# import sys
# import os
# from contextlib import contextmanager

# from ..reranker import rerank_docs
# from ...knowledge.retriever import ClimateQARetriever




# def divide_into_parts(target, parts):
#     # Base value for each part
#     base = target // parts
#     # Remainder to distribute
#     remainder = target % parts
#     # List to hold the result
#     result = []
    
#     for i in range(parts):
#         if i < remainder:
#             # These parts get base value + 1
#             result.append(base + 1)
#         else:
#             # The rest get the base value
#             result.append(base)
    
#     return result


# @contextmanager
# def suppress_output():
#     # Open a null device
#     with open(os.devnull, 'w') as devnull:
#         # Store the original stdout and stderr
#         old_stdout = sys.stdout
#         old_stderr = sys.stderr
#         # Redirect stdout and stderr to the null device
#         sys.stdout = devnull
#         sys.stderr = devnull
#         try:
#             yield
#         finally:
#             # Restore stdout and stderr
#             sys.stdout = old_stdout
#             sys.stderr = old_stderr



# def make_retriever_node(vectorstore,reranker,rerank_by_question=True, k_final=15, k_before_reranking=100, k_summary=5):

#     def retrieve_documents(state):
        
#         POSSIBLE_SOURCES = ["IPCC","IPBES","IPOS"] # ,"OpenAlex"]
#         questions = state["questions"]
        
#         # Use sources from the user input or from the LLM detection
#         if "sources_input" not in state or state["sources_input"] is None:
#             sources_input = ["auto"]
#         else:
#             sources_input = state["sources_input"]
#         auto_mode = "auto" in sources_input

#         # There are several options to get the final top k
#         # Option 1 - Get 100 documents by question and rerank by question
#         # Option 2 - Get 100/n documents by question and rerank the total
#         if rerank_by_question:
#             k_by_question = divide_into_parts(k_final,len(questions))
        
#         docs = []
        
#         for i,q in enumerate(questions):
            
#             sources = q["sources"]
#             question = q["question"]
            
#             # If auto mode, we use the sources detected by the LLM
#             if auto_mode:
#                 sources = [x for x in sources if x in POSSIBLE_SOURCES]
                
#             # Otherwise, we use the config
#             else:
#                 sources = sources_input
                
#             # Search the document store using the retriever
#             # Configure high top k for further reranking step
#             retriever = ClimateQARetriever(
#                 vectorstore=vectorstore,
#                 sources = sources,
#                 # reports = ias_reports,
#                 min_size = 200,
#                 k_summary = k_summary,
#                 k_total = k_before_reranking,
#                 threshold = 0.5,
#             )
#             docs_question = retriever.get_relevant_documents(question)
            
#             # Rerank
#             if reranker is not None:
#                 with suppress_output():
#                     docs_question = rerank_docs(reranker,docs_question,question)
#             else:
#                 # Add a default reranking score
#                 for doc in docs_question:
#                     doc.metadata["reranking_score"] = doc.metadata["similarity_score"]
                
#             # If rerank by question we select the top documents for each question
#             if rerank_by_question:
#                 docs_question = docs_question[:k_by_question[i]]
                
#             # Add sources used in the metadata
#             for doc in docs_question:
#                 doc.metadata["sources_used"] = sources
            
#             # Add to the list of docs
#             docs.extend(docs_question)
            
#         # Sorting the list in descending order by rerank_score
#         # Then select the top k
#         docs = sorted(docs, key=lambda x: x.metadata["reranking_score"], reverse=True)
#         docs = docs[:k_final]
        
#         new_state = {"documents":docs}
#         return new_state
    
#     return retrieve_documents