When a zip/image arrives via /upload, the LLM was classifying the
message as needs_clarification=True (because the chat body was just a
filename like "download (8).zip", not an instruction), and the early
return on line 91 fired before the receipts safety guard on line 106,
so the guard never executed.
master_agent: move the receipts safety guard to BEFORE the
needs_clarification early-return. If extra_context contains receipts,
unconditionally set needs_clarification=False and ensure expenses_agent
is in the agents list — the LLM cannot veto an upload with a question.
upload router: normalize empty or filename-only messages (e.g. when the
user drops a file in Discuss chat with no text) to
"Create an expense report from these uploaded receipts." so the LLM
intent classification also has a sensible string to work with.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previously ab_ai_mail.py intercepted file uploads before reaching the
LLM and responded with a hardcoded clarification template. The LLM had
no involvement in the file upload response.
Changes:
- ab_ai_mail.py: remove _post_file_clarification, _find_pending_attachments,
_describe_zip, and the two-step pending-attachment lookup. All messages
(text, files, or both) are dispatched to the agent service immediately.
Files with no text pass an empty message — the LLM decides what to do.
- upload.py: default message changed from hardcoded receipt instruction
to '' so the LLM determines intent from file content.
- master_agent._synthesize: always runs through the LLM for both single
and multi-agent cases — no raw templates reach the user.
- master_system.txt: add FILE UPLOADS routing rule so the LLM knows to
route receipts to expenses_agent without asking for clarification.
New flow: upload → parse → LLM classifies → agent acts → LLM synthesizes
natural response → user sees it. Zero scripted intercepts.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- master_agent: thread raw user message into extra_context and peer_data so
expenses_agent can check it directly without relying on LLM intent_summary
- master_agent: when receipts are in extra_context always route to expenses_agent,
so replies like 'skip duplicates' still trigger expense processing
- expenses_agent: _plan() checks peer_data raw_message alongside task so
skip/keep keywords are detected even when master rewrites the intent
- ab_ai_mail: wrap clarification message HTML in Markup() so Odoo does not
re-escape the tags; use <br> instead of <br/>
- ab_ai_mail: convert agent plain-text replies newlines to <br> for proper
line-break rendering in Discuss
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- _synthesize: short-circuit on any single-agent report (avoids extra
Ollama call that can timeout); wrap multi-agent LLM call in try/except
- _update_memory: catch exceptions so DB/memory failures don't kill reply
- _log_directive_start: use 0 instead of NULL for channel_id (NOT NULL col)
- create_expense: drop 'description' field (not valid on hr.expense in Odoo 18)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
AGENT_ACCESS_GROUPS uses XML IDs (e.g. hr_expense.group_hr_expense_user)
but the check compared them against res.groups.full_name strings which
never matched, denying every user access to all restricted agents.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Discuss bot now reads ir.attachment from incoming messages; file-only
messages no longer silently dropped
- ZIP files are described (contents listed) and bot asks clarifying
question before acting; user's follow-up reply looks back for pending
attachments so files don't need to be re-uploaded
- receipt_parser: extracts text from ZIP (recursive), JPG/PNG/etc (OCR),
PDF (pdfplumber), HTML, TXT
- expenses_agent: full rewrite fixing broken method signatures; adds
create_expense_sheet / create_expense / attach_receipt flow driven by
LLM receipt parsing (Ollama, HIPAA-locked)
- master_agent: extra_context threads receipts + user_id into directives
- FastAPI /upload multipart endpoint; registered in main.py
- Odoo /ai/upload controller proxies files to agent service
- ab_ai_bot: dispatch_message_with_files() for multipart uploads
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The classifier was silently falling back to a clarification prompt every
time the LLM wrapped its JSON in markdown fences, prefixed it with
'json', or added surrounding prose. The bot then asked 'Could you
clarify what you need?' to every message regardless of clarity.
Now: strip code fences, slice to the first {...} block, and on parse
failure log the raw content (truncated) and treat the message as 'no
specialist agent' so the direct-answer fallback responds instead of
looping on clarification.
Previously when the LLM classified a message as needing no specialist
agent, the dispatcher built zero directives and _synthesize returned
'No agent responses received.' Greetings, follow-up clarifications,
and general questions all fell into this dead end.
Now when intent.agents is empty and no clarification is needed, the
master makes a second LLM call with the recent conversation as context
and answers directly. Updated master_system.txt to steer the classifier
toward agents=[] for chitchat instead of forcing a clarification loop.
The f-string only spanned the first fragment ('You don') so the
{chr(44).join(...)} placeholder leaked into chat output as literal
text. Build the message with plain string concat.
User messages were only saved inside _update_memory at the end of a
successful directive. The clarification and access-denied branches
returned early without ever calling it, so when a clarification turn
asked 'what do you mean?' and the user replied, the original question
was missing from context — the bot looked at a transcript of nothing
but its own clarifying questions and asked yet another.
Save the user message at the top of handle_message so every branch
includes it. Drop the now-duplicate write from _update_memory.
The prompt template contains a literal JSON example block ({"needs_clarification": ...})
which str.format() tried to interpret as format fields, raising KeyError on every
Discuss DM. Switch to .replace() so braces in the template are taken literally.
Without exc_info we only see the bare exception string, which has been
unhelpful for debugging Discuss DM failures (e.g. a KeyError whose
message is just a JSON key, with no clue where it was raised).
Odoo's bot model serialises user_id as a string (str(uid)) over the
HTTP boundary, but the asyncpg memory queries ($1) expect an integer.
This caused 'str object cannot be interpreted as an integer' on every
Discuss DM. Cast at the entry point so downstream stores get an int.
