ducoterra
15c1272828
Grounded chat turns now run the agent loop (app/rag/agent.py) instead
of a bare chat_stream: while the per-turn budgets last
(BOR_AGENT_LIST_CALLS / BOR_AGENT_READ_CALLS, default 1 each) the model
gets list_documents (the indexed catalog, /api/docs order) and
read_document (full text, never truncated — A7-revised contract); once
both budgets are spent the tools key is dropped from the request and
the model must answer. Rejected calls (unknown tool, unknown/missing
path, document already in context, spent budget) consume no budget.
Budgets 0/0 make exactly one tools=None request — byte-identical to
the pre-phase path (budgets-as-kill-switch). Deflected turns keep the
direct chat_stream (A8 unchanged; the LOW prompt never carries the
<tools> section).
SSE contract gains {"type":"tool","name":...,"argument":
"source/path"|null} frames ahead of the answer deltas (PLAN §4
extension, owner permission 2026-08-26); done.sources, query_log.sources
and the per-turn log line (gains tool_calls=N) report the retrieval
docs + read docs, deduped. The UI shows a "calling tool"
button/label state and one visible .tool-call line per call above the
answer; the lines persist with the chat record and re-render on
reload. chat_stream passes tools through and accumulates streaming
tool_calls deltas into ToolCallPiece (tools=None stays byte-identical).
E2E: deterministic mock tool flow ("use your tools" + <tools> marker:
list -> read first catalog line -> quoted answer) plus the story suite
(marker flow, reload re-render, plain/deflected no-tool regressions).
Docs: .env.example + README (the two tools, the budgets, the SSE tool
frame, the "calling tool" UI state).
probe: turbo tool_calls=supported 2026-08-26 (uv run python -m
scripts.llm_probe --tools — non-streaming + streaming
finish_reason=tool_calls, indexed delta.tool_calls partials)
198 lines
8.9 KiB
Python
198 lines
8.9 KiB
Python
"""Application settings.
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Every setting can be overridden with an environment variable prefixed
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``BOR_`` (or a local gitignored ``.env`` file — see ``.env.example``).
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"""
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from __future__ import annotations
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import os
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from functools import lru_cache
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from pydantic import field_validator
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from pydantic_settings import BaseSettings, SettingsConfigDict
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#: The A9 import formats (PLAN anchor A9, revised 2026-08-21).
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#: ``BOR_IMPORT_EXTENSIONS`` may narrow — but never widen — this set.
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_ALLOWED_IMPORT_EXTENSIONS: frozenset[str] = frozenset(
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{"md", "markdown", "txt", "yaml", "yml", "json", "py"}
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)
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(
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env_file=".env",
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env_file_encoding="utf-8",
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env_prefix="BOR_",
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extra="ignore",
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)
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# --- App ---
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app_name: str = "Brain of Reese"
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app_version: str = "0.1.0"
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environment: str = "development"
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log_level: str = "INFO"
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static_dir: str = "frontend"
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# --- Database (PostgreSQL 17 + pgvector) ---
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database_url: str = "postgresql+psycopg://reese:reese@localhost:5432/brain_of_reese"
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# --- LLM (self-hosted, OpenAI-compatible "aipi" endpoint) ---
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llm_base_url: str = "https://aipi.reeseapps.com/v1"
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llm_api_key: str = ""
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llm_chat_model: str = "turbo"
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llm_embed_model: str = "embed"
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#: One-shot (non-streaming) completion model (A5 extended, phase 30):
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#: document summaries at import time and the KB overview (phase 31).
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#: Served by the same OpenAI-compatible endpoint — no new model
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#: management. Called via ``LLMClient.chat()``.
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llm_summary_model: str = "lite"
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#: Operator kill-switch for the ``thinking`` SSE events (phase 17,
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#: ``BOR_STREAM_THINKING``; ``0``/``false`` → off). When off, thinking
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#: pieces are still counted for the per-turn log line but never
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#: emitted — the answer stream itself is unchanged.
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stream_thinking: bool = True
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# --- RAG tuning ---
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embedding_dim: int = 768 # verified against aipi /v1 (embed model)
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top_n_docs: int = 2
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# Honesty gate (A8, re-tuned 2026-08-21): the ``embed`` model's cosine
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# scores compress into 0.41–0.84 on the real corpus, so the old 0.30
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# default never discriminated. LOW only fires when best cosine < this
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# AND no candidate chunk matches the question lexically (see A8).
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relevance_threshold: float = 0.62
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#: Maximum output tokens a chat answer may use (owner instruction
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#: 2026-08-22: answers must run to their natural end — the old hard
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#: 700-token cap cut long answers off mid-sentence).
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max_output_tokens: int = 32_768
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chunk_target_chars: int = 2_000
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chunk_overlap_chars: int = 200
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embed_batch_size: int = 16
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#: Total char budget for the ``<tuning>`` section of the system prompt
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#: (phase 15, steering notes). The newest-fitting notes are kept and the
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#: overflow is replaced by the ``[…truncated…]`` marker.
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steering_max_chars: int = 8_000
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#: Cap on the document content sent to the ``lite`` summary model in one
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#: call (phase 30, ``BOR_SUMMARY_MAX_CHARS``). Overflow is cut at the cap
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#: and the shared ``[…truncated…]`` marker is appended (see
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#: ``app.rag.summarizer``).
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summary_max_chars: int = 12_000
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#: Char budget for the ``<knowledge_base>`` section of the system prompt
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#: (phase 31: lite-generated KB overview, ``app.rag.overview``). The
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#: newest-fitting prefix of the stored outline is kept and the overflow
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#: is replaced by the shared ``[…truncated…]`` marker (phase 15
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#: convention — ``app.rag.prompts``).
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kb_overview_max_chars: int = 4_000
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#: Cap on the document list (source/path/title/first summary line per
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#: row) sent to the ``lite`` overview model in one call (phase 31,
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#: ``app.rag.overview``). Overflow is cut at the cap and the shared
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#: ``[…truncated…]`` marker is appended (summarizer convention).
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overview_input_max_chars: int = 40_000
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#: Per-turn opportunities to call the ``list_documents`` agent tool
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#: (phase 37, ``app.rag.agent``); 0 disables the tool entirely
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#: (pre-phase behavior with both budgets at 0).
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agent_list_calls: int = 1
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#: Per-turn opportunities to call the ``read_document`` agent tool
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#: (phase 37, ``app.rag.agent``); 0 disables the tool entirely.
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agent_read_calls: int = 1
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# --- Hybrid retrieval (A7, revised 2026-08-21) ---
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# cosine top-N ∪ Postgres FTS top-N, fused with Reciprocal Rank Fusion
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# (score = Σ 1/(rrf_k + rank) over the lists a chunk appears in).
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#
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# The vector window is deliberately wider than the lexical one: a
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# name-your-tool question's best *lexical* chunk (e.g. the "Install"
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# section of gitlab.md) can sit far down the vector ranking because the
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# question embeds close to generic templates. A 100-wide window is what
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# lets such chunks double-hit (one RRF term per list) and outrank a
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# template that owns vector rank 1 — measured 2026-08-22 against the
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# live 2774-chunk KB for "How did I install gitlab?" (gitlab.md:1 at
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# vrank 100 / lrank 3 → fused 0.0221 vs the template's 0.0164).
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hybrid_vector_candidates: int = 100
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hybrid_lexical_candidates: int = 30
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rrf_k: int = 60
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# --- Admin & sign-in (phase 16; A10 revised 2026-08-22) ---
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# Single-admin auth via a signed session cookie (Starlette
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# SessionMiddleware — no new services, no DB tables). Both secrets are
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# REQUIRED at startup: ``create_app()`` refuses to boot when either is
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# empty (``app.core.auth.ensure_admin_configured``). The password is
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# plaintext on purpose (homelab scope, owner decision 2026-08-22);
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# the session secret signs the cookie (``secrets.token_hex(32)``).
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admin_password: str = ""
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session_secret: str = ""
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#: Signed-cookie lifetime in seconds (default 12 h, refreshed on
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#: session writes — sliding for an active admin).
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session_max_age: int = 43_200
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session_cookie: str = "bor_session"
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# --- Import scope (A9, revised 2026-08-21) ---
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# Comma-separated list of lowercased file extensions (no dot) imported
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# by ``scripts/import_docs.py``. Hidden (dot) path components are always
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# skipped, plus the importer's exclusion list.
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# Stored as a raw CSV string (env-native — no JSON) and parsed on demand
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# via :py:meth:`import_extension_set`. ``mode="after"`` validation runs
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# against the raw string so a typo fails loudly at startup.
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import_extensions: str = "md,markdown,txt,yaml,yml,json,py"
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#: List of git repo URLs to clone/pull into ``sources_dir`` before
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#: indexing (phase 28); comma-separated, stored raw. Empty means no git
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#: sources — ``import_docs`` then falls back to ``--source`` / the old
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#: ``DEFAULT_SOURCES``.
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git_sources: str = ""
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#: Where ``import_docs`` clones/pulls the ``git_sources`` repos (phase
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#: 28). Stored as a raw string — ``Path.expanduser()`` is applied in
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#: the import script, not here.
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sources_dir: str = "~/bor-sources"
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@field_validator("import_extensions")
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@classmethod
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def _import_extensions_known(cls, v: str) -> str:
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"""Reject unknown/empty formats loudly instead of silently importing
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nothing (a typo like ``md,jsonn`` would otherwise walk zero files)."""
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exts = {part.strip().lstrip(".").lower() for part in v.split(",") if part.strip()}
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if not exts:
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raise ValueError("import_extensions must name at least one format")
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unknown = exts - _ALLOWED_IMPORT_EXTENSIONS
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if unknown:
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raise ValueError(
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f"unknown import extension(s): {', '.join(sorted(unknown))} — "
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f"allowed: {', '.join(sorted(_ALLOWED_IMPORT_EXTENSIONS))}"
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)
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return v
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# Suggested questions (onboarding + empty state).
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suggestions: list[str] = [
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"How is my Kubernetes cluster set up?",
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"What's my backup strategy?",
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"How do I deploy a new service?",
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"What's currently running in the homelab?",
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]
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@property
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def import_extension_set(self) -> frozenset[str]:
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"""Lowercased, dotted extension set (``.md``) for path filtering."""
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return frozenset(
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f".{part.strip().lstrip('.').lower()}"
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for part in self.import_extensions.split(",")
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if part.strip()
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)
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@property
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def git_source_list(self) -> list[str]:
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"""Non-empty, stripped git URLs from :py:attr:`git_sources` (phase 28).
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Whitespace around each entry is trimmed and empty entries dropped;
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an unset/empty value yields ``[]`` (the import script then uses its
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legacy local-directory defaults).
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"""
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return [part.strip() for part in self.git_sources.split(",") if part.strip()]
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@property
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def effective_api_key(self) -> str:
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"""API key for aipi: explicit setting, then $AIPI_KEY, then a placeholder."""
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return self.llm_api_key or os.environ.get("AIPI_KEY", "") or "not-needed"
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@lru_cache
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def get_settings() -> Settings:
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return Settings()
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