Create pdfquery.py

pdfquery.py

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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
+        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        # self.llm = OpenAI(temperature=0, openai_api_key=openai_api_key)
+        self.llm = ChatOpenAI(temperature=0, 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)
+            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(OpenAI(temperature=0), chain_type="stuff")
+        self.chain = load_qa_chain(ChatOpenAI(temperature=0), chain_type="stuff")
+
+    def forget(self) -> None:
+        self.db = None
+        self.chain = None