集成：Llama API

pip install meta-llama-haystack

# 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},
    }
)

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}})]}