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+---
+layout: integration
+name: Gradient
+description: Gradient AI is a self-managed cloud service for Large Language Models, offering fine-tuning and inference of open-source embedding and generative models.
+authors:
+  - name: Mateusz Haligowski
+  - name: deepset
+    socials:
+      github: deepset-ai
+      twitter: deepset_ai
+      linkedin: deepset-ai
+pypi: https://pypi.org/project/gradient-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/gradient
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/gradient.png
+version: Haystack 2.0
+---
+# Gradient AI integration
+
+[![PyPI - Version](https://img.shields.io/pypi/v/gradient-haystack.svg)](https://pypi.org/project/gradient-haystack)
+
+-----
+
+**Table of Contents**
+
+- [Gradient AI integration](#gradient-ai-integration)
+  - [Installation](#installation)
+  - [Usage](#usage)
+  - [Examples](#examples)
+  - [License](#license)
+
+## Installation
+Use `pip` to install the integration:
+
+```console
+pip install gradient-haystack
+```
+## Usage
+Once installed, you will have access to a Generator and two Embedder objects. 
+- `GradientDocumentEmbedder`: Use this component to create embeddings of documents. This is commonly used in indexing pipelines to store documents and their embeddings in document stores.
+- `GradientTextEmbedder`: Use this component to create embeddings of text, such as queries. This is commonly used as the first component of query pipelines to create embeddings of the query.
+- `GradientGenerator`: Use this component to query generative models with Gradient. This is commonly used in query pipelines to generate responses to queries.
+
+### Use the GradientDocumentEmbedder
+You can use embedding models with `GradientDocumentEmbedder`` to create embeddings of your documents. This is commonly used in indexing pipelines to write documents and their embeddings into a document store.
+
+```python
+import os
+
+from haystack import Pipeline
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.components.writers import DocumentWriter
+
+from gradient_haystack.embedders.gradient_document_embedder import GradientDocumentEmbedder
+
+os.environ["GRADIENT_ACCESS_TOKEN"] = "Your Gradient Access Token"
+os.environ["GRADIENT_WORKSPACE_ID"] = "Your Gradient Workspace id: "
+
+documents = [
+    Document(content="My name is Jean and I live in Paris."),
+    Document(content="My name is Mark and I live in Berlin."),
+    Document(content="My name is Giorgio and I live in Rome."),
+]
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_component(instance=GradientDocumentEmbedder(), name="document_embedder")
+indexing_pipeline.add_component(instance=DocumentWriter(document_store=InMemoryDocumentStore()), name="document_writer")
+indexing_pipeline.connect("document_embedder", "document_writer")
+indexing_pipeline.run({"document_embedder": {"documents": documents}})
+```
+
+### Use the GradientTextEmbedder and GradientGenerator
+You can use embedding models with `GradientTextEmbedder` and generative models with `GradientGenerator`. These two are commonly used together in a query pipeline such as a retrievel-augmented generative (RAG) pipeline such as the one below. 
+
+```python
+from haystack.components.builders.answer_builder import AnswerBuilder
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.retrievers import InMemoryEmbeddingRetriever
+
+from gradient_haystack.embedders.gradient_text_embedder import GradientTextEmbedder
+from gradient_haystack.generator.base import GradientGenerator
+
+from getpass import getpass
+
+prompt_template = """
+Given these documents, answer the question.\nDocuments:
+{% for doc in documents %}
+    {{ doc.content }}
+{% endfor %}
+\nQuestion: {{question}}
+\nAnswer:
+"""
+
+gradient_access_token = os.environ.get("GRADIENT_ACCESS_TOKEN")
+
+retriever = InMemoryEmbeddingRetriever(document_store)
+prompt_builder = PromptBuilder(template=prompt_template)
+embedder = GradientTextEmbedder(access_token=gradient_access_token)
+generator = GradientGenerator(access_token=gradient_access_token, base_model_slug="llama2-7b-chat")
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component(instance=embedder, name="text_embedder")
+rag_pipeline.add_component(
+    instance=retriever, name="retriever"
+)
+rag_pipeline.add_component(instance=prompt_builder, name="prompt_builder")
+rag_pipeline.add_component(instance=generator, name="llm")
+rag_pipeline.add_component(instance=AnswerBuilder(), name="answer_builder")
+
+rag_pipeline.connect("text_embedder", "retriever")
+rag_pipeline.connect("retriever", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder", "llm")
+rag_pipeline.connect("llm.replies", "answer_builder.replies")
+rag_pipeline.connect("retriever", "answer_builder.documents")
+
+rag_pipeline.run(
+        {
+            "text_embedder": {"text": question},
+            "prompt_builder": {"question": question},
+            "answer_builder": {"query": question},
+        }
+    )
+```
+
+## Examples
+You can find a full code example showing how to use the integration in [this Colab](https://colab.research.google.com/drive/1kE_NAKKgZztQJMbgm2esyTVkAxlrpGtd#scrollTo=coE-fMtTJ-Pp).
+
+## License
+
+`gradient-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.
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