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Merged
and27 merged 3 commits into from
Jan 21, 2026
Merged

Feat/service layer extract #2

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e1eb0a4
docs: update roadmap status and next steps
and27 Jan 21, 2026
3efb94e
feat(agent): extract chat service layer
and27 Jan 21, 2026
222fe55
Merge branch 'main' of https://github.com/and27/supportops into feat/…
and27 Jan 21, 2026
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333 changes: 11 additions & 322 deletions services/agent/app/main.py
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Original file line number Diff line number Diff line change
@@ -1,22 +1,16 @@
from __future__ import annotations

import logging
import os
import uuid
from pathlib import Path
from time import perf_counter
from typing import Any

from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request, Response
from fastapi.responses import JSONResponse

from .auth_utils import auth_enabled, get_auth_user
from .context_utils import build_context, load_recent_messages
from .embeddings import get_embedding_provider
from .ingest import get_ingest_config, run_ingest
from .logging_utils import log_event
from .prompts import get_clarify_prompt
from .service import ChatDependencies, ServiceError, handle_chat
from .adapters.retriever_adapter import get_retriever
from .adapters.supabase_repos import (
SupabaseConversationsRepo,
Expand All @@ -35,7 +29,7 @@
resolve_org_context,
resolve_org_id,
)
from .retrieval import decide_response, normalize_tags, precheck_action
from .retrieval import normalize_tags
from .schemas import (
AgentRunResponse,
ChatRequest,
Expand Down Expand Up @@ -64,20 +58,6 @@
logging.basicConfig(level=logging.INFO, format="%(message)s")


def extract_eval_metadata(
metadata: dict[str, Any] | None,
) -> tuple[str | None, str | None]:
if not metadata:
return None, None
eval_payload = metadata.get("eval")
if isinstance(eval_payload, dict):
expected = eval_payload.get("expected_action") or eval_payload.get("action")
category = eval_payload.get("category")
return expected, category
expected = metadata.get("expected_action") or metadata.get("eval_expected_action")
category = metadata.get("category") or metadata.get("eval_category")
return expected, category

@app.exception_handler(HTTPException)
async def http_exception_handler(_: Request, exc: HTTPException) -> JSONResponse:
log_event(logging.WARNING, "http_error", status_code=exc.status_code, detail=exc.detail)
Expand All @@ -97,7 +77,6 @@ async def health() -> dict[str, bool]:

@app.post("/v1/chat", response_model=ChatResponse)
async def chat(payload: ChatRequest, request: Request) -> ChatResponse:
start_time = perf_counter()
try:
supabase = get_supabase_client()
except RuntimeError as exc:
Expand All @@ -109,314 +88,24 @@ async def chat(payload: ChatRequest, request: Request) -> ChatResponse:
org_id, auth_user_id = resolve_org_context(
orgs_repo, members_repo, request, payload.org_id
)
user_id = auth_user_id or payload.user_id
conversation_id = payload.conversation_id or str(uuid.uuid4())
input_length_chars = len(payload.message or "")
kb_repo = SupabaseKBRepo(supabase)
retriever = get_retriever(supabase, kb_repo)
conversations_repo = SupabaseConversationsRepo(supabase)
messages_repo = SupabaseMessagesRepo(supabase)
tickets_repo = SupabaseTicketsRepo(supabase)
runs_repo = SupabaseRunsRepo(supabase)

log_event(
logging.INFO,
"chat_request",
conversation_id=conversation_id,
user_id=user_id,
org_id=org_id,
channel=payload.channel,
)
log_event(
logging.INFO,
"request_started",
conversation_id=conversation_id,
tenant_id=org_id,
channel=payload.channel,
input_length_chars=input_length_chars,
)

try:
if payload.conversation_id is None:
conversations_repo.create_conversation(
{
"id": conversation_id,
"org_id": org_id,
"user_id": user_id,
"channel": payload.channel,
"metadata": payload.metadata,
}
)

context_message_limit = int(os.getenv("CONTEXT_MESSAGE_LIMIT", "6"))
context_max_chars = int(os.getenv("CONTEXT_MAX_CHARS", "1200"))
prior_messages = []
context_text = ""
if payload.conversation_id:
prior_messages = load_recent_messages(
messages_repo, conversation_id, context_message_limit
)
context_text = build_context(prior_messages, context_max_chars)

messages_repo.create_message(
{
"conversation_id": conversation_id,
"role": "user",
"content": payload.message,
}
deps = ChatDependencies(
conversations_repo=conversations_repo,
messages_repo=messages_repo,
tickets_repo=tickets_repo,
runs_repo=runs_repo,
retriever=retriever,
)

kb_reply = None
citations = None
run_metadata: dict[str, Any] = {"retrieval_source": "none"}
guardrail_reason = None
decision_reason: str | None = None
decision_message = payload.message
retrieval_query = payload.message
retrieval_ms = 0
clarify_prompt = get_clarify_prompt()
if context_text:
decision_message = f"{context_text}\nuser: {payload.message}"
run_metadata["context_messages"] = len(prior_messages)
run_metadata["context_chars"] = len(context_text)
run_metadata["context_used"] = True
user_context = [
message.get("content", "").strip()
for message in prior_messages
if message.get("role") == "user" and message.get("content")
]
last_assistant = next(
(
message.get("content", "").strip()
for message in reversed(prior_messages)
if message.get("role") == "assistant"
),
"",
)
if last_assistant == clarify_prompt and user_context:
recent_users = user_context[-2:]
retrieval_query = "\n".join(recent_users + [payload.message]).strip()
else:
run_metadata["context_used"] = False

precheck = precheck_action(decision_message)
if precheck:
reply, action, confidence, decision_reason = precheck
run_metadata["precheck_action"] = action
run_metadata["decision_source"] = "precheck"
else:
retrieval_start = perf_counter()
kb_reply = retriever.retrieve(retrieval_query, org_id, conversation_id)
retrieval_ms = int((perf_counter() - retrieval_start) * 1000)
if kb_reply:
reply, citations, confidence, run_metadata = kb_reply
action = "reply"
run_metadata["decision_source"] = "kb"
decision_reason = (
"kb_vector_match"
if run_metadata.get("retrieval_source") == "vector"
else "kb_document_match"
)
else:
reply, action, confidence, decision_reason = decide_response(
decision_message
)
run_metadata["decision_source"] = "heuristic"
retrieval_source = run_metadata.get("retrieval_source") or "none"
if retrieval_source == "vector":
retrieval_candidates_count = int(run_metadata.get("match_count") or 0)
elif retrieval_source == "document":
retrieval_candidates_count = int(run_metadata.get("document_match_count") or 0)
else:
retrieval_candidates_count = 0
top_similarity = run_metadata.get("top_similarity")
log_event(
logging.INFO,
"retrieval_done",
conversation_id=conversation_id,
tenant_id=org_id,
channel=payload.channel,
retrieval_ms=retrieval_ms,
retrieval_candidates_count=retrieval_candidates_count,
top_similarity=top_similarity,
retrieval_source=retrieval_source,
)
reply_min_similarity = float(os.getenv("REPLY_MIN_SIMILARITY", "0.35"))
if action == "reply" and run_metadata.get("retrieval_source") == "vector":
clarify_prompt = get_clarify_prompt()
run_metadata["reply_min_similarity"] = reply_min_similarity
top_similarity = run_metadata.get("top_similarity")
if isinstance(top_similarity, (int, float)) and top_similarity < reply_min_similarity:
guardrail_reason = "low_similarity"
run_metadata["guardrail"] = guardrail_reason
run_metadata["guardrail_original_action"] = action
run_metadata["decision_reason_original"] = decision_reason
action = "ask_clarifying"
confidence = min(confidence, 0.4)
reply = clarify_prompt
citations = None
run_metadata["decision_source"] = "guardrail"
decision_reason = "guardrail_low_similarity"
if action == "reply" and not citations:
clarify_prompt = get_clarify_prompt()
guardrail_reason = "missing_citations"
run_metadata["guardrail"] = guardrail_reason
run_metadata["guardrail_original_action"] = action
run_metadata["decision_reason_original"] = decision_reason
action = "ask_clarifying"
confidence = min(confidence, 0.4)
reply = clarify_prompt
citations = None
run_metadata["decision_source"] = "guardrail"
decision_reason = "guardrail_missing_citations"
if decision_reason:
run_metadata["decision_reason"] = decision_reason
if not decision_reason:
decision_reason = "unspecified"
run_metadata["decision_reason"] = decision_reason
decision = action
handoff_type = None
if action in ("create_ticket", "escalate"):
decision = "handoff"
handoff_type = action
guardrails_triggered = [guardrail_reason] if guardrail_reason else []
log_event(
logging.INFO,
"decision_made",
conversation_id=conversation_id,
tenant_id=org_id,
channel=payload.channel,
decision=decision,
decision_reason=decision_reason,
guardrails_triggered=guardrails_triggered,
has_citations=bool(citations),
handoff_type=handoff_type,
)

expected_action, eval_category = extract_eval_metadata(payload.metadata)
if expected_action:
run_metadata["eval_expected_action"] = expected_action
run_metadata["eval_category"] = eval_category or "uncategorized"
run_metadata["eval_action_match"] = action == expected_action
log_event(
logging.INFO,
"eval_action_result",
conversation_id=conversation_id,
expected_action=expected_action,
actual_action=action,
category=eval_category or "uncategorized",
match=action == expected_action,
)
ticket_id = None
if action in ("create_ticket", "escalate"):
ticket_result = tickets_repo.create_ticket(
{
"org_id": org_id,
"conversation_id": conversation_id,
"subject": payload.message[:160],
}
)
if ticket_result:
ticket_id = ticket_result.get("id")
if not ticket_id:
raise RuntimeError("ticket_insert_failed")

messages_repo.create_message(
{
"conversation_id": conversation_id,
"role": "assistant",
"content": reply,
"metadata": {"citations": citations} if citations else None,
}
)

latency_ms = int((perf_counter() - start_time) * 1000)
response_tokens_estimated = int(len(reply or "") / 4)
response_event = "handoff_sent" if decision == "handoff" else "reply_sent"
log_event(
logging.INFO,
response_event,
conversation_id=conversation_id,
tenant_id=org_id,
channel=payload.channel,
response_tokens_estimated=response_tokens_estimated,
latency_ms_total=latency_ms,
handoff_type=handoff_type,
)
run_input = {
"message": payload.message,
"decision_message": decision_message,
"channel": payload.channel,
"conversation_id": conversation_id,
"user_id": user_id,
"org_id": org_id,
}
run_output = {
"reply": reply,
"action": action,
"confidence": confidence,
"ticket_id": ticket_id,
"citations": citations,
"decision_reason": decision_reason,
"decision_source": run_metadata.get("decision_source"),
"guardrail": guardrail_reason,
}
try:
runs_repo.create_run(
{
"org_id": org_id,
"conversation_id": conversation_id,
"action": action,
"confidence": confidence,
"input": run_input,
"output": run_output,
"citations": citations,
"latency_ms": latency_ms,
"metadata": run_metadata,
}
)
except Exception as exc:
log_event(
logging.WARNING,
"agent_run_insert_failed",
conversation_id=conversation_id,
error=str(exc),
)
except Exception as exc:
log_event(
logging.ERROR,
"db_error",
conversation_id=conversation_id,
error=str(exc),
)
raise HTTPException(status_code=500, detail="db_error")

log_event(
logging.INFO,
"chat_response",
conversation_id=conversation_id,
action=action,
confidence=confidence,
ticket_id=ticket_id,
citations=citations,
latency_ms=latency_ms,
retrieval_source=run_metadata.get("retrieval_source"),
decision_source=run_metadata.get("decision_source"),
guardrail=guardrail_reason,
decision_reason=decision_reason,
)

return ChatResponse(
conversation_id=conversation_id,
reply=reply,
action=action,
confidence=confidence,
ticket_id=ticket_id,
citations=citations,
decision_reason=decision_reason,
decision_source=run_metadata.get("decision_source"),
guardrail=guardrail_reason,
)
return handle_chat(payload, org_id, auth_user_id or payload.user_id, deps)
except ServiceError as exc:
raise HTTPException(status_code=500, detail=exc.detail)


@app.get("/v1/orgs", response_model=list[OrgResponse])
Expand Down
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