Read attached chat images with a vision model (#82)

## Summary
- Image attachments (PNG, JPEG, WebP, GIF) skip the document parser and go to a vision-capable Ollama model (`OLLAMA_MODEL_VISION`, default `gemma4:latest`).
- The image is stored on the prompt. `PromptSerializer` returns `file_type` and base64 `image` so history can redraw it.
- Unsupported image types and files over 8 MB return a websocket error and are not saved.
- A later text turn does not reuse a previous image as a spreadsheet.

Pairs with `chat_web_app` `feat/128-vision-image-prompt` ([#128](ai_ml_operations/chat_web_app#128)).

## Test plan
- [ ] `manage.py test chat_backend.tests.test_vision chat_backend.tests.test_consumers.GraphNodeTestCase`
- [ ] Send a prompt plus PNG over `ws/chat_again/` and confirm the vision model is called.
- [ ] `GET conversation_details` includes `image` and `file_type` for that turn.
- [ ] CSV upload still follows the document path.

Reviewed-on: #82

.env.example

@@ -16,6 +16,8 @@ OLLAMA_BASE_URL=http://127.0.0.1:11434
 # OLLAMA_MODEL=gpt-oss:20b
 # OLLAMA_MODEL_THINKING=gpt-oss:20b
 # OLLAMA_MODEL_FAST=gemma4:latest
+# Vision (image + prompt). Must be a vision-capable tag. Default gemma4:latest.
+# OLLAMA_MODEL_VISION=gemma4:latest
 # OLLAMA_MODEL_UTILITY=llama3.2
 # OLLAMA_EMBED_MODEL=nomic-embed-text
 # OLLAMA_NUM_CTX_THINKING=16384

.env.prod.example

@@ -37,6 +37,8 @@ OLLAMA_BASE_URL=http://10.0.0.128:11434
 OLLAMA_MODEL=gpt-oss:20b
 OLLAMA_MODEL_THINKING=gpt-oss:20b
 OLLAMA_MODEL_FAST=gemma4:latest
+# Vision (image + prompt). Must be a vision-capable tag.
+OLLAMA_MODEL_VISION=gemma4:latest
 OLLAMA_MODEL_UTILITY=llama3.2
 OLLAMA_EMBED_MODEL=nomic-embed-text
 OLLAMA_NUM_CTX_THINKING=16384

llm_be/chat_backend/consumers.py

@@ -1,5 +1,4 @@
 import json
-import base64
 import logging
 import pandas as pd
 from datetime import datetime
@@ -14,6 +13,7 @@ from langchain_core.messages import HumanMessage, AIMessage
 from langchain_community.vectorstores import Chroma
 from langchain_ollama import OllamaEmbeddings
 from chat_backend.ollama_config import (
+    ROLE_VISION,
     ollama_embeddings_kwargs,
     ollama_model_for_role,
     resolve_chat_role,
@@ -38,6 +38,7 @@ from .services.title_generator import title_generator
 from .services.moderation_classifier import moderation_classifier, ModerationLabel
 from .services.prompt_classifier.prompt_classifier import PromptClassifier, PromptType
 from .services.data_analysis_service import AsyncDataAnalysisService
+from .services.vision import AsyncVisionService, is_image_type, prepare_attachment
 from .services.grounded_chat import prepare_grounded_chat
 from .services.status_context import (
     emit_status,
@@ -278,7 +279,9 @@ async def get_conversation_file_async(conversation_id):
         # Get the very first prompt in the conversation that has a file
         prompt_with_file = await Prompt.objects.filter(
             conversation_id=conversation_id
-        ).exclude(file='').order_by('created').afirst()
+        ).exclude(file='').exclude(
+            file_type__in=["png", "jpeg", "jpg", "webp", "gif"]
+        ).order_by('created').afirst()
 
         if prompt_with_file and prompt_with_file.file:
             # Opening a DatabaseStorage file hits the DB, so read inside the thread.
@@ -414,29 +417,19 @@ class ChatConsumerAgain(AsyncWebsocketConsumer):
                 decoded_file = None
 
                 if file:
-                    decoded_file = base64.b64decode(file)
+                    decoded_file, file_type, attachment_error = prepare_attachment(
-                    logger.debug(decoded_file)
+                        file, file_type
-                    # The `altered_message` should only be created if a file exists
+                    )
-                    # and you want to pass its content directly to the classifier.
+                    if attachment_error:
-                    # Here, we'll let the classifier decide based on the user's prompt
+                        await self.send_json_message(
-                    # and then handle the file content separately.
+                            json.dumps(
-                    altered_message = message
+                                {
-                    if "csv" in file_type:
+                                    "type": "error",
-                        file_type = "csv"
+                                    "content": attachment_error,
-                        #altered_message = f"{message}\n The file type is csv and the file contents are: {decoded_file}"
+                                }
-                    elif "xmlformats-officedocument" in file_type:
+                            )
-                        file_type = "xlsx"
+                        )
-                        #df = pd.read_excel(decoded_file)
+                        return
-                        #altered_message = f"{message}\n The file type is xlsx and the file contents are: {df}"
-                    elif "word" in file_type:
-                        file_type = "docx"
-                    elif "pdf" in file_type:
-                        file_type = "pdf"
-                    elif "text" in file_type:
-                        file_type = "txt"
-                        #altered_message = f"{message}\n The file type is txt and the file contents are: {decoded_file}"
-                    else:
-                        file_type = "Not Sure"
 
                 logger.info(f'received: "{message}" for conversation {conversation_id}')
 
@@ -475,7 +468,16 @@ class ChatConsumerAgain(AsyncWebsocketConsumer):
                     conversation_id = input_dict["conversation_id"]
                     decoded_file = input_dict.get("decoded_file")
                     file_type = input_dict.get("file_type")
-                    
+
+                    if is_image_type(file_type) and decoded_file:
+                        service = AsyncVisionService()
+                        input_dict["_resolved_model"] = service.model_name
+                        input_dict["_citations"] = []
+                        await emit_status("reading_image")
+                        return service.generate_response(
+                            prompt_instance.message, decoded_file, file_type
+                        )
+
                     # Feature Flag + plan gate: Image Generation
                     if prompt_type == PromptType.IMAGE_GENERATION:
                         if not getattr(settings, "ALLOW_IMAGE_GENERATION", False):
@@ -603,7 +605,9 @@ class ChatConsumerAgain(AsyncWebsocketConsumer):
                      #    messages = messages[:-1] + [HumanMessage(content=altered_message)]
                      # I'll add it to the input_dict if needed.
 
-                resolved_model = ollama_model_for_role(resolve_chat_role(model))
+                resolved_model = ollama_model_for_role(
+                    ROLE_VISION if is_image_type(file_type) else resolve_chat_role(model)
+                )
                 prompt_metric = await create_prompt_metric(
                     prompt_instance.id,
                     prompt_instance.message,

llm_be/chat_backend/consumers_graph.py

@@ -1,5 +1,4 @@
 import json
-import base64
 import logging
 import pandas as pd
 from datetime import datetime
@@ -29,6 +28,7 @@ from .services.title_generator import title_generator
 from .services.moderation_classifier import moderation_classifier, ModerationLabel
 from .services.prompt_classifier.prompt_classifier import PromptClassifier, PromptType
 from .services.data_analysis_service import AsyncDataAnalysisService
+from .services.vision import AsyncVisionService, is_image_type, prepare_attachment
 from .services.grounded_chat import prepare_grounded_chat
 from .services.status_context import (
     emit_status,
@@ -36,7 +36,11 @@ from .services.status_context import (
     set_status_emitter,
 )
 from .services.ws_frames import citations_frame, status_frame
-from chat_backend.ollama_config import ollama_model_for_role, resolve_chat_role
+from chat_backend.ollama_config import (
+    ROLE_VISION,
+    ollama_model_for_role,
+    resolve_chat_role,
+)
 from .utils import (
     TokenUsageCollector,
     aiter_text_chunks,
@@ -202,7 +206,9 @@ async def get_conversation_file_async(conversation_id):
     try:
         prompt_with_file = await Prompt.objects.filter(
             conversation_id=conversation_id
-        ).exclude(file='').order_by('created').afirst()
+        ).exclude(file='').exclude(
+            file_type__in=["png", "jpeg", "jpg", "webp", "gif"]
+        ).order_by('created').afirst()
 
         if prompt_with_file and prompt_with_file.file:
             # Opening a DatabaseStorage file hits the DB, so read inside the thread.
@@ -249,7 +255,11 @@ async def classification_node(state: ChatState) -> ChatState:
     decoded_file = state.get("decoded_file")
     
     prompt_type = await PROMPT_CLASSIFIER.classify_async(msg)
-    
+
+    # Images are read by the vision model, not the spreadsheet parser.
+    if is_image_type(state.get("file_type")):
+        return {"prompt_type": PromptType.GENERAL_CHAT}
+
     # Override logic
     if decoded_file and (prompt_type == PromptType.DATA_ANALYSIS or 'analyze' in msg.lower() or 'data' in msg.lower()):
         prompt_type = PromptType.DATA_ANALYSIS
@@ -269,7 +279,18 @@ async def generation_node(state: ChatState) -> ChatState:
     conversation_id = state["conversation_id"]
     decoded_file = state.get("decoded_file")
     file_type = state.get("file_type")
-    
+
+    if is_image_type(file_type) and decoded_file:
+        service = AsyncVisionService()
+        await emit_status("reading_image")
+        generator = service.generate_response(
+            prompt_instance.message, decoded_file, file_type
+        )
+        return {
+            "response_generator": generator,
+            "resolved_model": service.model_name,
+        }
+
     # Feature Flag + plan gate: Image Generation
     if prompt_type == PromptType.IMAGE_GENERATION:
         if not getattr(settings, "ALLOW_IMAGE_GENERATION", False):
@@ -485,13 +506,19 @@ class ChatConsumerGraph(AsyncWebsocketConsumer):
                 print("Conversation ID: ", conversation_id)
                 decoded_file = None
                 if file:
-                    decoded_file = base64.b64decode(file)
+                    decoded_file, file_type, attachment_error = prepare_attachment(
-                    if "csv" in file_type: file_type = "csv"
+                        file, file_type
-                    elif "xmlformats-officedocument" in file_type: file_type = "xlsx"
+                    )
-                    elif "word" in file_type: file_type = "docx"
+                    if attachment_error:
-                    elif "pdf" in file_type: file_type = "pdf"
+                        await self.send_json_message(
-                    elif "text" in file_type: file_type = "txt"
+                            json.dumps(
-                    else: file_type = "Not Sure"
+                                {
+                                    "type": "error",
+                                    "content": attachment_error,
+                                }
+                            )
+                        )
+                        return
 
                 # Pre-fetch messages and file
                 messages, prompt_instance = await get_messages(
@@ -501,7 +528,9 @@ class ChatConsumerGraph(AsyncWebsocketConsumer):
                 if not decoded_file:
                     decoded_file, file_type = await get_conversation_file_async(conversation_id)
 
-                resolved_model = ollama_model_for_role(resolve_chat_role(model))
+                resolved_model = ollama_model_for_role(
+                    ROLE_VISION if is_image_type(file_type) else resolve_chat_role(model)
+                )
                 prompt_metric = await create_prompt_metric(
                     prompt_instance.id,
                     prompt_instance.message,
@@ -556,7 +585,7 @@ class ChatConsumerGraph(AsyncWebsocketConsumer):
                     citations = []
                     final_model = resolved_model
 
-                    if should_use_agent(message):
+                    if should_use_agent(message) and not is_image_type(file_type):
                         try:
                             await enforce_feature_gate(chat_user, "agentic_tasks")
                         except FeatureNotAllowed as exc:

llm_be/chat_backend/ollama_config.py

@@ -17,6 +17,8 @@ ROLE_EMBED = "embed"
 # independent plan steps concurrently on a smaller/cheaper model.
 ROLE_ORCHESTRATOR = "orchestrator"
 ROLE_SUBAGENT = "subagent"
+# Image + prompt turns (#128). Never falls back to the text chat model.
+ROLE_VISION = "vision"
 
 _VALID_ROLES = {
     ROLE_THINKING,
@@ -25,6 +27,7 @@ _VALID_ROLES = {
     ROLE_EMBED,
     ROLE_ORCHESTRATOR,
     ROLE_SUBAGENT,
+    ROLE_VISION,
 }
 
 
@@ -56,6 +59,7 @@ def ollama_model_for_role(role: str) -> str:
         ROLE_EMBED: "OLLAMA_EMBED_MODEL",
         ROLE_ORCHESTRATOR: "OLLAMA_MODEL_ORCHESTRATOR",
         ROLE_SUBAGENT: "OLLAMA_MODEL_SUBAGENT",
+        ROLE_VISION: "OLLAMA_MODEL_VISION",
     }[role]
     role_default = {
         ROLE_THINKING: "gpt-oss:20b",
@@ -64,14 +68,15 @@ def ollama_model_for_role(role: str) -> str:
         ROLE_EMBED: "nomic-embed-text",
         ROLE_ORCHESTRATOR: "gpt-oss:20b",
         ROLE_SUBAGENT: "llama3.2",
+        ROLE_VISION: "gemma4:latest",
     }[role]
 
     configured = getattr(settings, role_setting, None)
     if configured:
         return configured
 
-    # Embeddings must never silently fall back to a chat model (#62).
+    # Embeddings and vision must never silently fall back to a text chat model.
-    if role == ROLE_EMBED:
+    if role in (ROLE_EMBED, ROLE_VISION):
         return role_default
 
     legacy = getattr(settings, "OLLAMA_MODEL", None)
@@ -94,6 +99,8 @@ def ollama_num_ctx_for_role(role: str) -> int:
         return int(getattr(settings, "OLLAMA_NUM_CTX_SUBAGENT", 8192) or 8192)
     if role == ROLE_ORCHESTRATOR:
         return int(getattr(settings, "OLLAMA_NUM_CTX_ORCHESTRATOR", 16384) or 16384)
+    if role == ROLE_VISION:
+        return int(getattr(settings, "OLLAMA_NUM_CTX_VISION", 8192) or 8192)
     return int(getattr(settings, "OLLAMA_NUM_CTX_THINKING", 16384) or 16384)
 
 

llm_be/chat_backend/serializers.py

@@ -257,6 +257,7 @@ class PromptSerializer(serializers.ModelSerializer):
     tokens_in = serializers.SerializerMethodField()
     tokens_out = serializers.SerializerMethodField()
     feedback = serializers.SerializerMethodField()
+    image = serializers.SerializerMethodField()
 
     class Meta:
         model = Prompt
@@ -269,8 +270,10 @@ class PromptSerializer(serializers.ModelSerializer):
             "tokens_out",
             "citations",
             "feedback",
+            "file_type",
+            "image",
         )
-        read_only_fields = ("citations", "feedback")
+        read_only_fields = ("citations", "feedback", "file_type", "image")
 
     def _token_pair(self, obj):
         cache = self.context.setdefault("_prompt_token_cache", {})
@@ -314,6 +317,11 @@ class PromptSerializer(serializers.ModelSerializer):
             self.context["_prompt_feedback_by_id"] = by_prompt
         return by_prompt.get(obj.id)
 
+    def get_image(self, obj):
+        from chat_backend.services.vision import image_base64
+
+        return image_base64(obj)
+
     def validate_message(self, value: str) -> str:
         if value is None or not str(value).strip():
             raise serializers.ValidationError("Message text cannot be empty.")

llm_be/chat_backend/services/vision.py

@@ -0,0 +1,186 @@
+"""Image attachments on a chat turn (chat_web_app#128).
+
+Document uploads stay on the csv/xlsx/pdf path. An image is stored on the
+prompt and sent to a vision-capable Ollama model with the user's text.
+"""
+
+from __future__ import annotations
+
+import base64
+import json
+import logging
+
+import httpx
+
+from chat_backend.ollama_config import (
+    ROLE_VISION,
+    ollama_base_url,
+    ollama_model_for_role,
+    ollama_num_ctx_for_role,
+)
+
+logger = logging.getLogger(__name__)
+
+MAX_IMAGE_BYTES = 8 * 1024 * 1024
+
+IMAGE_MIME = {
+    "png": "image/png",
+    "jpeg": "image/jpeg",
+    "jpg": "image/jpeg",
+    "webp": "image/webp",
+    "gif": "image/gif",
+}
+
+_IMAGE_EXTENSIONS = frozenset(IMAGE_MIME)
+
+
+class VisionChunk:
+    """Stream piece that ``chunk_text`` and ``extract_token_usage`` both read."""
+
+    def __init__(self, text: str = "", info: dict | None = None):
+        self.content = text
+        self.generation_info = info or None
+
+
+def canonical_file_type(file_type: str) -> str:
+    """Map a browser MIME (or short extension) to the stored file_type.
+
+    Order matches the historical websocket normalizer so spreadsheets that
+    share the openxml MIME family still land on ``xlsx``.
+    """
+    lowered = (file_type or "").lower()
+    if "csv" in lowered:
+        return "csv"
+    if "xmlformats-officedocument" in lowered:
+        return "xlsx"
+    if "word" in lowered:
+        return "docx"
+    if "pdf" in lowered:
+        return "pdf"
+    if "text" in lowered:
+        return "txt"
+    if "png" in lowered:
+        return "png"
+    if "jpeg" in lowered or "jpg" in lowered:
+        return "jpeg"
+    if "webp" in lowered:
+        return "webp"
+    if "gif" in lowered:
+        return "gif"
+    if lowered.startswith("image/"):
+        return "unsupported_image"
+    return "Not Sure"
+
+
+def is_image_type(file_type: str | None) -> bool:
+    return (file_type or "").lower() in _IMAGE_EXTENSIONS
+
+
+def image_mime(file_type: str | None) -> str:
+    return IMAGE_MIME.get((file_type or "").lower(), "application/octet-stream")
+
+
+def prepare_attachment(file_b64: str, file_type: str) -> tuple[bytes, str, str | None]:
+    """Decode an upload. ``error`` is a user-facing string when the image is rejected."""
+    try:
+        decoded = base64.b64decode(file_b64, validate=False)
+    except Exception:
+        return b"", "Not Sure", "Could not read the attached file."
+
+    canonical = canonical_file_type(file_type)
+    if canonical == "unsupported_image":
+        return (
+            decoded,
+            canonical,
+            "Unsupported image. Use PNG, JPEG, WebP, or GIF.",
+        )
+    if is_image_type(canonical) and len(decoded) > MAX_IMAGE_BYTES:
+        return (
+            decoded,
+            canonical,
+            "Image is too large. Maximum size is 8 MB.",
+        )
+    return decoded, canonical, None
+
+
+def image_base64(prompt) -> str | None:
+    """Base64 of a stored prompt image, or None for non-images."""
+    if not is_image_type(getattr(prompt, "file_type", None)):
+        return None
+    if not prompt.file_exists():
+        return None
+    try:
+        data = prompt.file.read()
+    except Exception:
+        logger.exception(
+            "failed to read prompt image prompt_id=%s", getattr(prompt, "id", None)
+        )
+        return None
+    if not data:
+        return None
+    if isinstance(data, str):
+        data = data.encode()
+    return base64.b64encode(data).decode("ascii")
+
+
+class AsyncVisionService:
+    """Stream an answer from Ollama's chat API with one image."""
+
+    def __init__(self):
+        self.model_name = ollama_model_for_role(ROLE_VISION)
+        self.num_ctx = ollama_num_ctx_for_role(ROLE_VISION)
+
+    async def generate_response(self, query: str, image_bytes: bytes, file_type: str):
+        payload = {
+            "model": self.model_name,
+            "stream": True,
+            "options": {"num_ctx": self.num_ctx},
+            "messages": [
+                {
+                    "role": "user",
+                    "content": (
+                        "The user attached an image. Answer their question using what "
+                        "is visible in the image, including any text in it.\n\n"
+                        f"{query}"
+                    ),
+                    "images": [base64.b64encode(image_bytes).decode("ascii")],
+                }
+            ],
+        }
+        url = f"{ollama_base_url().rstrip('/')}/api/chat"
+        try:
+            async with httpx.AsyncClient(timeout=httpx.Timeout(180.0)) as client:
+                async with client.stream("POST", url, json=payload) as response:
+                    if response.status_code >= 400:
+                        body = await response.aread()
+                        logger.error(
+                            "vision model rejected request status=%s body=%s",
+                            response.status_code,
+                            body[:500],
+                        )
+                        yield VisionChunk(
+                            text="The vision model could not read that image."
+                        )
+                        return
+                    async for line in response.aiter_lines():
+                        if not line:
+                            continue
+                        try:
+                            event = json.loads(line)
+                        except json.JSONDecodeError:
+                            continue
+                        message = event.get("message") or {}
+                        text = message.get("content") or ""
+                        if text:
+                            yield VisionChunk(text=text)
+                        if event.get("done"):
+                            info = {
+                                key: event[key]
+                                for key in ("prompt_eval_count", "eval_count")
+                                if key in event
+                            }
+                            if info:
+                                yield VisionChunk(info=info)
+        except httpx.HTTPError:
+            logger.exception("vision request failed model=%s", self.model_name)
+            yield VisionChunk(text="The vision model could not read that image.")

llm_be/chat_backend/services/ws_frames.py

@@ -23,6 +23,7 @@ STATUS_LABELS: dict[str, str] = {
     "reading_sources": "Reading sources",
     "retrieving_docs": "Searching your documents",
     "analysing": "Analysing your file",
+    "reading_image": "Reading your image",
     "refining": "Refining the answer",
     "writing": "",  # tokens streaming — no label
 }

llm_be/chat_backend/tests/test_consumers.py

@@ -355,6 +355,42 @@ class GraphNodeTestCase(TransactionTestCase):
             self.prompt.message, b"a,b\n1,2\n", "csv"
         )
 
+    async def test_generation_node_sends_images_to_vision(self):
+        with mock.patch.object(consumers_graph, "AsyncVisionService") as service:
+            service.return_value.model_name = "gemma4:latest"
+            service.return_value.generate_response.return_value = "generator"
+
+            result = await consumers_graph.generation_node(
+                self._state(
+                    prompt_type=PromptType.DATA_ANALYSIS,
+                    message="what does this say?",
+                    decoded_file=b"\x89PNG",
+                    file_type="png",
+                )
+            )
+
+        self.assertEqual(result["response_generator"], "generator")
+        self.assertEqual(result["resolved_model"], "gemma4:latest")
+        service.return_value.generate_response.assert_called_once_with(
+            self.prompt.message, b"\x89PNG", "png"
+        )
+
+    async def test_attached_image_is_not_data_analysis(self):
+        with mock.patch.object(
+            consumers_graph.PROMPT_CLASSIFIER,
+            "classify_async",
+            mock.AsyncMock(return_value=PromptType.DATA_ANALYSIS),
+        ):
+            result = await consumers_graph.classification_node(
+                self._state(
+                    message="analyze this chart",
+                    decoded_file=b"\x89PNG",
+                    file_type="png",
+                )
+            )
+
+        self.assertEqual(result["prompt_type"], PromptType.GENERAL_CHAT)
+
     async def test_generation_node_routes_rag_prompts_with_workspace(self):
         with mock.patch.object(consumers_graph, "AsyncRAGService") as service:
             service.return_value.generate_response.return_value = "generator"

llm_be/chat_backend/tests/test_vision.py

@@ -0,0 +1,64 @@
+import base64
+
+from django.core.files.uploadedfile import SimpleUploadedFile
+from django.test import TestCase
+
+from chat_backend.services.vision import (
+    canonical_file_type,
+    image_base64,
+    is_image_type,
+    prepare_attachment,
+)
+from chat_backend.tests.factories import make_conversation, make_prompt
+
+
+class VisionAttachmentTestCase(TestCase):
+    def test_canonical_types(self):
+        self.assertEqual(canonical_file_type("image/png"), "png")
+        self.assertEqual(canonical_file_type("image/jpeg"), "jpeg")
+        self.assertEqual(canonical_file_type("image/jpg"), "jpeg")
+        self.assertEqual(canonical_file_type("image/webp"), "webp")
+        self.assertEqual(canonical_file_type("image/gif"), "gif")
+        self.assertEqual(canonical_file_type("text/plain"), "txt")
+        self.assertEqual(canonical_file_type("application/pdf"), "pdf")
+        self.assertEqual(canonical_file_type("image/bmp"), "unsupported_image")
+        self.assertEqual(canonical_file_type("image/svg+xml"), "unsupported_image")
+
+    def test_prepare_attachment_rejects_oversize_and_unknown_images(self):
+        png = base64.b64encode(b"\x89PNG").decode()
+        decoded, kind, error = prepare_attachment(png, "image/png")
+        self.assertEqual(decoded, b"\x89PNG")
+        self.assertEqual(kind, "png")
+        self.assertIsNone(error)
+        self.assertTrue(is_image_type(kind))
+
+        huge = base64.b64encode(b"x" * (8 * 1024 * 1024 + 1)).decode()
+        _, _, error = prepare_attachment(huge, "image/png")
+        self.assertIn("too large", error)
+
+        _, _, error = prepare_attachment(png, "image/bmp")
+        self.assertIn("Unsupported image", error)
+
+    def test_document_types_unchanged(self):
+        raw = base64.b64encode(b"a,b\n1,2\n").decode()
+        decoded, kind, error = prepare_attachment(raw, "text/csv")
+        self.assertEqual(kind, "csv")
+        self.assertIsNone(error)
+        self.assertEqual(decoded, b"a,b\n1,2\n")
+        self.assertFalse(is_image_type(kind))
+
+    def test_image_base64_reads_stored_png(self):
+        prompt = make_prompt(make_conversation())
+        prompt.file_type = "png"
+        prompt.file.save("shot.png", SimpleUploadedFile("shot.png", b"\x89PNG"))
+        prompt.save()
+
+        self.assertEqual(image_base64(prompt), base64.b64encode(b"\x89PNG").decode())
+
+    def test_image_base64_skips_documents(self):
+        prompt = make_prompt(make_conversation())
+        prompt.file_type = "csv"
+        prompt.file.save("data.csv", SimpleUploadedFile("data.csv", b"a,b"))
+        prompt.save()
+
+        self.assertIsNone(image_base64(prompt))

llm_be/llm_be/settings.py

@@ -169,6 +169,10 @@ OLLAMA_NUM_CTX_UTILITY = int(env("OLLAMA_NUM_CTX_UTILITY", "4096") or "4096")
 # sub-agents run independent plan steps on a smaller/cheaper model.
 OLLAMA_MODEL_ORCHESTRATOR = env("OLLAMA_MODEL_ORCHESTRATOR", "") or OLLAMA_MODEL
 OLLAMA_MODEL_SUBAGENT = env("OLLAMA_MODEL_SUBAGENT", "") or OLLAMA_MODEL_UTILITY
+# Vision turns (image + prompt). Must be a vision-capable Ollama tag.
+# Empty falls back to gemma4:latest inside ollama_model_for_role, not OLLAMA_MODEL.
+OLLAMA_MODEL_VISION = env("OLLAMA_MODEL_VISION", "") or ""
+OLLAMA_NUM_CTX_VISION = int(env("OLLAMA_NUM_CTX_VISION", "8192") or "8192")
 OLLAMA_NUM_CTX_ORCHESTRATOR = int(
     env("OLLAMA_NUM_CTX_ORCHESTRATOR", "16384") or "16384"
 )