NLU (Natural Language Understanding)

The NLU: block configures how the agent classifies user intent, extracts entities, resolves synonyms, and matches utterances. NLU definitions are used by the runtime for intent routing, digression matching, and entity extraction during conversation.

Overview

ABL’s NLU configuration supports:

Intent classification with keyword patterns and example utterances.
Entity extraction with typed extractors (enum, pattern, location, date, number, free text).
Synonyms for normalizing variant expressions to canonical values.
Embeddings-based matching for semantic similarity when keyword patterns are insufficient.
Multi-language support with per-language model configuration.
A glossary for domain-specific terminology.

NLU:
  intents:
    - NAME: send_wire
      PATTERNS: ["wire transfer", "send money", "wire funds"]
      EXAMPLES: ["I need to wire $50,000 to Germany", "Can I send a domestic wire?"]

  entities:
    - NAME: currency_code
      TYPE: enum
      VALUES: [USD, EUR, GBP, JPY]
      SYNONYMS:
        USD: [dollars, usd, bucks]
        EUR: [euros, eur]

  glossary:
    - "SWIFT/BIC -- Code identifying a bank globally"
    - "Fedwire -- Federal Reserve real-time settlement system"

Intent classification

Intents represent categories of user messages. Each intent has a name, keyword patterns for quick matching, and optional example utterances for model-based classification.

Syntax

NLU:
  intents:
    - NAME: book_flight
      PATTERNS: ["book flight", "find flights", "search flights", "fly to"]
      EXAMPLES:
        - "I want to fly to Paris next Tuesday"
        - "Find me a round trip to London"
        - "Book two seats on the morning flight to NYC"
      ENTITIES: [destination, travel_date, passenger_count]

Intent properties

Property	Type	Required	Default	Description
`NAME`	`string`	Yes	—	Unique intent identifier.
`PATTERNS`	`string[]`	Yes	—	Keyword patterns for quick substring matching.
`EXAMPLES`	`string[]`	No	—	Full example utterances for model-based classification. Improves accuracy.
`EXAMPLES_FILE`	`string`	No	—	Path to an external file containing example utterances (one per line).
`ENTITIES`	`string[]`	No	—	Entity types expected to co-occur with this intent.

Pattern matching

Patterns are matched as case-insensitive substrings against the user’s message. If any pattern appears in the message, the intent is a candidate match. Pattern matching is the first tier of classification; it is fast but imprecise.

Example-based classification

When EXAMPLES are provided, the runtime uses an LLM or embedding model to classify messages based on semantic similarity to the examples. This is more accurate than pattern matching but requires more compute.

Intent with external examples file

For intents with many examples, reference an external file:

NLU:
  intents:
    - NAME: product_inquiry
      PATTERNS: ["tell me about", "what is", "describe"]
      EXAMPLES_FILE: "./nlu/product_inquiry_examples.txt"

Entity extraction

Entities are structured values extracted from user messages. ABL supports six entity types.

Syntax

NLU:
  entities:
    - NAME: currency_code
      TYPE: enum
      VALUES: [USD, EUR, GBP, JPY, CHF, CAD, AUD]
      SYNONYMS:
        USD: [dollars, usd, bucks, us dollars]
        EUR: [euros, eur]
        GBP: [pounds, sterling, gbp, quid]

    - NAME: phone_number
      TYPE: pattern
      PATTERN: "\\b\\d{3}[-.]?\\d{3}[-.]?\\d{4}\\b"
      VALIDATION: "Must be a valid US phone number"

    - NAME: destination
      TYPE: location

    - NAME: travel_date
      TYPE: date

    - NAME: passenger_count
      TYPE: number

    - NAME: special_request
      TYPE: free_text

Entity types

Type	Description	Additional properties
`enum`	Matches against a fixed set of values with optional synonyms.	`VALUES`, `SYNONYMS`
`pattern`	Matches against a regular expression.	`PATTERN`, `VALIDATION`
`location`	Extracts geographic locations (cities, countries, addresses).	—
`date`	Extracts date and time references, including relative expressions (“next Tuesday”).	—
`number`	Extracts numeric values.	—
`free_text`	Captures arbitrary text spans. Useful for notes, descriptions, or comments.	—

Entity properties

Property	Type	Required	Default	Description
`NAME`	`string`	Yes	—	Unique entity identifier.
`TYPE`	`string`	Yes	—	Entity type. See Entity types.
`VALUES`	`string[]`	No	—	Valid values (for `enum` type).
`SYNONYMS`	`Record<string, string[]>`	No	—	Synonym mappings. Keys are canonical values.
`PATTERN`	`string`	No	—	Regular expression (for `pattern` type).
`VALIDATION`	`string`	No	—	Validation description or expression.

Synonyms

Synonyms map variant expressions to canonical values. When a synonym is detected, the runtime normalizes it to the canonical form before storing:

SYNONYMS:
  USD: [dollars, usd, bucks, us dollars]
  EUR: [euros, eur]
  GBP: [pounds, sterling, gbp, quid]

If the user says “100 bucks”, the entity extraction yields currency_code: "USD".

Embeddings-based matching

For more accurate semantic matching, enable embeddings-based NLU. This uses vector similarity to match user messages against intent examples.

NLU:
  embeddings:
    enabled: true
    provider: "bge-m3"
    model: "BAAI/bge-m3"
    threshold: 0.75
    cache_ttl: 3600

Embeddings properties

Property	Type	Required	Default	Description
`enabled`	`boolean`	Yes	—	Whether embeddings-based matching is active.
`provider`	`string`	No	—	Embedding model provider name.
`model`	`string`	No	—	Specific model identifier.
`baseUrl`	`string`	No	—	Custom endpoint URL for the embedding service.
`threshold`	`number`	No	—	Similarity threshold (0.0—1.0). Below this, the match is rejected.
`cache_ttl`	`number`	No	—	Cache duration in seconds for computed embeddings.

Multi-language support

ABL NLU supports multiple languages with per-language model configuration and optional code-switching detection.

NLU:
  languages: [en, es, fr]
  default_language: en
  allow_code_switching: true
  language_models:
    en: "gpt-4o"
    es: "gpt-4o"
    fr: "gpt-4o"

Multi-language properties

Property	Type	Required	Default	Description
`languages`	`string[]`	No	—	Supported language codes.
`default_language`	`string`	No	—	Default language when detection is ambiguous.
`allow_code_switching`	`boolean`	No	`false`	Allow users to switch languages mid-conversation.
`language_models`	`Record<string,string>`	No	—	Per-language model assignments for classification.

Model configuration

Specify which models to use for NLU classification tasks:

NLU:
  models:
    fast: "claude-haiku-4-5-20251001"
    balanced: "claude-sonnet-4-5-20250929"

Property	Type	Required	Default	Description
`fast`	`string`	No	—	Model for fast, low-latency classification.
`balanced`	`string`	No	—	Model for higher-accuracy classification.

Evaluation configuration

Control NLU evaluation behavior for monitoring and testing:

NLU:
  evaluation:
    log_predictions: true
    ab_test: true
    confidence_threshold: 0.6

Property	Type	Required	Default	Description
`log_predictions`	`boolean`	No	—	Log all NLU predictions for analysis.
`ab_test`	`boolean`	No	—	Enable A/B testing between NLU models.
`confidence_threshold`	`number`	No	—	Minimum confidence score for accepting a prediction.

Glossary

The glossary defines domain-specific terms and abbreviations. These definitions are injected into the LLM context to improve understanding of specialized vocabulary.

NLU:
  glossary:
    - "SWIFT/BIC -- Society for Worldwide Interbank Financial Telecommunication code"
    - "IBAN -- International Bank Account Number"
    - "Fedwire -- Federal Reserve real-time gross settlement system"
    - "OFAC -- Office of Foreign Assets Control"
    - "Beneficiary -- The person or entity receiving the wire transfer"

Each glossary entry is a plain string in "term -- definition" format.

External NLU configuration

For complex NLU setups, reference an external configuration file:

NLU:
  config_file: "./nlu/config.yaml"

The external file is merged with any inline NLU configuration. Inline values take precedence.

Complete example

NLU:
  models:
    fast: "claude-haiku-4-5-20251001"
    balanced: "claude-sonnet-4-5-20250929"

  languages: [en, es]
  default_language: en
  allow_code_switching: true

  intents:
    - NAME: send_wire
      PATTERNS: ["wire transfer", "send money", "wire funds", "remittance"]
      EXAMPLES:
        - "I need to wire $50,000 to a vendor in Germany"
        - "Can I send a domestic wire from my business account?"

    - NAME: check_status
      PATTERNS: ["wire status", "where is my wire", "tracking"]
      EXAMPLES:
        - "What's the status of confirmation WR-2024-88431?"

    - NAME: cancel_wire
      PATTERNS: ["cancel the wire", "stop the transfer", "recall"]

  entities:
    - NAME: currency_code
      TYPE: enum
      VALUES: [USD, EUR, GBP, JPY, CHF, CAD, AUD]
      SYNONYMS:
        USD: [dollars, usd, bucks]
        EUR: [euros, eur]
        GBP: [pounds, sterling, quid]

    - NAME: transfer_type
      TYPE: enum
      VALUES: [domestic, international]
      SYNONYMS:
        domestic: [within the us, fedwire, local wire]
        international: [overseas, swift, cross-border]

  embeddings:
    enabled: true
    provider: "bge-m3"
    threshold: 0.75
    cache_ttl: 3600

  glossary:
    - "SWIFT/BIC -- Code identifying a bank globally"
    - "Fedwire -- Federal Reserve real-time settlement system"
    - "OFAC -- Office of Foreign Assets Control"

Multi-Agent & Supervisor — intent-based routing in Supervisors
Expressions & functions — expression syntax used in intent conditions
Memory & Constraints — NLU results stored in session variables

​Overview

​Intent classification

​Syntax

​Intent properties

​Pattern matching

​Example-based classification

​Intent with external examples file

​Entity extraction

​Syntax

​Entity types

​Entity properties

​Synonyms

​Embeddings-based matching

​Embeddings properties

​Multi-language support

​Multi-language properties

​Model configuration

​Evaluation configuration

​Glossary

​External NLU configuration

​Complete example

​Related pages

Overview

Intent classification

Syntax

Intent properties

Pattern matching

Example-based classification

Intent with external examples file

Entity extraction

Syntax

Entity types

Entity properties

Synonyms

Embeddings-based matching

Embeddings properties

Multi-language support

Multi-language properties

Model configuration

Evaluation configuration

Glossary

External NLU configuration

Complete example

Related pages