Construct fewer AiConfig objects and instead pass around as needed

src/paperless_ai/ai_classifier.py

@@ -24,9 +24,14 @@ def get_language_name(language_code: str) -> str:
 
 def build_prompt_without_rag(
     document: Document,
+    config: AIConfig,
 ) -> str:
     filename = document.filename or ""
-    content = truncate_content(document.content[:4000] or "")
+    content = truncate_content(
+        document.content[:4000] or "",
+        chunk_size=config.llm_embedding_chunk_size,
+        context_size=config.llm_context_size,
+    )
 
     return f"""
     You are a document classification assistant.
@@ -49,10 +54,15 @@ def build_prompt_without_rag(
 
 def build_prompt_with_rag(
     document: Document,
+    config: AIConfig,
     user: User | None = None,
 ) -> str:
-    base_prompt = build_prompt_without_rag(document)
-    context = truncate_content(get_context_for_document(document, user))
+    base_prompt = build_prompt_without_rag(document, config)
+    context = truncate_content(
+        get_context_for_document(document, user),
+        chunk_size=config.llm_embedding_chunk_size,
+        context_size=config.llm_context_size,
+    )
 
     return f"""{base_prompt}
 
@@ -130,9 +140,9 @@ def get_ai_document_classification(
     ai_config = AIConfig()
 
     prompt = (
-        build_prompt_with_rag(document, user)
+        build_prompt_with_rag(document, ai_config, user)
         if ai_config.llm_embedding_backend
-        else build_prompt_without_rag(document)
+        else build_prompt_without_rag(document, ai_config)
     )
 
     client = AIClient()

src/paperless_ai/chat.py

@@ -3,6 +3,7 @@ import logging
 import sys
 
 from documents.models import Document
+from paperless.config import AIConfig
 from paperless_ai.client import AIClient
 from paperless_ai.indexing import _document_id_filters
 from paperless_ai.indexing import get_rag_prompt_helper
@@ -94,7 +95,8 @@ def _stream_chat_with_documents(query_str: str, documents: list[Document]):
     from llama_index.core.response_synthesizers import get_response_synthesizer
     from llama_index.core.retrievers import VectorIndexRetriever
 
-    index = load_or_build_index()
+    config = AIConfig()
+    index = load_or_build_index(config)
     filters = _document_id_filters(str(doc.pk) for doc in documents)
 
     retriever = VectorIndexRetriever(
@@ -116,7 +118,10 @@ def _stream_chat_with_documents(query_str: str, documents: list[Document]):
     prompt_template = PromptTemplate(template=CHAT_PROMPT_TMPL)
     response_synthesizer = get_response_synthesizer(
         llm=client.llm,
-        prompt_helper=get_rag_prompt_helper(),
+        prompt_helper=get_rag_prompt_helper(
+            chunk_size=config.llm_embedding_chunk_size,
+            context_size=config.llm_context_size,
+        ),
         text_qa_template=prompt_template,
         streaming=True,
     )

src/paperless_ai/embedding.py

@@ -20,9 +20,7 @@ OCR_LEADER_REGEX = re.compile(r"[._\-\u00b7]{4,}")
 HORIZONTAL_WHITESPACE_REGEX = re.compile(r"[ \t\u00a0]+")
 
 
-def get_embedding_model() -> "BaseEmbedding":
-    config = AIConfig()
-
+def get_embedding_model(config: AIConfig) -> "BaseEmbedding":
     match config.llm_embedding_backend:
         case LLMEmbeddingBackend.OPENAI_LIKE:
             from llama_index.embeddings.openai_like import OpenAILikeEmbedding
@@ -99,9 +97,8 @@ _DEFAULT_MODEL_NAMES = {
 }
 
 
-def get_configured_model_name() -> str:
+def get_configured_model_name(config: AIConfig) -> str:
     """Return the canonical name of the currently configured embedding model."""
-    config = AIConfig()
     default = _DEFAULT_MODEL_NAMES.get(
         config.llm_embedding_backend,
         "sentence-transformers/all-MiniLM-L6-v2",

src/paperless_ai/indexing.py

@@ -139,12 +139,12 @@ def build_document_node(
     return parser.get_nodes_from_documents([doc])
 
 
-def load_or_build_index():
+def load_or_build_index(config: AIConfig):
     """Return a VectorStoreIndex backed by the vector store."""
     import llama_index.core.settings as llama_settings
     from llama_index.core import VectorStoreIndex
 
-    embed_model = get_embedding_model()
+    embed_model = get_embedding_model(config)
     llama_settings.Settings.embed_model = embed_model
     vector_store = get_vector_store()
     return VectorStoreIndex.from_vector_store(
@@ -223,7 +223,16 @@ def update_llm_index(
     rebuild=False,
 ) -> str:
     """Rebuild or incrementally update the LLM index."""
-    model_name = get_configured_model_name()
+    documents = Document.objects.all()
+    no_documents = not documents.exists()
+
+    # Fast exit before touching config: nothing to index and no existing index.
+    if no_documents and not rebuild and not llm_index_exists():
+        logger.warning("No documents found to index.")
+        return "No documents found to index."
+
+    config = AIConfig()
+    model_name = get_configured_model_name(config)
 
     if (
         not rebuild
@@ -233,14 +242,11 @@ def update_llm_index(
         logger.warning("Embedding model changed; forcing LLM index rebuild.")
         rebuild = True
 
-    documents = Document.objects.all()
-    if not documents.exists():
+    if no_documents:
         logger.warning("No documents found to index.")
-        if not rebuild and not llm_index_exists():
-            return "No documents found to index."
 
-    chunk_size = AIConfig().llm_embedding_chunk_size
-    embed_model = get_embedding_model()
+    chunk_size = config.llm_embedding_chunk_size
+    embed_model = get_embedding_model(config)
 
     with write_store(embed_model_name=model_name) as store:
         if rebuild or not store.table_exists():
@@ -277,11 +283,15 @@ def update_llm_index(
 
 def llm_index_add_or_update_document(document: Document):
     """Add or atomically replace a document's chunks in the index."""
-    new_nodes = build_document_node(document, chunk_size=get_rag_chunk_size())
+    config = AIConfig()
+    new_nodes = build_document_node(
+        document,
+        chunk_size=config.llm_embedding_chunk_size,
+    )
     if new_nodes:
-        _embed_nodes(new_nodes, get_embedding_model())
+        _embed_nodes(new_nodes, get_embedding_model(config))
 
-    with write_store(embed_model_name=get_configured_model_name()) as store:
+    with write_store(embed_model_name=get_configured_model_name(config)) as store:
         store.upsert_document(str(document.id), new_nodes)
         store.ensure_document_id_scalar_index()
 
@@ -352,9 +362,11 @@ def query_similar_documents(
         )
         return []
 
+    config = AIConfig()
+
     from llama_index.core.retrievers import VectorIndexRetriever
 
-    index = load_or_build_index()
+    index = load_or_build_index(config)
 
     filters = (
         _document_id_filters(allowed_document_ids)
@@ -368,7 +380,6 @@ def query_similar_documents(
         filters=filters,
     )
 
-    config = AIConfig()
     query_text = truncate_content(
         (document.title or "") + "\n" + (document.content or ""),
         chunk_size=config.llm_embedding_chunk_size,

src/paperless_ai/tests/test_ai_classifier.py

@@ -6,6 +6,7 @@ import pytest
 from django.test import override_settings
 
 from documents.models import Document
+from paperless.config import AIConfig
 from paperless_ai.ai_classifier import build_localization_prompt
 from paperless_ai.ai_classifier import build_prompt_with_rag
 from paperless_ai.ai_classifier import build_prompt_without_rag
@@ -211,11 +212,12 @@ def test_prompt_with_without_rag(mock_document):
         "paperless_ai.ai_classifier.get_context_for_document",
         return_value="Context from similar documents",
     ):
-        prompt = build_prompt_without_rag(mock_document)
+        config = AIConfig()
+        prompt = build_prompt_without_rag(mock_document, config)
         assert "Additional context from similar documents" not in prompt
         assert "for generated" not in prompt
 
-        prompt = build_prompt_with_rag(mock_document)
+        prompt = build_prompt_with_rag(mock_document, config)
         assert "Additional context from similar documents" in prompt
 
         prompt = build_localization_prompt(

src/paperless_ai/tests/test_chat.py

@@ -185,6 +185,7 @@ def test_stream_chat_empty_document_list() -> None:
 
 def test_stream_chat_no_matching_nodes() -> None:
     with (
+        patch("paperless_ai.chat.AIConfig"),
         patch("paperless_ai.chat.AIClient") as mock_client_cls,
         patch("paperless_ai.chat.load_or_build_index") as mock_load_index,
     ):
@@ -204,6 +205,7 @@ def test_stream_chat_no_matching_nodes() -> None:
 
 def test_stream_chat_unexpected_failure_returns_generic_error(caplog) -> None:
     with (
+        patch("paperless_ai.chat.AIConfig"),
         patch("paperless_ai.chat.AIClient") as mock_client_cls,
         patch("paperless_ai.chat.load_or_build_index") as mock_load_index,
     ):

src/paperless_ai/tests/test_embedding.py

@@ -66,7 +66,7 @@ def test_get_embedding_model_openai(mock_ai_config):
     with patch(
         "llama_index.embeddings.openai_like.OpenAILikeEmbedding",
     ) as MockOpenAIEmbedding:
-        model = get_embedding_model()
+        model = get_embedding_model(mock_ai_config.return_value)
         MockOpenAIEmbedding.assert_called_once_with(
             model_name="text-embedding-3-small",
             api_key="test_api_key",
@@ -87,7 +87,7 @@ def test_get_embedding_model_openai_prefers_embedding_endpoint(mock_ai_config):
     with patch(
         "llama_index.embeddings.openai_like.OpenAILikeEmbedding",
     ) as MockOpenAIEmbedding:
-        model = get_embedding_model()
+        model = get_embedding_model(mock_ai_config.return_value)
         MockOpenAIEmbedding.assert_called_once_with(
             model_name="text-embedding-3-small",
             api_key="test_api_key",
@@ -108,7 +108,7 @@ def test_get_embedding_model_openai_blocks_internal_endpoint_when_disallowed(
     mock_ai_config.return_value.llm_allow_internal_endpoints = False
 
     with pytest.raises(ValueError, match="non-public address"):
-        get_embedding_model()
+        get_embedding_model(mock_ai_config.return_value)
 
 
 def test_get_embedding_model_huggingface(mock_ai_config):
@@ -120,7 +120,7 @@ def test_get_embedding_model_huggingface(mock_ai_config):
     with patch(
         "llama_index.embeddings.huggingface.HuggingFaceEmbedding",
     ) as MockHuggingFaceEmbedding:
-        model = get_embedding_model()
+        model = get_embedding_model(mock_ai_config.return_value)
         MockHuggingFaceEmbedding.assert_called_once_with(
             model_name="sentence-transformers/all-MiniLM-L6-v2",
             cache_folder=str(settings.DATA_DIR / "hf_cache"),
@@ -136,7 +136,7 @@ def test_get_embedding_model_ollama(mock_ai_config):
     with patch(
         "llama_index.embeddings.ollama.OllamaEmbedding",
     ) as MockOllamaEmbedding:
-        model = get_embedding_model()
+        model = get_embedding_model(mock_ai_config.return_value)
         MockOllamaEmbedding.assert_called_once_with(
             model_name="embeddinggemma",
             base_url="http://test-url",
@@ -154,7 +154,7 @@ def test_get_embedding_model_ollama_prefers_embedding_endpoint(mock_ai_config):
     with patch(
         "llama_index.embeddings.ollama.OllamaEmbedding",
     ) as MockOllamaEmbedding:
-        model = get_embedding_model()
+        model = get_embedding_model(mock_ai_config.return_value)
         MockOllamaEmbedding.assert_called_once_with(
             model_name="embeddinggemma",
             base_url="http://embedding-url",
@@ -172,7 +172,7 @@ def test_get_embedding_model_ollama_blocks_internal_endpoint_when_disallowed(
     mock_ai_config.return_value.llm_allow_internal_endpoints = False
 
     with pytest.raises(ValueError, match="non-public address"):
-        get_embedding_model()
+        get_embedding_model(mock_ai_config.return_value)
 
 
 def test_get_embedding_model_invalid_backend(mock_ai_config):
@@ -182,7 +182,7 @@ def test_get_embedding_model_invalid_backend(mock_ai_config):
         ValueError,
         match="Unsupported embedding backend: INVALID_BACKEND",
     ):
-        get_embedding_model()
+        get_embedding_model(mock_ai_config.return_value)
 
 
 @pytest.mark.parametrize(
@@ -199,18 +199,20 @@ def test_get_configured_model_name_falls_back_to_backend_default(
     expected_default,
 ):
     """When no model is explicitly configured, each backend has a distinct default."""
-    mock_ai_config.return_value.llm_embedding_backend = backend
-    mock_ai_config.return_value.llm_embedding_model = None
-    assert get_configured_model_name() == expected_default
+    config = mock_ai_config.return_value
+    config.llm_embedding_backend = backend
+    config.llm_embedding_model = None
+    assert get_configured_model_name(config) == expected_default
 
 
 def test_get_configured_model_name_explicit_overrides_default(mock_ai_config):
     """An explicit model name overrides the backend default for all backends."""
-    mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.OPENAI_LIKE
-    mock_ai_config.return_value.llm_embedding_model = "my-custom-model"
+    config = mock_ai_config.return_value
+    config.llm_embedding_backend = LLMEmbeddingBackend.OPENAI_LIKE
+    config.llm_embedding_model = "my-custom-model"
     # The backend default for OPENAI_LIKE is "text-embedding-3-small", so if
     # the explicit name was ignored we'd get the wrong result.
-    assert get_configured_model_name() == "my-custom-model"
+    assert get_configured_model_name(config) == "my-custom-model"
 
 
 def test_build_llm_index_text(mock_document):