---
title: Cohere → SIE
description: Migrate Cohere embed-v3 and rerank-v3 to self-hosted SIE with catalog embedding models and bge-reranker-v2-m3.
canonical_url: https://superlinked.com/docs/migrate/cohere
last_updated: 2026-05-08
---

Cohere offers `embed-v3` (asymmetric query/document embeddings) and
`rerank-v3.5` (cross-encoder reranker). Both have direct SIE
equivalents.

## Why migrate

- **Self-hosted.** No API key, no per-call cost, no rate limits, no
  data egress.
- **Embedding + reranking from one stack.** Cohere bills both
  separately; SIE serves both from one cluster.
- **Open weights.** SIE serves permissive-licensed embedding models
  and `BAAI/bge-reranker-v2-m3` from one self-hosted stack.

## What stays the same

- **Asymmetric query/document hint.** Cohere uses
  `input_type="search_query"` vs `"search_document"`. SIE uses
  `is_query=True` vs `is_query=False`.
- **Rerank shape.** Query + list of docs in, sorted scores out.
- **Multilingual coverage.** If you picked Cohere for
  `embed-multilingual-v3.0`, use a multilingual SIE model such as
  `intfloat/multilingual-e5-large-instruct` and validate on your own
  labeled multilingual set before cutting over; model rankings shift
  by language.

## Before

```python
import os
import cohere

docs = [
    "Mitochondria are the powerhouse of the cell.",
    "The Eiffel Tower is in Paris.",
]

client = cohere.ClientV2(api_key=os.environ["COHERE_API_KEY"])

resp = client.embed(
    texts=["The mitochondrion is the powerhouse of the cell."],
    model="embed-english-v3.0",
    input_type="search_document",
    embedding_types=["float"],
)
vectors = resp.embeddings.float_

resp = client.rerank(
    model="rerank-v3.5",
    query="What is the powerhouse of the cell?",
    documents=docs,
    top_n=10,
)
```

## After

```python
from sie_sdk import SIEClient
from sie_sdk.types import Item

client = SIEClient("http://localhost:8080")

texts = ["The mitochondrion is the powerhouse of the cell."]
docs = [
    "Mitochondria are the powerhouse of the cell.",
    "The Eiffel Tower is in Paris.",
]

# Document side: is_query=False (or omitted)
docs_result = client.encode(
    "NovaSearch/stella_en_400M_v5",
    [Item(text=t) for t in texts],
    is_query=False,
)
vectors = [r["dense"].tolist() for r in docs_result]

# Rerank
result = client.score(
    "BAAI/bge-reranker-v2-m3",
    Item(text="What is the powerhouse of the cell?"),
    [Item(text=d) for d in docs],
)
# result["scores"] is sorted by relevance desc; each entry has item_id and score.
```

## Mapping

| Cohere                              | SIE equivalent                                                    |
|-------------------------------------|-------------------------------------------------------------------|
| `embed-english-v3.0`                | `NovaSearch/stella_en_400M_v5`                                    |
| `embed-multilingual-v3.0`           | `intfloat/multilingual-e5-large-instruct`                         |
| `embed-english-light-v3.0`          | `intfloat/e5-small-v2`                                            |
| `input_type="search_query"`         | `client.encode(..., is_query=True)`                               |
| `input_type="search_document"`      | `client.encode(..., is_query=False)`                              |
| `rerank-v3.5` / `rerank-english-v3.0` | `BAAI/bge-reranker-v2-m3` or `jinaai/jina-reranker-v2-base-multilingual` |
| `rerank-multilingual-v3.0`          | `BAAI/bge-reranker-v2-m3`                                         |

## Score range caveat

Cohere returns calibrated relevance scores in roughly the [0, 1] range.
SIE's `score()` returns raw model-native scores from the cross-encoder,
which for `bge-reranker-v2-m3` are **logits** (any real number; sigmoid
to get a probability if you need [0,1]). The **ordering** is what
matters for reranking; don't compare absolute scores across the two
providers.

## Re-embed required?

**Yes.** Cohere's `embed-v3` and SIE's catalog embedding models
produce vectors in unrelated spaces, even at matched dimensions.

## Run it yourself

```bash
mise run serve -- -m NovaSearch/stella_en_400M_v5,BAAI/bge-reranker-v2-m3
export COHERE_API_KEY=...
uv add cohere
```

Run the Cohere 'before' and SIE 'after' snippets from this page on a
small slice of your own corpus. Compare embeddings and rerank
ordering.