NomicEmbeddings

This notebook covers how to get started with Nomic embedding models.

Installation

# install package
!pip install -U langchain-nomic

Environment Setup

Make sure to set the following environment variables:

• NOMIC_API_KEY

Usage

from langchain_nomic.embeddings import NomicEmbeddings

embeddings = NomicEmbeddings(model="nomic-embed-text-v1.5")

API Reference:

• NomicEmbeddings

embeddings.embed_query("My query to look up")

embeddings.embed_documents(
    ["This is a content of the document", "This is another document"]
)

# async embed query
await embeddings.aembed_query("My query to look up")

# async embed documents
await embeddings.aembed_documents(
    ["This is a content of the document", "This is another document"]
)

Custom Dimensionality

Nomic's nomic-embed-text-v1.5 model was trained with Matryoshka learning to enable variable-length embeddings with a single model. This means that you can specify the dimensionality of the embeddings at inference time. The model supports dimensionality from 64 to 768.

embeddings = NomicEmbeddings(model="nomic-embed-text-v1.5", dimensionality=256)

embeddings.embed_query("My query to look up")