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docs/set_env_for_training_data_and_reference_doc.md

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5. *Copy the SAS URL:* After creating the SAS, click `Copy` to get the URL with token. This will be used as the value for **TRAINING_DATA_SAS_URL** or **REFERENCE_DOC_SAS_URL** when running the sample code.
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<img src="./copy-access-signature.png" width="600" />
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6. *Set Environment Variables in ".env" File:* Depending on the sample that you will run, you will need to set required environment variables in [.env](../notebooks/.env).
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> NOTE: **REFERENCE_DOC_SAS_URL** can be the same as the **TRAINING_DATA_SAS_URL** to re-use the same blob container
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- [analyzer_training](../notebooks/analyzer_training.ipynb): Add the SAS URL as value of **TRAINIGN_DATA_SAS_URL**, and a prefix for **TRAINING_DATA_PATH**. You can choose any folder name you like for **TRAINING_DATA_PATH**. For example, you could use "training_files".
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```env
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TRAINING_DATA_SAS_URL=<Blob container SAS URL>
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TRAINING_DATA_PATH=<Designated folder path under the blob container>
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```
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- [field_extraction_pro_mode](../notebooks/field_extraction_pro_mode.ipynb): Add the SAS URL as value of **REFERENCE_DOC_SAS_URL**, and a prefix for **REFERENCE_DOC_PATH**. You can choose any folder name you like for **REFERENCE_DOC_PATH**. For example, you could use "training_files".
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- [field_extraction_pro_mode](../notebooks/field_extraction_pro_mode.ipynb): Add the SAS URL as value of **REFERENCE_DOC_SAS_URL**, and a prefix for **REFERENCE_DOC_PATH**. You can choose any folder name you like for **REFERENCE_DOC_PATH**. For example, you could use "reference_docs".
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```env
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REFERENCE_DOC_SAS_URL=<Blob container SAS URL>
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REFERENCE_DOC_PATH=<Designated folder path under the blob container>

notebooks/field_extraction_pro_mode.ipynb

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"metadata": {},
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"source": [
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"### Generate knowledge base for the second sample\n",
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"Let's try reference docuemnts with existing OCR results for the second sample"
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"Let's upload [refernce documents](../data/field_extraction_pro_mode/insurance_claims_review/reference_docs/) with existing OCR results for the second sample. These documents contain driver coverage policy that are useful in reviewing insurance claims."
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Analyze the multiple input documents with the second analyzer"
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"### Analyze the multiple input documents with the second analyzer\n",
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"Please note that the [input_docs_2](../data/field_extraction_pro_mode/insurance_claims_review/input_docs/) directory contains two PDF files as input: one is a car accident report, and the other is a repair estimate.\n",
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"\n",
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"The first document includes details such as the car’s license plate number, vehicle model, and other incident-related information.\n",
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"The second document provides a breakdown of the estimated repair costs.\n",
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"\n",
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"Due to the complexity of this multi-document scenario and the processing involved, it may take a few minutes to generate the results."
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"> In the LineItemCorroboration field, we see that each line item is extracted with its corresponding information, claim status, and evidence. Items that are not covered by the policy, such as the Starbucks drink and hotel stay, are not confirmed, while damage repairs that are supported by the supplied documents in the claim and are permitted by the policy are confirmed."
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"> In the LineItemCorroboration field, we see that each line item is extracted with its corresponding information, claim status, and evidence. Items that are not covered by the policy, such as the Starbucks drink and hotel stay, are marked as suspicious, while damage repairs that are supported by the supplied documents in the claim and are permitted by the policy are confirmed."
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]
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{

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