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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 |