@asktext/core/dist/index.d.ts

Core embedding and vector store utilities for AskText voice Q&A.

import { PrismaClient } from '@prisma/client';

/**
 * Abstract interface for vector storage and similarity search operations.
 * Implementations can use different backends (Prisma, external vector DBs, etc).
 */
interface VectorStore {
    /** Insert or update an embedding */
    upsertEmbedding(opts: {
        id: string;
        vector: number[];
        metadata?: Record<string, any>;
    }): Promise<void>;
    /**
     * Return the top-k most similar vectors to `vector`.
     * Implementations may ignore the filter if unsupported.
     */
    query(opts: {
        vector: number[];
        topK: number;
        filter?: Record<string, any>;
    }): Promise<Array<{
        id: string;
        score: number;
        metadata?: Record<string, any>;
    }>>;
}

/**
 * Text chunking utilities for splitting content into overlapping segments suitable for embedding.
 */
interface TextChunk {
    content: string;
    startChar: number;
    endChar: number;
    chunkIndex: number;
}
/**
 * Splits plain text into overlapping chunks suitable for embedding.
 * @param text Input plain text
 * @param maxLen Max characters per chunk (default 1500)
 * @param overlap Overlap between chunks to preserve context (default 200)
 */
declare function chunkText(text: string, maxLen?: number, overlap?: number): TextChunk[];

interface EmbedderOptions {
    apiKey: string;
    model?: string;
}
declare class OpenAIEmbedder {
    private openai;
    private model;
    constructor(opts: EmbedderOptions);
    /** Embed multiple texts in a single API call */
    embed(texts: string[]): Promise<number[][]>;
    /** Embed a single text string */
    embedOne(text: string): Promise<number[]>;
}

/**
 * High-level retrieval utilities for semantic search over embedded content.
 */

interface RetrieveOptions {
    query: string;
    store: VectorStore;
    topK?: number;
    embedder: OpenAIEmbedder;
    filter?: Record<string, any>;
}
/** Retrieve the most semantically similar text passages for a query */
declare function retrievePassages({ query, store, topK, embedder, filter }: RetrieveOptions): Promise<Array<{
    id: string;
    score: number;
    content?: string;
}>>;

/**
 * Text processing utilities for content normalization.
 */
/** Remove HTML tags and normalize whitespace from HTML/markdown content */
declare function stripHtml(html: string): string;

/**
 * Prisma-based vector store implementation using JSON serialization.
 * Expects an ArticleChunk model with embedding field stored as JSON string.
 */

/**
 * Creates a VectorStore implementation using Prisma with JSON-serialized embeddings.
 * Requires ArticleChunk model with fields: id, postId, chunkIndex, content, startChar, endChar, embedding.
 */
declare function createPrismaJsonStore(prisma: PrismaClient): VectorStore;

interface EmbedAndStoreOptions {
    articleId: string;
    htmlOrMarkdown: string;
    embedder: OpenAIEmbedder;
    store: VectorStore;
    /** Max characters per chunk (default 1500) */
    maxLen?: number;
    /** Overlap between chunks (default 200) */
    overlap?: number;
}
/**
 * Convenience helper that strips markup, splits the text, embeds every chunk
 * with OpenAI and upserts rows to the given VectorStore.
 */
declare function embedAndStore({ articleId, htmlOrMarkdown, embedder, store, maxLen, overlap, }: EmbedAndStoreOptions): Promise<void>;

export { type EmbedderOptions, OpenAIEmbedder, type RetrieveOptions, type TextChunk, type VectorStore, chunkText, createPrismaJsonStore, embedAndStore, retrievePassages, stripHtml };