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Custom Endpoints
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Custom Endpoints
Custom endpoints let you run server-side business logic “inside” Curiosity Workspace. They are typically used to:
- compute aggregates and analytics over the graph
- implement domain-specific retrieval (similarity, scoping, enrichment)
- orchestrate AI workflows safely (retrieve → validate → generate)
- expose controlled APIs to external systems
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Example: “similar cases” endpoint (pattern)
The demo repository includes endpoints that combine embeddings-based similarity with graph constraints. A simplified pattern looks like:
class SimilarRequest { public string Query { get; set; } }
var req = Body.FromJson<SimilarRequest>();
// Semantic retrieval for similar items (optionally constrain by type)
return Q().StartAtSimilarText(req.Query, nodeTypes: new[] { "SupportCase" }, count: 50)
.EmitWithScores();
In production, you typically add:
- input validation (max length, required fields)
- permission-aware scoping
- additional constraints (facets or graph relationships)
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Why endpoints (instead of doing everything in the UI)
Endpoints provide:
- security: authorization and token scoping
- performance: run close to the data
- reusability: one endpoint can serve multiple interfaces/integrations
- governance: endpoints can be reviewed and promoted like code
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Endpoint design guidelines
- Keep endpoints small and composable (single responsibility).
- Prefer read-only endpoints for retrieval and analytics.
- For long-running work, use an asynchronous/polling pattern (if supported in your deployment).
- Always enforce permission-aware retrieval for user-facing endpoints.
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Common endpoint categories
- Query endpoints
- return nodes, neighbors, and graph projections
- Search endpoints
- wrap search requests with custom defaults and scoping
- Similarity endpoints
- compute “similar items” using embeddings + constraints
- AI orchestration endpoints
- retrieve context, construct prompts, trigger generation, store results
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External access
If an endpoint is called externally:
- require an endpoint token
- scope tokens to the minimum required endpoint path(s)
- validate inputs (schema and limits) and return safe errors
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Next steps
- Schedule periodic endpoint runs: Scheduled Tasks
- Build a UI that calls endpoints: Interfaces and Integrations
See also
AI Tools are specialized wrappers around Custom Endpoints that allow Large Language Models (LLMs) to interact with Curiosity Workspace data and...
Curiosity Workspace provides a tightly integrated front-end and back-end experience, where the core functionality is exposed through a powerful C#...
Curiosity Workspace provides comprehensive APIs for programmatic access to the graph, search engine, and administrative functions.
Curiosity Workspace is a platform for building data applications that integrate graph, search, and AI capabilities.
Curiosity Workspace is a product that combines a knowledge graph, a search engine, and AI capabilities (NLP, embeddings, LLM-driven workflows) into a...
Graph reasoning refers to using the structure of your knowledge graph to answer questions and drive workflows. It can be done with:
Integrations connect Curiosity Workspace to external systems for ingestion, synchronization, and workflow automation.
LLM configuration is where you control:
This page documents practical prompting patterns that work well in Curiosity Workspace because they align with a retrieval + graph-first architecture.
Scaling Curiosity Workspace is about maintaining good user experience as your data size, query volume, and workload complexity increase.
Scheduled tasks run work periodically inside a Curiosity Workspace environment. They are commonly used for:
This page describes security concepts and operational practices for Curiosity Workspace deployments.