Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.neuronsearchlab.com/llms.txt

Use this file to discover all available pages before exploring further.

Contexts describe the placement, audience, and intent behind a recommendation request. Properly configuring them keeps results relevant across your product surfaces.

​
When to create a new context

Create a dedicated context whenever:
  • The UI surface has unique layout or business goals (homepage vs. detail page).
  • You need a different blend of retrieval settings, filters, or pipeline behaviour.
  • You want to measure performance separately from other experiences.
Start with a handful of high-impact contexts, then expand as you identify more nuanced placements.

​
Configure contexts in the console

1

Open Contexts

Navigate to Console -> Contexts.
2

Create a context

Click New context and note the numeric Context ID shown in the table, for example 101.
3

Define the context behavior

Fill in the modal to define how the context should behave:
  • Context Name and Description document the placement.
  • Select the Recommendation Type / model family that should serve this surface.
  • Configure Filters and optional combinations (AND/OR) to enforce inventory and eligibility rules.
  • Use the context to decide what gets retrieved before scoring.
  • Use the Rules Engine for post-scoring controls such as boost, bury, pin, dedupe, reorder, or grouping.
4

Share the context ID

Save the configuration and share the context ID with your engineering team.

​
Reference contexts in API calls

Attach the context ID to every recommendation request so NeuronSearchLab can apply the proper logic. 
curl -X GET "https://api.neuronsearchlab.com/v1/recommendations" \
  -H "Authorization: Bearer <access_token>" \
  -G \
  --data-urlencode "user_id=user-123" \
  --data-urlencode "context_id=101"
The Core API accepts the numeric console ID. For backwards compatibility, ctx_101 is also accepted and is normalized in recommendation explanations and served-recommendation telemetry.

​
Iterate continuously

Monitor performance metrics and adjust context settings as business goals evolve. Combine context changes with explicit Rules Engine updates and event tracking to keep the learning loop tight.