Using GraphQLite with Other SQLite Extensions

GraphQLite is a standard SQLite extension and can share a connection with other SQLite extensions, including sqlite-vec (vector search), sqlite-fts5 (full-text search), and others.

Loading GraphQLite into an Existing Connection

If you already have a sqlite3.Connection, use graphqlite.load() to add GraphQLite functions to it:

import sqlite3
import graphqlite

conn = sqlite3.connect("combined.db")
graphqlite.load(conn)

# Both the graph functions and the raw database are now available on conn
conn.execute("SELECT cypher('CREATE (n:Page {title: \"Home\"})')")
conn.execute("SELECT * FROM nodes")

Wrapping an Existing Connection

graphqlite.wrap() loads GraphQLite into an existing sqlite3.Connection and returns a Connection object that exposes the cypher() method:

import sqlite3
import graphqlite

raw_conn = sqlite3.connect("combined.db")
conn = graphqlite.wrap(raw_conn)

# Use the graphqlite Connection API
conn.cypher("CREATE (n:Page {title: 'Home'})")
results = conn.cypher("MATCH (n:Page) RETURN n.title")

# Access the underlying sqlite3.Connection for raw SQL
raw_conn.execute("SELECT COUNT(*) FROM nodes").fetchone()

Loading Multiple Extensions

Load GraphQLite first (it creates the graph schema tables), then load other extensions:

import sqlite3
import graphqlite

conn = sqlite3.connect("combined.db")

# 1. Load GraphQLite (creates schema tables)
graphqlite.load(conn)

# 2. Load other extensions
conn.enable_load_extension(True)
conn.load_extension("/path/to/other_extension")
conn.enable_load_extension(False)

This order matters: GraphQLite creates nodes, edges, and related tables on first load. If another extension has conflicting table names, loading GraphQLite first lets you detect the conflict early.

Example: GraphQLite + sqlite-vec

Combine graph traversal with vector similarity search. This pattern is the foundation of GraphRAG systems: find semantically similar documents with vectors, then expand to related content via graph edges.

import sqlite3
import graphqlite
import sqlite_vec
import json

# Create and configure the connection
conn = sqlite3.connect("knowledge.db")
graphqlite.load(conn)
sqlite_vec.load(conn)

# Create graph nodes (documents)
conn.execute("SELECT cypher('CREATE (n:Document {doc_id: \"doc1\", title: \"Introduction to Graphs\"})')")
conn.execute("SELECT cypher('CREATE (n:Document {doc_id: \"doc2\", title: \"Graph Algorithms\"})')")
conn.execute("SELECT cypher('CREATE (n:Document {doc_id: \"doc3\", title: \"Vector Search\"})')")

# Link related documents in the graph
conn.execute("""
    SELECT cypher('
        MATCH (a:Document {doc_id: "doc1"}), (b:Document {doc_id: "doc2"})
        CREATE (a)-[:RELATED_TO {strength: 0.9}]->(b)
    ')
""")
conn.execute("""
    SELECT cypher('
        MATCH (b:Document {doc_id: "doc2"}), (c:Document {doc_id: "doc3"})
        CREATE (b)-[:RELATED_TO {strength: 0.7}]->(c)
    ')
""")

# Create a vector table for document embeddings
conn.execute("""
    CREATE VIRTUAL TABLE IF NOT EXISTS doc_embeddings
    USING vec0(
        doc_id TEXT PRIMARY KEY,
        embedding FLOAT[4]
    )
""")

# Insert mock embeddings (replace with real model output)
embeddings = {
    "doc1": [0.1, 0.2, 0.9, 0.3],
    "doc2": [0.1, 0.3, 0.8, 0.4],
    "doc3": [0.9, 0.1, 0.1, 0.8],
}
for doc_id, emb in embeddings.items():
    conn.execute(
        "INSERT INTO doc_embeddings(doc_id, embedding) VALUES (?, ?)",
        [doc_id, json.dumps(emb)]
    )
conn.commit()

# --- Query: vector search + graph expansion ---

# Step 1: Find the most similar document to a query vector
query_embedding = json.dumps([0.1, 0.25, 0.85, 0.35])
similar = conn.execute("""
    SELECT doc_id
    FROM doc_embeddings
    WHERE embedding MATCH ?
      AND k = 2
    ORDER BY distance
""", [query_embedding]).fetchall()

print("Similar documents:", [row[0] for row in similar])

# Step 2: Expand to graph neighbors for each similar document
for (doc_id,) in similar:
    related = conn.execute(f"""
        SELECT cypher('
            MATCH (d:Document {{doc_id: "{doc_id}"}})-[:RELATED_TO]->(other:Document)
            RETURN other.doc_id AS id, other.title AS title
        ')
    """).fetchall()
    if related:
        print(f"  {doc_id} is related to:")
        for (row_json,) in related:
            row = json.loads(row_json)
            print(f"    - {row['id']}: {row['title']}")

Sharing Connections: In-Memory Databases

In-memory SQLite databases are private to a single connection. All extensions that need to share data must use the same conn object:

# Correct: one connection, multiple extensions
conn = sqlite3.connect(":memory:")
graphqlite.load(conn)
sqlite_vec.load(conn)
# Both extensions operate on the same in-memory database

# Wrong: two separate connections, two separate databases
conn1 = sqlite3.connect(":memory:")
conn2 = sqlite3.connect(":memory:")
# conn1 and conn2 cannot see each other's data

For file-based databases, this restriction does not apply — separate connections can open the same file, subject to SQLite locking rules.

Extension Loading Order

In general:

  1. GraphQLite first. It creates the graph schema (nodes, edges, property tables) on first load. Other extensions that reference these tables will find them already present.

  2. Other extensions next. Load them with enable_load_extension(True) / load_extension() / enable_load_extension(False).

  3. Commit between loads if any extension creates tables, to ensure schema visibility:

    graphqlite.load(conn)
conn.commit()

conn.enable_load_extension(True)
conn.load_extension("my_extension")
conn.enable_load_extension(False)
conn.commit()

Using GraphQLite with the Python Graph API Alongside Other Extensions

When you use graphqlite.Graph or graphqlite.connect(), access the underlying sqlite3.Connection to load additional extensions:

import graphqlite
import sqlite_vec

g = graphqlite.Graph("knowledge.db")

# Access the raw sqlite3.Connection
raw_conn = g.connection.sqlite_connection
sqlite_vec.load(raw_conn)

# Now use both APIs on the same connection
g.upsert_node("doc1", {"title": "Introduction"}, "Document")
raw_conn.execute("CREATE VIRTUAL TABLE IF NOT EXISTS vecs USING vec0(embedding FLOAT[4])")

Troubleshooting

Extension conflicts

If two extensions register functions with the same name, the later-loaded extension wins. Check for collisions between GraphQLite's functions (cypher, regexp, gql_load_graph, etc.) and the functions registered by your other extension.

Missing tables after loading

Ensure GraphQLite was loaded before any query that references graph tables. The schema is created lazily on first load; if the connection is closed and reopened, graphqlite.load() must be called again.

Transaction isolation

Some extensions use their own transaction management. If you encounter "table is locked" errors, commit between extension operations:

graphqlite.load(conn)
conn.execute("SELECT cypher('CREATE (n:Test {v: 1})')")
conn.commit()   # Flush GraphQLite writes

# Now safe to use the other extension
conn.execute("INSERT INTO vec_table VALUES (?)", ["..."])
conn.commit()