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Creating a terminal-based notebook TUI (Text User Interface) application in Termux that processes notes and converts them into vectors for AI embedding and retrieval-augmented generation involves several steps. Here’s a guide to help you set up such an application using Python:

Step 1: Set Up Termux

First, ensure that you have Termux installed on your Android device. You can download it from the Termux GitHub page or directly from the Google Play Store.

Step 2: Install Necessary Packages

Open Termux and install Python and other required packages:

pkg update
pkg upgrade
pkg install python
pkg install clang
pip install transformers torch faiss-cpu

Step 3: Create the TUI Application

You'll use Python and a TUI library like npyscreen or urwid. Here, I'll use npyscreen for simplicity.

Install npyscreen:

pip install npyscreen

Step 4: Write the TUI Application

Create a Python script (e.g., notebook_app.py) and implement the TUI application.

Here's an example:

import npyscreen
import re
import torch
import faiss
from transformers import BertTokenizer, BertModel

# Preprocessing, tokenization, and embedding functions
def preprocess_text(text):
    text = text.lower()
    text = re.sub(r'[^a-zA-Z\s]', '', text)
    text = re.sub(r'\s+', ' ', text).strip()
    return text

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

def tokenize_text(text):
    return tokenizer(text, padding='max_length', truncation=True, return_tensors="pt")

def get_embeddings(tokens):
    with torch.no_grad():
        outputs = model(**tokens)
    return outputs.last_hidden_state.mean(dim=1)

class NotebookApp(npyscreen.NPSAppManaged):
    def onStart(self):
        self.notes = []
        self.embeddings = None
        self.index = None
        self.addForm('MAIN', MainForm)

class MainForm(npyscreen.Form):
    def create(self):
        self.add(npyscreen.TitleText, name="New Note:", max_width=50)
        self.notes_box = self.add(npyscreen.BoxTitle, name="Notes", max_height=10)
        self.query = self.add(npyscreen.TitleText, name="Query:", max_width=50)
        self.result_box = self.add(npyscreen.BoxTitle, name="Results", max_height=5)
        self.add(npyscreen.ButtonPress, name="Add Note", when_pressed_function=self.add_note)
        self.add(npyscreen.ButtonPress, name="Search", when_pressed_function=self.search_notes)

    def add_note(self):
        note = self.get_widget('New Note:').value
        if note:
            preprocessed_note = preprocess_text(note)
            tokens = tokenize_text(preprocessed_note)
            embedding = get_embeddings(tokens).numpy()
            app = self.parentApp
            app.notes.append(note)
            if app.embeddings is None:
                app.embeddings = embedding
            else:
                app.embeddings = np.vstack([app.embeddings, embedding])
            self.notes_box.values = app.notes
            self.notes_box.display()

    def search_notes(self):
        query = self.get_widget('Query:').value
        if query:
            preprocessed_query = preprocess_text(query)
            tokens = tokenize_text(preprocessed_query)
            query_embedding = get_embeddings(tokens).numpy()
            app = self.parentApp
            if app.index is None:
                d = app.embeddings.shape[1]
                app.index = faiss.IndexFlatL2(d)
                app.index.add(app.embeddings)
            D, I = app.index.search(query_embedding, 5)
            results = [app.notes[i] for i in I[0]]
            self.result_box.values = results
            self.result_box.display()

if __name__ == '__main__':
    app = NotebookApp().run()

Step 5: Run the Application

In Termux, navigate to the directory containing your script and run it:

python notebook_app.py

Explanation

  1. Preprocessing, Tokenization, and Embedding Functions: These functions preprocess the text, tokenize it, and convert it into embeddings using BERT.

  2. NotebookApp Class: Manages the TUI application.

  3. MainForm Class: Defines the main form with widgets for adding notes and querying them. It handles note addition and search functionalities.

  4. Running the Application: The script is executed, which starts the TUI application.

This setup should help you create a functional TUI application in Termux that processes notes and performs vector-based searches using AI embeddings. Adjust and expand the code based on your specific needs.

Yes, Milvus typically uses Docker for easy deployment and management, especially for production environments or when running on systems where setup and configuration might be complex. However, Milvus can also be installed without Docker, especially for development or testing purposes, albeit with more manual setup.

Installing Milvus without Docker

If you prefer to install Milvus without Docker, you can follow these general steps:

  1. Check Requirements: Ensure your system meets the hardware and software requirements for Milvus, such as supported operating systems and dependencies like CUDA (if using GPU).

  2. Download Milvus: Download the Milvus binary or source code from the Milvus GitHub releases page.

  3. Install Dependencies: Install any required dependencies specified for Milvus. This may include libraries like libevent-dev, libyaml-cpp-dev, etc., depending on your operating system.

  4. Configure Milvus: Configure Milvus according to your setup. This typically involves editing configuration files (server_config.yaml, advanced_config.yaml) to adjust settings like storage paths, ports, and resource allocations.

  5. Start Milvus: Start the Milvus server using the provided executable or script. Ensure that necessary permissions are set (e.g., executable permissions on the binaries).

  6. Verify Installation: Check if Milvus is running correctly by accessing the web console or using the Python SDK (pymilvus) to connect and interact with the Milvus server.

Example Installation Steps

Here’s a simplified example assuming you are using Linux (adapt as necessary for your environment):

  1. Download Milvus: 

    wget https://github.com/milvus-io/milvus/releases/download/v2.0.0/milvus-2.0.0-cpu-d092721-linux.tar.gz
tar -zxvf milvus-2.0.0-cpu-d092721-linux.tar.gz
cd milvus-2.0.0-cpu-d092721-linux

  2. Install Dependencies (example for Ubuntu): 

    sudo apt-get install libevent-dev libyaml-cpp-dev

  3. Configure and Start Milvus:

  4. Edit conf/server_config.yaml to configure server settings.

  5. Start Milvus server: 

    ./bin/milvus run

  6. Verify Installation:

  7. Access the Milvus web console (default: http://localhost:19530) to verify server status.
  8. Use pymilvus Python library to connect and interact with Milvus from your applications.

Considerations

  • Performance: Running Milvus without Docker might require manual tuning for optimal performance and resource management.
  • Compatibility: Ensure compatibility with your system's architecture and dependencies.
  • Updates: Manually manage updates by downloading and installing new releases when available.

By following these steps, you can install and run Milvus without Docker, although Docker simplifies deployment and management in many scenarios.