Add Meta Llama integration (#326)

* init: meta_llama doc

* Update integrations/meta_llama.md

---------

Co-authored-by: Julian Risch <julian.risch@deepset.ai>

Author: seyeong-han

integrations/meta_llama.md

@@ -0,0 +1,132 @@
+---
+layout: integration
+name: Meta Llama API
+description: Use Llama Models with Haystack
+authors:
+    - name: Young Han
+      socials:
+        github: https://github.com/seyeong-han
+        twitter: deepset_ai
+        linkedin: https://www.linkedin.com/company/deepset-ai/
+pypi: https://pypi.org/project/meta-llama-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/meta_llama
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/meta_llama.png
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+
+## Overview
+
+This integration supports Meta Llama models provided through Meta’s own inferencing infrastructure. To get the `LLAMA_API_KEY`, check out [the Llama API website](https://llama.developer.meta.com?utm_source=partner-haystack&utm_medium=website).
+
+You can use Llama models with `MetaLlamaChatGenerator`.
+
+Currently, available models are:
+
+| Model ID | Input context length | Output context length | Input Modalities | Output Modalities |
+| --- | --- | --- | --- | --- |
+| `Llama-4-Scout-17B-16E-Instruct-FP8` | 128k | 4028 | Text, Image | Text |
+| `Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Text, Image | Text |
+| `Llama-3.3-70B-Instruct` | 128k | 4028 | Text | Text |
+| `Llama-3.3-8B-Instruct` | 128k | 4028 | Text | Text |
+
+## Installation
+
+```bash
+pip install meta-llama-haystack
+```
+
+## Usage
+
+Based on your use case, you can choose between `MetaLlamaChatGenerator`.
+Before using, make sure to set the `LLAMA_API_KEY` environment variable.
+
+### Using `MetaLlamaChatGenerator`
+
+This example showcases how to build a complete RAG system that can answer questions based on the information in your document store using Meta's Llama models.
+
+```python
+# To run this example, you will need to set a `LLAMA_API_KEY` environment variable.
+
+from haystack import Document, Pipeline
+from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
+from haystack.components.generators.utils import print_streaming_chunk
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
+from haystack.dataclasses import ChatMessage
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.utils import Secret
+
+from haystack_integrations.components.generators.meta_llama import MetaLlamaChatGenerator
+
+# Write documents to InMemoryDocumentStore
+document_store = InMemoryDocumentStore()
+document_store.write_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."),
+    ]
+)
+
+# Build a RAG pipeline
+prompt_template = [
+    ChatMessage.from_user(
+        "Given these documents, answer the question.\n"
+        "Documents:\n{% for doc in documents %}{{ doc.content }}{% endfor %}\n"
+        "Question: {{question}}\n"
+        "Answer:"
+    )
+]
+
+# Define required variables explicitly
+prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables={"question", "documents"})
+
+retriever = InMemoryBM25Retriever(document_store=document_store)
+llm = MetaLlamaChatGenerator(
+    api_key=Secret.from_env_var("LLAMA_API_KEY"),
+    streaming_callback=print_streaming_chunk,
+)
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component("retriever", retriever)
+rag_pipeline.add_component("prompt_builder", prompt_builder)
+rag_pipeline.add_component("llm", llm)
+rag_pipeline.connect("retriever", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder", "llm.messages")
+
+# Ask a question
+question = "Who lives in Paris?"
+rag_pipeline.run(
+    {
+        "retriever": {"query": question},
+        "prompt_builder": {"question": question},
+    }
+)
+```
+
+### Using `MetaLlamaChatGenerator`
+
+Below is an example of using `MetaLlamaChatGenerator`:
+
+```python
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.components.generators.meta_llama import (
+    MetaLlamaChatGenerator,
+)
+
+client = MetaLlamaChatGenerator()
+response = client.run(
+    messages=[ChatMessage.from_user("What is the best French cheese?")]
+)
+print(response)
+
+>>{'replies': [ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>, _content=[TextContent(text='The best French cheese is a matter of personal preference, but some of the most popular and highly-regarded French cheeses include:\n\n1. **Camembert**: A soft, creamy, and earthy cheese from Normandy, often served with bread and fruit.\n2. **Brie**: A soft, white, and mild cheese from the Île-de-France region, often baked or served with crackers.\n3. **Roquefort**: A pungent, blue-veined cheese from the Roquefort-sur-Soulzon region, often served as a dessert or used in salad dressings.\n4. **Époisses**: A strong, golden, and washed-rind cheese from Burgundy, often served with fruit and bread.\n5. **Pont l\'Évêque**: A semi-soft, golden, and washed-rind cheese from Normandy, often served with crackers or bread.\n\nOf course, there are many other excellent French cheeses, and the "best" one will depend on your personal taste preferences. Some other notable mentions include:\n\n* **Comté**: A firm, nutty, and golden cheese from Franche-Comté.\n* **Gruyère**: A nutty, creamy, and firm cheese from the Savoie region.\n* **Bucheron**: A semi-soft, white, and mild cheese from the Loire Valley.\n* **Bleu d\'Auvergne**: A creamy, blue-veined cheese from the Auvergne region.\n\nFrance is home to over 400 different types of cheese, each with its own unique characteristics and flavor profiles. So, feel free to explore and find your own favorite French cheese!')], _name=None, _meta={'model': 'Llama-4-Scout-17B-16E-Instruct-FP8', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 335, 'prompt_tokens': 17, 'total_tokens': 352, 'completion_tokens_details': None, 'prompt_tokens_details': None}})]}
+```