Netmind AI Embeddings
This notebook shows how to use Netmind AI for embeddings.
Visit https://www.netmind.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-netmind!pip install llama-index# You can set the API key in the embeddings or env# import os# os.environ["NETMIND_API_KEY"] = "your-api-key"
from llama_index.embeddings.netmind import NetmindEmbedding
embed_model = NetmindEmbedding( model_name="BAAI/bge-m3", api_key="your-api-key")Get Embeddings
Section titled “Get Embeddings”embeddings = embed_model.get_text_embedding("hello world")print(len(embeddings))1024print(embeddings[:5])[-0.04039396345615387, 0.03703497350215912, -0.02897450141608715, 0.016117244958877563, -0.03569157049059868]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/