OpenAIEmbeddings

This will help you get started with OpenAI embedding models using LangChain. For detailed documentation on OpenAIEmbeddings features and configuration options, please refer to the API reference.

Overview

Integration details

Provider	Package
OpenAI	langchain-openai

Setup

To access OpenAI embedding models you'll need to create a/an OpenAI account, get an API key, and install the langchain-openai integration package.

Credentials

Head to platform.openai.com to sign up to OpenAI and generate an API key. Once you’ve done this set the OPENAI_API_KEY environment variable:

import getpass
import os

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

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Installation

The LangChain OpenAI integration lives in the langchain-openai package:

%pip install -qU langchain-openai

Instantiation

Now we can instantiate our model object and generate chat completions:

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large",
    # With the `text-embedding-3` class
    # of models, you can specify the size
    # of the embeddings you want returned.
    # dimensions=1024
)

API Reference:OpenAIEmbeddings

Indexing and Retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the working with external knowledge tutorials.

Below, see how to index and retrieve data using the embeddings object we initialized above. In this example, we will index and retrieve a sample document in the InMemoryVectorStore.

# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content

API Reference:InMemoryVectorStore

'LangChain is the framework for building context-aware reasoning applications'

Direct Usage

Under the hood, the vectorstore and retriever implementations are calling embeddings.embed_documents(...) and embeddings.embed_query(...) to create embeddings for the text(s) used in from_texts and retrieval invoke operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

Embed single texts

You can embed single texts or documents with embed_query:

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector

[-0.019276829436421394, 0.0037708976306021214, -0.03294256329536438, 0.0037671267054975033, 0.008175

Embed multiple texts

You can embed multiple texts with embed_documents:

text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector

[-0.019260549917817116, 0.0037612367887049913, -0.03291035071015358, 0.003757466096431017, 0.0082049
[-0.010181212797760963, 0.023419594392180443, -0.04215526953339577, -0.001532090245746076, -0.023573

API Reference

For detailed documentation on OpenAIEmbeddings features and configuration options, please refer to the API reference.

Related

• Embedding model conceptual guide
• Embedding model how-to guides