LLMRails Embeddings
If youβre opening this Notebook on colab, you will probably need to install LlamaIndex π¦.
%pip install llama-index-embeddings-llm-rails!pip install llama-index# imports
from llama_index.embeddings.llm_rails import LLMRailsEmbedding# get credentials and create embeddings
import os
api_key = os.environ.get("API_KEY", "your-api-key")model_id = os.environ.get("MODEL_ID", "your-model-id")
embed_model = LLMRailsEmbedding(model_id=model_id, api_key=api_key)
embeddings = embed_model.get_text_embedding( "It is raining cats and dogs here!")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 β e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md
- Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ β 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/