MetaLlamaChatGenerator

Generate text using models available on Meta's Llama API.

Basic Information

Type: haystack_integrations.components.generators.meta_llama.chat.chat_generator.MetaLlamaChatGenerator
Components it can connect with:
- ChatPromptBuilder: MetaLlamaChatGenerator receives a rendered prompt from ChatPromptBuilder.
- DeepsetAnswerBuilder: MetaLlamaChatGenerator sends the generated replies to DeepsetAnswerBuilder through OutputAdapter.

Inputs

Parameter	Type	Default	Description
messages	List[ChatMessage]		A list of `ChatMessage` instances representing the input messages.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function that is called when a new token is received from the stream.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for the model. For details, see model documentation.
tools	Optional[Union[List[Tool], Toolset]]	None	A list of Tool objects or a Toolset that the model can use.

Outputs

Parameter	Type	Default	Description
replies	List[ChatMessage]		A list containing the generated ChatMessage responses.

Overview

Use MetaLlamaChatGenerator to generate text using models available on Meta's Llama API. For supported models, see Llama API Docs.

You can pass any text generation parameters valid for the Llama Chat Completion API directly to this component using the generation_kwargs parameter.

Use this component to:

Work seamlessly with the Llama API Chat Completion endpoint.
Stream responses from the Llama API Chat Completion endpoint.

Response Format

MetaLlamaChatGenerator currently only supports json_schema response format.

Usage Example

Using the Component in a Pipeline

This is an example RAG pipeline with MetaLlamaChatGenerator and DeepsetAnswerBuilder:

components:
  bm25_retriever: # Selects the most similar documents from the document store
    type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'Standard-Index-English'
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      top_k: 20 # The number of results to return
      fuzziness: 0

  query_embedder:
    type: deepset_cloud_custom_nodes.embedders.nvidia.text_embedder.DeepsetNvidiaTextEmbedder
    init_parameters:
      normalize_embeddings: true
      model: intfloat/e5-base-v2

  embedding_retriever: # Selects the most similar documents from the document store
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'Standard-Index-English'
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      top_k: 20 # The number of results to return

  document_joiner:
    type: haystack.components.joiners.document_joiner.DocumentJoiner
    init_parameters:
      join_mode: concatenate

  ranker:
    type: deepset_cloud_custom_nodes.rankers.nvidia.ranker.DeepsetNvidiaRanker
    init_parameters:
      model: intfloat/simlm-msmarco-reranker
      top_k: 8

  meta_field_grouping_ranker:
    type: haystack.components.rankers.meta_field_grouping_ranker.MetaFieldGroupingRanker
    init_parameters:
      group_by: file_id
      subgroup_by:
      sort_docs_by: split_id

  answer_builder:
    type: deepset_cloud_custom_nodes.augmenters.deepset_answer_builder.DeepsetAnswerBuilder
    init_parameters:
      reference_pattern: acm

  ChatPromptBuilder:
    type: haystack.components.builders.chat_prompt_builder.ChatPromptBuilder
    init_parameters:
      template:
      - _content:
        - text: "You are a helpful assistant answering the user's questions based on the provided documents.\nIf the answer is not in the documents, rely on the web_search tool to find information.\nDo not use your own knowledge.\n"
        _role: system
      - _content:
        - text: "Provided documents:\n{% for document in documents %}\nDocument [{{ loop.index }}] :\n{{ document.content }}\n{% endfor %}\n\nQuestion: {{ query }}\n"
        _role: user
      required_variables:
      variables:
  OutputAdapter:
    type: haystack.components.converters.output_adapter.OutputAdapter
    init_parameters:
      template: '{{ replies[0] }}'
      output_type: List[str]
      custom_filters:
      unsafe: false

  MetaLlamaChatGenerator:
    type: haystack_integrations.components.generators.meta_llama.chat.chat_generator.MetaLlamaChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - LLAMA_API_KEY
        strict: false
      model: Llama-4-Scout-17B-16E-Instruct-FP8
      api_base_url: https://api.llama.com/compat/v1/
      generation_kwargs:
      streaming_callback:
      tools:

connections:  # Defines how the components are connected
- sender: bm25_retriever.documents
  receiver: document_joiner.documents
- sender: query_embedder.embedding
  receiver: embedding_retriever.query_embedding
- sender: embedding_retriever.documents
  receiver: document_joiner.documents
- sender: document_joiner.documents
  receiver: ranker.documents
- sender: ranker.documents
  receiver: meta_field_grouping_ranker.documents
- sender: meta_field_grouping_ranker.documents
  receiver: answer_builder.documents
- sender: meta_field_grouping_ranker.documents
  receiver: ChatPromptBuilder.documents
- sender: OutputAdapter.output
  receiver: answer_builder.replies
- sender: ChatPromptBuilder.prompt
  receiver: MetaLlamaChatGenerator.messages
- sender: MetaLlamaChatGenerator.replies
  receiver: OutputAdapter.replies

inputs:  # Define the inputs for your pipeline
  query:  # These components will receive the query as input
  - "bm25_retriever.query"
  - "query_embedder.text"
  - "ranker.query"
  - "answer_builder.query"
  - "ChatPromptBuilder.query"
  filters:  # These components will receive a potential query filter as input
  - "bm25_retriever.filters"
  - "embedding_retriever.filters"

outputs:  # Defines the output of your pipeline
  documents: "meta_field_grouping_ranker.documents"  # The output of the pipeline is the retrieved documents
  answers: "answer_builder.answers"  # The output of the pipeline is the generated answers

max_runs_per_component: 100

metadata: {}

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var('LLAMA_API_KEY')	The Llama API key.
model	str	Llama-4-Scout-17B-16E-Instruct-FP8	The name of the Llama chat completion model to use.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function that is called when a new token is received from the stream. The callback function accepts StreamingChunk as an argument.
api_base_url	Optional[str]	https://api.llama.com/compat/v1/	The Llama API Base url. For more details, see LlamaAPI docs.
generation_kwargs	Optional[Dict[str, Any]]	None	Other parameters to use for the model. These parameters are all sent directly to the Llama API endpoint. See Llama API docs for more details. Some of the supported parameters: - `max_tokens`: The maximum number of tokens the output text can have. - `temperature`: What sampling temperature to use. Higher values mean the model will take more risks. Try 0.9 for more creative applications and 0 (argmax sampling) for ones with a well-defined answer. - `top_p`: An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. - `stream`: Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. - `safe_prompt`: Whether to inject a safety prompt before all conversations. - `random_seed`: The seed to use for random sampling.
tools	Optional[Union[List[Tool], Toolset]]	None	A list of tools for which the model can prepare calls.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
messages	List[ChatMessage]		A list of ChatMessage instances representing the input messages.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function that is called when a new token is received from the stream.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for the model. For details, see model documentation.
tools	Optional[Union[List[Tool], Toolset]]	None	A list of Tool objects or a Toolset that the model can use.

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Basic Information​

Inputs​

Outputs​

Overview​

Response Format​

Usage Example​

Using the Component in a Pipeline​

Parameters​

Init Parameters​

Run Method Parameters​