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vector-search-full-text-search-python.md

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title Full-Text Search with Python
summary Full-text search lets you retrieve documents for exact keywords. In Retrieval-Augmented Generation (RAG) scenarios, you can use full-text search together with vector search to improve the retrieval quality.
aliases
/tidb/stable/vector-search-full-text-search-python/
/tidbcloud/vector-search-full-text-search-python/

Full-Text Search with Python

Unlike Vector Search, which focuses on semantic similarity, full-text search lets you retrieve documents for exact keywords. In Retrieval-Augmented Generation (RAG) scenarios, you can use full-text search together with vector search to improve the retrieval quality.

The full-text search feature in TiDB provides the following capabilities:

  • Query text data directly: you can search any string columns directly without the embedding process.

  • Support for multiple languages: no need to specify the language for high-quality search. TiDB supports documents in multiple languages stored in the same table and automatically chooses the best text analyzer for each document.

  • Order by relevance: the search result can be ordered by relevance using the widely adopted BM25 ranking algorithm.

  • Fully compatible with SQL: all SQL features, such as pre-filtering, post-filtering, grouping, and joining, can be used with full-text search.

Tip:

For SQL usage, see Full-Text Search with SQL.

To use full-text search and vector search together in your AI apps, see Hybrid Search.

Prerequisites

Full-text search is still in the early stages, and we are continuously rolling it out to more customers. Currently, full-text search is only available on {{{ .starter }}} and {{{ .essential }}} in the following regions:

  • TiDB Cloud Serverless: Frankfurt (eu-central-1) and Singapore (ap-southeast-1)

To complete this tutorial, make sure you have a {{{ .starter }}} instance in a supported region. If you don't have one, follow Creating a {{{ .starter }}} instance to create it.

Get started

Step 1. Install the pytidb Python SDK

pytidb is the official Python SDK for TiDB, designed to help developers build AI applications efficiently. It includes built-in support for vector search and full-text search.

To install the SDK, run the following command:

pip install pytidb

# (Alternative) To use the built-in embedding functions and rerankers:
# pip install "pytidb[models]"

# (Optional) To convert query results into pandas DataFrames:
# pip install pandas

Step 2. Connect to TiDB

from pytidb import TiDBClient

db = TiDBClient.connect(
    host="HOST_HERE",
    port=4000,
    username="USERNAME_HERE",
    password="PASSWORD_HERE",
    database="DATABASE_HERE",
)

You can get these connection parameters from the TiDB Cloud console as follows:

  1. Navigate to the My TiDB page, and then click the name of your target {{{ .starter }}} or Essential instance to go to its overview page.

  2. Click Connect in the upper-right corner. A connection dialog is displayed, with connection parameters listed.

    For example, if the connection parameters are displayed as follows:

    HOST:     gateway01.us-east-1.prod.shared.aws.tidbcloud.com
PORT:     4000
USERNAME: 4EfqPF23YKBxaQb.root
PASSWORD: abcd1234
DATABASE: test
CA:       /etc/ssl/cert.pem

    The corresponding Python code to connect to the {{{ .starter }}} instance would be as follows:

    db = TiDBClient.connect(
    host="gateway01.us-east-1.prod.shared.aws.tidbcloud.com",
    port=4000,
    username="4EfqPF23YKBxaQb.root",
    password="abcd1234",
    database="test",
)

    Note that the preceding example is for demonstration purposes only. You need to fill in the parameters with your own values and keep them secure.

Step 3. Create a table and a full-text index

As an example, create a table named chunks with the following columns:

  • id (int): the ID of the chunk.
  • text (text): the text content of the chunk.
  • user_id (int): the ID of the user who created the chunk.
from pytidb.schema import TableModel, Field

class Chunk(TableModel, table=True):
    __tablename__ = "chunks"

    id: int = Field(primary_key=True)
    text: str = Field()
    user_id: int = Field()

table = db.create_table(schema=Chunk)

if not table.has_fts_index("text"):
    table.create_fts_index("text")   # 👈 Create a fulltext index on the text column.

Step 4. Insert data

table.bulk_insert(
    [
        Chunk(id=2, text="the quick brown", user_id=2),
        Chunk(id=3, text="fox jumps", user_id=3),
        Chunk(id=4, text="over the lazy dog", user_id=4),
    ]
)

Step 5. Perform a full-text search

After inserting data, you can perform a full-text search as follows:

df = (
  table.search("brown fox", search_type="fulltext")
    .limit(2)
    .to_pandas() # optional
)

#    id             text  user_id
# 0   3        fox jumps        3
# 1   2  the quick brown        2

For a complete example, see pytidb full-text search demo.

See also

Feedback & Help

Full-text search is still in the early stages with limited accessibility. If you would like to try full-text search in a region that is not yet available, or if you have feedback or need help, feel free to reach out to us: