mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
Xu Yang
110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
from __future__ import annotations
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from pathlib import Path
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import yaml
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from azure.ai.formrecognizer import DocumentAnalysisClient
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from azure.core.credentials import AzureKeyCredential
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from finco.conf import FincoSettings as Config
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from oai.llm_utils import APIBackend, create_embedding_with_multiprocessing
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from core.log import FinCoLog
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if TYPE_CHECKING:
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from langchain_core.documents import Document
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from langchain.document_loaders import PyPDFDirectoryLoader, PyPDFLoader
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with (Path(__file__).parent / "util_prompt.yaml").open(encoding="utf8") as f:
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UTIL_PROMPT = yaml.safe_load(
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f,
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)
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def load_documents_by_langchain(path: Path) -> list:
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"""Load documents from the specified path.
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Args:
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path (str): The path to the directory or file containing the documents.
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Returns:
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list: A list of loaded documents.
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"""
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loader = PyPDFDirectoryLoader(str(path), silent_errors=True) if path.is_dir() else PyPDFLoader(str(path))
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return loader.load()
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def process_documents_by_langchain(docs: list[Document]) -> dict[str, str]:
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"""Process a list of documents and group them by document name.
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Args:
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docs (list): A list of documents.
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Returns:
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dict: A dictionary where the keys are document names and the values are
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the concatenated content of the documents.
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"""
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content_dict = {}
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for doc in docs:
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doc_name = str(Path(doc.metadata["source"]).resolve())
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doc_content = doc.page_content
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if doc_name not in content_dict:
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content_dict[str(doc_name)] = doc_content
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else:
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content_dict[str(doc_name)] += doc_content
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return content_dict
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def load_and_process_pdfs_by_langchain(path: Path) -> dict[str, str]:
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return process_documents_by_langchain(load_documents_by_langchain(path))
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def load_and_process_one_pdf_by_azure_document_intelligence(
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path: Path,
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key: str,
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endpoint: str,
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) -> str:
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pages = len(PyPDFLoader(str(path)).load())
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document_analysis_client = DocumentAnalysisClient(
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endpoint=endpoint,
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credential=AzureKeyCredential(key),
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)
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with path.open("rb") as file:
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result = document_analysis_client.begin_analyze_document(
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"prebuilt-document",
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file,
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pages=f"1-{pages}",
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).result()
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return result.content
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def load_and_process_pdfs_by_azure_document_intelligence(path: Path) -> dict[str, str]:
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config = Config()
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assert config.azure_document_intelligence_key is not None
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assert config.azure_document_intelligence_endpoint is not None
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content_dict = {}
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ab_path = path.resolve()
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if ab_path.is_file():
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assert ".pdf" in ab_path.suffixes, "The file must be a PDF file."
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proc = load_and_process_one_pdf_by_azure_document_intelligence
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content_dict[str(ab_path)] = proc(
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ab_path,
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config.azure_document_intelligence_key,
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config.azure_document_intelligence_endpoint,
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)
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else:
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for file_path in ab_path.rglob("*"):
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if file_path.is_file() and ".pdf" in file_path.suffixes:
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content_dict[str(file_path)] = load_and_process_one_pdf_by_azure_document_intelligence(
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file_path,
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config.azure_document_intelligence_key,
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config.azure_document_intelligence_endpoint,
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)
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return content_dict
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