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Provider Configuration Guide

Configure Hyper-Extract to work with OpenAI, Bailian (Alibaba Cloud), or local vLLM deployments.

Unified Example

All three platforms run the same extraction task below. Only the client setup (first 3 lines) changes.

OpenAI

from hyperextract import create_client, AutoGraph

llm, emb = create_client("openai", api_key="sk-xxx")

Alibaba Bailian

from hyperextract import create_client, AutoGraph

llm, emb = create_client("bailian", api_key="sk-xxx")
# Or override model: create_client("bailian:qwen3.6-plus", api_key="sk-xxx")

Anthropic (Claude)

from hyperextract import create_client, AutoGraph

# Anthropic is LLM only — pair it with an OpenAI-compatible embedder.
# Keys: ANTHROPIC_API_KEY (or CLAUDE_API_KEY) for the LLM, OPENAI_API_KEY for embeddings.
llm, emb = create_client(
    llm="anthropic",  # default model: claude-opus-4-8 (override with "anthropic:<model>")
    embedder="openai:text-embedding-3-small",
)

DeepSeek

from hyperextract import create_client, AutoGraph

# DeepSeek is OpenAI-compatible but has no embeddings API — pair it with an
# OpenAI-compatible embedder. The V4 models default to "thinking" mode, which
# Hyper-Extract auto-disables so structured extraction works out of the box.
llm, emb = create_client(
    llm="deepseek",  # default: deepseek-v4-flash; override with "deepseek:deepseek-v4-pro"
    embedder="openai:text-embedding-3-small",
)

Local vLLM

from hyperextract import create_client, AutoGraph

llm, emb = create_client(
    llm="vllm:Qwen3.5-9B@http://localhost:8000/v1",
    embedder="vllm:bge-m3@http://localhost:8001/v1",
    api_key="dummy",
)

Extraction Task (same for all)

graph = AutoGraph(
    instruction="Extract people and their relationships",
    llm_client=llm,
    embedder=emb,
    node_key_extractor=lambda n: n.name,
    edge_key_extractor=lambda e: (e.source, e.target, e.type),
    nodes_in_edge_extractor=lambda e: (e.source, e.target),
)

text = "Zhang San founded ByteDance. Li Si serves as CEO."
graph.parse(text)
print(f"Nodes: {len(graph.nodes)}, Edges: {len(graph.edges)}")

CLI Equivalents

Platform Command
OpenAI he config init -p openai -k sk-xxx
Bailian he config init -p bailian -k sk-xxx
Anthropic he config llm -p anthropic -k sk-ant-xxx + he config embedder -p openai -k sk-xxx
DeepSeek he config llm -p deepseek -k sk-xxx + he config embedder -p openai -k sk-xxx
vLLM he config init → select "local vLLM"
Mixed (LLM=Bailian, Embedder=vLLM) he config llm -p bailian -k sk-xxx + he config embedder -p vllm -u http://localhost:8001/v1 -k dummy

String Shorthand Format

create_client() supports a compact string syntax for quick configuration:

Format Example Result
provider "bailian" Uses preset defaults for LLM + embedder
provider:model "bailian:qwen3.6-plus" Overrides LLM model, keeps preset embedder
provider:model@url "vllm:Qwen3.5-9B@localhost:8000/v1" Full manual specification

Using Config File Instead

If you prefer file-based configuration, run he config init once and then use Template.create() or get_client():

from hyperextract import get_client, AutoGraph

llm, emb = get_client()  # Reads ~/.he/config.toml
graph = AutoGraph(..., llm_client=llm, embedder=emb)

See Also