Alibaba cloud mysql integration

Alibaba Cloud MySQL is a fully managed relational database service that provides high availability, scalability, and security.

Alibaba Cloud MySQL provides deep integration for enterprise-level vector data processing. It natively supports storing and computing vector data of up to 16,383 dimensions. The service integrates mainstream vector operation functions and uses a highly optimized Hierarchical Navigable Small World (HNSW) algorithm to deliver efficient approximate nearest neighbor searches. This feature also supports creating indexes on full-dimension vector columns.

This guide provides a quick overview for getting started with the AlibabaCloudMySQL vector store. For a detailed listing of all alibabacloud-mysql vector store features, paramaters, and configurations, head to the langchain-alibabacloud-mysql.

Setup

To access the alibabacloud-mysql vector store, you’ll need to create an Alibaba Cloud RDS for MySQL instance with minor version 8.0.36 or higher, open the vector feature, make it accessible, and install the langchain-alibabacloud-mysql integration package.

Credentials

To connect to your Alibaba Cloud RDS MySQL instance, you’ll need to set the following environment variables:

ALIBABACLOUD_MYSQL_HOST: Your RDS MySQL host address
ALIBABACLOUD_MYSQL_PORT: MySQL port (default: 3306)
ALIBABACLOUD_MYSQL_USER: MySQL username
ALIBABACLOUD_MYSQL_PASSWORD: MySQL password
ALIBABACLOUD_MYSQL_DATABASE: Database name

Installation

The LangChain alibabacloud-mysql integration lives in the langchain-alibabacloud-mysql package:

pip install -U langchain-alibabacloud-mysql

Instantiation

Now we can instantiate the vector store with your RDS MySQL connection informations:

Initialize vector store

import os
from langchain_alibabacloud_mysql import AlibabaCloudMySQL
from langchain_community.embeddings import DashScopeEmbeddings

# Initialize DashScope embeddings (Alibaba Cloud's embedding service)
embeddings = DashScopeEmbeddings(
    model="text-embedding-v4",
    dashscope_api_key=os.environ.get("DASHSCOPE_API_KEY"),
)
# Or you can use OpenAI embeddings
# embeddings = OpenAIEmbeddings()

# Initialize vector store
vector_store = AlibabaCloudMySQL(
    host=os.environ.get("ALIBABACLOUD_MYSQL_HOST", "localhost"),
    port=int(os.environ.get("ALIBABACLOUD_MYSQL_PORT", "3306")),
    user=os.environ.get("ALIBABACLOUD_MYSQL_USER", "root"),
    password=os.environ.get("ALIBABACLOUD_MYSQL_PASSWORD", ""),
    database=os.environ.get("ALIBABACLOUD_MYSQL_DATABASE", "test"),
    embedding=embeddings,
    table_name="langchain_vectors",
    distance_strategy="cosine",  # or "euclidean"
    hnsw_m=6,  # HNSW index M parameter (3-200)
)

To instantiate the vector store, you need to provide an embedding model. You can use DashScope embeddings (recommended for Alibaba Cloud) or other embedding models (OpenAI, etc.) integrated into LangChain. If you choose to use dashscope model, you can get your API key from Model Studio, and set it in the following codes.

Manage vector store

Add items

Add documents

from langchain_core.documents import Document

document_1 = Document(page_content="Alibaba", metadata={"source": "https://example.com"})
document_2 = Document(page_content="Cloud", metadata={"source": "https://example.com"})
document_3 = Document(page_content="RDS for MySQL", metadata={"source": "https://example.com"})
documents = [document_1, document_2, document_3]

vector_store.add_documents(documents=documents, ids=["1", "2", "3"])

Update items

Update document by ID

updated_document = Document(
    page_content="Alibaba Cloud", metadata={"source": "https://another-example.com"}
)

vector_store.update_documents(document_id="1", document=updated_document)

Delete items

Delete documents by IDs

vector_store.delete(ids=["3"])

Query vector store

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

Directly

Performing a simple similarity search can be done as follows:

Similarity search

results = vector_store.similarity_search(
    query="mysql", k=1, filter={"source": "https://example.com"}
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

If you want to execute a similarity search and receive the corresponding scores you can run:

Similarity search with scores

results = vector_store.similarity_search_with_score(
    query="mysql", k=1, filter={"source": "https://example.com"}
)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

By turning into retriever

You can also transform the vector store into a retriever for easier usage in your chains.

Create retriever

retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 1})
retriever.invoke("alibaba")

Features

Alibaba Cloud MySQL vector store supports most standard vector store features:

Feature	Supported
Delete by ID	✅
Filtering	✅
Search by Vector	✅
Search with score	✅
Async	✅
Passes Standard Tests	✅
Multi Tenancy	❌
IDs in add Documents	✅

Metadata filtering

You can filter search results by metadata using dictionary-style filters:

Filter by metadata

# Search with metadata filter
results = vector_store.similarity_search(
    query="technology",
    k=5,
    filter={"category": "tech", "year": {"$gte": 2023}}
)

Supported filter operators:

$eq: Equal to
$ne: Not equal to
$gt: Greater than
$gte: Greater than or equal to
$lt: Less than
$lte: Less than or equal to
$in: In list
$nin: Not in list
$like: LIKE pattern matching

Maximal Marginal Relevance (MMR) search

MMR search provides diverse results by balancing relevance and diversity:

MMR search

results = vector_store.max_marginal_relevance_search(
    query="artificial intelligence",
    k=4,
    fetch_k=20,  # Number of candidates to consider
    lambda_mult=0.5,  # 0 = max diversity, 1 = max relevance
)

Batch operations

Efficiently add multiple documents at once:

Batch add documents

texts = ["Document 1", "Document 2", "Document 3"]
metadatas = [
    {"source": "doc1.pdf"},
    {"source": "doc2.pdf"},
    {"source": "doc3.pdf"},
]
ids = vector_store.add_texts(texts, metadatas=metadatas)

Get documents by IDs

Retrieve specific documents by their IDs:

Get by IDs

documents = vector_store.get_by_ids(["id1", "id2", "id3"])
for doc in documents:
    print(f"{doc.page_content} - {doc.metadata}")

Count and clear

Get the total number of vectors or clear all data:

Count and clear

# Count total vectors
count = vector_store.count()
print(f"Total vectors: {count}")

# Clear all vectors
vector_store.clear()

Async operations

AlibabaCloud MySQL vector store supports async operations for all major methods:

aadd_texts() - Add texts asynchronously
aadd_documents() - Add documents asynchronously
asimilarity_search() - Similarity search asynchronously
asimilarity_search_with_score() - Similarity search with scores asynchronously
amax_marginal_relevance_search() - MMR search asynchronously
adelete() - Delete vectors asynchronously
aget_by_ids() - Get documents by IDs asynchronously
aclear() - Clear all vectors asynchronously
acount() - Count vectors asynchronously
aclose() - Close connection pool asynchronously

Usage for retrieval-augmented generation

Retrieval-Augmented Generation (RAG) combines vector search with language model generation to provide contextual, accurate answers based on your documents.

Basic RAG workflow

Here’s a complete example of building a RAG application with Alibaba Cloud MySQL:

RAG example

import os
from langchain_alibabacloud_mysql import AlibabaCloudMySQL
from langchain_community.embeddings import DashScopeEmbeddings
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.chat_models.tongyi import ChatTongyi
from langchain_classic.chains import create_retrieval_chain
from langchain_classic.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate

# Step 1: Initialize embeddings and vector store
embeddings = DashScopeEmbeddings(
    model="text-embedding-v4",
    dashscope_api_key=os.environ.get("DASHSCOPE_API_KEY"),
)

vector_store = AlibabaCloudMySQL(
    host=os.environ.get("ALIBABACLOUD_MYSQL_HOST", "localhost"),
    port=int(os.environ.get("ALIBABACLOUD_MYSQL_PORT", "3306")),
    user=os.environ.get("ALIBABACLOUD_MYSQL_USER", "root"),
    password=os.environ.get("ALIBABACLOUD_MYSQL_PASSWORD", ""),
    database=os.environ.get("ALIBABACLOUD_MYSQL_DATABASE", "test"),
    embedding=embeddings,
    table_name="langchain_vectors_rag",
)

# Step 2: Load and split documents
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
docs = loader.load()

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
)
splits = text_splitter.split_documents(docs)

# Step 3: Add documents to vector store
vector_store.add_documents(documents=splits)

# Step 4: Create retriever
retriever = vector_store.as_retriever(search_kwargs={"k": 3})

# Step 5: Create RAG chain
llm = ChatTongyi()

prompt = ChatPromptTemplate.from_template(
    """Answer the following question based only on the provided context:

Context: {context}

Question: {input}"""
)

document_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, document_chain)

# Step 6: Query
response = rag_chain.invoke({"input": "What is task decomposition?"})
print(response["answer"])

Using retriever with agents

You can also use the vector store as a retrieval tool in an agent:

RAG agent

from langchain.agents import create_agent
from langchain.tools import tool

@tool
def retrieve_context(query: str) -> str:
    """Retrieve information to help answer a query."""
    retrieved_docs = vector_store.similarity_search(query, k=2)
    return "\n\n".join(
        f"Source: {doc.metadata}\nContent: {doc.page_content}"
        for doc in retrieved_docs
    )

tools = [retrieve_context]
llm = ChatTongyi()
agent = create_agent(
    llm,
    tools,
    system_prompt="You have access to a tool that retrieves context. Use it to help answer user queries.",
)

response = agent.invoke({"messages": [{"role": "user", "content": "What is task decomposition?"}]})

For more RAG guides and patterns, see:

For detailed RAG demo with Alibaba Cloud MySQL and more examples, see:

API reference

We will update the API reference soon, please refer to the langchain-alibabacloud-mysql for more details.

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Integrations by component

Alibaba cloud mysql integration

Setup

Credentials

Installation

Instantiation

Manage vector store

Add items

Update items

Delete items

Query vector store

Directly

By turning into retriever

Features

Metadata filtering

Maximal Marginal Relevance (MMR) search

Batch operations

Get documents by IDs

Count and clear

Async operations

Usage for retrieval-augmented generation

Basic RAG workflow

Using retriever with agents

API reference

Popular Providers

Integrations by component

Documentation Index

​Setup

​Credentials

​Installation

​Instantiation

​Manage vector store

​Add items

​Update items

​Delete items

​Query vector store

​Directly

​By turning into retriever

​Features

​Metadata filtering

​Maximal Marginal Relevance (MMR) search

​Batch operations

​Get documents by IDs

​Count and clear

​Async operations

​Usage for retrieval-augmented generation

​Basic RAG workflow

​Using retriever with agents

​API reference

Setup

Credentials

Installation

Instantiation

Manage vector store

Add items

Update items

Delete items

Query vector store

Directly

By turning into retriever

Features

Metadata filtering

Maximal Marginal Relevance (MMR) search

Batch operations

Get documents by IDs

Count and clear

Async operations

Usage for retrieval-augmented generation

Basic RAG workflow

Using retriever with agents

API reference