feat(vectorisers): Add FastEmbedVectoriser implementation of VectoriserBase and add fast embed dependency group

[Tom-Owen-ONS]

✨ Summary

Add a new FastEmbedVectoriser to ClassifAI as a lightweight local embedding backend that avoids torch and transformers at runtime, alongside the new fastembed optional dependency group, tests, and documentation updates.

I have run a manual check in general_workflow_demo.ipynb with FastEmbedVectoriser and it runs end to end.

I haven't done a performance benchmark, however happy to do so with guidance on how this is done for classifai.

I have not included the model caching or normalisation logic in survey-assist-embed-core to remain consistent with other implementations of VectoriserBase.

📜 Changes Introduced

• Added FastEmbedVectoriser as a new VectoriserBase implementation using fastembed.TextEmbedding with ClassifAI-style error handling.
• Added the fastembed optional dependency group and included it in the all extra.
• Updated vectoriser exports and overview documentation so FastEmbed appears in the public vectoriser surface and generated API docs.
• Updated installation and usage documentation to explain the lighter classifai[fastembed] install path.
• Added unit tests covering single-string input, batch input, load failures, transform failures, and empty input handling.
• Feature implementation (feat:) / bug fix (fix:) / refactoring (chore:) / documentation (docs:) / testing (test:)
• Updates to tests and/or documentation
• Terraform changes (if applicable)

✅ Checklist

• Code passes linting with Ruff
• Security checks pass using Bandit
• API and Unit tests are written and pass using pytest
• Terraform files (if applicable) follow best practices and have been validated (terraform fmt & terraform validate)
• DocStrings follow Google-style and are added as per Pylint recommendations
• Documentation has been updated if needed

🔍 How to Test

1. Sync the repo environment, including optional extras:
   uv sync --all-extras
2. Run the test suite:
   uv run pytest -q
3. Smoke test the new backend directly:
   uv run --extra fastembed python -c "from classifai.vectorisers import FastEmbedVectoriser; vectoriser = FastEmbedVectoriser(model_name='sentence-transformers/all-MiniLM-L6-v2'); print(vectoriser.transform('hello world').shape)"
4. Open DEMO/general_workflow_demo.ipynb and instantiate FastEmbedVectoriser there. This has been manually validated on this branch.
5. Review documentation updates.