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integrations/cerebras.md

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+---
+layout: integration
+name: Cerebras
+description: Use LLMs served by Cerebras API
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: Haystack_AI
+        linkedin: https://www.linkedin.com/company/deepset-ai
+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/cerebras.png
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Overview](#overview)
+- [Usage](#usage)
+
+## Overview
+
+[Cerebras](https://cerebras.ai/) is the go-to platform for fast and effortless AI training and inference.
+
+## Usage
+
+[Cerebras API](https://cerebras.ai/inference) is OpenAI compatible, making it easy to use in Haystack via OpenAI Generators.
+
+### Using `Generator`
+
+Here's an example of using `llama3.1-8b` served via Cerebras to perform question answering on a web page.
+You need to set the environment variable `CEREBRAS_API_KEY` and choose a [compatible model](https://inference-docs.cerebras.ai/introduction).
+
+```python
+from haystack import Pipeline
+from haystack.utils import Secret
+from haystack.components.fetchers import LinkContentFetcher
+from haystack.components.converters import HTMLToDocument
+from haystack.components.builders import PromptBuilder
+from haystack.components.generators import OpenAIGenerator
+
+fetcher = LinkContentFetcher()
+converter = HTMLToDocument()
+prompt_template = """
+According to the contents of this website:
+{% for document in documents %}
+  {{document.content}}
+{% endfor %}
+Answer the given question: {{query}}
+Answer:
+"""
+prompt_builder = PromptBuilder(template=prompt_template)
+llm = OpenAIGenerator(
+    api_key=Secret.from_env_var("CEREBRAS_API_KEY"),
+    api_base_url="https://api.cerebras.ai/v1",
+    model="llama3.1-8b"
+)
+pipeline = Pipeline()
+pipeline.add_component("fetcher", fetcher)
+pipeline.add_component("converter", converter)
+pipeline.add_component("prompt", prompt_builder)
+pipeline.add_component("llm", llm)
+
+pipeline.connect("fetcher.streams", "converter.sources")
+pipeline.connect("converter.documents", "prompt.documents")
+pipeline.connect("prompt.prompt", "llm.prompt")
+
+result = pipeline.run({"fetcher": {"urls": ["https://cerebras.ai/inference"]},
+              "prompt": {"query": "Why should I use Cerebras for serving LLMs?"}})
+
+print(result["llm"]["replies"][0])
+```
+
+### Using `ChatGenerator`
+
+See an example of engaging in a multi-turn conversation with `llama3.1-8b`.
+You need to set the environment variable `CEREBRAS_API_KEY` and choose a [compatible model](https://inference-docs.cerebras.ai/introduction).
+
+```python
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack.utils import Secret
+
+generator = OpenAIChatGenerator(
+    api_key=Secret.from_env_var("CEREBRAS_API_KEY"),
+    api_base_url="https://api.cerebras.ai/v1",
+    model="llama3.1-8b",
+    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)
+```