CohereChatGenerator

Complete chats using Cohere's models through the Cohere ClientV2 chat endpoint.

Key Features

• Uses Cohere's ClientV2 chat endpoint for chat completions.
• Customizable generation through generation_kwargs, including temperature, citation quality, and more.
• Supports tool calling with a list of tools or a Toolset.
• Streaming support for token-by-token responses.

Configuration

1. Drag the CohereChatGenerator component onto the canvas from the Component Library.
2. Click on the component to open the configuration panel.
3. On the General tab:
   1. Select the Cohere model to use. Make sure Haystack Platform is connected to your Cohere account. For details, see Use Cohere Models.
4. Go to the Advanced tab to configure additional settings such as generation_kwargs, tools, api_base_url, and streaming_callback.

Connections

CohereChatGenerator receives a rendered prompt as a list of ChatMessage objects, typically from ChatPromptBuilder. It outputs a list of ChatMessage objects through its replies output.

Connect its replies output to DeepsetAnswerBuilder through an OutputAdapter to build answers with references. See the usage example below for a complete pipeline.

Source Code

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

Usage Examples

Basic Configuration

  CohereChatGenerator:
    type: haystack_integrations.components.generators.cohere.chat.chat_generator.CohereChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - COHERE_API_KEY
        - CO_API_KEY
        strict: false
      model: command-r-08-2024

Using the Component in a Pipeline

This is a RAG chat pipeline with CohereChatGenerator sending replies to DeepsetAnswerBuilder 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

  CohereChatGenerator:
    type: haystack_integrations.components.generators.cohere.chat.chat_generator.CohereChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - COHERE_API_KEY
        - CO_API_KEY
        strict: false
      model: command-r-08-2024
      streaming_callback:
      api_base_url:
      generation_kwargs:
      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: CohereChatGenerator.messages
- sender: CohereChatGenerator.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]		List of ChatMessage instances representing the input messages.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for chat generation. For details on the parameters supported by the Cohere API, refer to the Cohere 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]		A list of ChatMessage instances 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(['COHERE_API_KEY', 'CO_API_KEY'])	The API key for the Cohere API.
model	str	command-r-08-2024	The name of the model to use. You can use models from the command family.
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]	None	The base URL of the Cohere API.
generation_kwargs	Optional[Dict[str, Any]]	None	Other parameters to use for the model during generation. For a list of parameters, see Cohere Chat endpoint. Some of the parameters are: - 'messages': A list of messages between the user and the model, meant to give the model conversational context for responding to the user's message. - 'system_message': When specified, adds a system message at the beginning of the conversation. - 'citation_quality': Defaults to accurate. Dictates the approach taken to generating citations as part of the RAG flow by allowing the user to specify whether they want accurate results or fast results. - 'temperature': A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations.
tools	Optional[Union[List[Tool], Toolset]]	None	A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.

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]		List of ChatMessage instances representing the input messages.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for chat generation. These parameters will potentially override the parameters passed in the init method. For more details on the parameters supported by the Cohere API, refer to the Cohere 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 during component initialization.

Related Information

• Use Cohere Models
• Pipeline Components