sie-weaviate 0.6.0

SIE integration for Weaviate

sie-weaviate

SIE integration for Weaviate v4.

Two integration paths

1. Client-side (this package, works now)

sie-weaviate provides vectorizer and enrichment helpers that call SIE's encode() and extract() and return data in the format Weaviate expects. You configure collections with Configure.Vectors.self_provided() and pass vectors on insert/query.

pip install sie-weaviate

import weaviate
import weaviate.classes as wvc
from sie_weaviate import SIEVectorizer

vectorizer = SIEVectorizer(base_url="http://localhost:8080", model="BAAI/bge-m3")

client = weaviate.connect_to_local()
try:
    collection = client.collections.create(
        "Documents",
        properties=[wvc.config.Property(name="text", data_type=wvc.config.DataType.TEXT)],
        vector_config=wvc.config.Configure.Vectors.self_provided(),
    )

    texts = ["first doc", "second doc"]
    vectors = vectorizer.embed_documents(texts)
    collection.data.insert_many([
        wvc.data.DataObject(properties={"text": t}, vector=v)
        for t, v in zip(texts, vectors)
    ])

    query_vec = vectorizer.embed_query("search text")
    results = collection.query.near_vector(near_vector=query_vec, limit=5)
finally:
    client.close()

2. Server-side module (partnership, planned)

A text2vec-sie Go module for the Weaviate server that enables native vectorizer config (Configure.Vectorizer.text2vec_sie(...)). See weaviate-module-spec/ for the spec and reference implementation.

Named vectors (dense + multivector)

SIENamedVectorizer produces multiple vector types in one SIE call. Use it with ColBERT models that output both dense and multivector (per-token) embeddings:

from sie_weaviate import SIENamedVectorizer

vectorizer = SIENamedVectorizer(
    base_url="http://localhost:8080",
    model="jinaai/jina-colbert-v2",
    output_types=["dense", "multivector"],
)

collection = client.collections.create(
    "Documents",
    properties=[wvc.config.Property(name="text", data_type=wvc.config.DataType.TEXT)],
    vector_config=[
        wvc.config.Configure.Vectors.self_provided(name="dense"),
        wvc.config.Configure.Vectors.self_provided(name="multivector"),
    ],
)

named = vectorizer.embed_documents(["hello world"])
collection.data.insert_many([
    wvc.data.DataObject(properties={"text": "hello world"}, vector=named[0])
])

For hybrid search, Weaviate has built-in BM25 — no extra vectors needed:

results = collection.query.hybrid(query="search text", alpha=0.75)

Document enrichment for Query Agent

SIEDocumentEnricher combines SIE's embedding and entity extraction pipelines to produce documents with dense vectors and structured metadata. The extracted properties (persons, organizations, locations, categories) are exactly what Weaviate's Query Agent uses to construct filters from natural language queries.

import weaviate
import weaviate.classes as wvc
from sie_weaviate import SIEDocumentEnricher

enricher = SIEDocumentEnricher(
    base_url="http://localhost:8080",
    labels=["person", "organization", "location"],
    classify_model="knowledgator/gliclass-large-v3.0",
    classify_labels=["technical", "business", "legal"],
)

client = weaviate.connect_to_local()
try:
    collection = client.collections.create(
        "Documents",
        description="Documents with extracted entity and classification metadata.",
        properties=[
            wvc.config.Property(name="text", data_type=wvc.config.DataType.TEXT),
            wvc.config.Property(
                name="person", data_type=wvc.config.DataType.TEXT_ARRAY,
                description="People mentioned in the document",
            ),
            wvc.config.Property(
                name="organization", data_type=wvc.config.DataType.TEXT_ARRAY,
                description="Organizations mentioned in the document",
            ),
            wvc.config.Property(
                name="location", data_type=wvc.config.DataType.TEXT_ARRAY,
                description="Locations mentioned in the document",
            ),
            wvc.config.Property(
                name="classification", data_type=wvc.config.DataType.TEXT,
                description="Document category: technical, business, or legal",
            ),
            wvc.config.Property(
                name="classification_score", data_type=wvc.config.DataType.NUMBER,
                description="Confidence score for the classification",
            ),
        ],
        vector_config=wvc.config.Configure.Vectors.self_provided(),
    )

    # Embed + extract in one call
    texts = [
        "John Smith presented Google's new AI strategy in New York.",
        "The court ruling on patent law affects tech companies.",
    ]
    docs = enricher.enrich(texts)
    collection.data.insert_many([
        wvc.data.DataObject(properties=doc.properties, vector=doc.vector)
        for doc in docs
    ])

    # The Query Agent can now filter on extracted properties:
    # "find documents about Google" → organization filter + vector search
    # "show me legal documents mentioning John Smith" → classification + person filter
    query_vec = enricher.enrich_query("AI strategy announcements")
    results = collection.query.near_vector(near_vector=query_vec, limit=5)
finally:
    client.close()

Testing

# Unit tests (no server needed)
pytest

# Integration tests (requires SIE + Weaviate)
pytest -m integration