We need to add unit tests to ensure this code is working end to end between code check ins. The unit tests should be run against every PR.
Guidelines for copilot
They tests should utilize the following assets:
- 2 small prebuilt Cosmos DB collections under 1 account with full text and vector index. We will store its details and credentials in github secrets.
- A seperate Azure OpenAI LLM and inference endpoint with moderate rate limit. The details and credentials will be in github secrets.
- Th CI test should securely generate config.yaml from config.yaml.example by inserting GH secrets (if this is advisable, if not advise on best practice)
- Use sample questions stored in a test_file in the repo
The tests should check that:
- vector search and full text search to both cosmos DB accounts shoudl work and give non-null results
- that the LLM and embedding endpoints are functional.
- python rag_divdet.py with the test questions should generate reasoning traces and final answers
We need to add unit tests to ensure this code is working end to end between code check ins. The unit tests should be run against every PR.
Guidelines for copilot
They tests should utilize the following assets:
The tests should check that: