TiDB Vector Store¶

TiDB Cloud, is a comprehensive Database-as-a-Service (DBaaS) solution, that provides dedicated and serverless options. TiDB Serverless is now integrating a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly develop AI applications using TiDB Serverless without the need for a new database or additional technical stacks. Create a free TiDB Serverless cluster and start using the vector search feature at https://pingcap.com/ai.

This notebook provides a detailed guide on utilizing the tidb vector search in LlamaIndex.

Setting up environments¶

In [ ]:

%pip install llama-index-vector-stores-tidbvector
%pip install llama-index

%pip install llama-index-vector-stores-tidbvector %pip install llama-index

In [ ]:

import textwrap

from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.tidbvector import TiDBVectorStore

import textwrap from llama_index.core import SimpleDirectoryReader, StorageContext from llama_index.core import VectorStoreIndex from llama_index.vector_stores.tidbvector import TiDBVectorStore

Configuring your OpenAI Key

In [ ]:

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass("Input your OpenAI API key:")

import getpass import os os.environ["OPENAI_API_KEY"] = getpass.getpass("Input your OpenAI API key:")

Configure TiDB connection setting that you will need. To connect to your TiDB Cloud Cluster, follow these steps:

• Go to your TiDB Cloud cluster Console and navigate to the Connect page.
• Select the option to connect using SQLAlchemy with PyMySQL, and copy the provided connection URL (without password).
• Paste the connection URL into your code, replacing the tidb_connection_string_template variable.
• Type your password.

In [ ]:

# replace with your tidb connect string from tidb cloud console
tidb_connection_string_template = "mysql+pymysql://<USER>:<PASSWORD>@<HOST>:4000/<DB>?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true"
# type your tidb password
tidb_password = getpass.getpass("Input your TiDB password:")
tidb_connection_url = tidb_connection_string_template.replace(
    "<PASSWORD>", tidb_password
)

# replace with your tidb connect string from tidb cloud console tidb_connection_string_template = "mysql+pymysql://:@:4000/?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true" # type your tidb password tidb_password = getpass.getpass("Input your TiDB password:") tidb_connection_url = tidb_connection_string_template.replace( "", tidb_password )

Prepare data that used to show case

In [ ]:

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

!mkdir -p 'data/paul_graham/' !wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

In [ ]:

documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
for index, document in enumerate(documents):
    document.metadata = {"book": "paul_graham"}

documents = SimpleDirectoryReader("./data/paul_graham").load_data() print("Document ID:", documents[0].doc_id) for index, document in enumerate(documents): document.metadata = {"book": "paul_graham"}

Document ID: 86e12675-2e9a-4097-847c-8b981dd41806

Create TiDB Vectore Store¶

The code snippet below creates a table named VECTOR_TABLE_NAME in TiDB, optimized for vector searching. Upon successful execution of this code, you will be able to view and access the VECTOR_TABLE_NAME table directly within your TiDB database environment

In [ ]:

VECTOR_TABLE_NAME = "paul_graham_test"
tidbvec = TiDBVectorStore(
    connection_string=tidb_connection_url,
    table_name=VECTOR_TABLE_NAME,
    distance_strategy="cosine",
    vector_dimension=1536,
    drop_existing_table=False,
)

VECTOR_TABLE_NAME = "paul_graham_test" tidbvec = TiDBVectorStore( connection_string=tidb_connection_url, table_name=VECTOR_TABLE_NAME, distance_strategy="cosine", vector_dimension=1536, drop_existing_table=False, )

Create a query engine based on tidb vectore store

In [ ]:

storage_context = StorageContext.from_defaults(vector_store=tidbvec)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, show_progress=True
)

storage_context = StorageContext.from_defaults(vector_store=tidbvec) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context, show_progress=True )

Note: If you encounter errors during this process due to the MySQL protocol’s packet size limitation, such as when trying to insert a large number of vectors (e.g., 2000 rows) , you can mitigate this issue by splitting the insertion into smaller batches. For example, you can set the insert_batch_size parameter to a smaller value (e.g., 1000) to avoid exceeding the packet size limit, ensuring smooth insertion of your data into the TiDB vector store:

storage_context = StorageContext.from_defaults(vector_store=tidbvec)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, insert_batch_size=1000, show_progress=True
)

Semantic similarity search¶

This section focus on vector search basics and refining results using metadata filters. Please note that tidb vector only supports Deafult VectorStoreQueryMode.

In [ ]:

query_engine = index.as_query_engine()
response = query_engine.query("What did the author do?")
print(textwrap.fill(str(response), 100))

query_engine = index.as_query_engine() response = query_engine.query("What did the author do?") print(textwrap.fill(str(response), 100))

The author wrote a book.

Filter with metadata¶

perform searches using metadata filters to retrieve a specific number of nearest-neighbor results that align with the applied filters.

In [ ]:

from llama_index.core.vector_stores.types import (
    MetadataFilter,
    MetadataFilters,
)

query_engine = index.as_query_engine(
    filters=MetadataFilters(
        filters=[
            MetadataFilter(key="book", value="paul_graham", operator="!="),
        ]
    ),
    similarity_top_k=2,
)
response = query_engine.query("What did the author learn?")
print(textwrap.fill(str(response), 100))

from llama_index.core.vector_stores.types import ( MetadataFilter, MetadataFilters, ) query_engine = index.as_query_engine( filters=MetadataFilters( filters=[ MetadataFilter(key="book", value="paul_graham", operator="!="), ] ), similarity_top_k=2, ) response = query_engine.query("What did the author learn?") print(textwrap.fill(str(response), 100))

Empty Response

Query again

In [ ]:

from llama_index.core.vector_stores.types import (
    MetadataFilter,
    MetadataFilters,
)

query_engine = index.as_query_engine(
    filters=MetadataFilters(
        filters=[
            MetadataFilter(key="book", value="paul_graham", operator="=="),
        ]
    ),
    similarity_top_k=2,
)
response = query_engine.query("What did the author learn?")
print(textwrap.fill(str(response), 100))

from llama_index.core.vector_stores.types import ( MetadataFilter, MetadataFilters, ) query_engine = index.as_query_engine( filters=MetadataFilters( filters=[ MetadataFilter(key="book", value="paul_graham", operator="=="), ] ), similarity_top_k=2, ) response = query_engine.query("What did the author learn?") print(textwrap.fill(str(response), 100))

The author learned valuable lessons from his experiences.

Delete documents¶

In [ ]:

tidbvec.delete(documents[0].doc_id)

tidbvec.delete(documents[0].doc_id)

Check whether the documents had been deleted

In [ ]:

query_engine = index.as_query_engine()
response = query_engine.query("What did the author learn?")
print(textwrap.fill(str(response), 100))

query_engine = index.as_query_engine() response = query_engine.query("What did the author learn?") print(textwrap.fill(str(response), 100))

Empty Response