OpenAI Embeddings
If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
%pip install llama-index-embeddings-openai!pip install llama-indeximport os
os.environ["OPENAI_API_KEY"] = "sk-..."from llama_index.embeddings.openai import OpenAIEmbeddingfrom llama_index.core import Settings
embed_model = OpenAIEmbedding(embed_batch_size=10)Settings.embed_model = embed_modelUsing OpenAI text-embedding-3-large and text-embedding-3-small
Section titled “Using OpenAI text-embedding-3-large and text-embedding-3-small”Note, you may have to update your openai client: pip install -U openai
# get API key and create embeddingsfrom llama_index.embeddings.openai import OpenAIEmbedding
embed_model = OpenAIEmbedding(model="text-embedding-3-large")
embeddings = embed_model.get_text_embedding( "Open AI new Embeddings models is great.")print(embeddings[:5])[-0.011500772088766098, 0.02457442320883274, -0.01760469563305378, -0.017763426527380943, 0.029841400682926178]print(len(embeddings))3072# get API key and create embeddingsfrom llama_index.embeddings.openai import OpenAIEmbedding
embed_model = OpenAIEmbedding( model="text-embedding-3-small",)
embeddings = embed_model.get_text_embedding( "Open AI new Embeddings models is awesome.")print(len(embeddings))1536Change the dimension of output embeddings
Section titled “Change the dimension of output embeddings”Note: Make sure you have the latest OpenAI client
# get API key and create embeddingsfrom llama_index.embeddings.openai import OpenAIEmbedding
embed_model = OpenAIEmbedding( model="text-embedding-3-large", dimensions=512,)
embeddings = embed_model.get_text_embedding( "Open AI new Embeddings models with different dimensions is awesome.")print(len(embeddings))512Note 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/