/**
 * LlmTextMutationPlugin
 *
 * Adds `{columnName}Text: String` companion fields on create/update mutation
 * inputs for every vector column. When the client provides a text string in
 * the companion field, the plugin embeds it server-side and injects the
 * resulting vector into the actual column.
 *
 * Example:
 *   mutation { createArticle(input: { embeddingText: "Machine learning concepts" }) }
 *
 * If the embedder returns null (e.g. quota exceeded when the metering plugin
 * is loaded), the mutation throws an error — unlike search, mutations cannot
 * silently skip writing a vector the user asked for.
 *
 * This is the mutation counterpart to LlmTextSearchPlugin (which handles
 * filter/query-side text-to-vector). Together they let clients work entirely
 * with text/prompts instead of raw float vectors.
 *
 * Runtime embedding uses the v4-style resolver wrapping approach (same as
 * graphile-upload-plugin and graphile-bucket-provisioner-plugin). grafserv v5
 * supports this through its backwards-compatibility layer.
 *
 * The companion fields are only added when the LLM plugin is loaded.
 * If no embedder is configured, the fields are still registered for schema
 * stability but return a clear error at execution time.
 */
import 'graphile-build';
import 'graphile-build-pg';
import type { GraphileConfig } from 'graphile-config';
declare global {
    namespace GraphileConfig {
        interface Plugins {
            LlmTextMutationPlugin: true;
        }
    }
}
/**
 * Creates the LlmTextMutationPlugin.
 *
 * Hooks into GraphQLInputObjectType_fields for create/update input types
 * and adds `{columnName}Text: String` for each vector column.
 *
 * Also wraps mutation resolvers via GraphQLObjectType_fields_field to
 * intercept `*Text` companion field values, embed them, and inject the
 * resulting vectors before the mutation executes.
 */
export declare function createLlmTextMutationPlugin(): GraphileConfig.Plugin;