Jul 4, 2025
Sometimes to understand the big picture you need to zoom out a simple example.
Let me show you one that:
For this example, the data that we are going to store and then retrieve is:
import time from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship from langchain_core.documents import Document from langchain_core.prompts import ChatPromptTemplate from langchain_ollama import OllamaEmbeddings from langchain_ollama.llms import OllamaLLM from surrealdb import Surreal from langchain_surrealdb.experimental.surrealdb_graph import SurrealDBGraph from langchain_surrealdb.vectorstores import SurrealDBVectorStore conn = Surreal("ws://localhost:8000/rpc") conn.signin({"username": "root", "password": "root"}) conn.use("langchain", "demo") vector_store = SurrealDBVectorStore(OllamaEmbeddings(model="llama3.2"), conn) graph_store = SurrealDBGraph(conn) vector_store.delete() graph_store.delete_nodes()
doc1 = Document( page_content="SurrealDB is the ultimate multi-model database for AI applications", metadata={"key": "sdb"}, ) doc2 = Document( page_content="Surrealism is an artistic and cultural movement that emerged in the early 20th century", metadata={"key": "surrealism"}, ) vector_store.add_documents(documents=[doc1, doc2], ids=["1", "2"])
# Document nodes node_sdb = Node(id="sdb", type="Document") node_surrealism = Node(id="surrealism", type="Document") # People nodes node_martin = Node(id="martin", type="People", properties={"name": "Martin"}) node_tobie = Node(id="tobie", type="People", properties={"name": "Tobie"}) node_max = Node(id="max", type="People", properties={"name":"Max Ernst"}) # Edges graph_documents = [ GraphDocument( nodes=[node_martin, node_tobie, node_sdb], relationships=[ Relationship(source=node_martin, target=node_sdb, type="KnowsAbout"), Relationship(source=node_tobie, target=node_sdb, type="KnowsAbout") ], source=doc1, ), GraphDocument( nodes=[node_max, node_surrealism], relationships=[ Relationship(source=node_max, target=node_surrealism, type="KnowsAbout") ], source=doc2, ), ] graph_store.add_graph_documents(graph_documents)
For this example we are using OllamaLLM from the LangChain components. You can use any other of the LLM components.
For Ollama, here are all the parameters. In the example I turned the temperature
up to the max to get the craziest outcomes possible. You may want to leave it at around 0.7, but it depends on your use case.
model = OllamaLLM(model="llama3.2", temperature=1, verbose=True) # Let's retrieve information about these 2 topics queries = ["database", "surrealism"] for q in queries: print(f'\n----------------------------------\nTopic: "{q}"\nVector search:') results = vector_store.similarity_search_with_score(query=q, k=2) for doc, score in results: print(f"• [{score:.0%}]: {doc.page_content}") top_match = results[0][0] # Graph query res = graph_store.query( """ SELECT <-relation_KnowsAbout<-graph_People as people FROM type::thing("graph_Document", $doc_key) FETCH people """, {"doc_key": top_match.metadata.get("key")}, ) people = [x.get("name") for x in res[0].get("people", [])] print(f"\nGraph result: {people}") # Template for the LLM template = """ You are a young, energetic database developer in your last 20s, who loves to talk tech, and who's also very geeky. Use the following pieces of retrieved context to answer the question. Use four sentences maximum and keep the answer concise. Try to be funny with a play on words. Context: {context}. People who know about this: {people}. Question: Explain "{topic}", summarize the context provided, and tell me who I can ask for more information. Answer: """ prompt = ChatPromptTemplate.from_template(template) chain = prompt | model answer = chain.invoke( {"context": top_match.page_content, "people": people, "topic": q} ) print(f"\nLLM answer:\n===========\n{answer}") time.sleep(4) print("\nBye!")
---------------------------------- Topic: "database" Vector search: • [34%]: SurrealDB is the ultimate multi-model database for AI applications • [22%]: Surrealism is an artistic and cultural movement that emerged in the early 20th century Graph result: ['Martin', 'Tobie'] LLM answer: =========== "Databases are like my brain, but less cluttered (mostly). Seriously though, a database is a storage system that organizes data in a way that allows efficient retrieval. Given our conversation about SurrealDB being the ultimate multi-model database for AI applications, it's no wonder Martin and Tobie are experts on the subject - they're the 'key' people to ask! ---------------------------------- Topic: "surrealism" Vector search: • [52%]: Surrealism is an artistic and cultural movement that emerged in the early 20th century • [46%]: SurrealDB is the ultimate multi-model database for AI applications Graph result: ['Max Ernst'] LLM answer: =========== It looks like we've stumbled into a "dream" world of art - Surrealism is all about exploring the subconscious mind through bizarre and fantastical creations. Think weird creatures, melting objects, and illogical scenarios - it's like someone poured a SQL query into my brain! According to the context, Max Ernst was one of the pioneers of this wild movement. If you want to learn more, I'd recommend checking out some online tutorials or asking a fellow art history geek (just don't ask me to explain it in code) Bye!
Did you notice the prompt in the code? Be creative and try different personalities:
template = """ You are rap singer, who always rhymes when talking. ... """
🥁 Drum roll…
LLM answer: =========== Yo, listen up, let me share a tale, About databases, it's time to prevail. A database's a collection, of data so fine, Storing info, making it all mine. Context's tight, about SurrealDB's the claim, Tobie and Martin know its AI fame. For more deets, ask them with glee, They'll hook you up with knowledge, just wait and see!
It never dissapoints.
LLM answer: =========== Yo, listen up, let me share a tale, Surrealism's the movement that set sail. It emerged in the 20th century's early prime, Influenced by Max Ernst, the pioneer of the time.
Find all the code in the langchain-surrealdb repository examples.
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Any questions or thoughts about this or semantic search using SurrealDB? Feel free to drop by our community to get in touch.
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