This content originally appeared on DEV Community and was authored by Mark Gyles
We’re thrilled to announce that SurrealDB now has an official integration with LangChain, one of the most popular frameworks for building powerful LLM-driven applications. This partnership brings together the strengths of SurrealDB’s multi-model flexibility and real-time capabilities with LangChain’s powerful orchestration layer, enabling developers to build smarter, faster, and more context-aware AI applications.
The integration includes the following LangChain components:
Vector Store (SurrealDBVectorStore)
Graph Store (SurrealDBGraph, currently experimental)
Graph QA Chain (SurrealDBGraphQAChain, currently experimental).
# DB connection
conn = Surreal(url)
conn.signin({"username": user, "password": password})
conn.use(ns, db)
# Vector Store
vector_store = SurrealDBVectorStore(
OllamaEmbeddings(model="llama3.2"),
conn
)
# Graph Store
graph_store = SurrealDBGraph(conn)
Get Started
You can start using the integration today via the SurrealDB docs, the LangChain docs, or our GitHub repository. We’ve also published example notebooks and sample apps to help you get up and running quickly:
Check out the official SurrealDB Docs or LangChain Docs
GitHub: langchain-surrealdb
Pypi: langchain-surrealdb
Tutorial: Make a GenAI chatbot using GraphRAG with SurrealDB + LangChain.
Join the Conversation
We’re excited to see what you build with LangChain and SurrealDB. If you have questions or want to showcase your project, join our community on Discord or tag us on X.
This content originally appeared on DEV Community and was authored by Mark Gyles