Delete agent.py

agent.py

@@ -1,47 +0,0 @@
-import os
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-
-from langchain.chains import ConversationalRetrievalChain
-from langchain.llms import OpenAI
-
-
-class Agent:
-    def __init__(self, openai_api_key: str | None = None) -> None:
-        # if openai_api_key is None, then it will look the enviroment variable OPENAI_API_KEY
-        self.embeddings = OpenAIEmbeddings(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.chat_history = None
-        self.chain = None
-        self.db = None
-
-    def ask(self, question: str) -> str:
-        if self.chain is None:
-            response = "Please, add a document."
-        else:
-            response = self.chain({"question": question, "chat_history": self.chat_history})
-            response = response["answer"].strip()
-            self.chat_history.append((question, response))
-        return response
-
-    def ingest(self, file_path: os.PathLike) -> None:
-        loader = PyPDFLoader(file_path)
-        documents = loader.load()
-        splitted_documents = self.text_splitter.split_documents(documents)
-
-        if self.db is None:
-            self.db = FAISS.from_documents(splitted_documents, self.embeddings)
-            self.chain = ConversationalRetrievalChain.from_llm(self.llm, self.db.as_retriever())
-            self.chat_history = []
-        else:
-            self.db.add_documents(splitted_documents)
-
-    def forget(self) -> None:
-        self.db = None
-        self.chain = None
-        self.chat_history = None