

🔹 ask

Ask question about your data in natural language

await client.ask{'question' : 'How many user over 25 years old do we have. Just return the number.', 'backend' : 'openai:gpt-4o-mini'});

• question: Your question in natural language.
• ref: The LLM backend. Must be openai:model-name or ollama:model-name, if not specified openai:gpt-4o-mini will be used as default. If you're using OpenAI as your backend you must specify the OPENAI_API_KEY env variable.

Schema Class (Data Model)

You can use the Schema class to model your data in an object-oriented way:

from satori_client import Satori, Schema
import asyncio

async def main():
    satori = Satori("username", "password", "ws://localhost:1234")
    await satori.connect()

    user = Schema(satori, "user", key="my_key", body={"name": "Anna"})
    await user.set()

asyncio.run(main())

It includes useful methods such as:

• set, delete, encrypt, decrypt, set_vertex, get_vertex, delete_vertex, dfs

• Array methods: push, pop, splice, remove

📝 Complete Example

import asyncio
from satori import Satori

async def main():
    client = Satori(username='user', password='password', host='ws://localhost:8000')
    await client.connect()
    await client.set({
        'key': 'user:1',
        'data': { 'name': 'Carlos', 'age': 30 },
        'type': 'user'
    })
    async def on_update(data):
        print('Real-time update:', data)
    await client.notify('user:1', on_update)

asyncio.run(main())

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🧠 Key Concepts

• key: Unique identifier of the object.
• type: Object type (e.g., 'user').
• field_array: Advanced filters for bulk operations.
• notifications: Subscription to real-time changes.
• vertices: Graph-like relationships between objects.

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Responses

All responses obbey the following pattern:

{
  data: any //the requested data if any
  message: string //status message
  type: string //SUCCESS || ERROR
}

AI responses obbey a different patern:

ask

{
  response: string //response to the question
}

query

{
  result: string //response from the operation made in the db
  status: string //status
}

ann

{
  results: array //response from the operation made in the db
}

💬 Questions or Suggestions?

Feel free to open an issue or contribute! With ❤️ from the Satori team.