Langchain Embeddings

langchain-embeddings (JavaScript/TypeScript)

Overview

Embedding models convert text into numerical vector representations that capture semantic meaning. These vectors enable semantic search, similarity comparison, and are essential for building RAG (Retrieval-Augmented Generation) systems with vector databases.

Key Concepts

• Embeddings: Dense vector representations of text that encode semantic meaning
• Vector Dimensions: Different models produce vectors of different sizes (e.g., 1536 for OpenAI, 768 for some open-source models)
• Similarity Search: Finding similar texts by comparing vector distances (cosine similarity, euclidean distance)
• Batch Processing: Efficiently embedding multiple texts at once
• Use Cases: Semantic search, document retrieval, clustering, recommendation systems

Provider Selection Decision Table

When to Choose Each Provider

Choose OpenAI if:

• You need high-quality embeddings for production
• You want reliable, fast API-based embeddings
• Cost is reasonable for your use case (~$0.13 per 1M tokens)

Choose Azure OpenAI if:

• You need enterprise support and SLAs
• Data compliance/residency is critical
• You're already using Azure infrastructure

Choose Cohere if:

• You need multilingual embeddings
• You want optimized embeddings for search vs. clustering
• You need competitive pricing

Choose HuggingFace if:

• You want to use open-source models
• You need specific model characteristics
• You want to run inference locally or on your own infrastructure

Choose Ollama if:

• Privacy is paramount (fully local)
• You want zero API costs after setup
• You have sufficient local compute resources

Code Examples

OpenAI Embeddings

import { OpenAIEmbeddings } from "@langchain/openai";

// Basic initialization
const embeddings = new OpenAIEmbeddings({
  modelName: "text-embedding-3-small",
  openAIApiKey: process.env.OPENAI_API_KEY, // Optional if set in env
});

// Embed a single query
const queryEmbedding = await embeddings.embedQuery(
  "What is the capital of France?"
);
console.log(`Vector dimensions: ${queryEmbedding.length}`);
console.log(`First few values: ${queryEmbedding.slice(0, 5)}`);

// Embed multiple documents
const documents = [
  "Paris is the capital of France.",
  "London is the capital of England.",
  "Berlin is the capital of Germany.",
];
const docEmbeddings = await embeddings.embedDocuments(documents);
console.log(`Embedded ${docEmbeddings.length} documents`);

// Using newer models with custom dimensions
const smallEmbeddings = new OpenAIEmbeddings({
  modelName: "text-embedding-3-small",
  dimensions: 512, // Reduce from default 1536 for efficiency
});

Azure OpenAI Embeddings

import { AzureOpenAIEmbeddings } from "@langchain/openai";

const embeddings = new AzureOpenAIEmbeddings({
  azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
  azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME,
  azureOpenAIApiEmbeddingsDeploymentName: "text-embedding-ada-002",
  azureOpenAIApiVersion: "2024-02-01",
});

const embedding = await embeddings.embedQuery("Hello world");

HuggingFace Embeddings (Local)

import { HuggingFaceTransformersEmbeddings } from "@langchain/community/embeddings/hf_transformers";

// Run embeddings locally with Transformers.js
const embeddings = new HuggingFaceTransformersEmbeddings({
  modelName: "Xenova/all-MiniLM-L6-v2",
});

const embedding = await embeddings.embedQuery("This runs locally!");

Ollama Embeddings (Local)

import { OllamaEmbeddings } from "@langchain/ollama";

// Requires Ollama running locally: ollama pull nomic-embed-text
const embeddings = new OllamaEmbeddings({
  model: "nomic-embed-text",
  baseUrl: "http://localhost:11434", // Default Ollama URL
});

const embedding = await embeddings.embedQuery("Fully local embeddings");

Cohere Embeddings

import { CohereEmbeddings } from "@langchain/cohere";

const embeddings = new CohereEmbeddings({
  apiKey: process.env.COHERE_API_KEY,
  model: "embed-english-v3.0",
  inputType: "search_query", // or "search_document", "classification", "clustering"
});

const queryEmbedding = await embeddings.embedQuery("Search query");
const docEmbeddings = await embeddings.embedDocuments(["doc1", "doc2"]);

Computing Similarity

import { OpenAIEmbeddings } from "@langchain/openai";

const embeddings = new OpenAIEmbeddings();

// Embed query and documents
const query = "What is machine learning?";
const docs = [
  "Machine learning is a branch of AI",
  "Paris is the capital of France",
  "Neural networks are used in deep learning",
];

const queryVec = await embeddings.embedQuery(query);
const docVecs = await embeddings.embedDocuments(docs);

// Compute cosine similarity
function cosineSimilarity(vecA: number[], vecB: number[]): number {
  const dotProduct = vecA.reduce((sum, a, i) => sum + a * vecB[i], 0);
  const magnitudeA = Math.sqrt(vecA.reduce((sum, a) => sum + a * a, 0));
  const magnitudeB = Math.sqrt(vecB.reduce((sum, b) => sum + b * b, 0));
  return dotProduct / (magnitudeA * magnitudeB);
}

// Find most similar document
const similarities = docVecs.map((docVec) => 
  cosineSimilarity(queryVec, docVec)
);
console.log("Similarities:", similarities);
const mostSimilarIdx = similarities.indexOf(Math.max(...similarities));
console.log("Most similar doc:", docs[mostSimilarIdx]);

Batch Processing for Efficiency

import { OpenAIEmbeddings } from "@langchain/openai";

const embeddings = new OpenAIEmbeddings({
  batchSize: 512, // OpenAI allows up to 2048 in one request
});

// Efficiently embed large document sets
const largeDocSet = Array.from({ length: 1000 }, (_, i) => 
  `Document ${i}: Some content here`
);

const docEmbeddings = await embeddings.embedDocuments(largeDocSet);
console.log(`Embedded ${docEmbeddings.length} documents in batches`);

Boundaries

What Agents CAN Do

✅ Initialize embedding models

• Set up OpenAI, Azure, Cohere, HuggingFace, or Ollama embeddings
• Configure API keys and model parameters

✅ Embed text content

• Embed single queries with embedQuery()
• Embed multiple documents with embedDocuments()
• Process large batches efficiently

✅ Use embeddings with vector stores

• Pass embeddings to vector store constructors
• Enable semantic search capabilities

✅ Choose appropriate models

• Select based on quality, cost, latency requirements
• Use local models for privacy concerns

✅ Optimize for use case

• Adjust batch sizes for efficiency
• Use smaller dimensions to reduce costs/storage

What Agents CANNOT Do

❌ Modify embedding dimensions arbitrarily

• Cannot change dimensions beyond what the model supports
• text-embedding-3-* models support custom dimensions, older models don't

❌ Mix embeddings from different models

• Cannot compare embeddings from different models directly
• Must use same model for all embeddings in a similarity search

❌ Exceed API rate limits

• Cannot bypass provider rate limits
• Must implement rate limiting for large-scale operations

❌ Generate embeddings without proper authentication

• Cannot use cloud providers without valid API keys
• Cannot access models without proper credentials

Gotchas

1. Model Consistency is Critical

// ❌ BAD: Using different models
const embeddings1 = new OpenAIEmbeddings({ 
  modelName: "text-embedding-3-small" 
});
const embeddings2 = new OpenAIEmbeddings({ 
  modelName: "text-embedding-ada-002" 
});

const queryVec = await embeddings1.embedQuery("query");
const docVec = await embeddings2.embedQuery("document");
// Similarity comparison will be meaningless!

// ✅ GOOD: Use same model for everything
const embeddings = new OpenAIEmbeddings({ 
  modelName: "text-embedding-3-small" 
});
const queryVec = await embeddings.embedQuery("query");
const docVec = await embeddings.embedQuery("document");
// Now similarity makes sense

Fix: Always use the same embedding model for all texts you want to compare.

2. Batch Size Limits

// ❌ Potential API error with too many docs
const embeddings = new OpenAIEmbeddings();
const hugeDocs = Array(5000).fill("text");
await embeddings.embedDocuments(hugeDocs); // May fail!

// ✅ Configure appropriate batch size
const embeddings = new OpenAIEmbeddings({
  batchSize: 512, // OpenAI limit is 2048, use smaller for safety
});
await embeddings.embedDocuments(hugeDocs); // Handles batching automatically

Fix: Set appropriate batchSize parameter for the provider.

3. API Keys in Environment

// ❌ Hardcoded API key
const embeddings = new OpenAIEmbeddings({
  openAIApiKey: "sk-...", // Never commit this!
});

// ✅ Use environment variables
const embeddings = new OpenAIEmbeddings({
  openAIApiKey: process.env.OPENAI_API_KEY,
});

// ✅ Even better: auto-detection
const embeddings = new OpenAIEmbeddings(); 
// Reads OPENAI_API_KEY from environment automatically

Fix: Use environment variables for API keys.

4. Text Length Limits

// ❌ Text too long
const embeddings = new OpenAIEmbeddings();
const veryLongText = "...".repeat(100000);
await embeddings.embedQuery(veryLongText); // Will fail!

// ✅ Chunk long texts first
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 8000, // OpenAI limit is ~8191 tokens
});
const chunks = await splitter.splitText(veryLongText);
const embeddings = await embeddings.embedDocuments(chunks);

Fix: Split long texts into chunks before embedding. Most models have 8k token limits.

5. Local Model Setup

// ❌ Ollama not running
import { OllamaEmbeddings } from "@langchain/ollama";
const embeddings = new OllamaEmbeddings({ model: "nomic-embed-text" });
await embeddings.embedQuery("test"); // Connection error!

// ✅ Ensure Ollama is running and model is pulled
// Terminal:
// ollama pull nomic-embed-text
// ollama serve

const embeddings = new OllamaEmbeddings({ model: "nomic-embed-text" });
await embeddings.embedQuery("test"); // Works!

Fix: For local models, ensure the service is running and model is downloaded.

6. Azure Configuration Complexity

// ❌ Missing required fields
const embeddings = new AzureOpenAIEmbeddings({
  azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
});

// ✅ All required fields
const embeddings = new AzureOpenAIEmbeddings({
  azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
  azureOpenAIApiInstanceName: "my-instance",
  azureOpenAIApiEmbeddingsDeploymentName: "text-embedding-ada-002",
  azureOpenAIApiVersion: "2024-02-01",
});

Fix: Azure requires instance name, deployment name, and API version.

7. Dimension Mismatch in Vector Stores

// ❌ Vector store expecting 1536 dimensions, model produces 512
const embeddings = new OpenAIEmbeddings({
  modelName: "text-embedding-3-small",
  dimensions: 512,
});

// Vector store created with default 1536 dimensions
const vectorStore = await MemoryVectorStore.fromTexts(
  ["text1"],
  embeddings, // Mismatch!
);

// ✅ Consistent dimensions
const embeddings = new OpenAIEmbeddings({
  modelName: "text-embedding-3-small",
  // Don't override dimensions, or ensure vector store matches
});

Fix: Ensure vector store and embeddings use compatible dimensions.

Links and Resources

Official Documentation

• LangChain JS Embeddings Overview
• OpenAI Embeddings
• Azure OpenAI Embeddings
• HuggingFace Embeddings
• Ollama Embeddings

Provider Documentation

• OpenAI Embeddings Guide
• Cohere Embeddings
• HuggingFace Models
• Ollama

Package Installation

# OpenAI
npm install @langchain/openai

# Cohere
npm install @langchain/cohere

# Ollama
npm install @langchain/ollama

# Community (HuggingFace, etc.)
npm install @langchain/community