@spec embed(
  String.t() | {atom(), keyword()} | struct(),
  String.t() | [String.t()],
  keyword()
) :: {:ok, [float()] | [[float()]]} | {:error, term()}

Generates embeddings for single or multiple text inputs.

Accepts either a single string or a list of strings, automatically handling both cases using pattern matching.

Parameters

• model_spec - Model specification in various formats
• input - Text string or list of text strings to generate embeddings for
• opts - Additional options (keyword list)

Options

• :dimensions - Number of dimensions for embeddings
• :encoding_format - Format for encoding ("float" or "base64")
• :user - User identifier for tracking
• :provider_options - Provider-specific options

Examples

# Single text input
{:ok, embedding} = ReqLLM.Embedding.embed("openai:text-embedding-3-small", "Hello world")
#=> {:ok, [0.1, -0.2, 0.3, ...]}

# Multiple text inputs
{:ok, embeddings} = ReqLLM.Embedding.embed(
  "openai:text-embedding-3-small",
  ["Hello", "World"]
)
#=> {:ok, [[0.1, -0.2, ...], [0.3, 0.4, ...]]}

@spec schema() :: NimbleOptions.t()

Returns the base embedding options schema.

This schema contains embedding-specific options that are vendor-neutral.

@spec supported_models() :: [String.t()]

Returns the list of supported embedding model specifications.

Examples

ReqLLM.Embedding.supported_models()
#=> ["openai:text-embedding-3-small", "openai:text-embedding-3-large", "openai:text-embedding-ada-002", "google:gemini-embedding-001"]

@spec validate_model(String.t() | {atom(), keyword()} | struct()) ::
  {:ok, ReqLLM.Model.t()} | {:error, term()}

Validates that a model supports embedding operations.

Parameters

• model_spec - Model specification in various formats

Examples

ReqLLM.Embedding.validate_model("openai:text-embedding-3-small")
#=> {:ok, %ReqLLM.Model{provider: :openai, model: "text-embedding-3-small"}}

ReqLLM.Embedding.validate_model("anthropic:claude-3-sonnet")
#=> {:error, :embedding_not_supported}