Hush workflow LLM, embedding, and reranking providers
Project description
hush-providers
LLM, embedding, and reranking provider integrations for Hush workflows.
Installation
pip install hush-providers
Quick Start
LLM (chain = prompt + LLM combined)
from hush.core import Hush, GraphOp, START, END, PARENT
from hush.providers import chain
async def main():
with GraphOp(name="chat") as graph:
chat = chain(
resource="gpt-4o",
template={"system": "You are a helpful assistant.", "user": "{question}"},
question=PARENT["question"],
)
START >> chat >> END
result = await Hush(graph).run(inputs={"question": "What is Python?"})
print(result["content"])
Embedding
from hush.providers import EmbeddingOp
embed = EmbeddingOp.of(resource="bge-m3", texts=PARENT["documents"])
Reranking
from hush.providers import RerankOp
rerank = RerankOp.of(resource="bge-reranker", query=PARENT["query"], documents=PARENT["docs"])
Supported Providers
| Type | Providers |
|---|---|
| LLM | OpenAI, Azure OpenAI, Google Gemini, vLLM |
| Embedding | OpenAI/vLLM, TEI, HuggingFace, ONNX |
| Reranking | vLLM, Pinecone, Cohere, HuggingFace, ONNX |
Configuration
Providers are configured via YAML resource files:
# resources.yaml
llm:
gpt-4o:
type: openai
model: gpt-4o
api_key: ${OPENAI_API_KEY}
embeddings:
bge-m3:
type: onnx
model_path: /models/bge-m3
Feature Flags
Install only the providers you need:
pip install "hush-providers[openai]" # OpenAI + Azure
pip install "hush-providers[gemini]" # Google Gemini
pip install "hush-providers[onnx]" # ONNX Runtime
pip install "hush-providers[all-light]" # All without PyTorch
pip install "hush-providers[all]" # Everything
Rust Backend
All providers have native Rust implementations via hush-providers (crate) — direct HTTP calls without Python overhead.
Related Packages
| Package | Description |
|---|---|
| hush-icore | Core workflow engine (required) |
| hush-telemetry | Tracing with token/cost tracking |
| hush-serve | Serve workflows as HTTP APIs |
License
Apache 2.0
Project details
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