After parsing all receipts, identify photos that are different shots of
the same physical receipt by comparing amount + date + vendor similarity
(difflib ratio >= 0.6). When a duplicate is found, keep whichever photo
produced the most OCR text (clearest shot) and report the skipped ones.
Zero-amount receipts (OCR failed entirely) are excluded from semantic
dedup to avoid false positives.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Dockerfile: add tesseract-ocr-osd for orientation detection data
- receipt_parser: resize large phone photos to 1800px, convert to
grayscale, sharpen before OCR; use psm 1 (auto + OSD) so rotated
receipts are correctly oriented before text extraction
- expenses_agent: tighten amount extraction prompt to pick the FINAL
total, not subtotal or tax line, reducing misreads like 42.90->409.00
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- ab_ai_bot: raise requests.post timeout 120s -> 600s so long OCR+LLM
runs don't silently drop the reply in Discuss
- upload: run parse_upload in ThreadPoolExecutor so tesseract OCR
doesn't block the FastAPI event loop
- expenses_agent: parse all receipts concurrently with asyncio.gather
(Ollama semaphore caps parallelism at 2); reduces 13-receipt LLM
time from ~39s sequential to ~20s parallel
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Dockerfile: install tesseract-ocr so Pillow+pytesseract can OCR receipt images
- operational_store: JSON-serialize raw_data before passing to asyncpg JSONB
- receipt_parser: add SHA256 hash + date extracted from filename timestamps
- expenses_agent: deduplicate receipts by hash before creating expense records
- expenses_agent: fetch all expensable Odoo products, pass list to LLM for
category selection (Meals, Flights, etc.) per receipt
- expenses_agent: pass date_hint from filename (e.g. 20260509_180857.jpg -> 2026-05-09)
as fallback when OCR text is unavailable
- expenses_tools: add get_expense_products() to fetch all expensable products
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>
The master agent was routing expense/receipt requests to finance_agent
instead of expenses_agent because only DB-registered agents appeared
in get_active_agents(). This adds auto-activation of all in-memory
registered agents with precise capability summaries so the LLM picks
the right specialist.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- ollama_backend: add format='json' for 'master' and receipt_parser
callers so llama3.1:8b returns valid JSON instead of plain English
- ab_ai_mail: add debug logging to trace attachment_ids from Discuss;
handle file-only messages and clarification look-back flow
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>
- ElearningTools: add create_course, update_course, publish_course,
add_section, create_slide, enroll_user write methods using OdooClient
- ElearningAgent: fix all BaseAgent method signatures (_plan/_gather/
_reason/_act/_report no longer take wrong positional args)
- Replace dead _dispatch_tool pattern with _tool_<name> methods so
BaseAgent._run_tool() can drive them via LLM tool calls in _loop()
- Add LLM-driven course creation in _reason(): when intent is create,
_loop() is called with a course-building system prompt and all tools;
the LLM calls create_course → add_section → create_slide → publish
- Fix handle_peer_request signature to match BaseAgent interface
- Fix AgentReport missing directive_id; fix SweepReport invalid kwargs
- Extend ELEARNING_TOOLS list with all new write-side tools
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
BaseAgent._lookup_odoo_context() calls odoo_doc_agent via PeerBus before
_plan() runs on every directive. The RAG answer is stored in
self._gathered['odoo_context'] and injected into every _loop() LLM call
so agents reason with correct Odoo 18 workflow steps automatically.
No changes required to individual agents. odoo_doc_agent opts out via
auto_rag=False to prevent self-referential calls.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Wraps odootrain RAG API (http://192.168.2.9:8000) as a BaseAgent so any
specialist agent can query Odoo 18 docs mid-execution via PeerBus
request_type=query_docs. Participates in sweep health checks.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Health endpoint called .ping() on both but neither implemented it,
causing ollama/odoo to always show as error and the bot to stay offline.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Routers calling /registry/agents raised AttributeError because
get_all() was not defined. Added method returning all registered
agents with active status, capabilities and instance flags.
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.
ollama-python 0.3.x returns the response as a dict, while newer releases
return pydantic objects. The backend assumed objects (response.message)
and crashed with AttributeError on every dispatch. Use a helper that
accepts either shape so the code works across versions.
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.
The router was calling handle_message(user_id, message, context, session_id)
but MasterAgent accepts (user_id, channel_id, message, directive_id) and
returns MasterResponse{response, status, ...} with no .reply or
.agent_reports fields. Discuss DMs to the bot crashed with TypeError.
Now the router:
- Derives directive_id from session_id (or generates one)
- Pulls channel_id out of req.context
- Maps MasterResponse.response -> DispatchResponse.reply
- Returns an empty agent_reports list (the field is reserved for future use;
per-agent reports aren't part of MasterResponse)
Some Odoo instances require the user's actual login/email for API key
auth rather than the __system__ special login. ODOO_USER defaults to
__system__ for standard Odoo 16+ installs.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- main.py: MemoryManager(pool=pool, llm=...) -> llm_router=...
Class signature is __init__(self, pool, llm_router=None).
- alembic.ini: script_location = migrations -> agent_service/migrations
When alembic runs from WORKDIR /app inside the container, 'migrations'
resolves to /app/migrations (missing). Correct path is
/app/agent_service/migrations where versions/ actually lives.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Fixes all errors reported in docker compose logs agent-service:
1. config.py: add ollama_max_concurrent, claude_timeout, claude_max_concurrent
fields so LLMRouter(config=settings) can read them without AttributeError.
2. main.py - LLM router: drop manual OllamaBackend/ClaudeBackend construction;
call LLMRouter(config=settings, pg_pool=pool) to match class signature.
Fixes: OllamaBackend.__init__() unexpected kwarg 'base_url'.
3. main.py - DB: add 5-attempt retry with 2s backoff and redacted DSN logging.
Fixes: connection refused race on startup before Postgres accepts connections.
4. main.py - AgentRegistry: call AgentRegistry() with no args (class takes none),
then await agent_registry.load_from_odoo(odoo) to populate active agents.
Fixes: AgentRegistry.__init__() unexpected kwarg 'odoo'.
5. main.py - PeerBus: pass registry=agent_registry at construction; register
specialist agents on agent_registry (not peer_bus, which has no register()).
peer_bus.py: make directive_id optional (default None) — bus is a singleton
at startup; directive_id is only needed per-request.
Fixes: PeerBus.__init__() missing positional args 'registry' and 'directive_id'.
6. main.py - MasterAgent: drop unexpected peer_bus= kwarg from constructor call.
Fixes: MasterAgent.__init__() unexpected kwarg 'peer_bus'.
7. mcp_router.py: pass NotificationOptions() instance instead of None.
Fixes: AttributeError 'NoneType' has no attribute 'tools_changed' (was applied
in running container but not committed; now committed).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
