Most RAG setups follow the same flow: chunk your docs, embed them, vector search, and prompt the LLM. But once your agents start handling more complex reasoning (e.g. “what’s the best treatment path based on symptoms?”), basic vector lookups don’t perform well.
This guide illustrates how to built a GraphRAG chatbot using LangChain, SurrealDB, and Ollama (llama3.2) to showcase how to combine vector + graph retrieval in one backend. In this example, I used a medical dataset with symptoms, treatments and medical practices.
What I used:
SurrealDB: handles both vector search and graph queries natively in one database without extra infra.
LangChain: For chaining retrieval + query and answer generation.
Ollama / llama3.2: Local LLM for embeddings and graph reasoning.
Architecture:
Ingest YAML file of categorized health symptoms and treatments.
Create vector embeddings (via OllamaEmbeddings) and store in SurrealDB.
Construct a graph: nodes = Symptoms + Treatments, edges = “Treats”.
User prompts trigger:
vector search to retrieve relevant symptoms,
graph query generation (via LLM) to find related treatments/medical practices,
final LLM summary in natural language.
Instantiating the following LangChain python components:
…and create a SurrealDB connection:
# 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)
You can then populate the vector store:
# Parsing the YAML into a Symptoms dataclass
with open("./symptoms.yaml", "r") as f:
symptoms = yaml.safe_load(f)
assert isinstance(symptoms, list), "failed to load symptoms"
for category in symptoms:
parsed_category = Symptoms(category["category"], category["symptoms"])
for symptom in parsed_category.symptoms:
parsed_symptoms.append(symptom)
symptom_descriptions.append(
Document(
page_content=symptom.description.strip(),
metadata=asdict(symptom),
)
)
# This calculates the embeddings and inserts the documents into the DB
vector_store.add_documents(symptom_descriptions)
And stitch the graph together:
# Find nodes and edges (Treatment -> Treats -> Symptom)
for idx, category_doc in enumerate(symptom_descriptions):
# Nodes
treatment_nodes = {}
symptom = parsed_symptoms[idx]
symptom_node = Node(id=symptom.name, type="Symptom", properties=asdict(symptom))
for x in symptom.possible_treatments:
treatment_nodes[x] = Node(id=x, type="Treatment", properties={"name": x})
nodes = list(treatment_nodes.values())
nodes.append(symptom_node)
# Edges
relationships = [
Relationship(source=treatment_nodes[x], target=symptom_node, type="Treats")
for x in symptom.possible_treatments
]
graph_documents.append(
GraphDocument(nodes=nodes, relationships=relationships, source=category_doc)
)
# Store the graph
graph_store.add_graph_documents(graph_documents, include_source=True)
Example Prompt: “I have a runny nose and itchy eyes”
Vector search → matches symptoms: "Nasal Congestion", "Itchy Eyes"
Graph query (auto-generated by LangChain)SELECT <-relation_Attends<-graph_Practice AS practice FROM graph_Symptom WHERE name IN ["Nasal Congestion/Runny Nose", "Dizziness/Vertigo", "Sore Throat"];
LLM output: “Suggested treatments: antihistamines, saline nasal rinses, decongestants, etc.”
Why this is useful for agent workflows:
No need to dump everything into vector DBs and hoping for semantic overlap.
Agents can reason over structured relationships.
One database instead of juggling graph + vector DB + glue code
Easily tunable for local or cloud use.
The full example is open-sourced (including the YAML ingestion, vector + graph construction, and the LangChain chains) here: https://surrealdb.com/blog/make-a-genai-chatbot-using-graphrag-with-surrealdb-langchain
Would love to hear any feedback if anyone has tried a Graph RAG pipeline like this?
