Update ai_classifier.py

See #10369

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().toISOString().split('T')[0]
+  public readonly today: string = new Date().toLocaleDateString('en-CA')
 
   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().toISOString().split('T')[0]
+  public readonly today: string = new Date().toLocaleDateString('en-CA')
 
   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().toISOString().split('T')[0]
+  public readonly today: string = new Date().toLocaleDateString('en-CA')
 
   getSuggestions() {
     return this.suggestions == null

src/paperless_ai/ai_classifier.py

@@ -1,8 +1,6 @@
-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
@@ -18,58 +16,34 @@ def build_prompt_without_rag(document: Document) -> str:
     filename = document.filename or ""
     content = truncate_content(document.content[:4000] or "")
 
-    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.
+    return f"""
+    You are a document classification assistant.
 
-    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
+    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 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:
     {filename}
 
-    CONTENT:
+    Content:
     {content}
-    """
-
-    return prompt
+    """.strip()
 
 
 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)
 
-    prompt += f"""
+    return f"""{base_prompt}
 
-    CONTEXT FROM SIMILAR DOCUMENTS:
+    Additional context from similar documents:
     {context}
-
-    ---------
-
-    DO NOT RESPOND WITH ANYTHING OTHER THAN THE JSON OBJECT.
-    """
-
-    return prompt
+    """.strip()
 
 
 def get_context_for_document(
@@ -100,36 +74,15 @@ def get_context_for_document(
     return "\n\n".join(context_blocks)
 
 
-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 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 get_ai_document_classification(

src/paperless_ai/client.py

@@ -1,10 +1,12 @@
 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")
 
@@ -18,7 +20,7 @@ class AIClient:
         self.settings = AIConfig()
         self.llm = self.get_llm()
 
-    def get_llm(self):
+    def get_llm(self) -> Ollama | OpenAI:
         if self.settings.llm_backend == "ollama":
             return Ollama(
                 model=self.settings.llm_model or "llama3",
@@ -39,9 +41,21 @@ class AIClient:
             self.settings.llm_backend,
             self.settings.llm_model,
         )
-        result = self.llm.complete(prompt)
-        logger.debug("LLM query result: %s", result)
-        return result
+
+        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()
 
     def run_chat(self, messages: list[ChatMessage]) -> str:
         logger.debug(

src/paperless_ai/tests/test_ai_classifier.py

@@ -10,7 +10,6 @@ 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
@@ -75,50 +74,14 @@ 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.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)
-
-    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_fallback_parse_success(
-    mock_run_llm_query,
-    mock_document,
-):
-    mock_run_llm_query.return_value.text = """
-    There is some text before the JSON.
-    ```json
-    {
+    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"]
+        "dates": ["2023-01-01"],
     }
-    ```
-    """
 
     result = get_ai_document_classification(mock_document)
 
@@ -130,22 +93,6 @@ def test_get_ai_document_classification_fallback_parse_success(
     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):
@@ -156,15 +103,6 @@ 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")
@@ -218,10 +156,10 @@ def test_prompt_with_without_rag(mock_document):
         return_value="Context from similar documents",
     ):
         prompt = build_prompt_without_rag(mock_document)
-        assert "CONTEXT FROM SIMILAR DOCUMENTS:" not in prompt
+        assert "Additional context from similar documents:" not in prompt
 
         prompt = build_prompt_with_rag(mock_document)
-        assert "CONTEXT FROM SIMILAR DOCUMENTS:" in prompt
+        assert "Additional context from similar documents:" in prompt
 
 
 @patch("paperless_ai.ai_classifier.query_similar_documents")

src/paperless_ai/tests/test_client.py

@@ -3,6 +3,7 @@ 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
 
@@ -69,13 +70,27 @@ 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
-    mock_llm_instance.complete.return_value = "test_result"
+
+    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]
 
     client = AIClient()
     result = client.run_llm_query("test_prompt")
 
-    mock_llm_instance.complete.assert_called_once_with("test_prompt")
-    assert result == "test_result"
+    assert result["title"] == "Test Title"
 
 
 def test_run_chat(mock_ai_config, mock_ollama_llm):

src/paperless_ai/tools.py

@@ -0,0 +1,10 @@
+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]