> ## Documentation Index
> Fetch the complete documentation index at: https://docs.autosnap.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Score Vehicles Against an Intent

> Score an LLM-supplied shopper intent against a caller-supplied list
of VINs using each vehicle's already-stored internal service vector — no
re-embedding of vehicles. One embedding call (the intent), one internal service
retrieve, cosine similarity in Python.

Scores are in the natural-language low range (~0.2–0.4), NOT the 0.9+
of /similar (which compares two rich vehicle vectors). That's expected:
short intent vs full vehicle vector. Do not normalize or inflate.

Takes a shopper intent (a question or short description) and a caller-supplied list of VINs. Returns each VIN's match score against the intent, an `in_stock` flag, and an optional `price_fit` against `price_min` / `price_max`. The vehicles are not re-encoded; Vault reuses each one's already-stored vector for the comparison.

Use this endpoint when an LLM holds a shortlist from a prior turn (a `/v2/search` result, a hand-picked set, a previously-shown group) and wants to re-rank just that shortlist as the shopper adds a constraint or shifts intent. For ranking against a dealer's full inventory, use `POST /v2/search` instead.

Scores fall in the natural-language low range (typically 0.20 to 0.40), which is expected for question-vector versus vehicle-vector comparisons. Treat the relative ranking, not the absolute value, as the signal.

For a worked end-to-end example showing `/v2/search` and `/v2/match-score` together in an agentic flow, see the [Semantic Search guide](/guides/semantic-search).


## OpenAPI

````yaml POST /v2/match-score
openapi: 3.0.3
info:
  title: AutosnapAI Origin API
  description: Dealer resolution, inventory management, and webhook APIs
  version: 1.0.0
servers:
  - url: https://api.autosnap.com
    description: Production
security: []
paths:
  /v2/match-score:
    post:
      tags:
        - search
      summary: Match Score
      description: >-
        Score an LLM-supplied shopper intent against a caller-supplied list

        of VINs using each vehicle's already-stored internal service vector — no

        re-embedding of vehicles. One embedding call (the intent), one internal
        service

        retrieve, cosine similarity in Python.


        Scores are in the natural-language low range (~0.2–0.4), NOT the 0.9+

        of /similar (which compares two rich vehicle vectors). That's expected:

        short intent vs full vehicle vector. Do not normalize or inflate.
      operationId: match_score_v2_match_score_post
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/MatchScoreRequest'
        required: true
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
components:
  schemas:
    MatchScoreRequest:
      properties:
        api_key:
          type: string
          title: Api Key
        intent:
          type: string
          title: Intent
        vins:
          items:
            type: string
          type: array
          title: Vins
        dealership_id:
          type: integer
          nullable: true
          title: Dealership Id
        price_min:
          type: integer
          nullable: true
          title: Price Min
        price_max:
          type: integer
          nullable: true
          title: Price Max
      type: object
      required:
        - api_key
        - intent
        - vins
      title: MatchScoreRequest
    HTTPValidationError:
      properties:
        detail:
          items:
            $ref: '#/components/schemas/ValidationError'
          type: array
          title: Detail
      type: object
      title: HTTPValidationError
    ValidationError:
      properties:
        loc:
          items:
            anyOf:
              - type: string
              - type: integer
          type: array
          title: Location
        msg:
          type: string
          title: Message
        type:
          type: string
          title: Error Type
        input:
          title: Input
        ctx:
          type: object
          title: Context
      type: object
      required:
        - loc
        - msg
        - type
      title: ValidationError

````