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Showing content from https://python.langchain.com/docs/integrations/text_embedding/nomic/ below:

NomicEmbeddings | 🦜️🔗 LangChain

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

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

Head to https://atlas.nomic.ai/ to sign up to Nomic and generate an API key. Once you've done this set the NOMIC_API_KEY environment variable:

import getpass
import os

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

To enable automated tracing of your model calls, set your LangSmith API key:

The LangChain Nomic integration lives in the langchain-nomic package:

%pip install -qU langchain-nomic

Note: you may need to restart the kernel to use updated packages.

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

from langchain_nomic import NomicEmbeddings

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

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.

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.

from langchain_core.vectorstores import InMemoryVectorStore

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

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


retriever = vectorstore.as_retriever()


retrieved_documents = retriever.invoke("What is LangChain?")


retrieved_documents[0].page_content

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

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.

You can embed single texts or documents with embed_query:

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  

[0.024642944, 0.029083252, -0.14013672, -0.09082031, 0.058898926, -0.07489014, -0.0138168335, 0.0037

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])  

[0.012771606, 0.023727417, -0.12365723, -0.083740234, 0.06530762, -0.07110596, -0.021896362, -0.0068
[-0.019058228, 0.04058838, -0.15222168, -0.06842041, -0.012130737, -0.07128906, -0.04534912, 0.00522

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

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

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