整合指南

LlamaIndex + OrcaRouter

LlamaIndex's OpenAI LLM class accepts api_base and api_key overrides. Route indexing, query, and synthesis calls through OrcaRouter for zero markup and automatic failover across providers.

取得 API 金鑰 LlamaIndex 官網 ↗

設定步驟

5 分鐘接入 OrcaRouter

1.Install: pip install llama-index-llms-openai
2.Import OpenAI from llama_index.llms.openai
3.Construct with api_base='https://api.orcarouter.ai/v1' and api_key='sk-orca-…'
4.Assign to Settings.llm so every query engine picks it up.
5.Build indices and query as usual — synthesis routes through OrcaRouter.

範例設定

from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

Settings.llm = OpenAI(
    api_base="https://api.orcarouter.ai/v1",
    api_key="sk-orca-...",
    model="gpt-4o",
)

# Now every query engine, agent, and chat engine uses OrcaRouter.
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
docs = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(docs)
response = index.as_query_engine().query("Summarize the key points.")

為什麼把 LlamaIndex 路由過 OrcaRouter?

RAG pipelines make many small calls per query (retrieve → rerank → synthesize). OrcaRouter's per-request routing means each of those calls independently picks the cheapest healthy backend, and you see the full breakdown in one dashboard.

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