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----
-layout: integration
-name: Gradient
-description: Gradient is an LLM development platform that offers simple web APIs for fine-tuning, embeddings, and inference on state-of-the-art open-source models.
-authors:
-  - name: Mateusz Haligowski
-  - name: deepset
-    socials:
-      github: deepset-ai
-      twitter: deepset_ai
-      linkedin: https://www.linkedin.com/company/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
----
-
-**Table of Contents**
-- [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`](https://docs.haystack.deepset.ai/docs/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`](https://docs.haystack.deepset.ai/docs/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`](https://docs.haystack.deepset.ai/docs/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.in_memory import InMemoryDocumentStore
-from haystack.components.writers import DocumentWriter
-
-from haystack_integrations.components.embedders.gradient 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
-import os
-
-from haystack.components.builders.answer_builder import AnswerBuilder
-from haystack.components.builders.prompt_builder import PromptBuilder
-from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
-
-from haystack_integrations.components.embedders.gradient import GradientTextEmbedder
-from haystack_integrations.components.generators.gradient 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:
-"""
-
-os.environ["GRADIENT_ACCESS_TOKEN"] = "YOUR-GRADIENT-ACCESS-TOKEN"
-os.environ["GRADIENT_WORKSPACE_ID"] = "YOUR-GRADIENT-WORKSPACE-ID"
-
-retriever = InMemoryEmbeddingRetriever(document_store)
-prompt_builder = PromptBuilder(template=prompt_template)
-embedder = GradientTextEmbedder()
-generator = GradientGenerator(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/github/deepset-ai/haystack-cookbook/blob/main/notebooks/gradient-embeders-and-generators-for-notion-rag.ipynb).
-
-## 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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