Add business-card photo intake to the Matrix bot (M3)

The intake bot now accepts a photo of a business card in the intake room and
turns it into the same new-investor proposal a typed note would. The only new
step is image -> text; everything downstream (parse, fuzzy match, in-thread
approval, log-communication write) is reused unchanged.

M3 was deferred only because Spark Control had no vision model. That blocker is
gone: the daily-driver Qwen is vision-capable under the same model id, and the
gateway forwards OpenAI multimodal content untouched, so no gateway/server/s9pk
change is needed -- this ships bot-only (git pull + rebuild on the Spark).

Transcribe-then-reuse (not vision-straight-to-JSON) is deliberate: the
transcription becomes the source text the email-integrity rule checks against,
so a mis-read address can't reach the CRM unapproved -- same guarantee as the
text path. Card commits tag source="matrix_card" for the audit log.

- llm.chat_vision: multimodal /v1/chat/completions, same model, same gateway
- spark.transcribe_card: faithful card->text, "" on a non-card (NONE sentinel)
- bot.on_image/handle_card: download image, transcribe, hand to handle_intake
- crm_client: source provenance overridable via the proposal's _source key
- tests: test_spark.py + a provenance case; 41/41 suite green

backend/ingest/llm.py

@@ -26,6 +26,31 @@ def chat(prompt, system=None, max_tokens=200, temperature=0.0):
     return (data["choices"][0]["message"].get("content") or "").strip()
 
 
+def chat_vision(prompt, image_b64, mime="image/jpeg", system=None, max_tokens=600, temperature=0.0):
+    """Multimodal chat: a text prompt + one base64 image to the local VL model via Spark Control.
+
+    Same endpoint and model as chat() — the daily-driver Qwen is vision-capable (capabilities
+    [vision, reasoning]); the only difference is the user message's `content` is the OpenAI
+    multimodal array (a text part + an image_url data-URI), which Spark Control forwards to vLLM
+    unchanged (it's a dumb passthrough). The server downscales to its max_pixels cap, so a
+    full-res phone photo is fine. Thinking stays off for fast, literal output."""
+    messages = []
+    if system:
+        messages.append({"role": "system", "content": system})
+    messages.append({"role": "user", "content": [
+        {"type": "text", "text": prompt},
+        {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{image_b64}"}},
+    ]})
+    body = {"model": config.CHAT_MODEL, "messages": messages,
+            "temperature": temperature, "max_tokens": max_tokens,
+            "chat_template_kwargs": {"enable_thinking": False}}
+    status, data = http_util.request("POST", f"{config.SPARK_CONTROL_URL}/v1/chat/completions",
+                                     body, verify=config.SPARK_VERIFY_TLS)
+    if status != 200:
+        raise RuntimeError(f"/v1/chat/completions (vision) -> {status}: {data}")
+    return (data["choices"][0]["message"].get("content") or "").strip()
+
+
 def chat_json(prompt, system=None, max_tokens=200):
     """Chat and parse the first JSON object from the reply (tolerant of fences)."""
     raw = chat(prompt, system=system, max_tokens=max_tokens)