Customizing Graph Structure

Zep enables the use of rich, domain-specific data structures in graphs through Entity Types and Edge Types, replacing generic graph nodes and edges with detailed models.

Zep classifies newly created nodes/edges as one of the default or custom types or leaves them unclassified. For example, a node representing a preference is classified as a Preference node, and attributes specific to that type are automatically populated. You may restrict graph queries to nodes/edges of a specific type, such as Preference.

The default entity and edge types are applied to user graphs (not all graphs) by default, but you may define additional custom types as needed.

Each node/edge is classified as a single type only. Multiple classifications are not supported.

Why use custom ontology

Custom entity and edge types improve graph construction by focusing extraction on the most important entities and relationships for your domain. When a custom ontology is defined, Zep’s extraction process prioritizes these types, resulting in more relevant and structured data for your use case.

Even if Zep’s default types cover your basic needs, defining a custom ontology tailored to your domain often produces better results because:

• Extraction focuses on entities and relationships that matter most to your application
• Graph structure aligns more closely with your domain model
• Retrieved context becomes more relevant and actionable

Default Entity and Edge Types

Definition

Zep provides default entity and edge types that are automatically applied to user graphs (not all graphs). These types help classify and structure the information extracted from conversations.

You can view the exact definition for the default ontology here.

Default Entity Types

The default entity types are:

• User: A Zep user specified by role in chat messages. There can only be a single User entity.
• Assistant: Represents the AI assistant in the conversation. This entity is a singleton.
• Preference: Entities mentioned in contexts expressing user preferences, choices, opinions, or selections. This classification is prioritized over most other classifications.
• Location: A physical or virtual place where activities occur or entities exist. Use this classification only after checking if the entity fits other more specific types.
• Event: A time-bound activity, occurrence, or experience.
• Object: A physical item, tool, device, or possession. Use this classification only as a last resort after checking other types.
• Topic: A subject of conversation, interest, or knowledge domain. Use this classification only as a last resort after checking other types.
• Organization: A company, institution, group, or formal entity.
• Document: Information content in various forms.

Default Edge Types

The default edge types are:

• LOCATED_AT: Represents that an entity exists or occurs at a specific location. Connects any entity to a Location.
• OCCURRED_AT: Represents that an event happened at a specific time or location. Connects an Event to any entity.

Default entity and edge types apply to user graphs. All nodes and edges in any user graph will be classified into one of these types or none.

Adding Data

When we add data to the graph, default entity and edge types are automatically created:

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from zep_cloud.types import Message
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message = {
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    "name": "John Doe",
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    "role": "user",
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    "content": "I really like pop music, and I don't like metal",
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}
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client.thread.add_messages(thread_id=thread_id, messages=[Message(**message)])

Searching

When searching nodes in the graph, you may provide a list of types to filter the search by. The provided types are ORed together. Search results will only include nodes that satisfy one of the provided types:

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search_results = client.graph.search(
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    user_id=user_id,
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    query="the user's music preferences",
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    scope="nodes",
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    search_filters={
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        "node_labels": ["Preference"]
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    }
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)
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for i, node in enumerate(search_results.nodes):
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    preference = node.attributes
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    print(f"Preference {i+1}:{preference}")

Preference 1: {'category': 'Music', 'description': 'Pop Music is a genre of music characterized by its catchy melodies and widespread appeal.', 'labels': ['Entity', 'Preference']}
Preference 2: {'category': 'Music', 'description': 'Metal Music is a genre of music characterized by its heavy sound and complex compositions.', 'labels': ['Entity', 'Preference']}

Disabling Default Ontology

In some cases, you may want to disable the default entity and edge types for specific users and only use custom types you define. You can do this by setting the disable_default_ontology flag when creating or updating a user.

When disable_default_ontology is set to true:

• Only custom entity and edge types you define will be used for classification
• The default entity and edge types (User, Assistant, Preference, Location, etc.) will not be applied
• Nodes and edges will only be classified as your custom types or remain unclassified

This is useful when you need precise control over your graph structure and want to ensure only domain-specific types are used.

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from zep_cloud.client import Zep
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client = Zep(
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    api_key=API_KEY,
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)
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# Create a user with default ontology disabled
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user = client.user.add(
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    user_id=user_id,
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    first_name="John",
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    last_name="Doe",
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    email="[email protected]",
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    disable_default_ontology=True
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)
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# Or update an existing user to disable default ontology
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client.user.update(
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    user_id=user_id,
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    disable_default_ontology=True
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)

Custom Entity and Edge Types

Definition

In addition to the default entity and edge types, you may specify your own custom entity and custom edge types. You need to provide a description of the type and a description for each of the fields. The syntax for this is different for each language.

You may not create more than 10 custom entity types and 10 custom edge types when setting ontology for a specific graph. The same limit of 10 custom entity types and 10 custom edge types also applies when setting ontology project-wide. The limit of 10 custom entity types does not include the default types. Each model may have up to 10 fields.

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from zep_cloud.external_clients.ontology import EntityModel, EntityText, EdgeModel, EntityBoolean
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from pydantic import Field
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class Restaurant(EntityModel):
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    """
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    Represents a specific restaurant.
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    """
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    cuisine_type: EntityText = Field(description="The cuisine type of the restaurant, for example: American, Mexican, Indian, etc.", default=None)
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    dietary_accommodation: EntityText = Field(description="The dietary accommodation of the restaurant, if any, for example: vegetarian, vegan, etc.", default=None)
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class Audiobook(EntityModel):
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    """
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    Represents an audiobook entity.
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    """
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    genre: EntityText = Field(description="The genre of the audiobook, for example: self-help, fiction, nonfiction, etc.", default=None)
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class RestaurantVisit(EdgeModel):
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    """
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    Represents the fact that the user visited a restaurant.
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    """
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    restaurant_name: EntityText = Field(description="The name of the restaurant the user visited", default=None)
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class AudiobookListen(EdgeModel):
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    """
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    Represents the fact that the user listened to or played an audiobook.
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    """
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    audiobook_title: EntityText = Field(description="The title of the audiobook the user listened to or played", default=None)
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class DietaryPreference(EdgeModel):
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    """
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    Represents the fact that the user has a dietary preference or dietary restriction.
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    """
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    preference_type: EntityText = Field(description="Preference type of the user: anything, vegetarian, vegan, peanut allergy, etc.", default=None)
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    allergy: EntityBoolean = Field(description="Whether this dietary preference represents a user allergy: True or false", default=None)

Setting Entity and Edge Types

You can set these custom entity and edge types as the graph ontology for your current Zep project. The ontology can be applied either project-wide to all users and graphs, or targeted to specific users and graphs only.

Setting Types Project Wide

When no user IDs or graph IDs are provided, the ontology is set for the entire project. All users and graphs within the project will use this ontology. Note that for custom edge types, you can require the source and destination nodes to be a certain type, or allow them to be any type:

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from zep_cloud import EntityEdgeSourceTarget
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client.graph.set_ontology(
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    entities={
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        "Restaurant": Restaurant,
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        "Audiobook": Audiobook,
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    },
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    edges={
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        "RESTAURANT_VISIT": (
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            RestaurantVisit,
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            [EntityEdgeSourceTarget(source="User", target="Restaurant")]
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        ),
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        "AUDIOBOOK_LISTEN": (
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            AudiobookListen,
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            [EntityEdgeSourceTarget(source="User", target="Audiobook")]
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        ),
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        "DIETARY_PREFERENCE": (
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            DietaryPreference,
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            [EntityEdgeSourceTarget(source="User")]
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        ),
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    }
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)

Setting Types For Specific Graphs

You can also set the ontology for specific users and/or graphs by providing user IDs and graph IDs. When these parameters are provided, the ontology will only apply to the specified users and graphs, while other users and graphs in the project will continue using the previously set ontology (whether that was due to a project-wide setting of ontology or due to a graph-specific setting of ontology):

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from zep_cloud import EntityEdgeSourceTarget
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await client.graph.set_ontology(
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    user_ids=["user_1234", "user_5678"],
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    graph_ids=["graph_1234", "graph_5678"],
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    entities={
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        "Restaurant": Restaurant,
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        "Audiobook": Audiobook,
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    },
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    edges={
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        "RESTAURANT_VISIT": (
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            RestaurantVisit,
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            [EntityEdgeSourceTarget(source="User", target="Restaurant")]
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        ),
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        "AUDIOBOOK_LISTEN": (
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            AudiobookListen,
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            [EntityEdgeSourceTarget(source="User", target="Audiobook")]
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        ),
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        "DIETARY_PREFERENCE": (
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            DietaryPreference,
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            [EntityEdgeSourceTarget(source="User")]
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        ),
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    }
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)

Adding Data

Now, when you add data to the graph, new nodes and edges are classified into exactly one of the overall set of entity or edge types respectively, or no type:

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from zep_cloud import Message
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import uuid
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messages_thread1 = [
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    Message(content="Take me to a lunch place", role="user", name="John Doe"),
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    Message(content="How about Panera Bread, Chipotle, or Green Leaf Cafe, which are nearby?", role="assistant", name="Assistant"),
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    Message(content="Do any of those have vegetarian options? I'm vegetarian", role="user", name="John Doe"),
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    Message(content="Yes, Green Leaf Cafe has vegetarian options", role="assistant", name="Assistant"),
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    Message(content="Let's go to Green Leaf Cafe", role="user", name="John Doe"),
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    Message(content="Navigating to Green Leaf Cafe", role="assistant", name="Assistant"),
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]
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messages_thread2 = [
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    Message(content="Play the 7 habits of highly effective people", role="user", name="John Doe"),
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    Message(content="Playing the 7 habits of highly effective people", role="assistant", name="Assistant"),
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]
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user_id = f"user-{uuid.uuid4()}"
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client.user.add(user_id=user_id, first_name="John", last_name="Doe", email="[email protected]")
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thread1_id = f"thread-{uuid.uuid4()}"
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thread2_id = f"thread-{uuid.uuid4()}"
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client.thread.create(thread_id=thread1_id, user_id=user_id)
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client.thread.create(thread_id=thread2_id, user_id=user_id)
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client.thread.add_messages(thread_id=thread1_id, messages=messages_thread1, ignore_roles=["assistant"])
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client.thread.add_messages(thread_id=thread2_id, messages=messages_thread2, ignore_roles=["assistant"])

Retrieving Custom Types

Once you’ve created custom entity and edge types in your graph, you’ll typically want to retrieve information filtered by these specific types. There are two primary approaches:

1. Context Templates (Recommended): Use context templates to create reusable context blocks that automatically filter by type. This is ideal when you want consistent formatting with automatic relevance detection.
2. Graph Search: Use direct graph search for more granular control over queries and when you need dynamic, query-based retrieval.

Using Context Templates

Context templates provide a convenient way to create context blocks that filter by your custom entity and edge types. You define a template once and Zep automatically retrieves and formats the relevant information.

Context templates support the types parameter on %{entities} and %{edges} variables to filter by your custom types:

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# Create a template that filters by custom types
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client.context.create_context_template(
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    template_id="restaurant-audiobook-context",
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    template="""# USER PREFERENCES
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%{edges types=[DIETARY_PREFERENCE] limit=5}
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# RESTAURANT VISITS
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%{edges types=[RESTAURANT_VISIT] limit=10}
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# RESTAURANTS
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%{entities types=[Restaurant] limit=5}
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# AUDIOBOOKS
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%{entities types=[Audiobook] limit=5}
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# AUDIOBOOK LISTENING HISTORY
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%{edges types=[AUDIOBOOK_LISTEN] limit=10}"""
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)
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# Use the template to get context
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results = client.thread.get_user_context(
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    thread_id=thread1_id,
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    template_id="restaurant-audiobook-context"
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)
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print(results.context)

The resulting context block will automatically include only the specified types, formatted according to your template:

# USER PREFERENCES
- (2024-11-20 10:30:00 - present) [DIETARY_PREFERENCE] <User states 'I'm vegetarian' indicating a dietary preference> { preference_type: vegetarian, allergy: false }
# RESTAURANT VISITS
- (2024-11-20 10:35:00 - present) [RESTAURANT_VISIT] <User John Doe is going to Green Leaf Cafe> { restaurant_name: Green Leaf Cafe }
# RESTAURANTS
- { name: Green Leaf Cafe, types: [Entity,Restaurant], summary: Green Leaf Cafe is a restaurant that offers vegetarian options, attributes: { dietary_accommodation: vegetarian } }
# AUDIOBOOKS
- { name: 7 habits of highly effective people, types: [Entity,Audiobook], summary: '7 habits of highly effective people' is an audiobook }
# AUDIOBOOK LISTENING HISTORY
- (2024-11-20 10:40:00 - present) [AUDIOBOOK_LISTEN] <John Doe requested to play the audiobook '7 habits of highly effective people'> { audiobook_title: 7 habits of highly effective people }

Using Graph Search

For more granular control or dynamic queries, you can use graph search to filter by entity or edge types. Graph search is ideal when you need to specify custom search queries, want direct access to individual nodes or edges, or are building dynamic filtering based on runtime conditions.

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search_results_restaurants = client.graph.search(
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    user_id=user_id,
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    query="Take me to a restaurant",
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    scope="nodes",
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    search_filters={
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        "node_labels": ["Restaurant"]
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    },
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    limit=1,
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)
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node = search_results_restaurants.nodes[0]
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print(f"Node name: {node.name}")
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print(f"Node labels: {node.labels}")
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print(f"Cuisine type: {node.attributes.get('cuisine_type')}")
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print(f"Dietary accommodation: {node.attributes.get('dietary_accommodation')}")

Node name: Green Leaf Cafe
Node labels: Entity,Restaurant
Cuisine type: undefined
Dietary accommodation: vegetarian

You can provide multiple types in search filters, and the types will be ORed together. For example, searching with edge_types: ["DIETARY_PREFERENCE", "RESTAURANT_VISIT"] will return edges matching either type.

Important Notes/Tips

Some notes regarding custom entity and edge types:

• The set_ontology method overwrites any previously defined custom entity and edge types, so the set of custom entity and edge types is always the list of types provided in the last set_ontology method call
• The overall set of entity and edge types for a project includes both the custom entity and edge types you set and the default entity and edge types
• You can overwrite the default entity and edge types by providing custom types with the same names
• Changing the custom entity or edge types will not update previously created nodes or edges. The classification and attributes of existing nodes and edges will stay the same. The only thing that can change existing classifications or attributes is adding data that provides new information.
• When creating custom entity or edge types, avoid using the following attribute names (including in Go struct tags), as they conflict with default attributes: uuid, name, graph_id, name_embedding, summary, and created_at
• Any custom entity or edge is required to have at least one custom property defined
• Tip: Design custom entity types to represent entities/nouns, and design custom edge types to represent relationships/verbs. Otherwise, your type might be represented in the graph as an edge more often than as a node or vice versa.
• Tip: If you have overlapping entity or edge types (e.g. ‘Hobby’ and ‘Hiking’), you can prioritize one type over another by mentioning which to prioritize in the entity or edge type descriptions