Integration · 60 sekunders installation · Ingen påslag
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.
Konfiguration
Fem steg.
- 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.
Konfiguration
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.")Varför routa LlamaIndex via 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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Routa LlamaIndex genom OrcaRouter idag.
Registrera dig på under en minut, hämta en sk-orca-…-nyckel och klistra in den i LlamaIndex. Inget påslag på token. Automatisk failover mellan alla leverantörer.
