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.
