feat: check document store and retriever dimensions before calculating embeddings for all documents

[AnushreeBannadabhavi]

Related Issues

• fixes Check document_store and embedding_model dimensions before calculating embeddings #5188

Proposed Changes:

• Currently, in haystack v1.x, when document_store.update_embeddings() gets called, the embeddings are calculated for all the documents and then saved to the document store. If the embedding dimension set in the document store differs from that of the retriever model, a runtime error is raised.
• However, we don't need to calculate embeddings for all the documents in the document store to raise this error. This is especially an issue when using paid APIs (openai) or when the embedding calculation is time consuming.
• A more efficient alternative is to calculate the embedding of a single document and verify that the embedding dimensions match between the document store and the retriever.

How did you test it?

• Added two unit tests in test_faiss.py
• Modified the basic_faq_pipeline.py to test the change.
  - Used FAISSDocumentStore instead of ElasticsearchDocumentStore. I instantiated the docustore without the embedding_dim parameter. Therefore the default value 768 is set in the docustore making the embedding dimension of the retriever and docustore different.

Checklist

• I have read the contributors guidelines and the code of conduct
• I have updated the related issue with new insights and changes
• I added unit tests and updated the docstrings
• I've used one of the conventional commit types for my PR title: fix:, feat:, build:, chore:, ci:, docs:, style:, refactor:, perf:, test:.
• I documented my code
• I ran pre-commit hooks and fixed any issue

[AnushreeBannadabhavi]

@anakin87 Thank you for reviewing my PR 7323! I had to close it because I accidentally changed the upstream branch from v1.x to main. This PR has the same changes as the previous one.

[anakin87]

Looks good.

Thank you for this contribution!