feat(indexers): add framework for default hooks

[lukeroantreeONS]

📌 Add framework for flexible, common hooks to be provided as part of the package

✨ Summary

Introduces a hooks submodule within indexers to provide robust, flexible hooks to the user to cover common pre- and post-processing tasks, and provide a base class should an advanced user want to implement custom ones.

Note: this does not remove the ability of a user to define a hook function themselves, without relying on the base class, as is currently the recommended approach.

📜 Changes Introduced

• feat(indexers): hook_factory defines base classes and shared utilities
• feat(indexers): default_hooks contains particular flexible pre- or post-processing hooks we offer as part of the package

✅ Checklist

Please confirm you've completed these checks before requesting a review.

• 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. re-build the package with uv build
2. re-install classifai from dist/classifai<version>.whl with any optional dependencies you want to test with
3. create a test.py file as described below:

from classifai.indexers import VectorStore
from classifai.indexers.dataclasses import VectorStoreSearchInput
from classifai.indexers.hooks import CapitalisationStandardisingHook
from classifai.vectorisers import HuggingFaceVectoriser

demo_vectoriser = HuggingFaceVectoriser(model_name="sentence-transformers/all-MiniLM-L6-v2")

default_hook_capitalise = CapitalisationStandardisingHook(method="upper")

demo_vectorstore = VectorStore(
    file_name="./data/fake_soc_dataset.csv",
    data_type="csv",
    vectoriser=demo_vectoriser,
    output_dir="./demo_vdb",
    overwrite=True,
    hooks={"search_preprocess": default_hook_capitalise},
)

query_df = VectorStoreSearchInput({"id": [1, 2], "query": ["apple merchant", "pub landlord"]})

results = demo_vectorstore.search(query_df, n_results=5)

print(results)

4. run uv run test.py; confirm it runs successfully, and the query_text column in the output is all capitalised.
5. repeat, changing the method parameter within the CapitalisationStandardisingHook object initialisation