Semantic Querying with Airflow and Astronomer

mkdir qdrant-airflow-tutorial && cd qdrant-airflow-tutorial
astro dev init

apache-airflow-providers-qdrant

HUGGINGFACE_TOKEN="<YOUR_HUGGINGFACE_ACCESS_TOKEN>"
AIRFLOW_CONN_QDRANT_DEFAULT='{
    "conn_type": "qdrant",
    "host": "xyz-example.eu-central.aws.cloud.qdrant.io:6333",
    "password": "<YOUR_QDRANT_API_KEY>"
}'

1 | To Kill a Mockingbird (1960) | fiction | Harper Lee's Pulitzer Prize-winning novel explores racial injustice and moral growth through the eyes of young Scout Finch in the Deep South.
2 | Harry Potter and the Sorcerer's Stone (1997) | fantasy | J.K. Rowling's magical tale follows Harry Potter as he discovers his wizarding heritage and attends Hogwarts School of Witchcraft and Wizardry.
3 | The Great Gatsby (1925) | fiction | F. Scott Fitzgerald's classic novel delves into the glitz, glamour, and moral decay of the Jazz Age through the eyes of narrator Nick Carraway and his enigmatic neighbour, Jay Gatsby.
4 | 1984 (1949) | dystopian | George Orwell's dystopian masterpiece paints a chilling picture of a totalitarian society where individuality is suppressed and the truth is manipulated by a powerful regime.
5 | The Catcher in the Rye (1951) | fiction | J.D. Salinger's iconic novel follows disillusioned teenager Holden Caulfield as he navigates the complexities of adulthood and society's expectations in post-World War II America.
6 | Pride and Prejudice (1813) | romance | Jane Austen's beloved novel revolves around the lively and independent Elizabeth Bennet as she navigates love, class, and societal expectations in Regency-era England.
7 | The Hobbit (1937) | fantasy | J.R.R. Tolkien's adventure follows Bilbo Baggins, a hobbit who embarks on a quest with a group of dwarves to reclaim their homeland from the dragon Smaug.
8 | The Lord of the Rings (1954-1955) | fantasy | J.R.R. Tolkien's epic fantasy trilogy follows the journey of Frodo Baggins to destroy the One Ring and defeat the Dark Lord Sauron in the land of Middle-earth.
9 | The Alchemist (1988) | fiction | Paulo Coelho's philosophical novel follows Santiago, an Andalusian shepherd boy, on a journey of self-discovery and spiritual awakening as he searches for a hidden treasure.
10 | The Da Vinci Code (2003) | mystery/thriller | Dan Brown's gripping thriller follows symbologist Robert Langdon as he unravels clues hidden in art and history while trying to solve a murder mystery with far-reaching implications.

import os
import requests

from airflow.decorators import dag, task
from airflow.models.baseoperator import chain
from airflow.models.param import Param
from airflow.providers.qdrant.hooks.qdrant import QdrantHook
from airflow.providers.qdrant.operators.qdrant import QdrantIngestOperator
from pendulum import datetime
from qdrant_client import models


QDRANT_CONNECTION_ID = "qdrant_default"
DATA_FILE_PATH = "include/books.txt"
COLLECTION_NAME = "airflow_tutorial_collection"

EMBEDDING_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"
EMBEDDING_DIMENSION = 384
SIMILARITY_METRIC = models.Distance.COSINE


def embed(text: str) -> list:
    HUGGINFACE_URL = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{EMBEDDING_MODEL_ID}"
    response = requests.post(
        HUGGINFACE_URL,
        headers={"Authorization": f"Bearer {os.getenv('HUGGINGFACE_TOKEN')}"},
        json={"inputs": [text], "options": {"wait_for_model": True}},
    )
    return response.json()[0]


@dag(
    dag_id="books_recommend",
    start_date=datetime(2023, 10, 18),
    schedule=None,
    catchup=False,
    params={"preference": Param("Something suspenseful and thrilling.", type="string")},
)
def recommend_book():
    @task
    def import_books(text_file_path: str) -> list:
        data = []
        with open(text_file_path, "r") as f:
            for line in f:
                _, title, genre, description = line.split("|")
                data.append(
                    {
                        "title": title.strip(),
                        "genre": genre.strip(),
                        "description": description.strip(),
                    }
                )

        return data

    @task
    def init_collection():
        hook = QdrantHook(conn_id=QDRANT_CONNECTION_ID)
        if not  hook.conn..collection_exists(COLLECTION_NAME):
            hook.conn.create_collection(
                COLLECTION_NAME,
                vectors_config=models.VectorParams(
                    size=EMBEDDING_DIMENSION, distance=SIMILARITY_METRIC
                ),
            )

    @task
    def embed_description(data: dict) -> list:
        return embed(data["description"])

    books = import_books(text_file_path=DATA_FILE_PATH)
    embeddings = embed_description.expand(data=books)

    qdrant_vector_ingest = QdrantIngestOperator(
        conn_id=QDRANT_CONNECTION_ID,
        task_id="qdrant_vector_ingest",
        collection_name=COLLECTION_NAME,
        payload=books,
        vectors=embeddings,
    )

    @task
    def embed_preference(**context) -> list:
        user_mood = context["params"]["preference"]
        response = embed(text=user_mood)

        return response

    @task
    def search_qdrant(
        preference_embedding: list,
    ) -> None:
        hook = QdrantHook(conn_id=QDRANT_CONNECTION_ID)

        result = hook.conn.query_points(
            collection_name=COLLECTION_NAME,
            query=preference_embedding,
            limit=1,
            with_payload=True,
        ).points

        print("Book recommendation: " + result[0].payload["title"])
        print("Description: " + result[0].payload["description"])

    chain(
        init_collection(),
        qdrant_vector_ingest,
        search_qdrant(embed_preference()),
    )


recommend_book()