Botok Integration with Python’s FastAPI and MongoDB

Botok Integration with Python’s FastAPI and MongoDB

Botok is a Python Tibetan Tokenizer that can tokenize Tibetan text into words with optional attributes such as lemma, POS, clean form. FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints. MongoDB is a popular NoSQL document database. In this article, we will explore how to integrate Botok with FastAPI and MongoDB to build a powerful Tibetan language processing application.

Installation

To get started, make sure you have Python 3 installed and create a new virtual environment. Then, install the required packages using pip:

pip install botok fastapi uvicorn pymongo

Example

To integrate Botok, FastAPI, and MongoDB, we can define a FastAPI route that receives a Tibetan text, tokenizes it using Botok, and then stores the tokens in MongoDB. Here is an example implementation:

# python
from fastapi import FastAPI
from botok import WordTokenizer
from pymongo import MongoClient

app = FastAPI()

# Connect to MongoDB
client = MongoClient("mongodb://localhost:27017/")
db = client["mydatabase"]
collection = db["tokens"]

# Initialize Botok tokenizer
tokenizer = WordTokenizer()

@app.post("/tokenize")
async def tokenize_tibetan_text(text: str):
    # Tokenize the text using Botok
    tokens = tokenizer.tokenize(text, split_affixes=False)

    # Store the tokens in MongoDB
    for token in tokens:
        collection.insert_one({"text": token.text, "pos": token.pos})

    return {"message": "Text tokenized and stored in MongoDB"}

To run the FastAPI server, use the following command:

uvicorn main:app --reload

Once the server is running, you can send a POST request to the /tokenize route with a JSON body containing the Tibetan text to be tokenized. The tokens will be stored in MongoDB for further processing or analysis.

Advantages of the Integration

Integrating Botok with FastAPI and MongoDB offers several advantages for Tibetan language processing applications:

1. FastAPI provides a high-performance web framework with automatic request validation, serialization, and documentation generation. It allows for efficient processing of HTTP requests and responses.
2. Botok enables accurate and efficient tokenization of Tibetan text, providing insights into the structure and attributes of the text. The tokens can be used for various natural language processing tasks such as sentiment analysis, named entity recognition, and part-of-speech tagging.
3. MongoDB offers a flexible and scalable NoSQL document database solution for storing and retrieving the tokenized Tibetan text. It allows for fast querying and indexing, making it ideal for handling large volumes of text data.

Overall, the integration of Botok, FastAPI, and MongoDB provides a powerful and efficient solution for Tibetan language processing applications, enabling developers to build robust and scalable systems that can handle complex linguistic tasks.

Conclusion

In this article, we explored how to integrate Botok, a Python Tibetan Tokenizer, with FastAPI and MongoDB. We provided code snippets and examples for integrating these technologies to build a powerful and efficient Tibetan language processing application. By leveraging the strengths of each technology, developers can create sophisticated systems that can process and analyze Tibetan text with ease.

By Lake Davenberg