OpenAIChatGenerator

Complete chats using OpenAI's large language models (LLMs).

Basic Information

• Type: haystack_integrations.generators.chat.openai.OpenAIChatGenerator
• Components it can connect with:
  • ChatPromptBuilder: OpenAIChatGenerator receives a rendered prompt from ChatPromptBuilder.
  • DeepsetAnswerBuilder: OpenAIChatGenerator sends the generated replies to DeepsetAnswerBuilder through OutputAdapter.

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	Additional keyword arguments for text generation. These parameters override the parameters in pipeline configuration. For a list of supported parameters, see OpenAI 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. This parameter can accept either a list of Tool objects or a Toolset instance.
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 in pipeline configuration.

Outputs

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

Overview

OpenAIChatGenerator works with the gpt-4 and o-series 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 initialize the component or when you run it. Any parameter that works with openai.ChatCompletion.create will work here too.

For a list of supported OpenAI API parameters, see OpenAI documentation.

Authorization

You need an OpenAI API key to use this component. Connect deepset to your OpenAI account on the Integrations page.

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:
  OpenAIChatGenerator:
    type: components.generators.chat.openai.OpenAIChatGenerator
    init_parameters:

Using the Component in a Pipeline

This is an example RAG pipeline with OpenAIChatGenerator 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

  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
api_key	Secret	Secret.from_env_var('OPENAI_API_KEY')	The OpenAI API key. Set it on the Integrations page.
model	str	gpt-4o-mini	The name of the model to use.
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	An optional base URL.
organization	Optional[str]	None	Your organization ID, defaults to None. See production best practices.
generation_kwargs	Optional[Dict[str, Any]]	None	Other parameters to use for the model. These parameters are sent directly to the OpenAI endpoint. See OpenAI documentation 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. For example, 0.1 means only the tokens comprising the top 10% probability mass are considered. - n: How many completions to generate for each prompt. For example, if the LLM gets 3 prompts and n is 2, it will generate two completions for each of the three prompts, ending up with 6 completions in total. - stop: One or more sequences after which the LLM should stop generating tokens. - presence_penalty: What penalty to apply if a token is already present at all. Bigger values mean the model will be less likely to repeat the same token in the text. - frequency_penalty: What penalty to apply if a token has already been generated in the text. Bigger values mean the model will be less likely to repeat the same token in the text. - logit_bias: Add a logit bias to specific tokens. The keys of the dictionary are tokens, and the values are the bias to add to that token.
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.
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.
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.
timeout	Optional[float]	None	Timeout for OpenAI client calls. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.

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.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for text generation. These parameters override the parameters in pipeline configuration. For a list of supported parameters, see OpenAI 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 overrides the t ools parameter in pipeline configuration. This parameter can accept either a list of Tool objects or a Toolset instance.
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 in pipeline configuration.