MariaDB
LangChain's MariaDB integration (langchain-mariadb) provides vector capabilities for working with MariaDB version 11.7.1 and above, distributed under the MIT license. Users can use the provided implementations as-is or customize them for specific needs. Key features include:
- Built-in vector similarity search
- Support for cosine and euclidean distance metrics
- Robust metadata filtering options
- Performance optimization through connection pooling
- Configurable table and column settings
Setupโ
Launch a MariaDB Docker container with:
!docker run --name mariadb-container -e MARIADB_ROOT_PASSWORD=langchain -e MARIADB_DATABASE=langchain -p 3306:3306 -d mariadb:11.7
Installing the Packageโ
The package uses SQLAlchemy but works best with the MariaDB connector, which requires C/C++ components:
# Debian, Ubuntu
!sudo apt install libmariadb3 libmariadb-dev
# CentOS, RHEL, Rocky Linux
!sudo yum install MariaDB-shared MariaDB-devel
# Install Python connector
!pip install -U mariadb
Then install langchain-mariadb
package
pip install -U langchain-mariadb
VectorStore works along with an LLM model, here using langchain-openai
as example.
pip install langchain-openai
export OPENAI_API_KEY=...
Initializationโ
from langchain_core.documents import Document
from langchain_mariadb import MariaDBStore
from langchain_openai import OpenAIEmbeddings
# connection string
url = f"mariadb+mariadbconnector://myuser:mypassword@localhost/langchain"
# Initialize vector store
vectorstore = MariaDBStore(
embeddings=OpenAIEmbeddings(),
embedding_length=1536,
datasource=url,
collection_name="my_docs",
)
API Reference:Document | OpenAIEmbeddings
Manage vector storeโ
Adding Dataโ
You can add data as documents with metadata:
docs = [
Document(
page_content="there are cats in the pond",
metadata={"id": 1, "location": "pond", "topic": "animals"},
),
Document(
page_content="ducks are also found in the pond",
metadata={"id": 2, "location": "pond", "topic": "animals"},
),
# More documents...
]
vectorstore.add_documents(docs)
Or as plain text with optional metadata:
texts = [
"a sculpture exhibit is also at the museum",
"a new coffee shop opened on Main Street",
]
metadatas = [
{"id": 6, "location": "museum", "topic": "art"},
{"id": 7, "location": "Main Street", "topic": "food"},
]
vectorstore.add_texts(texts=texts, metadatas=metadatas)
Query vector storeโ
# Basic similarity search
results = vectorstore.similarity_search("Hello", k=2)
# Search with metadata filtering
results = vectorstore.similarity_search("Hello", filter={"category": "greeting"})
Filter Optionsโ
The system supports various filtering operations on metadata:
- Equality: $eq
- Inequality: $ne
- Comparisons: $lt, $lte, $gt, $gte
- List operations: $in, $nin
- Text matching: $like, $nlike
- Logical operations: $and, $or, $not
Example:
# Search with simple filter
results = vectorstore.similarity_search(
"kitty", k=10, filter={"id": {"$in": [1, 5, 2, 9]}}
)
# Search with multiple conditions (AND)
results = vectorstore.similarity_search(
"ducks",
k=10,
filter={"id": {"$in": [1, 5, 2, 9]}, "location": {"$in": ["pond", "market"]}},
)
Usage for retrieval-augmented generationโ
TODO: document example
API referenceโ
See the repo here for more detail.
Relatedโ
- Vector store conceptual guide
- Vector store how-to guides