Adapter

Signature + Input → Adapter.format() → Prompt for LM
LM Response → Adapter.parse() → Typed Output

Your input fields are:
1. `question` (string): The question to answer

Your output fields are:
1. `answer` (string): A clear, direct answer

All interactions will be structured in the following way...

[[ ## question ## ]]
question

[[ ## answer ## ]]
Output field `answer` should be of type: string

[[ ## completed ## ]]

Respond with the corresponding output fields, starting with `[[ ## answer ## ]]`,
and then ending with the marker for `[[ ## completed ## ]]`.

In adhering to this structure, your objective is:
        Answer questions accurately and concisely.

[[ ## field_name ## ]]
value here

[[ ## another_field ## ]]
another value

[[ ## completed ## ]]

[[ ## analysis ## ]]
Output field `analysis` should be of type: Analysis

Definitions (used below):

Sentiment
----
- Positive
- Negative
- Neutral

{
  "sentiment": Sentiment,
  "confidence": float,
  "keywords": string[]
}

// Internally:
jsonish::from_str(output_format, &type_ir, &raw_text, true)

ParseError::Multiple {
    errors: vec![...],  // all field errors
    partial: Some(BamlValue::Map(...)),  // what did parse
}

use dspy_rs::ChatAdapter;

let adapter = ChatAdapter;

// See what prompt would be generated
let system = adapter.format_system_message_typed::<MySignature>()?;
let user = adapter.format_user_message_typed::<MySignature>(&input);

println!("System:\n{system}");
println!("User:\n{user}");

#[derive(Signature, Clone, Debug)]
struct Search {
    #[input]
    query: String,  // plain text

    #[input]
    #[format("yaml")]
    filters: Vec<Filter>,  // serialized as YAML

    #[output]
    results: Vec<Result>,
}

#[derive(Signature, Clone, Debug)]
/// Extract the insurance claim information from the following text.
/// - If you are unsure about a field, leave it as null.
pub struct InsuranceClaimInfo {
    #[input]
    claim_text: String,

    #[output]
    claim: InsuranceClaim,  // complex nested struct with enums
}

Your input fields are:
1. `claim_text` (string)

Your output fields are:
1. `claim` (InsuranceClaim)

All interactions will be structured in the following way, with the appropriate values filled in.

[[ ## claim_text ## ]]
claim_text

[[ ## claim ## ]]
Output field `claim` should be of type: InsuranceClaim

Definitions (used below):

// Specific standardized incident type.

IncidentType
----
- RearEndCollision
- SideImpactCollision
- HeadOnCollision
- ParkingLotCollision
- HouseFire
- KitchenFire
- ElectricalFire
- BurstPipeFlood
- StormDamage
- RoofLeak
- SlipAndFall
- PropertyInjury
- ProductLiability
- TheftBurglary
- Vandalism

// Standardized location type where incident occurred.

LocationType
----
- Intersection
- Highway
- ParkingLot
- Driveway
- ResidentialStreet
- ResidenceInterior
- ResidenceExterior
- CommercialProperty
- PublicProperty

{
  // Top-level insurance claim object aggregating all extracted fields.

  // Basic claim information.
  header: {
    // Basic claim information (metadata about the claim intake).

    // Claim ID in format `CLM-XXXXXX`, where `X` is a digit.
    claim_id: string or null,
    // Date claim was reported in `YYYY-MM-DD` format.
    report_date: string or null,
    // Date incident occurred in `YYYY-MM-DD` format.
    incident_date: string or null,
    // Full name of person reporting claim.
    reported_by: string or null,
    // Channel used to report claim.
    channel: 'Email' or 'Phone' or 'Portal' or 'InPerson' or null,
  },
  // Policy information if available.
  policy_details: {
    // Policy information if available.

    // Policy number in format `POL-XXXXXXXXX`, where `X` is a digit.
    policy_number: string or null,
    // Full legal name on policy.
    policyholder_name: string or null,
    // Type of insurance coverage.
    coverage_type: 'Property' or 'Auto' or 'Liability' or 'Health' or 'Travel' or 'Other' or null,
    // Policy effective start date in `YYYY-MM-DD` format.
    effective_date: string or null,
    // Policy expiration end date in `YYYY-MM-DD` format.
    expiration_date: string or null,
  } or null,
  // List of insured objects involved, if applicable.
  insured_objects: [
    {
      // An insured object involved in the claim (vehicle, building, person, etc.).

      // Unique identifier for insured object.
      //
      // For vehicles, use VIN format (e.g., `VIN12345678901234567`).
      // For buildings, use `PROP-XXXXXX` format.
      // For liability, use `LIAB-XXXXXX` format.
      // For other objects, use `OBJ-XXXXXX` format,
      // where `X` is a digit.
      object_id: string or null,
      // Type of insured object.
      object_type: 'Vehicle' or 'Building' or 'Person' or 'Other',
      // Make and model for vehicles (use standardized manufacturer names and models),
      // or building type for property.
      make_model: string or null,
      // Year for vehicles or year built for buildings.
      year: int or null,
      // Full street address where object is located or originated from.
      location_address: string or null,
      // Estimated monetary value in USD without currency symbol.
      estimated_value: int or null,
    }
  ] or null,
  // Structured incident details.
  incident_description: {
    // Structured incident details.

    // Specific standardized incident type.
    incident_type: IncidentType,
    // Standardized location type where incident occurred.
    location_type: LocationType,
    // Estimated damage in USD without currency symbol.
    estimated_damage_amount: int or null,
    // Police report number if applicable.
    police_report_number: string or null,
  } or null,
}

[[ ## completed ## ]]

Respond with the corresponding output fields, starting with the field `[[ ## claim ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.

In adhering to this structure, your objective is:
        Extract the insurance claim information from the following text.
        - If you are unsure about a field, leave it as null.

[[ ## claim_text ## ]]
A raccoon bumped a parked scooter in a driveway. Reported by Taylor P. via phone.