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Introduction

Claude 3.5 Sonnet, Anthropic’s latest AI model, offers groundbreaking advancements in intelligence, speed, and cost-efficiency. Combining Claude AI with TiDB’s robust database features, particularly TiDB Vector Search, can significantly enhance data management and search capabilities, making it ideal for complex applications requiring nuanced understanding and high performance.

Claude 3.5 Sonnet Overview

Claude 3.5 Sonnet excels in graduate-level reasoning, undergraduate-level knowledge, and coding proficiency. It supports complex workflows, context-sensitive tasks, and sophisticated content creation, operating at twice the speed of its predecessor while maintaining cost-effectiveness.

Key Features:

  • Advanced Reasoning and Knowledge: Excels in understanding complex instructions, humor, and nuance.
  • High-Speed Performance: Twice as fast as previous models, suitable for intensive tasks.
  • Collaborative Environment: New “Artifacts” feature allows for real-time content generation and editing.
  • Commitment to Safety: Rigorous testing to minimize misuse, with a focus on privacy and safety.

Enhancing TiDB with Claude AI

TiDB’s scalable, distributed SQL database combined with Claude AI’s advanced capabilities can create a powerful data ecosystem. Here’s how integrating these technologies can benefit your applications:

Semantic Search and Similarity Search

TiDB Vector Search allows for semantic and similarity searches using vector embeddings, making it possible to search based on meaning rather than keywords. This can be greatly enhanced with Claude AI’s ability to generate high-quality, semantically rich embeddings for various data types.

Real-Time Data Processing and Management

Claude AI’s ability to handle complex workflows and context-sensitive tasks complements TiDB’s strengths in handling large-scale, distributed data. This combination can streamline real-time data processing and management tasks.

Intelligent Data Insights

Utilize Claude AI for advanced data analysis and insights generation, leveraging TiDB’s robust data storage and retrieval capabilities. This integration can lead to more accurate and actionable insights from your data.

Practical Example: Implementing AI-Powered Search with TiDB and Claude

Here’s a practical example of integrating Claude AI with TiDB Vector Search to perform semantic searches.

Setting Up the Environment

  1. Create a TiDB Serverless Cluster: Follow the quickstart guide to set up your cluster.
  2. Enable Vector Search: Contact support if the feature is not visible.

Example Code for Semantic Search

import os
from openai import OpenAI
from peewee import Model, MySQLDatabase, TextField
from tidb_vector.peewee import VectorField

# Initialize OpenAI client
client = OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))
embedding_model = "text-embedding-ada-002"
embedding_dimensions = 1536

# Connect to TiDB
db = MySQLDatabase(
    'test',
    user=os.environ.get('TIDB_USERNAME'),
    password=os.environ.get('TIDB_PASSWORD'),
    host=os.environ.get('TIDB_HOST'),
    port=4000,
    ssl_verify_cert=True,
    ssl_verify_identity=True
)

# Sample documents
documents = [
    "TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads.",
    "TiFlash is the key component that makes TiDB essentially an HTAP database.",
    "TiKV is a distributed and transactional key-value database, providing transactional APIs with ACID compliance."
]

# Define model
class DocModel(Model):
    text = TextField()
    embedding = VectorField(dimensions=embedding_dimensions)

    class Meta:
        database = db
        table_name = "doc_test"

# Setup database
db.connect()
db.drop_tables([DocModel])
db.create_tables([DocModel])

# Generate embeddings
embeddings = [r.embedding for r in client.embeddings.create(input=documents, model=embedding_model).data]
data_source = [{"text": doc, "embedding": emb} for doc, emb in zip(documents, embeddings)]
DocModel.insert_many(data_source).execute()

# Query example
question = "What is TiKV?"
question_embedding = client.embeddings.create(input=question, model=embedding_model).data[0].embedding
related_docs = DocModel.select(DocModel.text, DocModel.embedding.cosine_distance(question_embedding).alias("distance")).order_by(SQL("distance")).limit(3)

print("Question:", question)
print("Related documents:")
for doc in related_docs:
    print(doc.distance, doc.text)

db.close()

Conclusion

Integrating Claude AI with TiDB can revolutionize how you manage and search your data. With Claude’s advanced AI capabilities and TiDB’s robust database features, you can implement powerful, AI-driven data solutions that are both efficient and cost-effective.

Explore TiDB Serverless and Claude 3.5 Sonnet to elevate your data management and search capabilities. Get started with TiDB and learn more about Claude 3.5 Sonnet today!


Last updated June 26, 2024

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