Integrate OceanBase vector search with Hugging Face

obclient -h$host -P$port -u$user_name -p$password -D$database_name

export OCEANBASE_DATABASE_URL=YOUR_OCEANBASE_DATABASE_URL
export OCEANBASE_DATABASE_USER=YOUR_OCEANBASE_DATABASE_USER
export OCEANBASE_DATABASE_DB_NAME=YOUR_OCEANBASE_DATABASE_DB_NAME
export OCEANBASE_DATABASE_PASSWORD=YOUR_OCEANBASE_DATABASE_PASSWORD
export HUGGING_FACE_API_KEY=YOUR_HUGGING_FACE_API_KEY

import os,shutil,torch,requests
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
from datasets import load_dataset
from sqlalchemy import Column, Integer, String
from pyobvector import ObVecClient, VECTOR, IndexParam, l2_distance

# delete cache directory
if os.path.exists("./cache"):
    shutil.rmtree("./cache")

HUGGING_FACE_API_KEY = os.getenv('HUGGING_FACE_API_KEY')
DATASET = "squad"  # Name of dataset from HuggingFace Datasets
INSERT_RATIO = 0.001  # Ratio of example dataset to be inserted
data = load_dataset(DATASET, split="validation", cache_dir="./cache")

# Generates a fixed subset. To generate a random subset, remove the seed.
data = data.train_test_split(test_size=INSERT_RATIO, seed=42)["test"]
# Clean up the data structure in the dataset.
data = data.map(
    lambda val: {"answer": val["answers"]["text"][0]},
    remove_columns=["id", "answers", "context"],
)

# HuggingFace API config
import os
from sentence_transformers import SentenceTransformer
# Set the HF Mirror for model download.
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
print("Downloading the model...")
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
print("Model download completed!")

def encode_text(batch):
    questions = batch["question"]

    # Perform inference using the local model.
    embeddings = model.encode(questions)

    # Format the embeddings.
    formatted_embeddings = []
    for embedding in embeddings:
        formatted_embedding = [round(float(val), 6) for val in embedding]
        formatted_embeddings.append(formatted_embedding)

    batch["embedding"] = formatted_embeddings
    return batch

INFERENCE_BATCH_SIZE = 64  # Batch size of model inference
data = data.map(encode_text, batched=True, batch_size=INFERENCE_BATCH_SIZE)
data_list = data.to_list()

OCEANBASE_DATABASE_URL = os.getenv('OCEANBASE_DATABASE_URL')
OCEANBASE_DATABASE_USER = os.getenv('OCEANBASE_DATABASE_USER')
OCEANBASE_DATABASE_DB_NAME = os.getenv('OCEANBASE_DATABASE_DB_NAME')
OCEANBASE_DATABASE_PASSWORD = os.getenv('OCEANBASE_DATABASE_PASSWORD')

client = ObVecClient(uri=OCEANBASE_DATABASE_URL, user=OCEANBASE_DATABASE_USER,password=OCEANBASE_DATABASE_PASSWORD,db_name=OCEANBASE_DATABASE_DB_NAME)

table_name = "huggingface_oceanbase_demo_documents"
client.drop_table_if_exist(table_name)

cols = [
    Column("id", Integer, primary_key=True, autoincrement=True),
    Column("title", String(255), nullable=False),
    Column("question", String(255), nullable=False),
    Column("answer", String(255), nullable=False),
    Column("embedding", VECTOR(384))
]

# Create vector index
vector_index_params = IndexParam(
    index_name="idx_question_embedding",
    field_name="embedding",
    index_type="HNSW",
    distance_metric="l2"
)

client.create_table_with_index_params(
    table_name=table_name,
    columns=cols,
    vidxs=[vector_index_params]
)

print('- Inserting Data to OceanBase...')
client.insert(table_name, data=data_list)

# Step 5. Query the most relevant document based on the query.
questions = {
    "question": [
        "What is LGM?",
        "When did Massachusetts first mandate that children be educated in schools?",
    ]
}

# Generate question embeddings
question_embeddings =  encode_text(questions)["embedding"]

for i, question in enumerate(questions["question"]):
    print(f"Question: {question}")

    # Search across OceanBase
    search_results = client.ann_search(
        table_name,
        vec_data=question_embeddings[i],
        vec_column_name="embedding",
        distance_func=l2_distance,
        with_dist=True,
        topk=3,
        output_column_names=["id", "answer", "question"],
    )

    # Print out results
    results_list = list(search_results)

    for r in results_list:
        print({
            "answer": r[1],
            "score": r[3] if len(r) > 3 else "N/A",
            "original question": r[2],
            "id": r[0]
        })
    print("\n")

- Inserting Data to OceanBase...
Question: What is LGM?
{'answer': 'Last Glacial Maximum', 'score': 0.29572604605808755, 'original question': 'What does LGM stands for?', 'id': 10}
{'answer': 'coordinate the response to the embargo', 'score': 1.2553772660960183, 'original question': 'Why was this short termed organization created?', 'id': 9}
{'answer': '"Reducibility Among Combinatorial Problems"', 'score': 1.2691888905109625, 'original question': 'What is the paper written by Richard Karp in 1972 that ushered in a new era of understanding between intractability and NP-complete problems?', 'id': 11}

Question: When did Massachusetts first mandate that children be educated in schools?
{'answer': '1852', 'score': 0.2408329167590669, 'original question': 'In what year did Massachusetts first require children to be educated in schools?', 'id': 1}
{'answer': 'several regional colleges and universities', 'score': 1.1474774558319025, 'original question': 'In 1890, who did the university decide to team up with?', 'id': 4}
{'answer': '1962', 'score': 1.2703532682776688, 'original question': 'When were stromules discovered?', 'id': 2}