alvaro
dfed03ac7c
Vocero now retrieves AquaPortal content through authenticated MCP. This removes the obsolete ApiPilot integration from the API and browser while preserving the separately deployed speech pipeline. Release notes: - Removed ApiPilot endpoints, configuration, credential overrides, and HTTP/realtime tool execution. - Removed browser ApiPilot catalog loading and prompt generation while retaining authenticated MCP discovery. - Updated regression coverage and operating documentation for the retired routes and settings. Reviewed-on: AquaPortal/s2s#3
180 lines
11 KiB
Python
180 lines
11 KiB
Python
from __future__ import annotations
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import os
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from dotenv import load_dotenv
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from vocero_api.core import logging as app_logging
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ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
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load_dotenv(os.path.join(ROOT_DIR, ".env"), override=False)
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app_logging.logger.debug("Loaded .env from %s", ROOT_DIR)
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def _normalize_reasoning_type(value: str) -> str:
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normalized = (value or "").strip().strip('"').strip("'").replace("-", "_").upper()
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if normalized == "OPENAI":
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return "OPENAI"
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return "PROXY"
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SERPER_KEY = os.environ.get("SERPER_API_KEY", "").strip()
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S2S_PRIMARY_PROMPT = os.environ.get("S2S_PRIMARY_PROMPT", "").strip()
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if S2S_PRIMARY_PROMPT:
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S2S_PRIMARY_PROMPT = S2S_PRIMARY_PROMPT.replace("\\n", "\n")
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# External user-by-phone info endpoint. When set, GET /api/user/phone proxies
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# to this URL (passing ?phoneNumber=) and forwards the JSON payload to the
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# browser so the UI can render caller identity / caller context.
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VOCERO_USER_PHONE_AUTH_URL = os.environ.get("VOCERO_USER_PHONE_AUTH_URL", "").strip().rstrip("/")
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# Feature flag: when "true" (case-insensitive) => injects the resolved caller
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# profile into the realtime session (patch session.update client->upstream,
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# append profile to the LLM system instructions prompt instructions, and prepend it to the
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# reasoning user_context for the /api/reasoning HTTP body.
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# Default = disabled (generic deploy; pure passthrough; all characterization lives
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# in the backend RAG per original architectural decision.)
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S2S_INJECT_PROFILE = (os.environ.get("S2S_INJECT_PROFILE", "") or "").strip().lower() in {"1", "true", "yes", "on"}
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# Endpoint URL for fetching the latest per-caller conversation summary, used when
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# both S2S_INJECT_PROFILE=true AND S2S_LATEST_SUMMARY is set. Completely
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# generic: the proxy performs a plain GET S2S_LATEST_SUMMARY?phoneNumber=<x>
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# and expects JSON back (optionally wrapped as {"result": <value>}). Empty
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# string means the feature is off (no network call, nothing injected).
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S2S_LATEST_SUMMARY = (os.environ.get("S2S_LATEST_SUMMARY", "") or "").strip()
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# Static / hardcoded latest-summary text. When set (non-empty string), it takes
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# PRIORITY over S2S_LATEST_SUMMARY: the proxy SKIPS the HTTP fetch entirely and
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# injects this raw plain-text value directly into the prompt as the latest
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# conversation summary. Designed for cases where the operator wants a clean,
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# essential, pre-cleaned summary snippet without depending on an upstream API.
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# Bypasses all HTTP logic (no login-by-phone bearer forwarding, no timeout, no
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# JSON unwrapping) — the string is passed as-is to _resolve_latest_summary_context
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# (which still applies the 1600-char cap + the LATEST_SUMMARY_HINT wrapper).
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S2S_LATEST_SUMMARY_TEXT = (os.environ.get("S2S_LATEST_SUMMARY_TEXT", "") or "").strip()
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if S2S_LATEST_SUMMARY_TEXT:
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# Interpret \n escape sequences so multi-line summaries can be written as a
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# single env line (matches S2S_PRIMARY_PROMPT convention above).
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S2S_LATEST_SUMMARY_TEXT = S2S_LATEST_SUMMARY_TEXT.replace("\\n", "\n")
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# Optional: login-by-phone endpoint. When set (and S2S_INJECT_PROFILE=true and
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# phoneNumber is available), before fetching user profile / latest summary the proxy
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# first performs a POST S2S_LOGIN_BY_PHONE with {"phoneNumber": "..."} JSON body,
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# extracts an auth token from the response (if any), and forwards it as
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# Authorization: Bearer <token> for subsequent profile / summary / reports HTTP calls.
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S2S_LOGIN_BY_PHONE = (os.environ.get("S2S_LOGIN_BY_PHONE", "") or "").strip().rstrip("/")
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# Optional: list of scheduled reports / podcasts endpoint. When set (and S2S_INJECT_PROFILE=true)
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# the proxy performs a plain GET S2S_REPORT_LIST, unwraps the JSON array (or {"result": [...]}),
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# filters per-item fields (sourceScheduleLabel, date, podcastScript), sorts by date DESC,
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# and injects the compiled block into the LLM instructions / user_context between
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# the caller profile block and the latest conversation summary block.
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S2S_REPORT_LIST = (os.environ.get("S2S_REPORT_LIST", "") or "").strip().rstrip("/")
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# Feature flag: when "true" (case-insensitive) => actually injects the REPORT LIST
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# contents (podcastScript) into the LLM instructions / context. When "false" (DEFAULT)
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# the proxy NEVER injects scheduled reports into the prompt — not even the TOC — and
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# skips the upstream S2S_REPORT_LIST network call entirely, fixing the user issue:
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# "All reports are currently injected with their complete podcastScript."
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#
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# Historical note (2026-09-11): before this flag existed, if S2S_INJECT_PROFILE=true AND
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# S2S_REPORT_LIST was set the server eagerly fetched every scheduled report for the
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# caller, serialized all their full podcastScript fields into a single block (capped at
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# 12000 chars) and appended it to every LLM turn. That was a huge prompt-bloat injection
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# the user explicitly flagged as unwanted. The safe default is therefore DISABLED: to
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# restore mass-report-injection, operators must opt in with S2S_INJECT_REPORTS=true.
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S2S_INJECT_REPORTS = (os.environ.get("S2S_INJECT_REPORTS", "") or "").strip().lower() in {"1", "true", "yes", "on"}
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# Max number of scheduled reports to surface in the QUICK-HINT line block (the TOC of
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# label + date shown before the serialized JSON entries). Also applied as a hard cap to
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# the final filtered+serialized entries list — so the wrapped prompt block never carries
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# content for more than N reports, even when the upstream API returns 20+. Configurable
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# from .env; value is integer-parsed and clamped to [1, 50] (invalid values fall back to
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# the default = 5, which is the user's requested "max 3-5" default range). User request
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# 2026-09-11: "Limit report quick hints to 3-5 entries, configurable through .env."
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try:
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_S2S_REPORTS_QUICK_HINT_MAX_RAW = int((os.environ.get("S2S_REPORTS_QUICK_HINT_MAX", "") or "5").strip())
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except Exception:
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_S2S_REPORTS_QUICK_HINT_MAX_RAW = 5
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S2S_REPORTS_QUICK_HINT_MAX = max(1, min(50, _S2S_REPORTS_QUICK_HINT_MAX_RAW))
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del _S2S_REPORTS_QUICK_HINT_MAX_RAW
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# Feature flag: when "true" → prepend a TODAY date + anti-hallucination guard to
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# every LLM prompt and session instructions. Fixes user bug 3 2026-09-11:
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# "Qwen2.5 has a training cutoff around mid-2024 and produces Chinese meta-comments
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# about 2026 reports: 'Today is 2023; why are these reports dated 2026?'"
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# Default = ON (safe even when empty/unset). Explicit S2S_INJECT_TODAY="false" to disable.
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# Injected text contains:
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# • The actual date: "TODAY is YYYY-MM-DD" (ISO, server local date).
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# • Earlier reports are historical records; do not question their dates.
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# • No internal meta-comments or prompt introspection.
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# • No thinking leaks with GPTQ Int4: only words Vocero says aloud.
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# • Retrieve report content through RAG rather than inventing it from summaries.
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S2S_INJECT_TODAY = (os.environ.get("S2S_INJECT_TODAY", "") or "").strip().lower() not in {"0", "false", "no", "off"}
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# Feature flag: S2S_REPORTS_INJECT_MODE controls what the server sends into the LLM
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# prompt for scheduled reports. Values (case-insensitive, default="minimal"):
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# • "minimal" → ONLY last N = S2S_REPORTS_QUICK_HINT_MAX items as a pure
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# TOC (label + date ONLY). NO podcastScript is ever exposed. This
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# the user's "Keep reports only as a MINIMAL summary"
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# fix. Token budget: ~50 tokens instead of ~3200.
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# • "full" → legacy behaviour: full JSON array with podcastScript
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# (still capped to S2S_REPORTS_QUICK_HINT_MAX items). Used to be
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# the default before 2026-09-11; kept only for explicit opt-in
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# because of the user explicitly flagged the massive script injection
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# as unwanted on this session.
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# • "off" / "none" → no reports injection at all (same as S2S_INJECT_REPORTS=false)
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# even when S2S_INJECT_REPORTS=true. Use this to keep the HTTP
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# fetch running while keeping the prompt clean, or as a redundant kill.
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S2S_REPORTS_INJECT_MODE = (os.environ.get("S2S_REPORTS_INJECT_MODE", "") or "").strip().lower()
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if S2S_REPORTS_INJECT_MODE not in {"minimal", "full", "off", "none"}:
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S2S_REPORTS_INJECT_MODE = "minimal"
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# Feature flag: when "true" the proxy logs the FULL PROMPT TEXT (not just
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# lengths) going to the upstream LLM/realtime backends. Includes:
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# - WS session.update session.instructions (before/after patch)
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# - /api/reasoning OPENAI full system message + user transcript
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# Default = off; only length summaries are logged (current behaviour).
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# WARNING: setting this on will log potentially PII / sensitive caller
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# profile data / user transcripts. Use only in development / debugging.
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S2S_DEBUG_PROMPT = (os.environ.get("S2S_DEBUG_PROMPT", "") or "").strip().lower() in {"1", "true", "yes", "on"}
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S2S_START_SESSION_MODE = (os.environ.get("S2S_START_SESSION_MODE", "") or "").strip().upper()
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S2S_THEME = (os.environ.get("S2S_THEME", "") or "").strip().lower()
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if S2S_THEME and S2S_THEME not in {"white", "dark"}:
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S2S_THEME = ""
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S2S_REASONING_TYPE = _normalize_reasoning_type(os.environ.get("S2S_REASONING_TYPE", "PROXY"))
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OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "").strip()
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OPENAI_BASE_URL = os.environ.get("OPENAI_BASE_URL", "https://api.openai.com/v1").strip().rstrip("/")
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OPENAI_REASONING_MODEL = os.environ.get("OPENAI_REASONING_MODEL", "gpt-4.1-mini").strip()
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OPENAI_ORGANIZATION = os.environ.get("OPENAI_ORGANIZATION", "").strip()
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OPENAI_PROJECT = os.environ.get("OPENAI_PROJECT", "").strip()
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# Speech-to-speech load balancer URL. When set, the browser POSTs /api/session
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# (which proxies <lb>/session here, server-side) and connects to the URL the LB
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# returns (the original flow). The LB address itself is never sent to the browser.
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# When empty, the user may instead set a direct s2s server URL in Settings and the
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# browser connects to it straight (no load balancer).
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LOAD_BALANCER_URL = os.environ.get("LOAD_BALANCER_URL", "").strip()
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# Direct s2s server URL pinned by the deploy. Takes priority over the load
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# balancer: when set, LB allocation and queuing are disabled, and the browser
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# connects to this URL directly. Unlike the LB address it is NOT a secret —
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# /api/config sends it to the client, which
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# shows it read-only in Settings.
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SPEECH_TO_SPEECH_URL = os.environ.get("SPEECH_TO_SPEECH_URL", "").strip()
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# Optional same-origin WebSocket relay target. When set, the browser dials this
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# app over ws(s)://<this-app>/v1/realtime and the server relays frames to the
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# upstream speech-to-speech endpoint, keeping the internal host private.
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S2S_PROXY_TARGET = os.environ.get("S2S_PROXY_TARGET", "").strip()
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if SPEECH_TO_SPEECH_URL or S2S_PROXY_TARGET:
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LOAD_BALANCER_URL = ""
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SERPER_URL = "https://google.serper.dev/search"
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# Cap results so the tool output stays small enough to feed back to the model.
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MAX_RESULTS = 5
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# Optional authenticated AquaPortal MCP integration. Legacy deployments remain unchanged.
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S2S_MCP_URL = os.environ.get("S2S_MCP_URL", "").strip()
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def _bounded_mcp_int(name: str, default: int, minimum: int, maximum: int) -> int:
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try:
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value = int(os.environ.get(name, str(default)))
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except ValueError:
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value = default
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return max(minimum, min(maximum, value))
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S2S_MCP_TIMEOUT_SEC = _bounded_mcp_int("S2S_MCP_TIMEOUT_SEC", 20, 1, 120)
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S2S_MCP_MAX_CALLS_PER_TURN = _bounded_mcp_int("S2S_MCP_MAX_CALLS_PER_TURN", 6, 1, 20)
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S2S_CONVERSATION_TTL_SEC = _bounded_mcp_int("S2S_CONVERSATION_TTL_SEC", 3600, 60, 86400)
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S2S_MAX_CONVERSATIONS = _bounded_mcp_int("S2S_MAX_CONVERSATIONS", 1000, 1, 10000)
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