LlamaCppChatGenerator

Complete chats using large language models running on llama.cpp.

Basic Information

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

Inputs

Parameter	Type	Default	Description
messages	List[ChatMessage]		A list of ChatMessage objects representing the input messages.
generation_kwargs	Optional[Dict[str, Any]]	None	A dictionary containing keyword arguments to customize text generation. For more information on the arguments you can use, see llama.cpp documentation.
tools	Optional[Union[List[Tool], Toolset]]	None	A list of tools or a Toolset for which the model can prepare calls.

Outputs

Parameter	Type	Default	Description
replies	List[ChatMessage]		The responses from the model.

Overview

llama.cpp is a library written in C/C++ for efficient inference of LLMs. It employs the quantized GGUF format that reduces memory requiremens and accelerates inference, making it suitable for running these models on standard machines (even without GPUs).

Llama.cpp uses the quantized binary file of the LLM in GGUF format, which you can download from Hugging Face. LlamaCppChatGenerator supports models running on Llama.cpp. Pass the path to the locally saved GGUF file as the model parameter.

Prerequisites

To use LlamaCppChatGenerator, download the GGUF version of the model you want to use from Hugging Face. Then, pass the path to the locally saved GGUF file as the model parameter.

Usage Example

Initializing the Component

components:
  LlamaCppChatGenerator:
    type: llama_cpp.src.haystack_integrations.components.generators.llama_cpp.chat.chat_generator.LlamaCppChatGenerator
    init_parameters:

Using the Component in a Pipeline

This is an example RAG pipeline with LlamaCppChatGenerator and DeepsetAnswerBuilder connected through OutputAdapter:

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

  LlamaCppChatGenerator:
    type: haystack_integrations.components.generators.llama_cpp.chat.chat_generator.LlamaCppChatGenerator
    init_parameters:
      model: /Downloads/gemma-3-1b-it-q4_0.gguf
      n_ctx: 0
      n_batch: 512
      model_kwargs:
      generation_kwargs:

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: LlamaCppChatGenerator.messages
- sender: LlamaCppChatGenerator.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
model	str		The path of a quantized model for text generation, for example, "zephyr-7b-beta.Q4_0.gguf". If the model path is also specified in the model_kwargs, this parameter is ignored.
n_ctx	Optional[int]	0	The number of tokens in the context. When set to 0, the context is taken from the model.
n_batch	Optional[int]	512	Prompt processing maximum batch size.
model_kwargs	Optional[Dict[str, Any]]	None	Dictionary containing keyword arguments used to initialize the LLM for text generation. These keyword arguments provide fine-grained control over the model loading. In case of duplication, these kwargs override model, n_ctx, and n_batch init parameters. For more information on the available kwargs, see llama.cpp documentation.
generation_kwargs	Optional[Dict[str, Any]]	None	A dictionary containing keyword arguments to customize text generation. For more information on the available kwargs, see llama.cpp documentation.
tools	Optional[Union[List[Tool], Toolset]]	None	A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a list of Tool objects or a Toolset instance.

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.
generation_kwargs	Optional[Dict[str, Any]]	None	A dictionary containing keyword arguments to customize text generation. For more information on the available kwargs, see llama.cpp documentation.
tools	Optional[Union[List[Tool], Toolset]]	None	A list of tools or a Toolset for which the model can prepare calls. If set, it will override the tools parameter set in pipeline configuration.