kglite 0.5.69

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

KGLite

A knowledge graph that runs inside your Python process. Load data, query with Cypher, do semantic search — no server, no setup, no infrastructure.

Two APIs: Use Cypher for querying, mutations, and semantic search. Use the fluent API (add_nodes / add_connections) for bulk-loading DataFrames. Most agent and application code only needs cypher().

Embedded, in-process	No server, no network; import and go
In-memory	Persistence via save()/load() snapshots
Cypher subset	Querying + mutations + text_score() for semantic search
Single-label nodes	Each node has exactly one type
Fluent bulk loading	Import DataFrames with add_nodes() / add_connections()

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

pip install kglite

---

Table of Contents

• Quick Start
• Using with AI Agents
• Core Concepts
• How It Works
• Return Types
• Schema Introspection
• Cypher Queries | Full Cypher Reference
• Fluent API: Data Loading
• Fluent API: Querying
• Semantic Search
• Graph Algorithms
• Spatial Operations
• Timeseries
• Analytics
• Schema and Indexes
• Import and Export
• Blueprints
• Performance
• Common Gotchas
• Graph Maintenance
• Common Recipes
• Code Tree
• API Quick Reference

---

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 a ResultView (lazy; data stays in Rust until accessed)
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'])

# Quick peek at first rows
result.head()      # first 5 rows (returns a new ResultView)
result.head(3)     # first 3 rows

# 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")

---

Using with AI Agents

KGLite is designed to work as a self-contained knowledge layer for AI agents. No external database, no server process, no network — just a Python object with a Cypher interface that an agent can query directly.

The idea

1. Load or build a graph from your data (DataFrames, CSVs, APIs)
2. Give the agent describe() — a progressive-disclosure XML schema that scales from tiny to massive graphs
3. The agent writes Cypher queries using graph.cypher() — no other API to learn
4. Semantic search works natively — text_score() in Cypher, backed by any embedding model you wrap

No vector database, no graph database, no infrastructure. The graph lives in memory and persists to a single .kgl file.

Quick setup

xml = graph.describe()  # inventory overview — types, connections, Cypher extensions
prompt = f"You have a knowledge graph:\n{xml}\nAnswer the user's question using graph.cypher()."

MCP server

Expose the graph to any MCP-compatible agent (Claude, etc.) with a thin server:

from mcp.server.fastmcp import FastMCP
import kglite

graph = kglite.load("my_graph.kgl")
mcp = FastMCP("knowledge-graph")

@mcp.tool()
def describe(types: list[str] | None = None) -> str:
    """Get the graph schema and Cypher reference."""
    return graph.describe(types=types)

@mcp.tool()
def query(cypher: str) -> str:
    """Run a Cypher query and return results."""
    result = graph.cypher(cypher, to_df=True)
    return result.to_markdown()

mcp.run(transport="stdio")

The agent calls describe() once to learn the schema, then uses query() for everything — traversals, aggregations, filtering, and semantic search via text_score(). For large graphs, call describe(types=['Field', 'Well']) to drill into specific types.

For code graphs, additional tools make exploration easier — see examples/mcp_server.py for a full example with find_entity, read_source, and entity_context tools.

Adding semantic search (5-minute setup)

Semantic search lets agents find nodes by meaning, not just exact property matches. Here's the minimal path:

# 1. Wrap any embedding model (local or remote)
class Embedder:
    dimension = 384
    def embed(self, texts: list[str]) -> list[list[float]]:
        from sentence_transformers import SentenceTransformer
        model = SentenceTransformer("all-MiniLM-L6-v2")
        return model.encode(texts).tolist()

# 2. Register it on the graph
graph.set_embedder(Embedder())

# 3. Embed a text column (one-time, incremental on re-run)
graph.embed_texts("Article", "summary")

# 4. Now agents can search by meaning in Cypher — no extra API
graph.cypher("""
    MATCH (a:Article)
    WHERE text_score(a, 'summary', 'climate policy') > 0.5
    RETURN a.title, text_score(a, 'summary', 'climate policy') AS score
    ORDER BY score DESC LIMIT 10
""")

The model wrapper works with any provider — OpenAI, Cohere, local sentence-transformers, Ollama. See Semantic Search for the full API including load/unload lifecycle, incremental embedding, and low-level vector access.

Tips for agent prompts

1. Start with describe() — gives the agent an inventory of types with capability flags, connection map, and non-standard Cypher extensions
2. Drill into types with describe(types=['Field']) — shows properties, connections, timeseries/spatial config, supporting children, and sample nodes
3. Use properties(type) for deeper column discovery — shows types, nullability, unique counts, and sample values
4. Use sample(type, n=3) before writing queries — lets the agent see real data shapes
5. Prefer Cypher over the fluent API in agent contexts — closer to natural language, easier for LLMs to generate
6. Use parameters (params={'x': val}) to prevent injection when passing user input to queries
7. ResultView is lazy — agents can call len(result) to check row count without converting all rows

What describe() returns

• Inventory mode (describe()): node types as compact descriptors TypeName[size,complexity,flags] sorted by count, connection map, Cypher extensions (timeseries, spatial, etc.). Core/supporting type tiers hide child types behind +N suffixes. For small graphs (≤15 types), full detail is inlined automatically.
• Focused mode (describe(types=['Field'])): detailed properties with types, connection topology, timeseries/spatial config, supporting children, and sample nodes.

---

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 select().where().traverse(). They don't copy data.

Atomicity. Each cypher() call is atomic — if any clause fails, the graph remains unchanged. For multi-statement atomicity, use graph.begin() transactions. Durability only via explicit save().

---

How It Works

KGLite stores nodes and relationships in a Rust graph structure (petgraph). Python only sees lightweight handles — data converts to Python objects on access, not on query.

• Cypher queries parse, optimize, and execute entirely in Rust, then return a ResultView (lazy — rows convert to Python dicts only when accessed)
• Fluent API chains build a selection (a set of node indices) — no data is copied until you call collect(), to_df(), etc.
• Persistence is via save()/load() binary snapshots — there is no WAL or auto-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)	ResultView — lazy container, rows converted on access
Read with to_df=True	pandas.DataFrame
Mutation (CREATE, SET, DELETE, MERGE)	ResultView with .stats dict
EXPLAIN prefix	str (query plan, not executed)

Spatial return types: point() values are returned as {'latitude': float, 'longitude': float} dicts.

ResultView

ResultView is a lazy result container returned by cypher(), centrality methods, collect(), and sample(). Data stays in Rust and is only converted to Python objects when you access it — making cypher() calls fast even for large result sets.

result = graph.cypher("MATCH (n:Person) RETURN n.name, n.age ORDER BY n.age")

len(result)        # row count (O(1), no conversion)
result[0]          # single row as dict (converts that row only)
result[-1]         # negative indexing works

for row in result: # iterate rows as dicts (one at a time)
    print(row)

result.head()      # first 5 rows → new ResultView
result.head(3)     # first 3 rows → new ResultView
result.tail(2)     # last 2 rows → new ResultView

result.to_list()   # all rows as list[dict] (full conversion)
result.to_df()     # pandas DataFrame (full conversion)

result.columns     # column names: ['n.name', 'n.age']
result.stats       # mutation stats (None for read queries)

Because ResultView supports iteration and indexing, it works anywhere you'd use a list of dicts — existing code that iterates over cypher() results continues to work unchanged.

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
len()	int	No materialization
indices()	list[int]	Raw graph indices
ids()	list[Any]	Flat list of IDs
titles()	list[str]	Flat list (see below)
get_properties(['a','b'])	list[tuple]	Flat list (see below)
collect()	ResultView or grouped dict	Full node dicts
to_df()	DataFrame	Columns: type, title, id, ...props
node(type, id)	dict | None	O(1) hash lookup

Flat vs. grouped results

titles(), get_properties(), and collect() 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.select('Person').titles()
# ['Alice', 'Bob', 'Charlie']

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

# Override with flatten_single_parent=False to always get grouped
graph.select('Person').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	ResultView of {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

---

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 a ResultView:

result = graph.sample('Person', n=3)
result[0]          # {'type': 'Person', 'title': 'Alice', 'id': 1, 'age': 28, 'city': 'Oslo'}
result.to_list()   # all rows as list[dict]
result.to_df()     # as DataFrame

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'},
# ]

describe() — AI agent context

Progressive-disclosure schema description designed for AI agents. Scales from tiny to massive graphs.

# Inventory overview — types, connections, Cypher extensions
xml = graph.describe()

# Focused detail for specific types — properties, connections, samples
detail = graph.describe(types=['Field', 'Well'])

Inventory mode (no args): compact descriptors TypeName[size,complexity,flags] sorted by count descending, connection map, non-standard Cypher extensions. Supporting types hidden behind +N suffixes on their parent. For small graphs (≤15 types), full detail is inlined automatically.

Focused mode (types=[...]): detailed properties with types, connection topology, timeseries/spatial config, <supporting> children, and sample nodes.

Cypher reference (cypher=True): full language reference including all supported clauses, operators (||, =~, IN, etc.), built-in functions, predicates, and examples. Useful for AI agents discovering available Cypher features.

# Full Cypher language reference
ref = graph.describe(cypher=True)

---

Cypher Queries

A substantial Cypher subset. See CYPHER.md for the full reference with examples of every clause.

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 → ResultView (iterate, index, or convert)
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)

Mutations

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

# SET
graph.cypher("MATCH (n:Person {name: 'Bob'}) SET n.age = 26")

# DELETE / DETACH DELETE
graph.cypher("MATCH (n:Person {name: 'Alice'}) DETACH DELETE n")

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

Transactions

with graph.begin() as tx:
    tx.cypher("CREATE (:Person {name: 'Alice', age: 30})")
    tx.cypher("CREATE (:Person {name: 'Bob', age: 25})")
    tx.cypher("""
        MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
        CREATE (a)-[:KNOWS]->(b)
    """)
    # Commits on exit; rolls back on exception

Parameters

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

Semantic search in Cypher

text_score() enables semantic search directly in Cypher. Requires set_embedder() + embed_texts():

graph.cypher("""
    MATCH (n:Article)
    WHERE text_score(n, 'summary', 'machine learning') > 0.8
    RETURN n.title, text_score(n, 'summary', 'machine learning') AS score
    ORDER BY score DESC LIMIT 10
""")

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 ))
Functions	toUpper, toLower, toString, toInteger, toFloat, size, type, id, labels, coalesce, count, sum, avg, min, max, collect, std, text_score
Spatial	point, distance, contains, intersects, centroid, area, perimeter, latitude, longitude
Timeseries	ts_sum, ts_avg, ts_min, ts_max, ts_count, ts_at, ts_first, ts_last, ts_delta, ts_series — date-string args
Not supported	CALL/stored procedures, FOREACH, subqueries, SET n:Label (label mutation), multi-label

See CYPHER.md for full examples of every feature.

---

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 mapped to id and title. The original column names become aliases — they work in Cypher queries and filter(), but results always use the canonical names.

Your DataFrame Column	Stored As	Alias?
unique_id_field (e.g., user_id)	id	n.user_id resolves to n.id
node_title_field (e.g., name)	title	n.name resolves to n.title
All other columns	Same name	—

# After adding with unique_id_field='user_id', node_title_field='name':
graph.cypher("MATCH (u:User) WHERE u.user_id = 1001 RETURN u")  # OK — alias resolves to id
graph.select('User').where({'user_id': 1001})              # OK — alias works here too
graph.select('User').where({'id': 1001})                   # Also OK — canonical name

# Results always use canonical names:
# {'id': 1001, 'title': 'Alice', 'type': 'User', ...}  — NOT 'user_id' or 'name'

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

Batch Property Updates

result = graph.select('Prospect').where({'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.last_report()       # most recent operation report
graph.operation_index()   # sequential index of last operation
graph.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.select('Product').where({'price': 999.99})
graph.select('Product').where({'price': {'<': 500.0}, 'stock': {'>': 50}})
graph.select('Product').where({'id': {'in': [101, 103]}})
graph.select('Product').where({'category': {'is_null': True}})

# Regex matching
graph.select('Person').where({'name': {'regex': '^A.*'}})   # or {'=~': '^A.*'}
graph.select('Person').where({'name': {'regex': '(?i)^alice'}})  # case-insensitive

# Negated conditions
graph.select('Person').where({'city': {'not_in': ['Oslo', 'Bergen']}})
graph.select('Person').where({'name': {'not_contains': 'test'}})
graph.select('Person').where({'name': {'not_regex': '^[A-C].*'}})

# OR logic — where_any keeps nodes matching ANY condition set
graph.select('Person').where_any([
    {'city': 'Oslo'},
    {'city': 'Bergen'},
])

# Connection existence — filter without changing the selection target
graph.select('Person').where_connected('KNOWS')                        # any direction
graph.select('Person').where_connected('KNOWS', direction='outgoing')  # outgoing only

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

Sorting and Pagination

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

# Pagination with offset + limit
graph.select('Person').sort('name').offset(20).limit(10)  # page 3 of 10

Traversing the Graph

alice = graph.select('User').where({'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',
    limit=10
)

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

Set Operations

n3 = graph.select('Prospect').where({'geoprovince': 'N3'})
m3 = graph.select('Prospect').where({'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.select('Person')

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

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

# Full materialization
people.collect()                      # → [{'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.node('Person', 1)       # → {'type': 'Person', 'title': 'Alice', ...} or None

Debugging Selections

result = graph.select('User').where({'id': 1001})
print(result.explain())
# SELECT User (1000 nodes) -> WHERE (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)

---

Semantic Search

Store embedding vectors alongside nodes and query them with fast similarity search. Embeddings are stored separately from node properties — they don't appear in collect(), to_df(), or regular Cypher property access.

Text-Level API (Recommended)

Register an embedding model once, then embed and search using text column names. The model runs on the Python side — KGLite only stores the resulting vectors.

from sentence_transformers import SentenceTransformer

class Embedder:
    def __init__(self, model_name="all-MiniLM-L6-v2"):
        self._model_name = model_name
        self._model = None
        self._timer = None
        self.dimension = 384  # set in load() if unknown

    def load(self):
        """Called automatically before embedding. Loads model on demand."""
        import threading
        if self._timer:
            self._timer.cancel()
            self._timer = None
        if self._model is None:
            self._model = SentenceTransformer(self._model_name)
            self.dimension = self._model.get_sentence_embedding_dimension()

    def unload(self, cooldown=60):
        """Called automatically after embedding. Releases after cooldown."""
        import threading
        def _release():
            self._model = None
            self._timer = None
        self._timer = threading.Timer(cooldown, _release)
        self._timer.start()

    def embed(self, texts: list[str]) -> list[list[float]]:
        return self._model.encode(texts).tolist()

# Register once on the graph
graph.set_embedder(Embedder())

# Embed a text column — stores vectors as "summary_emb" automatically
graph.embed_texts("Article", "summary")
# Embedding Article.summary: 100%|████████| 1000/1000 [00:05<00:00]
# → {'embedded': 1000, 'skipped': 3, 'skipped_existing': 0, 'dimension': 384}

# Search with text — resolves "summary" → "summary_emb" internally
results = graph.select("Article").search_text("summary", "machine learning", top_k=10)
# [{'id': 42, 'title': '...', 'type': 'Article', 'score': 0.95, ...}, ...]

Key details:

• Auto-naming: text column "summary" → embedding store key "summary_emb" (auto-derived)
• Incremental: re-running embed_texts skips nodes that already have embeddings — only new nodes get embedded. Pass replace=True to force re-embed.
• Progress bar: shows a tqdm progress bar by default. Disable with show_progress=False.
• Load/unload lifecycle: if the model has optional load() / unload() methods, they are called automatically before and after each embedding operation. Use this to load on demand and release after a cooldown.
• Not serialized: the model is not saved with save() — call set_embedder() again after deserializing.

# Add new articles, then re-embed — only new ones are processed
graph.embed_texts("Article", "summary")
# → {'embedded': 50, 'skipped': 0, 'skipped_existing': 1000, ...}

# Force full re-embed
graph.embed_texts("Article", "summary", replace=True)

# Combine with filters
results = (graph
    .select("Article")
    .where({"category": "politics"})
    .search_text("summary", "foreign policy", top_k=10))

Calling embed_texts() or search_text() without set_embedder() raises an error with a full skeleton showing the required model interface.

Storing Embeddings (Low-Level)

If you manage vectors yourself, use the low-level API:

# Explicit: pass a dict of {node_id: vector}
graph.set_embeddings('Article', 'summary', {
    1: [0.1, 0.2, 0.3, ...],
    2: [0.4, 0.5, 0.6, ...],
})

# Or auto-detect during add_nodes with column_types
df = pd.DataFrame({
    'id': [1, 2, 3],
    'title': ['A', 'B', 'C'],
    'text_emb': [[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]],
})
graph.add_nodes(df, 'Doc', 'id', 'title', column_types={'text_emb': 'embedding'})

Vector Search (Low-Level)

Search operates on the current selection — combine with select() and filter() for scoped queries:

# Basic search — returns list of dicts sorted by similarity
results = graph.select('Article').vector_search('summary', query_vec, top_k=10)
# [{'id': 5, 'title': '...', 'type': 'Article', 'score': 0.95, ...}, ...]
# 'score' is always included: cosine similarity [-1,1], dot_product, or negative euclidean distance

# Filtered search — only search within a subset
results = (graph
    .select('Article')
    .where({'category': 'politics'})
    .vector_search('summary', query_vec, top_k=10))

# DataFrame output
df = graph.select('Article').vector_search('summary', query_vec, top_k=10, to_df=True)

# Distance metrics: 'cosine' (default), 'dot_product', 'euclidean'
results = graph.select('Article').vector_search(
    'summary', query_vec, top_k=10, metric='dot_product')

Semantic Search in Cypher

text_score() enables semantic search directly in Cypher queries. It automatically embeds the query text using the registered model (via set_embedder()) and computes similarity:

# Requires: set_embedder() + embed_texts()
graph.cypher("""
    MATCH (n:Article)
    RETURN n.title, text_score(n, 'summary', 'machine learning') AS score
    ORDER BY score DESC LIMIT 10
""")

# With parameters
graph.cypher("""
    MATCH (n:Article)
    WHERE text_score(n, 'summary', $query) > 0.8
    RETURN n.title
""", params={'query': 'artificial intelligence'})

# Combine with graph filters
graph.cypher("""
    MATCH (n:Article)-[:CITED_BY]->(m:Article)
    WHERE n.category = 'politics'
    RETURN m.title, text_score(m, 'summary', 'foreign policy') AS score
    ORDER BY score DESC LIMIT 5
""")

Embedding Utilities

graph.list_embeddings()
# [{'node_type': 'Article', 'text_column': 'summary', 'dimension': 384, 'count': 1000}]

graph.remove_embeddings('Article', 'summary')

# Retrieve all embeddings for a type (no selection needed)
embs = graph.embeddings('Article', 'summary')
# {1: [0.1, 0.2, ...], 2: [0.4, 0.5, ...], ...}

# Retrieve embeddings for current selection only
embs = graph.select('Article').where({'category': 'politics'}).embeddings('summary')

# Get a single node's embedding (O(1) lookup, returns None if not found)
vec = graph.embedding('Article', 'summary', node_id)

Embeddings persist across save()/load() cycles automatically.

Embedding Export / Import

Export embeddings to a standalone .kgle file so they survive graph rebuilds. Embeddings are keyed by node ID — import resolves IDs against the current graph, skipping any that no longer exist.

# Export all embeddings
stats = graph.export_embeddings("embeddings.kgle")
# {'stores': 2, 'embeddings': 5000}

# Export only specific node types
graph.export_embeddings("embeddings.kgle", ["Article", "Author"])

# Export specific (node_type, property) pairs — empty list = all properties for that type
graph.export_embeddings("embeddings.kgle", {
    "Article": ["summary", "title"],  # only these two
    "Author": [],                     # all embedding properties for Author
})

# Import into a fresh graph — matches by (node_type, node_id)
graph2 = kglite.KnowledgeGraph()
graph2.add_nodes(articles_df, 'Article', 'id', 'title')
result = graph2.import_embeddings("embeddings.kgle")
# {'stores': 2, 'imported': 4800, 'skipped': 200}

This is useful when rebuilding a graph from scratch (e.g., re-running a build script) without re-generating expensive embeddings.

---

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.

Batch variant for computing many distances at once:

distances = graph.shortest_path_lengths_batch('Person', [(1, 5), (2, 8), (3, 10)])
# → [2, None, 5]  (None where no path exists, same order as input)

Much faster than calling shortest_path_length in a loop — builds the adjacency list once.

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 a ResultView of {type, title, id, score} rows, 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)

Clustering

General-purpose clustering via Cypher CALL cluster(). Reads nodes from a preceding MATCH clause.

# Spatial DBSCAN — auto-detects lat/lon from set_spatial() config
result = graph.cypher("""
    MATCH (f:Field)
    CALL cluster({method: 'dbscan', eps: 50000, min_points: 2})
    YIELD node, cluster
    RETURN cluster, count(*) AS n, collect(node.name) AS fields
    ORDER BY n DESC
""")

# Property-based K-means — cluster on explicit numeric properties
result = graph.cypher("""
    MATCH (w:Wellbore)
    CALL cluster({
        properties: ['totalDepth', 'bottomHoleTemp'],
        method: 'kmeans', k: 5, normalize: true
    })
    YIELD node, cluster
    RETURN cluster, count(*) AS n
""")

Parameter	Type	Default	Notes
method	string	"dbscan"	"dbscan" or "kmeans"
properties	list	(none)	If omitted, uses spatial config
eps	float	0.5	DBSCAN neighborhood radius (meters for spatial, raw units for properties)
min_points	int	3	DBSCAN minimum neighbors for core point
k	int	5	K-means cluster count
max_iterations	int	100	K-means iteration limit
normalize	bool	false	Min-max scale features to [0,1] before clustering

Noise points (DBSCAN only) get cluster = -1. Filter with WHERE cluster >= 0.

Node Degrees

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

---

Spatial Operations

Spatial queries are also available in Cypher via distance(), contains(), intersects(), centroid(), area(), perimeter(), and point(). See CYPHER.md.

Spatial Types

Declare spatial properties via column_types when loading data. This enables auto-resolution in Cypher queries and fluent API methods — no need to specify field names on every call.

Type	Cardinality	Purpose
location	0..1 per type	Primary lat/lon coordinate
geometry	0..1 per type	Primary WKT geometry
point.<name>	0..N	Named lat/lon coordinates
shape.<name>	0..N	Named WKT geometries

graph.add_nodes(df, 'Field', 'id', 'name', column_types={
    'latitude': 'location.lat',
    'longitude': 'location.lon',
    'wkt_polygon': 'geometry',
})

With spatial types declared, queries become simpler:

# Auto-resolves location fields — no lat_field/lon_field needed
graph.select('Field').near_point_m(center_lat=60.5, center_lon=3.2, max_distance_m=50000.0)

# Cypher distance between nodes — resolves via location, falls back to geometry centroid
graph.cypher("""
    MATCH (a:Field {name:'Troll'}), (b:Field {name:'Draugen'})
    RETURN distance(a, b) AS dist_m
""")

# Node-aware spatial functions — auto-resolve geometry from spatial config
graph.cypher("MATCH (c:City), (a:Area) WHERE contains(a, c) RETURN c.name, a.name")
graph.cypher("MATCH (n:Field) RETURN n.name, area(n) AS m2, centroid(n) AS center")
graph.cypher("MATCH (a:Field), (b:Field) WHERE intersects(a, b) RETURN a.name, b.name")

# Geometry-aware distance — 0 if inside/touching, boundary distance otherwise
graph.cypher("RETURN distance(point(60.5, 3.5), n.geometry)")  # 0 if inside polygon

# Virtual properties
graph.cypher("MATCH (n:Field) RETURN n.name, n.location, n.geometry")

Multiple named points and shapes:

graph.add_nodes(df, 'Well', 'id', 'name', column_types={
    'surface_lat': 'location.lat',
    'surface_lon': 'location.lon',
    'bh_lat': 'point.bottom_hole.lat',
    'bh_lon': 'point.bottom_hole.lon',
    'boundary_wkt': 'shape.boundary',
})

# Distance between named points
graph.cypher("... RETURN distance(a.bottom_hole, b.bottom_hole)")

Retroactive configuration (for data loaded without column_types):

graph.set_spatial('Field',
    location=('latitude', 'longitude'),
    geometry='wkt_polygon',
)

Bounding Box

# With spatial config — field names auto-resolved
graph.select('Discovery').within_bounds(
    min_lat=58.0, max_lat=62.0, min_lon=1.0, max_lon=5.0
)

# Without spatial config — explicit field names
graph.select('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 (Geodesic)

graph.select('Wellbore').near_point_m(
    center_lat=60.5, center_lon=3.2, max_distance_m=50000.0
)

WKT Geometry Intersection

graph.select('Field').intersects_geometry(
    'POLYGON((1 58, 5 58, 5 62, 1 62, 1 58))'
)

Accepts WKT strings or shapely geometry objects:

from shapely.geometry import box
graph.select('Field').intersects_geometry(box(1, 58, 5, 62))

Point-in-Polygon

graph.select('Block').contains_point(lat=60.5, lon=3.2)

GeoDataFrame Export

Convert query results with WKT columns to geopandas GeoDataFrames:

rv = graph.cypher("MATCH (n:Field) RETURN n.name, n.geometry")
gdf = rv.to_gdf(geometry_column='n.geometry', crs='EPSG:4326')

---

Analytics

Statistics

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

# Group by a property — like SQL GROUP BY
graph.select('Person').count(group_by='city')
# → {'Oslo': 42, 'Bergen': 15, 'Trondheim': 8}

graph.select('Person').statistics('age', group_by='city')
# → {'Oslo': {'count': 42, 'mean': 35.2, 'std': 8.1, 'min': 22, 'max': 65, 'sum': 1478},
#    'Bergen': {'count': 15, ...}, ...}

Calculations

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

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

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

Connection Aggregation

graph.select('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.schema_text()

Indexes

Two index types:

Method	Accelerates	Use for
create_index()	Equality (= value)	Exact lookups
create_range_index()	Range (>, <, >=, <=)	Numeric/date filtering

Both also accelerate Cypher WHERE clauses. Composite indexes support multi-property equality.

graph.create_index('Prospect', 'prospect_geoprovince')        # equality index
graph.create_range_index('Person', 'age')                      # B-Tree range index
graph.create_composite_index('Person', ['city', 'age'])        # composite equality

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")

Save files (.kgl) use a pinned binary format (bincode with explicit little-endian, fixed-int encoding). Files are forward-compatible within the same major version. For sharing across machines or long-term archival, prefer a portable format (GraphML, CSV).

Embedding Snapshots

Export embeddings separately so they survive graph rebuilds. See Embedding Export / Import under Semantic Search for full details.

graph.export_embeddings("embeddings.kgle")                          # all embeddings
graph.export_embeddings("embeddings.kgle", ["Article"])             # by node type
graph.export_embeddings("embeddings.kgle", {"Article": ["summary"]})  # by type + property

result = graph.import_embeddings("embeddings.kgle")
# {'stores': 2, 'imported': 4800, 'skipped': 200}

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

---

Blueprints

Build a complete graph from CSV files using a declarative JSON blueprint. Instead of writing add_nodes / add_connections calls, describe your node types, properties, connections, and timeseries in JSON — and from_blueprint() handles the rest.

graph = kglite.from_blueprint("blueprint.json", verbose=True)

Blueprint Structure

{
  "settings": {
    "root": "/path/to/csv/files",
    "output": "output/graph.kgl"
  },
  "nodes": {
    "Person": {
      "csv": "persons.csv",
      "pk": "person_id",
      "title": "name",
      "properties": {
        "age": "int",
        "city": "string",
        "salary": "float",
        "hired": "date"
      },
      "skipped": ["internal_code"],
      "filter": {"status": "Active", "age": {">": 18}},
      "connections": {
        "fk_edges": {
          "WORKS_AT": {"target": "Company", "fk": "company_id"}
        },
        "junction_edges": {
          "KNOWS": {
            "csv": "friendships.csv",
            "source_fk": "person_a",
            "target": "Person",
            "target_fk": "person_b",
            "properties": ["since"],
            "property_types": {"since": "date"}
          }
        }
      },
      "sub_nodes": {
        "Review": {
          "csv": "reviews.csv",
          "pk": "auto",
          "parent_fk": "person_id",
          "title": "summary",
          "properties": {"rating": "int"},
          "skipped": ["person_id"]
        }
      },
      "timeseries": {
        "time_key": {"year": "yr", "month": "mo"},
        "resolution": "month",
        "channels": {"sales": "monthly_sales"},
        "units": {"sales": "USD"}
      }
    },
    "Company": {
      "pk": "company_id",
      "title": "company_id",
      "properties": {},
      "skipped": []
    }
  }
}

Key Concepts

Concept	Description
pk	Primary key column. Use "auto" for auto-generated sequential IDs.
title	Column used as the node's display name.
properties	Map column names to types: "string", "int", "float", "date", "geometry", "location.lat", "location.lon". Unspecified columns are auto-detected.
skipped	Columns to exclude from properties (e.g., FK columns you don't want stored).
filter	Row-level filtering. Equality: {"status": "Active"}. Operators: {"age": {">": 18}} (supports =, !=, >, <, >=, <=).
FK edges	One-to-many: a column in the source CSV references the PK of a target node type.
Junction edges	Many-to-many via a separate CSV lookup table. Can attach properties to the edges.
Sub-nodes	Hierarchical children. Must have parent_fk pointing to the parent's PK column.
Manual nodes	Node types without a csv field — created automatically from distinct FK values pointing to them.
Timeseries	Time-indexed channels attached to nodes. time_key can be a single column ("date_col") or a composite ({"year": "yr", "month": "mo"}).

Loading Options

# Basic load
graph = kglite.from_blueprint("blueprint.json")

# Verbose output + auto-save to settings.output path
graph = kglite.from_blueprint("blueprint.json", verbose=True, save=True)

# Skip auto-save (just build in memory)
graph = kglite.from_blueprint("blueprint.json", save=False)

Inspecting the Result

After loading, use structure lookups to verify the graph:

graph.schema()                        # full overview: types, counts, connections, indexes
graph.properties("Person")            # per-property stats (type, non_null, unique, values)
graph.neighbors_schema("Person")      # connection topology (outgoing/incoming)
graph.sample("Person", n=3)           # inspect actual nodes
graph.connection_types()              # all edge types with counts and endpoint types
graph.describe()                      # XML description for AI agents

Loading Phases

from_blueprint() processes nodes in dependency order:

1. Manual nodes — types without CSV (created from distinct FK values)
2. Core nodes — types with CSV files
3. Sub-nodes — hierarchical children of core nodes
4. FK edges — direct foreign key relationships
5. Junction edges — many-to-many via lookup tables

Missing CSV files and invalid rows are handled gracefully — the graph is still created with whatever data is available.

---

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 — select() before filter() for narrower scans
4. Create indexes — on frequently filtered equality conditions (~3x on 100k+ nodes)
5. Use lightweight methods — len(), indices(), node() skip property materialization
6. Cypher LIMIT — use LIMIT to avoid scanning entire result sets

Threading

The Python GIL is released during heavy Rust operations, allowing other Python threads to run concurrently:

Operation	GIL Released?	Notes
save()	Yes	Serialization + compression + file write
load()	Yes	File read + decompression + deserialization
export_embeddings()	Yes	Serialization + compression + file write
cypher() (reads)	Yes	Query parsing, optimization, and execution
vector_search()	Yes	Similarity computation (uses rayon internally)
search_text()	Partial	Model embedding needs GIL; vector search releases it
add_nodes()	No	DataFrame conversion requires GIL throughout
import_embeddings()	No	Mutates graph in-place
cypher() (mutations)	No	Must hold exclusive lock on graph

For concurrent access from multiple threads, mutations (add_nodes, CREATE/SET/DELETE Cypher) require external synchronization. Read-only operations (cypher reads, vector_search, save) can run while other Python threads execute.

---

Common Gotchas

• Single-label only. Each node has exactly one type. labels(n) returns a string, not a list. SET n:OtherLabel is not supported.
• id and title are canonical. add_nodes(unique_id_field='user_id') stores the column as id. The original name works as an alias in Cypher (n.user_id resolves to n.id), but results always return canonical names (id, title).
• Save files use a pinned binary format. .kgl and .kgle files use bincode with explicitly pinned encoding options (little-endian, fixed-int). Files are compatible across OS and CPU architecture within the same major version. For long-term archival or sharing with non-kglite tools, use export() (GraphML, CSV).
• Indexes: create_index() accelerates equality only (=). For range queries (>, <, >=, <=), use create_range_index().
• Flat vs. grouped results. After traversal with multiple parents, titles(), collect(), and get_properties() return grouped dicts instead of flat lists. Use flatten_single_parent=False to always get grouped output.
• No auto-persistence. The graph lives in memory. save() is manual — crashes lose unsaved work.

---

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.select('Person')
    .where({'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
""")

---

Timeseries

Attach time-indexed numeric data directly to nodes — no need to create separate nodes per data point. Data is stored as compact columnar arrays with resolution-aware date-string queries through Cypher ts_*() functions.

Configuration

Configure timeseries metadata per node type: resolution, channel names, units, and bin type.

graph.set_timeseries("Field",
    resolution="month",                         # "year", "month", "day", "hour", "minute"
    channels=["oil", "gas"],                    # channel names
    units={"oil": "MSm3", "gas": "BSm3"},      # optional: per-channel units
    bin_type="total",                            # optional: "total", "mean", or "sample"
)

graph.timeseries_config("Field")
# {'resolution': 'month', 'channels': ['oil', 'gas'],
#  'units': {'oil': 'MSm3', 'gas': 'BSm3'}, 'bin_type': 'total'}

Loading Data

# Bulk load from a DataFrame (most common)
graph.add_timeseries(
    "Field",
    data=production_df,
    fk="npdid",                              # FK column → matches node.id
    time_key=["year", "month"],              # composite time key columns
    channels={"oil": "prfOilCol", "gas": "prfGasCol"},  # channel → column
    resolution="month",                       # required if set_timeseries() wasn't called
    units={"oil": "MSm3"},                   # optional, merged into config
)

# Or manually per node
graph.set_time_index(node_id, [[2020,1], [2020,2], [2020,3]])
graph.add_ts_channel(node_id, "oil", [1.23, 1.18, 1.25])
graph.add_ts_channel(node_id, "gas", [0.45, 0.42, 0.48])

Validation: time_key column count must match resolution depth (1 for year, 2 for month, 3 for day, 4 for hour, 5 for minute).

Inline Loading via add_nodes

When your DataFrame has one row per time step per entity (e.g., a production CSV), use the timeseries parameter on add_nodes to load nodes and timeseries in a single call. Rows are automatically deduplicated by unique_id_field for node properties; all rows are used for timeseries data.

# Each row is a monthly production record per field
prod_df = pd.DataFrame({
    'field_id': ['Troll']*3 + ['Draugen']*3,
    'field_name': ['Troll']*3 + ['Draugen']*3,
    'date': ['2020-01', '2020-02', '2020-03']*2,
    'oil': [100, 110, 120, 200, 210, 220],
    'gas': [50, 55, 60, 80, 85, 90],
})

# Single call — creates 2 nodes with 3 time steps each
graph.add_nodes(prod_df, 'Production', 'field_id', 'field_name',
    timeseries={
        'time': 'date',                   # date string column
        'channels': ['oil', 'gas'],       # value columns
    }
)

The timeseries dict accepts:

Key	Type	Required	Description
time	str or dict	Yes	Date string column name, or dict mapping resolution levels to column names
channels	list[str]	Yes	Column names containing numeric time-varying data
resolution	str	No	"year", "month", "day", "hour", "minute" — auto-detected if omitted
units	dict[str, str]	No	Per-channel unit labels

Separate time columns — when time is split across multiple columns (e.g., Norwegian CSVs with år, måned):

graph.add_nodes(df, 'Production', 'field_id', 'field_name',
    timeseries={
        'time': {'year': 'ar', 'month': 'maned'},
        'channels': ['oil', 'gas'],
    }
)

High-frequency data — hour and minute resolution:

graph.add_nodes(sensor_df, 'Reading', 'sensor_id', 'name',
    timeseries={
        'time': 'timestamp',              # e.g., "2020-01-15 10:30"
        'channels': ['temperature'],
        'resolution': 'minute',
    }
)

Timeseries columns are automatically excluded from node properties. Resolution is auto-detected from the time format when not specified.

Querying via Cypher

All ts_*() functions use date strings ('2020', '2020-2', '2020-2-15', '2020-2-15 10', '2020-2-15 10:30'). Precision is validated against the data resolution — querying with day precision on month data produces an error.

# Aggregate monthly data by year
graph.cypher("MATCH (f:Field) RETURN f.title, ts_sum(f.oil, '2020') AS prod")

# Top 10 fields by production
graph.cypher("""
    MATCH (f:Field)
    RETURN f.title, ts_sum(f.oil, '2020') AS prod
    ORDER BY prod DESC LIMIT 10
""")

# Month-level range
graph.cypher("MATCH (f:Field) RETURN ts_avg(f.oil, '2020-1', '2020-6') AS h1_avg")

# Multi-year range
graph.cypher("MATCH (f:Field) RETURN ts_sum(f.oil, '2018', '2023') AS total")

# Exact month lookup
graph.cypher("MATCH (f:Field) RETURN ts_at(f.oil, '2020-3') AS march")

# Change between periods
graph.cypher("MATCH (f:Field) RETURN ts_delta(f.oil, '2019', '2021') AS change")

# Latest sensor reading
graph.cypher("MATCH (s:Sensor) RETURN s.title, ts_last(s.temperature)")

# Extract full series for plotting
graph.cypher("MATCH (f:Field {title: 'TROLL'}) RETURN ts_series(f.oil, '2015', '2020')")

Retrieval

# All channels
graph.timeseries(node_id)
# {'keys': [[2020,1], [2020,2], ...], 'channels': {'oil': [...], 'gas': [...]}}

# Single channel
graph.timeseries(node_id, channel="oil")
# {'keys': [...], 'values': [...]}

# Date-string range filter
graph.timeseries(node_id, start='2020', end='2020')

Available functions: ts_at, ts_sum, ts_avg, ts_min, ts_max, ts_count, ts_first, ts_last, ts_series, ts_delta. See CYPHER.md for the full reference.

---

API Quick Reference

Graph lifecycle

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

Cypher (recommended for most tasks)

graph.cypher("MATCH (n:Person) RETURN n.name")                          # → ResultView
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'})")                        # → ResultView (.stats has counts)

Data loading (fluent API)

graph.add_nodes(data=df, node_type='T', unique_id_field='id')           # → report dict
graph.add_nodes(data=df, node_type='T', unique_id_field='id',          # with inline timeseries
    timeseries={'time': 'date', 'channels': ['oil', 'gas']})
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.select('Person')                        # select by type → KnowledgeGraph
    .where({'age': {'>': 25}})                    # AND filter → KnowledgeGraph
    .where_any([{'city': 'Oslo'}, {'city': 'Bergen'}])  # OR filter → KnowledgeGraph
    .where_connected('KNOWS', direction='outgoing') # edge existence → KnowledgeGraph
    .sort('age', ascending=False)                  # sort → KnowledgeGraph
    .offset(20).limit(10)                      # pagination → KnowledgeGraph
    .traverse('KNOWS', direction='outgoing')       # traverse → KnowledgeGraph
    .collect()                                   # materialize → ResultView or grouped dict

Semantic search

# Text-level API (recommended) — register model once, embed & search by column name
graph.set_embedder(model)                                                    # register model (.dimension, .embed())
graph.embed_texts('Article', 'summary')                                      # embed text column → stored as summary_emb
graph.select('Article').search_text('summary', 'find AI papers', top_k=10)  # text query search

# Low-level vector API — bring your own vectors
graph.set_embeddings('Article', 'summary', {id: vec, ...})             # store embeddings
graph.select('Article').vector_search('summary', qvec, top_k=10)  # similarity search
graph.list_embeddings()                                                 # list all embedding stores
graph.remove_embeddings('Article', 'summary')                           # remove an embedding store
graph.embeddings('Article', 'summary')                              # retrieve all vectors for type
graph.select('Article').embeddings('summary')                  # retrieve vectors for selection
graph.embedding('Article', 'summary', node_id)                      # single node vector (or None)
graph.export_embeddings('emb.kgle')                                     # export all embeddings to file
graph.export_embeddings('emb.kgle', ['Article'])                        # export by node type
graph.export_embeddings('emb.kgle', {'Article': ['summary']})           # export by type + property
graph.import_embeddings('emb.kgle')                                     # import embeddings from file
# Cypher: text_score(n, 'summary', 'query text') — semantic search in Cypher, needs set_embedder()

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 ResultView
graph.indexes()                               # → all indexes with type info
graph.describe()                              # → XML inventory for AI agents
graph.describe(types=['Person'])              # → focused detail for specific types

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)                                             # → ResultView of {type, title, id, score}
graph.betweenness_centrality(top_k=10)                               # → ResultView of {type, title, id, score}
graph.louvain_communities()                                          # → {communities, modularity, num_communities}
graph.connected_components()                                         # → list[list[node_dict]]

---

Code Tree

Parse multi-language codebases into KGLite knowledge graphs using tree-sitter. Extracts functions, classes/structs, enums, traits/interfaces, modules, and their relationships.

pip install kglite[code-tree]

Quick start

from kglite.code_tree import build

graph = build(".")  # auto-detects pyproject.toml / Cargo.toml

# What are the most-called functions?
graph.cypher("""
    MATCH (caller:Function)-[:CALLS]->(f:Function)
    RETURN f.name AS function, count(caller) AS callers
    ORDER BY callers DESC LIMIT 10
""")

# Label-optional matching — search across all node types
graph.cypher("""
    MATCH (n {name: 'execute'})
    RETURN n.type, n.name, n.file_path, n.line_number
""")

# Save for later
graph.save("codebase.kgl")

Code exploration methods

# Find entities by name (searches all code entity types)
graph.find("execute")
graph.find("KnowledgeGraph", node_type="Struct")
graph.find("exec", match_type="contains")       # case-insensitive substring
graph.find("Knowl", match_type="starts_with")    # case-insensitive prefix

# Get source location — single or batch
graph.source("execute_single_clause")
# {'file_path': 'src/graph/cypher/executor.rs', 'line_number': 165,
#  'end_line': 205, 'line_count': 41, 'signature': '...'}
graph.source(["KnowledgeGraph", "build", "execute"])

# Get full neighborhood of an entity
graph.context("KnowledgeGraph")
# {'node': {...}, 'defined_in': 'src/graph/mod.rs',
#  'HAS_METHOD': [...], 'IMPLEMENTS': [...], 'called_by': [...]}

# File table of contents — all entities defined in a file
graph.toc("src/graph/mod.rs")
# {'file': '...', 'entities': [...], 'summary': {'Function': 4, 'Struct': 2}}

Supported languages

Language	Extensions
Rust	.rs
Python	.py, .pyi
TypeScript	.ts, .tsx
JavaScript	.js, .jsx, .mjs
Go	.go
Java	.java
C#	.cs
C	.c, .h
C++	.cpp, .cc, .cxx, .hpp, .hh, .hxx

Graph schema

Node types: Project, Dependency, File, Module, Function, Struct, Class, Enum, Trait, Protocol, Interface, Attribute, Constant

Relationship types: DEPENDS_ON (Project→Dependency), HAS_SOURCE (Project→File), DEFINES (File→item), CALLS (Function→Function), HAS_METHOD (Struct/Class→Function), HAS_ATTRIBUTE (Struct/Class→Attribute), HAS_SUBMODULE (Module→Module), IMPLEMENTS (type→trait), EXTENDS (class→class), IMPORTS (File→Module), USES_TYPE, EXPOSES (Module→item)

Options

graph = build(".")                           # auto-detect manifest (pyproject.toml, Cargo.toml)
graph = build("pyproject.toml")              # explicit manifest file
graph = build("/path/to/src")                # directory scan (fallback when no manifest)
graph = build(".", include_tests=True)       # include test directories
graph = build(".", save_to="code.kgl", verbose=True)

When a manifest is detected, build() reads project metadata (name, version, dependencies) and only scans declared source directories — avoiding .venv/, target/, node_modules/, etc.