langchain

[samsam0615]

is it possible to pass questions from a pdf into an LLM using langchain or other ways?

[dosubot[bot]]

Answer generated by a 🤖

Answer

Yes, it is possible to pass questions from a PDF into a language model using LangChain. LangChain provides several built-in parsers for extracting text from PDFs, including PDFMinerParser, PDFPlumberParser, PyMuPDFParser, PyPDFium2Parser, and PyPDFParser. Once you have the text, you can pass it into the context and question variables in the QUESTION_PROMPT template.

Here's an example of how you might do this using PyPDFParser:

from langchain.document_loaders.parsers.pdf import PyPDFParser
from langchain.chains.qa_with_sources.map_reduce_prompt import QUESTION_PROMPT

# Open the PDF and extract the text
parser = PyPDFParser('your_pdf_file.pdf')
text = parser.extract_text()

# Create the prompt with the extracted text
QUESTION_PROMPT = QUESTION_PROMPT.create(context=text, question="Your question here")

This will create a prompt with the text from the PDF, which you can then pass into a language model.

The specific functionalities of the different PDF parsers (PDFMinerParser, PDFPlumberParser, PyMuPDFParser, PyPDFium2Parser, and PyPDFParser) provided by LangChain are as follows:

1. PDFMinerParser: This parser does not follow the default behavior of splitting by page. It treats the entire PDF document as a single unit for parsing. It uses the extract_text function from the pdfminer.high_level module to extract text from the PDF document.

2. PyMuPDFParser: This parser follows the default behavior of splitting by page. It treats each page of the PDF document as a separate unit for parsing. It uses the fitz.Page.get_text() function from the fitz module to extract text from each page of the PDF document.

3. PyPDFium2Parser: This parser follows the default behavior of splitting by page. It treats each page of the PDF document as a separate unit for parsing. It uses the pypdfium2.PdfDocument class to read the PDF document and the get_text_range method of the text_page object to extract text from each page.

4. PyPDFParser: This parser follows the default behavior of splitting by page. It uses the pypdf.PdfReader class to read the PDF document and the extract_text method of the page object to extract text from each page.

5. PDFPlumberParser: This parser follows the default behavior of splitting by page. It treats each page of the PDF document as a separate unit for parsing. It uses the pdfplumber.Page.extract_text() function to extract text from each page of the PDF document.

Please note that all these parsers are used to extract text from PDF documents, but the exact details of their functionalities (like handling of images, tables, etc.) are not provided in the context shared.

You can find more details about these parsers in the LangChain repository.

Sources

• libs/langchain/langchain/evaluation/qa/generate_prompt.py
• libs/langchain/langchain/chains/retrieval_qa/prompt.py
• libs/langchain/langchain/chains/qa_with_sources/map_reduce_prompt.py
• libs/langchain/langchain/document_loaders/parsers/init.py
• libs/langchain/tests/unit_tests/document_loaders/parsers/test_pdf_parsers.py
• libs/langchain/tests/integration_tests/document_loaders/parsers/test_pdf_parsers.py
• libs/langchain/langchain/document_loaders/parsers/pdf.py

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[lucasjinreal]

No such extrac_text method any more