Fireworks Embeddings
This guide shows you how to use Fireworks Embeddings through Fireworks Endpoints.
First, letβs install LlamaIndex and the Fireworks dependencies
%pip install llama-index-embeddings-fireworks!pip install llama-indexWe can then query embeddings on Fireworks
from llama_index.embeddings.fireworks import FireworksEmbedding
embed_model = FireworksEmbedding(api_key="YOUR API KEY", embed_batch_size=10)# Basic embedding exampleembeddings = embed_model.get_text_embedding("How do I sail to the moon?")print(len(embeddings), embeddings[:10])768 [-0.67973792552948, 1.5226128101348877, -3.9547336101531982, 0.3112764358520508, -0.19723102450370789, 1.8839401006698608, -1.1595842838287354, -0.20612922310829163, 0.16740809381008148, -0.9071207046508789]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/