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

TogetherEmbeddings | 🦜️🔗 LangChain

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

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

Head to https://api.together.xyz/ to sign up to Together and generate an API key. Once you've done this set the TOGETHER_API_KEY environment variable:

import getpass
import os

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

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

The LangChain Together integration lives in the langchain-together package:

%pip install -qU langchain-together

[notice] A new release of pip is available: 24.0 -> 24.2
[notice] To update, run: python -m pip install --upgrade pip
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_together import TogetherEmbeddings

embeddings = TogetherEmbeddings(
    model="togethercomputer/m2-bert-80M-8k-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.

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])  

[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011,

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])  

[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011, 
[0.066308185, -0.032866564, 0.115751594, 0.19082588, 0.14017, -0.26976448, -0.056340694, -0.26923394

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

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

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