semvec/pypi-packages huggingface dataset:
isaacus-haystack 0.1.0
Isaacus embedders for Haystack 2.x
License: Apache-2.0
Requires Python: >=3.9

## Overview
[Isaacus](https://isaacus.com/) is a foundational legal AI research company building AI models, apps, and tools for the legal tech ecosystem.

Isaacus' offering includes [Kanon 2 Embedder](https://isaacus.com/blog/introducing-kanon-2-embedder), the world's best legal embedding model (as measured on the [Massive Legal Embedding Benchmark](https://isaacus.com/blog/introducing-mleb)), as well as [legal zero-shot classification](https://docs.isaacus.com/models/introduction#universal-classification) and [legal extractive question answering models](https://docs.isaacus.com/models/introduction#answer-extraction).

Isaacus offers first-class support for Haystack through the `isaacus-haystack` integration package.

## Installation
```bash
pip install isaacus-haystack
```

## Components
- `IsaacusTextEmbedder` – embeds query text into a vector.
- `IsaacusDocumentEmbedder` – embeds Haystack `Document`s and writes to `document.embedding`.

## Quick Example
```python
from haystack import Pipeline, Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.utils import Secret
from haystack_integrations.components.embedders.isaacus import (IsaacusTextEmbedder, IsaacusDocumentEmbedder)

store = InMemoryDocumentStore(embedding_similarity_function="dot_product")
embedder = IsaacusDocumentEmbedder(
    api_key=Secret.from_env_var("ISAACUS_API_KEY"),
    model="kanon-2-embedder",          # choose any supported Isaacus embedding model
    # dimensions=1792,                 # optionally set to match your vector DB
)

raw_docs = [Document(content="Isaacus releases Kanon 2 Embedder: the world's best legal embedding model."),
            Document(content="Isaacus also offers legal zero-shot classification and extractive question answering models.")]
store.write_documents(embedder.run(raw_docs)["documents"])

pipe = Pipeline()
pipe.add_component("q", IsaacusTextEmbedder(
    api_key=Secret.from_env_var("ISAACUS_API_KEY"),
    model="kanon-2-embedder",
))
pipe.add_component("ret", InMemoryEmbeddingRetriever(document_store=store))
pipe.connect("q.embedding", "ret.query_embedding")

print(pipe.run({"q": {"text": "Who built Kanon 2 Embedder?"}}))
```

## Docs
- Isaacus Embeddings API: https://docs.isaacus.com/capabilities/embedding
- Haystack: https://haystack.deepset.ai/

## License
Apache-2.0