Connect with LangChain

Install langchain-singlestore

The langchain-singlestore package can be installed using the standard Python package installation process:

Shell

pip install -U langchain-singlestore

If the package is already installed, this command upgrades it to the latest version.

The langchain-singlestore package requires langchain-core, singlestoredb (SingleStore Python client), and sqlalchemy. These dependencies are automatically installed if they are not already present.

Connect to SingleStore

Specify the connection configuration of your SingleStore deployment either in the constructor argument while instantiating the class or using environment variables.

Constructor Argument	Environment Variable	Description
-	`SINGLESTOREDB_URL`	Connection URL in the following format: `singlestoredb://<username>:<password>@<host>:<port>/<database>`
`host`	`SINGLESTOREDB_HOST`	IP address or the hostname of the SingleStore cluster.
`port`	`SINGLESTOREDB_PORT`	Port of the SingleStore cluster. The default is `3306`.
`user`	`SINGLESTOREDB_USER`	Username of the SingleStore database user.
`password`	`SINGLESTOREDB_PASSWORD`	Password for the SingleStore database user.
`database`	`SINGLESTOREDB_DATABASE`	Name of the SingleStore database to connect with.
`table_name`	-	Name of the table in the specified SingleStore database.

For example:

Python

retriever = SingleStoreSQLDatabaseRetriever(
    host="svchost",
    user="s2user",
    password="pa55w0rd",
    database="dbTest",
    table_name="t1"
)

Supported Components

The langchain-singlestore package implements LangChain integrations for SingleStore and provides the following core components:

SingleStoreChatMessageHistory
SingleStoreSemanticCache
SingleStoreVectorStore
SingleStoreSQLDatabaseRetriever
SingleStoreLoader

SingleStoreChatMessageHistory

The SingleStoreChatMessageHistory class provides support for persistent chat message storage and session management, which is essential for AI applications that need to maintain conversation context across sessions.

Key features:

Automatic schema creation and management
Multiple conversation sessions
Message storage and retrieval
Integration with LangChain chat models

SingleStoreSemanticCache

The SingleStoreSemanticCache class implements vector-based semantic caching for LLM responses. Instead of exact string matching, it uses embeddings to find semantically similar cached queries.

Key features:

Vector-based semantic similarity for cached queries
Reduces LLM API calls for similar queries
Configurable similarity threshold
Thread-safe caching operations

SingleStoreVectorStore

The SingleStoreVectorStore class provides support for document storage and retrieval with vector and full-text search indexes. It supports multiple search strategies, advanced metadata filtering, and both vector and text-based indexing.

Key features:

Hybrid search (using a combination of vector and text indexes)
Multiple search strategies (VECTOR_ONLY, TEXT_ONLY, FILTER_BY_TEXT, FILTER_BY_VECTOR, WEIGHTED_SUM)
Simple and advanced metadata filtering
Efficient document management (add, delete, update)
Configurable distance metrics

SingleStoreSQLDatabaseRetriever

The SingleStoreSQLDatabaseRetriever class enables LangChain agents and chains to directly run SQL queries on the SingleStore database and retrieve results as structured documents.

Key features:

Run SQL queries and convert results to documents
Flexible row-to-document conversion with custom handlers
Connection pooling for efficient resource management
Integration with LangChain agents for database-aware AI
Support for complex queries with JSON results

SingleStoreLoader

The SingleStoreLoader class provides support for directly loading documents from SingleStore, which enables AI applications to process documents stored in the SingleStore database.

Key features:

Load documents from any database table
Configurable content and metadata fields
Efficient batch processing
Support for complex metadata structures

Example

The following is a basic example that shows how to store documents as embeddings in SingleStore and retrieve them via semantic search using LangChain and Hugging Face. For more examples, refer to the langchain-singlestore GitHub repository.

Install the required packages:

Shell

pip install -U langchain langchain-singlestore langchain-huggingface sentence-transformers

Add the following code to main.py:

Python

from langchain_huggingface import HuggingFaceEmbeddings
from langchain_singlestore import SingleStoreVectorStore

embedding = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)

# Connect to SingleStore
vectorstore = SingleStoreVectorStore(
    host="svchost",
    port=3306,
    user="s2user",
    password="passkey",
    database="dbTest",
    embedding=embedding
)

# Sample products
products = [
    "Wireless noise-cancelling headphones with long battery life.",
    "Smartwatch with heart rate and sleep tracking.",
    "Portable Bluetooth speaker with deep bass.",
    "Ergonomic office chair with lumbar support.",
    "4K UHD monitor for gaming and productivity."
]

# Store products in SingleStore
vectorstore.add_texts(products)
print("Products stored in SingleStore.")

# User interest
user_query = "I want headphones for running and music"

# Retrieve top 2 recommended products
results = vectorstore.similarity_search(user_query, k=2)

print("\nRecommended products:")
for  doc in results:
    print("- ", doc.page_content)

Run the code:

Shell

python main.py

Products stored in SingleStore.

Recommended products:
-  Wireless noise-cancelling headphones with long battery life
-  Portable Bluetooth speaker with deep bass

Connect with LangChain

On this page

Install langchain-singlestore

Connect to SingleStore

Supported Components

SingleStoreChatMessageHistory

SingleStoreSemanticCache

SingleStoreVectorStore

SingleStoreSQLDatabaseRetriever

SingleStoreLoader

Example

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