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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"
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]
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"
"term -- definition" format.
External NLU configuration
For complex NLU setups, reference an external configuration file:
NLU:
config_file: "./nlu/config.yaml"
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"
Related pages
