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Copy file name to clipboardExpand all lines: articles/ai-services/content-safety/quickstart-groundedness.md
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@@ -81,7 +81,7 @@ Create a new Python file named _quickstart.py_. Open the new file in your prefer
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"groundingSources": [
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"I'm 21 years old and I need to make a decision about the next two years of my life. Within a week. I currently work for a bank that requires strict sales goals to meet. IF they aren't met three times (three months) you're canned. They pay me 10/hour and it's not unheard of to get a raise in 6ish months. The issue is, **I'm not a salesperson**. That's not my personality. I'm amazing at customer service, I have the most positive customer service \"reports\" done about me in the short time I've worked here. A coworker asked \"do you ask for people to fill these out? you have a ton\". That being said, I have a job opportunity at Chase Bank as a part time teller. What makes this decision so hard is that at my current job, I get 40 hours and Chase could only offer me 20 hours/week. Drive time to my current job is also 21 miles **one way** while Chase is literally 1.8 miles from my house, allowing me to go home for lunch. I do have an apartment and an awesome roommate that I know wont be late on his portion of rent, so paying bills with 20hours a week isn't the issue. It's the spending money and being broke all the time.\n\nI previously worked at Wal-Mart and took home just about 400 dollars every other week. So I know i can survive on this income. I just don't know whether I should go for Chase as I could definitely see myself having a career there. I'm a math major likely going to become an actuary, so Chase could provide excellent opportunities for me **eventually**."
Copy file name to clipboardExpand all lines: articles/ai-services/document-intelligence/concept-custom-generative.md
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***Field Description**. Provide more contextual information in description to help clarify the field that needs to be extracted. Examples include location in the document, potential field labels it can be associated with, and ways to differentiate with other terms that could be ambiguous.
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***Variation**. Custom generative models can generalize across different document templates of the same document type. As a best practice, create a single model for all variations of a document type. Ideally, include a visual template for each type, especially for ones that
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***Variation**. Custom generative models can generalize across different document templates of the same document type. As a best practice, create a single model for all variations of a document type. Ideally, include a visual template for each type, especially for ones that involve distinct formatting or structural elements, to improve the model's accuracy and consistency in generating or processing documents.
|`Loan.RefinanceProgramType`|`selectionGroup`|Loan refinance program type|:unselected: Full Documentation:selected: Interest Rate Reduction<br>:unselected: Streamlined without Appraisal<br>:unselected: Other|
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|`Loan.OtherRefinanceProgram`|`string`|Other loan refinance program type|Cash-out refinance|
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|`Property`|`object`|||
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|`Property.Address`|`address`|Property address|1634 W Glenoaks Blvd<br>Glendale CA 91201 Los Angeles|
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|`Property.NumberOfUnits`|`integer`|Number of units|1|
|`Property.OccupancyStatus`|`selectionGroup`|Property occupancy status|:selected: Primary Residence<br>:unselected: Second Home<br>:unselected: Investment Property|
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|`Property.IsFhaSecondaryResidence`|`boolean`|Checkbox state of '`FHA` Secondary Residence'|:unselected:|
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|`Property.MixedUseProperty`|`selectionGroup`|Is the property a mixed-use property?|:selected: NO:unselected: YES|
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|`Property.ManufacturedHome`|`selectionGroup`|Is the property a manufactured home?|:selected: NO:unselected: YES|
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The 1003 URLA key-value pairs and line items extracted are in the `documentResults` section of the JSON output.
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## Field extraction 1004 Uniform Residential Appraisal Report (URAR)
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The following are the fields extracted from a 1004 URAR form in the JSON output response.
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* Try processing your own forms and documents with the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio).
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* Complete a [Document Intelligence quickstart](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.
Copy file name to clipboardExpand all lines: articles/ai-services/openai/concepts/assistants.md
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titleSuffix: Azure OpenAI Service
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description: Learn about the concepts behind the Azure OpenAI Assistants API.
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ms.topic: conceptual
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ms.date: 03/04/2024
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ms.date: 08/21/2024
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ms.service: azure-ai-openai
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manager: nitinme
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author: mrbullwinkle
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ms.author: mbullwin
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author: aahill
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ms.author: aahi
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recommendations: false
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---
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## Context window management
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Assistants automatically truncates text to ensure it stays within the model's maximum context length. You can customize this behavior by specifying the maximum tokens you'd like a run to utilize and/or the maximum number of recent messages you'd like to include in a run.
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Assistants automatically truncate text to ensure it stays within the model's maximum context length. You can customize this behavior by specifying the maximum tokens you'd like a run to utilize and/or the maximum number of recent messages you'd like to include in a run.
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### Max completion and max prompt tokens
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To control the token usage in a single Run, set `max_prompt_tokens` and `max_completion_tokens` when you create the Run. These limits apply to the total number of tokens used in all completions throughout the Run's lifecycle.
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For example, initiating a Run with `max_prompt_tokens` set to 500 and `max_completion_tokens` set to 1000 means the first completion will truncate the thread to 500 tokens and cap the output at 1000 tokens. If only 200 prompt tokens and 300 completion tokens are used in the first completion, the second completion will have available limits of 300 prompt tokens and 700 completion tokens.
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For example, initiating a Run with `max_prompt_tokens` set to 500 and `max_completion_tokens` set to 1000 means the first completion will truncate the thread to 500 tokens and cap the output at 1,000 tokens. If only 200 prompt tokens and 300 completion tokens are used in the first completion, the second completion will have available limits of 300 prompt tokens and 700 completion tokens.
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If a completion reaches the `max_completion_tokens` limit, the Run will terminate with a status of incomplete, and details will be provided in the `incomplete_details` field of the Run object.
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When using the File Search tool, we recommend setting the `max_prompt_tokens` to no less than 20,000. For longer conversations or multiple interactions with File Search, consider increasing this limit to 50,000, or ideally, removing the `max_prompt_tokens` limits altogether to get the highest quality results.
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## Truncation strategy
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You may also specify a truncation strategy to control how your thread should be rendered into the model's context window. Using a truncation strategy of type `auto` will use OpenAI's default truncation strategy. Using a truncation strategy of type `last_messages` will allow you to specify the number of the most recent messages to include in the context window.
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You can also specify a truncation strategy to control how your thread should be rendered into the model's context window. Using a truncation strategy of type `auto` will use OpenAI's default truncation strategy. Using a truncation strategy of type `last_messages` will allow you to specify the number of the most recent messages to include in the context window.
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## See also
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* Learn more about Assistants and [File Search](../how-to/file-search.md)
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