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| import os | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import Chroma | |
| from langchain.document_loaders import PyPDFium2Loader | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain.llms import OpenAI | |
| from langchain.chat_models import ChatOpenAI | |
| class PDFQuery: | |
| def __init__(self, openai_api_key=None) -> None: | |
| self.embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) | |
| os.environ["OPENAI_API_KEY"] = openai_api_key | |
| # Adjust chunk_size and chunk_overlap for better handling of large documents | |
| self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500) | |
| self.llm = ChatOpenAI(model="gpt-4", temperature=0.5, openai_api_key=openai_api_key) | |
| self.chain = None | |
| self.db = None | |
| def ask(self, question: str) -> str: | |
| if self.chain is None: | |
| response = "Please, add a document." | |
| else: | |
| docs = self.db.get_relevant_documents(question) | |
| # to better suit GPT-4's input format for optimal results. | |
| response = self.chain.run(input_documents=docs, question=question) | |
| return response | |
| def ingest(self, file_path: os.PathLike) -> None: | |
| loader = PyPDFium2Loader(file_path) | |
| documents = loader.load() | |
| splitted_documents = self.text_splitter.split_documents(documents) | |
| self.db = Chroma.from_documents(splitted_documents, self.embeddings).as_retriever() | |
| self.chain = load_qa_chain(self.llm, chain_type="stuff") | |
| def forget(self) -> None: | |
| self.db = None | |
| self.chain = None |