Environment
Overview
In our knowledge graph service, an Environment acts as a logical container that isolates sources within a user’s workspace. This allows users to create and manage multiple knowledge graphs independently under the same account.
Why Use Environments?
AI agents often interact with diverse and unrelated sets of information. Environments provide a clean way to separate these contexts, ensuring that data from one use case or project does not interfere with another. This is especially useful when:
- Building independent knowledge graphs for different agents or domains
- Running isolated queries and evaluations
- Managing context-specific memory for AI workflows
Key Characteristics
- Isolation: Sources stored in one environment are not accessible from another. This guarantees clean separation of information.
- Scoped Queries: All graph operations and queries are performed within the context of a specific environment.
- Flexibility: Users can create as many environments as needed, allowing for fine-grained control over their memory architecture.
Structure
Each environment has the following properties:
| Field | Type | Description |
|---|---|---|
name | string | Unique name of the environment (required) |
description | string | Optional description providing context or purpose |
Environment names must be unique across all of a user’s environments. Attempting to create another environment with the same name will result in an error.
Relationship with Sources
Every source must be associated with exactly one environment. This ensures that all knowledge derived from a source is scoped correctly and remains isolated within its intended graph.