kglite 0.5.10

A high-performance graph database library with Python bindings written in Rust

KGLite

An embedded knowledge graph engine for Python. Import and go — no server, no setup.

For AI agents: see kglite.pyi for full type stubs with signatures and docstrings.

Embedded, in-process	No server, no network; import and go
In-memory	Persistence via save()/load() snapshots
Cypher subset	Querying + mutations; returns dict or DataFrame
Single-label nodes	Each node has exactly one type
Single-threaded	Designed for single-threaded use (see Threading)

Requirements: Python 3.10+ (CPython) | macOS (ARM/Intel), Linux (x86_64/aarch64), Windows (x86_64) | pandas >= 1.5

pip install kglite

---

Quick Start

import kglite

graph = kglite.KnowledgeGraph()

# Create nodes and relationships
graph.cypher("CREATE (:Person {name: 'Alice', age: 28, city: 'Oslo'})")
graph.cypher("CREATE (:Person {name: 'Bob', age: 35, city: 'Bergen'})")
graph.cypher("CREATE (:Person {name: 'Charlie', age: 42, city: 'Oslo'})")
graph.cypher("""
    MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
    CREATE (a)-[:KNOWS]->(b)
""")

# Query — returns list[dict]
result = graph.cypher("""
    MATCH (p:Person) WHERE p.age > 30
    RETURN p.name AS name, p.city AS city
    ORDER BY p.age DESC
""")
for row in result:
    print(row['name'], row['city'])

# Or get a pandas DataFrame
df = graph.cypher("MATCH (p:Person) RETURN p.name, p.age ORDER BY p.age", to_df=True)

# Persist to disk and reload
graph.save("my_graph.kgl")
loaded = kglite.load("my_graph.kgl")

Loading Data from DataFrames

For bulk loading (thousands of rows), use the fluent API:

import pandas as pd

users_df = pd.DataFrame({
    'user_id': [1001, 1002, 1003],
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [28, 35, 42]
})

graph.add_nodes(data=users_df, node_type='User', unique_id_field='user_id', node_title_field='name')

edges_df = pd.DataFrame({'source_id': [1001, 1002], 'target_id': [1002, 1003]})
graph.add_connections(data=edges_df, connection_type='KNOWS', source_type='User',
                      source_id_field='source_id', target_type='User', target_id_field='target_id')

graph.cypher("MATCH (u:User) WHERE u.age > 30 RETURN u.name, u.age")

---

Core Concepts

Nodes have three built-in fields — type (label), title (display name), id (unique within type) — plus arbitrary properties. Each node has exactly one type.

Relationships connect two nodes with a type (e.g., :KNOWS) and optional properties. The Cypher API calls them "relationships"; the fluent API calls them "connections" — same thing.

Selections (fluent API) are lightweight views — a set of node indices that flow through chained operations like type_filter().filter().traverse(). They don't copy data.

Atomicity. Each cypher() call is atomic — if any clause fails, the graph remains unchanged. There are no multi-statement transactions. Durability only via explicit save().

---

Return Types

All node-related methods use a consistent key order: type, title, id, then other properties.

Cypher

Query type	Returns
Read (MATCH...RETURN)	list[dict] — one dict per row, keyed by column alias
Read with to_df=True	pandas.DataFrame
Mutation (CREATE, SET, DELETE, MERGE)	{'stats': {'nodes_created': int, ...}}
Mutation with RETURN	{'rows': [...], 'stats': {...}}
EXPLAIN prefix	str (query plan, not executed)

Node dicts

Every method that returns node data uses the same dict shape:

{'type': 'Person', 'title': 'Alice', 'id': 1, 'age': 28, 'city': 'Oslo'}
#  ^^^^             ^^^^^             ^^^       ^^^ other properties

Retrieval methods (cheapest to most expensive)

Method	Returns	Notes
node_count()	int	No materialization
indices()	list[int]	Raw graph indices
id_values()	list[Any]	Flat list of IDs
get_ids()	list[{type, title, id}]	Identification only
get_titles()	list[str]	Flat list (see below)
get_properties(['a','b'])	list[tuple]	Flat list (see below)
get_nodes()	list[dict]	Full node dicts
to_df()	DataFrame	Columns: type, title, id, ...props
get_node_by_id(type, id)	dict | None	O(1) hash lookup

Flat vs. grouped results

get_titles(), get_properties(), and get_nodes() automatically flatten when there is only one parent group (the common case). After a traversal with multiple parent groups, they return grouped dicts instead:

# No traversal (single group) → flat list
graph.type_filter('Person').get_titles()
# ['Alice', 'Bob', 'Charlie']

# After traversal (multiple groups) → grouped dict
graph.type_filter('Person').traverse('KNOWS').get_titles()
# {'Alice': ['Bob'], 'Bob': ['Charlie']}

# Override with flatten_single_parent=False to always get grouped
graph.type_filter('Person').get_titles(flatten_single_parent=False)
# {'Root': ['Alice', 'Bob', 'Charlie']}

Centrality methods

All centrality methods (pagerank, betweenness_centrality, closeness_centrality, degree_centrality) return:

Mode	Returns
Default	list[{type, title, id, score}] sorted by score desc
as_dict=True	{id: score} — keyed by node ID (unique per type)
to_df=True	DataFrame with columns type, title, id, score

---

API Quick Reference

Graph lifecycle

graph = kglite.KnowledgeGraph()     # create
graph.save("file.kgl")              # persist
graph = kglite.load("file.kgl")     # reload
graph.graph_info()                   # → dict with node_count, edge_count, fragmentation_ratio, ...
graph.get_schema()                   # → str summary of types and connections
graph.node_types                     # → ['Person', 'Product', ...]

Cypher (recommended for most tasks)

graph.cypher("MATCH (n:Person) RETURN n.name")                          # → list[dict]
graph.cypher("MATCH (n:Person) RETURN n.name", to_df=True)              # → DataFrame
graph.cypher("MATCH (n:Person) RETURN n.name", params={'x': 1})         # parameterized
graph.cypher("CREATE (:Person {name: 'Alice'})")                        # → {'stats': {...}}

Data loading (fluent API)

graph.add_nodes(data=df, node_type='T', unique_id_field='id')           # → report dict
graph.add_connections(data=df, connection_type='REL',
    source_type='A', source_id_field='src',
    target_type='B', target_id_field='tgt')                              # → report dict

Selection chain (fluent API)

graph.type_filter('Person')                        # select by type → KnowledgeGraph
    .filter({'age': {'>': 25}})                    # filter → KnowledgeGraph
    .sort('age', ascending=False)                  # sort → KnowledgeGraph
    .traverse('KNOWS', direction='outgoing')       # traverse → KnowledgeGraph
    .get_nodes()                                   # materialize → list[dict]

Introspection

graph.schema()                                # → full graph overview (types, counts, connections, indexes)
graph.connection_types()                      # → list of edge types with counts and endpoint types
graph.properties('Person')                    # → per-property stats (type, non_null, unique, values)
graph.properties('Person', max_values=50)     # → include values list for up to 50 unique values
graph.neighbors_schema('Person')              # → outgoing/incoming connection topology
graph.sample('Person', n=5)                   # → first N nodes as dicts
graph.indexes()                               # → all indexes with type info
graph.agent_describe()                        # → XML string for LLM prompt context

Algorithms

graph.shortest_path(source_type, source_id, target_type, target_id)  # → {path, connections, length} | None
graph.all_paths(source_type, source_id, target_type, target_id)      # → list[{path, connections, length}]
graph.pagerank(top_k=10)                                             # → list[{type, title, id, score}]
graph.betweenness_centrality(top_k=10)                               # → list[{type, title, id, score}]
graph.louvain_communities()                                          # → {communities, modularity, num_communities}
graph.connected_components()                                         # → list[list[node_dict]]

---

Schema Introspection

Methods for exploring graph structure — what types exist, what properties they have, and how they connect. Useful for discovering an unfamiliar graph or building dynamic UIs.

schema() — Full graph overview

s = graph.schema()
# {
#   'node_types': {
#     'Person': {'count': 500, 'properties': {'age': 'Int64', 'city': 'String'}},
#     'Company': {'count': 50, 'properties': {'founded': 'Int64'}},
#   },
#   'connection_types': {
#     'KNOWS': {'count': 1200, 'source_types': ['Person'], 'target_types': ['Person']},
#     'WORKS_AT': {'count': 500, 'source_types': ['Person'], 'target_types': ['Company']},
#   },
#   'indexes': ['Person.city', 'Person.(city, age)'],
#   'node_count': 550,
#   'edge_count': 1700,
# }

connection_types() — Edge type inventory

graph.connection_types()
# [
#   {'type': 'KNOWS', 'count': 1200, 'source_types': ['Person'], 'target_types': ['Person']},
#   {'type': 'WORKS_AT', 'count': 500, 'source_types': ['Person'], 'target_types': ['Company']},
# ]

properties(node_type, max_values=20) — Property details

Per-property statistics for a single node type. Only properties that exist on at least one node are included. The values list is included when the unique count is at or below max_values (default 20). Set max_values=0 to never include values, or raise it to see more (e.g., max_values=100).

graph.properties('Person')
# {
#   'type':  {'type': 'str', 'non_null': 500, 'unique': 1, 'values': ['Person']},
#   'title': {'type': 'str', 'non_null': 500, 'unique': 500},
#   'id':    {'type': 'int', 'non_null': 500, 'unique': 500},
#   'city':  {'type': 'str', 'non_null': 500, 'unique': 3, 'values': ['Bergen', 'Oslo', 'Stavanger']},
#   'age':   {'type': 'int', 'non_null': 500, 'unique': 45},
#   'email': {'type': 'str', 'non_null': 250, 'unique': 250},
# }

# See all values even for higher-cardinality properties
graph.properties('Person', max_values=100)

Raises KeyError if the node type doesn't exist.

neighbors_schema(node_type) — Connection topology

Outgoing and incoming connections grouped by (connection type, endpoint type):

graph.neighbors_schema('Person')
# {
#   'outgoing': [
#     {'connection_type': 'KNOWS', 'target_type': 'Person', 'count': 1200},
#     {'connection_type': 'WORKS_AT', 'target_type': 'Company', 'count': 500},
#   ],
#   'incoming': [
#     {'connection_type': 'KNOWS', 'source_type': 'Person', 'count': 1200},
#   ],
# }

Raises KeyError if the node type doesn't exist.

sample(node_type, n=5) — Quick data peek

Returns the first N nodes of a type as full dicts:

graph.sample('Person', n=3)
# [
#   {'type': 'Person', 'title': 'Alice', 'id': 1, 'age': 28, 'city': 'Oslo'},
#   {'type': 'Person', 'title': 'Bob', 'id': 2, 'age': 35, 'city': 'Bergen'},
#   {'type': 'Person', 'title': 'Charlie', 'id': 3, 'age': 42, 'city': 'Oslo'},
# ]

Returns fewer than N if the type has fewer nodes. Raises KeyError if the node type doesn't exist.

indexes() — Unified index list

graph.indexes()
# [
#   {'node_type': 'Person', 'property': 'city', 'type': 'equality'},
#   {'node_type': 'Person', 'properties': ['city', 'age'], 'type': 'composite'},
# ]

agent_describe() — AI agent context

Returns a self-contained XML string summarizing the graph structure and supported Cypher syntax. Designed to be included directly in an LLM prompt:

xml = graph.agent_describe()
prompt = f"You have a knowledge graph:\n{xml}\nAnswer the user's question using cypher()."

The output includes:

• Dynamic (per-graph): node types with counts and property schemas, connection types, indexes
• Static (always the same): supported Cypher subset, key API methods, single-label model notes

---

Cypher Queries

A substantial Cypher subset. See the Supported Cypher Subset table for exact coverage.

Single-label note: Each node has exactly one type. labels(n) returns a string, not a list. SET n:OtherLabel is not supported.

result = graph.cypher("""
    MATCH (p:Person)-[:KNOWS]->(f:Person)
    WHERE p.age > 30 AND f.city = 'Oslo'
    RETURN p.name AS person, f.name AS friend, p.age AS age
    ORDER BY p.age DESC
    LIMIT 10
""")

# Read queries → list[dict]
for row in result:
    print(f"{row['person']} knows {row['friend']}")

# Pass to_df=True for a DataFrame
df = graph.cypher("MATCH (n:Person) RETURN n.name, n.age ORDER BY n.age", to_df=True)

WHERE Clause

# Comparisons: =, <>, <, >, <=, >=
graph.cypher("MATCH (n:Product) WHERE n.price >= 500 RETURN n.title, n.price")

# Boolean operators: AND, OR, NOT
graph.cypher("MATCH (n:Person) WHERE n.age > 25 AND NOT n.city = 'Oslo' RETURN n.name")

# Null checks
graph.cypher("MATCH (n:Person) WHERE n.email IS NOT NULL RETURN n.name")

# String predicates: CONTAINS, STARTS WITH, ENDS WITH
graph.cypher("MATCH (n:Person) WHERE n.name CONTAINS 'ali' RETURN n.name")

# IN lists
graph.cypher("MATCH (n:Person) WHERE n.city IN ['Oslo', 'Bergen'] RETURN n.name")

# Regex matching with =~
graph.cypher("MATCH (n:Person) WHERE n.name =~ '(?i)^ali.*' RETURN n.name")
graph.cypher("MATCH (n:Person) WHERE n.email =~ '.*@example\\.com$' RETURN n.name")

Relationship Properties

Relationships can have properties. Access them with r.property syntax:

# Create relationships with properties
graph.cypher("""
    MATCH (p:Person {name: 'Alice'}), (m:Movie {title: 'Inception'})
    CREATE (p)-[:RATED {score: 5, comment: 'Excellent'}]->(m)
""")

# Access, filter, aggregate, sort by relationship properties
graph.cypher("MATCH (p)-[r:RATED]->(m) RETURN p.name, r.score, r.comment, type(r)")
graph.cypher("MATCH (p)-[r:RATED]->(m) WHERE r.score >= 4 RETURN p.name, m.title")
graph.cypher("MATCH (p)-[r:RATED]->(m) RETURN avg(r.score) AS avg_rating")
graph.cypher("MATCH ()-[r:RATED]->(m) RETURN m.title, r.score ORDER BY r.score DESC")

Aggregation

graph.cypher("MATCH (n:Person) RETURN n.city, count(*) AS population ORDER BY population DESC")
graph.cypher("MATCH (n:Person) RETURN avg(n.age) AS avg_age, min(n.age), max(n.age)")

# DISTINCT
graph.cypher("MATCH (n:Person) RETURN DISTINCT n.city")
graph.cypher("MATCH (n:Person) RETURN count(DISTINCT n.city) AS unique_cities")

WITH Clause

graph.cypher("""
    MATCH (p:Person)-[:KNOWS]->(f:Person)
    WITH p, count(f) AS friend_count
    WHERE friend_count > 3
    RETURN p.name, friend_count
    ORDER BY friend_count DESC
""")

OPTIONAL MATCH

Left outer join — keeps rows even when no match:

graph.cypher("""
    MATCH (p:Person)
    OPTIONAL MATCH (p)-[:KNOWS]->(f:Person)
    RETURN p.name, count(f) AS friends
""")

Built-in Functions

Function	Description
toUpper(expr)	Convert to uppercase
toLower(expr)	Convert to lowercase
toString(expr)	Convert to string
toInteger(expr)	Convert to integer
toFloat(expr)	Convert to float
size(expr)	Length of string or list
type(r)	Relationship type
id(n)	Node ID
labels(n)	Node type (string, not list — single-label)
coalesce(a, b, ...)	First non-null argument
length(p)	Path hop count
nodes(p)	Nodes in a path
relationships(p)	Relationships in a path

Arithmetic

graph.cypher("MATCH (n:Product) RETURN n.title, n.price * 1.25 AS price_with_tax")

CASE Expressions

# Generic form
graph.cypher("""
    MATCH (n:Person)
    RETURN n.name,
           CASE WHEN n.age >= 18 THEN 'adult' ELSE 'minor' END AS category
""")

# Simple form
graph.cypher("""
    MATCH (n:Person)
    RETURN n.name,
           CASE n.city WHEN 'Oslo' THEN 'capital' WHEN 'Bergen' THEN 'west coast' ELSE 'other' END AS region
""")

List Comprehensions

[x IN list WHERE predicate | expression] syntax:

# Map: double each number
graph.cypher("UNWIND [1] AS _ RETURN [x IN [1, 2, 3, 4, 5] | x * 2] AS doubled")
# [2, 4, 6, 8, 10]

# Filter only
graph.cypher("UNWIND [1] AS _ RETURN [x IN [1, 2, 3, 4, 5] WHERE x > 3] AS filtered")
# [4, 5]

# Filter + map
graph.cypher("UNWIND [1] AS _ RETURN [x IN [1, 2, 3, 4, 5] WHERE x > 3 | x * 2] AS result")
# [8, 10]

# With collect() — transform aggregated values
graph.cypher("""
    MATCH (p:Person)
    WITH collect(p.name) AS names
    RETURN [x IN names | toUpper(x)] AS upper_names
""")

Note: List comprehensions require at least one row in the pipeline. Use UNWIND [1] AS _ or a preceding MATCH/WITH to provide the row context.

Map Projections

n {.prop1, .prop2, alias: expr} syntax — select specific properties from a node:

# Select only name and age (returns a dict per row)
graph.cypher("MATCH (p:Person) RETURN p {.name, .age} AS info")
# [{'info': {'name': 'Alice', 'age': 30}}, {'info': {'name': 'Bob', 'age': 25}}]

# Mix shorthand properties with computed values
graph.cypher("""
    MATCH (p:Person)-[:WORKS_AT]->(c:Company)
    RETURN p {.name, .age, company: c.name} AS info
""")

# System properties (id, type) work too
graph.cypher("MATCH (p:Person) RETURN p {.name, .type, .id} AS info LIMIT 1")
# [{'info': {'name': 'Alice', 'type': 'Person', 'id': 1}}]

Parameters

graph.cypher(
    "MATCH (n:Person) WHERE n.age > $min_age RETURN n.name, n.age",
    params={'min_age': 25}
)

# Parameters in inline pattern properties
graph.cypher(
    "MATCH (n:Person {name: $name}) RETURN n.age",
    params={'name': 'Alice'}
)

# Parameters with DataFrame output
df = graph.cypher(
    "MATCH (n:Person) WHERE n.age > $min_age RETURN n.name, n.age ORDER BY n.age",
    params={'min_age': 20}, to_df=True
)

UNWIND

Expand a list into rows:

graph.cypher("UNWIND [1, 2, 3] AS x RETURN x, x * 2 AS doubled")

UNION

graph.cypher("""
    MATCH (n:Person) WHERE n.city = 'Oslo' RETURN n.name AS name
    UNION
    MATCH (n:Person) WHERE n.age > 30 RETURN n.name AS name
""")

Variable-Length Paths

# 1 to 3 hops
graph.cypher("MATCH (a:Person)-[:KNOWS*1..3]->(b:Person) WHERE a.name = 'Alice' RETURN b.name")

# Exact 2 hops
graph.cypher("MATCH (a:Person)-[:KNOWS*2]->(b:Person) RETURN a.name, b.name")

WHERE EXISTS

Check for subpattern existence. Both brace { } and parenthesis (( )) syntax are supported:

# Brace syntax
graph.cypher("MATCH (p:Person) WHERE EXISTS { (p)-[:KNOWS]->(:Person) } RETURN p.name")

# Parenthesis syntax (equivalent)
graph.cypher("MATCH (p:Person) WHERE EXISTS((p)-[:KNOWS]->(:Person)) RETURN p.name")

# Negation
graph.cypher("""
    MATCH (p:Person)
    WHERE NOT EXISTS { (p)-[:PURCHASED]->(:Product) }
    RETURN p.name
""")

shortestPath()

BFS shortest path between two nodes. Supports directed (->) and undirected (-) syntax:

# Directed — only follows edges in their defined direction
result = graph.cypher("""
    MATCH p = shortestPath((a:Person {name: 'Alice'})-[:KNOWS*..10]->(b:Person {name: 'Dave'}))
    RETURN length(p), nodes(p), relationships(p), a.name, b.name
""")

# Undirected — traverses edges in both directions (same as fluent API)
result = graph.cypher("""
    MATCH p = shortestPath((a:Person {name: 'Alice'})-[:KNOWS*..10]-(b:Person {name: 'Dave'}))
    RETURN length(p), nodes(p), relationships(p)
""")

# No path → empty list (not an error)

Path functions: length(p) returns hop count, nodes(p) returns node list, relationships(p) returns edge type list.

CREATE / SET / DELETE / REMOVE / MERGE

# CREATE — returns stats
result = graph.cypher("CREATE (n:Person {name: 'Alice', age: 30, city: 'Oslo'})")
print(result['stats']['nodes_created'])  # 1

# CREATE relationship between existing nodes
graph.cypher("""
    MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
    CREATE (a)-[:KNOWS]->(b)
""")

# SET — update properties
result = graph.cypher("MATCH (n:Person {name: 'Bob'}) SET n.age = 26, n.city = 'Stavanger'")
print(result['stats']['properties_set'])  # 2

# DELETE — plain DELETE errors if node has relationships; DETACH removes all
graph.cypher("MATCH (n:Person {name: 'Alice'}) DETACH DELETE n")

# REMOVE — remove properties (id/type are immutable)
graph.cypher("MATCH (n:Person {name: 'Alice'}) REMOVE n.city")

# MERGE — match or create
graph.cypher("""
    MERGE (n:Person {name: 'Alice'})
    ON CREATE SET n.created = 'today'
    ON MATCH SET n.updated = 'today'
""")

DataFrame Output

df = graph.cypher("""
    MATCH (p:Person)-[:KNOWS]->(f:Person)
    WITH p, count(f) AS friends
    RETURN p.name, p.city, friends
    ORDER BY friends DESC
""", to_df=True)

EXPLAIN

Prefix any Cypher query with EXPLAIN to see the query plan without executing it:

plan = graph.cypher("""
    EXPLAIN
    MATCH (p:Person)
    OPTIONAL MATCH (p)-[:KNOWS]->(f:Person)
    WITH p, count(f) AS friends
    RETURN p.name, friends
""")
print(plan)
# Query Plan:
#   1. NodeScan (MATCH) :Person
#   2. FusedOptionalMatchAggregate (optimized OPTIONAL MATCH + count)
#   3. Projection (RETURN) [p.name, friends]
# Optimizations: optional_match_fusion=1

Supported Cypher Subset

Category	Supported
Clauses	MATCH, OPTIONAL MATCH, WHERE, RETURN, WITH, ORDER BY, SKIP, LIMIT, UNWIND, UNION/UNION ALL, CREATE, SET, DELETE, DETACH DELETE, REMOVE, MERGE, EXPLAIN
Patterns	Node (n:Type), relationship -[:REL]->, variable-length *1..3, undirected -[:REL]-, properties {key: val}, p = shortestPath(...)
WHERE	=, <>, <, >, <=, >=, =~ (regex), AND, OR, NOT, IS NULL, IS NOT NULL, IN [...], CONTAINS, STARTS WITH, ENDS WITH, EXISTS { pattern }, EXISTS(( pattern ))
RETURN	n.prop, r.prop, AS aliases, DISTINCT, arithmetic +/-/*//, map projections n {.prop1, .prop2}
Aggregation	count(*), count(expr), sum, avg/mean, min, max, collect, std
Expressions	CASE WHEN...THEN...ELSE...END, $param, [x IN list WHERE ... | expr]
Functions	toUpper, toLower, toString, toInteger, toFloat, size, length, type, id, labels, coalesce, nodes(p), relationships(p)
Mutations	CREATE (n:Label {props}), CREATE (a)-[:TYPE]->(b), SET n.prop = expr, DELETE, DETACH DELETE, REMOVE n.prop, MERGE ... ON CREATE SET ... ON MATCH SET
Not supported	CALL/stored procedures, FOREACH, subqueries, SET n:Label (label mutation), REMOVE n:Label, multi-label

---

Fluent API: Data Loading

For most use cases, use Cypher queries. The fluent API is for bulk operations from DataFrames or complex data pipelines.

Adding Nodes

products_df = pd.DataFrame({
    'product_id': [101, 102, 103],
    'title': ['Laptop', 'Phone', 'Tablet'],
    'price': [999.99, 699.99, 349.99],
    'stock': [45, 120, 30]
})

report = graph.add_nodes(
    data=products_df,
    node_type='Product',
    unique_id_field='product_id',
    node_title_field='title',
    columns=['product_id', 'title', 'price', 'stock'],       # whitelist columns (None = all)
    column_types={'launch_date': 'datetime'},                  # explicit type hints
    conflict_handling='update'  # 'update' | 'replace' | 'skip' | 'preserve'
)
print(f"Created {report['nodes_created']} nodes in {report['processing_time_ms']}ms")

Property Mapping

When adding nodes, unique_id_field and node_title_field are renamed to id and title. The original column names no longer exist as properties.

Your DataFrame Column	Stored As	Why
unique_id_field (e.g., user_id)	id	Canonical identifier
node_title_field (e.g., name)	title	Display/label field
All other columns	Same name	Preserved as-is

# After adding with unique_id_field='user_id', node_title_field='name':
graph.type_filter('User').filter({'user_id': 1001})  # WRONG — field was renamed
graph.type_filter('User').filter({'id': 1001})        # CORRECT

Creating Connections

purchases_df = pd.DataFrame({
    'user_id': [1001, 1001, 1002],
    'product_id': [101, 103, 102],
    'date': ['2023-01-15', '2023-02-10', '2023-01-20'],
    'quantity': [1, 2, 1]
})

graph.add_connections(
    data=purchases_df,
    connection_type='PURCHASED',
    source_type='User',
    source_id_field='user_id',
    target_type='Product',
    target_id_field='product_id',
    columns=['date', 'quantity']
)

source_type and target_type each refer to a single node type. To connect nodes of the same type, set both to the same value (e.g., source_type='Person', target_type='Person').

Working with Dates

graph.add_nodes(
    data=estimates_df,
    node_type='Estimate',
    unique_id_field='estimate_id',
    node_title_field='name',
    column_types={'valid_from': 'datetime', 'valid_to': 'datetime'}
)

graph.type_filter('Estimate').filter({'valid_from': {'>=': '2020-06-01'}})
graph.type_filter('Estimate').valid_at('2020-06-15')
graph.type_filter('Estimate').valid_during('2020-01-01', '2020-06-30')

Batch Property Updates

result = graph.type_filter('Prospect').filter({'status': 'Inactive'}).update({
    'is_active': False,
    'deactivation_reason': 'status_inactive'
})

updated_graph = result['graph']
print(f"Updated {result['nodes_updated']} nodes")

Operation Reports

Operations that modify the graph return detailed reports:

report = graph.add_nodes(data=df, node_type='Product', unique_id_field='product_id')
# report keys: operation, timestamp, nodes_created, nodes_updated, nodes_skipped,
#              processing_time_ms, has_errors, errors

graph.get_last_report()       # most recent operation report
graph.get_operation_index()   # sequential index of last operation
graph.get_report_history()    # all reports

---

Fluent API: Querying

For most queries, prefer Cypher. The fluent API is for building reusable query chains or when you need explain() and selection-based workflows.

Filtering

graph.type_filter('Product').filter({'price': 999.99})
graph.type_filter('Product').filter({'price': {'<': 500.0}, 'stock': {'>': 50}})
graph.type_filter('Product').filter({'id': {'in': [101, 103]}})
graph.type_filter('Product').filter({'category': {'is_null': True}})

# Orphan nodes (no connections)
graph.filter_orphans(include_orphans=True)

Sorting

graph.type_filter('Product').sort('price')
graph.type_filter('Product').sort('price', ascending=False)
graph.type_filter('Product').sort([('stock', False), ('price', True)])

Traversing the Graph

alice = graph.type_filter('User').filter({'title': 'Alice'})
alice_products = alice.traverse(connection_type='PURCHASED', direction='outgoing')

# Filter and sort traversal targets
expensive = alice.traverse(
    connection_type='PURCHASED',
    filter_target={'price': {'>=': 500.0}},
    sort_target='price',
    max_nodes=10
)

# Get connection information
alice.get_connections(include_node_properties=True)

Set Operations

n3 = graph.type_filter('Prospect').filter({'geoprovince': 'N3'})
m3 = graph.type_filter('Prospect').filter({'geoprovince': 'M3'})

n3.union(m3)                    # all nodes from both (OR)
n3.intersection(m3)             # nodes in both (AND)
n3.difference(m3)               # nodes in n3 but not m3
n3.symmetric_difference(m3)     # nodes in exactly one (XOR)

Retrieving Results

people = graph.type_filter('Person')

# Lightweight (no property materialization)
people.node_count()                     # → 3
people.indices()                        # → [0, 1, 2]
people.id_values()                      # → [1, 2, 3]

# Medium (partial materialization)
people.get_ids()                        # → [{'type': 'Person', 'title': 'Alice', 'id': 1}, ...]
people.get_titles()                     # → ['Alice', 'Bob', 'Charlie']
people.get_properties(['age', 'city'])  # → [(28, 'Oslo'), (35, 'Bergen'), (42, 'Oslo')]

# Full materialization
people.get_nodes()                      # → [{'type': 'Person', 'title': 'Alice', 'id': 1, 'age': 28, ...}, ...]
people.to_df()                          # → DataFrame with columns type, title, id, age, city, ...

# Single node lookup (O(1))
graph.get_node_by_id('Person', 1)       # → {'type': 'Person', 'title': 'Alice', ...} or None

Debugging Selections

result = graph.type_filter('User').filter({'id': 1001})
print(result.explain())
# TYPE_FILTER User (1000 nodes) -> FILTER (1 nodes)

Pattern Matching

For simpler pattern-based queries without full Cypher clause support:

results = graph.match_pattern(
    '(p:Play)-[:HAS_PROSPECT]->(pr:Prospect)-[:BECAME_DISCOVERY]->(d:Discovery)'
)

for match in results:
    print(f"Play: {match['p']['title']}, Discovery: {match['d']['title']}")

# With property conditions
graph.match_pattern('(u:User)-[:PURCHASED]->(p:Product {category: "Electronics"})')

# Limit results for large graphs
graph.match_pattern('(a:Person)-[:KNOWS]->(b:Person)', max_matches=100)

---

Graph Algorithms

Shortest Path

result = graph.shortest_path(source_type='Person', source_id=1, target_type='Person', target_id=100)
if result:
    for node in result["path"]:
        print(f"{node['type']}: {node['title']}")
    print(f"Connections: {result['connections']}")
    print(f"Path length: {result['length']}")

Lightweight variants when you don't need full path data:

graph.shortest_path_length(...)    # → int | None (hop count only)
graph.shortest_path_ids(...)       # → list[id] | None (node IDs along path)
graph.shortest_path_indices(...)   # → list[int] | None (raw graph indices, fastest)

All path methods support connection_types, via_types, and timeout_ms for filtering and safety.

All Paths

paths = graph.all_paths(
    source_type='Play', source_id=1,
    target_type='Wellbore', target_id=100,
    max_hops=4,
    max_results=100  # Prevent OOM on dense graphs
)

Connected Components

components = graph.connected_components()
# Returns list of lists: [[node_dicts...], [node_dicts...], ...]
print(f"Found {len(components)} connected components")
print(f"Largest component: {len(components[0])} nodes")

graph.are_connected(source_type='Person', source_id=1, target_type='Person', target_id=100)

Centrality Algorithms

All centrality methods return list[{type, title, id, score}], sorted by score descending.

graph.betweenness_centrality(top_k=10)
graph.betweenness_centrality(normalized=True, sample_size=500)
graph.pagerank(top_k=10, damping_factor=0.85)
graph.degree_centrality(top_k=10)
graph.closeness_centrality(top_k=10)

# Alternative output formats
graph.pagerank(as_dict=True)      # → {1: 0.45, 2: 0.32, ...} (keyed by id)
graph.pagerank(to_df=True)        # → DataFrame with type, title, id, score columns

Community Detection

# Louvain modularity optimization (recommended)
result = graph.louvain_communities()
# {'communities': {0: [{type, title, id}, ...], 1: [...]},
#  'modularity': 0.45, 'num_communities': 2}

for comm_id, members in result['communities'].items():
    names = [m['title'] for m in members]
    print(f"Community {comm_id}: {names}")

# With edge weights and resolution tuning
result = graph.louvain_communities(weight_property='strength', resolution=1.5)

# Label propagation (faster, less precise)
result = graph.label_propagation(max_iterations=100)

Node Degrees

degrees = graph.type_filter('Person').get_degrees()
# Returns: {'Alice': 5, 'Bob': 3, ...}

---

Spatial Operations

Bounding Box

graph.type_filter('Discovery').within_bounds(
    lat_field='latitude', lon_field='longitude',
    min_lat=58.0, max_lat=62.0, min_lon=1.0, max_lon=5.0
)

Distance Queries (Haversine)

graph.type_filter('Wellbore').near_point_km(
    center_lat=60.5, center_lon=3.2, max_distance_km=50.0,
    lat_field='latitude', lon_field='longitude'
)

WKT Geometry Intersection

graph.type_filter('Field').intersects_geometry(
    'POLYGON((1 58, 5 58, 5 62, 1 62, 1 58))',
    geometry_field='wkt_geometry'
)

Point-in-Polygon

graph.type_filter('Block').contains_point(lat=60.5, lon=3.2, geometry_field='wkt_geometry')

---

Analytics

Statistics

price_stats = graph.type_filter('Product').statistics('price')
unique_cats = graph.type_filter('Product').unique_values(property='category', max_length=10)

Calculations

graph.type_filter('Product').calculate(expression='price * 1.1', store_as='price_with_tax')

graph.type_filter('User').traverse('PURCHASED').calculate(
    expression='sum(price * quantity)', store_as='total_spent'
)

graph.type_filter('User').traverse('PURCHASED').count(store_as='product_count', group_by_parent=True)

Connection Aggregation

graph.type_filter('Discovery').traverse('EXTENDS_INTO').calculate(
    expression='sum(share_pct)',
    aggregate_connections=True
)

Supported: sum, avg/mean, min, max, count, std.

---

Schema and Indexes

Schema Definition

graph.define_schema({
    'nodes': {
        'Prospect': {
            'required': ['npdid_prospect', 'prospect_name'],
            'optional': ['prospect_status'],
            'types': {'npdid_prospect': 'integer', 'prospect_name': 'string'}
        }
    },
    'connections': {
        'HAS_ESTIMATE': {'source': 'Prospect', 'target': 'ProspectEstimate'}
    }
})

errors = graph.validate_schema()
schema = graph.get_schema()

Indexes

Indexes accelerate equality lookups only (WHERE n.prop = value). Range conditions (<, >, <=, >=) always scan.

graph.create_index('Prospect', 'prospect_geoprovince')
graph.create_composite_index('Person', ['city', 'age'])

graph.list_indexes()
graph.drop_index('Prospect', 'prospect_geoprovince')

Indexes are maintained automatically by all mutation operations.

---

Import and Export

Saving and Loading

graph.save("my_graph.kgl")
loaded_graph = kglite.load("my_graph.kgl")

Portability: Save files use bincode serialization and are not guaranteed portable across OS, CPU architecture, or library versions. Always re-export via a portable format (GraphML, CSV) when sharing across machines.

Export Formats

graph.export('my_graph.graphml', format='graphml')  # Gephi, yEd
graph.export('my_graph.gexf', format='gexf')        # Gephi native
graph.export('my_graph.json', format='d3')           # D3.js
graph.export('my_graph.csv', format='csv')           # creates _nodes.csv + _edges.csv

graphml_string = graph.export_string(format='graphml')

Subgraph Extraction

subgraph = (
    graph.type_filter('Company')
    .filter({'title': 'Acme Corp'})
    .expand(hops=2)
    .to_subgraph()
)
subgraph.export('acme_network.graphml', format='graphml')

---

Performance

Tips

1. Batch operations — add nodes/connections in batches, not individually
2. Specify columns — only include columns you need to reduce memory
3. Filter by type first — type_filter() before filter() for narrower scans
4. Create indexes — on frequently filtered equality conditions (~3x on 100k+ nodes)
5. Use lightweight methods — node_count(), indices(), get_node_by_id() skip property materialization
6. Cypher LIMIT — use LIMIT to avoid scanning entire result sets

Threading

Designed for single-threaded use. The Rust code does not release the Python GIL during operations. If you share a graph instance across threads, guard access with your own lock.

---

Graph Maintenance

After heavy mutation workloads (DELETE, REMOVE), internal storage accumulates tombstones. Monitor with graph_info().

info = graph.graph_info()
# {'node_count': 950, 'node_capacity': 1000, 'node_tombstones': 50,
#  'edge_count': 2800, 'fragmentation_ratio': 0.05,
#  'type_count': 3, 'property_index_count': 2, 'composite_index_count': 0}

if info['fragmentation_ratio'] > 0.3:
    result = graph.vacuum()
    print(f"Reclaimed {result['tombstones_removed']} slots, remapped {result['nodes_remapped']} nodes")

vacuum() rebuilds the graph with contiguous indices and rebuilds all indexes. Resets the current selection — call between query chains.

reindex() rebuilds indexes only. Recovery tool, not routine maintenance — indexes are maintained automatically by all mutations.

---

Common Recipes

Upsert with MERGE

graph.cypher("""
    MERGE (p:Person {email: '[e-mail]'})
    ON CREATE SET p.created = '2024-01-01', p.name = 'Alice'
    ON MATCH SET p.last_seen = '2024-01-15'
""")

Top-K Nodes by Centrality

top_nodes = graph.pagerank(top_k=10)
for node in top_nodes:
    print(f"{node['title']}: {node['score']:.3f}")

2-Hop Neighborhood

graph.cypher("""
    MATCH (me:Person {name: 'Alice'})-[:KNOWS*2]-(fof:Person)
    WHERE fof <> me
    RETURN DISTINCT fof.name
""")

Export Subgraph

subgraph = (
    graph.type_filter('Person')
    .filter({'name': 'Alice'})
    .expand(hops=2)
    .to_subgraph()
)
subgraph.export('alice_network.graphml', format='graphml')

Parameterized Queries

graph.cypher(
    "MATCH (p:Person) WHERE p.city = $city AND p.age > $min_age RETURN p.name",
    params={'city': 'Oslo', 'min_age': 25}
)

Delete Subgraph

graph.cypher("""
    MATCH (u:User) WHERE u.status = 'inactive'
    DETACH DELETE u
""")

Aggregation with Relationship Properties

graph.cypher("""
    MATCH (p:Person)-[r:RATED]->(m:Movie)
    RETURN p.name, avg(r.score) AS avg_rating, count(m) AS movies_rated
    ORDER BY avg_rating DESC
""")