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+---
+layout: integration
+name: Sambanova
+description: Use open Language Models served by Sambanova
+authors:
+    - name: Sambanova Team
+      socials:
+        twitter: SambaNovaAI
+        linkedin: https://www.linkedin.com/company/sambanova
+pypi: https://pypi.org/project/haystack-ai/
+repo: https://github.com/deepset-ai/haystack
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack/issues
+logo: /logos/sambanova.png
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Overview](#overview)
+- [Usage](#usage)
+
+## Overview
+
+SambaNova is an AI company that develops SN40L Reconfigurable Dataflow Unit (RDU), a processor that provides native dataflow processing and high-performance for fast inference of Large Language Models.
+
+To start using SambaNova, sign up for an API key [here](https://cloud.sambanova.ai/).
+This will give you access to SambaNova Cloud API, which offers rapid inference of open Language Models like Llama 3 and Qwen.
+
+## Usage
+
+SambaNova Cloud API is OpenAI compatible, making it easy to use in Haystack via OpenAI Generators.
+
+
+### Using `Generator`
+
+Here's an example of using Llama served via SambaNova to perform question answering using RAG with `PromptBuilder`.
+You need to set the environment variable `SAMBANOVA_API_KEY` and choose a [compatible model](https://cloud.sambanova.ai/).
+
+```python
+from haystack import Document, Pipeline
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+
+from haystack.components.generators import OpenAIGenerator
+import os
+
+os.environ["SAMBANOVA_API_KEY"] = "YOUR_SAMBANOVA_API_KEY"
+
+document_store = InMemoryDocumentStore()
+document_store.write_documents(
+    [
+        Document(content="The Function-Calling API enables dynamic, agentic workflows by allowing the model to suggest and select function calls based on user input."
+                "This feature facilitates flexible agentic workflows that adapt to varied needs."),
+        Document(content="Interact with multimodal models directly through the Inference API (OpenAI compatible) and Playground"
+                 "for seamless text and image processing."),
+        Document(
+            content="New Python and Gradio code samples make it easier to build and deploy applications on SambaNova Cloud. These examples simplify"
+            "integrating AI models, enabling faster prototyping and reducing setup time."
+        ),
+    ]
+)
+
+template = """
+Given only the following information, answer the question.
+Ignore your own knowledge.
+
+Context:
+{% for document in documents %}
+    {{ document.content }}
+{% endfor %}
+
+Question: {{ query }}?
+"""
+
+llm = OpenAIGenerator(
+    api_key=Secret.from_env_var("SAMBANOVA_API_KEY"),
+    api_base_url="https://api.sambanova.ai/v1",
+    model="Meta-Llama-3.3-70B-Instruct",
+    generation_kwargs = {"max_tokens": 512}
+)
+
+pipe = Pipeline()
+
+pipe.add_component("retriever", InMemoryBM25Retriever(document_store=document_store))
+pipe.add_component("prompt_builder", PromptBuilder(template=template))
+pipe.add_component("llm", llm)
+pipe.connect("retriever", "prompt_builder.documents")
+pipe.connect("prompt_builder", "llm")
+
+query = "Functionalities of Sambanova API?"
+
+response = pipe.run({"prompt_builder": {"query": query}, "retriever": {"query": query}})
+
+print(response["llm"]["replies"])
+```
+
+### Using `ChatGenerator`
+
+See an example of engaging in a multi-turn conversation with Llama 3.3.
+You need to set the environment variable `SAMBANOVA_API_KEY` and choose a [compatible model](https://cloud.sambanova.ai/).
+
+```python
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack.utils import Secret
+import os
+
+os.environ["SAMBANOVA_API_KEY"] = "YOUR_SAMBANOVA_API_KEY"
+
+generator = OpenAIChatGenerator(
+    api_key=Secret.from_env_var("SAMBANOVA_API_KEY"),
+    api_base_url="https://api.sambanova.ai/v1",
+    model="Meta-Llama-3.3-70B-Instruct",
+    generation_kwargs = {"max_tokens": 512}
+)
+
+
+messages = []
+
+while True:
+    msg = input("Enter your message or Q to exit\n🧑 ")
+    if msg=="Q":
+        break
+    messages.append(ChatMessage.from_user(msg))
+    response = generator.run(messages=messages)
+    assistant_resp = response['replies'][0]
+    print("🤖 "+assistant_resp.content)
+    messages.append(assistant_resp)
+```
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