doc3

from typing import List class IngestionTask: def __init__(self, task_name: str, documents: List[str]): self.task_name = task_name self.documents = documents def process(self): # Process the documents for th…

self.documents = documents def process(self): # Process the documents for this task print(f"Processing {self.task_name} with {len(self.documents)} documents") class ModularIngestionSystem: def __init__(self…

# Indexing code pass except Exception as e: logging.error(f"Error indexing document: {e}", exc_info=True) # Example usage documents = ["doc1", "doc2", "doc3"] catch_bm25_indexing_failures(documents) ``` …

index.append(index_data) except IndexError as e: print(f"Error processing document '{document}': {e}") continue finally: # Monitor memory usage process = psutil…

with torch.no_grad(): doc_outputs = model(**doc_inputs) query_outputs = model(**query_inputs) doc_embeddings = doc_outputs.last_hidden_state.mean(dim=1) query_embedding = query_outputs.last_hidden_state.mean(dim…

return mean_precision, mean_recall, mean_f1, mean_ap def simulate_bm25_retrieval(query, documents): # Placeholder for actual BM25 retrieval logic # Return a subset of documents as retrieved documents return documents[:3] #…

[Turn 8827] Assistant: Certainly! Let's review your indexing code and suggest improvements to further optimize throughput. We'll also ensure that your LangChain implementation is properly integrated with your indexing pipeline. ### Optimiz…