schift 0.6.2

Embedding model migration SDK. Switch models without re-embedding.

Schift Python SDK

Python client and local migration toolkit for Schift.

The package currently exposes two public client styles:

• Schift: modular client for live API operations such as bucket ingest, catalog lookup, embedding, routing, search, usage, and hosted bucket management.
• Client: legacy projection client for fitting and downloading Projection objects that run locally.

Install

Base package:

pip install schift

Optional adapters:

pip install "schift[postgres]"
pip install "schift[qdrant]"
pip install "schift[all]"

Development extras:

pip install -e ".[dev]"

Authentication And Base URLs

The modular client reads SCHIFT_API_KEY automatically:

export SCHIFT_API_KEY=sch_your_key_here

Equivalent explicit construction:

from schift import Schift

client = Schift(
    api_key="sch_your_key_here",
    base_url="https://api.schift.io/v1",
    timeout=60.0,
)

Notes:

• Schift expects a /v1 base URL.
• The legacy Client defaults to https://api.schift.io and appends /v1/... internally.

Client Lifecycle

Schift holds a shared httpx.Client, so prefer a context manager for short-lived scripts:

from schift import Schift

with Schift() as client:
    models = client.catalog.list()

For long-running processes, keep one Schift instance and call close() during shutdown.

Bucket Ingest And Search

from schift import Schift

with Schift() as client:
    bucket = client.buckets.create(name="finance-docs")
    upload = client.buckets.upload(
        bucket["id"],
        [("files", ("q1-report.pdf", open("q1-report.pdf", "rb").read(), "application/pdf"))],
    )

    jobs = client.buckets.list_jobs(bucket_id=bucket["id"])
    hits = client.buckets.search(
        bucket["id"],
        "revenue guidance",
        top_k=5,
        filter={"metadata": {"quarter": "Q1"}},
    )

    print(upload["bucket_id"])
    print(jobs[0]["status"] if jobs else "queued")
    print(hits[0] if hits else "no hits")

Use client.buckets and client.query(..., bucket=...) for retrieval, POST /v1/chat for bucket-backed RAG chat with sources, and POST /v1/chat/completions for OpenAI-compatible LLM routing without bucket context.

The bucket helper also exposes list_collections(), get_job(), list_jobs(), wait_for_job(), and poll_job() so scripts can keep ingest orchestration in one place.

Quickstart

from schift import Schift

with Schift() as client:
    models = client.catalog.list()
    vector = client.embed(
        "quarterly revenue report",
        model="openai/text-embedding-3-small",
    )

    bucket = client.buckets.create("finance-docs")
    client.db.upsert(
        collection="finance-docs",
        vectors=[
            {
                "id": "doc-1",
                "values": vector.tolist(),
                "metadata": {"source": "q1-report"},
            }
        ],
    )

    hits = client.query(
        "revenue guidance",
        bucket="finance-docs",
        top_k=5,
    )

    print(models[0]["id"] if models else "no models")
    print(hits[0] if hits else "no hits")

Module Reference

catalog

List available models and inspect one model by ID.

from schift import Schift

with Schift() as client:
    models = client.catalog.list()
    model = client.catalog.get("openai/text-embedding-3-small")

embed

The embed module is callable for single-text requests and exposes batch() for multiple texts.

from schift import Schift

with Schift() as client:
    one = client.embed(
        "hello world",
        model="openai/text-embedding-3-small",
    )

    many = client.embed.batch(
        texts=["hello", "goodbye"],
        model="openai/text-embedding-3-small",
        dimensions=1024,
    )

Return values are NumPy arrays.

providers

Register your own LLM provider API key (BYOK) so client.chat and client.completions call the provider directly instead of consuming Schift Cloud's shared LLM quota. Supported providers: "openai", "google", "anthropic".

from schift import Schift

with Schift() as client:
    # Register a key
    client.providers.set(
        "google",
        api_key="AIza...",
        # endpoint_url="https://custom-proxy.example.com",  # optional
    )

    # Check whether a provider is configured (api_key is never returned)
    status = client.providers.get("openai")
    # {"provider": "openai", "configured": True | False, "endpoint_url": None | str}

Rotation: a stored BYOK record shadows any server-side env var or secret for that provider. To rotate, call set() again with the new key — changing env vars alone has no effect on orgs with a BYOK record.

routing

Read or update the server-side routing policy used by Schift when a model is omitted.

from schift import Schift

with Schift() as client:
    current = client.routing.get()
    updated = client.routing.set(
        primary="openai/text-embedding-3-small",
        fallback="google/gemini-embedding-001",
        mode="failover",
    )

pii

Mask Korean PII before sending text to an LLM, vector store, or workflow step. Use types when you want the masking contract to be visible and selectable.

from schift import Schift

PII_TYPES = [
    "resident_id",
    "alien_registration",
    "passport",
    "driver_license",
    "address",
    "phone",
    "bank_account",
]

with Schift() as client:
    result = client.redact_pii(
        "주민등록번호 850205-1234567, 연락처 010-1234-5678",
        types=PII_TYPES,
    )
    print(result["masked"])

    masked = client.mask(
        "계좌 123-45-678901",
        types=["bank_account"],
    )

The default token format returns tokens such as [PII_PHONE_1] and a reverse_map so the caller can restore the original values after the workflow step. Keep that map only in your temporary restore path; Schift does not persist or gateway-cache it for pii_type_index requests. Set token_format="label_index" only when you need legacy tokens such as [PHONE_1].

Send only masked into the LLM, agent, vector store, or workflow step. Never include reverse_map in the AI payload; use it only after the AI result returns if your app needs to restore values.

restored = client.restore_pii(
    "고객 연락처 [PII_PHONE_1]로 안내하세요.",
    result["reverse_map"],
)

restore_pii() is a stateless API call. It requires an API key and does not persist or cache the map.

bench

Run a server-side benchmark between two model IDs. The SDK returns BenchReport.

from schift import Schift

with Schift() as client:
    report = client.bench.run(
        source="openai/text-embedding-3-small",
        target="google/gemini-embedding-001",
        data="./eval_queries.jsonl",
    )

    print(report.verdict)
    print(report.summary())

db

Manage hosted buckets and write vectors or raw documents.

from schift import Schift

with Schift() as client:
    vector = client.embed(
        "Schift reduces vector migration downtime.",
        model="openai/text-embedding-3-small",
    )

    bucket = client.db.create_collection(
        name="product-docs",
        dimension=len(vector),
    )

    client.db.upsert(
        collection="product-docs",
        vectors=[
            {
                "id": "doc-1",
                "values": vector.tolist(),
                "metadata": {"title": "Launch plan"},
            }
        ],
    )

    client.db.upsert_text(
        collection="product-docs",
        documents=[
            {
                "id": "doc-2",
                "text": "Schift reduces vector migration downtime.",
                "metadata": {"title": "Overview"},
            }
        ],
        model="openai/text-embedding-3-small",
    )

    stats = client.db.collection_stats("product-docs")

Available methods:

• create_collection(name, dimension)
• list_buckets()
• get_collection(name)
• collection_stats(name)
• delete_collection(name)
• upsert(collection, vectors)
• upsert_text(collection, documents, model)

query

The query module is callable and supports hosted buckets or an external DB handle.

from schift import Schift

with Schift() as client:
    hosted = client.query(
        "vector migration rollback plan",
        bucket="product-docs",
        top_k=10,
        rerank=True,
        rerank_top_k=5,
    )

    passthrough = client.query(
        "incident response",
        db="prod-search-db",
        model="openai/text-embedding-3-small",
        top_k=5,
    )

rerank

Rerank a list of candidate documents with a cross-encoder style endpoint.

from schift import Schift

with Schift() as client:
    reranked = client.rerank(
        "incident response",
        documents=[
            {"id": "doc-1", "text": "..."},
            {"id": "doc-2", "text": "..."},
        ],
        top_k=2,
    )

usage

Fetch aggregate usage for billing or dashboards.

from schift import Schift

with Schift() as client:
    usage = client.usage.get(period="30d", granularity="day")

Workflows

Build and run RAG pipelines as composable DAGs.

Quick Start

from schift import Schift

with Schift() as client:
    # Create from template
    wf = client.workflow.create_rag("Product Search")

    # Run with inputs
    result = client.workflow.run(wf.id, inputs={"query": "best laptop"})
    print(result.outputs["answer"])

CRUD

wf = client.workflow.create("My Pipeline", description="Custom RAG")
workflows = client.workflow.list()
wf = client.workflow.get(wf.id)
wf = client.workflow.update(wf.id, name="Renamed")
client.workflow.delete(wf.id)

Building a Graph

wf = client.workflow.create("Custom RAG")

# Add blocks
start = client.workflow.add_block(wf.id, "start", title="Start")
retriever = client.workflow.add_block(wf.id, "retriever", config={
    "bucket": "my-docs",
    "top_k": 5,
    "rerank": True,
    "rerank_top_k": 3,
})
prompt = client.workflow.add_block(wf.id, "prompt_template", config={
    "template": "Answer based on:\n{{results}}\n\nQuestion: {{query}}",
})
llm = client.workflow.add_block(wf.id, "llm", config={
    "model": "openai/gpt-4o-mini",  # or "anthropic/claude-sonnet-4-20250514", "gemini-2.5-flash"
    "temperature": 0.7,
})
end = client.workflow.add_block(wf.id, "end")

# Connect blocks
client.workflow.add_edge(wf.id, start["id"], retriever["id"])
client.workflow.add_edge(wf.id, retriever["id"], prompt["id"])
client.workflow.add_edge(wf.id, prompt["id"], llm["id"])
client.workflow.add_edge(wf.id, llm["id"], end["id"])

Multiple Inputs

The start node forwards all input variables to downstream blocks:

result = client.workflow.run(wf.id, inputs={
    "query": "maternity leave policy",
    "user_id": "u-123",
    "language": "ko",
})

Reference variables in prompt templates with {{variable}} syntax.

Validation & Run History

# Validate graph (cycles, missing connections, etc.)
v = client.workflow.validate(wf.id)
print(v.valid, v.errors)

# List past runs
runs = client.workflow.runs(wf.id)
for r in runs:
    print(r.run_id, r.status, r.outputs)

YAML Import / Export

# Export to YAML
yaml_str = client.workflow.to_yaml(wf.id, path="pipeline.yaml")

# Load from file
definition = client.workflow.from_yaml("pipeline.yaml")

# Push YAML to create on server
wf = client.workflow.push_yaml("pipeline.yaml")

Templates

Method	Template
create_rag(name)	Retriever -> Prompt -> LLM
create_doc_qa(name)	Document QA with sources
create_chat(name)	Conversational RAG
create_ocr_ingest(name)	OCR -> Chunk -> Embed

Async Workflows

from schift import AsyncSchift

async with AsyncSchift() as client:
    wf = await client.workflow.create_rag("Async RAG")
    result = await client.workflow.run(wf.id, inputs={"query": "hello"})

Block Types

Category	Types
Control	start, end, conditional, loop
Retrieval	retriever (with rerank toggle), reranker
LLM	llm (OpenAI/Anthropic/Google), prompt_template, answer
Data	document_loader, chunker, embedder, text_processor
Integration	api_call, webhook, code_executor
Storage	vector_store, cache

API Reference

Method	Description
workflow.create(name)	Create workflow
workflow.get(id)	Get workflow
workflow.list()	List workflows
workflow.update(id, **kw)	Update workflow
workflow.delete(id)	Delete workflow
workflow.run(id, inputs)	Run workflow
workflow.runs(id)	List past runs
workflow.validate(id)	Validate graph
workflow.add_block(id, type, config)	Add block
workflow.add_edge(id, src, tgt)	Add edge
workflow.to_yaml(id)	Export as YAML
workflow.from_yaml(path)	Load YAML definition
workflow.push_yaml(path)	Import YAML to server

Projection Workflow

Projection creation still lives in the legacy Client API. It returns a local Projection object that can transform vectors without further API calls.

from schift import Client

legacy = Client(api_key="sch_your_key_here")

# Embed the same sample texts with both providers before fitting.
source_pairs = old_model_embeddings
target_pairs = new_model_embeddings

projection = legacy.fit(
    source=source_pairs,
    target=target_pairs,
    source_model="openai/text-embedding-3-small",
    target_model="google/gemini-embedding-001",
    project_name="openai-to-gemini",
)

converted = projection.transform(source_pairs[:10])
projection.save("./projection-openai-to-gemini")

You can later reload a saved projection:

from schift import Projection

projection = Projection.load("./projection-openai-to-gemini")

The legacy client also supports:

• fit(...)
• bench(...)
• list_projections()
• get_projection(project_id)

Local Migration With Adapters

The local migration engine reads vectors from a source adapter, applies a Projection, and writes to a sink adapter. This path does not depend on hosted collection APIs.

from schift import Projection
from schift.migrate import migrate
from schift.adapters.file import NpyAdapter

projection = Projection.load("./projection-openai-to-gemini")

source = NpyAdapter("old_embeddings.npy")
sink = NpyAdapter("new_embeddings.npy")

result = migrate(
    source=source,
    sink=sink,
    projection=projection,
    batch_size=2048,
    dry_run=True,
)

print(result)

The same engine is exposed through Schift().migrate.run(...).

Built-in Adapters

NumPy files

from schift.adapters.file import NpyAdapter

adapter = NpyAdapter("embeddings.npy")

pgvector

Requires pip install "schift[postgres]".

from schift.adapters.pgvector import PgVectorAdapter

adapter = PgVectorAdapter(
    conninfo="postgresql://user:password@localhost/mydb",
    table="documents",
    embedding_column="embedding",
    id_column="id",
)

Qdrant

Requires pip install "schift[qdrant]".

from schift.adapters.qdrant import QdrantAdapter

adapter = QdrantAdapter(
    url="http://localhost:6333",
    collection="documents",
    api_key=None,
)

Registry Helpers

from schift.adapters import get_adapter, list_adapters

print(list_adapters())

adapter = get_adapter(
    {
        "type": "npy",
        "path": "embeddings.npy",
    }
)

Errors

Both client styles raise exceptions from schift.client:

• AuthError
• QuotaError
• SchiftError

Example:

from schift import Schift
from schift.client import AuthError, QuotaError, SchiftError

try:
    with Schift() as client:
        client.catalog.list()
except AuthError:
    ...
except QuotaError:
    ...
except SchiftError:
    ...

Development

From clients/sdk/python/:

python -m pip install -e ".[dev]"
python -m ruff check .
python -m pytest

Source Layout

clients/sdk/python/
├── schift/
│   ├── schift_client.py   # modular SDK entry point
│   ├── client.py          # legacy projection client
│   ├── projection.py      # local projection object
│   ├── adapters/          # npy, pgvector, qdrant
│   ├── workflow.py         # workflow CRUD, blocks, edges, YAML, templates
│   └── *.py               # catalog, embed, routing, db, query, rerank, usage
└── docs/                  # request/response contracts and schemas