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# Azure AI Language
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Azure AI Language enables users with task-oriented and optimized pre-trained or custom language models to effectively understand and analyze documents and conversations. This Prompt flow tool is a wrapper for various Azure AI Language APIs. The current list of supported capabilities is as follows:
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| Name | Description |
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|-------------------------------------------|-------------------------------------------------------|
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| Abstractive Summarization | Generate abstractive summaries from documents. |
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| Extractive Summarization | Extract summaries from documents. |
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| Conversation Summarization | Summarize conversations. |
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| Entity Recognition | Recognize and categorize entities in documents. |
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| Key Phrase Extraction | Extract key phrases from documents. |
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| Language Detection | Detect the language of documents. |
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| PII Entity Recognition | Recognize and redact PII entities in documents. |
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| Conversational PII | Recognize and redact PII entities in conversations. |
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| Sentiment Analysis | Analyze the sentiment of documents. |
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| Conversational Language Understanding | Predict intents and entities from user's utterances. |
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| Translator | Translate documents. |
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## Requirements
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PyPI package: [`promptflow-azure-ai-language`](https://pypi.org/project/promptflow-azure-ai-language/).
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- For AzureML users:
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follow this [wiki](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-custom-tool-package-creation-and-usage?view=azureml-api-2#prepare-compute-session), starting from `Prepare compute session`.
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- For local users:
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```
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pip install promptflow-azure-ai-language
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```
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You may also want to install the [Prompt flow for VS Code extension](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow).
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## Prerequisites
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The tool calls APIs from Azure AI Language. To use it, you must create a connection to an [Azure AI Language resource](https://learn.microsoft.com/en-us/azure/ai-services/language-service/). [Create a Language Resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics) first, if necessary.
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- In Prompt flow, add a new `CustomConnection`.
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- Under the `secrets` field, specify the resource's API key: `api_key: <Azure AI Language Resource api key>`
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- Under the `configs` field, specify the resource's endpoint: `endpoint: <Azure AI Language Resource endpoint>`
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To use the `Translator` tool, you must set up an additional connection to an [Azure AI Translator resource](https://azure.microsoft.com/en-us/products/ai-services/ai-translator). [Create a Translator resource](https://learn.microsoft.com/en-us/azure/ai-services/translator/create-translator-resource) first, if necessary.
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- In Prompt flow, add a new `CustomConnection`.
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- Under the `secrets` field, specify the resource's API key: `api_key: <Azure AI Translator Resource api key>`
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- Under the `configs` field, specify the resource's endpoint: `endpoint: <Azure AI Translator Resource endpoint>`
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- If your Translator Resource is regional and non-global, specify its region under `configs` as well: `region: <Azure AI Translator Resource region>`
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## Inputs
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When a tool parameter is of type `Document`, it requires a `dict` object of [this](https://learn.microsoft.com/en-us/rest/api/language/text-analysis-runtime/analyze-text?view=rest-language-2023-04-01&tabs=HTTP#multilanguageinput) specification.
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Example:
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```
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my_document = {
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"id": "1",
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"text": "This is some document text!",
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"language": "en"
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}
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```
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When a tool parameter is of type `Conversation`, it requires a `dict` object.
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Example:
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```
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my_conversation = {
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"id": "meeting_1",
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"language": "en",
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"modality": "text",
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"domain": "generic",
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"conversationItems": [
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{
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"participantId": "person1",
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"role": "generic",
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"id": "1",
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"text": "Hello!"
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},
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{
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"participantId": "person2",
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"role": "generic",
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"id": "2",
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"text": "How are you?"
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}
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]
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}
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```
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---------------------------
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All skills have the following (optional) inputs:
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| Name | Type | Description | Required |
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|--------------------|------------------|-------------|----------|
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| max_retries | int | The maximum number of HTTP request retries. Default value is `5`. | No |
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| max_wait | int | The maximum wait time (in seconds) in-between HTTP requests. Default value is `60`. | No |
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| parse_response | bool | Should the full API JSON output be parsed to extract the single task result. Default value is `False`. | No |
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HTTP request logic utilizes [exponential backoff](https://en.wikipedia.org/wiki/Exponential_backoff).
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See skill specific inputs below:
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---------------------------
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| Abstractive Summarization | Name | Type | Description | Required |
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|-|--------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Language resource. | Yes |
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|| document | `Document` | The input document. | Yes |
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|| query | string | The query used to structure summarization. | Yes |
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|| summary_length | string (enum) | The desired summary length. Enum values are `short`, `medium`, and `long`. | No |
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---------------------------
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| Extractive Summarization | Name | Type | Description | Required |
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|-|--------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Language resource. | Yes |
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|| document | `Document` | The input document. | Yes |
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|| query | string | The query used to structure summarization. | Yes |
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|| sentence_count | int | The desired number of output summary sentences. Default value is `3`. | No |
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|| sort_by | string (enum) | The sorting criteria for extractive summarization results. Enum values are `Offset` to sort results in order of appearance in the text and `Rank` to sort results in order of importance (i.e. rank score) according to model. Default value is `Offset`. | No |
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---------------------------
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| Conversation Summarization | Name | Type | Description | Required |
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|-|--------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Language resource. | Yes |
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|| conversation | `Conversation` | The input conversation. | Yes |
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|| summary_aspect | string (enum) | The desired summary "aspect" to obtain. Enum values are `chapterTitle` to obtain the chapter title of any conversation, `issue` to obtain the summary of issues in transcripts of web chats and service calls between customer-service agents and customers, `narrative` to obtain the generic summary of any conversation, `resolution` to obtain the summary of resolutions in transcripts of web chats and service calls between customer-service agents and customers, `recap` to obtain a general summary, and `follow-up tasks` to obtain a summary of follow-up or action items. | Yes |
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---------------------------
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| Entity Recognition | Name | Type | Description | Required |
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|-|--------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Language resource. | Yes |
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|| document | `Document` | The input document. | Yes |
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---------------------------
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| Key Phrase Extraction | Name | Type | Description | Required |
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|-|--------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Language resource. | Yes |
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|| document | `Document` | The input document. | Yes |
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---------------------------
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| Language Detection | Name | Type | Description | Required |
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|-|--------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Language resource. | Yes |
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|| text | string | The input text. | Yes |
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---------------------------
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| PII Entity Recognition | Name | Type | Description | Required |
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|-|--------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Language resource. | Yes |
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|| document | `Document` | The input document. | Yes |
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|| domain | string (enum) | The PII domain used for PII Entity Recognition. Enum values are `none` for no domain, or `phi` to indicate that entities in the Personal Health domain should be redacted. Default value is `none`. | No |
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|| pii_categories | list[string] | Describes the PII categories to return. | No |
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---------------------------
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| Conversational PII | Name | Type | Description | Required |
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|-|-----------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Language resource. | Yes |
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|| conversation | `Conversation` | The input conversation. | Yes |
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|| pii_categories | list[string] | Describes the PII categories to return for detection. Default value is `['Default']`. | No |
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|| redact_audio_timing | bool | Should audio stream offset and duration for any detected entities be redacted. Default value is `False`. | No |
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|| redaction source | string (enum) | For transcript conversations, this parameter provides information regarding which content type should be used for entity detection. The details of the entities detected - such as the offset, length, and the text itself - will correspond to the text type selected here. Enum values are `lexical`, `itn`, `maskedItn`, and `text`. Default value is `lexical`. | No |
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|| exclude_pii_categories | list[string] | Describes the PII categories to exclude for detection. Default value is `[]`. | No |
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---------------------------
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| Sentiment Analysis | Name | Type | Description | Required |
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|-|--------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Language resource. | Yes |
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|| document | `Document` | The input document. | Yes |
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|| opinion_mining | bool | Should opinion mining be enabled. Default value is `False`. | No |
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---------------------------
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| Conversational Language Understanding | Name | Type | Description | Required |
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|-|--------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Language resource. | Yes |
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|| language | string | The ISO 639-1 code for the language of the input. | Yes |
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|| utterances | string | A single user utterance or a json array of user utterances. | Yes |
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|| project_name | string | The Conversational Language Understanding project to be called. | Yes |
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|| deployment_name | string | The Conversational Language Understanding project deployment to be called. | Yes |
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---------------------------
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| Translator |Name | Type | Description | Required |
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|-|--------------------|------------------|-------------|----------|
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|| connection | CustomConnection | The created connection to an Azure AI Translator resource. | Yes |
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|| text | string | The input text. | Yes |
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|| to | list[string] | The languages to translate the input text to. | Yes |
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|| source_language | string | The language of the input text. | No |
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|| category | string | The category (domain) of the translation. This parameter is used to get translations from a customized system built with Custom Translator. Default value is `general`. | No |
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|| text_type | string (enum) | The type of the text being translated. Possible values are `plain` (default) or `html`. | No |
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## Outputs
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- When the input parameter `parse_response` is set to `False` (default value), the full API JSON response will be returned (as a `dict` object).
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- When the input parameter `parse_response` is set to `True`, the full API JSON response will be parsed to extract the single task result associated with the tool's given skill. Output will depend on the skill (but will still be a `dict` object).
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- **Note:** for Conversational Language Understanding (CLU), output will be a list of responses (either full or parsed), one for each detected user utterance in the input.
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Refer to Azure AI Language's [REST API reference](https://learn.microsoft.com/en-us/rest/api/language/) for details on API response format, specific task result formats, etc.
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## Sample Flows
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Find example flows using the `promptflow-azure-ai-language` package [here](https://github.com/microsoft/promptflow/tree/main/examples/flows/integrations/azure-ai-language).
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## Contact
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Please reach out to Azure AI Language (<taincidents@microsoft.com>) with any issues.
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# Custom Tools
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This section contains documentation for custom tools created by the community to extend Prompt flow's capabilities for specific use cases. These tools are developed following the guide on [Creating and Using Tool Packages](../../how-to-guides/develop-a-tool/create-and-use-tool-package.md). They are not officially maintained or endorsed by the Prompt flow team. For questions or issues when using a tool, please use the support contact link in the table below.
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## Tool Package Index
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The table below provides an index of custom tool packages. The columns contain:
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- **Package Name:** The name of the tool package. Links to the package documentation.
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- **Description:** A short summary of what the tool package does.
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- **Owner:** The creator/maintainer of the tool package.
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- **Support Contact:** Link to contact for support and reporting new issues.
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| Package Name | Description | Owner | Support Contact |
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|-|-|-|-|
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| promptflow-azure-ai-language | Collection of Azure AI Language Prompt flow tools. | Sean Murray | taincidents@microsoft.com |
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|llmlingua-promptflow|Speed up large language model's inference and enhance large language model's perceive of key information, compress the prompt with minimal performance loss.| LLMLingua Team|llmlingua@microsoft.com|
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```{toctree}
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:maxdepth: 1
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:hidden:
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azure-ai-language-tool
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llmlingua-prompt-compression-tool
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```
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# LLMLingua Prompt Compression
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## Introduction
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LLMLingua Prompt Compression tool enables you to speed up large language model's inference and enhance large language model's perceive of key information, compress the prompt with minimal performance loss.
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## Requirements
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PyPI package: [`llmlingua-promptflow`](https://pypi.org/project/llmlingua-promptflow/).
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- For Azure users:
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follow [the wiki for AzureML](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-custom-tool-package-creation-and-usage?view=azureml-api-2#prepare-compute-session) or [the wiki for AI Studio](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/prompt-flow-tools/prompt-flow-tools-overview#custom-tools) to prepare the compute session.
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- For local users:
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```
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pip install llmlingua-promptflow
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```
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You may also want to install the [Prompt flow for VS Code extension](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow).
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## Prerequisite
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Create a MaaS deployment for large language model in Azure model catalog. Take the Llama model as an example, you can learn how to deploy and consume Meta Llama models with model as a service by [the guidance for Azure AI Studio](https://learn.microsoft.com/azure/ai-studio/how-to/deploy-models-llama?tabs=llama-three).
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## Inputs
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The tool accepts the following inputs:
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| Name | Type | Description | Required |
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| ---- | ---- | ----------- | -------- |
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| prompt | string | The prompt that needs to be compressed. | Yes |
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| myconn | CustomConnection | The created connection to a MaaS resource for calculating log probability. | Yes |
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| rate | float | The maximum compression rate target to be achieved. Default value is 0.5. | No |
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## Outputs
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| Return Type | Description |
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|-------------|----------------------------------------------------------------------|
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| string | The resulting compressed prompt. |
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## Sample Flows
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Find example flows using the `llmlingua-promptflow` package [here](https://github.com/microsoft/promptflow/tree/main/examples/flows/integrations/llmlingua-prompt-compression).
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## Contact
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Please reach out to LLMLingua Team (<llmlingua@microsoft.com>) with any issues.
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