Benchmarking Llama 3 with TiDB Vector Search

As artificial intelligence models continue to evolve, evaluating their performance through rigorous benchmarking becomes crucial. Llama 3, a state-of-the-art language model, is no exception. This article explores the benchmarking of Llama 3, utilizing TiDB Vector Search to enhance the accuracy and efficiency of semantic search capabilities.

Understanding Llama 3

Llama 3 is designed to excel in natural language understanding and generation tasks. Its architecture leverages advanced transformer models, enabling it to process and generate human-like text based on the context provided.

The Role of TiDB Vector Search in Benchmarking

Benchmarking Llama 3 requires a robust system to handle large volumes of data and perform high-speed searches. TiDB Vector Search provides an optimal solution with its ability to store and search vector embeddings. This capability ensures that semantic searches, crucial for benchmarking language models, are performed efficiently and accurately.

Setting Up TiDB Vector Search

To benchmark Llama 3 with TiDB Vector Search, follow these steps:

1. Sign Up and Create a Cluster:
   • Sign up on tidbcloud.
   • Select the EU-Central-1 region and create a TiDB Serverless cluster with vector support.
2. Connect to Your Cluster:
   • Navigate to the Clusters page, select your target cluster, and click “Connect”.
   • Use the connection dialog to set up your connection parameters.
3. Create Tables and Insert Data:
   • Create a table with a vector field to store embeddings:

CREATE TABLE benchmark_table (id INT PRIMARY KEY, text TEXT, embedding VECTOR(1536));
INSERT INTO benchmark_table VALUES (1, 'Sample text 1', '[0.1, 0.2, ...]'), (2, 'Sample text 2', '[0.2, 0.1, ...]');

Benchmarking Process

1. Generate Embeddings with Llama 3:
   • Use Llama 3 to generate vector embeddings for your benchmark dataset. This dataset should include a variety of texts to comprehensively evaluate the model’s performance.
2. Store Embeddings in TiDB:
   • Insert the generated embeddings into the benchmark_table in your TiDB cluster.
3. Perform Semantic Searches:
   • Use TiDB Vector Search to perform semantic searches on the stored embeddings
   • Measure the response time and accuracy of the search results to evaluate Llama 3’s performance.

SELECT * FROM benchmark_table ORDER BY vec_cosine_distance(embedding, '[query_embedding]') LIMIT 10;

Performance Metrics

To effectively benchmark Llama 3, consider the following performance metrics:

1. Accuracy:
   • Measure how well the search results match the expected outcomes. This can be evaluated using precision, recall, and F1 score metrics.
2. Latency:
   • Record the time taken to perform searches. Lower latency indicates better performance in real-time applications.
3. Scalability:
   • Assess how the system performs with increasing data volumes. TiDB’s distributed architecture should maintain performance as data scales.

Example Benchmarking Code

Here’s a sample Python script to benchmark Llama 3 using TiDB Vector Search:

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

# Connect to TiDB
db = MySQLDatabase('benchmark', user=os.environ.get('TIDB_USERNAME'), password=os.environ.get('TIDB_PASSWORD'), host=os.environ.get('TIDB_HOST'), port=4000)
db.connect()

# Define model
class BenchmarkModel(Model):
    text = TextField()
    embedding = VectorField(dimensions=1536)
    class Meta:
        database = db
        table_name = "benchmark_table"

# Create table and insert data
db.create_tables([BenchmarkModel])

# Use Llama 3 to generate embeddings
client = OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))
documents = ["Sample text 1", "Sample text 2", "Sample text 3"]
embeddings = [r.embedding for r in client.embeddings.create(input=documents, model="text-embedding-3-small").data]

# Insert embeddings into TiDB
data_source = [{"text": doc, "embedding": emb} for doc, emb in zip(documents, embeddings)]
BenchmarkModel.insert_many(data_source).execute()

# Perform a search
query_embedding = client.embeddings.create(input="Query text", model="text-embedding-3-small").data[0].embedding
results = BenchmarkModel.select(BenchmarkModel.text, BenchmarkModel.embedding.cosine_distance(query_embedding).alias("distance")).order_by(SQL("distance")).limit(10)

# Display results
for result in results:
    print(result.text, result.distance)

db.close()

Conclusion

Benchmarking Llama 3 with TiDB Vector Search provides valuable insights into the model’s performance in real-world scenarios. By leveraging the power of vector embeddings and efficient search capabilities, you can ensure that your AI applications are both accurate and responsive. Start your benchmarking journey with TiDB Vector Search today by visiting TiDB Cloud and explore its potential for your AI projects.