Knowledge Graph for Agents

You are an expert in knowledge graphs for AI agent systems. Help the user add a graph layer that captures entities and relationships from their data, enabling multi-hop reasoning that vector and keyword search can't do.

Why a Knowledge Graph?#

Vector search finds similar documents. BM25 finds matching keywords. Neither answers:

• "Who works with Alice at Acme Corp?"
• "What services are running on the production server?"
• "Which projects depend on PostgreSQL?"

These require relationship traversal — following connections between entities. That's what a knowledge graph does.

Architecture#

text

Ingest → Entity Extraction → Graph Storage → Query
                                    ↓
                            Spreading Activation
                            (2-hop traversal)

Entity Extraction (at ingest time)#

Extract entities from every chunk of text you index:

python

def extract_entities(text):
    """Simple heuristic entity extraction — no LLM needed."""
    entities = []
    # Title-case words (proper nouns)
    for word in text.split():
        if word[0].isupper() and len(word) > 2:
            entities.append({"name": word, "label": "Entity"})
    # Email addresses → Person
    for email in re.findall(r'[\w.+-]+@[\w.-]+\.\w+', text):
        entities.append({"name": email.split("@")[0].title(), "label": "Person"})
    return entities

For production, use spaCy NER or an LLM-based extractor for higher quality.

Graph Storage#

SQLite graph (simple, zero dependencies):

sql

CREATE TABLE nodes (
    id INTEGER PRIMARY KEY,
    name TEXT UNIQUE,
    label TEXT,
    properties_json TEXT DEFAULT '{}'
);

CREATE TABLE edges (
    source_id INTEGER REFERENCES nodes(id),
    target_id INTEGER REFERENCES nodes(id),
    rel_type TEXT DEFAULT 'RELATED_TO',
    weight REAL DEFAULT 1.0
);

Neo4j (production, scales better):

cypher

CREATE (n:Entity {name: "Alice", label: "Person"})
CREATE (m:Entity {name: "Acme Corp", label: "Organisation"})
CREATE (n)-[:WORKS_AT]->(m)

Co-occurrence Edges#

When two named entities appear in the same chunk, create a CO_OCCURS edge:

python

NAMED_LABELS = {"Person", "Place", "Organisation", "Event", "Product"}

for i, e1 in enumerate(chunk_entities):
    for e2 in chunk_entities[i+1:]:
        if e1["label"] in NAMED_LABELS and e2["label"] in NAMED_LABELS:
            graph.add_edge(e1["name"], e2["name"], "CO_OCCURS")

This is what gives the graph traversal value — connecting entities that appear together in context.

Querying: Spreading Activation (2-hop)#

Don't just match entities — traverse their connections:

sql

-- Find entities connected to the query entity within 2 hops
WITH start_nodes AS (
    SELECT id, name FROM nodes WHERE name LIKE '%Alice%'
),
hop1 AS (
    SELECT CASE WHEN e.source_id = s.id THEN e.target_id ELSE e.source_id END as mid_id
    FROM start_nodes s JOIN edges e ON (e.source_id = s.id OR e.target_id = s.id)
    WHERE e.weight >= 0.5
),
hop2 AS (
    SELECT CASE WHEN e.source_id = h.mid_id THEN e.target_id ELSE e.source_id END as end_id,
           h.mid_id
    FROM hop1 h JOIN edges e ON (e.source_id = h.mid_id OR e.target_id = h.mid_id)
)
SELECT DISTINCT n.name, n.label FROM hop2 JOIN nodes n ON n.id = hop2.end_id;

Hebbian Strengthening#

Edges between co-accessed entities get stronger over time:

python

def hebbian_strengthen(accessed_entities):
    """Strengthen edges between entities accessed in the same query."""
    for i, e1 in enumerate(accessed_entities):
        for e2 in accessed_entities[i+1:]:
            graph.update_edge_weight(e1, e2, delta=0.1)

Integration with Hybrid Search#

The graph layer works alongside BM25 and vector search:

1. Extract entities from query — "What services does Alice use?" → entities: [Alice, services]
2. Query graph — find Alice node, traverse USES relationships
3. Boost matching chunks — chunks mentioning graph-discovered entities get a score boost
4. Fuse with other layers — graph results merge into the unified ranking

Common Patterns#

Resolving Ambiguity#

python

# Merge duplicate entities
graph.merge("Alice", "Alice Smith")  # Same person, different references

Temporal Edges#

python

# Add timestamps to relationships
graph.add_edge("Alice", "Project Alpha", "WORKS_ON",
               properties={"since": "2024-01-15"})

Entity Types for Agents#

LabelExamplesUse CasePersonteam members, contactsWho questionsOrganisationcompanies, teamsAffiliation queriesProjectinitiatives, reposWhat's connectedSystemservices, toolsInfrastructure queriesPlaceoffices, citiesLocation queries

Pitfalls#

1. Edge explosion — don't create edges between ALL entities, only named ones (Person, Place, Org). Topic words create too many low-value edges.
2. No graph layer — you'll hit a ceiling where flat retrieval can't answer relationship questions.
3. Over-reliance on LLM extraction — heuristic extraction (capitalisation + patterns) is 80% as good at 0% of the cost.
4. Forgetting to prune — graphs grow. Schedule periodic cleanup of orphan nodes and weak edges.

Getting Started#

1. Pick a backend: SQLite (simple) or Neo4j (production)
2. Add entity extraction to your ingest pipeline
3. Create co-occurrence edges between named entities per chunk
4. Add 2-hop spreading activation to your search
5. Fuse graph results with your existing BM25/vector search
6. Set up a simple evaluation (test queries → expected results)