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- Maintain the modularity of the architecture to allow for easy updates and modifications based on feedback. By following these steps and using the provided code structure, you can refine your architecture to better align with stakehold…

self.content = content self.metadata = {} def add_metadata(self, field, value): self.metadata[field] = value class DataModel: def __init__(self): self.documents = [] self.metadata_index = {}…

data_model.add_document(document1) document2 = Document(2, "Document 2", "This is the second document") document2.add_metadata("author", "Jane Smith") document2.add_metadata("date", "2022-01-02") data_model.add_document(document2) # Retri…

Create a controlled environment to isolate and test specific scenarios that lead to metadata mismatches to reproduce and debug the issue. ### Example Implementation Here's an enhanced version of your logging and debugging approach: ```py…

def __init__(self, id, metadata, retrieval_time, expected_metadata): self.id = id self.metadata = metadata self.retrieval_time = retrieval_time self.expected_metadata = expected_metadata self.meta…

### 4. **Implement Caching and Validation** Use caching to improve retrieval performance and implement validation to ensure metadata consistency. ### 5. **Testing and Monitoring** Thoroughly test the refactored structure and continue to mo…

# Process documents and retrieve metadata for doc in docs: doc.metadata = get_metadata(doc.id) if not validate_metadata(doc.metadata, doc.expected_metadata): logging.debug(f"Metadata mismatch found in doc {doc.id}: Expected …