sie-haystack
SIE integration for Haystack.
Installation
pip install sie-haystack
Imports
Preferred import paths follow Haystack's namespace convention:
from haystack_integrations.components.embedders.sie import (
SIEDocumentEmbedder,
SIETextEmbedder,
)
from haystack_integrations.components.rankers.sie import SIERanker
from haystack_integrations.components.extractors.sie import SIEExtractor
The legacy flat imports remain supported for compatibility:
from sie_haystack import SIEDocumentEmbedder, SIEExtractor, SIERanker, SIETextEmbedder
Usage
from haystack import Document
from haystack_integrations.components.embedders.sie import SIEDocumentEmbedder, SIETextEmbedder
from haystack_integrations.components.rankers.sie import SIERanker
# Embed a query
text_embedder = SIETextEmbedder(base_url="http://localhost:8080", model="BAAI/bge-m3")
result = text_embedder.run(text="What is machine learning?")
query_embedding = result["embedding"]
# Embed documents
doc_embedder = SIEDocumentEmbedder(base_url="http://localhost:8080", model="BAAI/bge-m3")
docs = [Document(content="Python is a programming language.")]
result = doc_embedder.run(documents=docs)
embedded_docs = result["documents"]
# Rerank documents
ranker = SIERanker(
base_url="http://localhost:8080",
model="jinaai/jina-reranker-v2-base-multilingual"
)
result = ranker.run(query="What is Python?", documents=embedded_docs, top_k=3)
ranked_docs = result["documents"]
Relation models such as GLiREL classify relations between supplied entity
spans. Extract those spans first, then pass them through the relation
extractor's entities input:
from haystack_integrations.components.extractors.sie import SIEExtractor
text = "Tim Cook is the CEO of Apple Inc."
entity_extractor = SIEExtractor(
model="urchade/gliner_multi-v2.1",
labels=["person", "organization"],
)
relation_extractor = SIEExtractor(
model="jackboyla/glirel-large-v0",
labels=["ceo_of"],
)
entities = entity_extractor.run(text=text)["entities"]
result = relation_extractor.run(text=text, entities=entities)
Metadata
Release files for sie-haystack 0.8.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sie_haystack-0.8.1.tar.gz | 19.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sie_haystack-0.8.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 31.4 kB
Release files / sie_haystack-0.8.1.tar.gz
| Download URL | sie_haystack-0.8.1.tar.gz |
|---|---|
| Size | 19.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
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Transparency logRelease files / sie_haystack-0.8.1-py3-none-any.whl
| Download URL | sie_haystack-0.8.1-py3-none-any.whl |
|---|---|
| Size | 11.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
46174d10adfd15ed8ce569592b279d2295711113c73d49a2950bc177573ffb59
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eace36cd3a4197bbf42254c38e1692ecb76ea2f221213234bea3e92b31c212ba
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 23, 2026.
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