import logging import shutil import faiss import llama_index.core.settings as llama_settings import tqdm from django.conf import settings from llama_index.core import Document as LlamaDocument from llama_index.core import StorageContext from llama_index.core import VectorStoreIndex from llama_index.core import load_index_from_storage from llama_index.core.node_parser import SimpleNodeParser from llama_index.core.retrievers import VectorIndexRetriever from llama_index.core.schema import BaseNode from llama_index.core.storage.docstore import SimpleDocumentStore from llama_index.core.storage.index_store import SimpleIndexStore from llama_index.vector_stores.faiss import FaissVectorStore from documents.models import Document from paperless_ai.embedding import build_llm_index_text from paperless_ai.embedding import get_embedding_dim from paperless_ai.embedding import get_embedding_model logger = logging.getLogger("paperless_ai.indexing") def get_or_create_storage_context(*, rebuild=False): """ Loads or creates the StorageContext (vector store, docstore, index store). If rebuild=True, deletes and recreates everything. """ if rebuild: shutil.rmtree(settings.LLM_INDEX_DIR, ignore_errors=True) settings.LLM_INDEX_DIR.mkdir(parents=True, exist_ok=True) if rebuild or not settings.LLM_INDEX_DIR.exists(): embedding_dim = get_embedding_dim() faiss_index = faiss.IndexFlatL2(embedding_dim) vector_store = FaissVectorStore(faiss_index=faiss_index) docstore = SimpleDocumentStore() index_store = SimpleIndexStore() else: vector_store = FaissVectorStore.from_persist_dir(settings.LLM_INDEX_DIR) docstore = SimpleDocumentStore.from_persist_dir(settings.LLM_INDEX_DIR) index_store = SimpleIndexStore.from_persist_dir(settings.LLM_INDEX_DIR) return StorageContext.from_defaults( docstore=docstore, index_store=index_store, vector_store=vector_store, persist_dir=settings.LLM_INDEX_DIR, ) def build_document_node(document: Document) -> list[BaseNode]: """ Given a Document, returns parsed Nodes ready for indexing. """ text = build_llm_index_text(document) metadata = { "document_id": str(document.id), "title": document.title, "tags": [t.name for t in document.tags.all()], "correspondent": document.correspondent.name if document.correspondent else None, "document_type": document.document_type.name if document.document_type else None, "created": document.created.isoformat() if document.created else None, "added": document.added.isoformat() if document.added else None, "modified": document.modified.isoformat(), } doc = LlamaDocument(text=text, metadata=metadata) parser = SimpleNodeParser() return parser.get_nodes_from_documents([doc]) def load_or_build_index(nodes=None): """ Load an existing VectorStoreIndex if present, or build a new one using provided nodes if storage is empty. """ embed_model = get_embedding_model() llama_settings.Settings.embed_model = embed_model storage_context = get_or_create_storage_context() try: return load_index_from_storage(storage_context=storage_context) except ValueError as e: logger.warning("Failed to load index from storage: %s", e) if not nodes: logger.info("No nodes provided for index creation.") raise return VectorStoreIndex( nodes=nodes, storage_context=storage_context, embed_model=embed_model, ) def remove_document_docstore_nodes(document: Document, index: VectorStoreIndex): """ Removes existing documents from docstore for a given document from the index. This is necessary because FAISS IndexFlatL2 is append-only. """ all_node_ids = list(index.docstore.docs.keys()) existing_nodes = [ node.node_id for node in index.docstore.get_nodes(all_node_ids) if node.metadata.get("document_id") == str(document.id) ] for node_id in existing_nodes: # Delete from docstore, FAISS IndexFlatL2 are append-only index.docstore.delete_document(node_id) def update_llm_index(*, progress_bar_disable=False, rebuild=False): """ Rebuild or update the LLM index. """ nodes = [] documents = Document.objects.all() if not documents.exists(): logger.warning("No documents found to index.") return if rebuild: embed_model = get_embedding_model() llama_settings.Settings.embed_model = embed_model storage_context = get_or_create_storage_context(rebuild=rebuild) # Rebuild index from scratch for document in tqdm.tqdm(documents, disable=progress_bar_disable): document_nodes = build_document_node(document) nodes.extend(document_nodes) index = VectorStoreIndex( nodes=nodes, storage_context=storage_context, embed_model=embed_model, show_progress=not progress_bar_disable, ) else: # Update existing index index = load_or_build_index() all_node_ids = list(index.docstore.docs.keys()) existing_nodes = { node.metadata.get("document_id"): node for node in index.docstore.get_nodes(all_node_ids) } for document in tqdm.tqdm(documents, disable=progress_bar_disable): doc_id = str(document.id) document_modified = document.modified.isoformat() if doc_id in existing_nodes: node = existing_nodes[doc_id] node_modified = node.metadata.get("modified") if node_modified == document_modified: continue # Again, delete from docstore, FAISS IndexFlatL2 are append-only index.docstore.delete_document(node.node_id) nodes.extend(build_document_node(document)) else: # New document, add it nodes.extend(build_document_node(document)) if nodes: logger.info( "Updating %d nodes in LLM index.", len(nodes), ) index.insert_nodes(nodes) else: logger.info("No changes detected, skipping llm index rebuild.") index.storage_context.persist(persist_dir=settings.LLM_INDEX_DIR) def llm_index_add_or_update_document(document: Document): """ Adds or updates a document in the LLM index. If the document already exists, it will be replaced. """ new_nodes = build_document_node(document) index = load_or_build_index(nodes=new_nodes) remove_document_docstore_nodes(document, index) index.insert_nodes(new_nodes) index.storage_context.persist(persist_dir=settings.LLM_INDEX_DIR) def llm_index_remove_document(document: Document): """ Removes a document from the LLM index. """ index = load_or_build_index() remove_document_docstore_nodes(document, index) index.storage_context.persist(persist_dir=settings.LLM_INDEX_DIR) def query_similar_documents( document: Document, top_k: int = 5, document_ids: list[int] | None = None, ) -> list[Document]: """ Runs a similarity query and returns top-k similar Document objects. """ index = load_or_build_index() # constrain only the node(s) that match the document IDs, if given doc_node_ids = ( [ node.node_id for node in index.docstore.docs.values() if node.metadata.get("document_id") in document_ids ] if document_ids else None ) retriever = VectorIndexRetriever( index=index, similarity_top_k=top_k, doc_ids=doc_node_ids, ) query_text = (document.title or "") + "\n" + (document.content or "") results = retriever.retrieve(query_text) document_ids = [ int(node.metadata["document_id"]) for node in results if "document_id" in node.metadata ] return list(Document.objects.filter(pk__in=document_ids))