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LlamaIndex Framework
Integrations
Embeddings

Together AI Embeddings

This notebook shows how to use Together AI for embeddings. Together AI provides access to many state-of-the-art embedding models.

Visit https://together.ai and sign up to get an API key.

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

%pip install llama-index-embeddings-together
!pip install llama-index
# You can set the API key in the embeddings or env
# import os
# os.environ["TOEGETHER_API_KEY"] = "your-api-key"
from llama_index.embeddings.together import TogetherEmbedding
embed_model = TogetherEmbedding(
model_name="togethercomputer/m2-bert-80M-8k-retrieval", api_key="..."
)
embeddings = embed_model.get_text_embedding("hello world")
print(len(embeddings))
768
print(embeddings[:5])
[-0.11657876, -0.012690996, 0.24342081, 0.32781482, 0.022501636]
Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/