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+---
+layout: integration
+name: Isaacus
+description: Use the latest foundational legal AI models from Isaacus in Haystack.
+authors:
+  - name: Isaacus
+    socials:
+      github: isaacus-dev
+      linkedin: https://www.linkedin.com/company/isaacus/
+type: Model Provider
+logo: /logos/isaacus.png
+version: Haystack 2.0
+repo: https://github.com/isaacus-dev/isaacus-haystack
+pypi: https://pypi.org/project/isaacus-haystack
+report_issue: https://github.com/isaacus-dev/isaacus-haystack/issues
+---
+
+### ***Table of Contents***
+- [Overview](#overview)
+- [Installation](#installation)
+- [Components](#components)
+- [Quick Example](#quick-example)
+- [Docs](#docs)
+- [License](#license)
+
+## Overview
+[Isaacus](https://isaacus.com/) is a foundational legal AI research company building AI models, apps, and tools for the legal tech ecosystem.
+
+Isaacus offers first-class support for Haystack via the `isaacus-haystack` package, providing embedders optimized for legal retrieval—most notably **Kanon 2**, a high-performing legal embedding model (see the [Kanon 2 overview](https://isaacus.com/blog/introducing-kanon-2-embedder) and the [Massive Legal Embedding Benchmark](https://isaacus.com/blog/introducing-mleb)).
+
+## 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
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