MistralChatGenerator

Generates text using Mistral's generative models through the Mistral API.

Key Features

Generates text using Mistral AI models via the Mistral Chat Completion API.
Supports streaming responses token by token.
Supports tool calling for agentic use cases.
Accepts any text generation parameters valid for the Mistral Chat Completion API via generation_kwargs.
Configurable timeout, retry behavior, and custom HTTP client.

Configuration

Drag the MistralChatGenerator component onto the canvas from the Component Library.
Click on the component to open the configuration panel.
On the General tab:
1. Create a secret with your Mistral API key. Use MISTRAL_API_KEY as the secret key. For instructions, see Create Secrets.
2. Select the model. For supported models, see Mistral AI docs.
Go to the Advanced tab to configure generation_kwargs, streaming_callback, tools, timeout, max_retries, http_client_kwargs, and api_base_url.

Connections

MistralChatGenerator accepts a list of ChatMessage objects through its messages input. It outputs generated responses as a list of ChatMessage objects through its replies output.

Connect ChatPromptBuilder's prompt output to MistralChatGenerator's messages input. Connect its replies output to OutputAdapter and then to DeepsetAnswerBuilder.

Source Code

To check this component's source code, open chat_generator.py in the Haystack Core Integrations repository.

Usage Examples

Basic Configuration

  MistralChatGenerator:
    type: haystack_integrations.components.generators.mistral.chat.chat_generator.MistralChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - MISTRAL_API_KEY
        strict: false
      model: mistral-small-latest
      api_base_url: https://api.mistral.ai/v1

This is an example RAG pipeline with MistralChatGenerator 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

  MistralChatGenerator:
    type: haystack_integrations.components.generators.mistral.chat.chat_generator.MistralChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - MISTRAL_API_KEY
        strict: false
      model: mistral-small-latest
      streaming_callback:
      api_base_url: https://api.mistral.ai/v1
      generation_kwargs:
      tools:
      timeout:
      max_retries:
      http_client_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: MistralChatGenerator.messages
- sender: MistralChatGenerator.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` 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 Mistral API docs.
`tools`	Optional[Union[List[Tool], Toolset]]	None	A list of tools or a Toolset for which the model can prepare calls. If set, it overrides the `tools` parameter set in pipeline configuration.
`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.
`tools_strict`	Optional[bool]	None	Whether to strictly enforce the tools provided in the `tools` parameter. If set to `True`, the model will only use the tools provided in the `tools` parameter. If set to `False`, the model can use other tools that are not provided in the `tools` parameter.

Outputs

Parameter	Type	Description
`replies`	List[ChatMessage]	A list of `ChatMessage` objects representing the generated responses.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var('MISTRAL_API_KEY')	The Mistral API key.
model	str	mistral-small-latest	The name of the Mistral chat completion model to use.
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.
api_base_url	Optional[str]	https://api.mistral.ai/v1	The Mistral API Base url. For more details, see Mistral docs.
generation_kwargs	Optional[Dict[str, Any]]	None	Other parameters to use for the model. These parameters are all sent directly to the Mistral endpoint. See Mistral 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 or a Toolset for which the model can prepare calls. This parameter can accept either a list of `Tool` objects or a `Toolset` instance.
timeout	Optional[float]	None	The timeout for the Mistral API call. If not set, it defaults to either the `OPENAI_TIMEOUT` environment variable, or 30 seconds.
max_retries	Optional[int]	None	Maximum number of retries to contact OpenAI after an internal error. If not set, it defaults to either the `OPENAI_MAX_RETRIES` environment variable, or set to 5.
http_client_kwargs	Optional[Dict[str, Any]]	None	A dictionary of keyword arguments to configure a custom `httpx.Client`or `httpx.AsyncClient`. For more information, see the HTTPX documentation.

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` 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 Mistral API docs.
tools	Optional[Union[List[Tool], Toolset]]	None	A list of tools or a Toolset for which the model can prepare calls. If set, it overrides the `tools` parameter set in pipeline configuration.
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.
tools_strict	Optional[bool]	None	Whether to strictly enforce the tools provided in the `tools` parameter. If set to `True`, the model will only use the tools provided in the `tools` parameter. If set to `False`, the model can use other tools that are not provided in the `tools` parameter.

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Key Features​

Configuration​

Connections​

Source Code​

Usage Examples​

Basic Configuration​

Parameters​

Inputs​

Outputs​

Init Parameters​

Run Method Parameters​