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In the ever-evolving landscape of database technologies, the integration of vector search capabilities into databases is opening new avenues for handling and querying large volumes of data. This article delves into how Mistral 8x22B can be leveraged alongside TiDB Vector Search to perform powerful semantic searches and similarity queries, thereby enhancing data processing and analysis.

Understanding Mistral 8x22B

Mistral 8x22B is a state-of-the-art AI model renowned for its proficiency in handling complex data types and performing advanced machine learning tasks. It excels in generating embeddings—a method of representing data such as text, images, or videos as points in a semantically meaningful space. These embeddings enable various AI-driven applications, including semantic search, recommendation systems, and more.

TiDB Vector Search: A Brief Overview

TiDB Vector Search is a feature currently in public beta, enabling the use of vector search within the TiDB ecosystem. This feature allows the storage and querying of vector embeddings, making it possible to search for data based on its semantic meaning rather than just textual content. Here’s how it works:

  1. Embeddings Representation: Data is transformed into embeddings, which are vectors representing the data points in a high-dimensional space.
  2. Similarity Search: Using distance metrics like cosine similarity, the system can find data points that are semantically similar to a given query.

Combining Mistral 8x22B with TiDB Vector Search

Creating Vector Embeddings

To utilize Mistral 8x22B for generating embeddings, we first need to create a TiDB Serverless cluster with vector support. Here’s a step-by-step guide:

  1. Sign Up: Register on tidbcloud.
  2. Cluster Setup: Create a TiDB Serverless cluster in the eu-central-1 region, enabling vector search support.
  3. Configuration: Follow the provided instructions to set up and connect to the cluster.

Inserting Data and Embeddings

Once the cluster is set up, you can create tables to store data and their corresponding vector embeddings. Here’s an example of creating a table and inserting data:

CREATE TABLE vector_table (
    id INT PRIMARY KEY,
    doc TEXT,
    embedding VECTOR(1536)
);

INSERT INTO vector_table VALUES (1, 'Sample text 1', '[0.1, 0.2, ..., 0.9]'),
                                (2, 'Sample text 2', '[0.3, 0.4, ..., 0.7]');

Performing Semantic Search

To perform a semantic search, you need to query the vector embeddings. Here’s how you can find the nearest neighbors to a given vector using cosine similarity:

SELECT * FROM vector_table
ORDER BY vec_cosine_distance(embedding, '[0.15, 0.25, ..., 0.95]')
LIMIT 3;

Use Case: Enhancing Search Capabilities

One of the primary use cases of combining Mistral 8x22B with TiDB Vector Search is to enhance search capabilities in applications. For instance, in a content recommendation system, you can recommend articles, videos, or products that are semantically similar to what a user has interacted with previously.

Example: Semantic Search with OpenAI and TiDB Vector Search

Here’s an example demonstrating how to use OpenAI embeddings for semantic search in TiDB:

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

client = OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))
db = MySQLDatabase(
    'test_db',
    user='your_username',
    password='your_password',
    host='your_host',
    port=4000
)

class Document(Model):
    text = TextField()
    embedding = VectorField(dimensions=1536)

    class Meta:
        database = db

db.connect()
db.create_tables([Document])

documents = ["Example document 1", "Example document 2"]
embeddings = client.embeddings.create(input=documents, model="text-embedding-ada-002")

for doc, emb in zip(documents, embeddings.data):
    Document.create(text=doc, embedding=emb.embedding)

# Querying for similar documents
query_embedding = client.embeddings.create(input="Example query", model="text-embedding-ada-002").data[0].embedding
similar_docs = Document.select().order_by(Document.embedding.cosine_distance(query_embedding)).limit(3)

for doc in similar_docs:
    print(doc.text)

Conclusion

The integration of Mistral 8x22B with TiDB Vector Search offers a robust framework for semantic searches and advanced data queries. This combination not only enhances the accuracy and relevance of search results but also provides a scalable solution for handling large datasets across various applications.

Explore more about TiDB Vector Search and start building your AI-driven applications by visiting TiDB Serverless. Whether you’re a beginner or an expert, TiDB Serverless provides an excellent SQL playground for your data-driven projects.


Last updated June 26, 2024

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