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from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.retrievers import ParentDocumentRetriever
from langchain.storage import InMemoryStore
from langchain_community.vectorstores import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyMuPDFLoader
import os
# Function to create embeddings
# def create_embeddings(text_chunks):
# embeddings = embeddings_model.encode(text_chunks, show_progress_bar=True)
# return embeddings
curr_dir = os.getcwd()
db_path = 'chroma_db'
def process_pdf_document(file_path_list):
'''
Process a PDF document and return the documents and text splitters
Args:
file_path (str): The path to the PDF document
parent_chunk_size (int): The size of the parent chunks
child_chunk_size (int): The size of the child chunks
Returns:
documents (list): The list of documents
parent_splitter (RecursiveCharacterTextSplitter): The text splitter for the parent documents
child_splitter (RecursiveCharacterTextSplitter): The text splitter for the child documents
'''
# # Load the PDF document
# loader = PyMuPDFLoader(file_path)
# documents = loader.load()
loaders = [PyMuPDFLoader(file_path) for file_path in file_path_list]
documents = []
for loader in loaders:
documents.extend(loader.load())
return documents
# Function to create the vectorstore
def create_vectorstore(embeddings_model="all-MiniLM-L6-v2"):
'''
Create the vectorstore and store for the documents
Args:
embeddings_model (HuggingFaceEmbeddings): The embeddings model
documents (list): The list of documents
Returns:
vectorstore (Chroma): The vectorstore
store (InMemoryStore): The store
'''
# Initialize the embedding model
embeddings_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
# # This text splitter is used to create the parent documents
# parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
# # This text splitter is used to create the child documents
# # It should create documents smaller than the parent
# child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# The vectorstore to use to index the child chunks
# vectorstore = Chroma(
# collection_name="split_parents", embedding_function=embeddings_model
# )
vectordb = Chroma(persist_directory=db_path,
embedding_function=embeddings_model)
# The storage layer for the parent documents
store = InMemoryStore()
return vectordb, store
def rag_retriever(vectorstore, store, documents, parent_splitter, child_splitter):
'''
Create the retriever for the RAG model
Args:
vectorstore (Chroma): The vectorstore
store (InMemoryStore): The store
parent_splitter (RecursiveCharacterTextSplitter): The text splitter for the parent documents
child_splitter (RecursiveCharacterTextSplitter): The text splitter for the child documents
Returns:
retriever (ParentDocumentRetriever): The retriever
'''
retriever = ParentDocumentRetriever(
vectorstore=vectorstore,
docstore=store,
child_splitter=child_splitter,
parent_splitter=parent_splitter,
docs=documents
)
# retriever.add_documents(documents)
# retriever = vectorstore.as_retriever()
return retriever |