diff --git a/docker/install_management_commands.sh b/docker/install_management_commands.sh index 17dae68a2..be972d605 100755 --- a/docker/install_management_commands.sh +++ b/docker/install_management_commands.sh @@ -11,6 +11,7 @@ for command in decrypt_documents \ mail_fetcher \ document_create_classifier \ document_index \ + document_llmindex \ document_renamer \ document_retagger \ document_thumbnails \ diff --git a/docker/rootfs/usr/local/bin/document_llmindex b/docker/rootfs/usr/local/bin/document_llmindex new file mode 100755 index 000000000..8e51245e1 --- /dev/null +++ b/docker/rootfs/usr/local/bin/document_llmindex @@ -0,0 +1,14 @@ +#!/command/with-contenv /usr/bin/bash +# shellcheck shell=bash + +set -e + +cd "${PAPERLESS_SRC_DIR}" + +if [[ $(id -u) == 0 ]]; then + s6-setuidgid paperless python3 manage.py document_llmindex "$@" +elif [[ $(id -un) == "paperless" ]]; then + python3 manage.py document_llmindex "$@" +else + echo "Unknown user." +fi diff --git a/pyproject.toml b/pyproject.toml index 452c17bc3..240a1a476 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,7 @@ dependencies = [ "drf-spectacular~=0.28", "drf-spectacular-sidecar~=2025.4.1", "drf-writable-nested~=0.7.1", + "faiss-cpu>=1.10", "filelock~=3.18.0", "flower~=2.0.1", "gotenberg-client~=0.10.0", @@ -47,8 +48,12 @@ dependencies = [ "inotifyrecursive~=0.3", "jinja2~=3.1.5", "langdetect~=1.0.9", + "llama-index>=0.12.33", + "llama-index-embeddings-huggingface>=0.5.3", + "llama-index-vector-stores-faiss>=0.3", "nltk~=3.9.1", "ocrmypdf~=16.10.0", + "openai>=1.76", "pathvalidate~=3.2.3", "pdf2image~=1.17.0", "python-dateutil~=2.9.0", @@ -60,6 +65,7 @@ dependencies = [ "rapidfuzz~=3.13.0", "redis[hiredis]~=5.2.1", "scikit-learn~=1.6.1", + "sentence-transformers>=4.1", "setproctitle~=1.3.4", "tika-client~=0.9.0", "tqdm~=4.67.1", diff --git a/src/documents/management/commands/document_llmindex.py b/src/documents/management/commands/document_llmindex.py new file mode 100644 index 000000000..2985a61e4 --- /dev/null +++ b/src/documents/management/commands/document_llmindex.py @@ -0,0 +1,19 @@ +from django.core.management import BaseCommand +from django.db import transaction + +from documents.management.commands.mixins import ProgressBarMixin +from documents.tasks import llm_index_rebuild + + +class Command(ProgressBarMixin, BaseCommand): + help = "Manages the LLM-based vector index for Paperless." + + def add_arguments(self, parser): + parser.add_argument("command", choices=["rebuild"]) + self.add_argument_progress_bar_mixin(parser) + + def handle(self, *args, **options): + self.handle_progress_bar_mixin(**options) + with transaction.atomic(): + if options["command"] == "rebuild": + llm_index_rebuild(progress_bar_disable=self.no_progress_bar) diff --git a/src/documents/tasks.py b/src/documents/tasks.py index 13c104185..903149ba0 100644 --- a/src/documents/tasks.py +++ b/src/documents/tasks.py @@ -6,6 +6,7 @@ import uuid from pathlib import Path from tempfile import TemporaryDirectory +import faiss import tqdm from celery import Task from celery import shared_task @@ -17,6 +18,11 @@ from django.db import transaction from django.db.models.signals import post_save from django.utils import timezone from filelock import FileLock +from llama_index.core import Document as LlamaDocument +from llama_index.core import StorageContext +from llama_index.core import VectorStoreIndex +from llama_index.core.settings import Settings +from llama_index.vector_stores.faiss import FaissVectorStore from whoosh.writing import AsyncWriter from documents import index @@ -52,6 +58,9 @@ from documents.sanity_checker import SanityCheckFailedException from documents.signals import document_updated from documents.signals.handlers import cleanup_document_deletion from documents.signals.handlers import run_workflows +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 if settings.AUDIT_LOG_ENABLED: from auditlog.models import LogEntry @@ -496,3 +505,52 @@ def check_scheduled_workflows(): workflow_to_run=workflow, document=document, ) + + +def llm_index_rebuild(*, progress_bar_disable=False, rebuild=False): + if rebuild: + shutil.rmtree(settings.LLM_INDEX_DIR, ignore_errors=True) + settings.LLM_INDEX_DIR.mkdir(parents=True, exist_ok=True) + + documents = Document.objects.all() + + embed_model = get_embedding_model() + + 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) + else: + vector_store = FaissVectorStore.from_persist_dir(settings.LLM_INDEX_DIR) + storage_context = StorageContext.from_defaults(vector_store=vector_store) + Settings.embed_model = embed_model + + llm_docs = [] + for document in tqdm.tqdm(documents, disable=progress_bar_disable): + if not document.content: + continue + llm_docs.append( + LlamaDocument( + text=build_llm_index_text(document), + metadata={ + "id": 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(), + "added": document.added.isoformat(), + }, + ), + ) + + index = VectorStoreIndex.from_documents( + llm_docs, + storage_context=storage_context, + ) + settings.LLM_INDEX_DIR.mkdir(exist_ok=True) + index.storage_context.persist(persist_dir=settings.LLM_INDEX_DIR) diff --git a/src/paperless/ai/embedding.py b/src/paperless/ai/embedding.py new file mode 100644 index 000000000..1c33f197c --- /dev/null +++ b/src/paperless/ai/embedding.py @@ -0,0 +1,67 @@ +from llama_index.embeddings.huggingface import HuggingFaceEmbedding +from llama_index.embeddings.openai import OpenAIEmbedding + +from documents.models import Document +from documents.models import Note +from paperless.config import AIConfig + +EMBEDDING_DIMENSIONS = { + "text-embedding-3-small": 1536, + "sentence-transformers/all-MiniLM-L6-v2": 384, +} + + +def get_embedding_model(): + config = AIConfig() + + match config.llm_embedding_backend: + case "openai": + return OpenAIEmbedding( + model=config.llm_embedding_model or "text-embedding-3-small", + api_key=config.llm_api_key, + ) + case "local": + return HuggingFaceEmbedding( + model_name=config.llm_embedding_model + or "sentence-transformers/all-MiniLM-L6-v2", + ) + case _: + raise ValueError( + f"Unsupported embedding backend: {config.llm_embedding_backend}", + ) + + +def get_embedding_dim() -> int: + config = AIConfig() + model = config.llm_embedding_model or ( + "text-embedding-3-small" + if config.llm_embedding_backend == "openai" + else "sentence-transformers/all-MiniLM-L6-v2" + ) + if model not in EMBEDDING_DIMENSIONS: + raise ValueError(f"Unknown embedding model: {model}") + return EMBEDDING_DIMENSIONS[model] + + +def build_llm_index_text(doc: Document) -> str: + lines = [ + f"Title: {doc.title}", + f"Filename: {doc.filename}", + f"Created: {doc.created}", + f"Added: {doc.added}", + f"Modified: {doc.modified}", + f"Tags: {', '.join(tag.name for tag in doc.tags.all())}", + f"Document Type: {doc.document_type.name if doc.document_type else ''}", + f"Correspondent: {doc.correspondent.name if doc.correspondent else ''}", + f"Storage Path: {doc.storage_path.name if doc.storage_path else ''}", + f"Archive Serial Number: {doc.archive_serial_number or ''}", + f"Notes: {','.join([str(c.note) for c in Note.objects.filter(document=doc)])}", + ] + + for instance in doc.custom_fields.all(): + lines.append(f"Custom Field - {instance.field.name}: {instance}") + + lines.append("\nContent:\n") + lines.append(doc.content or "") + + return "\n".join(lines) diff --git a/src/paperless/ai/indexing.py b/src/paperless/ai/indexing.py new file mode 100644 index 000000000..271b5f3cd --- /dev/null +++ b/src/paperless/ai/indexing.py @@ -0,0 +1,52 @@ +import logging + +import llama_index.core.settings as llama_settings +from django.conf import settings +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.retrievers import VectorIndexRetriever +from llama_index.vector_stores.faiss import FaissVectorStore + +from documents.models import Document +from paperless.ai.embedding import get_embedding_model + +logger = logging.getLogger("paperless.ai.indexing") + + +def load_index() -> VectorStoreIndex: + """Loads the persisted LlamaIndex from disk.""" + vector_store = FaissVectorStore.from_persist_dir(settings.LLM_INDEX_DIR) + embed_model = get_embedding_model() + + llama_settings.Settings.embed_model = embed_model + llama_settings.Settings.chunk_size = 512 + + storage_context = StorageContext.from_defaults( + vector_store=vector_store, + persist_dir=settings.LLM_INDEX_DIR, + ) + return load_index_from_storage(storage_context) + + +def query_similar_documents(document: Document, top_k: int = 5) -> list[Document]: + """Runs a similarity query and returns top-k similar Document objects.""" + + # Load index + index = load_index() + retriever = VectorIndexRetriever(index=index, similarity_top_k=top_k) + + # Build query from the document text + query_text = (document.title or "") + "\n" + (document.content or "") + + # Query + results = retriever.retrieve(query_text) + + # Each result.node.metadata["document_id"] should match our stored doc + 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)) diff --git a/src/paperless/config.py b/src/paperless/config.py index 4a20ea461..2c38b4056 100644 --- a/src/paperless/config.py +++ b/src/paperless/config.py @@ -178,6 +178,8 @@ class AIConfig(BaseConfig): """ ai_enabled: bool = dataclasses.field(init=False) + llm_embedding_backend: str = dataclasses.field(init=False) + llm_embedding_model: str = dataclasses.field(init=False) llm_backend: str = dataclasses.field(init=False) llm_model: str = dataclasses.field(init=False) llm_api_key: str = dataclasses.field(init=False) @@ -187,6 +189,12 @@ class AIConfig(BaseConfig): app_config = self._get_config_instance() self.ai_enabled = app_config.ai_enabled or settings.AI_ENABLED + self.llm_embedding_backend = ( + app_config.llm_embedding_backend or settings.LLM_EMBEDDING_BACKEND + ) + self.llm_embedding_model = ( + app_config.llm_embedding_model or settings.LLM_EMBEDDING_MODEL + ) self.llm_backend = app_config.llm_backend or settings.LLM_BACKEND self.llm_model = app_config.llm_model or settings.LLM_MODEL self.llm_api_key = app_config.llm_api_key or settings.LLM_API_KEY diff --git a/src/paperless/migrations/0004_applicationconfiguration_ai_enabled_and_more.py b/src/paperless/migrations/0004_applicationconfiguration_ai_enabled_and_more.py index 55833dffc..da5180bf2 100644 --- a/src/paperless/migrations/0004_applicationconfiguration_ai_enabled_and_more.py +++ b/src/paperless/migrations/0004_applicationconfiguration_ai_enabled_and_more.py @@ -19,6 +19,27 @@ class Migration(migrations.Migration): verbose_name="Enables AI features", ), ), + migrations.AddField( + model_name="applicationconfiguration", + name="llm_embedding_backend", + field=models.CharField( + blank=True, + choices=[("openai", "OpenAI"), ("local", "Local")], + max_length=32, + null=True, + verbose_name="Sets the LLM Embedding backend", + ), + ), + migrations.AddField( + model_name="applicationconfiguration", + name="llm_embedding_model", + field=models.CharField( + blank=True, + max_length=32, + null=True, + verbose_name="Sets the LLM Embedding model", + ), + ), migrations.AddField( model_name="applicationconfiguration", name="llm_api_key", diff --git a/src/paperless/models.py b/src/paperless/models.py index d2a15d9ac..fb8a44986 100644 --- a/src/paperless/models.py +++ b/src/paperless/models.py @@ -74,6 +74,11 @@ class ColorConvertChoices(models.TextChoices): CMYK = ("CMYK", _("CMYK")) +class LLMEmbeddingBackend(models.TextChoices): + OPENAI = ("openai", _("OpenAI")) + LOCAL = ("local", _("Local")) + + class LLMBackend(models.TextChoices): """ Matches to --llm-backend @@ -284,6 +289,21 @@ class ApplicationConfiguration(AbstractSingletonModel): default=False, ) + llm_embedding_backend = models.CharField( + verbose_name=_("Sets the LLM embedding backend"), + null=True, + blank=True, + max_length=32, + choices=LLMEmbeddingBackend.choices, + ) + + llm_embedding_model = models.CharField( + verbose_name=_("Sets the LLM embedding model"), + null=True, + blank=True, + max_length=32, + ) + llm_backend = models.CharField( verbose_name=_("Sets the LLM backend"), null=True, diff --git a/src/paperless/settings.py b/src/paperless/settings.py index 37bdc42dd..76a0a8c61 100644 --- a/src/paperless/settings.py +++ b/src/paperless/settings.py @@ -283,6 +283,7 @@ MODEL_FILE = __get_path( "PAPERLESS_MODEL_FILE", DATA_DIR / "classification_model.pickle", ) +LLM_INDEX_DIR = DATA_DIR / "llm_index" LOGGING_DIR = __get_path("PAPERLESS_LOGGING_DIR", DATA_DIR / "log") @@ -1281,7 +1282,12 @@ OUTLOOK_OAUTH_ENABLED = bool( # AI Settings # ################################################################################ AI_ENABLED = __get_boolean("PAPERLESS_AI_ENABLED", "NO") -LLM_BACKEND = os.getenv("PAPERLESS_LLM_BACKEND", "openai") # or "ollama" +LLM_EMBEDDING_BACKEND = os.getenv( + "PAPERLESS_LLM_EMBEDDING_BACKEND", + "local", +) # or "openai" +LLM_EMBEDDING_MODEL = os.getenv("PAPERLESS_LLM_EMBEDDING_MODEL") +LLM_BACKEND = os.getenv("PAPERLESS_LLM_BACKEND", "ollama") # or "openai" LLM_MODEL = os.getenv("PAPERLESS_LLM_MODEL") LLM_API_KEY = os.getenv("PAPERLESS_LLM_API_KEY") LLM_URL = os.getenv("PAPERLESS_LLM_URL") diff --git a/uv.lock b/uv.lock index 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