TiDB

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. Be among the first to experience it by joining the waitlist for the private beta at https://tidb.cloud/ai.

As part of our ongoing efforts to empower TiDB users in leveraging AI application development, we provide support for

• Memory, enabling the storage of chat history messages directly within TiDB;
• TiDB Loader streamlining the process of loading data from TiDB using Langchain;
• TiDB Vector Store, enabling the use of TiDB Cloud as a vector store, capitalizing on TiDB's robust database infrastructure.

Memory

Utilize TiDB Cloud to store chat message history, leveraging the unlimited scalability of TiDB Cloud Serverless. This enables the storage of massive amounts of historical data without the need to maintain message retention windows.

from langchain_community.chat_message_histories import TiDBChatMessageHistory
from langchain_community.chat_message_histories import TiDBChatMessageHistory

history = TiDBChatMessageHistory(
    connection_string=tidb_connection_string,
    session_id="code_gen",
)

history.add_user_message("How's our feature going?")
history.add_ai_message(
    "It's going well. We are working on testing now. It will be released in Feb."
)

Please refer the details here.

TiDB Loader

Effortlessly load data from TiDB into other LangChain components using SQL. This simplifies the integration process, allowing for seamless data manipulation and utilization within your AI applications.

from langchain_community.document_loaders import TiDBLoader

# Setup TiDBLoader to retrieve data
loader = TiDBLoader(
    connection_string=tidb_connection_string,
    query=f"SELECT * FROM {table_name};",
    page_content_columns=["name", "description"],
    metadata_columns=["id"],
)

# Load data
documents = loader.load()

Please refer the details here.

TiDB Vector Store

With TiDB's exceptional database capabilities, easily manage and store billions of vectorized data. This enhances the performance and scalability of AI applications, providing a robust foundation for your vector storage needs.

from typing import List, Tuple
from langchain.docstore.document import Document
from langchain_community.vectorstores import TiDBVectorStore
from langchain_openai import OpenAIEmbeddings

db = TiDBVectorStore.from_texts(
    embedding=embeddings,
    texts=['Andrew like eating oranges', 'Alexandra is from England', 'Ketanji Brown Jackson is a judge'],
    table_name="tidb_vector_langchain",
    connection_string=tidb_connection_url,
    distance_strategy="cosine",
)

query = "Can you tell me about Alexandra?"
docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)
for doc, score in docs_with_score:
    print("-" * 80)
    print("Score: ", score)
    print(doc.page_content)
    print("-" * 80)

Please refer the details here.