Update pdfquery.py

pdfquery.py

@@ -7,32 +7,36 @@ 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:
+    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)
+        # 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(OpenAI(temperature=0), chain_type="stuff")
-        self.chain = load_qa_chain(ChatOpenAI(temperature=0), chain_type="stuff")
+            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
+        self.chain = None