DeepsetAzureOpenAIVisionGenerator

Generate text using text and image capabilities of OpenAI's LLMs through Azure services.

Basic Information

• Type: deepset_cloud_custom_nodes.azure_openai_vision.DeepsetAzureOpenAIVisionGenerator
• Components it can connect with:
  • PromptBuilder: Receives the prompt from PromptBuilder.
  • DeepsetPDFDocumentToBase64Image: Receives images from DeepsetPDFDocumentToBase64Image, which extracts them from PDF files.
  • DeepsetAnswerBuilder: Sends the generated replies to DeepsetAnswerBuilder, which uses them to build GeneratedAnswer objects.

Inputs

Parameter	Type	Default	Description
prompt	str		The prompt with instructions for the model.
images	List[Base64Image]		A list of Base64Image's that represent the image content of the message. The base64 encoded images are passed on to OpenAI to be used as images for the text generation.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for text generation. These parameters potentially override the parameters in pipeline configuration. For more details on the parameters supported by the OpenAI API, refer to the OpenAI documentation.

Outputs

Parameter	Type	Default	Description
replies	List[str]		A list of strings containing the generated responses.
meta	List[Dict[str, Any]]		A list of dictionaries containing the metadata for each response.

Overview

DeepsetAzureOpenAIVisionGenerator works with GPT-4 and GPT-3.5 turbo families of models hosted on Azure. These models can understand images, making it possible to describe them, analyze details, and answer questions based on images. For details and limitations, check OpenAI's Vision documentation.

Authentication

To work with Azure components, you will need an Azure OpenAI API key, as well as an Azure OpenAI endpoint. You can learn more about them in Azure documentation.

Once you have the API key, connect deepset to Azure OpenAI:

Add Workspace-Level Integration

1. Click your profile icon and choose Settings.
2. Go to Workspace>Integrations.
3. Find the provider you want to connect and click Connect next to them.
4. Enter the API key and any other required details.
5. Click Connect. You can use this integration in pipelines and indexes in the current workspace.

Add Organization-Level Integration

1. Click your profile icon and choose Settings.
2. Go to Organization>Integrations.
3. Find the provider you want to connect and click Connect next to them.
4. Enter the API key and any other required details.
5. Click Connect. You can use this integration in pipelines and indexes in all workspaces in the current organization.

Usage Example

Initializing the Component

components:
  DeepsetAzureOpenAIVisionGenerator:
    type: generators.azure_openai_vision.DeepsetAzureOpenAIVisionGenerator
    init_parameters:

Using the Component in a Pipeline

Here's an example of a query pipeline with DeepsetAzureOpenAIVisionGenerator. It's preceded by DeepsetFileDownloader (image_downloader), which downloads the documents returned by previous components, such as a Ranker or DocumentJoiner. It then sends the downloaded files to DeepsetPDFDocumentToBase64Image (pdf_to_image), which converts them into Base64Image objects that DeepsetAzureOpenAIVisionGenerator can take in. The Generator also receives the prompt from the PromptBuilder. It then sends the generated replies to DeepsetAnswerBuilder.

And here's the full YAML configuration:

components:
  bm25_retriever:
    type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          use_ssl: true
          verify_certs: false
          hosts:
            - ${OPENSEARCH_HOST}
          http_auth:
            - ${OPENSEARCH_USER}
            - ${OPENSEARCH_PASSWORD}
          embedding_dim: 1024
          similarity: cosine
      top_k: 20
  query_embedder:
    type: haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder
    init_parameters:
      model: BAAI/bge-m3
      tokenizer_kwargs:
        model_max_length: 1024
  embedding_retriever:
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          use_ssl: true
          verify_certs: false
          hosts:
            - ${OPENSEARCH_HOST}
          http_auth:
            - ${OPENSEARCH_USER}
            - ${OPENSEARCH_PASSWORD}
          embedding_dim: 1024
          similarity: cosine
      top_k: 20
  document_joiner:
    type: haystack.components.joiners.document_joiner.DocumentJoiner
    init_parameters:
      join_mode: concatenate
  ranker:
    type: haystack.components.rankers.transformers_similarity.TransformersSimilarityRanker
    init_parameters:
      model: BAAI/bge-reranker-v2-m3
      top_k: 8
      model_kwargs:
        torch_dtype: torch.float16
      tokenizer_kwargs:
        model_max_length: 1024
      meta_fields_to_embed:
        - file_name
  image_downloader:
    type: deepset_cloud_custom_nodes.augmenters.deepset_file_downloader.DeepsetFileDownloader
    init_parameters:
      file_extensions:
        - .pdf
  pdf_to_image:
    type: deepset_cloud_custom_nodes.converters.pdf_to_image.DeepsetPDFDocumentToBase64Image
    init_parameters:
      detail: high
  prompt_builder:
    type: haystack.components.builders.prompt_builder.PromptBuilder
    init_parameters:
      template: |-
        Answer the questions briefly and precisely using the images and text passages provided.
        Only use images and text passages that are related to the question to answer it.
        In your answer, only refer to images and text passages that are relevant in answering the query.
        Only use references in the form [NUMBER OF IMAGE] if you are using information from an image.
        Or [NUMBER OF DOCUMENT] if you are using information from a document.

        These are the documents:
        {% for document in documents %}
        Document[ {{ loop.index }} ]:
        File Name: {{ document.meta['file_name'] }}
        Text only version of image number {{ loop.index }} that is also provided.
        {{ document.content }}
        {% endfor %}
        Question: {{ question }}
        Answer: 
  answer_builder:
    type: deepset_cloud_custom_nodes.augmenters.deepset_answer_builder.DeepsetAnswerBuilder
    init_parameters:
      reference_pattern: acm
  TopKDocuments:
    type: haystack.components.joiners.document_joiner.DocumentJoiner
    init_parameters:
      top_k: 8
  DeepsetAzureOpenAIVisionGenerator:
    type: deepset_cloud_custom_nodes.generators.azure_openai_vision.DeepsetAzureOpenAIVisionGenerator
    init_parameters:
      azure_endpoint: <endpoint>
      api_version: '2023-05-15'
      azure_deployment: gpt-4o
      generation_kwargs:
        max_tokens: 650
        temperature: 0
        seed: 0
connections:
  - 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: image_downloader.documents
    receiver: pdf_to_image.documents
  - sender: prompt_builder.prompt
    receiver: answer_builder.prompt
  - sender: ranker.documents
    receiver: prompt_builder.documents
  - sender: ranker.documents
    receiver: TopKDocuments.documents
  - sender: TopKDocuments.documents
    receiver: image_downloader.documents
  - sender: ranker.documents
    receiver: answer_builder.documents
  - sender: prompt_builder.prompt
    receiver: DeepsetAzureOpenAIVisionGenerator.prompt
  - sender: pdf_to_image.base64_images
    receiver: DeepsetAzureOpenAIVisionGenerator.images
  - sender: DeepsetAzureOpenAIVisionGenerator.replies
    receiver: answer_builder.replies
max_loops_allowed: 100
metadata: {}
inputs:
  query:
    - bm25_retriever.query
    - query_embedder.text
    - ranker.query
    - prompt_builder.question
    - answer_builder.query
  filters:
    - embedding_retriever.filters
    - bm25_retriever.filters
outputs:
  answers: answer_builder.answers
  documents: ranker.documents

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.
azure_deployment	Optional[str]	gpt-4o	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[Callable[[StreamingChunk], None]]	None	A callback function called when a new token is received from the stream. It accepts StreamingChunk as an argument.
system_prompt	Optional[str]	None	The system prompt to use for text generation. If not provided, the Generator omits the system prompt and uses the default system prompt.
timeout	Optional[float]	None	Timeout for AzureOpenAI client. If not set, it is inferred from the OPENAI_TIMEOUT environment variable or set to 30.
max_retries	Optional[int]	None	Maximum retries to establish contact with AzureOpenAI if it returns an internal error. If not set, it is inferred from the OPENAI_MAX_RETRIES environment variable or set to 5.
generation_kwargs	Optional[Dict[str, Any]]	None	Other parameters to use for the model, 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: 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: 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.

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
prompt	str		The prompt with instructions for the model.
images	List[Base64Image]		A list of Base64Image's that represent the image content of the message. The base64 encoded images are passed on to OpenAI to be used as images for the text generation.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for text generation. These parameters potentially override the parameters in pipeline configuration. For more details on the parameters supported by the OpenAI API, refer to the OpenAI documentation.