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| # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: MIT | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining a | |
| # copy of this software and associated documentation files (the "Software"), | |
| # to deal in the Software without restriction, including without limitation | |
| # the rights to use, copy, modify, merge, publish, distribute, sublicense, | |
| # and/or sell copies of the Software, and to permit persons to whom the | |
| # Software is furnished to do so, subject to the following conditions: | |
| # | |
| # The above copyright notice and this permission notice shall be included in | |
| # all copies or substantial portions of the Software. | |
| # | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL | |
| # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING | |
| # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER | |
| # DEALINGS IN THE SOFTWARE. | |
| import faiss | |
| import os | |
| import shutil | |
| import gc | |
| import torch | |
| from llama_index.vector_stores import FaissVectorStore | |
| from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document | |
| from llama_index import StorageContext, load_index_from_storage | |
| class FaissEmbeddingStorage: | |
| def __init__(self, data_dir, dimension): | |
| self.d = dimension | |
| self.data_dir = data_dir | |
| self.engine = None | |
| self.persist_dir = f"{self.data_dir}_vector_embedding" | |
| def initialize_index(self, force_rewrite=False): | |
| # Check if the persist directory exists and delete it if force_rewrite is true | |
| if force_rewrite and os.path.exists(self.persist_dir): | |
| print("Deleting existing directory for a fresh start.") | |
| self.delete_persist_dir() | |
| if os.path.exists(self.persist_dir) and os.listdir(self.persist_dir): | |
| print("Using the persisted value form " + self.persist_dir) | |
| vector_store = FaissVectorStore.from_persist_dir(self.persist_dir) | |
| storage_context = StorageContext.from_defaults( | |
| vector_store=vector_store, persist_dir=self.persist_dir | |
| ) | |
| self.index = load_index_from_storage(storage_context=storage_context) | |
| else: | |
| print("Generating new values") | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| if os.path.exists(self.data_dir) and os.listdir(self.data_dir): | |
| file_metadata = lambda x: {"filename": x} | |
| documents = SimpleDirectoryReader(self.data_dir, file_metadata=file_metadata, | |
| recursive=True, required_exts= [".pdf", ".doc", ".docx", ".txt", ".xml"]).load_data() | |
| else: | |
| print("No files found in the directory. Initializing an empty index.") | |
| documents = [] | |
| faiss_index = faiss.IndexFlatL2(self.d) | |
| #faiss_index = faiss.IndexFlatIP(self.d) | |
| vector_store = FaissVectorStore(faiss_index=faiss_index) | |
| storage_context = StorageContext.from_defaults(vector_store=vector_store) | |
| index = VectorStoreIndex.from_documents(documents, storage_context=storage_context ,show_progress = True) | |
| index.storage_context.persist(persist_dir=self.persist_dir) | |
| self.index = index | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| def delete_persist_dir(self): | |
| if os.path.exists(self.persist_dir) and os.path.isdir(self.persist_dir): | |
| try: | |
| shutil.rmtree(self.persist_dir) | |
| except Exception as e: | |
| print(f"Error occurred while deleting directory: {str(e)}") | |
| def get_engine(self,is_chat_engine ,streaming , similarity_top_k): | |
| if is_chat_engine == True: | |
| self.engine = self.index.as_chat_engine( | |
| chat_mode="condense_question", | |
| streaming=streaming, | |
| similarity_top_k = similarity_top_k | |
| ) | |
| else: | |
| query_engine = self.index.as_query_engine( | |
| streaming=streaming, | |
| similarity_top_k = similarity_top_k, | |
| ) | |
| self.engine = query_engine | |
| return self.engine | |
| def reset_engine(self, engine): | |
| engine.reset() | |