WangShen007
54b949b392
The upstream project name is gone from code, assets, docs and build
output: brand strings, domains (replypier.site and its app/api/docs/help
subdomains), env prefixes, image and network names, the widget's public
JS API (window.ReplyPier, replypierId/BaseUrl/Config/Token), CSS tokens
(--rp-*), file and directory names, and the WordPress plugin package.
- widget loader is now webclient/replypier.min.js; both bundles rebuilt
from source (backend/assets/widget.{js,css}, frontend/public/webclient)
- NOTICE, llms.txt, CONTRIBUTING, HELP_CENTER_INFRA and config.dev.js
rewritten rather than string-swapped; the demo config no longer ships
the previous vendor's Firebase/Razorpay/Google keys
- new migration replypier_ai_model_001 adds REPLYPIER to the managed
AIModelType and rewrites pre-rename rows
- local compose project, database, role and volume renamed; LOCAL_DEPLOY
documents carrying the old local data over
350 lines
13 KiB
Python
350 lines
13 KiB
Python
"""
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Copyright 2024-2026 ReplyPier
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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Phase 3 investigation engine: hypothesis-driven root-cause analysis with every
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tool call captured as a first-class InvestigationEvent row (the glass box).
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Orchestrated here, executed by the ticket_investigator worker.
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"""
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import asyncio
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import json
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import time
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from typing import List, Optional, Tuple
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from uuid import UUID
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from app.core.logger import get_logger
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from app.database import SessionLocal
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from app.models.investigation import (
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HypothesisStatus,
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InvestigationEvent,
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InvestigationEventType,
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InvestigationHypothesis,
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InvestigationRun,
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InvestigationRunStatus,
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RCADocument,
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)
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from app.models.ticket import Ticket
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from app.utils.guardrails import GuardrailAction, PIIDetector
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logger = get_logger(__name__)
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# Per-hypothesis tool budget; the run-level max_tool_calls caps the total.
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HYPOTHESIS_TOOL_CALL_LIMIT = 6
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# Truncation applied at capture time — raw payloads never land in the DB.
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MAX_EVENT_INPUT_CHARS = 2000
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MAX_EVENT_RESULT_CHARS = 4000
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# Bounded time for the final RCA synthesis (outside the main wall clock so a
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# timed-out investigation can still produce a partial RCA).
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RCA_SYNTHESIS_TIMEOUT_SECONDS = 180
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VALID_VERDICT_STATUSES = {
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HypothesisStatus.VALIDATED.value,
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HypothesisStatus.INVALIDATED.value,
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HypothesisStatus.INCONCLUSIVE.value,
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}
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def redact_snippet(text: str, max_chars: int) -> str:
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"""PII-redact and truncate a tool payload before it is persisted."""
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if not text:
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return ""
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result = PIIDetector.detect(text[: max_chars * 2], action=GuardrailAction.REDACT)
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redacted = result.redacted_text if result.redacted_text is not None else text
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redacted = redacted[:max_chars]
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return redacted
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class EvidenceRecorder:
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"""Writes InvestigationEvent rows as the investigator works.
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Each write uses its own short-lived session so evidence is visible live
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(the glass-box UI polls during active runs) and survives a failed run.
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Also the run's tool-call meter: the budget check reads self.tool_calls.
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"""
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def __init__(self, run_id: UUID, ticket_id: UUID):
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self.run_id = run_id
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self.ticket_id = ticket_id
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self.seq = 0
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self.tool_calls = 0
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self.hypothesis_id: Optional[UUID] = None
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# Per-hypothesis tool-call outcomes: {hypothesis_id: [total, errored]}.
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# A verdict is only worth as much as the evidence under it, and a run
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# where every query failed must not be able to assert a fact (#318).
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self.calls_by_hypothesis: dict = {}
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# Run-level totals, for the dashboard's connector banner.
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self.failed_tool_calls = 0
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# tool function name -> configured connector (MCP server) name.
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self.connector_by_function: dict = {}
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# In-memory digest for RCA synthesis, mirroring the persisted rows.
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self.evidence_lines: List[str] = []
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def map_connectors(self, mcp_tools: list) -> None:
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for tool in mcp_tools or []:
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connector = getattr(tool, "_connector_name", None) or "MCP"
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for function_name in getattr(tool, "functions", {}) or {}:
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self.connector_by_function[function_name] = connector
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def _write(self, **fields) -> None:
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self.seq += 1
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try:
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with SessionLocal() as db:
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db.add(
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InvestigationEvent(
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run_id=self.run_id,
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ticket_id=self.ticket_id,
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hypothesis_id=self.hypothesis_id,
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seq=self.seq,
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**fields,
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)
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)
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db.commit()
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except Exception as e:
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logger.error(f"Failed to record investigation event: {e}")
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def record_phase(self, label: str) -> None:
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"""Phase marker — powers the live ticker line in the UI."""
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self._write(event_type=InvestigationEventType.PHASE, label=label[:300])
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async def tool_hook(self, function_name: str, function, arguments: dict):
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"""agno tool hook: times the call and captures redacted input/result.
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Registered on the hypothesis-testing agents only."""
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# The Groq structured-output capture tool is plumbing, not evidence.
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if function_name == "json":
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return await function(**arguments)
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started = time.monotonic()
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error: Optional[str] = None
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result = None
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try:
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result = await function(**arguments)
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return result
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except Exception as e:
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error = str(e)
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raise
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finally:
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self.tool_calls += 1
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if error:
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self.failed_tool_calls += 1
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if self.hypothesis_id is not None:
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tally = self.calls_by_hypothesis.setdefault(self.hypothesis_id, [0, 0])
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tally[0] += 1
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if error:
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tally[1] += 1
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duration_ms = int((time.monotonic() - started) * 1000)
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try:
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input_text = json.dumps(arguments, default=str)
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except Exception:
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input_text = str(arguments)
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input_snippet = redact_snippet(input_text, MAX_EVENT_INPUT_CHARS)
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result_snippet = redact_snippet(str(result), MAX_EVENT_RESULT_CHARS) if result is not None else None
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self._write(
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event_type=InvestigationEventType.TOOL_CALL,
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tool_name=function_name[:200],
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connector_name=self.connector_by_function.get(function_name, "MCP")[:200],
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tool_input=input_snippet,
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tool_result=result_snippet,
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duration_ms=duration_ms,
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error=error[:2000] if error else None,
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)
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self.evidence_lines.append(
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f"[E{self.seq}] {function_name}({input_snippet[:300]}) -> "
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+ (f"ERROR: {error[:200]}" if error else (result_snippet or "")[:500])
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)
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def tool_outcomes(self, hypothesis_id: UUID) -> tuple:
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"""(total, errored) tool calls made while testing this hypothesis."""
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total, errored = self.calls_by_hypothesis.get(hypothesis_id, [0, 0])
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return total, errored
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def enforce_evidence_backed_verdict(
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hypothesis: InvestigationHypothesis, recorder: "EvidenceRecorder"
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) -> bool:
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"""Downgrade a definite verdict that no successful query supports.
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The model returns status and confidence as free text, and nothing else
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checks them against what actually happened. A run whose every search
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errored — a missing index, a rejected client version — can still come back
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"the record does not exist, validated, 0.90", which a support agent would
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reasonably act on. Tool errors are the absence of evidence, never evidence
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of absence.
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Only fires when the phase made tool calls and every one of them failed;
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a hypothesis reasoned from ticket context alone is left as the model
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returned it. Returns True when it changed the verdict.
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"""
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total, errored = recorder.tool_outcomes(hypothesis.id)
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if not total or errored < total:
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return False
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if str(hypothesis.status) == HypothesisStatus.INCONCLUSIVE.value:
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return False
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original = str(hypothesis.status)
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confidence = hypothesis.confidence
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hypothesis.status = HypothesisStatus.INCONCLUSIVE
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hypothesis.confidence = 0.0
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# The model's own conclusion is dropped rather than kept: it reads as a
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# finding, and the RCA writer would quote it as one.
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hypothesis.conclusion = (
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f"Not tested — all {total} tool call{'' if total == 1 else 's'} in this phase "
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f"failed, so no evidence was gathered. The tester reported "
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f"\"{original}\"{f' at {confidence:.2f} confidence' if confidence is not None else ''}, "
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"which nothing supports. Check the connector errors in the evidence log."
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)
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return True
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def hypotheses_digest(hypotheses: List[InvestigationHypothesis]) -> str:
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lines = []
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for h in hypotheses:
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lines.append(
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f"H{h.idx}: {h.title} — {h.status} (confidence {h.confidence if h.confidence is not None else 'n/a'})\n"
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f" {h.conclusion or h.rationale or ''}"
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)
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return "\n".join(lines)
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async def run_investigation_phases(
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db,
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run: InvestigationRun,
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ticket: Ticket,
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agent,
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context_message: str,
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mcp_tools: list,
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recorder: EvidenceRecorder,
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) -> Tuple[List[InvestigationHypothesis], bool]:
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"""Generate hypotheses, then test each within the run's tool budget.
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Mutates hypothesis rows (committed per step so the UI streams progress).
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Returns (hypotheses, budget_exhausted)."""
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recorder.record_phase("Generating hypotheses")
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plan = await agent.generate_hypotheses(context_message)
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if plan is None or not plan.hypotheses:
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raise RuntimeError("Hypothesis generation produced no result")
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hypotheses: List[InvestigationHypothesis] = []
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for i, spec in enumerate(plan.hypotheses, start=1):
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hypothesis = InvestigationHypothesis(
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run_id=run.id,
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ticket_id=ticket.id,
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idx=i,
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title=spec.title[:300],
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rationale=spec.rationale,
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status=HypothesisStatus.PENDING,
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)
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db.add(hypothesis)
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hypotheses.append(hypothesis)
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db.commit()
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budget_exhausted = False
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max_tool_calls = run.max_tool_calls if run.max_tool_calls is not None else 25
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for hypothesis in hypotheses:
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remaining_budget = max(0, max_tool_calls - recorder.tool_calls)
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if remaining_budget <= 0 and mcp_tools:
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budget_exhausted = True
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hypothesis.status = HypothesisStatus.INCONCLUSIVE
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hypothesis.confidence = 0.0
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hypothesis.conclusion = "Not tested — the run's tool-call budget was exhausted."
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db.commit()
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continue
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recorder.hypothesis_id = hypothesis.id
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recorder.record_phase(f"Testing H{hypothesis.idx}: {hypothesis.title}")
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hypothesis.status = HypothesisStatus.TESTING
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db.commit()
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verdict = await agent.test_hypothesis(
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context_message,
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hypothesis.title,
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hypothesis.rationale,
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mcp_tools=mcp_tools,
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tool_call_limit=min(HYPOTHESIS_TOOL_CALL_LIMIT, remaining_budget) if mcp_tools else None,
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tool_hooks=[recorder.tool_hook],
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)
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if verdict is not None and verdict.status in VALID_VERDICT_STATUSES:
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hypothesis.status = verdict.status
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hypothesis.confidence = max(0.0, min(1.0, verdict.confidence))
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hypothesis.conclusion = verdict.conclusion
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if enforce_evidence_backed_verdict(hypothesis, recorder):
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logger.warning(
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f"H{hypothesis.idx} claimed '{verdict.status}' with every tool "
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f"call failing; forced to inconclusive"
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)
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else:
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hypothesis.status = HypothesisStatus.INCONCLUSIVE
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hypothesis.confidence = 0.0
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# A provider that refused the request is a hard, deterministic
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# failure — saying "no parseable verdict" for it reads as a model
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# quality problem and hides a fixable connector issue (#303).
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hypothesis.conclusion = (
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getattr(agent, "last_provider_error", None)
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or "The tester produced no parseable verdict."
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)
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db.commit()
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recorder.hypothesis_id = None
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return hypotheses, budget_exhausted
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async def synthesize_and_store_rca(
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db,
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run: InvestigationRun,
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ticket: Ticket,
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agent,
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context_message: str,
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hypotheses: List[InvestigationHypothesis],
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recorder: EvidenceRecorder,
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partial: bool,
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) -> Optional[RCADocument]:
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"""Write the versioned RCA document from whatever evidence exists."""
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from app.repositories.ticket import InvestigationRepository
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recorder.record_phase("Writing root-cause analysis")
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try:
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result = await asyncio.wait_for(
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agent.synthesize_rca(
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context_message,
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hypotheses_digest(hypotheses),
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"\n".join(recorder.evidence_lines[-60:]),
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partial=partial,
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),
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timeout=RCA_SYNTHESIS_TIMEOUT_SECONDS,
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)
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except asyncio.TimeoutError:
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logger.error(f"RCA synthesis timed out for {ticket.display_number}")
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return None
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if result is None:
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return None
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rca = RCADocument(
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ticket_id=ticket.id,
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run_id=run.id,
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version=InvestigationRepository(db).next_rca_version(ticket.id),
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summary=result.summary,
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impact=result.impact,
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timeline=[entry.model_dump() for entry in result.timeline],
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investigation_log=result.investigation_log,
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contributing_factors=result.contributing_factors,
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conclusion=result.conclusion,
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remediation=result.remediation,
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prevention=result.prevention,
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customer_summary=result.customer_summary,
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confidence=max(0.0, min(1.0, result.confidence)),
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is_partial=partial,
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generated_by="ai",
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)
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db.add(rca)
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db.commit()
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return rca
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