e5e1ad8dee
Give the agent recall of things said beyond the verbatim window, without breaking the RAM-only philosophy — nothing is persisted to disk. - MemoryIndex: a capped, in-memory pool of embedded messages with pure-Python cosine search (no numpy). Retains far more than the rolling transcript so old lines can be surfaced on demand; oldest evicted past the cap to bound RAM. - OllamaEmbedder: local embeddings via nomic-embed-text, on by default and independent of the chat provider (reuses the Ollama host when chat is Ollama). - Bridge: captured room messages (live + backfilled) are embedded on a background worker so a slow embedder can't stall frame draining. On a /ai question the agent retrieves top-k relevant lines, drops weak (<min_score) and windowed-duplicate hits, and prepends them as a clearly-fenced "recalled context" preamble — kept at user role, never elevated to system, so untrusted room text informs without instructing. Falls back to recency-only if the embedder is unreachable. - CLI: --no-rag, --embed-model, --embed-host, --rag-top-k. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
"""In-RAM semantic memory for the hack-house AI agent.
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Holds embedded past messages in process memory only — no disk, no DB. The
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store is bounded and dies with the agent, exactly like the room's own history
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and the rolling transcript. Cosine similarity is computed in pure Python (the
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vectors are small and the store is capped), so there's no numpy dependency.
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"""
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from __future__ import annotations
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import math
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from dataclasses import dataclass
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from .providers import Msg
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@dataclass
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class _Entry:
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msg: Msg
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vec: list[float]
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norm: float # precomputed ||vec|| so search is a dot product + divide
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class MemoryIndex:
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"""A capped, in-memory pool of embedded messages for semantic recall.
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This is the *long-term* store — it deliberately retains far more than the
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verbatim transcript window, so the agent can recall something said long
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before the recent slice. Oldest entries are evicted past ``max_entries`` to
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bound RAM (≈3 MB at 500 × 768-float vectors).
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"""
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def __init__(self, max_entries: int = 500):
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self.max_entries = max_entries
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self._entries: list[_Entry] = []
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def __len__(self) -> int:
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return len(self._entries)
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def add(self, msg: Msg, vec: list[float]) -> None:
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norm = math.sqrt(sum(x * x for x in vec)) if vec else 0.0
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if norm == 0.0:
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return # empty / failed embedding — skip rather than poison search
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self._entries.append(_Entry(msg, vec, norm))
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if len(self._entries) > self.max_entries:
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self._entries = self._entries[-self.max_entries:]
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def search(self, qvec: list[float], k: int) -> list[tuple[float, Msg]]:
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"""Top-``k`` entries by cosine similarity, highest first."""
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qnorm = math.sqrt(sum(x * x for x in qvec)) if qvec else 0.0
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if qnorm == 0.0 or not self._entries:
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return []
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scored = [
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(sum(a * b for a, b in zip(qvec, e.vec)) / (qnorm * e.norm), e.msg)
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for e in self._entries
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]
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scored.sort(key=lambda t: t[0], reverse=True)
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return scored[:k]
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