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="...")Get Embeddings
Section titled “Get Embeddings”embeddings = embed_model.get_text_embedding("hello world")print(len(embeddings))768print(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/