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Andrew Desousa
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update scripts and data ingestion documentation
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scripts/.env.sample

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# resource switch
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FLAG_EMBEDDING_MODEL = "AOAI" # "AOAI" or "COHERE"
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FLAG_COHERE = "ENGLISH" # "MULTILINGUAL" or "ENGLISH" options for Cohere embedding models
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FLAG_AOAI = "V3" # "V2" or "V3" options for AOAI embedding models
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FLAG_EMBEDDING_MODEL = "AOAI"
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# update vector dimension based on model chosen
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VECTOR_DIMENSION = 1536 # change it to desired, e.g., 1536 for AOAI ada 002, 1024 for COHERE
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VECTOR_DIMENSION = 1536
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# AOAI resource
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AZURE_OPENAI_API_VERSION = '2023-05-15'
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AZURE_OPENAI_ENDPOINT = ""
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AZURE_OPENAI_API_KEY = ""
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# Cohere multilingual resource
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COHERE_MULTILINGUAL_ENDPOINT = ""
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COHERE_MULTILINGUAL_API_KEY = ""
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# Cohere English resource
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COHERE_ENGLISH_ENDPOINT = ""
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COHERE_ENGLISH_API_KEY = ""
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EMBEDDING_MODEL_ENDPOINT = ""
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scripts/data_utils.py

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endpoint = embedding_model_endpoint if embedding_model_endpoint else os.environ.get("EMBEDDING_MODEL_ENDPOINT")
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FLAG_EMBEDDING_MODEL = os.getenv("FLAG_EMBEDDING_MODEL", "AOAI")
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FLAG_COHERE = os.getenv("FLAG_COHERE", "ENGLISH")
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FLAG_AOAI = os.getenv("FLAG_AOAI", "V3")
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if azure_credential is None and (endpoint is None):
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raise Exception("EMBEDDING_MODEL_ENDPOINT and EMBEDDING_MODEL_KEY are required for embedding")

scripts/push_to_acs.py

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scripts/readme.md

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# Data Preparation
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# Prepare Data Locally
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Follow the instructions in this section to prepare your data locally. This is easiest for small data sets. For much larger sets, see the [instructions to use AML below](#use-aml-to-prepare-data).
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## Setup
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- Install the necessary packages listed in requirements.txt, e.g. `pip install --user -r requirements.txt`
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- Install the necessary packages listed in requirements.txt, e.g. `pip install --user -r requirements-dev.txt`
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## Configure
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- Create a .env file similar to the .env.example file. Fill in the values for the environment variables.

scripts/run_batch_create_index.py

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