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Semantic Cache with Jina AI and TiDB Vector

Semantic cache is a cache that stores the semantic information of the data. It can be used to speed up the search process by storing the embeddings of the data and searching for similar embeddings. This example demonstrates how to use Jina AI to generate embeddings for text data and store the embeddings in TiDB Vector Storage. It also shows how to search for similar embeddings in TiDB Vector Storage.

Prerequisites

  • A running TiDB Serverless cluster with vector search enabled
  • Python 3.8 or later
  • Jina AI API key

Run the example

Clone this repo

git clone https://github.com/pingcap/tidb-vector-python.git

Create a virtual environment

cd tidb-vector-python/examples/semantic-cache
python3 -m venv .venv
source .venv/bin/activate

Install dependencies

pip install -r requirements.txt

Set the environment variables

Get the HOST, PORT, USERNAME, PASSWORD, and DATABASE from the TiDB Cloud console, as described in the Prerequisites section. Then set the following environment variables:

export DATABASE_URI="mysql+pymysql://34u7xMnnDLSkjV1.root:<PASSWORD>@gateway01.eu-central-1.prod.aws.tidbcloud.com:4000/test?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true"

or create a .env file with the above environment variables.

Run this example

Start the semantic cache server

fastapi dev cache.py

Test the API

Get the Jina AI API key from the Jina AI Embedding API page, and save it somewhere safe for later use.

POST /set

curl --location ':8000/set' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <your jina token>' \
--data '{
    "key": "what is tidb",
    "value": "tidb is a mysql-compatible and htap database"
}'

GET /get/<key>

curl --location ':8000/get/what%27s%20tidb%20and%20tikv?max_distance=0.5' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <your jina token>'