feat(box): bidirectional attachment transfer for sandbox (#2257)

* feat(box): bidirectional attachment transfer for sandbox

Materialize inbound attachments into the sandbox workspace so agents can
process user-sent files, and collect agent-produced files from the outbox
to attach them back to the reply.

- box(service): add materialize_inbound_attachments / collect_outbound
  attachments. Prefer direct host-filesystem read/write on the bind-mounted
  workspace (no size limit), falling back to chunked exec only for
  non-shared backends (e2b/remote). Clear per-query inbox/outbox dirs at
  turn start to avoid query_id-reuse collisions.
- provider(localagent): inject inbound attachment descriptors into the
  sandbox and append a system note telling the agent the inbox/outbox paths.
- pipeline(wrapper): collect outbox files on the final stream chunk and
  append them as attachment components to the response chain.
- web(debug-dialog): render File components with a download link when
  base64/url is present; add base64/path fields to the File entity.
- tests: cover inbound/outbound, large-file transfer without truncation,
  and stale-dir clearing (86 passing).

* feat(box): support voice/file attachment round-trip end-to-end

Extends the bidirectional attachment transfer to audio and arbitrary files
through the real webchat UI, and fixes the model-payload errors that
non-image attachments triggered.

- platform(websocket_adapter): resolve Voice/File component storage keys to
  base64 (previously only Image), so audio/documents reach the sandbox inbox.
- web(debug-dialog): accept audio/* and any file in the uploader (was
  image-only), classify by mimetype, upload Voice/File via the documents
  endpoint, and render non-image staged attachments as a chip.
- provider(litellmchat): drop non-image file parts (file_base64 / file_url)
  when building the OpenAI/LiteLLM payload. These come from Voice/File
  attachments — including ones replayed from conversation history — and the
  agent reads their bytes from the sandbox, not the model. Without this the
  provider rejects the request: 'invalid content type=file_base64'.
- provider(localagent): also strip those parts from the current user message
  alongside the sandbox-path note (model-facing clarity; the requester is the
  real safety net for history).
- tests: cover the requester strip/keep behavior (file dropped, image kept and
  reshaped to image_url, mixed history, plain-string content).

* test(box): cover inbound/outbound attachment helpers; fix ruff format

- ruff format localagent.py (CI ruff format --check was failing)
- add unit tests for ResponseWrapper outbound-attachment helpers (wrapper.py 78%->98%)
- add unit tests for LocalAgentRunner._inject_inbound_attachments
- add unit tests for WebSocketAdapter._process_image_components (0%->covered)

Lifts PR patch coverage from 68.97% to ~88% (>75% target).

src/langbot/pkg/provider/modelmgr/requesters/litellmchat.py

@@ -216,11 +216,22 @@ class LiteLLMRequester(requester.ProviderAPIRequester):
             content = msg_dict.get('content')
 
             if isinstance(content, list):
+                converted_parts = []
                 for part in content:
                     if isinstance(part, dict) and part.get('type') == 'image_base64':
                         part['image_url'] = {'url': part['image_base64']}
                         part['type'] = 'image_url'
                         del part['image_base64']
+                    # OpenAI-compatible chat models reject non-image file parts
+                    # (audio/document base64 or url). These originate from Voice /
+                    # File attachments — including ones replayed from conversation
+                    # history — and the agent already accesses their bytes via the
+                    # sandbox. Drop them from the model payload to avoid
+                    # "Invalid user message ... invalid content type=file_base64".
+                    if isinstance(part, dict) and part.get('type') in ('file_base64', 'file_url'):
+                        continue
+                    converted_parts.append(part)
+                msg_dict['content'] = converted_parts
 
             req_messages.append(msg_dict)