GoogleAIGeminiChatGenerator

Complete chats using Gemini models through Google AI Studio.

Basic Information

Type: haystack_integrations.components.generators.google_ai.chat.gemini.GoogleAIGeminiChatGenerator
Components it can connect with:
- ChatPromptBuilder: GoogleAIGeminiChatGenerator receives a rendered prompt from ChatPromptBuilder.
- DeepsetAnswerBuilder: GoogleAIGeminiChatGenerator sends the generated replies to DeepsetAnswerBuilder through OutputAdapter (see Usage Examples below).

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 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.

Outputs

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

Overview

Deprecation Notice

GoogleAIGeminiChatGenerator will be deprecated after August 2025. Switch to the new GoogleGenAIChatGenerator instead.

Use GoogleAIGeminiChatGenerator to generate replies in the ChatMessage format using models from the Google Gemini family. For a list of supported models, see Google documentation.

Authorization

You need Google Studio API key to use this component. Connect deepset to your Google AI Studio account on the Integrations page.

Connection Instructions

Click your profile icon in the top right corner and choose Integrations.
Click Connect next to the provider.
Enter your API key and submit it.

Usage Example

Initializing the Component

components:
  GoogleAIGeminiChatGenerator:
    type: google_ai.src.haystack_integrations.components.generators.google_ai.chat.gemini.GoogleAIGeminiChatGenerator
    init_parameters:

Using the Component in a Pipeline

This is an example RAG pipeline with GoogleAIGeminiChatGenerator 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

  GoogleAIGeminiChatGenerator:
    type: haystack_integrations.components.generators.google_ai.chat.gemini.GoogleAIGeminiChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - GOOGLE_API_KEY
        strict: false
      model: gemini-1.5-flash
      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: GoogleAIGeminiChatGenerator.messages
- sender: GoogleAIGeminiChatGenerator.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('GOOGLE_API_KEY')	Google AI Studio API key. To get a key, see Google AI Studio.
model	str	gemini-1.5-flash	Name of the model to use. For available models, see Google documentation.
generation_config	Optional[Union[GenerationConfig, Dict[str, Any]]]	None	The generation configuration to use. This can either be a `GenerationConfig` object or a dictionary of parameters. For available parameters, see the API reference.
safety_settings	Optional[Dict[HarmCategory, HarmBlockThreshold]]	None	The safety settings to use. A dictionary with `HarmCategory` as keys and `HarmBlockThreshold` as values. For more information, see the API reference
tools	Optional[List[Tool]]	None	A list of tools for which the model can prepare calls.
tool_config	Optional[content_types.ToolConfigDict]	None	The tool config to use. See the documentation for ToolConfig.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function 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 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.

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Basic Information​

Inputs​

Outputs​

Overview​

Authorization​

Connection Instructions

Usage Example​

Initializing the Component​

Using the Component in a Pipeline​

Parameters​

Init Parameters​

Run Method Parameters​