Backends¶

Every backend speaks the same protocol: given a Pydantic schema and a document, return a JSON-shaped extraction. Switching backends is a single argument.

Listing backends¶

idp providers    # prints the full table

Calling from Python¶

from idp.llm import get_backend

backend = get_backend("anthropic")
# or: "openai", "ollama", "nanonets", "china:qwen", "china:deepseek", ...

get_backend("auto") resolves in this order: anthropic > openai > ollama (if OLLAMA_HOST is set) > mock (last resort, with an INFO log line).

International providers¶

provider	env var	default model	multimodal
`anthropic`	`ANTHROPIC_API_KEY`	claude-3-5-sonnet-latest	yes
`openai`	`OPENAI_API_KEY`	gpt-4o-mini	yes
`ollama`	(none — uses `OLLAMA_HOST`)	whatever you have running	depends on the model
`nanonets`	(none — local model)	Nanonets-OCR2-3B	yes (it's a VLM)

China LLM providers (8, OpenAI-compatible)¶

All eight speak the OpenAI Chat-Completions protocol, so the API-key-and-HTTP path is identical — only the env var name and the default model differ.

from idp.llm import get_china_backend

backend = get_china_backend("qwen", multimodal=True)
# backend.model == "qwen2.5-vl-72b-instruct"

provider	env var	default model	multimodal model
`deepseek`	`DEEPSEEK_API_KEY`	deepseek-chat	— (text only)
`qwen`	`DASHSCOPE_API_KEY`	qwen-plus	qwen2.5-vl-72b-instruct
`zhipu`	`ZHIPUAI_API_KEY`	glm-4-plus	glm-4v-plus
`moonshot`	`MOONSHOT_API_KEY`	moonshot-v1-128k	moonshot-v1-128k-vision-preview
`yi`	`YI_API_KEY`	yi-large	yi-vision
`doubao`	`ARK_API_KEY`	doubao-pro-32k	doubao-1-5-vision-pro-32k
`hunyuan`	`HUNYUAN_API_KEY`	hunyuan-pro	hunyuan-vision
`baichuan`	`BAICHUAN_API_KEY`	baichuan4	— (text only)

MockBackend¶

get_backend("mock") returns a deterministic backend that produces empty defaults. Used for:

CI runs without API keys (the full test suite runs against MockBackend).
Showing the pipeline structure to a new contributor.
Reproducing a known-bad extraction to debug a schema.

The mock backend is not a real LLM and shouldn't be confused with one — fields come back as defaults, the confidence is uniform 0.10, and the schema doesn't validate.

Self-hosted OCR (Nanonets-OCR2-3B)¶

A standalone, fully offline alternative for sensitive documents. No API key, no network. Heavy install: ~3 GB pip packages + ~7 GB model weights.

pip install py-idp[hf-vlm]
export IDP_ENABLE_NANONETS=1
export IDP_BACKEND=nanonets

Speed: ~5–15 seconds per page on Apple M4 (CPU+GPU mixed, float16). First call downloads the model; subsequent calls use the ~/.cache/huggingface/hub/ cache.