Lamini

Lamini Embeddings API

Text embedding generation.

Documentation GitHub OpenAPI

Operations 1

POST /v1/embedding Get the vector embedding for a given prompt #

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OpenAPI Specification

openapi: 3.2.0
info:
  title: Lamini Embeddings API
  description: LLM inference and tuning for the enterprise. Factual LLMs. Classifier and embeddings. Deployed anywhere in 10 min.
  version: 1.13.6
security:
- APIKeyAuth: []
tags:
- name: Embeddings
  description: Get vector embeddings for your data.
paths:
  /v1/embedding:
    post:
      tags:
      - Embeddings
      summary: Get the vector embedding for a given prompt
      operationId: embedding_v1_embedding_post
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/EmbeddingParams'
        required: true
      responses:
        '200':
          description: Successful response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/EmbeddingResponse'
              example:
                embedding:
                - 0.013080810196697712
                - -0.05404408276081085
                - 0.0183291956782341
                - -0.03727744519710541
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
      - API Key Auth: []
      parameters:
      - name: Authorization
        in: header
        required: true
        schema:
          type: string
        description: API key for authentication
components:
  schemas:
    HTTPValidationError:
      properties:
        detail:
          items:
            $ref: '#/components/schemas/ValidationError'
          type: array
          title: Detail
      type: object
      title: HTTPValidationError
    EmbeddingParams:
      properties:
        prompt:
          anyOf:
          - type: string
          - items:
              type: string
            type: array
          title: Prompt
          description: The input text to embed. Can be a single string or a list of strings.
          example: What is the capital of France?
        model_name:
          type: string
          title: Model Name
          description: The name of the embedding model to use. If not provided, a default model will be used.
          default: sentence-transformers/all-MiniLM-L6-v2
        latency_sensitive:
          type: boolean
          title: Latency Sensitive
          description: Whether the request is latency-sensitive. If true, faster processing may be prioritized.
          default: true
      type: object
      required:
      - prompt
      title: EmbeddingParams
    ValidationError:
      properties:
        loc:
          items:
            anyOf:
            - type: string
            - type: integer
          type: array
          title: Location
        msg:
          type: string
          title: Message
        type:
          type: string
          title: Error Type
      type: object
      required:
      - loc
      - msg
      - type
      title: ValidationError
    EmbeddingResponse:
      properties:
        embedding:
          anyOf:
          - items:
              type: number
            type: array
          - items:
              items:
                type: number
              type: array
            type: array
          title: Embedding
      type: object
      required:
      - embedding
      title: EmbeddingResponse
  securitySchemes:
    APIKeyAuth:
      type: apiKey
      in: header
      name: Authorization
      description: Enter your API token. Get your API key from http://localhost:5001/account
x-provenance:
  method: harvested
  publisher: Lamini
  source: https://docs.lamini.ai/openapi.json
  note: split by refine-openapis from the provider's own published document, verified by fetch (openapi/_original/*.fetch.json);
    stamped by stamp-openapi-provenance.py (roadmap#793)