embedding-pipeline-builder

name	embedding-pipeline-builder
description	Builds document embedding pipelines with text chunking, embedding generation, indexing, and retrieval optimization. Use when users request "embedding pipeline", "document indexing", "text chunking", "RAG preprocessing", or "semantic indexing".

Embedding Pipeline Builder

Build production-ready document embedding and retrieval pipelines.

Core Workflow

1. Load documents: Ingest from various sources
2. Preprocess text: Clean and normalize
3. Chunk documents: Split into optimal sizes
4. Generate embeddings: Create vector representations
5. Index vectors: Store in vector database
6. Optimize retrieval: Tune for accuracy

Pipeline Architecture

┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│   Loader    │───▶│ Preprocessor │───▶│   Chunker   │
└─────────────┘    └─────────────┘    └─────────────┘
                                            │
                                            ▼
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│  Retriever  │◀───│   Indexer   │◀───│  Embedder   │
└─────────────┘    └─────────────┘    └─────────────┘

Document Loading

Multi-Source Loader

// pipeline/loaders.ts
import { readFile, readdir } from 'fs/promises';
import { join, extname } from 'path';
import pdf from 'pdf-parse';
import mammoth from 'mammoth';

interface LoadedDocument {
  id: string;
  content: string;
  metadata: {
    source: string;
    type: string;
    title?: string;
    createdAt?: Date;
    [key: string]: any;
  };
}

export class DocumentLoader {
  async loadFile(filePath: string): Promise<LoadedDocument> {
    const ext = extname(filePath).toLowerCase();
    const content = await this.extractContent(filePath, ext);

    return {
      id: this.generateId(filePath),
      content,
      metadata: {
        source: filePath,
        type: ext.slice(1),
      },
    };
  }

  async loadDirectory(dirPath: string): Promise<LoadedDocument[]> {
    const files = await readdir(dirPath, { recursive: true });
    const documents: LoadedDocument[] = [];

    for (const file of files) {
      const filePath = join(dirPath, file);
      try {
        const doc = await this.loadFile(filePath);
        documents.push(doc);
      } catch (error) {
        console.error(`Failed to load ${filePath}:`, error);
      }
    }

    return documents;
  }

  private async extractContent(filePath: string, ext: string): Promise<string> {
    const buffer = await readFile(filePath);

    switch (ext) {
      case '.txt':
      case '.md':
        return buffer.toString('utf-8');

      case '.pdf':
        const pdfData = await pdf(buffer);
        return pdfData.text;

      case '.docx':
        const result = await mammoth.extractRawText({ buffer });
        return result.value;

      case '.json':
        const json = JSON.parse(buffer.toString('utf-8'));
        return this.flattenJson(json);

      default:
        throw new Error(`Unsupported file type: ${ext}`);
    }
  }

  private flattenJson(obj: any, prefix = ''): string {
    const parts: string[] = [];

    for (const [key, value] of Object.entries(obj)) {
      const path = prefix ? `${prefix}.${key}` : key;

      if (typeof value === 'object' && value !== null) {
        parts.push(this.flattenJson(value, path));
      } else {
        parts.push(`${path}: ${value}`);
      }
    }

    return parts.join('\n');
  }

  private generateId(source: string): string {
    return `doc_${Buffer.from(source).toString('base64url').slice(0, 16)}`;
  }
}

Web Loader

// pipeline/web-loader.ts
import { JSDOM } from 'jsdom';
import { Readability } from '@mozilla/readability';

export class WebLoader {
  async loadUrl(url: string): Promise<LoadedDocument> {
    const response = await fetch(url);
    const html = await response.text();

    const dom = new JSDOM(html, { url });
    const reader = new Readability(dom.window.document);
    const article = reader.parse();

    return {
      id: this.generateId(url),
      content: article?.textContent || '',
      metadata: {
        source: url,
        type: 'webpage',
        title: article?.title,
        byline: article?.byline,
      },
    };
  }

  async loadSitemap(: ): <[]> {
     response =  (sitemapUrl);
     xml =  response.();

     urlMatches = xml.() || [];
     urls = urlMatches.(
      match.(, ).(, )
    );

     : [] = [];
     ( url  urls.(, )) { 
       {
         doc =  .(url);
        documents.(doc);
          ( (r, )); 
      }  (error) {
        .(, error);
      }
    }

     documents;
  }
}

Text Preprocessing

// pipeline/preprocessor.ts
export class TextPreprocessor {
  process(text: string): string {
    return this.pipeline(text, [
      this.normalizeWhitespace,
      this.removeSpecialCharacters,
      this.normalizeUnicode,
      this.removeExcessiveNewlines,
    ]);
  }

  private pipeline(text: string, transforms: Array<(t: string) => string>): string {
    return transforms.reduce((t, fn) => fn(t), text);
  }

  private normalizeWhitespace(text: string): string {
    return text
      .replace(/\t/g, ' ')
      .replace(/[ ]+/g, ' ')
      .trim();
  }

  private removeSpecialCharacters(: ):  {
     text
      .(, )
      .(, );
  }

   (: ):  {
     text.();
  }

   (: ):  {
     text.(, );
  }
}

Text Chunking

Smart Chunker

// pipeline/chunker.ts
export interface Chunk {
  id: string;
  content: string;
  metadata: {
    documentId: string;
    chunkIndex: number;
    startChar: number;
    endChar: number;
    [key: string]: any;
  };
}

export interface ChunkerOptions {
  chunkSize: number;
  chunkOverlap: number;
  separators?: string[];
}

export class RecursiveChunker {
  private options: ChunkerOptions;
  private separators: string[];

  constructor(options: ChunkerOptions) {
    this.options = options;
    this.separators = options.separators || [
      '\n\n',  // Paragraphs
      '\n',    // Lines
      '. ',    // Sentences
      ,    
      ,     
      ,      
    ];
  }

  (: ): [] {
     : [] = [];
     textChunks = .(., );

    textChunks.( {
      chunks.({
        : ,
        : text.,
        : {
          : .,
          : index,
          : text.,
          : text.,
          ....,
        },
      });
    });

     chunks;
  }

   (
    : ,
    : 
  ): <{ : ; : ; :  }> {
     separator = .[separatorIndex];
     { chunkSize, chunkOverlap } = .;

    
     (text. <= chunkSize) {
       [{ : text, : , : text. }];
    }

    
     parts = separator ? text.(separator) : text.();
     : <{ : ; : ; :  }> = [];
     currentChunk = ;
     currentStart = ;
     position = ;

     ( part  parts) {
       partWithSep = part + (separator || );

       ((currentChunk + partWithSep). > chunkSize) {
         (currentChunk) {
          
           (currentChunk. > chunkSize && separatorIndex < .. - ) {
             subChunks = .(currentChunk, separatorIndex + );
            results.(...subChunks.( ({
              ...c,
              : c. + currentStart,
              : c. + currentStart,
            })));
          }  {
            results.({
              : currentChunk.(),
              : currentStart,
              : position,
            });
          }

          
           overlapText = .(currentChunk, chunkOverlap);
          currentChunk = overlapText + partWithSep;
          currentStart = position - overlapText.;
        }  {
          currentChunk = partWithSep;
        }
      }  {
        currentChunk += partWithSep;
      }

      position += partWithSep.;
    }

    
     (currentChunk.()) {
      results.({
        : currentChunk.(),
        : currentStart,
        : position,
      });
    }

     results;
  }

   (: , : ):  {
     (overlapSize === )  ;
     text.(-overlapSize);
  }
}

Semantic Chunker

// pipeline/semantic-chunker.ts
import { generateEmbedding } from '../embeddings';

export class SemanticChunker {
  private similarityThreshold: number;
  private minChunkSize: number;
  private maxChunkSize: number;

  constructor(options: {
    similarityThreshold?: number;
    minChunkSize?: number;
    maxChunkSize?: number;
  } = {}) {
    this.similarityThreshold = options.similarityThreshold || 0.8;
    this.minChunkSize = options.minChunkSize || 100;
    this.maxChunkSize = options.maxChunkSize || 2000;
  }

  async chunk(document: LoadedDocument): Promise<Chunk[]> {
    // Split into sentences
    const sentences = this.splitIntoSentences(document.content);

    // Generate embeddings for each sentence
    const embeddings = await .(
      sentences.( (s))
    );

    
     groups = .(sentences, embeddings);

    
     groups.( ({
      : ,
      : group.(),
      : {
        : .,
        : index,
        : ,
        ....,
      },
    }));
  }

   (: ): [] {
     text
      .()
      .( s. > );
  }

   (: [], : [][]): [][] {
     : [][] = [];
     : [] = [];
     currentLength = ;

     ( i = ; i < sentences.; i++) {
       sentence = sentences[i];

       (currentGroup. === ) {
        currentGroup.(sentence);
        currentLength = sentence.;
        ;
      }

      
       similarity = .(embeddings[i], embeddings[i - ]);

       (
        similarity > . &&
        currentLength + sentence. < .
      ) {
        currentGroup.(sentence);
        currentLength += sentence.;
      }  {
         (currentLength >= .) {
          groups.(currentGroup);
        }
        currentGroup = [sentence];
        currentLength = sentence.;
      }
    }

     (currentGroup. > ) {
      groups.(currentGroup);
    }

     groups;
  }

   (: [], : []):  {
     dotProduct = ;
     normA = ;
     normB = ;

     ( i = ; i < a.; i++) {
      dotProduct += a[i] * b[i];
      normA += a[i] * a[i];
      normB += b[i] * b[i];
    }

     dotProduct / (.(normA) * .(normB));
  }
}

Embedding Generation

// pipeline/embedder.ts
import OpenAI from 'openai';
import pLimit from 'p-limit';

const openai = new OpenAI();

export interface EmbeddedChunk extends Chunk {
  embedding: number[];
}

export class Embedder {
  private model: string;
  private batchSize: number;
  private concurrency: number;

  constructor(options: {
    model?: string;
    batchSize?: number;
    concurrency?: number;
  } = {}) {
    this.model = options.model || 'text-embedding-3-small';
    this.batchSize = options.batchSize || 100;
    this.concurrency = options.concurrency || 5;
  }

  async embedChunks(chunks: Chunk[]): <[]> {
     limit = (.);
     batches = .(chunks);

     embeddedBatches =  .(
      batches.(
        ( .(batch))
      )
    );

     embeddedBatches.();
  }

   (: []): [][] {
     : [][] = [];
     ( i = ; i < chunks.; i += .) {
      batches.(chunks.(i, i + .));
    }
     batches;
  }

    (: []): <[]> {
     response =  openai..({
      : .,
      : chunks.( c.),
    });

     chunks.( ({
      ...chunk,
      : response.[i].,
    }));
  }
}

Complete Pipeline

// pipeline/index.ts
import { DocumentLoader } from './loaders';
import { TextPreprocessor } from './preprocessor';
import { RecursiveChunker } from './chunker';
import { Embedder } from './embedder';
import { VectorStore } from './vectorstore';

export interface PipelineOptions {
  chunkSize?: number;
  chunkOverlap?: number;
  embeddingModel?: string;
  namespace?: string;
}

export class EmbeddingPipeline {
  private loader: DocumentLoader;
  private preprocessor: TextPreprocessor;
  private chunker: RecursiveChunker;
  private embedder: Embedder;
  private vectorStore: VectorStore;

  constructor(options:  = {}) {
    . =  ();
    . =  ();
    . =  ({
      : options. || ,
      : options. || ,
    });
    . =  ({
      : options.,
    });
    . =  (options.);
  }

   (: ): <{ :  }> {
    
      =  ..(filePath);

     .();
  }

   (: ): <{ : ; :  }> {
     documents =  ..(dirPath);
     totalChunks = ;

     ( doc  documents) {
       result =  .(doc);
      totalChunks += result.;
    }

     { : documents., : totalChunks };
  }

   (: ): <{ :  }> {
     webLoader =  ();
      =  webLoader.(url);

     .();
  }

    (: ): <{ :  }> {
    
    . = ..(.);

    
     chunks = ..();

    
     embeddedChunks =  ..(chunks);

    
     ..(embeddedChunks);

     { : embeddedChunks. };
  }

   (: , topK = ): <[]> {
     ..(query, topK);
  }
}


 pipeline =  ({
  : ,
  : ,
  : ,
});


 pipeline.();
 pipeline.();


 results =  pipeline.();

Best Practices

1. Chunk size: Balance context vs noise (500-1500 chars)
2. Overlap: 10-20% prevents context loss
3. Preprocessing: Clean but preserve meaning
4. Batch embeddings: Reduce API calls
5. Add metadata: Enable filtering
6. Semantic chunking: For high-quality retrieval
7. Hybrid search: Combine vector and keyword
8. Monitor quality: Test retrieval accuracy

Output Checklist

Every embedding pipeline should include:

• Multi-format document loading
• Text preprocessing and cleaning
• Smart chunking with overlap
• Batch embedding generation
• Metadata preservation
• Vector store indexing
• Semantic search capability
• Error handling and logging
• Progress tracking
• Retrieval quality testing