DumiFlex
87dd6ab12c
A short note (up to 600 characters) in Settings > AI assistant > About my library. The chat, every Draft with AI panel and the pack builder read it after their own instructions, and it wins over how existing items are written. A chat keeps the note it opened with, so an edit never changes an open chat's prompt. Suggest drafts a note from the library summary and sample option text for the user to edit; nothing is saved until Save. The note is stored with the shared AI settings (ai.json), not per model, and a note that asks for a minor or non-consent in a sexual context is refused like a pack request.
580 lines
28 KiB
Python
580 lines
28 KiB
Python
"""What a chat turn sends the model: step asks, tail notes (permissions,
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lazy tool groups, vocab, canvas), the context window, the fixed prefix,
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compaction and library retrieval."""
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from __future__ import annotations
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import asyncio
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import json as _json
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import sqlite3
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from typing import Any
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from engine.ai import budget as ai_budget
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from engine.ai import chat as ai_chat
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from engine.ai import chatlog as ai_chatlog
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from engine.ai import compact as ai_compact
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from engine.ai import docs as ai_docs
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from engine.ai import index as ai_index
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from engine.ai import note as ai_note
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from engine.ai import pack as ai_pack
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from engine.ai import safety as ai_safety
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from engine.ai import tools as ai_tools
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from engine.ai import vocab as ai_vocab
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from wp_api import (
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_ai_chat,
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_ai_client,
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_ai_embed,
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_ai_search,
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_ai_vocab,
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_ai_web,
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)
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def _results_tail(question: str, *, last: bool, native: bool, split: bool = False) -> str:
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"""What follows a step's results. The user's question goes with them:
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once a step's results are the newest message, small models forget what
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was asked and answer with a greeting. `split`: the chat asks each step
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in two parts (`_ai_chat.split_step`), so being ready means calling
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`answer` (native) or no calls (JSON); the message comes after."""
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asked = f"\n\nThe user's message you are answering: \"{ai_budget.clip(question, 600)}\"" \
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if question else ""
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if split:
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done = ("call answer, then write your reply" if native
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else "answer with \"calls\": [], then write your reply")
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else:
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done = ("answer in plain text without calling a tool" if native
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else "answer with \"calls\": [] and a message")
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if last:
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tail = ("\n\nNo more tool calls. Answer the user's message now from these results"
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+ (", in plain text, with" if split else f": {done} that gives")
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+ " the actual answer.")
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else:
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tail = (f"\n\nIf these results answer the user's message, {done} that gives the "
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"actual answer. Otherwise make the next calls. Never repeat a call you already "
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"made.")
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return asked + tail
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def _decls(cfg: dict[str, Any], meta: dict[str, Any], fmt: str) -> list[dict[str, Any]]:
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"""The chat's native tool declarations, in its own order (later
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additions at the end), with the `answer` tool when its steps split."""
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if fmt != "native":
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return []
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out = ai_tools.native(ai_tools.tools(meta["tools"]), cfg["api"])
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if _splits(cfg, meta, fmt):
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out.append(ai_tools.answer_decl(cfg["api"]))
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return out
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def _splits(cfg: dict[str, Any], meta: dict[str, Any], fmt: str) -> bool:
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"""Whether this chat asks each step in two parts (`_ai_chat.split_step`):
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set when the chat starts (small and medium models), JSON mode or a
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native server that takes tool_choice."""
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return bool(meta.get("split")) and (fmt == "json" or _ai_chat.split_native(cfg))
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async def _chat_step(
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cfg: dict[str, Any], *, system: str, log: list[dict[str, Any]], fmt: str,
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names: list[str], prof: dict[str, Any], num_ctx: int, cache_key: str,
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emit: Any = None, typed: bool = False, decls: list[dict[str, Any]] | None = None,
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split: bool = False, answer_only: bool = False,
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offer: list[Any] | None = None, must_call: bool = False,
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plain: list[Any] | None = None,
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) -> dict[str, Any]:
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"""One model step, asked again once with the problem named when a
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JSON-mode answer isn't usable: small models usually fix a named error.
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`typed`: JSON-mode args follow each tool's schema (local servers); a
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server that can't take that schema is asked again with the string form.
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`split`: asked in two parts, act then answer (`_ai_chat.split_step`);
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`answer_only` skips the act part (the step is told to answer).
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`offer` (typed JSON only): the tools this step's grammar allows instead
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of all of the chat's (a shortlist, a plan, a plan item); the prompt and
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native declarations stay the same, so the cached prefix does too.
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`must_call`: a split act ask that must call (with `offer`). `plain`:
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the offer before `engine/ai/narrow.py` limited its fields, asked
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instead if the server's grammar refuses the narrowed one."""
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chosen = ai_tools.tools(names)
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native = decls if decls is not None else ai_tools.native(chosen, cfg["api"])
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typed = typed and fmt == "json" and _ai_chat.checks_schema(cfg)
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if typed and offer:
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chosen = list(offer)
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else:
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must_call = False
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schema = ai_tools.step_schema([t.name for t in chosen], chosen if typed else None)
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problem = ""
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attempt = 0
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while attempt < 2:
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asked = log if not problem else [*log, {"role": "user", "content": (
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f"Your last answer couldn't be used ({problem}). Answer again with only the "
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"JSON object.")}]
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try:
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if split:
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return await _ai_chat.split_step(
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cfg, system=system, log=asked, fmt=fmt, offered=chosen, tools=native,
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typed=typed, act_temp=prof.get("act_temp", prof["chat_temp"]),
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write_temp=prof.get("write_temp", prof["chat_temp"]),
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max_calls=int(prof.get("act_calls") or 0), num_ctx=num_ctx,
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cache_key=cache_key, emit=emit, answer_only=answer_only,
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must_call=must_call,
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)
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return await _ai_chat.step(
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cfg, system=system, log=asked, fmt=fmt, tools=native,
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names=[t.name for t in chosen],
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schema=schema, temperature=prof["chat_temp"], num_ctx=num_ctx,
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cache_key=cache_key, emit=emit,
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)
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except _ai_chat.ToolsUnsupported:
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raise
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except _ai_client.AIError as e:
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if typed and plain is not None and _ai_chat.refused_enums(cfg, e):
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chosen, plain = list(plain), None
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schema = ai_tools.step_schema([t.name for t in chosen], chosen)
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continue # the same ask, without the enums
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if typed and _ai_chat.refused_schema(cfg, e):
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chosen, must_call = ai_tools.tools(names), False
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typed, schema = False, ai_tools.step_schema(names)
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continue # the same ask, args as a string
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if attempt or fmt != "json" or "JSON" not in e.message:
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raise
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problem = "it wasn't valid JSON"
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except ValueError as e:
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if attempt:
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raise _ai_client.AIError(
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f"the model's answer couldn't be used ({e}). Try again, or a larger "
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"model.") from e
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problem = str(e)
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attempt += 1
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raise _ai_client.AIError("the model's answer couldn't be used")
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def _final_step(fmt: str, message: str) -> dict[str, Any]:
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"""The reply the user saw, as a step that calls nothing, when the model's
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last step called tools: the turn must end on an answer so the next one
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reads like a conversation (and strict chat templates accept it)."""
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answer = {"calls": [], "message": message, "plan": ""} if fmt == "json" else None
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return ai_chatlog.step_entry(ai_chatlog.dumps(answer) if answer else message, [],
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answer=answer, synthetic=True)
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#: Told to the model when the user changes a permission mid-chat.
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_PERM_NOTES = {
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("delete", True): "(The user turned on deleting: delete_items is available now. Use it "
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"only for what they ask to delete.)",
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("delete", False): "(The user turned off deleting: delete_items is gone. If they ask to "
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"delete something, tell them it is off in Settings › AI assistant.)",
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("auto", True): "(The user turned on auto-apply: description, tag and category changes "
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"from update_info, and new categories, are applied as soon as you "
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"propose them; the user can undo them. Other changes stay cards.)",
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("auto", False): "(Auto-apply is off: every change is a card the user applies.)",
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("web", True): "(The user set up web search: web_search is available now. Use it on "
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"your own whenever references would help; the user allows each query.)",
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("web", False): "(Web search is off now: web_search is gone.)",
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("reader", True): "(read_result is available now: when a search snippet isn't enough, "
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"read one of its result pages; the user allows each read.)",
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("reader", False): "(Reading result pages is off now: read_result is gone.)",
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("danbooru", True): "(The user turned on the Danbooru lookup: danbooru looks up a tag's "
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"wiki, aliases, implications and related tags; the user allows each.)",
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("danbooru", False): "(The Danbooru lookup is off now: danbooru is gone.)",
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}
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#: The same, for a lazy chat whose web or Danbooru tools aren't loaded yet.
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_LAZY_NOTES = {
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"web": "(The user set up web search: call more_tools with {\"group\": \"web\"} to get "
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"web_search when references would help; the user allows each query.)",
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"reader": "(Reading result pages is on too: more_tools {\"group\": \"web\"} also gives "
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"read_result.)",
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"danbooru": "(The user turned on the Danbooru lookup: call more_tools with {\"group\": "
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"\"danbooru\"} to look up a tag's wiki, aliases and related tags.)",
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}
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async def _vocab_kinds(body: dict[str, Any]) -> set[str]:
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"""The lookup sources this request may use (`_ai_vocab.available`);
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the first call may load the tag list, so it runs off the event loop."""
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if not ai_vocab.flags(body.get("vocab")):
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return set()
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loop = asyncio.get_running_loop()
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try:
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return await loop.run_in_executor(None, _ai_vocab.available, body.get("vocab"))
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except Exception: # noqa: BLE001 - a broken tag file only turns the lookup off
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return set()
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def _sync_vocab(meta: dict[str, Any], kinds: set[str]) -> str:
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"""Offer `lookup` while a source is on, and say which (once, in the
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tail, when it changes)."""
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now = sorted(kinds)
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if list(meta.get("vocab") or []) == now:
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return ""
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meta["vocab"] = now
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meta["tools"] = ai_tools.sync_delete(list(meta.get("tools") or []), bool(now),
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ai_tools.VOCAB)
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if not now:
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return "(The user's tag, LoRA and embedding lookups are off now: lookup is gone.)"
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names = ", ".join(ai_vocab.LABELS[k] for k in now)
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return (f"(lookup can search the user's {names} now: check tag-style options with it "
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"and name the user's real LoRAs.)")
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def _canvas_note(meta: dict[str, Any], body: dict[str, Any]) -> str:
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"""What the user's open canvas sets at run time, when it changed."""
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canvas = ai_pack.canvas_vars(body.get("canvas"))
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if not isinstance(body.get("canvas"), list):
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return ""
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names = [f"{c['source']}:{c['name']}" for c in canvas]
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if names == list(meta.get("canvas") or []):
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return ""
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meta["canvas"] = names
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return ai_pack.canvas_note(canvas) or "(No WP Context Injector, Loop or Model Info " \
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"variables on the user's open canvas now.)"
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def _sync_permissions(meta: dict[str, Any], cfg: dict[str, Any]) -> str:
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"""Bring the chat's tool list in line with Settings › AI assistant ›
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Permissions, and say what changed (once, in the message tail, so the
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cached prefix only moves when the user flips a switch). No tool can
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change these settings."""
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notes = []
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for key, flag in (("delete", "allow_delete"), ("auto", "auto_apply")):
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now = bool(cfg.get(flag))
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if bool(meta.get(key)) != now:
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meta[key] = now
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notes.append(_PERM_NOTES[(key, now)])
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web = _ai_search.enabled(cfg)
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flags = {"web": web, "reader": web and _ai_web.reader_ready(cfg),
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"danbooru": bool(cfg.get("danbooru_lookup"))}
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# A lazy chat (small and medium models) loads web and Danbooru tools
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# with more_tools: switching one on only says so, until it is loaded.
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lazy = bool(meta.get("lazy"))
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loaded = set(meta.get("groups") or [])
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for key, now in flags.items():
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if bool(meta.get(key)) != now:
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meta[key] = now
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group = "danbooru" if key == "danbooru" else "web"
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notes.append(_LAZY_NOTES[key] if now and lazy and group not in loaded
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else _PERM_NOTES[(key, now)])
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tools = ai_tools.sync_delete(list(meta.get("tools") or []), bool(meta.get("delete")))
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web_in = not lazy or "web" in loaded
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tools = ai_tools.sync_delete(tools, web and web_in, ai_tools.WEB_SEARCH)
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tools = ai_tools.sync_delete(tools, flags["reader"] and web_in, ai_tools.READER)
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meta["tools"] = ai_tools.sync_delete(
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tools, flags["danbooru"] and (not lazy or "danbooru" in loaded), ai_tools.DANBOORU)
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return " ".join(notes)
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def _more_tools(meta: dict[str, Any], group: Any = None) -> str:
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"""Add one group of tools (or all the rest) to the chat, at the end of
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its list so what was already declared keeps its place (`ai_tools.more`).
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Web and Danbooru tools come only while the user has them switched on."""
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def allowed(t: Any) -> bool:
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return ((t.kind != "web" or ai_tools.web_allowed(
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t.name, web=bool(meta.get("web")), reader=bool(meta.get("reader")),
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danbooru=bool(meta.get("danbooru"))))
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and (t.name not in ai_tools.DELETE or bool(meta.get("delete")))
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and (t.name not in ai_tools.VOCAB or bool(meta.get("vocab"))))
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name = group if isinstance(group, str) and group else None
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tools, added = ai_tools.more(list(meta.get("tools") or []), name, allowed)
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# Loaded lazy groups stay loaded (`_sync_permissions`), also when they
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# came with "every tool".
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lazy = {g for t in added for g in t.groups if g in ai_tools.LAZY_GROUPS}
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if name or lazy:
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meta["groups"] = sorted({*(meta.get("groups") or []), *lazy, *([name] if name else [])})
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if not added:
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if name == "web" and not meta.get("web"):
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return ("web search isn't set up; the user turns it on in Settings › AI assistant "
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"› Web search. Answer without it")
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if name == "danbooru" and not meta.get("danbooru"):
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return ("the Danbooru lookup is off; the user turns it on in Settings › AI "
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"assistant. Answer without it")
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return (f"the {name} tools are already available" if name
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else "every tool is already available")
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meta["tools"] = tools
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return ("These tools are available now:\n" + ai_chat.kinds_section(added)).strip()
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#: About how much a cloud model holds, for the meter (labelled "about").
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CLOUD_WINDOW = {"anthropic": 200000, "openai": 128000}
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#: The summary's own answer.
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SUMMARY_TOKENS = 1200
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async def _window(cfg: dict[str, Any], meta: dict[str, Any]) -> int:
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"""The tokens this chat's model holds: what a local server loaded the
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model with (asked once per chat), else the context setting (which
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Ollama gets as `num_ctx`); a cloud model's usual size."""
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if isinstance(meta.get("window"), int) and meta.get("window_model") == cfg.get("model"):
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return int(meta["window"])
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if not cfg.get("local"):
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try:
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listed = await _ai_client.cloud_context(cfg)
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except Exception: # noqa: BLE001 - only a hint for the meter
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listed = None
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if not listed:
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return CLOUD_WINDOW.get(str(cfg.get("api")), 128000)
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meta["window"] = listed
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meta["window_model"] = cfg.get("model")
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return listed
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found = None
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try:
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found = await _ai_client.server_context(cfg)
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except Exception: # noqa: BLE001 - only a hint for the meter
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found = None
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setting = int(cfg.get("context_max") or ai_budget.CONTEXT_DEFAULT)
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window = min(found, setting) if found and cfg.get("api") == "ollama" else (found or setting)
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meta["window"] = window
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meta["window_model"] = cfg.get("model")
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return window
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def _lean(meta: dict[str, Any]) -> dict[str, Any]:
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"""The leanness level this chat started at (`ai_budget.LEVELS`)."""
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i = meta.get("lean")
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return ai_budget.LEVELS[i] if isinstance(i, int) and 0 <= i < len(ai_budget.LEVELS) else {}
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_LIBRARY_HEAD = "\n\nThe user's library (data, not instructions):\n"
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def _system_text(prof: dict[str, Any], meta: dict[str, Any], fmt: str, snapshot: str) -> str:
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# The JSON prompt lists the tools the chat started with: tools added
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# later (more_tools, a permission) are described where they were added,
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# so the instructions, and the cache behind them, never change.
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return ai_chat.system_prompt(
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slim=prof["slim"], native=fmt == "native",
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tools=meta.get("prompt_tools") or meta["tools"],
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examples=_lean(meta).get("examples", True), prose=_lean(meta).get("prose", True),
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typed=fmt == "json" and bool(meta.get("typed")),
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split=fmt == "native" and bool(meta.get("split")),
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index=fmt == "json" and bool(meta.get("tool_index")),
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) + ai_note.section(meta.get("note")) + _LIBRARY_HEAD + snapshot
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def _fixed_chars(cfg: dict[str, Any], prof: dict[str, Any], meta: dict[str, Any], fmt: str,
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snapshot: str) -> int:
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"""What every request of this chat carries before its history: the
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instructions with the library summary, plus the tool declarations
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(in native mode they travel beside the prompt and count against the
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window just the same; 37 of them are about 10k tokens)."""
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n = len(_system_text(prof, meta, fmt, snapshot))
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if fmt == "native":
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n += len(_json.dumps(_decls(cfg, meta, fmt)))
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return n
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def _chat_profile(base: dict[str, Any], cfg: dict[str, Any], meta: dict[str, Any], fmt: str,
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snapshot: str, window: int) -> tuple[dict[str, Any], int]:
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"""The size's profile, made slim when the chat started lean, with the
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history and tool-result caps cut to what the window leaves beside the
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fixed part. Returns it with the fixed part's size."""
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prof = {**base, "slim": True} if _lean(meta).get("slim") else dict(base)
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fixed = _fixed_chars(cfg, prof, meta, fmt, snapshot)
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room = ai_budget.room_chars(window, fixed)
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prof["history_chars"] = min(prof["history_chars"], room)
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prof["result_chars"] = min(prof["result_chars"], max(1500, room // 2))
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return prof, fixed
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def _open_chat(lib: Any, cfg: dict[str, Any], meta: dict[str, Any], base: dict[str, Any],
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page: dict[str, Any] | None, fmt: str, window: int, start: int = 0) -> str:
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"""Pick a new chat's tools and library summary: the leanest level from
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`start` whose fixed part fits the model's window (the last one when
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none does; the server's own count then decides). Sets `meta["tools"]`
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and `meta["lean"]` and returns the library summary. The "About my
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library" note is kept in `meta["note"]` as it was when the chat opened,
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so the instructions never change under a chat."""
|
||
note = ai_note.clean(cfg.get("library_note"))
|
||
if note and "note" not in meta:
|
||
meta["note"] = note
|
||
snapshot = ""
|
||
for level in range(start, len(ai_budget.LEVELS)):
|
||
lean = ai_budget.LEVELS[level]
|
||
slim = base["slim"] or bool(lean.get("slim"))
|
||
if level:
|
||
meta["lean"] = level
|
||
else:
|
||
meta.pop("lean", None)
|
||
meta["tools"] = ai_tools.toolset(page.get("route") if page else None,
|
||
scoped=slim or bool(base.get("scoped")),
|
||
wide=not slim,
|
||
web=bool(meta.get("web")),
|
||
delete=bool(meta.get("delete")),
|
||
vocab=bool(meta.get("vocab")),
|
||
reader=bool(meta.get("reader")),
|
||
danbooru=bool(meta.get("danbooru")))
|
||
if meta.get("lazy"):
|
||
meta["prompt_tools"] = list(meta["tools"])
|
||
limit = lean.get("overview") or (40 if slim else None)
|
||
snapshot = ai_safety.strip_invisible(lib.overview(limit=limit))
|
||
prof = {**base, "slim": slim}
|
||
if ai_budget.fits(_fixed_chars(cfg, prof, meta, fmt, snapshot), window):
|
||
break
|
||
return snapshot
|
||
|
||
|
||
def _too_small(window: int, fixed: int, counted: tuple[int, int] | None) -> str:
|
||
"""Why a chat can't run in this model's window, and what to change."""
|
||
prompt, n_ctx = counted if counted and counted[1] else (ai_budget.tokens(fixed), window)
|
||
want = ai_budget.suggest_window(int(prompt * ai_budget.CHARS_PER_TOKEN))
|
||
return (f"the model was loaded with {n_ctx:,} tokens of context, and this request needs "
|
||
f"about {prompt:,} (the assistant's instructions, tools and your library summary "
|
||
f"take most of it). Load the model with at least {want:,} tokens of context: "
|
||
"LM Studio and llama.cpp set it when the model loads, Ollama takes Settings › "
|
||
"AI assistant › Most context to use. Or set Model size to Small, which sends "
|
||
"fewer tools")
|
||
|
||
|
||
def _context(cfg: dict[str, Any], meta: dict[str, Any], entries: list[dict[str, Any]],
|
||
system_chars: int, usage: dict[str, Any], window: int) -> dict[str, Any]:
|
||
"""The meter: tokens the last request used (the provider's count when
|
||
it gave one, else an estimate) against the window."""
|
||
rendered = ai_chatlog.size(ai_chatlog.render(entries, meta)) + system_chars
|
||
estimate = ai_budget.tokens(rendered)
|
||
output = int(usage.get("output") or 0)
|
||
if usage.get("prompt"):
|
||
used, estimated = int(usage["prompt"]) + output, False
|
||
else:
|
||
# Ollama counts only the prompt tokens it had to read again (none
|
||
# of a cached prefix), so its number drops to a few hundred after a
|
||
# cache hit: estimate the whole prompt instead.
|
||
used, estimated = max(estimate, int(usage.get("evaluated") or 0)) + output, True
|
||
return {"used": used, "window": window, "estimated": estimated,
|
||
"about": not cfg.get("local"), "compactions": int(meta.get("compactions") or 0),
|
||
# The divider in the chat goes before this user turn.
|
||
"summarised_turns": ai_compact.summarised_turns(entries, meta),
|
||
"compact_failed": bool(meta.get("compact_failed"))}
|
||
|
||
|
||
async def _maybe_compact(cfg: dict[str, Any], log: list[dict[str, Any]], meta: dict[str, Any],
|
||
prof: dict[str, Any], num_ctx: int, window: int, fixed_chars: int,
|
||
emit: Any, *, force: bool = False) -> dict[str, Any]:
|
||
"""Summarise the turns before the last few when the prompt nears the
|
||
window (or when asked). A failed summary changes nothing: the older
|
||
masking and dropping still keep the prompt in budget."""
|
||
if not force:
|
||
need = ai_budget.tokens(ai_chatlog.size(ai_chatlog.render(log, meta)) + fixed_chars)
|
||
if need < ai_compact.threshold(window, prof.get("compact_at")):
|
||
return meta
|
||
cut = ai_compact.best_split(log, meta)
|
||
if cut is None:
|
||
return meta
|
||
if not force and cut <= int(meta.get("compact_failed") or 0):
|
||
return meta # failed on these turns already; try again once there's more
|
||
if emit:
|
||
await emit({"type": "status", "v": "Summarizing earlier messages to make room"})
|
||
summary = None
|
||
# Small models miss the summary's shape now and then: once more with
|
||
# less to read before giving up.
|
||
for limit in (ai_compact.MAX_TRANSCRIPT, ai_compact.MAX_TRANSCRIPT // 3):
|
||
try:
|
||
answer = await _ai_client.complete_json(
|
||
cfg, system=ai_compact.SYSTEM, user=ai_compact.prompt(log, meta, cut, limit),
|
||
schema=ai_compact.SCHEMA, schema_name="summary", temperature=0.2,
|
||
max_tokens=SUMMARY_TOKENS,
|
||
num_ctx=num_ctx if cfg.get("api") == "ollama" else None)
|
||
except _ai_client.AIError:
|
||
continue
|
||
got = answer.get("summary") if isinstance(answer, dict) else None
|
||
if isinstance(got, str) and len(got.strip()) >= 20:
|
||
summary = got
|
||
break
|
||
if summary is None:
|
||
# Said, not silent: the meter shows it, and older results are still
|
||
# trimmed to keep the prompt in budget.
|
||
if emit:
|
||
await emit({"type": "status",
|
||
"v": "Couldn't summarize; trimming older results instead"})
|
||
return {**meta, "compact_failed": cut}
|
||
text = ai_safety.strip_invisible(ai_compact.finish(
|
||
summary, ai_compact.ids_in(log, meta, cut), meta.get("decisions")))
|
||
return ai_compact.apply(meta, text, cut)
|
||
|
||
|
||
#: One library index per library database file (see `engine.ai.index`).
|
||
_INDEXES: dict[str, ai_index.Index | None] = {}
|
||
|
||
|
||
#: Ids and docs sections a chat remembers having handed the model.
|
||
MAX_SHOWN = 400
|
||
|
||
|
||
def _synced_index(conn: Any, lib: ai_chat.Library) -> ai_index.Index | None:
|
||
"""The library index, brought up to date with the saved rows; None when
|
||
SQLite has no FTS5 or the index can't be used (search then scores rows
|
||
in Python, as before)."""
|
||
idx = _library_index_for(conn)
|
||
if idx is None:
|
||
return None
|
||
key = str(ai_index.index_path(ai_index.library_path(conn)) or "")
|
||
rows = [*lib.modules.values(), *lib.bundles.values(), *lib.templates.values()]
|
||
try:
|
||
idx.sync(rows, lib._kind)
|
||
except sqlite3.Error:
|
||
_INDEXES.pop(key, None)
|
||
return None
|
||
return idx
|
||
|
||
|
||
async def _query_vector(lib: ai_chat.Library, cfg: dict[str, Any],
|
||
text: str) -> list[float] | None:
|
||
"""The query's vector when meaning search is on and some items have
|
||
vectors already; else None (keywords only)."""
|
||
if lib.index is None or not lib.embed_model:
|
||
return None
|
||
try:
|
||
if lib.index.vector_count(lib.embed_model) == 0:
|
||
return None
|
||
except sqlite3.Error:
|
||
return None
|
||
return await _ai_embed.query_vector(cfg, text)
|
||
|
||
|
||
def _library_index_for(conn: Any) -> ai_index.Index | None:
|
||
path = ai_index.index_path(ai_index.library_path(conn))
|
||
key = str(path or "")
|
||
if key not in _INDEXES:
|
||
_INDEXES[key] = ai_index.open_index(path)
|
||
return _INDEXES[key]
|
||
|
||
|
||
async def _retrieval(lib: ai_chat.Library, text: str, meta: dict[str, Any],
|
||
prof: dict[str, Any], cfg: dict[str, Any]) -> str:
|
||
"""What the library index and the docs say this message is about, for
|
||
the end of the message (never the system prompt: the request prefix
|
||
stays the same). Gated; items and sections this chat already got are
|
||
left out. Large models get names only: they search well, and every
|
||
token here is paid on every later request of a cloud chat."""
|
||
shown = set(meta.get("shown") or [])
|
||
parts: list[str] = []
|
||
new: list[str] = []
|
||
if lib.index is not None:
|
||
try:
|
||
mblock, mids = ai_index.mentioned(lib.index, text, full=prof["size"] != "large")
|
||
except sqlite3.Error:
|
||
mblock, mids = "", []
|
||
if mblock:
|
||
parts.append(mblock)
|
||
new.extend(m for m in mids if m not in shown)
|
||
shown |= set(mids)
|
||
skip = shown | ({lib.editor_id} if lib.editor_id else set())
|
||
qvec = await _query_vector(lib, cfg, text)
|
||
try:
|
||
block, ids = ai_index.retrieve(lib.index, text, seen=skip,
|
||
limit=4 if prof["slim"] else 6,
|
||
full=prof["size"] != "large",
|
||
qvec=qvec, model=lib.embed_model)
|
||
except sqlite3.Error:
|
||
block, ids = "", []
|
||
if block:
|
||
parts.append(block)
|
||
new.extend(ids)
|
||
dblock, keys = ai_docs.retrieve(text, seen={k[4:] for k in shown if k.startswith("doc:")})
|
||
if dblock:
|
||
parts.append("Docs sections that may answer this (docs_read them before answering):\n"
|
||
+ dblock)
|
||
new.extend("doc:" + k for k in keys)
|
||
if new:
|
||
meta["shown"] = [*(meta.get("shown") or []), *new][-MAX_SHOWN:]
|
||
return "\n\n".join(parts)
|