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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_v2' | |
| class QuestionRetriever: | |
| def load_documents(self,file_name): | |
| current_directory = os.getcwd() | |
| data_directory = os.path.join(current_directory, "data") | |
| file_path = os.path.join(data_directory, file_name) | |
| loader = TextLoader(file_path) | |
| documents = loader.load() | |
| return documents | |
| def store_data_in_vector_db(self,documents): | |
| # global db | |
| text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0,separator="\n") | |
| docs = text_splitter.split_documents(documents) | |
| # create the open-source embedding function | |
| embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") | |
| # print(docs) | |
| # load it into Chroma | |
| db = Chroma.from_documents(docs, embedding_function) | |
| return db | |
| def get_response(self, user_query): | |
| db=self.store_data_in_vector_db(documents) | |
| docs = db.similarity_search(user_query) | |
| most_similar_question = docs[0].page_content.split("\n")[0] # Extract the first question | |
| if user_query==most_similar_question: | |
| most_similar_question=docs[1].page_content.split("\n")[0] | |
| print(most_similar_question) | |
| return most_similar_question | |
| def process_pdf_document(file_path, parent_chunk_size=2000, child_chunk_size=500): | |
| ''' | |
| 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() | |
| # Initialize text splitters for parent and child documents | |
| parent_splitter = RecursiveCharacterTextSplitter(chunk_size=parent_chunk_size) | |
| child_splitter = RecursiveCharacterTextSplitter(chunk_size=child_chunk_size) | |
| return documents, parent_splitter, child_splitter | |
| # 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): | |
| ''' | |
| 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=None, | |
| # parent_splitter=None, | |
| # docs=documents | |
| # ) | |
| # retriever.add_documents(documents) | |
| retriever = vectorstore.as_retriever() | |
| return retriever | |
| # def retrieve_context(query, top_k): | |
| # # Retrieve the top k similar documents | |
| # sub_docs = vectorstore.similarity_search(query, k=top_k, return_documents=True) | |
| # # Get the context of the first document | |
| # context = sub_docs[0].page_content | |
| # return context | |