VertexAIGeminiChatGenerator

Complete chats using Google Gemini models through Vertex AI.

Key Features

• Generates chat responses using Google Gemini models through Vertex AI.
• Authenticates using Google Cloud Application Default Credentials.
• Returns responses in the ChatMessage format for chat-based pipelines.
• Supports tool and function calling with configurable tool behavior.
• Configurable safety settings and generation parameters.
• Supports streaming for real-time token delivery.

Configuration

Authentication

This component authenticates using Google Cloud Application Default Credentials (ADCs). You must have a GCP account with a project authorized to use Vertex AI endpoints. For more information, see the Google documentation.

1. Drag the VertexAIGeminiChatGenerator component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Enter the model name (for example, gemini-1.5-flash). For available models, see Google documentation.
4. Go to the Advanced tab to configure the project ID, location, generation config, safety settings, tools, tool config, and streaming callback.

Connections

VertexAIGeminiChatGenerator accepts a list of ChatMessage instances as input (messages). It outputs a list of ChatMessage replies (replies).

Typically, you connect ChatPromptBuilder to the messages input to build the prompt. Connect the replies output to OutputAdapter and then to DeepsetAnswerBuilder to format the final answer.

Usage Example

Using the Component in a Pipeline

This is an example RAG pipeline with VertexAIGeminiChatGenerator 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

  VertexAIGeminiChatGenerator:
    type: haystack_integrations.components.generators.google_vertex.chat.gemini.VertexAIGeminiChatGenerator
    init_parameters:
      model: gemini-1.5-flash
      project_id:
      location:
      generation_config:
      safety_settings:
      tools:
      tool_config:
      streaming_callback:

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: VertexAIGeminiChatGenerator.messages
- sender: VertexAIGeminiChatGenerator.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

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.
tools	Optional[List[Tool]]	None	A list of tools for which the model can prepare calls. If set, it will override the tools parameter set during component initialization.

Outputs

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

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str	gemini-1.5-flash	Name of the model to use. For available models, see https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models.
project_id	Optional[str]	None	ID of the GCP project to use. By default, it is set during Google Cloud authentication.
location	Optional[str]	None	The default location to use when making API calls, if not set uses us-central-1. Defaults to None.
generation_config	Optional[Union[GenerationConfig, Dict[str, Any]]]	None	Configuration for the generation process. See the [GenerationConfig documentation](https://cloud.google.com/python/docs/reference/aiplatform/latest/vertexai.generative_models.GenerationConfig for a list of supported arguments.
safety_settings	Optional[Dict[HarmCategory, HarmBlockThreshold]]	None	Safety settings to use when generating content. See the documentation for HarmBlockThreshold and HarmCategory for more details.
tools	Optional[List[Tool]]	None	A list of tools for which the model can prepare calls.
tool_config	Optional[ToolConfig]	None	The tool config to use. See the documentation for [ToolConfig] (https://cloud.google.com/vertex-ai/generative-ai/docs/reference/python/latest/vertexai.generative_models.ToolConfig)
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.

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.
tools	Optional[List[Tool]]	None	A list of tools for which the model can prepare calls. If set, it will override the tools parameter set during component initialization.