src-ui/src/app/components/common/custom-fields-query-dropdown/custom-fields-query-dropdown.component.ts

@@ -246,7 +246,7 @@ export class CustomFieldsQueryDropdownComponent extends LoadingComponentWithPerm
 
   customFields: CustomField[] = []
 
-  public readonly today: string = new Date().toLocaleDateString('en-CA')
+  public readonly today: string = new Date().toISOString().split('T')[0]
 
   constructor() {
     super()

src-ui/src/app/components/common/dates-dropdown/dates-dropdown.component.ts

@@ -165,7 +165,7 @@ export class DatesDropdownComponent implements OnInit, OnDestroy {
   @Input()
   placement: string = 'bottom-start'
 
-  public readonly today: string = new Date().toLocaleDateString('en-CA')
+  public readonly today: string = new Date().toISOString().split('T')[0]
 
   get isActive(): boolean {
     return (

src-ui/src/app/components/common/input/date/date.component.ts

@@ -59,7 +59,7 @@ export class DateComponent
   @Output()
   filterDocuments = new EventEmitter<NgbDateStruct[]>()
 
-  public readonly today: string = new Date().toLocaleDateString('en-CA')
+  public readonly today: string = new Date().toISOString().split('T')[0]
 
   getSuggestions() {
     return this.suggestions == null

src/paperless_ai/ai_classifier.py

@@ -1,6 +1,8 @@
+import json
 import logging
 
 from django.contrib.auth.models import User
+from llama_index.core.base.llms.types import CompletionResponse
 
 from documents.models import Document
 from documents.permissions import get_objects_for_user_owner_aware
@@ -16,34 +18,58 @@ def build_prompt_without_rag(document: Document) -> str:
     filename = document.filename or ""
     content = truncate_content(document.content[:4000] or "")
 
-    return f"""
-    You are a document classification assistant.
+    prompt = f"""
+    You are an assistant that extracts structured information from documents.
+    Only respond with the JSON object as described below.
+    Never ask for further information, additional content or ask questions. Never include any other text.
+    Suggested tags and document types must be strictly based on the content of the document.
+    Do not change the field names or the JSON structure, only provide the values. Use double quotes and proper JSON syntax.
+    Each field must be a list of plain strings.
 
-    Analyze the following document and extract the following information:
-    - A short descriptive title
-    - Tags that reflect the content
-    - Names of people or organizations mentioned
-    - The type or category of the document
-    - Suggested folder paths for storing the document
-    - Up to 3 relevant dates in YYYY-MM-DD format
+    The JSON object must contain the following fields:
+    - title: A short, descriptive title
+    - tags: A list of simple tags like ["insurance", "medical", "receipts"]
+    - correspondents: A list of names or organizations mentioned in the document
+    - document_types: The type/category of the document (e.g. "invoice", "medical record")
+    - storage_paths: Suggested folder paths (e.g. "Medical/Insurance")
+    - dates: List up to 3 relevant dates in YYYY-MM-DD format
 
-    Filename:
+    The format of the JSON object is as follows:
+    {{
+        "title": "xxxxx",
+        "tags": ["xxxx", "xxxx"],
+        "correspondents": ["xxxx", "xxxx"],
+        "document_types": ["xxxx", "xxxx"],
+        "storage_paths": ["xxxx", "xxxx"],
+        "dates": ["YYYY-MM-DD", "YYYY-MM-DD", "YYYY-MM-DD"],
+    }}
+    ---------
+
+    FILENAME:
     {filename}
 
-    Content:
+    CONTENT:
     {content}
-    """.strip()
+    """
+
+    return prompt
 
 
 def build_prompt_with_rag(document: Document, user: User | None = None) -> str:
-    base_prompt = build_prompt_without_rag(document)
     context = truncate_content(get_context_for_document(document, user))
+    prompt = build_prompt_without_rag(document)
 
-    return f"""{base_prompt}
+    prompt += f"""
 
-    Additional context from similar documents:
+    CONTEXT FROM SIMILAR DOCUMENTS:
     {context}
-    """.strip()
+
+    ---------
+
+    DO NOT RESPOND WITH ANYTHING OTHER THAN THE JSON OBJECT.
+    """
+
+    return prompt
 
 
 def get_context_for_document(
@@ -74,15 +100,36 @@ def get_context_for_document(
     return "\n\n".join(context_blocks)
 
 
-def parse_ai_response(raw: dict) -> dict:
-    return {
-        "title": raw.get("title", ""),
-        "tags": raw.get("tags", []),
-        "correspondents": raw.get("correspondents", []),
-        "document_types": raw.get("document_types", []),
-        "storage_paths": raw.get("storage_paths", []),
-        "dates": raw.get("dates", []),
-    }
+def parse_ai_response(response: CompletionResponse) -> dict:
+    try:
+        raw = json.loads(response.text)
+        return {
+            "title": raw.get("title"),
+            "tags": raw.get("tags", []),
+            "correspondents": raw.get("correspondents", []),
+            "document_types": raw.get("document_types", []),
+            "storage_paths": raw.get("storage_paths", []),
+            "dates": raw.get("dates", []),
+        }
+    except json.JSONDecodeError:
+        logger.warning("Invalid JSON in AI response, attempting modified parsing...")
+        try:
+            # search for a valid json string like { ... } in the response
+            start = response.text.index("{")
+            end = response.text.rindex("}") + 1
+            json_str = response.text[start:end]
+            raw = json.loads(json_str)
+            return {
+                "title": raw.get("title"),
+                "tags": raw.get("tags", []),
+                "correspondents": raw.get("correspondents", []),
+                "document_types": raw.get("document_types", []),
+                "storage_paths": raw.get("storage_paths", []),
+                "dates": raw.get("dates", []),
+            }
+        except (ValueError, json.JSONDecodeError):
+            logger.exception("Failed to parse AI response")
+            return {}
 
 
 def get_ai_document_classification(

src/paperless_ai/client.py

@@ -1,12 +1,10 @@
 import logging
 
 from llama_index.core.llms import ChatMessage
-from llama_index.core.program.function_program import get_function_tool
 from llama_index.llms.ollama import Ollama
 from llama_index.llms.openai import OpenAI
 
 from paperless.config import AIConfig
-from paperless_ai.tools import DocumentClassifierSchema
 
 logger = logging.getLogger("paperless_ai.client")
 
@@ -20,7 +18,7 @@ class AIClient:
         self.settings = AIConfig()
         self.llm = self.get_llm()
 
-    def get_llm(self) -> Ollama | OpenAI:
+    def get_llm(self):
         if self.settings.llm_backend == "ollama":
             return Ollama(
                 model=self.settings.llm_model or "llama3",
@@ -41,21 +39,9 @@ class AIClient:
             self.settings.llm_backend,
             self.settings.llm_model,
         )
-
-        user_msg = ChatMessage(role="user", content=prompt)
-        tool = get_function_tool(DocumentClassifierSchema)
-        result = self.llm.chat_with_tools(
-            tools=[tool],
-            user_msg=user_msg,
-            chat_history=[],
-        )
-        tool_calls = self.llm.get_tool_calls_from_response(
-            result,
-            error_on_no_tool_calls=True,
-        )
-        logger.debug("LLM query result: %s", tool_calls)
-        parsed = DocumentClassifierSchema(**tool_calls[0].tool_kwargs)
-        return parsed.model_dump()
+        result = self.llm.complete(prompt)
+        logger.debug("LLM query result: %s", result)
+        return result
 
     def run_chat(self, messages: list[ChatMessage]) -> str:
         logger.debug(

src/paperless_ai/tests/test_ai_classifier.py

@@ -10,6 +10,7 @@ from paperless_ai.ai_classifier import build_prompt_with_rag
 from paperless_ai.ai_classifier import build_prompt_without_rag
 from paperless_ai.ai_classifier import get_ai_document_classification
 from paperless_ai.ai_classifier import get_context_for_document
+from paperless_ai.ai_classifier import parse_ai_response
 
 
 @pytest.fixture
@@ -74,14 +75,16 @@ def mock_similar_documents():
     LLM_MODEL="some_model",
 )
 def test_get_ai_document_classification_success(mock_run_llm_query, mock_document):
-    mock_run_llm_query.return_value = {
-        "title": "Test Title",
-        "tags": ["test", "document"],
-        "correspondents": ["John Doe"],
-        "document_types": ["report"],
-        "storage_paths": ["Reports"],
-        "dates": ["2023-01-01"],
-    }
+    mock_run_llm_query.return_value.text = json.dumps(
+        {
+            "title": "Test Title",
+            "tags": ["test", "document"],
+            "correspondents": ["John Doe"],
+            "document_types": ["report"],
+            "storage_paths": ["Reports"],
+            "dates": ["2023-01-01"],
+        },
+    )
 
     result = get_ai_document_classification(mock_document)
 
@@ -93,6 +96,56 @@ def test_get_ai_document_classification_success(mock_run_llm_query, mock_documen
     assert result["dates"] == ["2023-01-01"]
 
 
+@pytest.mark.django_db
+@patch("paperless_ai.client.AIClient.run_llm_query")
+@override_settings(
+    LLM_BACKEND="ollama",
+    LLM_MODEL="some_model",
+)
+def test_get_ai_document_classification_fallback_parse_success(
+    mock_run_llm_query,
+    mock_document,
+):
+    mock_run_llm_query.return_value.text = """
+    There is some text before the JSON.
+    ```json
+    {
+        "title": "Test Title",
+        "tags": ["test", "document"],
+        "correspondents": ["John Doe"],
+        "document_types": ["report"],
+        "storage_paths": ["Reports"],
+        "dates": ["2023-01-01"]
+    }
+    ```
+    """
+
+    result = get_ai_document_classification(mock_document)
+
+    assert result["title"] == "Test Title"
+    assert result["tags"] == ["test", "document"]
+    assert result["correspondents"] == ["John Doe"]
+    assert result["document_types"] == ["report"]
+    assert result["storage_paths"] == ["Reports"]
+    assert result["dates"] == ["2023-01-01"]
+
+
+@pytest.mark.django_db
+@patch("paperless_ai.client.AIClient.run_llm_query")
+@override_settings(
+    LLM_BACKEND="ollama",
+    LLM_MODEL="some_model",
+)
+def test_get_ai_document_classification_parse_failure(
+    mock_run_llm_query,
+    mock_document,
+):
+    mock_run_llm_query.return_value.text = "Invalid JSON response"
+
+    result = get_ai_document_classification(mock_document)
+    assert result == {}
+
+
 @pytest.mark.django_db
 @patch("paperless_ai.client.AIClient.run_llm_query")
 def test_get_ai_document_classification_failure(mock_run_llm_query, mock_document):
@@ -103,6 +156,15 @@ def test_get_ai_document_classification_failure(mock_run_llm_query, mock_documen
         get_ai_document_classification(mock_document)
 
 
+def test_parse_llm_classification_response_invalid_json():
+    mock_response = MagicMock()
+    mock_response.text = "Invalid JSON response"
+
+    result = parse_ai_response(mock_response)
+
+    assert result == {}
+
+
 @pytest.mark.django_db
 @patch("paperless_ai.client.AIClient.run_llm_query")
 @patch("paperless_ai.ai_classifier.build_prompt_with_rag")
@@ -156,10 +218,10 @@ def test_prompt_with_without_rag(mock_document):
         return_value="Context from similar documents",
     ):
         prompt = build_prompt_without_rag(mock_document)
-        assert "Additional context from similar documents:" not in prompt
+        assert "CONTEXT FROM SIMILAR DOCUMENTS:" not in prompt
 
         prompt = build_prompt_with_rag(mock_document)
-        assert "Additional context from similar documents:" in prompt
+        assert "CONTEXT FROM SIMILAR DOCUMENTS:" in prompt
 
 
 @patch("paperless_ai.ai_classifier.query_similar_documents")

src/paperless_ai/tests/test_client.py

@@ -3,7 +3,6 @@ from unittest.mock import patch
 
 import pytest
 from llama_index.core.llms import ChatMessage
-from llama_index.core.llms.llm import ToolSelection
 
 from paperless_ai.client import AIClient
 
@@ -70,27 +69,13 @@ def test_run_llm_query(mock_ai_config, mock_ollama_llm):
     mock_ai_config.llm_url = "http://test-url"
 
     mock_llm_instance = mock_ollama_llm.return_value
-
-    tool_selection = ToolSelection(
-        tool_id="call_test",
-        tool_name="DocumentClassifierSchema",
-        tool_kwargs={
-            "title": "Test Title",
-            "tags": ["test", "document"],
-            "correspondents": ["John Doe"],
-            "document_types": ["report"],
-            "storage_paths": ["Reports"],
-            "dates": ["2023-01-01"],
-        },
-    )
-
-    mock_llm_instance.chat_with_tools.return_value = MagicMock()
-    mock_llm_instance.get_tool_calls_from_response.return_value = [tool_selection]
+    mock_llm_instance.complete.return_value = "test_result"
 
     client = AIClient()
     result = client.run_llm_query("test_prompt")
 
-    assert result["title"] == "Test Title"
+    mock_llm_instance.complete.assert_called_once_with("test_prompt")
+    assert result == "test_result"
 
 
 def test_run_chat(mock_ai_config, mock_ollama_llm):

src/paperless_ai/tools.py

@@ -1,10 +0,0 @@
-from llama_index.core.bridge.pydantic import BaseModel
-
-
-class DocumentClassifierSchema(BaseModel):
-    title: str
-    tags: list[str]
-    correspondents: list[str]
-    document_types: list[str]
-    storage_paths: list[str]
-    dates: list[str]