LlamaIndex

LlamaIndex’s OpenAI LLM class accepts an api_base override. Point it at MirAPI and the whole indexing and querying stack (LLM, embeddings, and agents) runs on your key.

What you need

Element	Value
Base URL	`https://api.mirapi.ai/v1` (`api_base`)
API key	`MIRAPI_API_KEY`
Model	`deepseek-chat`

api_base must end in /v1: LlamaIndex appends the path (/chat/completions, /embeddings) itself.

Install

pip install llama-index llama-index-llms-openai

Configure

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

Settings.llm = OpenAI(
    model="deepseek-chat",
    api_key=os.environ["MIRAPI_API_KEY"],
    api_base="https://api.mirapi.ai/v1",
)

LlamaIndex passes api_base through to the OpenAI SDK’s base_url, so api_base is the parameter to set. The api_key here is any MirAPI key from the Platform. MirAPI accepts the standard OpenAI Authorization: Bearer scheme, and the OpenAI class sends it automatically.

Verify with a message

response = Settings.llm.complete("Reply with exactly: connected to MirAPI")
print(response.text)

The output should contain connected to MirAPI. If it does not, start with Errors and troubleshooting below.

Embeddings for RAG

Embeddings use a separate model class and a dedicated endpoint; a chat model will not work here.

from llama_index.embeddings.openai import OpenAIEmbedding

Settings.embed_model = OpenAIEmbedding(
    model="<embedding-model-id>",
    api_key=os.environ["MIRAPI_API_KEY"],
    api_base="https://api.mirapi.ai/v1",
)

Pick an embedding model ID from the catalog and set Settings.embed_model once. Every VectorStoreIndex you build then embeds through /v1/embeddings. Prompt caching applies to the chat model, not to embeddings. When the LLM reuses a large system prompt or index context, cache reads cost less than ordinary input, and hits are visible in usage.prompt_tokens_details.cached_tokens.

Tool calling and agents

MirAPI supports the OpenAI tools / tool_choice format, so LlamaIndex FunctionTool and agents that emit tool calls work without extra configuration. The model must advertise tool calling in the catalog, and deepseek-chat does. See the tool-calling agent tutorial for a full example.

Streaming, reasoning, and structured outputs

Streaming works through the standard stream_complete / stream_chat methods. MirAPI streams SSE data: lines ending with data: [DONE], and reasoning tokens arrive before the final answer.
For reasoning models, set reasoning_effort to control how much the model reasons; reasoning tokens are billed as output.
For typed extraction, LlamaIndex’s structured_predict / as_structured_llm map to the OpenAI-compatible response_format (JSON mode or json_schema); the model must advertise JSON output in the catalog. See Structured outputs.

Errors and troubleshooting

Code	Meaning	What to do
401	Key missing, invalid, or unknown	Check `MIRAPI_API_KEY` and that it comes from the Platform.
403	Key valid but rejected: empty balance, model outside the whitelist, quota, or IP allowlist	Fix the cause (top up, add the model, raise the quota) and retry.
413	Payload too large	Reduce the input size.
429	Rate limited	Retry with jittered exponential backoff (1s → 2s → 4s, cap ~30s). There is no `Retry-After` header and the gateway does not retry for you.
500	Upstream failure	Idempotent requests are safe to retry.

Every error message ends with a request ID; include it in any support ticket.

Billing

MirAPI bills text and multimodal chat per token, priced in USD per 1M tokens with separate input, output, and cache-read prices. Charges come out of a prepaid balance that never expires, and there is no subscription. Reasoning tokens count as output. When the balance runs out, the API returns 403; the same key works again immediately after you top up. Reconcile spend in the Platform billing log, GET /api/usage/token (account-level summary), or GET /api/log/token (per-request detail).

Tool-specific pitfalls

api_base must include /v1. LlamaIndex appends /chat/completions and /embeddings.
Embeddings are a separate class. Passing a chat model ID to OpenAIEmbedding (or vice versa) fails.
A valid key that returns 403 usually means an empty balance or a model outside the key’s whitelist; top up or fix the key and retry.
429 has no Retry-After. Implement your own backoff; the gateway will not retry on your behalf.