add way for tools (summarize) to stop agent run and return tool output to user

.gitignore

@@ -44,9 +44,6 @@ env.d/development/*
 !env.d/development/*.dist
 env.d/terraform
 
-# Configuration
-**/conversations/configuration/llm/dev.json
-
 # npm
 node_modules
 
@@ -82,6 +79,3 @@ db.sqlite3
 
 # Docker compose override
 compose.override.yml
-
-# Docling
-docling-models

CHANGELOG.md

@@ -10,22 +10,6 @@ and this project adheres to
 
 ### Added
 
-- ✨(backend) add FindRagBackend
-
-### Changed
-
-- 📦️(front) update react
-
-### Fixed
-
-- 🐛(e2e) fix test-e2e-chromium
-- 🐛(back) fix system prompt compatibility with self-hosted models #200
-- ⚰️(back) remove dead code and unused files
-
-## [0.0.10] - 2025-12-15
-
-### Added
-
 - ✨(front) add retry button
 
 ### Fixed
@@ -40,7 +24,6 @@ and this project adheres to
 ## [0.0.9] - 2025-11-17
 
 ### Added
-
 - ✨(front) add code copy button
 - ✨(RAG) add generic collection RAG tools #159
 
@@ -48,6 +31,7 @@ and this project adheres to
 
 - 🔊(langfuse) enable tracing with redacted content #162
 
+
 ## [0.0.8] - 2025-11-10
 
 ### Fixed
@@ -62,24 +46,28 @@ and this project adheres to
 
 - 🔥(posthog) remove posthog middleware for async mode fix #146
 
+
 ## [0.0.7] - 2025-10-28
 
 ### Fixed
 
 - 🚑️(posthog) fix the posthog middleware for async mode #133
 
+
 ## [0.0.6] - 2025-10-28
 
 ### Fixed
 
 - 🚑️(stats) fix tracking id in upload event #130
 
+
 ## [0.0.5] - 2025-10-27
 
 ### Fixed
 
 - 🚑️(drag-drop) fix the rejection display on Safari #127
 
+
 ## [0.0.4] - 2025-10-27
 
 ### Added
@@ -96,12 +84,14 @@ and this project adheres to
 - 🐛(front) fix mobile source
 - 🐛(attachments) reject the whole drag&drop if unsupported formats #123
 
+
 ## [0.0.3] - 2025-10-21
 
 ### Fixed
 
 - 🚑️(web-search) fix missing argument in RAG backend #116
 
+
 ## [0.0.2] - 2025-10-21
 
 ### Added
@@ -111,7 +101,6 @@ and this project adheres to
 - 📈(posthog) add `sub` field to tracking #95
 
 ### Changed
-
 - 🔧(front) change links feedback tchap + settings popup
 - 🐛(front) code activation fix session end #93
 - 💬(wording) error page wording #102
@@ -119,6 +108,7 @@ and this project adheres to
 - 🐛(activation-codes) create contact in brevo before add to list #108
 - ⚗️(summarization) add system prompt to handle tool #112
 
+
 ## [0.0.1] - 2025-10-19
 
 ### Changed
@@ -141,7 +131,7 @@ and this project adheres to
 - 🎨(front) change list attachment in chat
 - 🎨(front) move emplacement for attachment
 - 🎨(ui) retour ui sources files
-- ✨(ui) fix retour global ui
+- ✨(ui) fix retour global ui 
 - 🐛(fix) broken staging css
 - 🎨(alpha) adjustment for alpha version
 - ✨(ui) delete flex message
@@ -176,8 +166,7 @@ and this project adheres to
 - 💄(chat) add code highlighting for LLM responses #67
 
 
-[unreleased]: https://github.com/suitenumerique/conversations/compare/v0.0.10...main
-[0.0.10]: https://github.com/suitenumerique/conversations/releases/v0.0.10
+[unreleased]: https://github.com/suitenumerique/conversations/compare/v0.0.9...main
 [0.0.9]: https://github.com/suitenumerique/conversations/releases/v0.0.9
 [0.0.8]: https://github.com/suitenumerique/conversations/releases/v0.0.8
 [0.0.7]: https://github.com/suitenumerique/conversations/releases/v0.0.7

compose.yml

@@ -71,9 +71,6 @@ services:
       - "host.docker.internal:host-gateway"
     ports:
       - "8071:8000"
-    networks:
-      - default
-      - lasuite
     volumes:
       - ./src/backend:/app
       - ./data/static:/data/static
@@ -92,9 +89,6 @@ services:
     image: nginx:1.25
     ports:
       - "8083:8083"
-    networks:
-      - default
-      - lasuite
     volumes:
       - ./docker/files/etc/nginx/conf.d:/etc/nginx/conf.d:ro
     depends_on:
@@ -183,8 +177,3 @@ services:
       kc_postgresql:
         condition: service_healthy
         restart: true
-
-networks:
-  lasuite:
-    name: lasuite-network
-    driver: bridge

docs/env.md

@@ -95,9 +95,6 @@ These are the environment variables you can set for the `conversations-backend`
 | CACHES_KEY_PREFIX                               | The prefix used to every cache keys.                                                                                              | conversations                                           |
 | THEME_CUSTOMIZATION_FILE_PATH                   | full path to the file customizing the theme. An example is provided in src/backend/conversations/configuration/theme/default.json | BASE_DIR/conversations/configuration/theme/default.json |
 | THEME_CUSTOMIZATION_CACHE_TIMEOUT               | Cache duration for the customization settings                                                                                     | 86400                                                   |
-| FIND_API_KEY                                    | API key of Find                                                                                                                   |                                                         |
-| FIND_API_URL                                    | URL of Find                                                                                                                       | `https://app-find/api`                                  |
-| FIND_API_TIMEOUT                                | Find API timeout                                                                                                                  | 30                                                      |
 
 
 ## conversations-frontend image

docs/llm-configuration.md

@@ -244,9 +244,9 @@ For Mistral AI models using the Etalab platform:
 {
   "models": [
     {
-      "hrid": "mistral-medium",
-      "model_name": "mistral-medium-2508",
-      "human_readable_name": "Mistral Medium (Etalab)",
+      "hrid": "mistral-large",
+      "model_name": "mistral-large-latest",
+      "human_readable_name": "Mistral Large (Etalab)",
       "provider_name": "mistral-etalab",
       "profile": null,
       "settings": {

docs/tools/web_search_brave.md

@@ -357,7 +357,6 @@ The RAG backend performs semantic search to find the most relevant content:
 rag_results = document_store.search(
     query,
     results_count=settings.BRAVE_RAG_WEB_SEARCH_CHUNK_NUMBER,
-    **kwargs, # Additional search parameters like session with access_token
 )
 ```
 

package.json

@@ -0,0 +1,6 @@
+{
+  "dependencies": {
+    "@ai-sdk/react": "^1.2.12",
+    "@ai-sdk/ui-utils": "^1.2.11"
+  }
+}

src/backend/chat/agent_rag/document_converter/parser.py

@@ -1,100 +0,0 @@
-"""Document parsers for RAG backends."""
-
-import logging
-from io import BytesIO
-from urllib.parse import urljoin
-
-from django.conf import settings
-
-import requests
-from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
-from docling.datamodel.base_models import InputFormat
-from docling.datamodel.pipeline_options import PdfPipelineOptions, TableStructureOptions
-from docling.document_converter import DocumentConverter as DoclingDocumentConverter
-from docling.document_converter import PdfFormatOption
-from docling_core.types.io import DocumentStream
-
-from chat.agent_rag.document_converter.markitdown import (
-    DocumentConverter as MarkitdownDocumentConverter,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class BaseParser:
-    """Base class for document parsers."""
-
-    def parse_document(self, name: str, content_type: str, content: BytesIO) -> str:
-        """
-        Parse the document and prepare it for the search operation.
-        This method should handle the logic to convert the document
-        into a format suitable for storage.
-
-        Args:
-            name (str): The name of the document.
-            content_type (str): The MIME type of the document (e.g., "application/pdf").
-            content (BytesIO): The content of the document as a BytesIO stream.
-
-        Returns:
-            str: The document content in Markdown format.
-        """
-        raise NotImplementedError("Must be implemented in subclass.")
-
-
-class AlbertParser(BaseParser):
-    """Document parser using Albert API for PDFs and DocumentConverter for other formats."""
-
-    endpoint = urljoin(settings.ALBERT_API_URL, "/v1/parse-beta")
-
-    def parse_pdf_document(self, name: str, content_type: str, content: bytes) -> str:
-        """Parse PDF document using Albert API."""
-        response = requests.post(
-            self.endpoint,
-            headers={
-                "Authorization": f"Bearer {settings.ALBERT_API_KEY}",
-            },
-            files={
-                "file": (name, content, content_type),
-                "output_format": (None, "markdown"),
-            },
-            timeout=settings.ALBERT_API_PARSE_TIMEOUT,
-        )
-        response.raise_for_status()
-
-        return "\n\n".join(
-            document_page["content"] for document_page in response.json().get("data", [])
-        )
-
-    def parse_document(self, name: str, content_type: str, content: bytes) -> str:
-        """Parse document based on content type."""
-        if content_type == "application/pdf":
-            return self.parse_pdf_document(name=name, content_type=content_type, content=content)
-        return MarkitdownDocumentConverter().convert_raw(
-            name=name, content_type=content_type, content=content
-        )
-
-
-class DoclingParser(BaseParser):
-    """Document parser using Docling's DocumentConverter."""
-
-    artifacts_path = "src/backend/docling-models"
-
-    def __init__(self):
-        pipeline_options = PdfPipelineOptions(artifacts_path=self.artifacts_path)
-        pipeline_options.do_ocr = True
-        pipeline_options.do_table_structure = True
-        pipeline_options.table_structure_options = TableStructureOptions(do_cell_matching=False)
-
-        self.converter = DoclingDocumentConverter(
-            format_options={
-                InputFormat.PDF: PdfFormatOption(
-                    pipeline_options=pipeline_options,
-                    backend=PyPdfiumDocumentBackend
-                )}
-        )
-
-    def parse_document(self, name: str, content_type: str, content: bytes) -> str:
-        """Parse document using Docling's DocumentConverter."""
-        return self.converter.convert(
-            DocumentStream(name=name, stream=BytesIO(content))
-        ).document.export_to_markdown()

src/backend/chat/agent_rag/document_rag_backends/albert_rag_backend.py

@@ -13,7 +13,7 @@ import requests
 
 from chat.agent_rag.albert_api_constants import Searches
 from chat.agent_rag.constants import RAGWebResult, RAGWebResults, RAGWebUsage
-from chat.agent_rag.document_converter.parser import DoclingParser
+from chat.agent_rag.document_converter.markitdown import DocumentConverter
 from chat.agent_rag.document_rag_backends.base_rag_backend import BaseRagBackend
 
 logger = logging.getLogger(__name__)
@@ -26,6 +26,9 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
 
     It provides methods to:
     - Create a collection for the search operation.
+    - Parse documents and convert them to Markdown format:
+       + Handle PDF parsing using the Albert API.
+       + Use the DocumentConverter (markitdown) for other formats.
     - Store parsed documents in the Albert collection.
     - Perform a search operation using the Albert API.
     """
@@ -43,9 +46,10 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
         }
         self._collections_endpoint = urljoin(self._base_url, "/v1/collections")
         self._documents_endpoint = urljoin(self._base_url, "/v1/documents")
+        self._pdf_parser_endpoint = urljoin(self._base_url, "/v1/parse-beta")
         self._search_endpoint = urljoin(self._base_url, "/v1/search")
+
         self._default_collection_description = "Temporary collection for RAG document search"
-        self.parser = DoclingParser()
 
     def create_collection(self, name: str, description: Optional[str] = None) -> str:
         """
@@ -110,7 +114,59 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
             )
             response.raise_for_status()
 
-    def store_document(self, name: str, content: str, **kwargs) -> None:
+    def parse_pdf_document(self, name: str, content_type: str, content: BytesIO) -> str:
+        """
+        Parse the PDF document content and return the text content.
+        This method should handle the logic to convert the PDF into
+        a format suitable for the Albert API.
+        """
+        response = requests.post(
+            self._pdf_parser_endpoint,
+            headers=self._headers,
+            files={
+                "file": (
+                    name,
+                    content,
+                    content_type,
+                ),  # Use the name as the filename in the request
+                "output_format": (None, "markdown"),  # Specify the output format as Markdown,
+            },
+            timeout=settings.ALBERT_API_PARSE_TIMEOUT,
+        )
+        response.raise_for_status()
+
+        return "\n\n".join(
+            document_page["content"] for document_page in response.json().get("data", [])
+        )
+
+    def parse_document(self, name: str, content_type: str, content: BytesIO):
+        """
+        Parse the document and prepare it for the search operation.
+        This method should handle the logic to convert the document
+        into a format suitable for the Albert API.
+
+        Args:
+            name (str): The name of the document.
+            content_type (str): The MIME type of the document (e.g., "application/pdf").
+            content (BytesIO): The content of the document as a BytesIO stream.
+
+        Returns:
+            str: The document content in Markdown format.
+        """
+        # Implement the parsing logic here
+        if content_type == "application/pdf":
+            # Handle PDF parsing
+            markdown_content = self.parse_pdf_document(
+                name=name, content_type=content_type, content=content
+            )
+        else:
+            markdown_content = DocumentConverter().convert_raw(
+                name=name, content_type=content_type, content=content
+            )
+
+        return markdown_content
+
+    def store_document(self, name: str, content: str) -> None:
         """
         Store the document content in the Albert collection.
         This method should handle the logic to send the document content to the Albert API.
@@ -118,7 +174,6 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
         Args:
             name (str): The name of the document.
             content (str): The content of the document in Markdown format.
-            **kwargs: Additional arguments.
         """
         response = requests.post(
             urljoin(self._base_url, self._documents_endpoint),
@@ -133,7 +188,7 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
         logger.debug(response.json())
         response.raise_for_status()
 
-    async def astore_document(self, name: str, content: str, **kwargs) -> None:
+    async def astore_document(self, name: str, content: str) -> None:
         """
         Store the document content in the Albert collection.
         This method should handle the logic to send the document content to the Albert API.
@@ -141,7 +196,6 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
         Args:
             name (str): The name of the document.
             content (str): The content of the document in Markdown format.
-            **kwargs: Additional arguments.
         """
         async with httpx.AsyncClient(timeout=settings.ALBERT_API_TIMEOUT) as client:
             response = await client.post(
@@ -159,14 +213,13 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
             logger.debug(response.json())
             response.raise_for_status()
 
-    def search(self, query: str, results_count: int = 4, **kwargs) -> RAGWebResults:
+    def search(self, query, results_count: int = 4) -> RAGWebResults:
         """
         Perform a search using the Albert API based on the provided query.
 
         Args:
             query (str): The search query.
             results_count (int): The number of results to return.
-            **kwargs: Additional arguments.
 
         Returns:
             RAGWebResults: The search results.
@@ -203,14 +256,13 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
             ),
         )
 
-    async def asearch(self, query, results_count: int = 4, **kwargs) -> RAGWebResults:
+    async def asearch(self, query, results_count: int = 4) -> RAGWebResults:
         """
         Perform an asynchronous search using the Albert API based on the provided query.
 
         Args:
             query (str): The search query.
             results_count (int): The number of results to return.
-            **kwargs: Additional arguments.
 
         Returns:
             RAGWebResults: The search results.

src/backend/chat/agent_rag/document_rag_backends/base_rag_backend.py

@@ -8,7 +8,6 @@ from typing import List, Optional
 from asgiref.sync import sync_to_async
 
 from chat.agent_rag.constants import RAGWebResults
-from chat.agent_rag.document_converter.parser import BaseParser
 
 logger = logging.getLogger(__name__)
 
@@ -39,7 +38,6 @@ class BaseRagBackend:
         self.collection_id = collection_id
         self.read_only_collection_id = read_only_collection_id or []
         self._default_collection_description = "Temporary collection for RAG document search"
-        self.parser: BaseParser = BaseParser()
 
     def get_all_collection_ids(self) -> List[str]:
         """
@@ -55,7 +53,7 @@ class BaseRagBackend:
 
         collection_ids = []
         if self.collection_id:
-            collection_ids.append(self.collection_id)
+            collection_ids.append(int(self.collection_id))
         if self.read_only_collection_id:
             collection_ids.extend(
                 [int(collection_id) for collection_id in self.read_only_collection_id]
@@ -90,9 +88,9 @@ class BaseRagBackend:
         Returns:
             str: The document content in Markdown format.
         """
-        return self.parser.parse_document(name, content_type, content)
+        raise NotImplementedError("Must be implemented in subclass.")
 
-    def store_document(self, name: str, content: str, **kwargs) -> None:
+    def store_document(self, name: str, content: str) -> None:
         """
         Store the document content in the collection.
         This method should handle the logic to send the document content to the API.
@@ -100,11 +98,10 @@ class BaseRagBackend:
         Args:
             name (str): The name of the document.
             content (str): The content of the document in Markdown format.
-            **kwargs: Additional arguments. ex: "user_sub" for access control.
         """
         raise NotImplementedError("Must be implemented in subclass.")
 
-    async def astore_document(self, name: str, content: str, **kwargs) -> None:
+    async def astore_document(self, name: str, content: str) -> None:
         """
         Store the document content in the collection.
         This method should handle the logic to send the document content to the API.
@@ -112,13 +109,10 @@ class BaseRagBackend:
         Args:
             name (str): The name of the document.
             content (str): The content of the document in Markdown format.
-            **kwargs: Additional arguments. ex: "user_sub" for access control.
         """
-        return await sync_to_async(self.store_document)(name=name, content=content, **kwargs)
+        return await sync_to_async(self.store_document)(name=name, content=content)
 
-    def parse_and_store_document(
-        self, name: str, content_type: str, content: BytesIO, **kwargs
-    ) -> str:
+    def parse_and_store_document(self, name: str, content_type: str, content: BytesIO) -> str:
         """
         Parse the document and store it in the Albert collection.
 
@@ -126,13 +120,12 @@ class BaseRagBackend:
             name (str): The name of the document.
             content_type (str): The MIME type of the document (e.g., "application/pdf").
             content (BytesIO): The content of the document as a BytesIO stream.
-            **kwargs: Additional arguments. ex: "user_sub" for access control.
         """
         if not self.collection_id:
             raise RuntimeError("The RAG backend requires collection_id")
 
         document_content = self.parse_document(name, content_type, content)
-        self.store_document(name, document_content, **kwargs)
+        self.store_document(name, document_content)
         return document_content
 
     def delete_collection(self) -> None:
@@ -149,27 +142,17 @@ class BaseRagBackend:
         """
         return await sync_to_async(self.delete_collection)()
 
-    def search(self, query: str, results_count: int = 4, **kwargs) -> RAGWebResults:
+    def search(self, query, results_count: int = 4) -> RAGWebResults:
         """
         Search the collection for the given query.
-
-        Args:
-            query: The search query string.
-            results_count: Number of results to return.
-            **kwargs: Additional arguments. ex: 'session' for OIDC authentication.
         """
         raise NotImplementedError("Must be implemented in subclass.")
 
-    async def asearch(self, query: str, results_count: int = 4, **kwargs) -> RAGWebResults:
+    async def asearch(self, query, results_count: int = 4) -> RAGWebResults:
         """
-        Search the collection for the given query asynchronously.
-
-        Args:
-            query: The search query string.
-            results_count: Number of results to return.
-            **kwargs: Additional arguments. ex: 'session' for OIDC authentication.
+        Search the collection for the given query.
         """
-        return await sync_to_async(self.search)(query=query, results_count=results_count, **kwargs)
+        return await sync_to_async(self.search)(query=query, results_count=results_count)
 
     @classmethod
     @contextmanager

src/backend/chat/agent_rag/document_rag_backends/find_rag_backend.py

@@ -1,153 +0,0 @@
-"""Implementation of the Find API for RAG document search."""
-
-import logging
-import uuid
-from typing import List, Optional
-from urllib.parse import urljoin
-from uuid import uuid4
-
-from django.conf import settings
-from django.utils import timezone
-
-import requests
-
-from chat.agent_rag.constants import RAGWebResult, RAGWebResults, RAGWebUsage
-from chat.agent_rag.document_converter.parser import DoclingParser
-from chat.agent_rag.document_rag_backends.base_rag_backend import BaseRagBackend
-from utils.oidc import with_fresh_access_token
-
-logger = logging.getLogger(__name__)
-
-
-SUPPORTED_LANGUAGE_CODES = ["en", "fr", "de", "nl"]
-
-
-class FindRagBackend(BaseRagBackend):
-    """
-    This class is a placeholder for the Find API implementation.
-    It is designed to be used with the RAG (Retrieval-Augmented Generation) document search system.
-
-    It provides methods to:
-    - Store parsed documents in the Find index.
-    - Perform a search operation using the Find API.
-    """
-
-    def __init__(
-        self,
-        collection_id: Optional[str] = None,
-        read_only_collection_id: Optional[List[str]] = None,
-    ):
-        # Initialize any necessary parameters or configurations here
-        super().__init__(collection_id, read_only_collection_id)
-        self.api_key = settings.FIND_API_KEY
-        self.search_endpoint = "api/v1.0/documents/search/"
-        self.indexing_endpoint = "api/v1.0/documents/index/"
-        self.parser = DoclingParser()
-
-    def create_collection(self, name: str, description: Optional[str] = None) -> str:
-        """
-        init collection_id
-        """
-        self.collection_id = self.collection_id or str(uuid.uuid4())
-        return self.collection_id
-
-    def delete_collection(self) -> None:
-        """
-        Deletion not available
-        """
-        logger.warning("deletion of collections is not yet supported in FindRagBackend")
-
-    def store_document(self, name: str, content: str, **kwargs) -> None:
-        """
-        index document in Find
-
-        Args:
-            name (str): The name of the document.
-            content (str): The content of the document in Markdown format.
-            user_sub (str): The user subject identifier for access control.
-        """
-        logger.debug("index document '%s' in Find", name)
-
-        user_sub = kwargs.get("user_sub")
-        if not user_sub:
-            raise ValueError("user_sub is required to store document in FindRagBackend")
-
-        response = requests.post(
-            urljoin(settings.FIND_API_URL, self.indexing_endpoint),
-            headers={"Authorization": f"Bearer {self.api_key}"},
-            json={
-                "id": str(uuid4()),
-                "title": str(name) or "",
-                "depth": 0,
-                "path": str(name) or "",
-                "numchild": 0,
-                "content": content or "",
-                "created_at": timezone.now().isoformat(),
-                "updated_at": timezone.now().isoformat(),
-                "tags": [f"collection-{self.collection_id}"],
-                "size": len(content.encode("utf-8")),
-                "users": [user_sub],
-                "groups": [],
-                "reach": "authenticated",
-                "is_active": True,
-            },
-            timeout=settings.FIND_API_TIMEOUT,
-        )
-        response.raise_for_status()
-
-    @with_fresh_access_token
-    def search(self, query: str, results_count: int = 4, **kwargs) -> RAGWebResults:
-        """
-        Perform a search using the Find API.
-        Uses the user's OIDC token from the request session.
-
-        Args:
-            query: The search query.
-            results_count: Number of results to return.
-            **kwargs: Additional arguments. Expected: 'session' containing OIDC tokens,
-
-        Returns:
-            RAGWebResults: The search results.
-        """
-        logger.debug("search documents in Find with query '%s'", query)
-
-        response = requests.post(
-            urljoin(settings.FIND_API_URL, self.search_endpoint),
-            headers={"Authorization": f"Bearer {kwargs['session'].get('oidc_access_token')}"},
-            json={
-                "q": query,
-                "tags": [
-                    f"collection-{collection_id}" for collection_id in self.get_all_collection_ids()
-                ],
-                "k": results_count,
-            },
-            timeout=settings.FIND_API_TIMEOUT,
-        )
-        response.raise_for_status()
-
-        return RAGWebResults(
-            data=[
-                RAGWebResult(
-                    url=get_language_value(result["_source"], "title"),
-                    content=get_language_value(result["_source"], "content"),
-                    score=result["_score"],
-                )
-                for result in response.json()
-            ],
-            usage=RAGWebUsage(
-                prompt_tokens=0,
-                completion_tokens=0,
-            ),
-        )
-
-
-def get_language_value(source, language_field):
-    """
-    extract the value of the language field with the correct language_code extension.
-    "title" and "content" have extensions like "title.en" or "title.fr".
-    get_language_value will return the value regardless of the extension.
-    """
-    for language_code in SUPPORTED_LANGUAGE_CODES:
-        if f"{language_field}.{language_code}" in source:
-            return source[f"{language_field}.{language_code}"]
-    raise ValueError(f"No '{language_field}' field with any supported language code in object")

src/backend/chat/agent_rag/document_search/albert_api.py

@@ -11,7 +11,7 @@ import requests
 
 from chat.agent_rag.albert_api_constants import Searches
 from chat.agent_rag.constants import RAGWebResult, RAGWebResults, RAGWebUsage
-from chat.agent_rag.document_converter.parser import DoclingParser
+from chat.agent_rag.document_converter.markitdown import DocumentConverter
 from chat.models import ChatConversation
 
 logger = logging.getLogger(__name__)
@@ -80,6 +80,58 @@ class AlbertRagDocumentSearch:
         self.conversation.collection_id = str(response.json()["id"])
         return True
 
+    def _parse_pdf_document(self, name: str, content_type: str, content: BytesIO) -> str:
+        """
+        Parse the PDF document content and return the text content.
+        This method should handle the logic to convert the PDF into
+        a format suitable for the Albert API.
+        """
+        response = requests.post(
+            self._pdf_parser_endpoint,
+            headers=self._headers,
+            files={
+                "file": (
+                    name,
+                    content,
+                    content_type,
+                ),  # Use the name as the filename in the request
+                "output_format": (None, "markdown"),  # Specify the output format as Markdown,
+            },
+            timeout=settings.ALBERT_API_PARSE_TIMEOUT,
+        )
+        response.raise_for_status()
+
+        return "\n\n".join(
+            document_page["content"] for document_page in response.json().get("data", [])
+        )
+
+    def parse_document(self, name: str, content_type: str, content: BytesIO):
+        """
+        Parse the document and prepare it for the search operation.
+        This method should handle the logic to convert the document
+        into a format suitable for the Albert API.
+
+        Args:
+            name (str): The name of the document.
+            content_type (str): The MIME type of the document (e.g., "application/pdf").
+            content (BytesIO): The content of the document as a BytesIO stream.
+
+        Returns:
+            str: The document content in Markdown format.
+        """
+        # Implement the parsing logic here
+        if content_type == "application/pdf":
+            # Handle PDF parsing
+            markdown_content = self._parse_pdf_document(
+                name=name, content_type=content_type, content=content
+            )
+        else:
+            markdown_content = DocumentConverter().convert_raw(
+                name=name, content_type=content_type, content=content
+            )
+
+        return markdown_content
+
     def _store_document(self, name: str, content: str):
         """
         Store the document content in the Albert collection.
@@ -104,7 +156,7 @@ class AlbertRagDocumentSearch:
         logger.debug(response.json())
         response.raise_for_status()
 
-    def parse_and_store_document(self, name: str, content_type: str, content: bytes):
+    def parse_and_store_document(self, name: str, content_type: str, content: BytesIO):
         """
         Parse the document and store it in the Albert collection.
 
@@ -113,9 +165,7 @@ class AlbertRagDocumentSearch:
             content_type (str): The MIME type of the document (e.g., "application/pdf").
             content (BytesIO): The content of the document as a BytesIO stream.
         """
-        document_content = DoclingParser().parse_document(
-            name=name, content_type=content_type, content=content
-        )
+        document_content = self.parse_document(name, content_type, content)
         self._store_document(name, document_content)
         return document_content
 

src/backend/chat/agents/base.py

@@ -190,4 +190,6 @@ class BaseAgent(Agent):
 
         _tools = [get_pydantic_tools_by_name(tool_name) for tool_name in self.configuration.tools]
 
-        super().__init__(model=_model_instance, instructions=_system_prompt, tools=_tools, **kwargs)
+        super().__init__(
+            model=_model_instance, system_prompt=_system_prompt, tools=_tools, **kwargs
+        )

src/backend/chat/agents/conversation.py

@@ -16,6 +16,7 @@ from .base import BaseAgent
 
 logger = logging.getLogger(__name__)
 
+
 MOCKED_RESPONSE = """
 # **Ode to the AI Assistant** 🤖✨
 
@@ -101,10 +102,10 @@ class ConversationAgent(BaseAgent):
         if settings.WARNING_MOCK_CONVERSATION_AGENT:
             self._model = FunctionModel(stream_function=mocked_agent_model)
 
-        @self.instructions
+        @self.system_prompt
         def add_the_date() -> str:
             """
-            Dynamic instruction function to add the current date.
+            Dynamic system prompt function to add the current date.
 
             Warning: this will always use the date in the server timezone,
             not the user's timezone...
@@ -112,9 +113,9 @@ class ConversationAgent(BaseAgent):
             _formatted_date = formats.date_format(timezone.now(), "l d/m/Y", use_l10n=False)
             return f"Today is {_formatted_date}."
 
-        @self.instructions
+        @self.system_prompt
         def enforce_response_language() -> str:
-            """Dynamic instruction function to set the expected language to use."""
+            """Dynamic system prompt function to set the expected language to use."""
             return f"Answer in {get_language_name(language).lower()}." if language else ""
 
     def get_web_search_tool_name(self) -> str | None:

src/backend/chat/clients/pydantic_ai.py

@@ -78,9 +78,6 @@ from chat.tools.document_summarize import document_summarize
 from chat.vercel_ai_sdk.core import events_v4, events_v5
 from chat.vercel_ai_sdk.encoder import EventEncoder
 
-# Keep at the top of the file to avoid mocking issues
-document_store_backend = import_string(settings.RAG_DOCUMENT_SEARCH_BACKEND)
-
 logger = logging.getLogger(__name__)
 
 User = get_user_model()
@@ -92,7 +89,6 @@ class ContextDeps:
 
     conversation: models.ChatConversation
     user: User
-    session: Optional[Dict] = None
     web_search_enabled: bool = False
 
 
@@ -107,14 +103,7 @@ def get_model_configuration(model_hrid: str):
 class AIAgentService:  # pylint: disable=too-many-instance-attributes
     """Service class for AI-related operations (Pydantic-AI edition)."""
 
-    def __init__(  # pylint: disable=too-many-arguments,too-many-positional-arguments
-        self,
-        conversation: models.ChatConversation,
-        user,
-        session=None,
-        model_hrid=None,
-        language=None,
-    ):
+    def __init__(self, conversation: models.ChatConversation, user, model_hrid=None, language=None):
         """
         Initialize the AI agent service.
 
@@ -144,7 +133,6 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
         self._context_deps = ContextDeps(
             conversation=conversation,
             user=user,
-            session=session,
             web_search_enabled=self._is_web_search_enabled,
         )
 
@@ -248,7 +236,6 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
         Parse and store input documents in the conversation's document store.
         """
         # Early external document URL rejection
-
         if any(
             not document.url.startswith("/media-key/")
             for document in documents
@@ -262,6 +249,8 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
         ):
             raise ValueError("Document URL does not belong to the conversation.")
 
+        document_store_backend = import_string(settings.RAG_DOCUMENT_SEARCH_BACKEND)
+
         document_store = document_store_backend(self.conversation.collection_id)
         if not document_store.collection_id:
             # Create a new collection for the conversation
@@ -287,7 +276,6 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                         name=document.identifier,
                         content_type=document.media_type,
                         content=document_data,
-                        user_sub=self.user.sub,
                     )
                 else:
                     # Remote URL
@@ -297,7 +285,6 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                     name=document.identifier,
                     content_type=document.media_type,
                     content=document.data,
-                    user_sub=self.user.sub,
                 )
 
             if not document.media_type.startswith("text/"):
@@ -433,7 +420,6 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                     ],
                 },
             )
-
             try:
                 await self.parse_input_documents(input_documents)
             except Exception as exc:  # pylint: disable=broad-except
@@ -471,7 +457,7 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
 
         if force_web_search:
 
-            @self.conversation_agent.instructions
+            @self.conversation_agent.system_prompt
             def force_web_search_prompt() -> str:
                 """Dynamic system prompt function to force web search."""
                 return (
@@ -519,7 +505,7 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                 )
 
             # Inform the model (system-level) that documents are attached and available
-            @self.conversation_agent.instructions
+            @self.conversation_agent.system_prompt
             def attached_documents_note() -> str:
                 return (
                     "[Internal context] User documents are attached to this conversation. "
@@ -563,6 +549,10 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
 
             _final_output_from_tool = None
             _ui_sources = []
+            # Track if a tool (like summarize) should directly provide the final answer
+            _final_response_from_tool_streamed = False
+            _summarize_tool_call_ids: set[str] = set()
+            _stop_after_tool = False
 
             # Help Mistral to prevent `Unexpected role 'user' after role 'tool'` error.
             if history and history[-1].kind == "request":
@@ -588,6 +578,10 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                             logger.debug("node.run result: %s", result)
                             for part in result.model_response.parts:
                                 if isinstance(part, TextPart):
+                                    # If a tool (like summarize) is the final answer,
+                                    # do not stream additional model text.
+                                    if _final_response_from_tool_streamed:
+                                        continue
                                     if self._fake_streaming_delay:
                                         for i in range(0, len(part.content), 4):
                                             await self._agent_stop_streaming()
@@ -619,7 +613,10 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                                     logger.debug("PartStartEvent: %s", dataclasses.asdict(event))
 
                                     if isinstance(event.part, TextPart):
-                                        yield events_v4.TextPart(text=event.part.content)
+                                        # If a tool (like summarize) is the final answer,
+                                        # do not stream additional model text.
+                                        if not _final_response_from_tool_streamed:
+                                            yield events_v4.TextPart(text=event.part.content)
                                     elif isinstance(event.part, ToolCallPart):
                                         yield events_v4.ToolCallStreamingStartPart(
                                             tool_call_id=event.part.tool_call_id,
@@ -637,7 +634,12 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                                         dataclasses.asdict(event),
                                     )
                                     if isinstance(event.delta, TextPartDelta):
-                                        yield events_v4.TextPart(text=event.delta.content_delta)
+                                        # If a tool (like summarize) is the final answer,
+                                        # do not stream additional model text.
+                                        if not _final_response_from_tool_streamed:
+                                            yield events_v4.TextPart(
+                                                text=event.delta.content_delta
+                                            )
                                     elif isinstance(event.delta, ToolCallPartDelta):
                                         _tool_is_streaming = True
                                         yield events_v4.ToolCallDeltaPart(
@@ -662,6 +664,10 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                                 )
                                 if isinstance(event, FunctionToolCallEvent):
                                     if not _tool_is_streaming:
+                                        # Track summarize tool calls so we can treat their
+                                        # result as the final answer.
+                                        if event.part.tool_name == "summarize":
+                                            _summarize_tool_call_ids.add(event.tool_call_id)
                                         yield events_v4.ToolCallPart(
                                             tool_call_id=event.tool_call_id,
                                             tool_name=event.part.tool_name,
@@ -670,6 +676,9 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                                             else {},
                                         )
                                 elif isinstance(event, FunctionToolResultEvent):
+                                    _is_summarize_result = (
+                                        event.tool_call_id in _summarize_tool_call_ids
+                                    )
                                     if isinstance(event.result, ToolReturnPart):
                                         if event.result.metadata and (
                                             sources := event.result.metadata.get("sources")
@@ -689,21 +698,40 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                                                     **_new_source_ui.source.model_dump()
                                                 )
 
-                                        yield events_v4.ToolResultPart(
-                                            tool_call_id=event.tool_call_id,
-                                            result=event.result.content,
-                                        )
+                                        if _is_summarize_result:
+                                            # For summarize, the tool output IS the final answer.
+                                            _final_output_from_tool = event.result.content
+                                            _final_response_from_tool_streamed = True
+                                            _stop_after_tool = True
+                                            if event.result.content:
+                                                yield events_v4.TextPart(
+                                                    text=event.result.content
+                                                )
+                                        else:
+                                            yield events_v4.ToolResultPart(
+                                                tool_call_id=event.tool_call_id,
+                                                result=event.result.content,
+                                            )
                                     elif isinstance(event.result, RetryPromptPart):
-                                        yield events_v4.ToolResultPart(
-                                            tool_call_id=event.tool_call_id,
-                                            result=event.result.content,
-                                        )
+                                        # RetryPrompts are internal hints for the model,
+                                        # they should not replace the final user-visible answer.
+                                        if not _is_summarize_result:
+                                            yield events_v4.ToolResultPart(
+                                                tool_call_id=event.tool_call_id,
+                                                result=event.result.content,
+                                            )
                                     else:
                                         logger.warning(
                                             "Unexpected tool result type: %s %s",
                                             type(event.result),
                                             dataclasses.asdict(event.result),
                                         )
+                            if _stop_after_tool:
+                                # Stop processing further tool events/nodes once summarize
+                                # has produced the final answer.
+                                break
+                    if _stop_after_tool:
+                        break
                     elif Agent.is_end_node(node):
                         # Once an End node is reached, the agent run is complete
                         logger.debug("v: %s", dataclasses.asdict(node))
@@ -724,7 +752,7 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                             message_id=_model_response_message_id,
                         )
 
-                # Final usage summary
+                # Final usage summary (even if we stopped early after a tool)
                 final_usage = run.usage()
                 usage["promptTokens"] = final_usage.input_tokens
                 usage["completionTokens"] = final_usage.output_tokens
@@ -732,19 +760,35 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
         await self._agent_stop_streaming(force_cache_check=True)
 
         # Persist conversation
-        await sync_to_async(self._update_conversation)(
-            final_output=run.result.new_messages(),
-            usage=usage,
-            final_output_from_tool=_final_output_from_tool,
-            ui_sources=_ui_sources,
-            model_response_message_id=_model_response_message_id,
-            image_key_mapping=image_key_mapping or None,
-        )
-
-        if self._langfuse_available:
-            langfuse.update_current_trace(
-                output=run.result.output if self._store_analytics else "REDACTED"
+        if _final_output_from_tool:
+            # When a tool (like summarize) produced the final answer, we don't rely
+            # on the agent's `run.result` (which might be incomplete if we stopped early).
+            await sync_to_async(self._update_conversation)(
+                final_output=[],
+                usage=usage,
+                final_output_from_tool=_final_output_from_tool,
+                ui_sources=_ui_sources,
+                model_response_message_id=_model_response_message_id,
+                image_key_mapping=image_key_mapping or None,
             )
+            if self._langfuse_available:
+                langfuse.update_current_trace(
+                    output=_final_output_from_tool if self._store_analytics else "REDACTED"
+                )
+        else:
+            await sync_to_async(self._update_conversation)(
+                final_output=run.result.new_messages(),
+                usage=usage,
+                final_output_from_tool=None,
+                ui_sources=_ui_sources,
+                model_response_message_id=_model_response_message_id,
+                image_key_mapping=image_key_mapping or None,
+            )
+            if self._langfuse_available:
+                langfuse.update_current_trace(
+                    output=run.result.output if self._store_analytics else "REDACTED"
+                )
+
         # Vercel finish message
         yield events_v4.FinishMessagePart(
             finish_reason=events_v4.FinishReason.STOP,
@@ -782,13 +826,24 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
             ],
             kind="request",
         )
-        _merged_final_output_message = ModelResponse(
-            parts=[
-                part for msg in final_output if isinstance(msg, ModelResponse) for part in msg.parts
-            ]
-            + ([TextPart(content=final_output_from_tool)] if final_output_from_tool else []),
-            kind="response",
-        )
+        if final_output_from_tool:
+            # When a tool (like summarize) produced the final answer, we only keep
+            # that content as the assistant message, to avoid the main model
+            # rephrasing or duplicating it.
+            _merged_final_output_message = ModelResponse(
+                parts=[TextPart(content=final_output_from_tool)],
+                kind="response",
+            )
+        else:
+            _merged_final_output_message = ModelResponse(
+                parts=[
+                    part
+                    for msg in final_output
+                    if isinstance(msg, ModelResponse)
+                    for part in msg.parts
+                ],
+                kind="response",
+            )
 
         if image_key_mapping:
             for part in _merged_final_output_request.parts:
@@ -799,18 +854,23 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                         ):
                             content.url = unsigned_url
 
+        _request_ui_message = model_message_to_ui_message(_merged_final_output_request)
         _output_ui_message = model_message_to_ui_message(_merged_final_output_message)
-        if ui_sources:
-            _output_ui_message.parts += ui_sources
-        if model_response_message_id:
-            _output_ui_message.id = model_response_message_id
-        else:
-            logger.warning("model_response_message_id is None")
 
-        self.conversation.messages += [
-            model_message_to_ui_message(_merged_final_output_request),
-            _output_ui_message,
+        if ui_sources and _output_ui_message is not None:
+            _output_ui_message.parts += ui_sources
+        if _output_ui_message is not None:
+            if model_response_message_id:
+                _output_ui_message.id = model_response_message_id
+            else:
+                logger.warning("model_response_message_id is None")
+
+        # Only append non-empty UI messages to avoid None values,
+        # which would break Pydantic validation on list[UIMessage].
+        new_messages = [
+            msg for msg in (_request_ui_message, _output_ui_message) if msg is not None
         ]
+        self.conversation.messages += new_messages
         self.conversation.agent_usage = usage
 
         final_output_json = json.loads(

src/backend/chat/tests/agent_rag/document_converter/run_docling.py

@@ -1,29 +0,0 @@
-"""
-Unit tests for the DocumentConverter.
-
-Only for coverage as the DocumentConverter is a simple wrapper around MarkItDown.
-"""
-
-from io import BytesIO
-
-from docling.document_converter import DocumentConverter
-from docling_core.types.io import DocumentStream
-
-
-def main():
-    """Test that the DocumentConverter calls the underlying MarkItDown converter."""
-    file_path = "test.pdf"
-    converter = DocumentConverter()
-
-    # Convert from file content instead of file path
-    with open(file_path, "rb") as file:
-        content = file.read()
-        stream = DocumentStream(name="test.pdf", stream=BytesIO(content))
-        result = converter.convert(stream)
-        markdown = result.document.export_to_markdown()
-
-    assert markdown == "Document PDF test"
-
-
-if __name__ == "__main__":
-    main()

src/backend/chat/tests/agent_rag/document_converter/test.pdf

@@ -1,90 +0,0 @@
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-%Çì�¢
-5 0 obj
-<</Length 6 0 R/Filter /FlateDecode>>
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-ÍQoŽÐÌ„WÆ „#h!¨³»ú‡À˜5Sò_a Œ&¦â§�°•Ÿƒ4‡!¢ÊÅ¿ÿÑϽwÊ%çÑC—Y4[ò/a�ö³n‡D¢
-‹æhû¨Z<nØö‡�1F3Ýaj–·úì«{mùµi:uendstream
-endobj
-6 0 obj
-180
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-4 0 obj
-<</Type/Page/MediaBox [0 0 595 842]
-/Rotate 0/Parent 3 0 R
-/Resources<</ProcSet[/PDF /Text]
-/Font 8 0 R
->>
-/Contents 5 0 R
->>
-endobj
-3 0 obj
-<< /Type /Pages /Kids [
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-] /Count 1
->>
-endobj
-1 0 obj
-<</Type /Catalog /Pages 3 0 R
-/Metadata 9 0 R
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-8 0 obj
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-7 0 obj
-<</BaseFont/Times-Roman/Type/Font
-/Subtype/Type1>>
-endobj
-9 0 obj
-<</Type/Metadata
-/Subtype/XML/Length 1549>>stream
-<?xpacket begin='' id='W5M0MpCehiHzreSzNTczkc9d'?>
-<?adobe-xap-filters esc="CRLF"?>
-<x:xmpmeta xmlns:x='adobe:ns:meta/' x:xmptk='XMP toolkit 2.9.1-13, framework 1.6'>
-<rdf:RDF xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#' xmlns:iX='http://ns.adobe.com/iX/1.0/'>
-<rdf:Description rdf:about='uuid:81d69fb9-8bc7-11e4-0000-66b1dd18110c' xmlns:pdf='http://ns.adobe.com/pdf/1.3/'><pdf:Producer>GPL Ghostscript 9.06</pdf:Producer>
-<pdf:Keywords>()</pdf:Keywords>
-</rdf:Description>
-<rdf:Description rdf:about='uuid:81d69fb9-8bc7-11e4-0000-66b1dd18110c' xmlns:xmp='http://ns.adobe.com/xap/1.0/'><xmp:ModifyDate>2014-12-22T00:49:20+01:00</xmp:ModifyDate>
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src/backend/chat/tests/agent_rag/document_converter/test_docling.py

@@ -1,18 +0,0 @@
-"""
-Unit tests for the DoclingParser.
-"""
-from chat.agent_rag.document_converter.parser import DoclingParser
-
-
-def test_document_converter():
-    """Test that the DocumentConverter calls the underlying MarkItDown converter."""
-    file_name = "test"
-    content_type = "application/pdf"
-    file_path = "src/backend/chat/tests/data/test.pdf"
-    parser = DoclingParser()
-
-    with open(file_path, "rb") as file:
-        content = file.read()
-        result = parser.parse_document(name= file_name, content_type= content_type, content= content)
-
-    assert "Document PDF test" in result

src/backend/chat/tests/agent_rag/document_converter/test_markitdown.py

@@ -5,21 +5,28 @@ Only for coverage as the DocumentConverter is a simple wrapper around MarkItDown
 """
 
 from io import BytesIO
+from unittest.mock import MagicMock, patch
 
 from chat.agent_rag.document_converter.markitdown import DocumentConverter
 
 
-def test_document_converter():
+@patch("chat.agent_rag.document_converter.markitdown.MarkItDown")
+def test_document_converter(mock_markitdown: MagicMock):
     """Test that the DocumentConverter calls the underlying MarkItDown converter."""
-    file_path = "src/backend/chat/tests/data/test.pdf"
+    mock_conversion = MagicMock()
+    mock_conversion.text_content = "converted text"
+    mock_markitdown.return_value.convert_stream.return_value = mock_conversion
+
     converter = DocumentConverter()
 
-    with open(file_path, "rb") as file:
-        content = file.read()
-        result = converter.convert_raw(
-            name="test.pdf",
-            content_type="application/pdf",
-            content=content,
-        )
+    result = converter.convert_raw(
+        name="test.pdf",
+        content_type="application/pdf",
+        content=b"test content",
+    )
 
-    assert result == "Document PDF test\n\n"
+    assert result == "converted text"
+    converter.converter.convert_stream.assert_called_once()  # pylint: disable=no-member
+    args, kwargs = converter.converter.convert_stream.call_args  # pylint: disable=no-member
+    assert isinstance(args[0], BytesIO)
+    assert kwargs["file_extension"] == ".pdf"

src/backend/chat/tests/agents/test_build_conversation_agent.py

@@ -27,14 +27,9 @@ def test_build_pydantic_agent_success_no_tools():
     """Test successful agent creation without tools."""
     agent = ConversationAgent(model_hrid="default-model")
     assert isinstance(agent, Agent)
-    assert agent._system_prompts == ()
-
-    instructions = agent._instructions
-    assert len(instructions) == 3
-    assert instructions[0] == "You are a helpful assistant"
-    assert instructions[1].__name__ == "add_the_date"
-    assert instructions[2].__name__ == "enforce_response_language"
 
+    assert agent._system_prompts == ("You are a helpful assistant",)
+    assert agent._instructions == []
     assert isinstance(agent.model, OpenAIChatModel)
     assert agent.model.model_name == "model-123"
     assert str(agent.model.client.base_url) == "https://api.llm.com/v1/"
@@ -42,7 +37,6 @@ def test_build_pydantic_agent_success_no_tools():
     assert agent._function_toolset.tools == {}
 
 
-@freeze_time("2025-07-25T10:36:35.297675Z")
 def test_build_pydantic_agent_with_tools(settings):
     """Test successful agent creation with tools."""
     settings.AI_AGENT_TOOLS = ["get_current_weather"]
@@ -50,14 +44,8 @@ def test_build_pydantic_agent_with_tools(settings):
     agent = ConversationAgent(model_hrid="default-model")
     assert isinstance(agent, Agent)
 
-    instructions = agent._instructions
-    assert len(instructions) == 3
-    assert instructions[0] == "You are a helpful assistant"
-    assert instructions[1].__name__ == "add_the_date"
-    assert instructions[1]() == "Today is Friday 25/07/2025."
-    assert instructions[2].__name__ == "enforce_response_language"
-    assert instructions[2]() == ""
-
+    assert agent._system_prompts == ("You are a helpful assistant",)
+    assert agent._instructions == []
     assert isinstance(agent.model, OpenAIChatModel)
     assert agent.model.model_name == "model-123"
     assert str(agent.model.client.base_url) == "https://api.llm.com/v1/"
@@ -68,23 +56,21 @@ def test_build_pydantic_agent_with_tools(settings):
 @freeze_time("2025-07-25T10:36:35.297675Z")
 def test_add_dynamic_system_prompt():
     """
-    Ensure add_the_date and enforce_response_language instructions are registered
+    Ensure add_the_date and enforce_response_language system prompt are registered
     and returns proper values.
     """
     agent = ConversationAgent(model_hrid="default-model")
 
-    assert len(agent._system_prompt_functions) == 0
+    assert len(agent._system_prompt_functions) == 2
 
-    instructions = agent._instructions
-    assert len(instructions) == 3
-    assert instructions[0] == "You are a helpful assistant"
-    assert instructions[1].__name__ == "add_the_date"
-    assert instructions[1]() == "Today is Friday 25/07/2025."
-    assert instructions[2].__name__ == "enforce_response_language"
-    assert instructions[2]() == ""
+    assert agent._system_prompt_functions[0].function.__name__ == "add_the_date"
+    assert agent._system_prompt_functions[0].function() == "Today is Friday 25/07/2025."
+
+    assert agent._system_prompt_functions[1].function.__name__ == "enforce_response_language"
+    assert agent._system_prompt_functions[1].function() == ""
 
     agent = ConversationAgent(model_hrid="default-model", language="fr-fr")
-    assert agent._instructions[2]() == "Answer in french."
+    assert agent._system_prompt_functions[1].function() == "Answer in french."
 
 
 def test_agent_get_web_search_tool_name(settings):

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src/backend/chat/tests/tools/test_web_search_brave.py

@@ -38,6 +38,9 @@ def brave_settings(settings):
     settings.BRAVE_SEARCH_EXTRA_SNIPPETS = True
     settings.BRAVE_SUMMARIZATION_ENABLED = False
     settings.BRAVE_CACHE_TTL = 3600
+    settings.RAG_DOCUMENT_SEARCH_BACKEND = (
+        "chat.agent_rag.document_rag_backends.albert_rag_backend.AlbertRagBackend"
+    )
     settings.BRAVE_RAG_WEB_SEARCH_CHUNK_NUMBER = 5
 
 

src/backend/chat/tests/views/chat/conftest.py

@@ -1,17 +0,0 @@
-"""Common test fixtures for chat views tests."""
-
-from unittest import mock
-
-import pytest
-
-
-@pytest.fixture(autouse=True)
-def mock_process_request():
-    """
-    Mock process_request to bypass OIDC authentication in tests.
-    """
-    with mock.patch(
-        "lasuite.oidc_login.decorators.RefreshOIDCAccessToken.process_request"
-    ) as mocked_process_request:
-        mocked_process_request.return_value = None
-        yield mocked_process_request

src/backend/chat/tests/views/chat/conversations/test_conversation.py

@@ -130,16 +130,6 @@ def test_post_conversation_data_protocol(api_client, mock_openai_stream):
 
     assert mock_openai_stream.called
 
-    # ensure instructions are merged as a system prompt
-    last_request_payload = json.loads(respx.calls.last.request.content)
-    assert last_request_payload["messages"][0] == {
-        "content": (
-            "You are a helpful test assistant :)\n\nToday is Friday 25/07/2025.\n\n"
-            "Answer in english."
-        ),
-        "role": "system",
-    }
-
     chat_conversation.refresh_from_db()
     assert chat_conversation.ui_messages == [
         {
@@ -180,15 +170,29 @@ def test_post_conversation_data_protocol(api_client, mock_openai_stream):
     )
 
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
-
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": (
-                "You are a helpful test assistant :)\n\n"
-                "Today is Friday 25/07/2025.\n\nAnswer in english."
-            ),
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Today is Friday 25/07/2025.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
                 {
                     "content": ["Hello"],
                     "part_kind": "user-prompt",
@@ -251,15 +255,6 @@ def test_post_conversation_text_protocol(api_client, mock_openai_stream):
     assert response_content == "Hello there"
 
     assert mock_openai_stream.called
-    # ensure instructions are merged as a system prompt
-    last_request_payload = json.loads(respx.calls.last.request.content)
-    assert last_request_payload["messages"][0] == {
-        "content": (
-            "You are a helpful test assistant :)\n\nToday is Friday 25/07/2025.\n\n"
-            "Answer in english."
-        ),
-        "role": "system",
-    }
 
     chat_conversation.refresh_from_db()
     assert chat_conversation.ui_messages == [
@@ -301,15 +296,29 @@ def test_post_conversation_text_protocol(api_client, mock_openai_stream):
     )
 
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
-
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": (
-                "You are a helpful test assistant :)\n\n"
-                "Today is Friday 25/07/2025.\n\nAnswer in english."
-            ),
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Today is Friday 25/07/2025.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
                 {
                     "content": ["Hello"],
                     "part_kind": "user-prompt",
@@ -400,12 +409,11 @@ def test_post_conversation_with_image(api_client, mock_openai_stream_image):
     # Check the exact structure expected by the AI service
     assert body["messages"] == [
         {
-            "content": (
-                "You are a helpful test assistant :)\n\nToday is Friday 25/07/2025."
-                "\n\nAnswer in english."
-            ),
+            "content": "You are a helpful test assistant :)",
             "role": "system",
         },
+        {"content": "Today is Friday 25/07/2025.", "role": "system"},
+        {"content": "Answer in english.", "role": "system"},
         {
             "content": [
                 {"text": "Hello, what do you see on this picture?", "type": "text"},
@@ -490,12 +498,27 @@ def test_post_conversation_with_image(api_client, mock_openai_stream_image):
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": (
-                "You are a helpful test assistant :)\n\n"
-                "Today is Friday 25/07/2025.\n\nAnswer in english."
-            ),
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Today is Friday 25/07/2025.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
                 {
                     "content": [
                         "Hello, what do you see on this picture?",
@@ -593,12 +616,11 @@ def test_post_conversation_tool_call(api_client, mock_openai_stream_tool, settin
 
     assert body["messages"] == [
         {
-            "content": (
-                "You are a helpful test assistant :)\n\n"
-                "Today is Friday 25/07/2025.\n\nAnswer in english."
-            ),
+            "content": "You are a helpful test assistant :)",
             "role": "system",
         },
+        {"content": "Today is Friday 25/07/2025.", "role": "system"},
+        {"content": "Answer in english.", "role": "system"},
         {"content": [{"text": "Weather in Paris?", "type": "text"}], "role": "user"},
     ]
 
@@ -656,12 +678,27 @@ def test_post_conversation_tool_call(api_client, mock_openai_stream_tool, settin
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": (
-                "You are a helpful test assistant :)\n\n"
-                "Today is Friday 25/07/2025.\n\nAnswer in english."
-            ),
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Today is Friday 25/07/2025.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
                 {
                     "content": ["Weather in Paris?"],
                     "part_kind": "user-prompt",
@@ -700,10 +737,7 @@ def test_post_conversation_tool_call(api_client, mock_openai_stream_tool, settin
             "run_id": _run_id,
         },
         {
-            "instructions": (
-                "You are a helpful test assistant :)\n\n"
-                "Today is Friday 25/07/2025.\n\nAnswer in english."
-            ),
+            "instructions": None,
             "kind": "request",
             "parts": [
                 {
@@ -795,12 +829,11 @@ def test_post_conversation_tool_call_fails(api_client, mock_openai_stream_tool,
 
     assert body["messages"] == [
         {
-            "content": (
-                "You are a helpful test assistant :)\n\n"
-                "Today is Friday 25/07/2025.\n\nAnswer in french."
-            ),
+            "content": "You are a helpful test assistant :)",
             "role": "system",
         },
+        {"content": "Today is Friday 25/07/2025.", "role": "system"},
+        {"content": "Answer in french.", "role": "system"},
         {"content": [{"text": "Weather in Paris?", "type": "text"}], "role": "user"},
     ]
 
@@ -858,12 +891,27 @@ def test_post_conversation_tool_call_fails(api_client, mock_openai_stream_tool,
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": (
-                "You are a helpful test assistant :)\n\n"
-                "Today is Friday 25/07/2025.\n\nAnswer in french."
-            ),
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Today is Friday 25/07/2025.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Answer in french.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
                 {
                     "content": ["Weather in Paris?"],
                     "part_kind": "user-prompt",
@@ -902,10 +950,7 @@ def test_post_conversation_tool_call_fails(api_client, mock_openai_stream_tool,
             "run_id": _run_id,
         },
         {
-            "instructions": (
-                "You are a helpful test assistant :)\n\n"
-                "Today is Friday 25/07/2025.\n\nAnswer in french."
-            ),
+            "instructions": None,
             "kind": "request",
             "parts": [
                 {
@@ -1169,11 +1214,27 @@ def test_post_conversation_data_protocol_no_stream(
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": (
-                "You are an amazing assistant.\n\nToday is Friday 25/07/2025.\n\nAnswer in english."
-            ),
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are an amazing assistant.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Today is Friday 25/07/2025.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
                 {
                     "content": ["Why the sky is blue?"],
                     "part_kind": "user-prompt",
@@ -1308,12 +1369,27 @@ async def test_post_conversation_async(api_client, mock_openai_stream, monkeypat
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": (
-                "You are a helpful test assistant :)\n\n"
-                "Today is Friday 25/07/2025.\n\nAnswer in english."
-            ),
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Today is Friday 25/07/2025.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-07-25T10:36:35.297675Z",
+                },
                 {
                     "content": ["Hello"],
                     "part_kind": "user-prompt",

src/backend/chat/tests/views/chat/conversations/test_conversation_with_document_upload.py

@@ -8,7 +8,6 @@ import logging
 from io import BytesIO
 from unittest import mock
 
-from django.contrib.sessions.backends.cache import SessionStore
 from django.utils import formats, timezone
 
 import httpx
@@ -42,49 +41,28 @@ from chat.tests.utils import replace_uuids_with_placeholder
 pytestmark = pytest.mark.django_db(transaction=True)
 
 
-@pytest.fixture(
-    autouse=True,
-    params=[
-        "chat.agent_rag.document_rag_backends.find_rag_backend.FindRagBackend",
-        "chat.agent_rag.document_rag_backends.albert_rag_backend.AlbertRagBackend",
-    ],
-)
-def ai_settings(request, settings):
+@pytest.fixture(autouse=True)
+def ai_settings(settings):
     """Fixture to set AI service URLs for testing."""
-
-    # enable on rag document search tool
-    settings.RAG_DOCUMENT_SEARCH_BACKEND = request.param
-    settings.RAG_WEB_SEARCH_PROMPT_UPDATE = (
-        "Based on the following document contents:\n\n{search_results}\n\n"
-        "Please answer the user's question: {user_prompt}"
-    )
-
     settings.AI_BASE_URL = "https://www.external-ai-service.com/"
     settings.AI_API_KEY = "test-api-key"
     settings.AI_MODEL = "test-model"
     settings.AI_AGENT_INSTRUCTIONS = "You are a helpful test assistant :)"
 
-    # Albert API settings
+    # Enable Albert API for document search
+    settings.RAG_DOCUMENT_SEARCH_BACKEND = (
+        "chat.agent_rag.document_rag_backends.albert_rag_backend.AlbertRagBackend"
+    )
     settings.ALBERT_API_URL = "https://albert.api.etalab.gouv.fr"
     settings.ALBERT_API_KEY = "albert-api-key"
-
-    # Find API settings
-    settings.FIND_API_URL = "https://find.api.example.com"
-    settings.FIND_API_KEY = "find-api-key"
+    settings.RAG_WEB_SEARCH_PROMPT_UPDATE = (
+        "Based on the following document contents:\n\n{search_results}\n\n"
+        "Please answer the user's question: {user_prompt}"
+    )
 
     return settings
 
 
-@pytest.fixture(autouse=True)
-def mock_refresh_access_token():
-    """Mock refresh_access_token to bypass token refresh in tests."""
-    with mock.patch("utils.oidc.refresh_access_token") as mocked_refresh_access_token:
-        session = SessionStore()
-        session["oidc_access_token"] = "mocked-access-token"
-        mocked_refresh_access_token.return_value = session
-        yield mocked_refresh_access_token
-
-
 @pytest.fixture(name="sample_pdf_content")
 def fixture_sample_pdf_content():
     """Create a dummy PDF content as BytesIO."""
@@ -103,25 +81,17 @@ def fixture_sample_pdf_content():
     return BytesIO(pdf_data)
 
 
-@pytest.fixture(name="mock_document_api")
-def fixture_mock_document_api():
+@pytest.fixture(name="mock_albert_api")
+def fixture_mock_albert_api():
     """Fixture to mock the Albert API endpoints."""
     # Mock collection creation
-
-    document_name = "sample.pdf"
-    document_content = "This is the content of the PDF."
-    prompt_tokens = 10
-    completion_tokens = 20
-    search_method = "semantic"
-    search_score = 0.9
-
     responses.post(
         "https://albert.api.etalab.gouv.fr/v1/collections",
         json={"id": "123", "name": "test-collection"},
         status=status.HTTP_200_OK,
     )
 
-    # Mock Albert PDF parsing -> deprecated
+    # Mock PDF parsing
     responses.post(
         "https://albert.api.etalab.gouv.fr/v1/parse-beta",
         json={
@@ -131,7 +101,7 @@ def fixture_mock_document_api():
                     "metadata": {"document_name": "sample.pdf"},
                 }
             ],
-            "usage": {"prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens},
+            "usage": {"prompt_tokens": 10, "completion_tokens": 20},
         },
         status=status.HTTP_200_OK,
     )
@@ -149,42 +119,20 @@ def fixture_mock_document_api():
         json={
             "data": [
                 {
-                    "method": search_method,
+                    "method": "semantic",
                     "chunk": {
                         "id": 123,
-                        "content": document_content,
-                        "metadata": {"document_name": document_name},
+                        "content": "This is the content of the PDF.",
+                        "metadata": {"document_name": "sample.pdf"},
                     },
-                    "score": search_score,
+                    "score": 0.9,
                 }
             ],
-            "usage": {"prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens},
+            "usage": {"prompt_tokens": 10, "completion_tokens": 20},
         },
         status=status.HTTP_200_OK,
     )
 
-    # Mock document indexing (Find API)
-    responses.post(
-        "https://find.api.example.com/api/v1.0/documents/index/",
-        json={"id": "456", "status": "indexed"},
-        status=status.HTTP_200_OK,
-    )
-
-    # Mock document search (Find API)
-    responses.post(
-        "https://find.api.example.com/api/v1.0/documents/search/",
-        json=[
-            {
-                "_source": {
-                    "title.fr": document_name,
-                    "content.fr": document_content,
-                },
-                "_score": search_score,
-            }
-        ],
-        status=status.HTTP_200_OK,
-    )
-
 
 @pytest.fixture(name="mock_summarization_agent")
 def fixture_mock_summarization_agent():
@@ -268,10 +216,9 @@ def fixture_mock_openai_stream():
 @responses.activate
 @respx.mock
 @freeze_time()
-def test_post_conversation_with_document_upload(
-    # pylint: disable=too-many-arguments,too-many-positional-arguments
+def test_post_conversation_with_document_upload(  # pylint: disable=too-many-arguments,too-many-positional-arguments
     api_client,
-    mock_document_api,  # pylint: disable=unused-argument
+    mock_albert_api,  # pylint: disable=unused-argument
     sample_pdf_content,
     today_promt_date,
     mock_ai_agent_service,
@@ -406,25 +353,53 @@ def test_post_conversation_with_document_upload(
     assert len(chat_conversation.pydantic_messages) == 4
 
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
-
     assert chat_conversation.pydantic_messages[0] == {
-        "instructions": "You are a helpful test assistant :)\n\n"
-        f"{today_promt_date}\n\n"
-        "Answer in english.\n\n"
-        "Use document_search_rag ONLY to retrieve specific passages from "
-        "attached documents. Do NOT use it to summarize; for summaries, "
-        "call the summarize tool instead.\n\nWhen you receive a result from the "
-        "summarization tool, you MUST return it directly to the user without "
-        "any modification, paraphrasing, or additional summarization."
-        "The tool already produces optimized summaries that should be "
-        "presented verbatim.You may translate the summary if required, "
-        "but you MUST preserve all the information from the original summary."
-        "You may add a follow-up question after the summary if needed.\n\n"
-        "[Internal context] User documents are attached to this conversation. "
-        "Do not request re-upload of documents; consider them already "
-        "available via the internal store.",
+        "instructions": "When you receive a result from the summarization tool, you "
+        "MUST return it directly to the user without any "
+        "modification, paraphrasing, or additional summarization.The "
+        "tool already produces optimized summaries that should be "
+        "presented verbatim.You may translate the summary if "
+        "required, but you MUST preserve all the information from the "
+        "original summary.You may add a follow-up question after the "
+        "summary if needed.",
         "kind": "request",
         "parts": [
+            {
+                "content": "You are a helpful test assistant :)",
+                "dynamic_ref": None,
+                "part_kind": "system-prompt",
+                "timestamp": timezone_now,
+            },
+            {
+                "content": today_promt_date,
+                "dynamic_ref": None,
+                "part_kind": "system-prompt",
+                "timestamp": timezone_now,
+            },
+            {
+                "content": "Answer in english.",
+                "dynamic_ref": None,
+                "part_kind": "system-prompt",
+                "timestamp": timezone_now,
+            },
+            {
+                "content": "Use document_search_rag ONLY to retrieve specific "
+                "passages from attached documents. Do NOT use it to "
+                "summarize; for summaries, call the summarize tool "
+                "instead.",
+                "dynamic_ref": None,
+                "part_kind": "system-prompt",
+                "timestamp": timezone_now,
+            },
+            {
+                "content": "[Internal context] User documents are attached to this "
+                "conversation. Do not request re-upload of documents; "
+                "consider them already available via the internal "
+                "store.",
+                "dynamic_ref": None,
+                "part_kind": "system-prompt",
+                "timestamp": timezone_now,
+            },
             {
                 "content": ["What does the document say?"],
                 "part_kind": "user-prompt",
@@ -464,21 +439,14 @@ def test_post_conversation_with_document_upload(
     }
     assert chat_conversation.pydantic_messages[2] == {
         "instructions": (
-            "You are a helpful test assistant :)\n\n"
-            f"{today_promt_date}\n\n"
-            "Answer in english.\n\n"
-            "Use document_search_rag ONLY to retrieve specific passages from "
-            "attached documents. Do NOT use it to summarize; for summaries, "
-            "call the summarize tool instead.\n\nWhen you receive a result from the "
-            "summarization tool, you MUST return it directly to the user without "
-            "any modification, paraphrasing, or additional summarization."
-            "The tool already produces optimized summaries that should be "
-            "presented verbatim.You may translate the summary if required, "
-            "but you MUST preserve all the information from the original summary."
-            "You may add a follow-up question after the summary if needed.\n\n"
-            "[Internal context] User documents are attached to this conversation. "
-            "Do not request re-upload of documents; consider them already "
-            "available via the internal store."
+            "When you receive a result from the summarization tool, you MUST "
+            "return it directly to the user without any modification, "
+            "paraphrasing, or additional summarization."
+            "The tool already produces optimized summaries that should "
+            "be presented verbatim."
+            "You may translate the summary if required, but you MUST preserve "
+            "all the information from the original summary."
+            "You may add a follow-up question after the summary if needed."
         ),
         "kind": "request",
         "parts": [
@@ -531,8 +499,7 @@ def test_post_conversation_with_document_upload(
 @responses.activate
 @respx.mock
 @freeze_time("2025-07-25T10:36:35.297675Z")
-def test_post_conversation_with_document_upload_feature_disabled(
-    # pylint: disable=too-many-arguments,too-many-positional-arguments
+def test_post_conversation_with_document_upload_feature_disabled(  # pylint: disable=too-many-arguments,too-many-positional-arguments
     api_client,
     caplog,
     mock_openai_stream,  # pylint: disable=unused-argument
@@ -585,12 +552,14 @@ def test_post_conversation_with_document_upload_feature_disabled(
 
     # Replace UUIDs with placeholders for assertion
     response_content = replace_uuids_with_placeholder(response_content)
+
     assert response_content == (
         '0:"From the document, I can see that "\n'
         "0:\"it says 'Hello PDF'.\"\n"
         'f:{"messageId":"<mocked_uuid>"}\n'
         'd:{"finishReason":"stop","usage":{"promptTokens":150,"completionTokens":25}}\n'
     )
+
     # This behavior must be improved in the future to inform the user properly
     assert "Document upload feature is disabled, ignoring input documents." in caplog.text
 
@@ -600,7 +569,7 @@ def test_post_conversation_with_document_upload_feature_disabled(
 @freeze_time()
 def test_post_conversation_with_document_upload_summarize(  # pylint: disable=too-many-arguments,too-many-positional-arguments  # noqa: PLR0913
     api_client,
-    mock_document_api,  # pylint: disable=unused-argument
+    mock_albert_api,  # pylint: disable=unused-argument
     sample_pdf_content,
     today_promt_date,
     mock_ai_agent_service,
@@ -613,7 +582,6 @@ def test_post_conversation_with_document_upload_summarize(  # pylint: disable=to
     api_client.force_authenticate(user=chat_conversation.owner)
 
     pdf_base64 = base64.b64encode(sample_pdf_content.read()).decode("utf-8")
-
     message = UIMessage(
         id="1",
         role="user",
@@ -675,7 +643,7 @@ def test_post_conversation_with_document_upload_summarize(  # pylint: disable=to
         'document discusses various topics."}\n'
         '0:"The document discusses various topics."\n'
         'f:{"messageId":"<mocked_uuid>"}\n'
-        'd:{"finishReason":"stop","usage":{"promptTokens":283,"completionTokens":19}}\n'
+        'd:{"finishReason":"stop","usage":{"promptTokens":317,"completionTokens":19}}\n'
     )
 
     # Check that the conversation was updated
@@ -737,25 +705,52 @@ def test_post_conversation_with_document_upload_summarize(  # pylint: disable=to
 
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages[0] == {
-        "instructions": (
-            "You are a helpful test assistant :)\n\n"
-            f"{today_promt_date}\n\n"
-            "Answer in english.\n\n"
-            "Use document_search_rag ONLY to retrieve specific passages from "
-            "attached documents. Do NOT use it to summarize; for summaries, "
-            "call the summarize tool instead.\n\nWhen you receive a result from the "
-            "summarization tool, you MUST return it directly to the user without "
-            "any modification, paraphrasing, or additional summarization."
-            "The tool already produces optimized summaries that should be "
-            "presented verbatim.You may translate the summary if required, "
-            "but you MUST preserve all the information from the original summary."
-            "You may add a follow-up question after the summary if needed.\n\n"
-            "[Internal context] User documents are attached to this conversation. "
-            "Do not request re-upload of documents; consider them already "
-            "available via the internal store."
-        ),
+        "instructions": "When you receive a result from the summarization tool, you "
+        "MUST return it directly to the user without any "
+        "modification, paraphrasing, or additional summarization.The "
+        "tool already produces optimized summaries that should be "
+        "presented verbatim.You may translate the summary if "
+        "required, but you MUST preserve all the information from the "
+        "original summary.You may add a follow-up question after the "
+        "summary if needed.",
         "kind": "request",
         "parts": [
+            {
+                "content": "You are a helpful test assistant :)",
+                "dynamic_ref": None,
+                "part_kind": "system-prompt",
+                "timestamp": timezone_now,
+            },
+            {
+                "content": today_promt_date,
+                "dynamic_ref": None,
+                "part_kind": "system-prompt",
+                "timestamp": timezone_now,
+            },
+            {
+                "content": "Answer in english.",
+                "dynamic_ref": None,
+                "part_kind": "system-prompt",
+                "timestamp": timezone_now,
+            },
+            {
+                "content": "Use document_search_rag ONLY to retrieve specific "
+                "passages from attached documents. Do NOT use it to "
+                "summarize; for summaries, call the summarize tool "
+                "instead.",
+                "dynamic_ref": None,
+                "part_kind": "system-prompt",
+                "timestamp": timezone_now,
+            },
+            {
+                "content": "[Internal context] User documents are attached to this "
+                "conversation. Do not request re-upload of documents; "
+                "consider them already available via the internal "
+                "store.",
+                "dynamic_ref": None,
+                "part_kind": "system-prompt",
+                "timestamp": timezone_now,
+            },
             {
                 "content": ["Make a summary of this document."],
                 "part_kind": "user-prompt",
@@ -795,21 +790,14 @@ def test_post_conversation_with_document_upload_summarize(  # pylint: disable=to
     }
     assert chat_conversation.pydantic_messages[2] == {
         "instructions": (
-            "You are a helpful test assistant :)\n\n"
-            f"{today_promt_date}\n\n"
-            "Answer in english.\n\n"
-            "Use document_search_rag ONLY to retrieve specific passages from "
-            "attached documents. Do NOT use it to summarize; for summaries, "
-            "call the summarize tool instead.\n\nWhen you receive a result from the "
-            "summarization tool, you MUST return it directly to the user without "
-            "any modification, paraphrasing, or additional summarization."
-            "The tool already produces optimized summaries that should be "
-            "presented verbatim.You may translate the summary if required, "
-            "but you MUST preserve all the information from the original summary."
-            "You may add a follow-up question after the summary if needed.\n\n"
-            "[Internal context] User documents are attached to this conversation. "
-            "Do not request re-upload of documents; consider them already "
-            "available via the internal store."
+            "When you receive a result from the summarization tool, you MUST "
+            "return it directly to the user without any modification, "
+            "paraphrasing, or additional summarization."
+            "The tool already produces optimized summaries that should "
+            "be presented verbatim."
+            "You may translate the summary if required, but you MUST preserve "
+            "all the information from the original summary."
+            "You may add a follow-up question after the summary if needed."
         ),
         "kind": "request",
         "parts": [

src/backend/chat/tests/views/chat/conversations/test_conversation_with_document_url.py

@@ -17,6 +17,7 @@ from pydantic_ai.messages import (
     DocumentUrl,
     ModelMessage,
     ModelResponse,
+    SystemPromptPart,
     TextPart,
     UserPromptPart,
 )
@@ -37,19 +38,11 @@ from chat.tests.utils import replace_uuids_with_placeholder
 pytestmark = pytest.mark.django_db(transaction=True)
 
 
-@pytest.fixture(
-    autouse=True,
-    params=[
-        "chat.agent_rag.document_rag_backends.find_rag_backend.FindRagBackend",
-        "chat.agent_rag.document_rag_backends.albert_rag_backend.AlbertRagBackend",
-    ],
-)
-def ai_settings(request, settings):
+@pytest.fixture(autouse=True)
+def ai_settings(settings):
     """Fixture to set AI service URLs for testing."""
-    settings.RAG_DOCUMENT_SEARCH_BACKEND = request.param
     settings.AI_BASE_URL = "https://www.external-ai-service.com/"
     settings.AI_API_KEY = "test-api-key"
-    settings.FIND_API_KEY = "find-api-key"
     settings.AI_MODEL = "test-model"
     settings.AI_AGENT_INSTRUCTIONS = "You are a helpful test assistant :)"
     return settings
@@ -68,8 +61,7 @@ def fixture_sample_document_content():
 
 @responses.activate
 @freeze_time()
-def test_post_conversation_with_local_pdf_document_url(
-    # pylint: disable=too-many-arguments,too-many-positional-arguments
+def test_post_conversation_with_local_pdf_document_url(  # pylint: disable=too-many-arguments,too-many-positional-arguments
     api_client,
     sample_document_content,
     today_promt_date,
@@ -93,10 +85,6 @@ def test_post_conversation_with_local_pdf_document_url(
         json={"id": "document_id", "object": "document"},
         status=200,
     )
-    responses.post(
-        "https://app-find/api/v1.0/documents/index/",
-        status=200,
-    )
 
     chat_conversation = ChatConversationFactory(owner__language="en-us")
     api_client.force_authenticate(user=chat_conversation.owner)
@@ -132,7 +120,7 @@ def test_post_conversation_with_local_pdf_document_url(
     )
 
     async def agent_model(messages: list[ModelMessage], _info: AgentInfo):
-        presigned_url = messages[0].parts[0].content[1].url
+        presigned_url = messages[0].parts[3].content[1].url
         assert presigned_url.startswith("http://localhost:9000/conversations-media-storage/")
         assert presigned_url.find("X-Amz-Signature=") != -1
         assert presigned_url.find("X-Amz-Date=") != -1
@@ -141,6 +129,11 @@ def test_post_conversation_with_local_pdf_document_url(
         assert messages == [
             ModelRequest(
                 parts=[
+                    SystemPromptPart(
+                        content="You are a helpful test assistant :)", timestamp=timezone.now()
+                    ),
+                    SystemPromptPart(content=today_promt_date, timestamp=timezone.now()),
+                    SystemPromptPart(content="Answer in english.", timestamp=timezone.now()),
                     UserPromptPart(
                         content=[
                             "What is in this document?",
@@ -153,8 +146,6 @@ def test_post_conversation_with_local_pdf_document_url(
                         timestamp=timezone.now(),
                     ),
                 ],
-                instructions=f"You are a helpful test assistant :)\n\n{today_promt_date}"
-                "\n\nAnswer in english.",
                 run_id=messages[0].run_id,
             )
         ]
@@ -230,11 +221,27 @@ def test_post_conversation_with_local_pdf_document_url(
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": "You are a helpful test assistant :)\n\n"
-            f"{today_promt_date}\n\n"
-            "Answer in english.",
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": timestamp,
+                },
+                {
+                    "content": today_promt_date,
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": timestamp,
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": timestamp,
+                },
                 {
                     "content": [
                         "What is in this document?",
@@ -422,6 +429,7 @@ def test_post_conversation_with_remote_document_url(
 @freeze_time("2025-10-18T20:48:20.286204Z")
 def test_post_conversation_with_local_document_url_in_history(  # pylint: disable=too-many-arguments,too-many-positional-arguments
     api_client,
+    today_promt_date,
     mock_ai_agent_service,
 ):
     """
@@ -429,8 +437,6 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
     """
     chat_conversation_pk = "0be55da5-8eb7-4dad-aa0f-fea454bd5809"
     document_url = f"/media-key/{chat_conversation_pk}/sample.pdf"
-    formatted_date = formats.date_format(timezone.now(), "l d/m/Y", use_l10n=False)
-
     chat_conversation = ChatConversationFactory(
         pk=chat_conversation_pk,
         owner__language="en-us",
@@ -466,11 +472,27 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
         ],
         pydantic_messages=[
             {
-                "instructions": "You are a helpful test assistant :)\n\n"
-                f"Today is {formatted_date}.\n\n"
-                "Answer in english.",
+                "instructions": None,
                 "kind": "request",
                 "parts": [
+                    {
+                        "content": "You are a helpful test assistant :)",
+                        "dynamic_ref": None,
+                        "part_kind": "system-prompt",
+                        "timestamp": "2025-10-18T20:48:20.286204Z",
+                    },
+                    {
+                        "content": today_promt_date,
+                        "dynamic_ref": None,
+                        "part_kind": "system-prompt",
+                        "timestamp": "2025-10-18T20:48:20.286204Z",
+                    },
+                    {
+                        "content": "Answer in english.",
+                        "dynamic_ref": None,
+                        "part_kind": "system-prompt",
+                        "timestamp": "2025-10-18T20:48:20.286204Z",
+                    },
                     {
                         "content": [
                             "What is in this document?",
@@ -533,7 +555,7 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
     )
 
     async def agent_model(messages: list[ModelMessage], _info: AgentInfo):
-        presigned_url = messages[0].parts[0].content[1].url
+        presigned_url = messages[0].parts[3].content[1].url
         assert presigned_url.startswith("http://localhost:9000/conversations-media-storage/")
         assert presigned_url.find("X-Amz-Signature=") != -1
         assert presigned_url.find("X-Amz-Date=") != -1
@@ -542,6 +564,18 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
         assert messages == [
             ModelRequest(
                 parts=[
+                    SystemPromptPart(
+                        content="You are a helpful test assistant :)",
+                        timestamp=timezone.now(),
+                    ),
+                    SystemPromptPart(
+                        content=today_promt_date,
+                        timestamp=timezone.now(),
+                    ),
+                    SystemPromptPart(
+                        content="Answer in english.",
+                        timestamp=timezone.now(),
+                    ),
                     UserPromptPart(
                         content=[
                             "What is in this document?",
@@ -554,9 +588,6 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
                         timestamp=timezone.now(),
                     ),
                 ],
-                instructions="You are a helpful test assistant :)\n\n"
-                "Today is Saturday 18/10/2025.\n\n"
-                "Answer in english.",
                 run_id=messages[0].run_id,
             ),
             ModelResponse(
@@ -575,9 +606,6 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
                         timestamp=timezone.now(),
                     )
                 ],
-                instructions="You are a helpful test assistant :)\n\n"
-                "Today is Saturday 18/10/2025.\n\n"
-                "Answer in english.",
                 run_id=messages[2].run_id,
             ),
         ]
@@ -677,11 +705,27 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
     _run_id = chat_conversation.pydantic_messages[2]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": "You are a helpful test assistant :)\n\n"
-            "Today is Saturday 18/10/2025.\n\n"
-            "Answer in english.",
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-10-18T20:48:20.286204Z",
+                },
+                {
+                    "content": today_promt_date,
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-10-18T20:48:20.286204Z",
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-10-18T20:48:20.286204Z",
+                },
                 {
                     "content": [
                         "What is in this document?",
@@ -728,9 +772,7 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
             # no run_id here
         },
         {
-            "instructions": "You are a helpful test assistant :)\n\n"
-            "Today is Saturday 18/10/2025.\n\n"
-            "Answer in english.",
+            "instructions": None,
             "kind": "request",
             "parts": [
                 {
@@ -781,8 +823,7 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
         ("data.csv", "text/csv"),
     ],
 )
-def test_post_conversation_with_local_not_pdf_document_url(
-    # pylint: disable=too-many-arguments,too-many-positional-arguments
+def test_post_conversation_with_local_not_pdf_document_url(  # pylint: disable=too-many-arguments,too-many-positional-arguments
     api_client,
     today_promt_date,
     mock_ai_agent_service,
@@ -807,10 +848,6 @@ def test_post_conversation_with_local_not_pdf_document_url(
         json={"id": "document_id", "object": "document"},
         status=200,
     )
-    responses.post(
-        "https://app-find/api/v1.0/documents/index/",
-        status=200,
-    )
 
     chat_conversation = ChatConversationFactory(owner__language="en-us")
     api_client.force_authenticate(user=chat_conversation.owner)
@@ -849,6 +886,27 @@ def test_post_conversation_with_local_not_pdf_document_url(
         assert messages == [
             ModelRequest(
                 parts=[
+                    SystemPromptPart(
+                        content="You are a helpful test assistant :)", timestamp=timezone.now()
+                    ),
+                    SystemPromptPart(content=today_promt_date, timestamp=timezone.now()),
+                    SystemPromptPart(content="Answer in english.", timestamp=timezone.now()),
+                    SystemPromptPart(
+                        content=(
+                            "Use document_search_rag ONLY to retrieve specific passages from "
+                            "attached documents. Do NOT use it to summarize; for summaries, "
+                            "call the summarize tool instead."
+                        ),
+                        timestamp=timezone.now(),
+                    ),
+                    SystemPromptPart(
+                        content=(
+                            "[Internal context] User documents are attached to this conversation. "
+                            "Do not request re-upload of documents; consider them already "
+                            "available via the internal store."
+                        ),
+                        timestamp=timezone.now(),
+                    ),
                     UserPromptPart(
                         content=[
                             "What is in this document?",
@@ -858,22 +916,14 @@ def test_post_conversation_with_local_not_pdf_document_url(
                     ),
                 ],
                 instructions=(
-                    "You are a helpful test assistant :)\n\n"
-                    f"{today_promt_date}\n\n"
-                    "Answer in english.\n\n"
-                    "Use document_search_rag ONLY to retrieve specific passages from "
-                    "attached documents. Do NOT use it to summarize; for summaries, "
-                    "call the summarize tool instead.\n\nWhen you receive a result "
-                    "from the summarization tool, you MUST return it directly to "
-                    "the user without any modification, paraphrasing, or additional "
-                    "summarization.The tool already produces optimized summaries "
-                    "that should be presented verbatim.You may translate the summary "
-                    "if required, but you MUST preserve all the information from the "
-                    "original summary.You may add a follow-up question after the "
-                    "summary if needed.\n\n"
-                    "[Internal context] User documents are attached to this conversation. "
-                    "Do not request re-upload of documents; "
-                    "consider them already available via the internal store."
+                    "When you receive a result from the summarization tool, you MUST "
+                    "return it directly to the user without any modification, "
+                    "paraphrasing, or additional summarization."
+                    "The tool already produces optimized summaries that should "
+                    "be presented verbatim."
+                    "You may translate the summary if required, but you MUST preserve "
+                    "all the information from the original summary."
+                    "You may add a follow-up question after the summary if needed."
                 ),
                 run_id=messages[0].run_id,
             )
@@ -949,25 +999,53 @@ def test_post_conversation_with_local_not_pdf_document_url(
     assert chat_conversation.pydantic_messages == [
         {
             "instructions": (
-                "You are a helpful test assistant :)\n\n"
-                f"{today_promt_date}\n\n"
-                "Answer in english.\n\n"
-                "Use document_search_rag ONLY to retrieve specific passages from "
-                "attached documents. Do NOT use it to summarize; for summaries, "
-                "call the summarize tool instead.\n\nWhen you receive a result "
-                "from the summarization tool, you MUST return it directly to "
-                "the user without any modification, paraphrasing, or additional "
-                "summarization.The tool already produces optimized summaries "
-                "that should be presented verbatim.You may translate the summary "
-                "if required, but you MUST preserve all the information from the "
-                "original summary.You may add a follow-up question after the "
-                "summary if needed.\n\n"
-                "[Internal context] User documents are attached to this conversation. "
-                "Do not request re-upload of documents; "
-                "consider them already available via the internal store."
+                "When you receive a result from the summarization tool, you MUST "
+                "return it directly to the user without any modification, "
+                "paraphrasing, or additional summarization."
+                "The tool already produces optimized summaries that should "
+                "be presented verbatim."
+                "You may translate the summary if required, but you MUST preserve "
+                "all the information from the original summary."
+                "You may add a follow-up question after the summary if needed."
             ),
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": timestamp,
+                },
+                {
+                    "content": today_promt_date,
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": timestamp,
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": timestamp,
+                },
+                {
+                    "content": "Use document_search_rag ONLY to retrieve specific "
+                    "passages from attached documents. Do NOT use it to "
+                    "summarize; for summaries, call the summarize tool "
+                    "instead.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": timestamp,
+                },
+                {
+                    "content": "[Internal context] User documents are attached to "
+                    "this conversation. Do not request re-upload of "
+                    "documents; consider them already available via the "
+                    "internal store.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": timestamp,
+                },
                 {
                     "content": [
                         "What is in this document?",

src/backend/chat/tests/views/chat/conversations/test_conversation_with_history.py

@@ -919,7 +919,7 @@ def history_conversation_with_tool_fixture():
     history_timestamp = timezone.now().replace(year=2025, month=6, day=15, hour=10, minute=30)
 
     # Create a conversation with pre-existing messages including a tool invocation
-    conversation = ChatConversationFactory(owner__language="nl-nl")
+    conversation = ChatConversationFactory()
 
     # Add previous user and assistant messages with tool invocation
     conversation.messages = [
@@ -1377,9 +1377,7 @@ def test_post_conversation_with_existing_tool_history(
 
     # Verify the new tool call request is included
     assert history_conversation_with_tool.pydantic_messages[8] == {
-        "instructions": "You are a helpful test assistant :)\n\n"
-        "Today is Friday 25/07/2025.\n\n"
-        "Answer in dutch.",
+        "instructions": None,
         "kind": "request",
         "parts": [
             {
@@ -1422,9 +1420,7 @@ def test_post_conversation_with_existing_tool_history(
     }
 
     assert history_conversation_with_tool.pydantic_messages[10] == {
-        "instructions": "You are a helpful test assistant :)\n\n"
-        "Today is Friday 25/07/2025.\n\n"
-        "Answer in dutch.",
+        "instructions": None,
         "kind": "request",
         "parts": [
             {

src/backend/chat/tests/views/chat/conversations/test_conversation_with_image_url.py

@@ -2,7 +2,7 @@
 
 import uuid
 
-from django.utils import formats, timezone
+from django.utils import timezone
 
 import pytest
 from dirty_equals import IsUUID
@@ -12,6 +12,7 @@ from pydantic_ai.messages import (
     ImageUrl,
     ModelMessage,
     ModelResponse,
+    SystemPromptPart,
     TextPart,
     UserPromptPart,
 )
@@ -86,15 +87,22 @@ def test_post_conversation_with_local_image_url(
     )
 
     async def agent_model(messages: list[ModelMessage], _info: AgentInfo):
-        presigned_url = messages[0].parts[0].content[1].url
-        # assert presigned_url.startswith("http://localhost:9000/conversations-media-storage/")
+        presigned_url = messages[0].parts[3].content[1].url
+        assert presigned_url.startswith("http://localhost:9000/conversations-media-storage/")
         assert presigned_url.find("X-Amz-Signature=") != -1
         assert presigned_url.find("X-Amz-Date=") != -1
         assert presigned_url.find("X-Amz-Expires=") != -1
-        formatted_date = formats.date_format(timezone.now(), "l d/m/Y", use_l10n=False)
+
         assert messages == [
             ModelRequest(
                 parts=[
+                    SystemPromptPart(
+                        content="You are a helpful test assistant :)", timestamp=timezone.now()
+                    ),
+                    SystemPromptPart(
+                        content="Today is Saturday 18/10/2025.", timestamp=timezone.now()
+                    ),
+                    SystemPromptPart(content="Answer in english.", timestamp=timezone.now()),
                     UserPromptPart(
                         content=[
                             "What is in this image?",
@@ -107,8 +115,6 @@ def test_post_conversation_with_local_image_url(
                         timestamp=timezone.now(),
                     ),
                 ],
-                instructions="You are a helpful test assistant :)\n\nToday is "
-                f"{formatted_date}.\n\nAnswer in english.",
                 run_id=messages[0].run_id,
             )
         ]
@@ -178,10 +184,27 @@ def test_post_conversation_with_local_image_url(
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": "You are a helpful test assistant :)\n\n"
-            "Today is Saturday 18/10/2025.\n\nAnswer in english.",
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-10-18T20:48:20.286204Z",
+                },
+                {
+                    "content": "Today is Saturday 18/10/2025.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-10-18T20:48:20.286204Z",
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-10-18T20:48:20.286204Z",
+                },
                 {
                     "content": [
                         "What is in this image?",
@@ -263,6 +286,11 @@ def test_post_conversation_with_local_image_wrong_url(
         assert messages == [
             ModelRequest(
                 parts=[
+                    SystemPromptPart(
+                        content="You are a helpful test assistant :)", timestamp=timezone.now()
+                    ),
+                    SystemPromptPart(content=today_promt_date, timestamp=timezone.now()),
+                    SystemPromptPart(content="Answer in english.", timestamp=timezone.now()),
                     UserPromptPart(
                         content=[
                             "What is in this image?",
@@ -275,8 +303,6 @@ def test_post_conversation_with_local_image_wrong_url(
                         timestamp=timezone.now(),
                     ),
                 ],
-                instructions=f"You are a helpful test assistant :)\n\n{today_promt_date}"
-                "\n\nAnswer in english.",
                 run_id=messages[0].run_id,
             )
         ]
@@ -348,6 +374,11 @@ def test_post_conversation_with_remote_image_url(
         assert messages == [
             ModelRequest(
                 parts=[
+                    SystemPromptPart(
+                        content="You are a helpful test assistant :)", timestamp=timezone.now()
+                    ),
+                    SystemPromptPart(content=today_promt_date, timestamp=timezone.now()),
+                    SystemPromptPart(content="Answer in english.", timestamp=timezone.now()),
                     UserPromptPart(
                         content=[
                             "What is in this image?",
@@ -360,8 +391,6 @@ def test_post_conversation_with_remote_image_url(
                         timestamp=timezone.now(),
                     ),
                 ],
-                instructions="You are a helpful test assistant :)\n\n"
-                f"{today_promt_date}\n\nAnswer in english.",
                 run_id=messages[0].run_id,
             )
         ]
@@ -475,10 +504,27 @@ def test_post_conversation_with_local_image_url_in_history(
         ],
         pydantic_messages=[
             {
-                "instructions": f"You are a helpful test assistant :)\n\n{today_promt_date}"
-                "\n\nAnswer in english.",
+                "instructions": None,
                 "kind": "request",
                 "parts": [
+                    {
+                        "content": "You are a helpful test assistant :)",
+                        "dynamic_ref": None,
+                        "part_kind": "system-prompt",
+                        "timestamp": "2025-10-18T20:48:20.286204Z",
+                    },
+                    {
+                        "content": today_promt_date,
+                        "dynamic_ref": None,
+                        "part_kind": "system-prompt",
+                        "timestamp": "2025-10-18T20:48:20.286204Z",
+                    },
+                    {
+                        "content": "Answer in english.",
+                        "dynamic_ref": None,
+                        "part_kind": "system-prompt",
+                        "timestamp": "2025-10-18T20:48:20.286204Z",
+                    },
                     {
                         "content": [
                             "What is in this image?",
@@ -541,7 +587,7 @@ def test_post_conversation_with_local_image_url_in_history(
     )
 
     async def agent_model(messages: list[ModelMessage], _info: AgentInfo):
-        presigned_url = messages[0].parts[0].content[1].url
+        presigned_url = messages[0].parts[3].content[1].url
         assert presigned_url.startswith("http://localhost:9000/conversations-media-storage/")
         assert presigned_url.find("X-Amz-Signature=") != -1
         assert presigned_url.find("X-Amz-Date=") != -1
@@ -550,6 +596,18 @@ def test_post_conversation_with_local_image_url_in_history(
         assert messages == [
             ModelRequest(
                 parts=[
+                    SystemPromptPart(
+                        content="You are a helpful test assistant :)",
+                        timestamp=timezone.now(),
+                    ),
+                    SystemPromptPart(
+                        content=today_promt_date,
+                        timestamp=timezone.now(),
+                    ),
+                    SystemPromptPart(
+                        content="Answer in english.",
+                        timestamp=timezone.now(),
+                    ),
                     UserPromptPart(
                         content=[
                             "What is in this image?",
@@ -561,9 +619,7 @@ def test_post_conversation_with_local_image_url_in_history(
                         ],
                         timestamp=timezone.now(),
                     ),
-                ],
-                instructions="You are a helpful test assistant :)\n\n"
-                f"{today_promt_date}\n\nAnswer in english.",
+                ]
             ),
             ModelResponse(
                 parts=[TextPart(content="This is an image of a single pixel.")],
@@ -581,8 +637,6 @@ def test_post_conversation_with_local_image_url_in_history(
                     )
                 ],
                 run_id=messages[2].run_id,
-                instructions="You are a helpful test assistant :)\n\n"
-                "Today is Saturday 18/10/2025.\n\nAnswer in english.",
             ),
         ]
         yield "This is an image of square, very small and nice."
@@ -681,10 +735,27 @@ def test_post_conversation_with_local_image_url_in_history(
     _run_id = chat_conversation.pydantic_messages[2]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": f"You are a helpful test assistant :)\n\n{today_promt_date}"
-            "\n\nAnswer in english.",
+            "instructions": None,
             "kind": "request",
             "parts": [
+                {
+                    "content": "You are a helpful test assistant :)",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-10-18T20:48:20.286204Z",
+                },
+                {
+                    "content": today_promt_date,
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-10-18T20:48:20.286204Z",
+                },
+                {
+                    "content": "Answer in english.",
+                    "dynamic_ref": None,
+                    "part_kind": "system-prompt",
+                    "timestamp": "2025-10-18T20:48:20.286204Z",
+                },
                 {
                     "content": [
                         "What is in this image?",
@@ -725,8 +796,7 @@ def test_post_conversation_with_local_image_url_in_history(
             },
         },
         {
-            "instructions": "You are a helpful test assistant :)\n\nToday is Saturday 18/10/2025."
-            "\n\nAnswer in english.",
+            "instructions": None,
             "kind": "request",
             "parts": [
                 {

src/backend/chat/tests/views/chat/conversations/test_stop_steaming.py

@@ -1,4 +1,4 @@
-"""Test the post_stop_streaming view."""
+"""Test the post_stop_steaming view."""
 
 from unittest.mock import patch
 

src/backend/chat/tools/document_search_rag.py

@@ -26,7 +26,7 @@ def add_document_rag_search_tool(agent: Agent) -> None:
 
         document_store = document_store_backend(ctx.deps.conversation.collection_id)
 
-        rag_results = document_store.search(query, session=ctx.deps.session)
+        rag_results = document_store.search(query)
 
         ctx.usage += RunUsage(
             input_tokens=rag_results.usage.prompt_tokens,
@@ -39,7 +39,7 @@ def add_document_rag_search_tool(agent: Agent) -> None:
             metadata={"sources": {result.url for result in rag_results.data}},
         )
 
-    @agent.instructions
+    @agent.system_prompt
     def document_rag_instructions() -> str:
         """Dynamic system prompt function to add RAG instructions if any."""
         return (

src/backend/chat/tools/document_summarize.py

@@ -26,12 +26,14 @@ def read_document_content(doc):
         return doc.file_name, f.read().decode("utf-8")
 
 
-async def summarize_chunk(idx, chunk, total_chunks, summarization_agent, ctx):
+async def summarize_chunk(idx, chunk, total_chunks, summarization_agent, ctx, language: str):
     """Summarize a single chunk of text."""
     sum_prompt = (
         "You are an agent specializing in text summarization. "
         "Generate a clear and concise summary of the following passage "
-        f"(part {idx}/{total_chunks}):\n'''\n{chunk}\n'''\n\n"
+        f"(part {idx}/{total_chunks}).\n"
+        f"The summary must be written in {language}.\n"
+        f"Passage:\n'''\n{chunk}\n'''\n\n"
     )
 
     logger.debug(
@@ -52,7 +54,7 @@ async def summarize_chunk(idx, chunk, total_chunks, summarization_agent, ctx):
 
 @last_model_retry_soft_fail
 async def document_summarize(  # pylint: disable=too-many-locals
-    ctx: RunContext, *, instructions: str | None = None
+    ctx: RunContext, *, instructions: str | None = None, language: str = "french"
 ) -> ToolReturn:
     """
     Generate a complete, ready-to-use summary of the documents in context
@@ -66,16 +68,19 @@ async def document_summarize(  # pylint: disable=too-many-locals
 
     Examples:
     "Summarize this doc in 2 paragraphs" -> instructions = "summary in 2 paragraphs"
-    "Summarize this doc in English" -> instructions = "In English"
-    "Summarize this doc" -> instructions = "" (default)
+    "Summarize this doc in English" -> language = "English"
+    "Summarize this doc with one paragraph on topic1 and one paragraph on topic2" -> instructions = "summary in 2 paragraphs, one paragraph on topic1 and one paragraph on topic2"
+    "Summarize this doc" -> instructions = "" (default) language = "french" (default)
 
     Args:
         instructions (str | None): The instructions the user gave to use for the summarization
+        language (str): The language in which the summary must be generated (default: "french")
     """
     try:
         instructions_hint = (
             instructions.strip() if instructions else "The summary should contain 2 or 3 parts."
         )
+        language_hint = (language or "french").strip()
         summarization_agent = SummarizationAgent()
 
         # Collect documents content
@@ -101,7 +106,7 @@ async def document_summarize(  # pylint: disable=too-many-locals
         )
         documents_chunks = chunker(
             [doc[1] for doc in documents],
-            # overlap=settings.SUMMARIZATION_OVERLAP_SIZE,
+            overlap=settings.SUMMARIZATION_OVERLAP_SIZE,
         )
 
         logger.info(
@@ -118,7 +123,9 @@ async def document_summarize(  # pylint: disable=too-many-locals
         async def summarize_chunk_with_semaphore(idx, chunk, total_chunks):
             """Summarize a chunk with semaphore-controlled concurrency."""
             async with semaphore:
-                return await summarize_chunk(idx, chunk, total_chunks, summarization_agent, ctx)
+                return await summarize_chunk(
+                    idx, chunk, total_chunks, summarization_agent, ctx, language_hint
+                )
 
         doc_chunk_summaries = []
         try:
@@ -154,7 +161,8 @@ async def document_summarize(  # pylint: disable=too-many-locals
             "- Harmonize style and terminology.\n"
             "- The final summary must be well-structured and formatted in markdown.\n"
             f"- Follow the instructions: {instructions_hint}\n"
-            "Respond directly with the final summary."
+            f"- The final summary must be written in {language_hint}.\n"
+            "Respond directly with the final summary. Begin with a title."
         )
 
         logger.debug("[summarize] MERGE prompt=> %s", merged_prompt)

src/backend/chat/tools/exceptions.py

@@ -3,6 +3,17 @@
 from pydantic_ai import ModelRetry
 
 
+class ModelRetryLast(ModelRetry):
+    """
+    Same as ModelRetry but also holds the last retry message to return when all attempts failed.
+    """
+
+    def __init__(self, message: str, last_retry_message: str):
+        """Initialize ModelRetryLast with message and last retry message."""
+        self.last_retry_message = last_retry_message
+        super().__init__(message)
+
+
 class ModelCannotRetry(ModelRetry):
     """
     Exception to raise when a tool function cannot be retried.

src/backend/chat/tools/web_search_brave.py

@@ -127,7 +127,7 @@ async def _extract_and_summarize_snippets_async(query: str, url: str) -> List[st
         return []
 
 
-async def _fetch_and_store_async(url: str, document_store, **kwargs) -> None:
+async def _fetch_and_store_async(url: str, document_store) -> None:
     """Fetch, extract and store text content from the URL in the document store."""
 
     try:
@@ -136,7 +136,7 @@ async def _fetch_and_store_async(url: str, document_store, **kwargs) -> None:
         logger.debug("Fetched document: %s", document)
 
         if document:
-            await document_store.astore_document(url, document, **kwargs)
+            await document_store.astore_document(url, document)
     except DocumentFetchError as e:
         logger.warning("Failed to fetch and store %s: %s", url, e)
         # Continue with other documents

src/backend/chat/views.py

@@ -7,13 +7,11 @@ from uuid import uuid4
 from django.conf import settings
 from django.core.files.storage import default_storage
 from django.http import Http404, StreamingHttpResponse
-from django.utils.decorators import method_decorator
 
 import langfuse
 import magic
 import posthog
 from lasuite.malware_detection import malware_detection
-from lasuite.oidc_login.decorators import refresh_oidc_access_token
 from rest_framework import decorators, filters, mixins, permissions, status, viewsets
 from rest_framework.exceptions import MethodNotAllowed, PermissionDenied, ValidationError
 from rest_framework.response import Response
@@ -124,7 +122,6 @@ class ChatViewSet(  # pylint: disable=too-many-ancestors, abstract-method
             self.permission_classes = []
         return super().get_permissions()
 
-    @method_decorator(refresh_oidc_access_token)
     @decorators.action(
         methods=["post"],
         detail=True,
@@ -176,7 +173,6 @@ class ChatViewSet(  # pylint: disable=too-many-ancestors, abstract-method
         ai_service = AIAgentService(
             conversation=conversation,
             user=self.request.user,
-            session=request.session,
             model_hrid=model_hrid,
             language=(
                 self.request.user.language
@@ -225,7 +221,7 @@ class ChatViewSet(  # pylint: disable=too-many-ancestors, abstract-method
         url_path="stop-streaming",
         url_name="stop-streaming",
     )
-    def post_stop_streaming(self, request, pk):  # pylint: disable=unused-argument
+    def post_stop_steaming(self, request, pk):  # pylint: disable=unused-argument
         """Handle POST requests to stop streaming the chat conversation.
 
         This action will put a poison pill in the redis cache to stop any ongoing streaming.

src/backend/conftest.py

@@ -22,7 +22,7 @@ def no_http_requests(monkeypatch):
     Credits: https://blog.jerrycodes.com/no-http-requests/
     """
 
-    allowed_hosts = {"localhost", "127.0.0.1", "minio", "minio:9000"}
+    allowed_hosts = {"localhost", "minio", "minio:9000"}
     original_urlopen = HTTPConnectionPool.urlopen
 
     def urlopen_mock(self, method, url, *args, **kwargs):

src/backend/conversations/configuration/llm/default_dev.json

@@ -0,0 +1,77 @@
+{
+  "models": [
+    {
+      "hrid": "default-model",
+      "model_name": "settings.AI_MODEL",
+      "human_readable_name": "Default Model",
+      "provider_name": "default-provider",
+      "profile": {
+        "openai_supports_strict_tool_definition": false,
+        "openai_supports_tool_choice_required": false
+      },
+      "supports_streaming": false,
+      "settings": {},
+      "is_active": true,
+      "icon": [
+        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAMAAABF0y+mAAAAn1BMVEUALosAKoovTZjw8vb////+9/jlPUniAAz",
+        "iABUAGIWbpsTwq7HhAAAAI4dle7DrdX4AJohRaaboXWj7+/zn6On5//9NZaT29vfoWmVHYKDoUl/k5OUAIYddc6vpbHYCM47Y3+v53+LiFCUA",
+        "HIWnsckYPJHi6PL77O7jJjW3wdf1w8jre4QgQ5TZ2txwg7Pr3+I8WZ6OnsTuoamClL7tlZ5xz5y8AAAAzUlEQVR4AZ3RRQKDQBBEUSTu7h5c4",
+        "vc/W6Yp3KG2Dz4ynDdeEBvOmq12xx2E1u0B+4NOEocj4DgNJ1PgLAvni8WyBq5Yc71ubFJx23C2q4P7dRYejg1xzvCUgvz5guz11k7gXYKF/1",
+        "8oyiYuvHAYeVkhXCzolVStHcGDjiQzNmMQxsMI5rEJRdQSPZvbpE2E8aY6gC6Z+2Hg4dFA0Yb4YedNL/v4Fk8WJuwiGhrChJNXI210rnib9Fs",
+        "JlXRUC/HwTscPIXf/iklq/tjb/gHAdxkCUjAg2QAAAABJRU5ErkJggg=="
+      ],
+      "system_prompt": "settings.AI_AGENT_INSTRUCTIONS",
+      "tools": "settings.AI_AGENT_TOOLS"
+    },
+    {
+      "hrid": "default-summarization-model",
+      "model_name": "settings.AI_MODEL",
+      "human_readable_name": "Default Summarization Model",
+      "provider_name": "default-provider",
+      "profile": {
+        "openai_supports_strict_tool_definition": false,
+        "openai_supports_tool_choice_required": false
+      },
+      "supports_streaming": false,
+      "settings": {},
+      "is_active": true,
+      "icon": null,
+      "system_prompt": "settings.SUMMARIZATION_SYSTEM_PROMPT",
+      "tools": []
+    },
+    {
+      "hrid": "etalab-plateform-mistral-medium-2508",
+      "model_name": "mistral-medium-2508",
+      "human_readable_name": "Mistral Medium 2508 (Plateforme Etalab)",
+      "provider_name": "mistral-plateform-etalab",
+      "profile": null,
+      "supports_streaming": false,
+      "settings": {},
+      "is_active": true,
+      "icon": [
+        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAMAAABF0y+mAAAAn1BMVEUALosAKoovTZjw8vb////+9/jlPUniAAz",
+        "iABUAGIWbpsTwq7HhAAAAI4dle7DrdX4AJohRaaboXWj7+/zn6On5//9NZaT29vfoWmVHYKDoUl/k5OUAIYddc6vpbHYCM47Y3+v53+LiFCUA",
+        "HIWnsckYPJHi6PL77O7jJjW3wdf1w8jre4QgQ5TZ2txwg7Pr3+I8WZ6OnsTuoamClL7tlZ5xz5y8AAAAzUlEQVR4AZ3RRQKDQBBEUSTu7h5c4",
+        "vc/W6Yp3KG2Dz4ynDdeEBvOmq12xx2E1u0B+4NOEocj4DgNJ1PgLAvni8WyBq5Yc71ubFJx23C2q4P7dRYejg1xzvCUgvz5guz11k7gXYKF/1",
+        "8oyiYuvHAYeVkhXCzolVStHcGDjiQzNmMQxsMI5rEJRdQSPZvbpE2E8aY6gC6Z+2Hg4dFA0Yb4YedNL/v4Fk8WJuwiGhrChJNXI210rnib9Fs",
+        "JlXRUC/HwTscPIXf/iklq/tjb/gHAdxkCUjAg2QAAAABJRU5ErkJggg=="
+      ],
+      "system_prompt": "settings.AI_AGENT_INSTRUCTIONS",
+      "tools": "settings.AI_AGENT_TOOLS"
+    }
+  ],
+  "providers": [
+    {
+      "hrid": "default-provider",
+      "base_url": "settings.AI_BASE_URL",
+      "api_key": "settings.AI_API_KEY",
+      "kind": "openai"
+    },
+    {
+      "hrid": "mistral-plateform-etalab",
+      "base_url": "https://api.mistral.etalab.gouv.fr/",
+      "api_key": "environ.MISTRAL_ETALAB_API_KEY",
+      "kind": "mistral"
+    }
+  ]
+}

src/backend/conversations/settings.py

@@ -841,23 +841,6 @@ USER QUESTION:
         environ_prefix=None,
     )
 
-    # Find
-    FIND_API_KEY = values.Value(
-        None,
-        environ_name="FIND_API_KEY",
-        environ_prefix=None,
-    )
-    FIND_API_URL = values.Value(
-        "https://app-find/api",
-        environ_name="FIND_API_URL",
-        environ_prefix=None,
-    )
-    FIND_API_TIMEOUT = values.PositiveIntegerValue(
-        default=30,  # seconds
-        environ_name="FIND_API_TIMEOUT",
-        environ_prefix=None,
-    )
-
     # Logging
     # We want to make it easy to log to console but by default we log production
     # to Sentry and don't want to log to console.

src/backend/core/api/__init__.py

@@ -1,9 +1,12 @@
 """Conversations core API endpoints"""
 
+from django.conf import settings
 from django.core.exceptions import ValidationError
 
 from rest_framework import exceptions as drf_exceptions
 from rest_framework import views as drf_views
+from rest_framework.decorators import api_view
+from rest_framework.response import Response
 
 
 def exception_handler(exc, context):
@@ -25,3 +28,14 @@ def exception_handler(exc, context):
         exc = drf_exceptions.ValidationError(detail=detail)
 
     return drf_views.exception_handler(exc, context)
+
+
+# pylint: disable=unused-argument
+@api_view(["GET"])
+def get_frontend_configuration(request):
+    """Returns the frontend configuration dict as configured in settings."""
+    frontend_configuration = {
+        "LANGUAGE_CODE": settings.LANGUAGE_CODE,
+    }
+    frontend_configuration.update(settings.FRONTEND_CONFIGURATION)
+    return Response(frontend_configuration)

src/backend/core/api/serializers.py

@@ -20,3 +20,23 @@ class UserSerializer(serializers.ModelSerializer):
             "sub",
         ]
         read_only_fields = ["id", "email", "full_name", "short_name", "sub"]
+
+
+class UserLightSerializer(UserSerializer):
+    """Serialize users with limited fields."""
+
+    id = serializers.SerializerMethodField(read_only=True)
+    email = serializers.SerializerMethodField(read_only=True)
+
+    def get_id(self, _user):
+        """Return always None. Here to have the same fields than in UserSerializer."""
+        return None
+
+    def get_email(self, _user):
+        """Return always None. Here to have the same fields than in UserSerializer."""
+        return None
+
+    class Meta:
+        model = models.User
+        fields = ["id", "email", "full_name", "short_name"]
+        read_only_fields = ["id", "email", "full_name", "short_name"]

src/backend/core/authentication/__init__.py

@@ -0,0 +1,52 @@
+"""Custom authentication classes for the Conversations core app"""
+
+from django.conf import settings
+
+from rest_framework.authentication import BaseAuthentication
+from rest_framework.exceptions import AuthenticationFailed
+
+
+class ServerToServerAuthentication(BaseAuthentication):
+    """
+    Custom authentication class for server-to-server requests.
+    Validates the presence and correctness of the Authorization header.
+    """
+
+    AUTH_HEADER = "Authorization"
+    TOKEN_TYPE = "Bearer"  # noqa S105
+
+    def authenticate(self, request):
+        """
+        Authenticate the server-to-server request by validating the Authorization header.
+
+        This method checks if the Authorization header is present in the request, ensures it
+        contains a valid token with the correct format, and verifies the token against the
+        list of allowed server-to-server tokens. If the header is missing, improperly formatted,
+        or contains an invalid token, an AuthenticationFailed exception is raised.
+
+        Returns:
+            None: If authentication is successful
+                  (no user is authenticated for server-to-server requests).
+
+        Raises:
+            AuthenticationFailed: If the Authorization header is missing, malformed,
+            or contains an invalid token.
+        """
+        auth_header = request.headers.get(self.AUTH_HEADER)
+        if not auth_header:
+            raise AuthenticationFailed("Authorization header is missing.")
+
+        # Validate token format and existence
+        auth_parts = auth_header.split(" ")
+        if len(auth_parts) != 2 or auth_parts[0] != self.TOKEN_TYPE:
+            raise AuthenticationFailed("Invalid authorization header.")
+
+        token = auth_parts[1]
+        if token not in settings.SERVER_TO_SERVER_API_TOKENS:
+            raise AuthenticationFailed("Invalid server-to-server token.")
+
+        # Authentication is successful, but no user is authenticated
+
+    def authenticate_header(self, request):
+        """Return the WWW-Authenticate header value."""
+        return f"{self.TOKEN_TYPE} realm='Create document server to server'"

src/backend/core/fields.py

@@ -0,0 +1,25 @@
+"""A JSONField for DRF to handle serialization/deserialization."""
+
+import json
+
+from rest_framework import serializers
+
+
+class JSONField(serializers.Field):
+    """
+    A custom field for handling JSON data.
+    """
+
+    def to_representation(self, value):
+        """
+        Convert the JSON string to a Python dictionary for serialization.
+        """
+        return value
+
+    def to_internal_value(self, data):
+        """
+        Convert the Python dictionary to a JSON string for deserialization.
+        """
+        if data is None:
+            return None
+        return json.dumps(data)

src/backend/core/filters.py

@@ -2,9 +2,31 @@
 
 import unicodedata
 
+import django_filters
+
 
 def remove_accents(value):
     """Remove accents from a string (vélo -> velo)."""
     return "".join(
         c for c in unicodedata.normalize("NFD", value) if unicodedata.category(c) != "Mn"
     )
+
+
+class AccentInsensitiveCharFilter(django_filters.CharFilter):
+    """
+    A custom CharFilter that filters on the accent-insensitive value searched.
+    """
+
+    def filter(self, qs, value):
+        """
+        Apply the filter to the queryset using the unaccented version of the field.
+
+        Args:
+            qs: The queryset to filter.
+            value: The value to search for in the unaccented field.
+        Returns:
+            A filtered queryset.
+        """
+        if value:
+            value = remove_accents(value)
+        return super().filter(qs, value)

src/backend/core/templates/core/generate_document.html

@@ -0,0 +1,14 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Generate Document</title>
+</head>
+<body>
+    <h2>Generate Document</h2>
+    <form method="post" enctype="multipart/form-data">
+        {% csrf_token %}
+        {{ form.as_p }}
+        <button type="submit">Generate PDF</button>
+    </form>
+</body>
+</html>

src/backend/core/templatetags/extra_tags.py

@@ -0,0 +1,58 @@
+"""Custom template tags for the core application of People."""
+
+import base64
+
+from django import template
+from django.contrib.staticfiles import finders
+
+from PIL import ImageFile as PillowImageFile
+
+register = template.Library()
+
+
+def image_to_base64(file_or_path, close=False):
+    """
+    Return the src string of the base64 encoding of an image represented by its path
+    or file opened or not.
+
+    Inspired by Django's "get_image_dimensions"
+    """
+    pil_parser = PillowImageFile.Parser()
+    if hasattr(file_or_path, "read"):
+        file = file_or_path
+        if file.closed and hasattr(file, "open"):
+            file_or_path.open()
+        file_pos = file.tell()
+        file.seek(0)
+    else:
+        try:
+            # pylint: disable=consider-using-with
+            file = open(file_or_path, "rb")
+        except OSError:
+            return ""
+        close = True
+
+    try:
+        image_data = file.read()
+        if not image_data:
+            return ""
+        pil_parser.feed(image_data)
+        if pil_parser.image:
+            mime_type = pil_parser.image.get_format_mimetype()
+            encoded_string = base64.b64encode(image_data)
+            return f"data:{mime_type:s};base64, {encoded_string.decode('utf-8'):s}"
+        return ""
+    finally:
+        if close:
+            file.close()
+        else:
+            file.seek(file_pos)
+
+
+@register.simple_tag
+def base64_static(path):
+    """Return a static file into a base64."""
+    full_path = finders.find(path)
+    if full_path:
+        return image_to_base64(full_path, True)
+    return ""

src/backend/locale/de_DE/LC_MESSAGES/django.po

@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: la-suite-conversations\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2025-10-20 21:48+0000\n"
-"PO-Revision-Date: 2025-12-15 13:49\n"
+"PO-Revision-Date: 2025-11-17 16:37\n"
 "Last-Translator: \n"
 "Language-Team: German\n"
 "Language: de_DE\n"

src/backend/locale/en_US/LC_MESSAGES/django.po

@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: la-suite-conversations\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2025-10-20 21:48+0000\n"
-"PO-Revision-Date: 2025-12-15 13:49\n"
+"PO-Revision-Date: 2025-11-17 16:37\n"
 "Last-Translator: \n"
 "Language-Team: English\n"
 "Language: en_US\n"

src/backend/locale/fr_FR/LC_MESSAGES/django.po

@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: la-suite-conversations\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2025-10-20 21:48+0000\n"
-"PO-Revision-Date: 2025-12-15 13:49\n"
+"PO-Revision-Date: 2025-11-17 16:37\n"
 "Last-Translator: \n"
 "Language-Team: French\n"
 "Language: fr_FR\n"

src/backend/locale/nl_NL/LC_MESSAGES/django.po

@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: la-suite-conversations\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2025-10-20 21:48+0000\n"
-"PO-Revision-Date: 2025-12-15 13:49\n"
+"PO-Revision-Date: 2025-11-17 16:37\n"
 "Last-Translator: \n"
 "Language-Team: Dutch\n"
 "Language: nl_NL\n"

src/backend/locale/ru_RU/LC_MESSAGES/django.po

@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: la-suite-conversations\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2025-10-20 21:48+0000\n"
-"PO-Revision-Date: 2025-12-15 13:49\n"
+"PO-Revision-Date: 2025-11-17 16:37\n"
 "Last-Translator: \n"
 "Language-Team: Russian\n"
 "Language: ru_RU\n"

src/backend/locale/uk_UA/LC_MESSAGES/django.po

@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: la-suite-conversations\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2025-10-20 21:48+0000\n"
-"PO-Revision-Date: 2025-12-15 13:49\n"
+"PO-Revision-Date: 2025-11-17 16:37\n"
 "Last-Translator: \n"
 "Language-Team: Ukrainian\n"
 "Language: uk_UA\n"

src/backend/pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "conversations"
-version = "0.0.10"
+version = "0.0.9"
 authors = [{ "name" = "DINUM", "email" = "dev@mail.numerique.gouv.fr" }]
 classifiers = [
     "Development Status :: 5 - Production/Stable",
@@ -43,9 +43,7 @@ dependencies = [
     "djangorestframework==3.16.1",
     "drf_spectacular==0.29.0",
     "dockerflow==2024.4.2",
-    "docling",
     "easy_thumbnails==2.10.1",
-    "easyocr",
     "factory_boy==3.3.3",
     "gunicorn==23.0.0",
     "jsonschema==4.25.1",

src/backend/utils/oidc.py

@@ -1,54 +0,0 @@
-"""Utility functions for OIDC token management."""
-
-from functools import wraps
-
-from django.conf import settings
-
-import requests
-from lasuite.oidc_login.backends import get_oidc_refresh_token, store_tokens
-from rest_framework.exceptions import AuthenticationFailed
-
-
-def refresh_access_token(session):
-    """Refresh the OIDC access token using the refresh token."""
-    refresh_token = get_oidc_refresh_token(session)
-    if not refresh_token:
-        raise AuthenticationFailed({"error": "Refresh token is missing from session"})
-
-    response = requests.post(
-        settings.OIDC_OP_TOKEN_ENDPOINT,
-        data={
-            "grant_type": "refresh_token",
-            "client_id": settings.OIDC_RP_CLIENT_ID,
-            "client_secret": settings.OIDC_RP_CLIENT_SECRET,
-            "refresh_token": refresh_token,
-        },
-        timeout=5,
-    )
-    response.raise_for_status()
-    token_info = response.json()
-
-    store_tokens(
-        session,
-        access_token=token_info.get("access_token"),
-        id_token=None,
-        refresh_token=token_info.get("refresh_token"),
-    )
-    return session
-
-
-def with_fresh_access_token(func):
-    """
-    Decorator to handle OIDC token refresh and extraction.
-    Expects 'session' in kwargs and update it with the fresh token.
-    """
-
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        session = kwargs.pop("session", None)
-        if session is None:
-            raise AuthenticationFailed({"error": "Session is required but not provided"})
-        refreshed_session = refresh_access_token(session)
-        return func(*args, session=refreshed_session, **kwargs)
-
-    return wrapper

src/frontend/apps/conversations/package.json

@@ -1,6 +1,6 @@
 {
   "name": "app-conversations",
-  "version": "0.0.10",
+  "version": "0.0.9",
   "private": true,
   "scripts": {
     "dev": "next dev",
@@ -39,11 +39,11 @@
     "lottie-react": "^2.4.1",
     "luxon": "3.6.1",
     "micromark-extension-llm-math": "3.1.1-20250610",
-    "next": "15.3.8",
+    "next": "15.3.3",
     "posthog-js": "1.249.3",
-    "react": "19.2.1",
+    "react": "*",
     "react-aria-components": "1.9.0",
-    "react-dom": "19.2.1",
+    "react-dom": "18.3.1",
     "react-i18next": "15.5.2",
     "react-intersection-observer": "9.16.0",
     "react-markdown": "10.1.0",

src/frontend/apps/conversations/src/features/chat/components/Chat.tsx

@@ -709,11 +709,6 @@ export const Chat = ({
                       {message.content && (
                         <Box
                           className="mainContent-chat"
-                          data-testid={
-                            message.role === 'assistant'
-                              ? 'assistant-message-content'
-                              : undefined
-                          }
                           $padding={{ all: 'xxs' }}
                         >
                           <p className="sr-only">
@@ -796,20 +791,39 @@ export const Chat = ({
                               <Loader />
                               <Text $variation="600" $size="md">
                                 {(() => {
-                                  const toolInvocation = message.parts?.find(
+                                  // Find the tool invocation that is currently running (not completed)
+                                  const toolInvocations = message.parts?.filter(
                                     (part) =>
                                       part.type === 'tool-invocation' &&
                                       part.toolInvocation.toolName !==
                                         'document_parsing',
-                                  );
+                                  ) || [];
+                                  
+                                  // Find the last tool invocation that is not yet completed
+                                  const activeToolInvocation = [...toolInvocations]
+                                    .reverse()
+                                    .find(
+                                      (part) =>
+                                        part.type === 'tool-invocation' &&
+                                        part.toolInvocation.state !== 'result',
+                                    );
+                                  
                                   if (
-                                    toolInvocation?.type ===
+                                    activeToolInvocation?.type ===
                                       'tool-invocation' &&
-                                    toolInvocation.toolInvocation.toolName ===
+                                    activeToolInvocation.toolInvocation.toolName ===
                                       'summarize'
                                   ) {
                                     return t('Summarizing...');
                                   }
+                                  if (
+                                    activeToolInvocation?.type ===
+                                      'tool-invocation' &&
+                                    activeToolInvocation.toolInvocation.toolName ===
+                                      'fetch_url'
+                                  ) {
+                                    return t('Fetching URL...');
+                                  }
                                   return t('Search...');
                                 })()}
                               </Text>

src/frontend/apps/conversations/src/i18n/translations.json

@@ -60,7 +60,7 @@
       "Give feedback": "Faire un retour",
       "History": "Historique",
       "Home": "Accueil",
-      "If enabled, this allows us to analyse your exchanges to improve the Assistant. If disabled, all conversations remain confidential and are not used in any way. ": "Si cette option est activée, cela nous permet d'analyser vos conversations afin d'améliorer l'Assistant. Si elle est désactivée, toutes les conversations restent confidentielles et ne sont utilisées d'aucune manière. ",
+      "If enabled, this allows us to analyse your exchanges to improve the Assistant. If disabled, all conversations remain confidential and are not used in any way. ": "Si cette option est activée, cela nous permet d'analyser vos conversations afin d'améliorer l'Assistant. Si elle est désactivée, toutes les conversations restent confidentielles et ne sont utilisées d'aucune manière ",
       "Illustration": "Image",
       "Image 401": "Image 401",
       "Image 403": "Image 403",

src/frontend/apps/e2e/__tests__/app-conversations/chat.spec.ts

@@ -47,12 +47,16 @@ test.describe('Chat page', () => {
 
     await page.keyboard.press('Enter');
 
+    // Wait for the response to appear
+    await page
+      .getByRole('button', { name: 'See more' })
+      .waitFor({ timeout: 10000 });
+
     const copyButton = page.getByRole('button', { name: 'Copy' });
     await expect(copyButton).toBeVisible();
 
-    const messageContent = page.getByTestId('assistant-message-content');
+    const messageContent = page.getByText('Lorem ipsum dolor sit amet');
     await expect(messageContent).toBeVisible();
-    await expect(messageContent).not.toBeEmpty(); 
 
     // Check history
     const chatHistoryLink = page

src/frontend/apps/e2e/package.json

@@ -1,6 +1,6 @@
 {
   "name": "app-e2e",
-  "version": "0.0.10",
+  "version": "0.0.9",
   "private": true,
   "scripts": {
     "lint": "eslint . --ext .ts",

src/frontend/package.json

@@ -1,6 +1,6 @@
 {
   "name": "conversations",
-  "version": "0.0.10",
+  "version": "0.0.9",
   "private": true,
   "workspaces": {
     "packages": [
@@ -32,8 +32,8 @@
     "@typescript-eslint/eslint-plugin": "8.33.1",
     "@typescript-eslint/parser": "8.33.1",
     "eslint": "8.57.0",
-    "react": "19.2.1",
-    "react-dom": "19.2.1",
+    "react": "19.1.0",
+    "react-dom": "19.1.0",
     "typescript": "5.8.3"
   }
 }

src/frontend/packages/eslint-config-conversations/package.json

@@ -1,6 +1,6 @@
 {
   "name": "eslint-config-conversations",
-  "version": "0.0.10",
+  "version": "0.0.9",
   "license": "MIT",
   "scripts": {
     "lint": "eslint --ext .js ."

src/frontend/packages/i18n/package.json

@@ -1,6 +1,6 @@
 {
   "name": "packages-i18n",
-  "version": "0.0.10",
+  "version": "0.0.9",
   "private": true,
   "scripts": {
     "extract-translation": "yarn extract-translation:conversations",

src/frontend/yarn.lock

@@ -1971,10 +1971,10 @@
     "@emnapi/runtime" "^1.4.3"
     "@tybys/wasm-util" "^0.9.0"
 
-"@next/env@15.3.8":
-  version "15.3.8"
-  resolved "https://registry.yarnpkg.com/@next/env/-/env-15.3.8.tgz#02326c38d315d72f2ab8b2bcc9a5c81ec5482873"
-  integrity sha512-SAfHg0g91MQVMPioeFeDjE+8UPF3j3BvHjs8ZKJAUz1BG7eMPvfCKOAgNWJ6s1MLNeP6O2InKQRTNblxPWuq+Q==
+"@next/env@15.3.3":
+  version "15.3.3"
+  resolved "https://registry.npmjs.org/@next/env/-/env-15.3.3.tgz"
+  integrity sha512-OdiMrzCl2Xi0VTjiQQUK0Xh7bJHnOuET2s+3V+Y40WJBAXrJeGA3f+I8MZJ/YQ3mVGi5XGR1L66oFlgqXhQ4Vw==
 
 "@next/eslint-plugin-next@15.3.3":
   version "15.3.3"
@@ -1983,45 +1983,45 @@
   dependencies:
     fast-glob "3.3.1"
 
-"@next/swc-darwin-arm64@15.3.5":
-  version "15.3.5"
-  resolved "https://registry.yarnpkg.com/@next/swc-darwin-arm64/-/swc-darwin-arm64-15.3.5.tgz#75606cb72e1659a23f15195dba760dc01b186c5d"
-  integrity sha512-lM/8tilIsqBq+2nq9kbTW19vfwFve0NR7MxfkuSUbRSgXlMQoJYg+31+++XwKVSXk4uT23G2eF/7BRIKdn8t8w==
+"@next/swc-darwin-arm64@15.3.3":
+  version "15.3.3"
+  resolved "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-15.3.3.tgz"
+  integrity sha512-WRJERLuH+O3oYB4yZNVahSVFmtxRNjNF1I1c34tYMoJb0Pve+7/RaLAJJizyYiFhjYNGHRAE1Ri2Fd23zgDqhg==
 
-"@next/swc-darwin-x64@15.3.5":
-  version "15.3.5"
-  resolved "https://registry.yarnpkg.com/@next/swc-darwin-x64/-/swc-darwin-x64-15.3.5.tgz#78ffad7ef26685e5b8150891b467a4ecef94e179"
-  integrity sha512-WhwegPQJ5IfoUNZUVsI9TRAlKpjGVK0tpJTL6KeiC4cux9774NYE9Wu/iCfIkL/5J8rPAkqZpG7n+EfiAfidXA==
+"@next/swc-darwin-x64@15.3.3":
+  version "15.3.3"
+  resolved "https://registry.yarnpkg.com/@next/swc-darwin-x64/-/swc-darwin-x64-15.3.3.tgz#71588bad245180ffd1af1e1f894477287e739eb0"
+  integrity sha512-XHdzH/yBc55lu78k/XwtuFR/ZXUTcflpRXcsu0nKmF45U96jt1tsOZhVrn5YH+paw66zOANpOnFQ9i6/j+UYvw==
 
-"@next/swc-linux-arm64-gnu@15.3.5":
-  version "15.3.5"
-  resolved "https://registry.yarnpkg.com/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-15.3.5.tgz#d9a405ceec729d62033dbdc48f8c331c544f09fd"
-  integrity sha512-LVD6uMOZ7XePg3KWYdGuzuvVboxujGjbcuP2jsPAN3MnLdLoZUXKRc6ixxfs03RH7qBdEHCZjyLP/jBdCJVRJQ==
+"@next/swc-linux-arm64-gnu@15.3.3":
+  version "15.3.3"
+  resolved "https://registry.yarnpkg.com/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-15.3.3.tgz#66a15f749c14f04a89f8c7e21c7a8d343fc34e6e"
+  integrity sha512-VZ3sYL2LXB8znNGcjhocikEkag/8xiLgnvQts41tq6i+wql63SMS1Q6N8RVXHw5pEUjiof+II3HkDd7GFcgkzw==
 
-"@next/swc-linux-arm64-musl@15.3.5":
-  version "15.3.5"
-  resolved "https://registry.yarnpkg.com/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-15.3.5.tgz#65f19ad3ecd2881381ec2a149afba261ba180dde"
-  integrity sha512-k8aVScYZ++BnS2P69ClK7v4nOu702jcF9AIHKu6llhHEtBSmM2zkPGl9yoqbSU/657IIIb0QHpdxEr0iW9z53A==
+"@next/swc-linux-arm64-musl@15.3.3":
+  version "15.3.3"
+  resolved "https://registry.yarnpkg.com/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-15.3.3.tgz#14bd66213f7f33d6909574750bcb05037221a2ac"
+  integrity sha512-h6Y1fLU4RWAp1HPNJWDYBQ+e3G7sLckyBXhmH9ajn8l/RSMnhbuPBV/fXmy3muMcVwoJdHL+UtzRzs0nXOf9SA==
 
-"@next/swc-linux-x64-gnu@15.3.5":
-  version "15.3.5"
-  resolved "https://registry.yarnpkg.com/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-15.3.5.tgz#cd7f7e002212360b99f7e791a2d2fedb352f2374"
-  integrity sha512-2xYU0DI9DGN/bAHzVwADid22ba5d/xrbrQlr2U+/Q5WkFUzeL0TDR963BdrtLS/4bMmKZGptLeg6282H/S2i8A==
+"@next/swc-linux-x64-gnu@15.3.3":
+  version "15.3.3"
+  resolved "https://registry.yarnpkg.com/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-15.3.3.tgz#4a19434545e5e752d9a3ed71f9b34982725f6293"
+  integrity sha512-jJ8HRiF3N8Zw6hGlytCj5BiHyG/K+fnTKVDEKvUCyiQ/0r5tgwO7OgaRiOjjRoIx2vwLR+Rz8hQoPrnmFbJdfw==
 
-"@next/swc-linux-x64-musl@15.3.5":
-  version "15.3.5"
-  resolved "https://registry.yarnpkg.com/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-15.3.5.tgz#302c9e4ace935c963d45fce9584754a19295c452"
-  integrity sha512-TRYIqAGf1KCbuAB0gjhdn5Ytd8fV+wJSM2Nh2is/xEqR8PZHxfQuaiNhoF50XfY90sNpaRMaGhF6E+qjV1b9Tg==
+"@next/swc-linux-x64-musl@15.3.3":
+  version "15.3.3"
+  resolved "https://registry.yarnpkg.com/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-15.3.3.tgz#41ab140dd0a04ab7291adbec5836c1ce251a588c"
+  integrity sha512-HrUcTr4N+RgiiGn3jjeT6Oo208UT/7BuTr7K0mdKRBtTbT4v9zJqCDKO97DUqqoBK1qyzP1RwvrWTvU6EPh/Cw==
 
-"@next/swc-win32-arm64-msvc@15.3.5":
-  version "15.3.5"
-  resolved "https://registry.yarnpkg.com/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-15.3.5.tgz#5bbe1434afa2360634d45fc7860a038d11e4e296"
-  integrity sha512-h04/7iMEUSMY6fDGCvdanKqlO1qYvzNxntZlCzfE8i5P0uqzVQWQquU1TIhlz0VqGQGXLrFDuTJVONpqGqjGKQ==
+"@next/swc-win32-arm64-msvc@15.3.3":
+  version "15.3.3"
+  resolved "https://registry.yarnpkg.com/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-15.3.3.tgz#fcd1d7e0007b7b73d1acdbf0ad6d91f7aa2deb15"
+  integrity sha512-SxorONgi6K7ZUysMtRF3mIeHC5aA3IQLmKFQzU0OuhuUYwpOBc1ypaLJLP5Bf3M9k53KUUUj4vTPwzGvl/NwlQ==
 
-"@next/swc-win32-x64-msvc@15.3.5":
-  version "15.3.5"
-  resolved "https://registry.yarnpkg.com/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-15.3.5.tgz#9629b2eac3159c70f3449cecc2a29bfd4bcb2d5a"
-  integrity sha512-5fhH6fccXxnX2KhllnGhkYMndhOiLOLEiVGYjP2nizqeGWkN10sA9taATlXwake2E2XMvYZjjz0Uj7T0y+z1yw==
+"@next/swc-win32-x64-msvc@15.3.3":
+  version "15.3.3"
+  resolved "https://registry.yarnpkg.com/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-15.3.3.tgz#c0e33e069d7922dd0546cac77a0247ad81d4a1aa"
+  integrity sha512-4QZG6F8enl9/S2+yIiOiju0iCTFd93d8VC1q9LZS4p/Xuk81W2QDjCFeoogmrWWkAD59z8ZxepBQap2dKS5ruw==
 
 "@nodelib/fs.scandir@2.1.5":
   version "2.1.5"
@@ -10838,12 +10838,12 @@ neo-async@^2.6.2:
   resolved "https://registry.npmjs.org/neo-async/-/neo-async-2.6.2.tgz"
   integrity sha512-Yd3UES5mWCSqR+qNT93S3UoYUkqAZ9lLg8a7g9rimsWmYGK8cVToA4/sF3RrshdyV3sAGMXVUmpMYOw+dLpOuw==
 
-next@15.3.8:
-  version "15.3.8"
-  resolved "https://registry.yarnpkg.com/next/-/next-15.3.8.tgz#c7df2fa890c66fa3042e85437e3c1e8e6bd38b26"
-  integrity sha512-L+4c5Hlr84fuaNADZbB9+ceRX9/CzwxJ+obXIGHupboB/Q1OLbSUapFs4bO8hnS/E6zV/JDX7sG1QpKVR2bguA==
+next@15.3.3:
+  version "15.3.3"
+  resolved "https://registry.npmjs.org/next/-/next-15.3.3.tgz"
+  integrity sha512-JqNj29hHNmCLtNvd090SyRbXJiivQ+58XjCcrC50Crb5g5u2zi7Y2YivbsEfzk6AtVI80akdOQbaMZwWB1Hthw==
   dependencies:
-    "@next/env" "15.3.8"
+    "@next/env" "15.3.3"
     "@swc/counter" "0.1.3"
     "@swc/helpers" "0.5.15"
     busboy "1.6.0"
@@ -10851,14 +10851,14 @@ next@15.3.8:
     postcss "8.4.31"
     styled-jsx "5.1.6"
   optionalDependencies:
-    "@next/swc-darwin-arm64" "15.3.5"
-    "@next/swc-darwin-x64" "15.3.5"
-    "@next/swc-linux-arm64-gnu" "15.3.5"
-    "@next/swc-linux-arm64-musl" "15.3.5"
-    "@next/swc-linux-x64-gnu" "15.3.5"
-    "@next/swc-linux-x64-musl" "15.3.5"
-    "@next/swc-win32-arm64-msvc" "15.3.5"
-    "@next/swc-win32-x64-msvc" "15.3.5"
+    "@next/swc-darwin-arm64" "15.3.3"
+    "@next/swc-darwin-x64" "15.3.3"
+    "@next/swc-linux-arm64-gnu" "15.3.3"
+    "@next/swc-linux-arm64-musl" "15.3.3"
+    "@next/swc-linux-x64-gnu" "15.3.3"
+    "@next/swc-linux-x64-musl" "15.3.3"
+    "@next/swc-win32-arm64-msvc" "15.3.3"
+    "@next/swc-win32-x64-msvc" "15.3.3"
     sharp "^0.34.1"
 
 no-case@^3.0.4:
@@ -11779,12 +11779,12 @@ react-dnd@^14.0.3:
     fast-deep-equal "^3.1.3"
     hoist-non-react-statics "^3.3.2"
 
-react-dom@19.0.0, react-dom@19.2.1:
-  version "19.2.1"
-  resolved "https://registry.yarnpkg.com/react-dom/-/react-dom-19.2.1.tgz#ce3527560bda4f997e47d10dab754825b3061f59"
-  integrity sha512-ibrK8llX2a4eOskq1mXKu/TGZj9qzomO+sNfO98M6d9zIPOEhlBkMkBUBLd1vgS0gQsLDBzA+8jJBVXDnfHmJg==
+react-dom@18.3.1, react-dom@19.0.0, react-dom@19.1.0:
+  version "19.1.0"
+  resolved "https://registry.yarnpkg.com/react-dom/-/react-dom-19.1.0.tgz#133558deca37fa1d682708df8904b25186793623"
+  integrity sha512-Xs1hdnE+DyKgeHJeJznQmYMIBG3TKIHJJT95Q58nHLSrElKlGQqDTR2HQ9fx5CN/Gk6Vh/kupBTDLU11/nDk/g==
   dependencies:
-    scheduler "^0.27.0"
+    scheduler "^0.26.0"
 
 react-i18next@15.5.2:
   version "15.5.2"
@@ -12006,10 +12006,10 @@ react-window@^1.8.11:
     "@babel/runtime" "^7.0.0"
     memoize-one ">=3.1.1 <6"
 
-react@19.0.0, react@19.2.1:
-  version "19.2.1"
-  resolved "https://registry.yarnpkg.com/react/-/react-19.2.1.tgz#8600fa205e58e2e807f6ef431c9f6492591a2700"
-  integrity sha512-DGrYcCWK7tvYMnWh79yrPHt+vdx9tY+1gPZa7nJQtO/p8bLTDaHp4dzwEhQB7pZ4Xe3ok4XKuEPrVuc+wlpkmw==
+react@*, react@19.0.0, react@19.1.0:
+  version "19.1.0"
+  resolved "https://registry.yarnpkg.com/react/-/react-19.1.0.tgz#926864b6c48da7627f004795d6cce50e90793b75"
+  integrity sha512-FS+XFBNvn3GTAWq26joslQgWNoFu08F4kl0J4CgdNKADkdSGXQyTCnKteIAJy96Br6YbpEU1LSzV5dYtjMkMDg==
 
 readable-stream@^3.4.0:
   version "3.6.2"
@@ -12444,10 +12444,10 @@ scheduler@0.25.0-rc-603e6108-20241029:
   resolved "https://registry.npmjs.org/scheduler/-/scheduler-0.25.0-rc-603e6108-20241029.tgz"
   integrity sha512-pFwF6H1XrSdYYNLfOcGlM28/j8CGLu8IvdrxqhjWULe2bPcKiKW4CV+OWqR/9fT52mywx65l7ysNkjLKBda7eA==
 
-scheduler@^0.27.0:
-  version "0.27.0"
-  resolved "https://registry.yarnpkg.com/scheduler/-/scheduler-0.27.0.tgz#0c4ef82d67d1e5c1e359e8fc76d3a87f045fe5bd"
-  integrity sha512-eNv+WrVbKu1f3vbYJT/xtiF5syA5HPIMtf9IgY/nKg0sWqzAUEvqY/xm7OcZc/qafLx/iO9FgOmeSAp4v5ti/Q==
+scheduler@^0.26.0:
+  version "0.26.0"
+  resolved "https://registry.yarnpkg.com/scheduler/-/scheduler-0.26.0.tgz#4ce8a8c2a2095f13ea11bf9a445be50c555d6337"
+  integrity sha512-NlHwttCI/l5gCPR3D1nNXtWABUmBwvZpEQiD4IXSbIDq8BzLIK/7Ir5gTFSGZDUu37K5cMNp0hFtzO38sC7gWA==
 
 schema-utils@^4.3.0, schema-utils@^4.3.2:
   version "4.3.2"

src/mail/package.json

@@ -1,6 +1,6 @@
 {
   "name": "mail_mjml",
-  "version": "0.0.10",
+  "version": "0.0.9",
   "description": "An util to generate html and text django's templates from mjml templates",
   "type": "module",
   "dependencies": {