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from langchain_community.vectorstores.faiss import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import text, PyPDFLoader, WebBaseLoader
from langchain_community.document_transformers import Html2TextTransformer
from langchain_community.embeddings import HuggingFaceEmbeddings
class KnowledgeBase:
def __init__(self) -> None:
self.embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-mpnet-base-v2"
)
self.text_splitter = RecursiveCharacterTextSplitter(
chunk_size=100, chunk_overlap=50
)
self.retriever = None
def load_txt(self, path):
loader = text.TextLoader(file_path=path)
documents = loader.load()
chunked_docs = self.text_splitter.split_documents(documents=documents)
print(chunked_docs.__len__())
db = FAISS.from_documents(embedding=self.embeddings, documents=chunked_docs)
self.retriever = db.as_retriever()
def load_pdf(self, path):
loader = PyPDFLoader(file_path=path)
documents = loader.load()
chunked_docs = self.text_splitter.split_documents(documents=documents)
db = FAISS.from_documents(embedding=self.embeddings, documents=chunked_docs)
self.retriever = db.as_retriever()
def load_url(self, path):
loader = WebBaseLoader(web_path=path)
docs = loader.load()
html2text = Html2TextTransformer()
docs_transformed = html2text.transform_documents(docs)
chunked_docs = self.text_splitter.split_documents(docs_transformed)
db = FAISS.from_documents(embedding=self.embeddings, documents=chunked_docs)
self.retriever = db.as_retriever()
def invoke(self, query):
return self.retriever.invoke(query)
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