✨(backend) use docling

I save my changes  but this is deprecated
as we want must use docling-serve.

.gitignore

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

CHANGELOG.md

@@ -8,14 +8,19 @@ and this project adheres to
 
 ## [Unreleased]
 
+### Added
+
+- ✨(backend) add FindRagBackend
+
 ### Changed
-- 🐛(front) optimize chat
+
 - 📦️(front) update react
-- ✨(chat) Generate and edit conversation title
 
 ### Fixed
 
-- 🐛(e2e) fix test-e2e-chronium
+- 🐛(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
 
@@ -35,6 +40,7 @@ and this project adheres to
 ## [0.0.9] - 2025-11-17
 
 ### Added
+
 - ✨(front) add code copy button
 - ✨(RAG) add generic collection RAG tools #159
 
@@ -42,7 +48,6 @@ and this project adheres to
 
 - 🔊(langfuse) enable tracing with redacted content #162
 
-
 ## [0.0.8] - 2025-11-10
 
 ### Fixed
@@ -57,28 +62,24 @@ 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
@@ -95,14 +96,12 @@ 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
@@ -112,6 +111,7 @@ 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
@@ -119,7 +119,6 @@ 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
@@ -142,7 +141,7 @@ 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

compose.yml

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

docs/env.md

@@ -95,6 +95,9 @@ 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-large",
-      "model_name": "mistral-large-latest",
-      "human_readable_name": "Mistral Large (Etalab)",
+      "hrid": "mistral-medium",
+      "model_name": "mistral-medium-2508",
+      "human_readable_name": "Mistral Medium (Etalab)",
       "provider_name": "mistral-etalab",
       "profile": null,
       "settings": {

docs/tools/web_search_brave.md

@@ -357,6 +357,7 @@ 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

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

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

@@ -0,0 +1,100 @@
+"""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.markitdown import DocumentConverter
+from chat.agent_rag.document_converter.parser import DoclingParser
 from chat.agent_rag.document_rag_backends.base_rag_backend import BaseRagBackend
 
 logger = logging.getLogger(__name__)
@@ -26,9 +26,6 @@ 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.
     """
@@ -46,10 +43,9 @@ 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:
         """
@@ -114,59 +110,7 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
             )
             response.raise_for_status()
 
-    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:
+    def store_document(self, name: str, content: str, **kwargs) -> None:
         """
         Store the document content in the Albert collection.
         This method should handle the logic to send the document content to the Albert API.
@@ -174,6 +118,7 @@ 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),
@@ -188,7 +133,7 @@ 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) -> None:
+    async def astore_document(self, name: str, content: str, **kwargs) -> None:
         """
         Store the document content in the Albert collection.
         This method should handle the logic to send the document content to the Albert API.
@@ -196,6 +141,7 @@ 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(
@@ -213,13 +159,14 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
             logger.debug(response.json())
             response.raise_for_status()
 
-    def search(self, query, results_count: int = 4) -> RAGWebResults:
+    def search(self, query: str, results_count: int = 4, **kwargs) -> 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.
@@ -256,13 +203,14 @@ class AlbertRagBackend(BaseRagBackend):  # pylint: disable=too-many-instance-att
             ),
         )
 
-    async def asearch(self, query, results_count: int = 4) -> RAGWebResults:
+    async def asearch(self, query, results_count: int = 4, **kwargs) -> 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

@@ -8,6 +8,7 @@ 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__)
 
@@ -38,6 +39,7 @@ 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]:
         """
@@ -53,7 +55,7 @@ class BaseRagBackend:
 
         collection_ids = []
         if self.collection_id:
-            collection_ids.append(int(self.collection_id))
+            collection_ids.append(self.collection_id)
         if self.read_only_collection_id:
             collection_ids.extend(
                 [int(collection_id) for collection_id in self.read_only_collection_id]
@@ -88,9 +90,9 @@ class BaseRagBackend:
         Returns:
             str: The document content in Markdown format.
         """
-        raise NotImplementedError("Must be implemented in subclass.")
+        return self.parser.parse_document(name, content_type, content)
 
-    def store_document(self, name: str, content: str) -> None:
+    def store_document(self, name: str, content: str, **kwargs) -> None:
         """
         Store the document content in the collection.
         This method should handle the logic to send the document content to the API.
@@ -98,10 +100,11 @@ 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) -> None:
+    async def astore_document(self, name: str, content: str, **kwargs) -> None:
         """
         Store the document content in the collection.
         This method should handle the logic to send the document content to the API.
@@ -109,10 +112,13 @@ 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)
+        return await sync_to_async(self.store_document)(name=name, content=content, **kwargs)
 
-    def parse_and_store_document(self, name: str, content_type: str, content: BytesIO) -> str:
+    def parse_and_store_document(
+        self, name: str, content_type: str, content: BytesIO, **kwargs
+    ) -> str:
         """
         Parse the document and store it in the Albert collection.
 
@@ -120,12 +126,13 @@ 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)
+        self.store_document(name, document_content, **kwargs)
         return document_content
 
     def delete_collection(self) -> None:
@@ -142,17 +149,27 @@ class BaseRagBackend:
         """
         return await sync_to_async(self.delete_collection)()
 
-    def search(self, query, results_count: int = 4) -> RAGWebResults:
+    def search(self, query: str, results_count: int = 4, **kwargs) -> 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, results_count: int = 4) -> RAGWebResults:
+    async def asearch(self, query: str, results_count: int = 4, **kwargs) -> RAGWebResults:
         """
-        Search the collection for the given query.
+        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.
         """
-        return await sync_to_async(self.search)(query=query, results_count=results_count)
+        return await sync_to_async(self.search)(query=query, results_count=results_count, **kwargs)
 
     @classmethod
     @contextmanager

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

@@ -0,0 +1,153 @@
+"""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.markitdown import DocumentConverter
+from chat.agent_rag.document_converter.parser import DoclingParser
 from chat.models import ChatConversation
 
 logger = logging.getLogger(__name__)
@@ -80,58 +80,6 @@ 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.
@@ -156,7 +104,7 @@ class AlbertRagDocumentSearch:
         logger.debug(response.json())
         response.raise_for_status()
 
-    def parse_and_store_document(self, name: str, content_type: str, content: BytesIO):
+    def parse_and_store_document(self, name: str, content_type: str, content: bytes):
         """
         Parse the document and store it in the Albert collection.
 
@@ -165,7 +113,9 @@ 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 = self.parse_document(name, content_type, content)
+        document_content = DoclingParser().parse_document(
+            name=name, content_type=content_type, content=content
+        )
         self._store_document(name, document_content)
         return document_content
 

src/backend/chat/agents/base.py

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

src/backend/chat/agents/conversation.py

@@ -16,7 +16,6 @@ from .base import BaseAgent
 
 logger = logging.getLogger(__name__)
 
-
 MOCKED_RESPONSE = """
 # **Ode to the AI Assistant** 🤖✨
 
@@ -102,10 +101,10 @@ class ConversationAgent(BaseAgent):
         if settings.WARNING_MOCK_CONVERSATION_AGENT:
             self._model = FunctionModel(stream_function=mocked_agent_model)
 
-        @self.system_prompt
+        @self.instructions
         def add_the_date() -> str:
             """
-            Dynamic system prompt function to add the current date.
+            Dynamic instruction function to add the current date.
 
             Warning: this will always use the date in the server timezone,
             not the user's timezone...
@@ -113,9 +112,9 @@ class ConversationAgent(BaseAgent):
             _formatted_date = formats.date_format(timezone.now(), "l d/m/Y", use_l10n=False)
             return f"Today is {_formatted_date}."
 
-        @self.system_prompt
+        @self.instructions
         def enforce_response_language() -> str:
-            """Dynamic system prompt function to set the expected language to use."""
+            """Dynamic instruction 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

@@ -60,7 +60,6 @@ from chat.agents.local_media_url_processors import (
     update_history_local_urls,
     update_local_urls,
 )
-from chat.agents.summarize import SummarizationAgent
 from chat.ai_sdk_types import (
     LanguageModelV1Source,
     SourceUIPart,
@@ -79,6 +78,9 @@ 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()
@@ -90,6 +92,7 @@ class ContextDeps:
 
     conversation: models.ChatConversation
     user: User
+    session: Optional[Dict] = None
     web_search_enabled: bool = False
 
 
@@ -104,7 +107,14 @@ 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__(self, conversation: models.ChatConversation, user, model_hrid=None, language=None):
+    def __init__(  # pylint: disable=too-many-arguments,too-many-positional-arguments
+        self,
+        conversation: models.ChatConversation,
+        user,
+        session=None,
+        model_hrid=None,
+        language=None,
+    ):
         """
         Initialize the AI agent service.
 
@@ -134,6 +144,7 @@ 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,
         )
 
@@ -237,6 +248,7 @@ 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
@@ -250,8 +262,6 @@ 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
@@ -277,6 +287,7 @@ 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
@@ -286,6 +297,7 @@ 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/"):
@@ -357,16 +369,7 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
         messages: List[UIMessage],
         force_web_search: bool = False,
     ) -> events_v4.Event | events_v5.Event:
-        """
-        Drive the agent for the provided user message, stream Vercel-AI-SDK event parts representing model and tool activity, and persist the final conversation state.
-        
-        Parameters:
-            messages (List[UIMessage]): UI messages for the conversation; the last message must be from the user.
-            force_web_search (bool): If true, require the agent to invoke the configured web search tool before answering (ignored if the feature or tool is unavailable).
-        
-        Returns:
-            events_v4.Event | events_v5.Event: Streamed event parts such as `TextPart`, `ToolCallPart`/`ToolCallStreamingStartPart`/`ToolCallDeltaPart`, `ToolResultPart`, `ReasoningPart`, `SourcePart`, `DataPart`, `StartStepPart`, and `FinishMessagePart` that drive frontend updates.
-        """
+        """Run the Pydantic AI agent and stream events."""
         if messages[-1].role != "user":
             return
 
@@ -430,6 +433,7 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                     ],
                 },
             )
+
             try:
                 await self.parse_input_documents(input_documents)
             except Exception as exc:  # pylint: disable=broad-except
@@ -456,28 +460,6 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
 
         await self._agent_stop_streaming(force_cache_check=True)
 
-        generated_title = None
-
-        # +1 because we're about to add a new user message
-        current_user_count = sum(1 for msg in self.conversation.messages if msg.role == "user") + 1
-        if (
-            current_user_count == settings.AUTO_TITLE_AFTER_USER_MESSAGES
-            and not self.conversation.title_set_by_user_at
-        ):
-            generated_title = await self._generate_title()
-
-        # Notify frontend about the title update
-        if generated_title:
-            yield events_v4.DataPart(
-                data=[
-                    {
-                        "type": "conversation_metadata",
-                        "conversationId": str(self.conversation.pk),
-                        "title": generated_title,
-                    }
-                ]
-            )
-
         if force_web_search and not self._is_web_search_enabled:
             logger.warning("Web search is forced but the feature is disabled, ignoring.")
             force_web_search = False
@@ -489,7 +471,7 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
 
         if force_web_search:
 
-            @self.conversation_agent.system_prompt
+            @self.conversation_agent.instructions
             def force_web_search_prompt() -> str:
                 """Dynamic system prompt function to force web search."""
                 return (
@@ -537,7 +519,7 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
                 )
 
             # Inform the model (system-level) that documents are attached and available
-            @self.conversation_agent.system_prompt
+            @self.conversation_agent.instructions
             def attached_documents_note() -> str:
                 return (
                     "[Internal context] User documents are attached to this conversation. "
@@ -757,14 +739,12 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
             ui_sources=_ui_sources,
             model_response_message_id=_model_response_message_id,
             image_key_mapping=image_key_mapping or None,
-            generated_title=generated_title,
         )
 
         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,
@@ -783,25 +763,18 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
         ui_sources: List[SourceUIPart] = None,
         model_response_message_id: str | None = None,
         image_key_mapping: Dict[str, str] = None,
-        generated_title: str | None = None,
     ):  # pylint: disable=too-many-arguments
         """
-        Merge the agent's final outputs into the conversation and persist updated conversation state.
-        
-        Parameters:
-            final_output (List[ModelRequest | ModelMessage]): Sequence of model requests and responses produced by the agent run; these will be merged into a single request and a single response before saving.
-            usage (Dict[str, int]): Token usage statistics to store on the conversation (e.g., promptTokens, completionTokens).
-            final_output_from_tool (str | None): Optional text produced by a tool that should be appended to the final model response.
-            ui_sources (List[SourceUIPart], optional): Optional UI-visible source parts to attach to the final response message.
-            model_response_message_id (str | None, optional): If provided, assign this id to the saved model response UI message; if omitted, a warning will be logged.
-            image_key_mapping (Dict[str, str], optional): Mapping from original (unsigned) media URLs to presigned/rewritten URLs; applied to image/document references in the merged request parts.
-            generated_title (str | None, optional): Optional auto-generated conversation title to apply to the conversation.
-        
-        Behavior:
-            - Merges multiple model request/response objects into a single ModelRequest and ModelResponse.
-            - Rewrites image/document URLs in user prompt parts when an image_key_mapping is provided.
-            - Converts merged model messages to UI messages, appends ui_sources if present, and sets the response message id when supplied.
-            - Appends the merged request and response messages to the conversation, updates agent usage and pydantic messages, applies a generated title if given, and saves the conversation.
+        Save everything related to the conversation.
+
+        Things to improve here:
+         - The way we need to add the UI sources to the final output message.
+
+        Args:
+            final_output (List[ModelRequest | ModelMessage]): The final output from the agent.
+            usage (Dict[str, int]): The token usage statistics.
+            user_initial_prompt_str (str | None): The initial user prompt string, if any.
+            ui_sources (List[SourceUIPart]): Optional UI sources to include in the conversation.
         """
         _merged_final_output_request = ModelRequest(
             parts=[
@@ -847,43 +820,5 @@ class AIAgentService:  # pylint: disable=too-many-instance-attributes
         self.conversation.pydantic_messages += json.loads(
             ModelMessagesTypeAdapter.dump_json(final_output).decode("utf-8")
         )
-        if generated_title:
-            self.conversation.title = generated_title
+
         self.conversation.save()
-
-    async def _generate_title(self) -> str | None:
-        """
-        Create a concise conversation title based on the conversation's first messages.
-        
-        Uses the summarization agent to produce a short title in the same language as the user's messages. Returns the generated title text trimmed to at most 100 characters, or `None` if generation fails or produces no text.
-        
-        Returns:
-            str | None: The generated title (trimmed to 100 characters), or `None` when no title is available.
-        """
-
-        # Build context from the first messages
-        context = "\n".join(
-            f"{msg.role}: {msg.content[:300]}"  # Limit content length per message
-            for msg in self.conversation.messages[:6]  # First few messages (3 user + 3 assistant)
-        )
-
-        prompt = (
-            "Generate a short, concise title (maximum 60 characters) for this conversation. "
-            "The title should capture the main topic or intent. "
-            "Return ONLY the title text, nothing else. No quotes, no explanations.\n\n"
-            "Return the title text in the same language the user messages are written."
-            f"If in doubt, use {self.language or 'French'}."
-            f"Conversation:\n{context}"
-        )
-
-        try:
-            agent = SummarizationAgent()
-            result = await agent.run(prompt)
-            title = (result.output or "").strip()[:100]  # Enforce max length
-            logger.info("Generated title for conversation %s: %s", self.conversation.pk, title)
-            return title if title else None
-        except Exception as exc:  # pylint: disable=broad-except #noqa: BLE001
-            logger.warning(
-                "Failed to generate title for conversation %s: %s", self.conversation.pk, exc
-            )
-            return None

src/backend/chat/migrations/0005_chatconversation_title_set_by_user_at.py

@@ -1,21 +0,0 @@
-# Generated by Django 5.2.9 on 2025-12-30 09:44
-
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-    dependencies = [
-        ("chat", "0004_chatconversationattachment_and_more"),
-    ]
-
-    operations = [
-        migrations.AddField(
-            model_name="chatconversation",
-            name="title_set_by_user_at",
-            field=models.DateTimeField(
-                blank=True,
-                help_text="Timestamp when the user manually set the title. If set, prevent automatic title generation.",
-                null=True,
-            ),
-        ),
-    ]

src/backend/chat/models.py

@@ -44,12 +44,7 @@ class ChatConversation(BaseModel):
         null=True,
         help_text="Title of the chat conversation",
     )
-    title_set_by_user_at = models.DateTimeField(
-        blank=True,
-        null=True,
-        help_text="Timestamp when the user manually set the title. If set, prevent automatic "
-        "title generation.",
-    )
+
     ui_messages = models.JSONField(
         default=list,
         blank=True,

src/backend/chat/serializers.py

@@ -4,7 +4,6 @@ from typing import Optional
 from urllib.parse import quote
 
 from django.conf import settings
-from django.utils import timezone
 
 from django_pydantic_field.rest_framework import SchemaField  # pylint: disable=no-name-in-module
 from drf_spectacular.utils import extend_schema_field
@@ -28,20 +27,6 @@ class ChatConversationSerializer(serializers.ModelSerializer):
         fields = ["id", "title", "created_at", "updated_at", "messages", "owner"]
         read_only_fields = ["id", "created_at", "updated_at", "messages"]
 
-    def update(self, instance, validated_data):
-        # If title is being changed, mark it as user-set
-        """
-        Update the ChatConversation instance and record when the title is changed by the user.
-        
-        If `validated_data` contains a `title` different from the instance's current title, sets `title_set_by_user_at` to the current time.
-        
-        Returns:
-            The updated ChatConversation instance.
-        """
-        if "title" in validated_data and validated_data["title"] != instance.title:
-            instance.title_set_by_user_at = timezone.now()
-        return super().update(instance, validated_data)
-
 
 class ChatConversationInputSerializer(serializers.Serializer):
     """
@@ -213,4 +198,4 @@ class CreateChatConversationAttachmentSerializer(serializers.ModelSerializer):
                 f"File size exceeds the maximum limit of {max_size:d} MB."
             )
 
-        return size
+        return size

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

@@ -0,0 +1,29 @@
+"""
+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

@@ -0,0 +1,90 @@
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+<</Length 6 0 R/Filter /FlateDecode>>
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+endobj
+6 0 obj
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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
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+/Contents 5 0 R
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+3 0 obj
+<< /Type /Pages /Kids [
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+<</Type /Catalog /Pages 3 0 R
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+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>
+<xmp:CreateDate>2014-12-22T00:49:20+01:00</xmp:CreateDate>
+<xmp:CreatorTool>PDFCreator Version 1.6.0</xmp:CreatorTool></rdf:Description>
+<rdf:Description rdf:about='uuid:81d69fb9-8bc7-11e4-0000-66b1dd18110c' xmlns:xapMM='http://ns.adobe.com/xap/1.0/mm/' xapMM:DocumentID='uuid:81d69fb9-8bc7-11e4-0000-66b1dd18110c'/>
+<rdf:Description rdf:about='uuid:81d69fb9-8bc7-11e4-0000-66b1dd18110c' xmlns:dc='http://purl.org/dc/elements/1.1/' dc:format='application/pdf'><dc:title><rdf:Alt><rdf:li xml:lang='x-default'>test_word</rdf:li></rdf:Alt></dc:title><dc:creator><rdf:Seq><rdf:li>Seb</rdf:li></rdf:Seq></dc:creator><dc:description><rdf:Seq><rdf:li>()</rdf:li></rdf:Seq></dc:description></rdf:Description>
+</rdf:RDF>
+</x:xmpmeta>
+                                                                        
+                                                                        
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+endstream
+endobj
+2 0 obj
+<</Producer(GPL Ghostscript 9.06)
+/CreationDate(D:20141222004920+01'00')
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+/Author(\376\377\000S\000e\000b)
+/Keywords()
+/Subject()>>endobj
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src/backend/chat/tests/agent_rag/document_converter/test_docling.py

@@ -0,0 +1,18 @@
+"""
+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

@@ -5,28 +5,21 @@ 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
 
 
-@patch("chat.agent_rag.document_converter.markitdown.MarkItDown")
-def test_document_converter(mock_markitdown: MagicMock):
+def test_document_converter():
     """Test that the DocumentConverter calls the underlying MarkItDown converter."""
-    mock_conversion = MagicMock()
-    mock_conversion.text_content = "converted text"
-    mock_markitdown.return_value.convert_stream.return_value = mock_conversion
-
+    file_path = "src/backend/chat/tests/data/test.pdf"
     converter = DocumentConverter()
 
-    result = converter.convert_raw(
-        name="test.pdf",
-        content_type="application/pdf",
-        content=b"test content",
-    )
+    with open(file_path, "rb") as file:
+        content = file.read()
+        result = converter.convert_raw(
+            name="test.pdf",
+            content_type="application/pdf",
+            content=content,
+        )
 
-    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"
+    assert result == "Document PDF test\n\n"

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

@@ -27,9 +27,14 @@ 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/"
@@ -37,6 +42,7 @@ 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"]
@@ -44,8 +50,14 @@ def test_build_pydantic_agent_with_tools(settings):
     agent = ConversationAgent(model_hrid="default-model")
     assert isinstance(agent, Agent)
 
-    assert agent._system_prompts == ("You are a helpful assistant",)
-    assert agent._instructions == []
+    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 isinstance(agent.model, OpenAIChatModel)
     assert agent.model.model_name == "model-123"
     assert str(agent.model.client.base_url) == "https://api.llm.com/v1/"
@@ -56,21 +68,23 @@ 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 system prompt are registered
+    Ensure add_the_date and enforce_response_language instructions are registered
     and returns proper values.
     """
     agent = ConversationAgent(model_hrid="default-model")
 
-    assert len(agent._system_prompt_functions) == 2
+    assert len(agent._system_prompt_functions) == 0
 
-    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() == ""
+    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]() == ""
 
     agent = ConversationAgent(model_hrid="default-model", language="fr-fr")
-    assert agent._system_prompt_functions[1].function() == "Answer in french."
+    assert agent._instructions[2]() == "Answer in french."
 
 
 def test_agent_get_web_search_tool_name(settings):

src/backend/chat/tests/data/test.pdf

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+<pdf:Keywords>()</pdf:Keywords>
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+<xmp:CreateDate>2014-12-22T00:49:20+01:00</xmp:CreateDate>
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src/backend/chat/tests/tools/test_web_search_brave.py

@@ -38,9 +38,6 @@ 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

@@ -0,0 +1,17 @@
+"""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/conftest.py

@@ -10,19 +10,15 @@ import respx
 from freezegun import freeze_time
 
 
-def build_openai_stream():
+@pytest.fixture(name="mock_openai_stream")
+@freeze_time("2025-07-25T10:36:35.297675Z")
+def fixture_mock_openai_stream():
     """
-    Constructs a string that simulates an OpenAI streaming response payload.
-    
-    The returned string contains three OpenAI-style `data:` blocks: a first chunk with content "Hello",
-    a second chunk with content " there" and a `finish_reason` of "stop" (including a `usage` object),
-    and a final `data: [DONE]` marker. Timestamp fields are generated from timezone.now() converted to
-    naive timestamps.
-    
-    Returns:
-        A string containing concatenated `data:` lines representing streaming chunks and a final `[DONE]` marker.
+    Fixture to mock the OpenAI stream response.
+
+    See https://platform.openai.com/docs/api-reference/chat-streaming/streaming
     """
-    return (
+    openai_stream = (
         "data: "
         + json.dumps(
             {
@@ -63,24 +59,7 @@ def build_openai_stream():
         "data: [DONE]\n\n"
     )
 
-
-@pytest.fixture(name="mock_openai_stream")
-@freeze_time("2025-07-25T10:36:35.297675Z")
-def fixture_mock_openai_stream():
-    """
-    Fixture to mock the OpenAI stream response.
-
-    See https://platform.openai.com/docs/api-reference/chat-streaming/streaming
-    """
-    openai_stream = build_openai_stream()
-
     async def mock_stream():
-        """
-        Yield each line of the prepared OpenAI-style streaming payload as encoded bytes.
-        
-        Yields:
-            AsyncGenerator[bytes, None]: Sequential byte chunks for each line in the constructed stream, preserving original line endings.
-        """
         for line in openai_stream.splitlines(keepends=True):
             yield line.encode()
 
@@ -91,100 +70,10 @@ def fixture_mock_openai_stream():
     return route
 
 
-@pytest.fixture(name="mock_openai_stream_with_title_generation")
-@freeze_time("2025-07-25T10:36:35.297675Z")
-def fixture_mock_openai_stream_with_title_generation():
-    """
-    Mock pytest fixture that intercepts POST requests to the external chat completions endpoint and returns either a streaming chat response or a non-streaming title-generation response depending on the incoming request.
-    
-    When the request JSON has "stream" set to True, the fixture returns an HTTP streaming response that imitates OpenAI's chat streaming payload; otherwise it returns a non-streaming JSON response containing a generated title and usage metadata.
-    
-    Returns:
-        respx.Route: A configured respx route that intercepts POST requests to
-        "https://www.external-ai-service.com/chat/completions" and replies based on the request body.
-    """
-
-    def create_stream_response():
-        """
-        Create an HTTP response whose body streams encoded lines of an OpenAI-style streaming payload.
-        
-        Returns:
-            httpx.Response: HTTP 200 response with a streaming body that yields encoded bytes for each line of the streaming payload.
-        """
-        openai_stream = build_openai_stream()
-
-        async def mock_stream():
-            """
-            Yield encoded byte chunks for each line of the OpenAI stream.
-            
-            Each yielded value is a bytes object containing one line (including its line ending) from the prebuilt OpenAI streaming payload, suitable for use as an HTTP streaming response body.
-            """
-            for line in openai_stream.splitlines(keepends=True):
-                yield line.encode()
-
-        return httpx.Response(200, stream=mock_stream())
-
-    def create_non_stream_response():
-        """
-        Create a non-streaming OpenAI-like chat completion response containing a generated title.
-        
-        Returns:
-            httpx.Response: HTTP 200 response whose JSON payload represents a chat completion with a single assistant message containing the generated title and accompanying metadata (id, model, timestamps, choices, and usage).
-        """
-        return httpx.Response(
-            200,
-            json={
-                "id": "chatcmpl-title",
-                "object": "chat.completion",
-                "created": int(timezone.make_naive(timezone.now()).timestamp()),
-                "model": "test-model",
-                "choices": [
-                    {
-                        "index": 0,
-                        "message": {
-                            "role": "assistant",
-                            "content": "GENERATED TITLE",
-                        },
-                        "finish_reason": "stop",
-                    }
-                ],
-                "usage": {"prompt_tokens": 50, "completion_tokens": 5, "total_tokens": 55},
-            },
-        )
-
-    def handle_request(request):
-        """
-        Selects a streaming or non-streaming HTTP response based on the request JSON `stream` flag.
-        
-        Parameters:
-            request (httpx.Request): Incoming request whose JSON body is inspected for the `stream` boolean flag.
-        
-        Returns:
-            httpx.Response: A response that streams the OpenAI-style event lines if `stream` is True, otherwise a non-streaming JSON response.
-        """
-        body = json.loads(request.content)
-        if body.get("stream", False):
-            return create_stream_response()
-        return create_non_stream_response()
-
-    route = respx.post("https://www.external-ai-service.com/chat/completions").mock(
-        side_effect=handle_request
-    )
-
-    return route
-
-
 @pytest.fixture(name="mock_openai_no_stream")
 @freeze_time("2025-07-25T10:36:35.297675Z")
 def fixture_mock_openai_no_stream():
-    """
-    Create a respx route that returns a fixed, non-streaming OpenAI chat completion response.
-    
-    The mocked response is an HTTP 200 JSON payload representing a completed assistant message (explaining Rayleigh scattering) with associated metadata and usage details.
-    
-    Returns:
-        respx.Route: The configured respx route intercepting POST requests to https://www.external-ai-service.com/chat/completions.
-    """
+    """Fixture to mock the OpenAI response."""
 
     route = respx.post("https://www.external-ai-service.com/chat/completions").mock(
         return_value=httpx.Response(
@@ -498,4 +387,4 @@ def fixture_mock_openai_stream_tool():
         ]
     )
 
-    return route
+    return route

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

@@ -130,6 +130,16 @@ 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 == [
         {
@@ -170,29 +180,15 @@ 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": None,
+            "instructions": (
+                "You are a helpful test assistant :)\n\n"
+                "Today is Friday 25/07/2025.\n\nAnswer in english."
+            ),
             "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",
@@ -255,6 +251,15 @@ 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 == [
@@ -296,29 +301,15 @@ 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": None,
+            "instructions": (
+                "You are a helpful test assistant :)\n\n"
+                "Today is Friday 25/07/2025.\n\nAnswer in english."
+            ),
             "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",
@@ -409,11 +400,12 @@ 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 :)",
+            "content": (
+                "You are a helpful test assistant :)\n\nToday is Friday 25/07/2025."
+                "\n\nAnswer in english."
+            ),
             "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"},
@@ -498,27 +490,12 @@ 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": None,
+            "instructions": (
+                "You are a helpful test assistant :)\n\n"
+                "Today is Friday 25/07/2025.\n\nAnswer in english."
+            ),
             "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?",
@@ -616,11 +593,12 @@ def test_post_conversation_tool_call(api_client, mock_openai_stream_tool, settin
 
     assert body["messages"] == [
         {
-            "content": "You are a helpful test assistant :)",
+            "content": (
+                "You are a helpful test assistant :)\n\n"
+                "Today is Friday 25/07/2025.\n\nAnswer in english."
+            ),
             "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"},
     ]
 
@@ -678,27 +656,12 @@ 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": None,
+            "instructions": (
+                "You are a helpful test assistant :)\n\n"
+                "Today is Friday 25/07/2025.\n\nAnswer in english."
+            ),
             "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",
@@ -737,7 +700,10 @@ def test_post_conversation_tool_call(api_client, mock_openai_stream_tool, settin
             "run_id": _run_id,
         },
         {
-            "instructions": None,
+            "instructions": (
+                "You are a helpful test assistant :)\n\n"
+                "Today is Friday 25/07/2025.\n\nAnswer in english."
+            ),
             "kind": "request",
             "parts": [
                 {
@@ -829,11 +795,12 @@ def test_post_conversation_tool_call_fails(api_client, mock_openai_stream_tool,
 
     assert body["messages"] == [
         {
-            "content": "You are a helpful test assistant :)",
+            "content": (
+                "You are a helpful test assistant :)\n\n"
+                "Today is Friday 25/07/2025.\n\nAnswer in french."
+            ),
             "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"},
     ]
 
@@ -891,27 +858,12 @@ 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": None,
+            "instructions": (
+                "You are a helpful test assistant :)\n\n"
+                "Today is Friday 25/07/2025.\n\nAnswer in french."
+            ),
             "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",
@@ -950,7 +902,10 @@ def test_post_conversation_tool_call_fails(api_client, mock_openai_stream_tool,
             "run_id": _run_id,
         },
         {
-            "instructions": None,
+            "instructions": (
+                "You are a helpful test assistant :)\n\n"
+                "Today is Friday 25/07/2025.\n\nAnswer in french."
+            ),
             "kind": "request",
             "parts": [
                 {
@@ -1214,27 +1169,11 @@ def test_post_conversation_data_protocol_no_stream(
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": None,
+            "instructions": (
+                "You are an amazing assistant.\n\nToday is Friday 25/07/2025.\n\nAnswer in english."
+            ),
             "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",
@@ -1369,27 +1308,12 @@ 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": None,
+            "instructions": (
+                "You are a helpful test assistant :)\n\n"
+                "Today is Friday 25/07/2025.\n\nAnswer in english."
+            ),
             "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

@@ -8,6 +8,7 @@ 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
@@ -41,28 +42,49 @@ from chat.tests.utils import replace_uuids_with_placeholder
 pytestmark = pytest.mark.django_db(transaction=True)
 
 
-@pytest.fixture(autouse=True)
-def ai_settings(settings):
+@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):
     """Fixture to set AI service URLs for testing."""
-    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 :)"
 
-    # 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"
+    # 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
+    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"
+
     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."""
@@ -81,17 +103,25 @@ def fixture_sample_pdf_content():
     return BytesIO(pdf_data)
 
 
-@pytest.fixture(name="mock_albert_api")
-def fixture_mock_albert_api():
+@pytest.fixture(name="mock_document_api")
+def fixture_mock_document_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 PDF parsing
+    # Mock Albert PDF parsing -> deprecated
     responses.post(
         "https://albert.api.etalab.gouv.fr/v1/parse-beta",
         json={
@@ -101,7 +131,7 @@ def fixture_mock_albert_api():
                     "metadata": {"document_name": "sample.pdf"},
                 }
             ],
-            "usage": {"prompt_tokens": 10, "completion_tokens": 20},
+            "usage": {"prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens},
         },
         status=status.HTTP_200_OK,
     )
@@ -119,20 +149,42 @@ def fixture_mock_albert_api():
         json={
             "data": [
                 {
-                    "method": "semantic",
+                    "method": search_method,
                     "chunk": {
                         "id": 123,
-                        "content": "This is the content of the PDF.",
-                        "metadata": {"document_name": "sample.pdf"},
+                        "content": document_content,
+                        "metadata": {"document_name": document_name},
                     },
-                    "score": 0.9,
+                    "score": search_score,
                 }
             ],
-            "usage": {"prompt_tokens": 10, "completion_tokens": 20},
+            "usage": {"prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens},
         },
         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():
@@ -216,9 +268,10 @@ 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_albert_api,  # pylint: disable=unused-argument
+    mock_document_api,  # pylint: disable=unused-argument
     sample_pdf_content,
     today_promt_date,
     mock_ai_agent_service,
@@ -353,53 +406,25 @@ def test_post_conversation_with_document_upload(  # pylint: disable=too-many-arg
     assert len(chat_conversation.pydantic_messages) == 4
 
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
+
     assert chat_conversation.pydantic_messages[0] == {
-        "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.",
+        "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.",
         "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",
@@ -439,14 +464,21 @@ def test_post_conversation_with_document_upload(  # pylint: disable=too-many-arg
     }
     assert chat_conversation.pydantic_messages[2] == {
         "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."
+            "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."
         ),
         "kind": "request",
         "parts": [
@@ -499,7 +531,8 @@ def test_post_conversation_with_document_upload(  # pylint: disable=too-many-arg
 @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
@@ -552,14 +585,12 @@ def test_post_conversation_with_document_upload_feature_disabled(  # pylint: dis
 
     # 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
 
@@ -569,7 +600,7 @@ def test_post_conversation_with_document_upload_feature_disabled(  # pylint: dis
 @freeze_time()
 def test_post_conversation_with_document_upload_summarize(  # pylint: disable=too-many-arguments,too-many-positional-arguments  # noqa: PLR0913
     api_client,
-    mock_albert_api,  # pylint: disable=unused-argument
+    mock_document_api,  # pylint: disable=unused-argument
     sample_pdf_content,
     today_promt_date,
     mock_ai_agent_service,
@@ -582,6 +613,7 @@ 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",
@@ -643,7 +675,7 @@ 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":317,"completionTokens":19}}\n'
+        'd:{"finishReason":"stop","usage":{"promptTokens":283,"completionTokens":19}}\n'
     )
 
     # Check that the conversation was updated
@@ -705,52 +737,25 @@ 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": "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.",
+        "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."
+        ),
         "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",
@@ -790,14 +795,21 @@ def test_post_conversation_with_document_upload_summarize(  # pylint: disable=to
     }
     assert chat_conversation.pydantic_messages[2] == {
         "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."
+            "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."
         ),
         "kind": "request",
         "parts": [

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

@@ -17,7 +17,6 @@ from pydantic_ai.messages import (
     DocumentUrl,
     ModelMessage,
     ModelResponse,
-    SystemPromptPart,
     TextPart,
     UserPromptPart,
 )
@@ -38,11 +37,19 @@ from chat.tests.utils import replace_uuids_with_placeholder
 pytestmark = pytest.mark.django_db(transaction=True)
 
 
-@pytest.fixture(autouse=True)
-def ai_settings(settings):
+@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):
     """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
@@ -61,7 +68,8 @@ 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,
@@ -85,6 +93,10 @@ def test_post_conversation_with_local_pdf_document_url(  # pylint: disable=too-m
         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)
@@ -120,7 +132,7 @@ def test_post_conversation_with_local_pdf_document_url(  # pylint: disable=too-m
     )
 
     async def agent_model(messages: list[ModelMessage], _info: AgentInfo):
-        presigned_url = messages[0].parts[3].content[1].url
+        presigned_url = messages[0].parts[0].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
@@ -129,11 +141,6 @@ def test_post_conversation_with_local_pdf_document_url(  # pylint: disable=too-m
         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?",
@@ -146,6 +153,8 @@ def test_post_conversation_with_local_pdf_document_url(  # pylint: disable=too-m
                         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,
             )
         ]
@@ -221,27 +230,11 @@ def test_post_conversation_with_local_pdf_document_url(  # pylint: disable=too-m
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": None,
+            "instructions": "You are a helpful test assistant :)\n\n"
+            f"{today_promt_date}\n\n"
+            "Answer in english.",
             "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?",
@@ -429,7 +422,6 @@ 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,
 ):
     """
@@ -437,6 +429,8 @@ 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",
@@ -472,27 +466,11 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
         ],
         pydantic_messages=[
             {
-                "instructions": None,
+                "instructions": "You are a helpful test assistant :)\n\n"
+                f"Today is {formatted_date}.\n\n"
+                "Answer in english.",
                 "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?",
@@ -555,7 +533,7 @@ 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[3].content[1].url
+        presigned_url = messages[0].parts[0].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
@@ -564,18 +542,6 @@ 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?",
@@ -588,6 +554,9 @@ 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(
@@ -606,6 +575,9 @@ 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,
             ),
         ]
@@ -705,27 +677,11 @@ 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": None,
+            "instructions": "You are a helpful test assistant :)\n\n"
+            "Today is Saturday 18/10/2025.\n\n"
+            "Answer in english.",
             "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?",
@@ -772,7 +728,9 @@ def test_post_conversation_with_local_document_url_in_history(  # pylint: disabl
             # no run_id here
         },
         {
-            "instructions": None,
+            "instructions": "You are a helpful test assistant :)\n\n"
+            "Today is Saturday 18/10/2025.\n\n"
+            "Answer in english.",
             "kind": "request",
             "parts": [
                 {
@@ -823,7 +781,8 @@ 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,
@@ -848,6 +807,10 @@ def test_post_conversation_with_local_not_pdf_document_url(  # pylint: disable=t
         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)
@@ -886,27 +849,6 @@ def test_post_conversation_with_local_not_pdf_document_url(  # pylint: disable=t
         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?",
@@ -916,14 +858,22 @@ def test_post_conversation_with_local_not_pdf_document_url(  # pylint: disable=t
                     ),
                 ],
                 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."
+                    "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."
                 ),
                 run_id=messages[0].run_id,
             )
@@ -999,53 +949,25 @@ def test_post_conversation_with_local_not_pdf_document_url(  # pylint: disable=t
     assert chat_conversation.pydantic_messages == [
         {
             "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."
+                "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."
             ),
             "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

@@ -29,24 +29,12 @@ pytestmark = pytest.mark.django_db(transaction=True)
 
 @pytest.fixture(autouse=True)
 def ai_settings(settings):
-    """
-    Configure AI-related settings for tests on the provided settings object.
-    
-    Sets test values for AI service base URL, API key, model, agent instructions, and sets
-    AUTO_TITLE_AFTER_USER_MESSAGES to 999 to disable automatic title generation during tests.
-    
-    Parameters:
-        settings (object): Django settings-like object to be mutated for test configuration.
-    
-    Returns:
-        object: The same settings object with AI-related test configuration applied.
-    """
+    """Fixture to set AI service URLs for testing."""
     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 :)"
 
-    settings.AUTO_TITLE_AFTER_USER_MESSAGES = 999  # disable auto title generation
     return settings
 
 
@@ -931,7 +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()
+    conversation = ChatConversationFactory(owner__language="nl-nl")
 
     # Add previous user and assistant messages with tool invocation
     conversation.messages = [
@@ -1389,7 +1377,9 @@ 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": None,
+        "instructions": "You are a helpful test assistant :)\n\n"
+        "Today is Friday 25/07/2025.\n\n"
+        "Answer in dutch.",
         "kind": "request",
         "parts": [
             {
@@ -1432,7 +1422,9 @@ def test_post_conversation_with_existing_tool_history(
     }
 
     assert history_conversation_with_tool.pydantic_messages[10] == {
-        "instructions": None,
+        "instructions": "You are a helpful test assistant :)\n\n"
+        "Today is Friday 25/07/2025.\n\n"
+        "Answer in dutch.",
         "kind": "request",
         "parts": [
             {
@@ -1581,184 +1573,3 @@ def test_post_conversation_add_image_to_conversation_with_tool_history(
         toolInvocations=None,
         parts=[TextUIPart(type="text", text="I see a cat in the picture.")],
     )
-
-
-@freeze_time("2025-07-25T10:36:35.297675Z")
-@respx.mock
-def test_post_conversation_triggers_automatic_title_generation(
-    api_client, mock_openai_stream_with_title_generation, settings, history_conversation
-):
-    """
-    Test that posting the 3rd user message triggers automatic title generation.
-
-    The history_conversation fixture has 2 user messages. Posting a 3rd message
-    should trigger title generation via the SummarizationAgent.
-    """
-    # Configure the title generation threshold
-    settings.AUTO_TITLE_AFTER_USER_MESSAGES = 3
-
-    url = f"/api/v1.0/chats/{history_conversation.pk}/conversation/?protocol=data"
-    data = {
-        "messages": [
-            {
-                "id": "third-user-msg",
-                "role": "user",
-                "parts": [{"text": "Can you explain backpropagation?", "type": "text"}],
-                "content": "Can you explain backpropagation?",
-                "createdAt": "2025-07-25T10:36:00.000Z",
-            }
-        ]
-    }
-    api_client.force_login(history_conversation.owner)
-
-    history_conversation.title = "initial title"
-    history_conversation.save()
-
-    assert not history_conversation.title_set_by_user_at
-
-    response = api_client.post(url, data, format="json")
-
-    assert response.status_code == status.HTTP_200_OK
-    assert response.get("Content-Type") == "text/event-stream"
-    assert response.streaming
-
-    # Wait for the streaming content to be fully received
-    response_content = b"".join(response.streaming_content).decode("utf-8")
-
-    # Verify the conversation_metadata event is in the stream
-
-    assert '"type": "conversation_metadata"' in response_content
-
-    # Refresh and verify title was updated
-    history_conversation.refresh_from_db()
-
-    assert history_conversation.title == "GENERATED TITLE"
-    # title_set_by_user_at should remain None since it was auto-generated
-    assert not history_conversation.title_set_by_user_at
-
-    assert mock_openai_stream_with_title_generation.called
-
-    assert mock_openai_stream_with_title_generation.call_count == 2
-
-
-@freeze_time("2025-07-25T10:36:35.297675Z")
-@respx.mock
-def test_post_conversation_does_not_regenerate_title_when_user_set(
-    api_client, mock_openai_stream_with_title_generation, settings, history_conversation
-):
-    """
-    Test that title is NOT regenerated if the user has manually set a title.
-    """
-    settings.AUTO_TITLE_AFTER_USER_MESSAGES = 3
-
-    # Simulate user having set a custom title
-    history_conversation.title = "My Custom Title"
-    history_conversation.title_set_by_user_at = timezone.now()
-    history_conversation.save()
-
-    url = f"/api/v1.0/chats/{history_conversation.pk}/conversation/?protocol=data"
-    data = {
-        "messages": [
-            {
-                "id": "third-user-msg",
-                "role": "user",
-                "parts": [{"text": "Can you explain backpropagation?", "type": "text"}],
-                "content": "Can you explain backpropagation?",
-                "createdAt": "2025-07-25T10:36:00.000Z",
-            }
-        ]
-    }
-    api_client.force_login(history_conversation.owner)
-
-    response = api_client.post(url, data, format="json")
-
-    assert response.status_code == status.HTTP_200_OK
-
-    # Consume the stream
-    response_content = b"".join(response.streaming_content).decode("utf-8")
-
-    # conversation_metadata should NOT be in the stream since title wasn't generated
-    assert "conversation_metadata" not in response_content
-
-    # Refresh and verify title was NOT changed
-    history_conversation.refresh_from_db()
-
-    assert history_conversation.title == "My Custom Title"
-    assert history_conversation.title_set_by_user_at is not None
-
-    assert mock_openai_stream_with_title_generation.called
-
-    assert mock_openai_stream_with_title_generation.call_count == 1
-
-
-@freeze_time("2025-07-25T10:36:35.297675Z")
-@respx.mock
-def test_post_conversation_does_not_generate_title_before_threshold(
-    api_client, mock_openai_stream_with_title_generation, settings
-):
-    """
-    Test that title is NOT generated before reaching the message threshold.
-    """
-    settings.AUTO_TITLE_AFTER_USER_MESSAGES = 3
-
-    # Create a conversation with only 1 user message
-    history_timestamp = timezone.now().replace(year=2025, month=6, day=15, hour=10, minute=30)
-    conversation = ChatConversationFactory(title="initial title")
-
-    conversation.messages = [
-        UIMessage(
-            id="prev-user-msg-1",
-            createdAt=history_timestamp,
-            content="Hello!",
-            reasoning=None,
-            experimental_attachments=None,
-            role="user",
-            annotations=None,
-            toolInvocations=None,
-            parts=[TextUIPart(type="text", text="Hello!")],
-        ),
-        UIMessage(
-            id="prev-assistant-msg-1",
-            createdAt=history_timestamp.replace(minute=31),
-            content="Hi there! How can I help you?",
-            reasoning=None,
-            experimental_attachments=None,
-            role="assistant",
-            annotations=None,
-            toolInvocations=None,
-            parts=[TextUIPart(type="text", text="Hi there! How can I help you?")],
-        ),
-    ]
-    conversation.save()
-
-    url = f"/api/v1.0/chats/{conversation.pk}/conversation/?protocol=data"
-    data = {
-        "messages": [
-            {
-                "id": "second-user-msg",
-                "role": "user",
-                "parts": [{"text": "What's machine learning?", "type": "text"}],
-                "content": "What's machine learning?",
-                "createdAt": "2025-07-25T10:36:00.000Z",
-            }
-        ]
-    }
-    api_client.force_login(conversation.owner)
-
-    response = api_client.post(url, data, format="json")
-
-    assert response.status_code == status.HTTP_200_OK
-
-    # Consume the stream
-    response_content = b"".join(response.streaming_content).decode("utf-8")
-
-    # conversation_metadata should NOT be in the stream (only 2 user messages)
-    assert "conversation_metadata" not in response_content
-
-    # Refresh and verify title was not updated
-    conversation.refresh_from_db()
-
-    assert conversation.title == "initial title"
-    assert not conversation.title_set_by_user_at
-
-    assert mock_openai_stream_with_title_generation.call_count == 1

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

@@ -2,7 +2,7 @@
 
 import uuid
 
-from django.utils import timezone
+from django.utils import formats, timezone
 
 import pytest
 from dirty_equals import IsUUID
@@ -12,7 +12,6 @@ from pydantic_ai.messages import (
     ImageUrl,
     ModelMessage,
     ModelResponse,
-    SystemPromptPart,
     TextPart,
     UserPromptPart,
 )
@@ -87,22 +86,15 @@ def test_post_conversation_with_local_image_url(
     )
 
     async def agent_model(messages: list[ModelMessage], _info: AgentInfo):
-        presigned_url = messages[0].parts[3].content[1].url
-        assert presigned_url.startswith("http://localhost:9000/conversations-media-storage/")
+        presigned_url = messages[0].parts[0].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?",
@@ -115,6 +107,8 @@ 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,
             )
         ]
@@ -184,27 +178,10 @@ def test_post_conversation_with_local_image_url(
     _run_id = chat_conversation.pydantic_messages[0]["run_id"]
     assert chat_conversation.pydantic_messages == [
         {
-            "instructions": None,
+            "instructions": "You are a helpful test assistant :)\n\n"
+            "Today is Saturday 18/10/2025.\n\nAnswer in english.",
             "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?",
@@ -286,11 +263,6 @@ 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?",
@@ -303,6 +275,8 @@ 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,
             )
         ]
@@ -374,11 +348,6 @@ 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?",
@@ -391,6 +360,8 @@ 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,
             )
         ]
@@ -504,27 +475,10 @@ def test_post_conversation_with_local_image_url_in_history(
         ],
         pydantic_messages=[
             {
-                "instructions": None,
+                "instructions": f"You are a helpful test assistant :)\n\n{today_promt_date}"
+                "\n\nAnswer in english.",
                 "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?",
@@ -587,7 +541,7 @@ def test_post_conversation_with_local_image_url_in_history(
     )
 
     async def agent_model(messages: list[ModelMessage], _info: AgentInfo):
-        presigned_url = messages[0].parts[3].content[1].url
+        presigned_url = messages[0].parts[0].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
@@ -596,18 +550,6 @@ 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?",
@@ -619,7 +561,9 @@ 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.")],
@@ -637,6 +581,8 @@ 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."
@@ -735,27 +681,10 @@ 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": None,
+            "instructions": f"You are a helpful test assistant :)\n\n{today_promt_date}"
+            "\n\nAnswer in english.",
             "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?",
@@ -796,7 +725,8 @@ def test_post_conversation_with_local_image_url_in_history(
             },
         },
         {
-            "instructions": None,
+            "instructions": "You are a helpful test assistant :)\n\nToday is Saturday 18/10/2025."
+            "\n\nAnswer in english.",
             "kind": "request",
             "parts": [
                 {

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

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

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

@@ -28,7 +28,6 @@ def test_create_conversation(api_client):
     conversation = ChatConversation.objects.get(id=response.data["id"])
     assert conversation.owner == user
     assert conversation.title == "New Conversation"
-    assert not conversation.title_set_by_user_at
 
 
 def test_create_conversation_other_owner(api_client):

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

@@ -2,7 +2,6 @@
 
 import pytest
 from rest_framework import status
-from rest_framework.exceptions import ErrorDetail
 
 from core.factories import UserFactory
 
@@ -27,34 +26,6 @@ def test_update_conversation(api_client):
     # Verify in database
     conversation = ChatConversation.objects.get(id=chat_conversation.pk)
     assert conversation.title == "Updated Title"
-    assert conversation.title_set_by_user_at is not None
-
-
-def test_update_conversation_limit_title_length(api_client):
-    """Test that updating a conversation with a title exceeding 100 characters fails validation."""
-    chat_conversation = ChatConversationFactory(title="Initial title")
-
-    url = f"/api/v1.0/chats/{chat_conversation.pk}/"
-    # Create a 101-character title to exceed the 100-character maximum limit
-    new_title = "X" * 101
-    data = {"title": new_title}
-    api_client.force_login(chat_conversation.owner)
-    response = api_client.put(url, data, format="json")
-
-    assert response.status_code == status.HTTP_400_BAD_REQUEST
-
-    assert response.data == {
-        "title": [
-            ErrorDetail(
-                string="Ensure this field has no more than 100 characters.", code="max_length"
-            )
-        ]
-    }
-
-    # Verify in database (title should remain unchanged)
-    conversation = ChatConversation.objects.get(id=chat_conversation.pk)
-    assert conversation.title == "Initial title"
-    assert not conversation.title_set_by_user_at
 
 
 def test_update_conversation_anonymous(api_client):

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)
+        rag_results = document_store.search(query, session=ctx.deps.session)
 
         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.system_prompt
+    @agent.instructions
     def document_rag_instructions() -> str:
         """Dynamic system prompt function to add RAG instructions if any."""
         return (

src/backend/chat/tools/document_summarize.py

@@ -101,7 +101,7 @@ 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(

src/backend/chat/tools/exceptions.py

@@ -3,17 +3,6 @@
 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) -> None:
+async def _fetch_and_store_async(url: str, document_store, **kwargs) -> 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) -> None:
         logger.debug("Fetched document: %s", document)
 
         if document:
-            await document_store.astore_document(url, document)
+            await document_store.astore_document(url, document, **kwargs)
     except DocumentFetchError as e:
         logger.warning("Failed to fetch and store %s: %s", url, e)
         # Continue with other documents

src/backend/chat/views.py

@@ -7,11 +7,13 @@ 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
@@ -122,6 +124,7 @@ 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,
@@ -173,6 +176,7 @@ 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
@@ -221,7 +225,7 @@ class ChatViewSet(  # pylint: disable=too-many-ancestors, abstract-method
         url_path="stop-streaming",
         url_name="stop-streaming",
     )
-    def post_stop_steaming(self, request, pk):  # pylint: disable=unused-argument
+    def post_stop_streaming(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", "minio", "minio:9000"}
+    allowed_hosts = {"localhost", "127.0.0.1", "minio", "minio:9000"}
     original_urlopen = HTTPConnectionPool.urlopen
 
     def urlopen_mock(self, method, url, *args, **kwargs):

src/backend/conversations/settings.py

@@ -841,6 +841,23 @@ 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.
@@ -911,9 +928,7 @@ USER QUESTION:
     LANGFUSE_MEDIA_UPLOAD_ENABLED = values.BooleanValue(
         default=False, environ_name="LANGFUSE_MEDIA_UPLOAD_ENABLED", environ_prefix=None
     )
-    AUTO_TITLE_AFTER_USER_MESSAGES = values.PositiveIntegerValue(
-        3, environ_name="AUTO_TITLE_AFTER_USER_MESSAGES", environ_prefix=None
-    )
+
     # WARNING: Testing purpose only. Do not use in production.
     WARNING_MOCK_CONVERSATION_AGENT = values.BooleanValue(
         default=False,

src/backend/core/api/__init__.py

@@ -1,12 +1,9 @@
 """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):
@@ -28,14 +25,3 @@ 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

@@ -20,23 +20,3 @@ 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

@@ -1,52 +0,0 @@
-"""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

@@ -1,25 +0,0 @@
-"""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

@@ -2,31 +2,9 @@
 
 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

@@ -1,14 +0,0 @@
-<!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

@@ -1,58 +0,0 @@
-"""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/pyproject.toml

@@ -43,7 +43,9 @@ 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

@@ -0,0 +1,54 @@
+"""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/src/components/Loader.tsx

@@ -1,8 +1,7 @@
 import dynamic from 'next/dynamic';
 
-import searchingAnimation from '@/assets/lotties/searching';
-
 const Lottie = dynamic(() => import('lottie-react'), { ssr: false });
+import searchingAnimation from '@/assets/lotties/searching';
 
 export function Loader() {
   return (

src/frontend/apps/conversations/src/features/chat/api/useChat.tsx

@@ -1,9 +1,6 @@
 import { UseChatOptions, useChat as useAiSdkChat } from '@ai-sdk/react';
-import { useQueryClient } from '@tanstack/react-query';
-import { useEffect } from 'react';
 
 import { fetchAPI } from '@/api';
-import { KEY_LIST_CONVERSATION } from '@/features/chat/api/useConversations';
 import { useChatPreferencesStore } from '@/features/chat/stores/useChatPreferencesStore';
 
 const fetchAPIAdapter = (input: RequestInfo | URL, init?: RequestInit) => {
@@ -39,55 +36,10 @@ const fetchAPIAdapter = (input: RequestInfo | URL, init?: RequestInit) => {
   return fetchAPI(url, init);
 };
 
-interface ConversationMetadataEvent {
-  type: 'conversation_metadata';
-  conversationId: string;
-  title: string;
-}
-/**
- * Type guard that determines whether a value is a ConversationMetadataEvent.
- *
- * @param item - Value to test
- * @returns `true` if `item` is a ConversationMetadataEvent, `false` otherwise.
- */
-function isConversationMetadataEvent(
-  item: unknown,
-): item is ConversationMetadataEvent {
-  return (
-    typeof item === 'object' &&
-    item !== null &&
-    'type' in item &&
-    item.type === 'conversation_metadata'
-  );
-}
-
-/**
- * Hook that provides chat functionality with a custom fetch adapter and automatic conversation-list cache invalidation.
- *
- * The hook invokes the underlying AI chat implementation with `maxSteps` set to 3 and a fetch wrapper that appends UI-driven query parameters; when the chat stream emits a `conversation_metadata` event the hook invalidates the conversation list cache (KEY_LIST_CONVERSATION).
- *
- * @param options - Chat configuration options (note: `maxSteps` is overridden to 3 and the `fetch` implementation is replaced)
- * @returns The chat hook result object containing `data`, status flags, and control methods for interacting with the chat stream.
- */
 export function useChat(options: Omit<UseChatOptions, 'fetch'>) {
-  const queryClient = useQueryClient();
-
-  const result = useAiSdkChat({
+  return useAiSdkChat({
     ...options,
     maxSteps: 3,
     fetch: fetchAPIAdapter,
   });
-
-  useEffect(() => {
-    if (result.data && Array.isArray(result.data)) {
-      for (const item of result.data) {
-        if (isConversationMetadataEvent(item)) {
-          void queryClient.invalidateQueries({
-            queryKey: [KEY_LIST_CONVERSATION],
-          });
-        }
-      }
-    }
-  }, [result.data, queryClient]);
-  return result;
-}
+}

src/frontend/apps/conversations/src/features/chat/api/useRenameConversation.tsx

@@ -1,62 +0,0 @@
-import {
-  UseMutationOptions,
-  useMutation,
-  useQueryClient,
-} from '@tanstack/react-query';
-
-import { APIError, errorCauses, fetchAPI } from '@/api';
-
-import { KEY_LIST_CONVERSATION } from './useConversations';
-
-interface RenameConversationProps {
-  conversationId: string;
-  title: string;
-}
-
-export const renameConversation = async ({
-  conversationId,
-  title,
-}: RenameConversationProps): Promise<void> => {
-  const response = await fetchAPI(`chats/${conversationId}/`, {
-    method: 'PUT',
-    body: JSON.stringify({
-      title,
-    }),
-  });
-
-  if (!response.ok) {
-    throw new APIError(
-      'Failed to rename the conversation',
-      await errorCauses(response),
-    );
-  }
-};
-
-type UseRenameConversationOptions = UseMutationOptions<
-  void,
-  APIError,
-  RenameConversationProps
->;
-
-export const useRenameConversation = (
-  options?: UseRenameConversationOptions,
-) => {
-  const queryClient = useQueryClient();
-  return useMutation<void, APIError, RenameConversationProps>({
-    mutationFn: renameConversation,
-    ...options,
-    onSuccess: (data, variables, context) => {
-      void queryClient.invalidateQueries({
-        queryKey: [KEY_LIST_CONVERSATION],
-      });
-      if (options?.onSuccess) {
-        void options.onSuccess(data, variables, context);
-      }
-    },
-    onError: (error, variables, context) => {
-      if (options?.onError) {
-        void options.onError(error, variables, context);
-      }
-    },
-  });
-};

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

@@ -1,386 +0,0 @@
-import {
-  Message,
-  ReasoningUIPart,
-  SourceUIPart,
-  ToolInvocationUIPart,
-} from '@ai-sdk/ui-utils';
-import 'katex/dist/katex.min.css';
-import { memo, useDeferredValue } from 'react';
-import { useTranslation } from 'react-i18next';
-import { MarkdownHooks } from 'react-markdown';
-import rehypeKatex from 'rehype-katex';
-import rehypePrettyCode from 'rehype-pretty-code';
-import remarkGfm from 'remark-gfm';
-import remarkMath from 'remark-math';
-
-import { Box, Icon, Text } from '@/components';
-import { useClipboard } from '@/hook';
-import { useResponsiveStore } from '@/stores';
-
-import { AttachmentList } from './AttachmentList';
-import { CodeBlock } from './CodeBlock';
-import { FeedbackButtons } from './FeedbackButtons';
-import { SourceItemList } from './SourceItemList';
-import { ToolInvocationItem } from './ToolInvocationItem';
-
-// Mémoriser les plugins Markdown en dehors du composant pour éviter les recréations
-const remarkPlugins = [remarkGfm, remarkMath];
-const rehypePlugins = [
-  [
-    rehypePrettyCode,
-    {
-      theme: 'github-dark-dimmed',
-    },
-  ],
-  rehypeKatex,
-];
-
-// Composants Markdown mémorisés
-// eslint-disable-next-line @typescript-eslint/no-explicit-any
-const markdownComponents: any = {
-  // eslint-disable-next-line @typescript-eslint/no-unused-vars, @typescript-eslint/no-explicit-any
-  p: ({ node, ...props }: any) => (
-    <Text
-      as="p"
-      $css="display: block"
-      $theme="greyscale"
-      $variation="850"
-      {...props}
-    />
-  ),
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  a: ({ children, ...props }: any) => (
-    <a target="_blank" rel="noopener noreferrer" {...props}>
-      {children}
-    </a>
-  ),
-  // eslint-disable-next-line @typescript-eslint/no-unused-vars, @typescript-eslint/no-explicit-any
-  pre: ({ node, children, ...props }: any) => (
-    <CodeBlock {...props}>{children}</CodeBlock>
-  ),
-};
-
-// Composant Markdown mémorisé pour éviter les recalculs inutiles
-const MemoizedMarkdown = memo(function MemoizedMarkdown({
-  content,
-}: {
-  content: string;
-}) {
-  return (
-    <MarkdownHooks
-      remarkPlugins={remarkPlugins}
-      // eslint-disable-next-line @typescript-eslint/no-explicit-any, @typescript-eslint/no-unsafe-assignment
-      rehypePlugins={rehypePlugins as any} // Type mismatch with react-markdown types
-      // eslint-disable-next-line @typescript-eslint/no-unsafe-assignment
-      components={markdownComponents}
-    >
-      {content}
-    </MarkdownHooks>
-  );
-});
-
-interface ChatMessageProps {
-  message: Message;
-  isLastAssistantMessageInConversation: boolean;
-  shouldApplyStreamingHeight: boolean;
-  streamingMessageHeight: number | null;
-  isCurrentlyStreaming: boolean;
-  status: 'idle' | 'streaming' | 'submitted' | 'ready' | 'error';
-  isSourceOpen: string | null;
-  conversationId: string | undefined;
-  onOpenSources: (messageId: string) => void;
-  getMetadata: (url: string) =>
-    | {
-        title: string | null;
-        favicon: string | null;
-        loading: boolean;
-        error: boolean;
-      }
-    | undefined;
-}
-
-export const ChatMessage = memo(function ChatMessage({
-  message,
-  isLastAssistantMessageInConversation,
-  shouldApplyStreamingHeight,
-  streamingMessageHeight,
-  isCurrentlyStreaming,
-  status,
-  isSourceOpen,
-  conversationId,
-  onOpenSources,
-  getMetadata,
-}: ChatMessageProps) {
-  const { t } = useTranslation();
-  const copyToClipboard = useClipboard();
-  const { isMobile } = useResponsiveStore();
-
-  const deferredContent = useDeferredValue(message.content);
-
-  const contentToRender =
-    message.role === 'assistant' ? deferredContent : message.content;
-
-  return (
-    <Box
-      key={message.id}
-      data-message-id={message.id}
-      $css={`
-        display: flex;
-        width: 100%;
-        margin: auto;
-        margin-bottom: ${isLastAssistantMessageInConversation ? '30px' : '0px'};
-        color: var(--c--theme--colors--greyscale-850);
-        padding-left: 12px;
-        padding-right: 12px;
-        max-width: 750px;
-        text-align: left;
-        overflow-wrap: anywhere;
-        flex-direction: ${message.role === 'user' ? 'row-reverse' : 'row'};
-      `}
-    >
-      <Box
-        $display="block"
-        $width={`${message.role === 'user' ? 'auto' : '100%'}`}
-      >
-        {message.experimental_attachments &&
-          message.experimental_attachments.length > 0 && (
-            <Box>
-              <AttachmentList
-                attachments={message.experimental_attachments}
-                isReadOnly={true}
-              />
-            </Box>
-          )}
-        <Box
-          $radius="8px"
-          $width={`${message.role === 'user' ? 'auto' : '100%'}`}
-          $maxWidth="100%"
-          $padding={`${message.role === 'user' ? '12px' : '0'}`}
-          $margin={{ vertical: 'base' }}
-          $background={`${message.role === 'user' ? '#EEF1F4' : 'white'}`}
-          $css={`
-            display: inline-block;
-            float: right;
-            ${shouldApplyStreamingHeight ? `min-height: ${streamingMessageHeight}px;` : ''}
-          `}
-        >
-          {message.content && (
-            <Box
-              className="mainContent-chat"
-              data-testid={
-                message.role === 'assistant'
-                  ? 'assistant-message-content'
-                  : undefined
-              }
-              $padding={{ all: 'xxs' }}
-            >
-              <p className="sr-only">
-                {message.role === 'user'
-                  ? t('You said: ')
-                  : t('Assistant IA replied: ')}
-              </p>
-              {message.role === 'user' ? (
-                <Text
-                  as="p"
-                  $css="white-space: pre-wrap; display: block;"
-                  $theme="greyscale"
-                  $variation="850"
-                >
-                  {message.content}
-                </Text>
-              ) : (
-                <MemoizedMarkdown content={contentToRender} />
-              )}
-            </Box>
-          )}
-
-          <Box $direction="column" $gap="2">
-            {isCurrentlyStreaming &&
-              isLastAssistantMessageInConversation &&
-              status === 'streaming' &&
-              message.parts?.some(
-                (part) =>
-                  part.type === 'tool-invocation' &&
-                  part.toolInvocation.toolName !== 'document_parsing',
-              ) && (
-                <Box
-                  $direction="row"
-                  $align="center"
-                  $gap="6px"
-                  $width="100%"
-                  $maxWidth="750px"
-                  $margin={{
-                    all: 'auto',
-                    top: 'base',
-                    bottom: 'md',
-                  }}
-                >
-                  <Text $variation="600" $size="md">
-                    {(() => {
-                      const toolInvocation = message.parts?.find(
-                        (part) =>
-                          part.type === 'tool-invocation' &&
-                          part.toolInvocation.toolName !== 'document_parsing',
-                      );
-                      if (
-                        toolInvocation?.type === 'tool-invocation' &&
-                        toolInvocation.toolInvocation.toolName === 'summarize'
-                      ) {
-                        return t('Summarizing...');
-                      }
-                      return t('Search...');
-                    })()}
-                  </Text>
-                </Box>
-              )}
-            {message.parts
-              ?.filter(
-                (part) =>
-                  part.type === 'reasoning' || part.type === 'tool-invocation',
-              )
-              .map(
-                (
-                  part: ReasoningUIPart | ToolInvocationUIPart,
-                  partIndex: number,
-                ) =>
-                  part.type === 'reasoning' ? (
-                    <Box
-                      key={`reasoning-${partIndex}`}
-                      $background="var(--c--theme--colors--greyscale-100)"
-                      $color="var(--c--theme--colors--greyscale-500)"
-                      $padding={{ all: 'sm' }}
-                      $radius="md"
-                      $css="font-size: 0.9em;"
-                    >
-                      {part.reasoning}
-                    </Box>
-                  ) : part.type === 'tool-invocation' &&
-                    isCurrentlyStreaming &&
-                    isLastAssistantMessageInConversation ? (
-                    <ToolInvocationItem
-                      key={`tool-invocation-${partIndex}`}
-                      toolInvocation={part.toolInvocation}
-                      status={status}
-                      hideSearchLoader={true}
-                    />
-                  ) : null,
-              )}
-          </Box>
-          {message.role === 'assistant' &&
-            !(
-              isLastAssistantMessageInConversation && status === 'streaming'
-            ) && (
-              <Box
-                $css="color: #222631; font-size: 12px;"
-                $direction="row"
-                $align="center"
-                $justify="space-between"
-                $gap="6px"
-                $margin={{ top: 'base' }}
-              >
-                <Box $direction="row" $gap="4px">
-                  <Box
-                    $direction="row"
-                    $align="center"
-                    $gap="4px"
-                    className="c__button--neutral action-chat-button"
-                    onClick={() => copyToClipboard(message.content)}
-                    onKeyDown={(e) => {
-                      if (e.key === 'Enter' || e.key === ' ') {
-                        e.preventDefault();
-                        copyToClipboard(message.content);
-                      }
-                    }}
-                    role="button"
-                    tabIndex={0}
-                  >
-                    <Icon
-                      iconName="content_copy"
-                      $theme="greyscale"
-                      $variation="550"
-                      $size="16px"
-                      className="action-chat-button-icon"
-                    />
-                    {!isMobile && (
-                      <Text $theme="greyscale" $variation="550">
-                        {t('Copy')}
-                      </Text>
-                    )}
-                  </Box>
-                  {message.parts?.some((part) => part.type === 'source') &&
-                    (() => {
-                      const sourceCount =
-                        message.parts?.filter((part) => part.type === 'source')
-                          .length || 0;
-                      return (
-                        <Box
-                          $direction="row"
-                          $align="center"
-                          $gap="4px"
-                          className={`c__button--neutral action-chat-button ${isSourceOpen === message.id ? 'action-chat-button--open' : ''}`}
-                          onClick={() => onOpenSources(message.id)}
-                          onKeyDown={(e) => {
-                            if (e.key === 'Enter' || e.key === ' ') {
-                              e.preventDefault();
-                              onOpenSources(message.id);
-                            }
-                          }}
-                          role="button"
-                          tabIndex={0}
-                        >
-                          <Icon
-                            iconName="book"
-                            $theme="greyscale"
-                            $variation="550"
-                            $size="16px"
-                            className="action-chat-button-icon"
-                          />
-                          <Text
-                            $theme="greyscale"
-                            $variation="550"
-                            $weight="500"
-                            $size="12px"
-                          >
-                            {t('Show')} {sourceCount}{' '}
-                            {sourceCount !== 1 ? t('sources') : t('source')}
-                          </Text>
-                        </Box>
-                      );
-                    })()}
-                </Box>
-                <Box $direction="row" $gap="4px">
-                  {conversationId &&
-                    message.id &&
-                    message.id.startsWith('trace-') && (
-                      <FeedbackButtons
-                        conversationId={conversationId}
-                        messageId={message.id}
-                      />
-                    )}
-                </Box>
-              </Box>
-            )}
-          {message.parts &&
-            isSourceOpen === message.id &&
-            (() => {
-              const sourceParts = message.parts.filter(
-                (part): part is SourceUIPart => part.type === 'source',
-              );
-              return (
-                <Box
-                  $css={`
-                    animation: fade-in 0.2s ease-out;
-                  `}
-                >
-                  <SourceItemList
-                    parts={sourceParts}
-                    getMetadata={getMetadata}
-                  />
-                </Box>
-              );
-            })()}
-        </Box>
-      </Box>
-    </Box>
-  );
-});

src/frontend/apps/conversations/src/features/chat/hooks/index.ts

@@ -1,3 +1,2 @@
 export { useChatScroll } from './useChatScroll';
 export { useSourceMetadataCache } from './useSourceMetadata';
-export { useModelSelection } from './useModelSelection';

src/frontend/apps/conversations/src/features/chat/hooks/useModelSelection.ts

@@ -1,44 +0,0 @@
-import { useEffect, useRef, useState } from 'react';
-
-import { LLMModel, useLLMConfiguration } from '../api/useLLMConfiguration';
-import { useChatPreferencesStore } from '../stores/useChatPreferencesStore';
-
-export const useModelSelection = () => {
-  const { data: llmConfig } = useLLMConfiguration();
-  const { selectedModelHrid, setSelectedModelHrid } = useChatPreferencesStore();
-  const [selectedModel, setSelectedModel] = useState<LLMModel | null>(null);
-  const hasInitializedRef = useRef(false);
-
-  useEffect(() => {
-    // Ne s'exécuter qu'une seule fois quand llmConfig est chargé
-    if (llmConfig?.models && !hasInitializedRef.current) {
-      let modelToSelect: LLMModel | undefined;
-
-      if (selectedModelHrid) {
-        // Try to find the previously selected model
-        modelToSelect = llmConfig.models.find(
-          (model) =>
-            model.hrid === selectedModelHrid && model.is_active !== false,
-        );
-      }
-
-      // If no saved model or saved model not found/inactive, use default
-      if (!modelToSelect) {
-        modelToSelect = llmConfig.models.find((model) => model.is_default);
-      }
-
-      if (modelToSelect) {
-        setSelectedModel(modelToSelect);
-        setSelectedModelHrid(modelToSelect.hrid);
-        hasInitializedRef.current = true;
-      }
-    }
-  }, [llmConfig?.models, selectedModelHrid, setSelectedModelHrid]);
-
-  const handleModelSelect = (model: LLMModel) => {
-    setSelectedModel(model);
-    setSelectedModelHrid(model.hrid);
-  };
-
-  return { selectedModel, handleModelSelect };
-};

src/frontend/apps/conversations/src/features/left-panel/components/ConversationItemActions.tsx

@@ -1,4 +1,4 @@
-import { useModal } from '@openfun/cunningham-react';
+import { Button as _Button, useModal } from '@openfun/cunningham-react';
 import { useTranslation } from 'react-i18next';
 import { css } from 'styled-components';
 
@@ -6,7 +6,6 @@ import { DropdownMenu, DropdownMenuOption, Icon } from '@/components';
 import { ChatConversation } from '@/features/chat/types';
 
 import { ModalRemoveConversation } from './ModalRemoveConversation';
-import { ModalRenameConversation } from './ModalRenameConversation';
 
 interface ConversationItemActionsProps {
   conversation: ChatConversation;
@@ -18,7 +17,6 @@ export const ConversationItemActions = ({
   const { t } = useTranslation();
 
   const deleteModal = useModal();
-  const renameModal = useModal();
 
   const options: DropdownMenuOption[] = [
     {
@@ -28,13 +26,6 @@ export const ConversationItemActions = ({
       disabled: false,
       testId: `conversation-item-actions-remove-${conversation.id}`,
     },
-    {
-      label: t('Rename chat'),
-      icon: 'tune',
-      callback: () => renameModal.open(),
-      disabled: false,
-      testId: `conversation-item-actions-rename-${conversation.id}`,
-    },
   ];
 
   return (
@@ -80,12 +71,6 @@ export const ConversationItemActions = ({
           conversation={conversation}
         />
       )}
-      {renameModal.isOpen && (
-        <ModalRenameConversation
-          onClose={renameModal.onClose}
-          conversation={conversation}
-        />
-      )}
     </>
   );
 };

src/frontend/apps/conversations/src/features/left-panel/components/ModalRemoveConversation.tsx

@@ -79,7 +79,7 @@ export const ModalRemoveConversation = ({
         </Text>
       }
     >
-      <Box className="--conversations--modal-remove-chat">
+      <Box className="--converstions--modal-remove-chat">
         <Text $size="sm" $variation="600">
           {t('Are you sure you want to delete this conversation ?')}
         </Text>

src/frontend/apps/conversations/src/features/left-panel/components/ModalRenameConversation.tsx

@@ -1,100 +0,0 @@
-import { Button, Input, Modal, ModalSize } from '@openfun/cunningham-react';
-import { t } from 'i18next';
-import { useState } from 'react';
-
-import { Box, Text, useToast } from '@/components';
-import { useRenameConversation } from '@/features/chat/api/useRenameConversation';
-import { ChatConversation } from '@/features/chat/types';
-
-interface ModalRenameConversation {
-  onClose: () => void;
-  conversation: ChatConversation;
-}
-
-export const ModalRenameConversation = ({
-  onClose,
-  conversation,
-}: ModalRenameConversation) => {
-  const { showToast } = useToast();
-
-  const { mutate: renameConversation } = useRenameConversation({
-    onSuccess: () => {
-      showToast(
-        'success',
-        t('The conversation has been renamed.'),
-        undefined,
-        4000,
-      );
-      onClose();
-    },
-    onError: (error) => {
-      const errorMessage =
-        error.cause?.[0] ||
-        error.message ||
-        t('An error occurred while renaming the conversation');
-      showToast('error', t(errorMessage), undefined, 4000);
-    },
-  });
-
-  const [newName, setNewName] = useState(conversation.title ?? '');
-  const handleSubmit = (e: React.FormEvent) => {
-    e.preventDefault();
-    if (newName.trim()) {
-      renameConversation({
-        conversationId: conversation.id,
-        title: newName,
-      });
-    }
-  };
-  return (
-    <Modal
-      isOpen
-      closeOnClickOutside
-      onClose={() => onClose()}
-      aria-label={t('Content modal to rename a conversation')}
-      rightActions={
-        <>
-          <Button
-            aria-label={t('Close the modal')}
-            color="secondary"
-            onClick={() => onClose()}
-          >
-            {t('Cancel')}
-          </Button>
-          <Button
-            aria-label={t('Rename chat')}
-            color="primary"
-            type="submit"
-            form="rename-chat-form"
-          >
-            {t('Rename')}
-          </Button>
-        </>
-      }
-      size={ModalSize.SMALL}
-      title={
-        <Text
-          $size="h6"
-          as="h6"
-          $margin={{ all: '0' }}
-          $align="flex-start"
-          $variation="1000"
-        >
-          {t('Rename chat')}
-        </Text>
-      }
-    >
-      <Box className="--conversations--modal-rename-chat">
-        <form onSubmit={handleSubmit} id="rename-chat-form" className="mt-s">
-          <Input
-            label={t('New name')}
-            maxLength={100}
-            onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
-              setNewName(e.target.value);
-            }}
-          />
-        </form>
-      </Box>
-    </Modal>
-  );
-};

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

@@ -31,7 +31,6 @@
       "Close the modal": "Fermer la modale",
       "Confirm deletion": "Confirmer la suppression",
       "Content modal to delete conversation": "Modale pour supprimer la conversation",
-      "Content modal to rename conversation": "Modale pour renommer la conversation",
       "Conversation analysis disabled": "Analyse de la conversation désactivée",
       "Conversation analysis enabled": "Analyse de la conversation activée",
       "Copied": "Copié",
@@ -77,7 +76,6 @@
       "Logout": "Se déconnecter",
       "New chat": "Nouvelle conversation",
       "New feedback": "Nouveaux commentaires",
-      "New name": "Nouveau nom",
       "No code? ": "Pas de code ? ",
       "No conversation found": "Aucune conversation trouvée",
       "Notify me": "Me notifier",
@@ -89,8 +87,6 @@
       "Proconnect Login": "Connexion Proconnect",
       "Quick search input": "Saisie de recherche rapide",
       "Remove attachment": "Supprimer la pièce jointe",
-      "Rename": "Renommer",
-      "Rename chat": "Renommer la conversation",
       "Research on the web": "Rechercher sur le web",
       "Search": "Rechercher",
       "Search for a chat": "Rechercher un chat",
@@ -111,7 +107,6 @@
       "The Assistant is a sovereign conversational AI designed for public servants. It helps you save time on daily tasks like rephrasing, summarising, translating, or searching information. Your data never leaves France and is stored on secure, state-compliant infrastructures. It is never used for commercial purposes.": "L'Assistant est une IA souveraine conçue pour les fonctionnaires. Il vous permet de gagner du temps sur des tâches quotidiennes telles que la reformulation, le résumé, la traduction ou la recherche d'informations. Vos données ne quittent jamais la France et sont stockées sur des infrastructures sûres et conformes à l'état et ne sont jamais utilisées à des fins commerciales.",
       "The Assistant is in Beta": "L'Assistant est en Bêta",
       "The conversation has been deleted.": "La conversation a été supprimée.",
-      "The conversation has been renamed": "La conversation a été renom.",
       "The summary feature is not supported yet.": "La fonctionnalité de résumé n'est pas encore prise en charge.",
       "Thinking...": "Réflexion...",
       "To add a file to the conversation, drop it here.": "Pour ajouter un fichier à la conversation, déposez-le ici.",