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Multi-Class Classification with OpenAI and Vector Search

Pre-Requisites

  • Azure OpenAI
    • Embedding Model (text-embedding-ada-002)
    • Chat Model (gpt-35-turbo)
  • Azure Cognitive Search

Setup

  1. Clone the repository.
  2. Create a Python virtual environment python3 -m venv env.
  3. Install Python depencies pip install -r requirements.txt.
  4. Rename config.ini.template to config.ini.
  5. Update the variables within config.ini based on your values from the Azure portal.
  6. Rename documents.json.template to documents.json. Update the contents of the JSON file with category and content pairs to pre-populate the vector database.
  7. Create a chat API messages template beneath a folder called messages. Note: See messages.json as an example structure.

Usage

  • populate_vector_index.py - Running this script will drop/create a vector search index in Azure Cognitive Search based on the documents within documents.json.
  • query_vector_index.py --query "YOUR_QUERY" - Running this script will query the populated vector database for the nearest neighbors.
  • classify_text_snippet.py --snippet "YOUR_SNIPPET" - Running this script will use the chat completion API to classify the snippet.

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