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Overview

This overview covers text-based embedding models. LangChain does not currently support multimodal embeddings.
Embedding models transform raw text—such as a sentence, paragraph, or tweet—into a fixed-length vector of numbers that captures its semantic meaning. These vectors allow machines to compare and search text based on meaning rather than exact words. In practice, this means that texts with similar ideas are placed close together in the vector space. For example, instead of matching only the phrase “machine learning”, embeddings can surface documents that discuss related concepts even when different wording is used.

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How it works

  1. Vectorization — The model encodes each input string as a high-dimensional vector.
  2. Similarity scoring — Vectors are compared using mathematical metrics to measure how closely related the underlying texts are.

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Similarity metrics

Several metrics are commonly used to compare embeddings:
  • Cosine similarity — measures the angle between two vectors.
  • Euclidean distance — measures the straight-line distance between points.
  • Dot product — measures how much one vector projects onto another.
Here’s an example of computing cosine similarity between two vectors: 
import numpy as np

def cosine_similarity(vec1, vec2):
    dot = np.dot(vec1, vec2)
    return dot / (np.linalg.norm(vec1) * np.linalg.norm(vec2))

similarity = cosine_similarity(query_embedding, document_embedding)
print("Cosine Similarity:", similarity)

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Embedding Interface in LangChain

LangChain provides a standard interface for text embedding models (e.g., OpenAI, Cohere, Hugging Face) via the Embeddings interface. Two main methods are available:
  • embed_documents(texts: List[str]) → List[List[float]]: Embeds a list of documents.
  • embed_query(text: str) → List[float]: Embeds a single query.
The interface allows queries and documents to be embedded with different strategies, though most providers handle them the same way in practice.

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Top integrations

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

embeddings.embed_query("Hello, world!")
ProviderPackage
AzureOpenAIlangchain-openai
Ollamalangchain-ollama
Fakelangchain-core
OpenAIlangchain-openai
Google Geminilangchain-google-genai
Togetherlangchain-together
Fireworkslangchain-fireworks
MistralAIlangchain-mistralai
Coherelangchain-cohere
AI/ML APIlangchain-aimlapi
Nomiclangchain-nomic
Databricksdatabricks-langchain
IBMlangchain-ibm
NVIDIAlangchain-nvidia

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Caching

Embeddings can be stored or temporarily cached to avoid needing to recompute them. Caching embeddings can be done using a CacheBackedEmbeddings. This wrapper stores embeddings in a key-value store, where the text is hashed and the hash is used as the key in the cache. The main supported way to initialize a CacheBackedEmbeddings is from_bytes_store. It takes the following parameters:
  • underlying_embedder: The embedder to use for embedding.
  • document_embedding_cache: Any ByteStore for caching document embeddings.
  • batch_size: (optional, defaults to None) The number of documents to embed between store updates.
  • namespace: (optional, defaults to "") The namespace to use for the document cache. Helps avoid collisions (e.g., set it to the embedding model name).
  • query_embedding_cache: (optional, defaults to None) A ByteStore for caching query embeddings, or True to reuse the same store as document_embedding_cache.
import time
from langchain.embeddings import CacheBackedEmbeddings  
from langchain.storage import LocalFileStore 
from langchain_core.vectorstores import InMemoryVectorStore

# Create your underlying embeddings model
underlying_embeddings = ... # e.g., OpenAIEmbeddings(), HuggingFaceEmbeddings(), etc.

# Store persists embeddings to the local filesystem
# This isn't for production use, but is useful for local
store = LocalFileStore("./cache/") 

cached_embedder = CacheBackedEmbeddings.from_bytes_store(
    underlying_embeddings,
    store,
    namespace=underlying_embeddings.model
)

# Example: caching a query embedding
tic = time.time()
print(cached_embedder.embed_query("Hello, world!"))
print(f"First call took: {time.time() - tic:.2f} seconds")

# Subsequent calls use the cache
tic = time.time()
print(cached_embedder.embed_query("Hello, world!"))
print(f"Second call took: {time.time() - tic:.2f} seconds")
In production, you would typically use a more robust persistent store, such as a database or cloud storage. Please see stores integrations for options.

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All integrations

Aleph Alpha

Anyscale

Ascend

AI/ML API

AwaDB

AzureOpenAI

Baichuan Text Embeddings

Baidu Qianfan

Bedrock

BGE on Hugging Face

Bookend AI

Clarifai

Cloudflare Workers AI

Clova Embeddings

Cohere

DashScope

Databricks

DeepInfra

EDEN AI

Elasticsearch

Embaas

Fake Embeddings

FastEmbed by Qdrant

Fireworks

Google Gemini

Google Vertex AI

GPT4All

Gradient

GreenNode

Hugging Face

IBM watsonx.ai

Infinity

Instruct Embeddings

IPEX-LLM CPU

IPEX-LLM GPU

Intel Extension for Transformers

Jina

John Snow Labs

LASER

Lindorm

Llama.cpp

Llamafile

LLMRails

LocalAI

MiniMax

MistralAI

Model2Vec

ModelScope

MosaicML

Naver

Nebius

Netmind

NLP Cloud

Nomic

NVIDIA NIMs

Oracle Cloud Infrastructure

Ollama

OpenClip

OpenAI

OpenVINO

Optimum Intel

Oracle AI Vector Search

OVHcloud

Pinecone Embeddings

PredictionGuard

PremAI

SageMaker

SambaNovaCloud

SambaStudio

Self Hosted

Sentence Transformers

Solar

SpaCy

SparkLLM

TensorFlow Hub

Text Embeddings Inference

TextEmbed

Titan Takeoff

Together AI

Upstage

Volc Engine

Voyage AI

Xinference

YandexGPT

ZhipuAI