Neural Search

Configure how neural search retrieves, ranks, and filters knowledge base content across all bots in your tenant.

The Neural Search configuration section controls how the neural search pipeline retrieves, ranks, and filters knowledge base content across your tenant.

These settings apply to all bots and cannot be overridden at the bot or data source level. Access these settings at Settings > Configuration > Neural Search.

Retrieval

Neural Inference Per Retriever Results Limit

Type	Integer
Default	8

Controls how many candidate documents each retriever fetches during neural search before neural search merges and re-ranks them. Raise this value when relevant documents are being missed in search results; lower it to reduce latency when retrieval speed is a priority.

See also: Neural Search Result Limit

Neural Search Result Limit

Type	Integer
Default	10

No description available.

Return top section of KB

Type	Checkbox
Default	Disabled

No description available.

Entity filtering

Neural Inference Retriever Intersection Filter List

Type	Text field
Default	Not configured

Specifies which retrievers apply intersection-based entity filtering during neural search. This field accepts a comma-separated list of internal retriever names. Contact Aisera support to configure this setting.

Neural Inference Retriever Union Filter List

Type	Text field
Default	BMRetriever,EMRetriever_new

Specifies which retrievers apply union-based entity filtering during neural search. This field accepts a comma-separated list of internal retriever names. Contact Aisera support to configure this setting.

Reranking and relevancy

Enable Cross-Encoder

Type	Checkbox
Default	Disabled

Activates a reranking step that scores each retrieved result by comparing the full query against the document's title and subject, replacing the initial retrieval ranking. Enable this when embedding-based ranking produces results that are semantically related but not precisely ordered by relevance to the exact query phrasing.

The cross-encoder evaluates document title and subject only, not full document content.

Enabling this adds inference latency proportional to the number of retrieved results. It also changes how relevance scores are scaled in the response, which may affect downstream systems that apply score thresholds.

Tenant Relevancy Confidence Cutoff Score (Normalized 0-100)

Type	Decimal
Default	0

No description available.

Per App Relevancy Confidence Cutoff Score (Normalized 0-100)

Type	Text field
Default	[{}]

No description available.

LLM model used for document summarization in Neural Search

Type	Dropdown
Default	gpt-5.1

Controls which large language model neural search uses to generate document summaries. The selected model determines both the summarization behavior and the prompts applied during summary generation.

Form intercept

Filter by Product Information in Form Intercept

Type	Checkbox
Default	Disabled

When a Form Intercept channel submits a query that includes a product identifier, this setting restricts neural search results to knowledge base content tagged with that identifier. Use this when your tenant supports multiple products with separate knowledge base content and you want to prevent cross-product results.

Tag your knowledge base documents with product information metadata matching the identifiers your Form Intercept channel sends.

See also: Form Intercept Delimiter

Form Intercept Delimiter

Type	Text field
Default	||

Specifies the delimiter neural search uses to separate the search query from the product identifier in Form Intercept queries. This value must match the delimiter your Form Intercept channel uses when constructing queries that include product context.

Neural search treats the delimiter as a literal string, not a regular expression.

See also: Filter by Product Information in Form Intercept