101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
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from typing import Dict, List
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from haystack.schema import Document
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from api.embeddingsServiceCaller import EmbeddingServiceCaller
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from helper.openai import (
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openai_doc_reference_prompt_v1,
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openai_doc_citation_prompt_v2,
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MAX_GPT4_TOKENS,
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GPT4_COMPLETION_TOKENS,
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MAX_GPT35_TURBO_TOKENS,
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RERANKING_TOKENS,
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count_prompt_tokens_gpt4,
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count_prompt_tokens_gpt35,
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)
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import json
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import ast
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class ReRanker:
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def __init__(self) -> None:
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"""
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Initializes the ReRanker class with an caller for MODEL SERVICE.
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"""
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self.caller = EmbeddingServiceCaller()
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def rerank_documents_with_gpt35(self, documents: List[Document], query: str):
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"""
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Reranks a list of documents using GPT-3.5 based on a given query.
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Args:
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documents (List[Document]): A list of Document objects to be reranked.
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query (str): The query string used for reranking.
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Returns:
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List[Document]: A list of reranked Document objects.
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"""
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formatted_documents = []
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reranked_documents_token_count = count_prompt_tokens_gpt35(
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openai_doc_citation_prompt_v2
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)
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for doc in documents:
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reranked_documents_token_count += count_prompt_tokens_gpt35(doc.content)
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if (
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reranked_documents_token_count
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< MAX_GPT35_TURBO_TOKENS - RERANKING_TOKENS
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):
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formatted_documents.append({"content": doc.content, "id": doc.id})
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payload = json.dumps(
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{
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"system_prompt": openai_doc_citation_prompt_v2,
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"documents": formatted_documents,
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"query": query,
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}
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)
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sorted_document_ids = self.caller.rerank_documents_gpt(payload=payload)
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print(sorted_document_ids, "sorted_document_ids")
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message_content = sorted_document_ids["choices"][0]["message"]["content"]
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# Check if the message content is a string representation of a list. If not then return empty list.
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# If yes then parse it, and check if the returned ids exists.
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try:
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content_list = ast.literal_eval(message_content)
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if isinstance(content_list, list):
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# Proceed with further processing
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return [doc for doc in documents for id in content_list if id == doc.id]
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else:
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return []
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except (SyntaxError, ValueError):
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return []
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def get_final_references(
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self, reranked_documents: List[Document], retrieved_documents: List[Document]
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) -> List[Document]:
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"""
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Combines reranked and retrieved documents, ensuring no duplicates and maintaining order.
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Args:
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reranked_documents (List[Document]): The documents after reranking.
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retrieved_documents (List[Document]): The original set of retrieved documents.
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Returns:
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List[Document]: A combined list of reranked and retrieved documents.
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"""
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final_references = list(reranked_documents)
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if not reranked_documents:
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return retrieved_documents
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# If The model in the Re-Ranking process did not return all document ids.
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# In that Case, we create a new sorted list. The first indexes are the existing documents
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# from the re-ranking, followed by the missing ones from the retriever.
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elif len(reranked_documents) < len(retrieved_documents):
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reranked_ids = set(doc.id for doc in reranked_documents)
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missing_documents = [
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doc for doc in retrieved_documents if doc.id not in reranked_ids
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]
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final_references.extend(missing_documents)
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return final_references
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elif len(reranked_documents) == len(retrieved_documents):
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return final_references
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else:
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return retrieved_documents
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