AzureOpenAIChatGenerator

Generate text using OpenAI's models on Azure.

Basic Information

• Type: components.generators.chat.azure.AzureOpenAIChatGenerator
• Components it can connect with:
  • ChatPromptBuilder: AzureOpenAIChatGenerator receives a rendered prompt from ChatPromptBuilder.
  • DeepsetAnswerBuilder: AzureOpenAIChatGenerator 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 that is called when a new token is received from the stream.
generation_kwargs	Optional[Dict[str, Any]]	None	Optional arguments to pass to the Anthropic generation endpoint.
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. If set, it will override the tools parameter set during component initialization.
tools_strict	Optional[Bool]	None	Whether to enable strict schema adherence for tool calls. If set to True, the model follows exactly the schema provided in the parameters field of the tool definition, but this may increase latency. If set, it overrides the tools_strict parameter set during component initialization.

Outputs

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

Overview

AzureOpenAIChatGenerator works with the gpt-4 - type models and supports streaming responses from OpenAI API.

You can customize how the text is generated by passing parameters to the OpenAI API. Use the **generation_kwargs argument when you configure the component in a pipeline. Any parameter that works with openai.ChatCompletion.create will work here too.

For details on OpenAI API parameters, see OpenAI documentation.

Authentication

To use this component, connect deepset with Azure OpenAI first:

Connection Instructions

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

Usage Example

Initializing the Component

components:
  AzureOpenAIChatGenerator:
    type: components.generators.chat.azure.AzureOpenAIChatGenerator
    init_parameters:

Using the Component in a Pipeline

This is a RAG pipeline where AzureOpenAIChatGenerator sends the generated 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

  AzureOpenAIChatGenerator:
    type: haystack.components.generators.chat.azure.AzureOpenAIChatGenerator
    init_parameters:
      azure_endpoint:
      api_version: '2023-05-15'
      azure_deployment: gpt-4o-mini
      api_key:
        type: env_var
        env_vars:
        - AZURE_OPENAI_API_KEY
        strict: false
      azure_ad_token:
        type: env_var
        env_vars:
        - AZURE_OPENAI_AD_TOKEN
        strict: false
      organization:
      streaming_callback:
      timeout:
      max_retries:
      generation_kwargs:
      default_headers:
      tools:
      tools_strict: false
      azure_ad_token_provider:
      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: AzureOpenAIChatGenerator.messages
- sender: AzureOpenAIChatGenerator.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
azure_endpoint	Optional[str]	None	The endpoint of the deployed model, for example "https://example-resource.azure.openai.com/".
api_version	Optional[str]	2023-05-15	The version of the API to use. Defaults to 2023-05-15.
azure_deployment	Optional[str]	gpt-4o-mini	The deployment of the model, usually the model name.
api_key	Optional[Secret]	Secret.from_env_var('AZURE_OPENAI_API_KEY', strict=False)	The API key to use for authentication.
azure_ad_token	Optional[Secret]	Secret.from_env_var('AZURE_OPENAI_AD_TOKEN', strict=False)	Azure Active Directory token.
organization	Optional[str]	None	Your organization ID, defaults to None. For help, see Setting up your organization.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function called when a new token is received from the stream. It accepts StreamingChunk as an argument.
timeout	Optional[float]	None	Timeout for OpenAI client calls. 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.
generation_kwargs	Optional[Dict[str, Any]]	None	Other parameters to use for the model. These parameters are sent directly to the OpenAI endpoint. For details, see OpenAI documentation. Some of the supported parameters: - max_tokens: The maximum number of tokens the output text can have. - temperature: The sampling temperature to use. Higher values mean the model takes more risks. Try 0.9 for more creative applications and 0 (argmax sampling) for ones with a well-defined answer. - top_p: Nucleus sampling is an alternative to sampling with temperature, where the model considers tokens with a top_p probability mass. For example, 0.1 means only the tokens comprising the top 10% probability mass are considered. - n: The number of completions to generate for each prompt. For example, with 3 prompts and n=2, the LLM will generate two completions per prompt, resulting in 6 completions total. - stop: One or more sequences after which the LLM should stop generating tokens. - presence_penalty: The penalty applied if a token is already present. Higher values make the model less likely to repeat the token. - frequency_penalty: Penalty applied if a token has already been generated. Higher values make the model less likely to repeat the token. - logit_bias: Adds a logit bias to specific tokens. The keys of the dictionary are tokens, and the values are the bias to add to that token.
default_headers	Optional[Dict[str, str]]	None	Default headers to use for the AzureOpenAI client.
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.
tools_strict	bool	False	Whether to enable strict schema adherence for tool calls. If set to True, the model will follow exactly the schema provided in the parameters field of the tool definition, but this may increase latency.
azure_ad_token_provider	Optional[Union[AzureADTokenProvider, AsyncAzureADTokenProvider]]	None	A function that returns an Azure Active Directory token, will be invoked on every request.
http_client_kwargs	Optional[Dict[str, Any]]	None	A dictionary of keyword arguments to configure a custom httpx.Clientor 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