Knowledge Graph

Memori automatically builds a knowledge graph from your AI conversations. Every time Advanced Augmentation processes a conversation, it extracts structured relationships — semantic triples — and connects them into a graph. Because you own the database, you can query the knowledge graph directly using SQL.

How It Works

Conversation captured — Your user talks to your AI through the Memori-wrapped LLM client
Augmentation processes — Memori analyzes the conversation in the background
NER extraction — Named-entity recognition identifies key entities and relationships
Triple creation — Relationships are expressed as subject-predicate-object triples
Graph storage — Triples are stored and deduplicated in your database
Recall ready — The graph is available for semantic search on the next LLM call

Semantic Triples

Every fact in the knowledge graph is a semantic triple — a three-part statement: [Subject] [Predicate] [Object].

"Alice" "prefers" "dark mode"
"PostgreSQL" "is" "a relational database"
"The project" "uses" "FastAPI"

Example Extraction

From "My favorite database is PostgreSQL and I use it with FastAPI for our REST APIs. I've been using Python for about 8 years":

Subject	Predicate	Object
user	favorite_database	PostgreSQL
user	uses	FastAPI
user	uses_for	REST APIs
user	uses_with	PostgreSQL + FastAPI
user	experience_years	Python (8 years)

Memori automatically deduplicates triples — frequently mentioned facts get a higher mention count and updated timestamp.

Database Tables

Table	Purpose
`memori_subject`	Stores unique subjects
`memori_predicate`	Stores unique predicates
`memori_object`	Stores unique objects
`memori_knowledge_graph`	Links subjects, predicates, and objects into triples
`memori_entity_fact`	Stores facts with vector embeddings for recall

Querying

Via Recall API

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from memori import Memori

engine = create_engine("sqlite:///memori.db")
SessionLocal = sessionmaker(bind=engine)

mem = Memori(conn=SessionLocal)
mem.attribution(entity_id="user_alice", process_id="my_agent")

facts = mem.recall("database preferences", limit=5)

for fact in facts:
    print(f"Fact: {fact.content}")
    print(f"Similarity: {fact.similarity:.4f}")

Via Direct SQL

Since the knowledge graph lives in your database, you can query it directly for debugging, dashboards, or exploration.

Direct Database Queries

SELECT
    s.name AS subject,
    p.name AS predicate,
    o.name AS object
FROM memori_knowledge_graph kg
JOIN memori_subject s ON kg.subject_id = s.id
JOIN memori_predicate p ON kg.predicate_id = p.id
JOIN memori_object o ON kg.object_id = o.id;

Scope

Aspect	Scope
Triples	Per entity — shared across all processes
Visibility	All processes for an entity can see and use the graph
Growth	Conversations from any process contribute to the entity's graph

If Alice tells your support bot about PostgreSQL, your code assistant also knows she uses PostgreSQL.