diff options
| -rw-r--r-- | pdf_reader.py | 25 |
1 files changed, 20 insertions, 5 deletions
diff --git a/pdf_reader.py b/pdf_reader.py index e70a88b..62d79fb 100644 --- a/pdf_reader.py +++ b/pdf_reader.py @@ -3,6 +3,7 @@ import hashlib import io import json import os +from typing import Any import fitz # PyMuPDF import pytesseract @@ -14,9 +15,10 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_chroma import Chroma from langchain_community.embeddings import OllamaEmbeddings from langchain_community.llms import Ollama +from langchain_core.language_models import LLM # Make sure Tesseract is installed and accessible -pytesseract.pytesseract.tesseract_cmd = r'/usr/local/bin/tesseract' # Update this path based on your tesseract installation +pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract' # Update this path based on your tesseract installation def extract_text_from_pdf(pdf_path): @@ -135,13 +137,26 @@ def process_pdf_for_qa(pdf_path, embeddings, base_persist_directory): return vector_store -def create_qa_chain(vector_store, llm): - """Create a QA chain for answering questions.""" +def create_qa_chain(vector_store: Any, llm: LLM) -> RetrievalQA: + """ + Create a QA chain for answering questions using the provided vector store and Large Language Model (LLM). + + Args: + vector_store: The database of vectors representing text embeddings. + llm: The Large Language Model instance. + + Returns: + A RetrievalQA object that can be used to answer questions by querying the LLM with user input. + """ # Use the vector store retriever directly - retriever = vector_store.as_retriever() + vector_store_retriever = vector_store.as_retriever() # Create a RetrievalQA chain using the new approach - qa_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever) + qa_chain = RetrievalQA.from_chain_type( + llm=llm, + chain_type="stuff", + retriever=vector_store_retriever # Use a more specific variable name + ) return qa_chain |