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atlasbot: rework reasoning pipeline

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This commit is contained in:
Brad Stein 2026-01-28 02:21:42 -03:00
parent 769d3f41bf
commit 666dcb3faa
2 changed files with 210 additions and 78 deletions
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2
services/comms/atlasbot-deployment.yaml
View File

@ -16,7 +16,7 @@ spec:
labels:
app: atlasbot
annotations:
checksum/atlasbot-configmap: manual-atlasbot-89
checksum/atlasbot-configmap: manual-atlasbot-90
vault.hashicorp.com/agent-inject: "true"
vault.hashicorp.com/role: "comms"
vault.hashicorp.com/agent-inject-secret-turn-secret: "kv/data/atlas/comms/turn-shared-secret"

258
services/comms/scripts/atlasbot/bot.py
View File

@ -16,7 +16,7 @@ PASSWORD = os.environ["BOT_PASS"]
ROOM_ALIAS = "#othrys:live.bstein.dev"
OLLAMA_URL = os.environ.get("OLLAMA_URL", "https://chat.ai.bstein.dev/")
MODEL = os.environ.get("OLLAMA_MODEL", "qwen2.5-coder:7b-instruct-q4_0")
MODEL = os.environ.get("OLLAMA_MODEL", "qwen2.5:14b-instruct")
MODEL_FAST = os.environ.get("ATLASBOT_MODEL_FAST", "")
MODEL_DEEP = os.environ.get("ATLASBOT_MODEL_DEEP", "")
FALLBACK_MODEL = os.environ.get("OLLAMA_FALLBACK_MODEL", "")
@ -2895,6 +2895,7 @@ def _open_ended_system() -> str:
"Use ONLY the provided fact pack and recent chat as your evidence. "
"You may draw light inferences if you label them as such. "
"Write concise, human sentences with a helpful, calm tone (not a list). "
"Be willing to take a light stance; do not over-hedge. "
"If the question is subjective (cool/interesting/unconventional), pick a standout fact and explain why it stands out. "
"If the question asks for a list, embed the list inline in a sentence (comma-separated). "
"If the question is ambiguous, pick a reasonable interpretation and state it briefly. "
@ -3002,20 +3003,27 @@ def _open_ended_plan(
*,
fact_pack: str,
history_lines: list[str],
focus_tags: set[str],
avoid_tags: set[str],
count: int,
state: ThoughtState | None,
step: int,
model: str | None,
) -> list[dict[str, Any]]:
if state:
state.update("planning", step=1, note="mapping angles")
state.update("planning", step=step, note="mapping angles")
count = max(1, count)
focus_hint = ", ".join(sorted(focus_tags)) if focus_tags else "any"
avoid_hint = ", ".join(sorted(avoid_tags)) if avoid_tags else "none"
prompt_text = (
"Analyze the question and propose up to "
f"{count} distinct answer angles that can be supported by the fact pack. "
"Keep them diverse (e.g., metrics, hardware, workload placement, recent changes). "
"If the question is subjective, propose at least one angle that surfaces a standout detail. "
f"Prefer angles that align with these tags: {focus_hint}. "
f"Avoid angles that overlap these tags if possible: {avoid_hint}. "
"Avoid repeating the same angle as the most recent response if possible. "
"Return JSON: {\"angles\":[{\"focus\":\"...\",\"reason\":\"...\",\"priority\":1-5}]}."
"Return JSON: {\"angles\":[{\"focus\":\"...\",\"reason\":\"...\",\"tags\":[\"tag\"],\"priority\":1-5}]}."
)
context = _append_history_context(fact_pack, history_lines)
result = _ollama_json_call(
@ -3037,10 +3045,12 @@ def _open_ended_plan(
priority = item.get("priority")
if not isinstance(priority, (int, float)):
priority = 3
tags = _sanitize_focus_tags(item.get("tags") or [])
cleaned.append(
{
"focus": focus,
"reason": str(item.get("reason") or ""),
"tags": tags,
"priority": int(max(1, min(5, priority))),
}
)
@ -3131,6 +3141,35 @@ def _preferred_tags_for_prompt(prompt: str) -> set[str]:
return tags & _ALLOWED_INSIGHT_TAGS
_TAG_KEYWORDS: dict[str, tuple[str, ...]] = {
"utilization": ("cpu", "ram", "memory", "net", "network", "io", "disk", "usage", "utilization", "hottest", "busy"),
"database": ("postgres", "db", "database", "connections"),
"pods": ("pod", "pods", "deployment", "daemonset", "job", "cron", "workload"),
"hardware": ("hardware", "architecture", "arch", "rpi", "raspberry", "jetson", "amd64", "arm64", "node", "nodes"),
"availability": ("ready", "not ready", "unready", "down", "missing"),
"workloads": ("workload", "service", "namespace", "app"),
"os": ("os", "kernel", "kubelet", "containerd", "runtime"),
}
def _tags_from_text(text: str) -> set[str]:
q = normalize_query(text)
if not q:
return set()
tags: set[str] = set()
for tag, keywords in _TAG_KEYWORDS.items():
if any(word in q for word in keywords):
tags.add(tag)
return tags & _ALLOWED_INSIGHT_TAGS
def _history_focus_tags(history_lines: list[str]) -> set[str]:
if not history_lines:
return set()
recent = " ".join(line for line in history_lines[-6:] if isinstance(line, str))
return _tags_from_text(recent)
def _open_ended_insights(
prompt: str,
*,
@ -3139,10 +3178,11 @@ def _open_ended_insights(
history_lines: list[str],
count: int,
state: ThoughtState | None,
step: int,
model: str | None,
) -> list[dict[str, Any]]:
if state:
state.update("analyzing", note="scouting insights")
state.update("analyzing", step=step, note="scouting insights")
count = max(1, count)
allowed_tags = ", ".join(sorted(_ALLOWED_INSIGHT_TAGS))
prompt_text = (
@ -3188,10 +3228,35 @@ def _open_ended_insights(
return cleaned
def _rank_insights(
insights: list[dict[str, Any]],
*,
focus_tags: set[str],
avoid_tags: set[str],
count: int,
) -> list[dict[str, Any]]:
if not insights:
return []
ranked: list[tuple[float, dict[str, Any]]] = []
for insight in insights:
relevance = _normalize_fraction(insight.get("relevance"), default=0.6)
novelty = _normalize_fraction(insight.get("novelty"), default=0.5)
tags = set(insight.get("tags") or [])
score = relevance * 0.65 + novelty * 0.35
if focus_tags and tags & focus_tags:
score += 0.1
if avoid_tags and tags & avoid_tags:
score -= 0.2
ranked.append((score, insight))
ranked.sort(key=lambda item: item[0], reverse=True)
return [item for _, item in ranked[:count]]
def _fallback_fact_ids(
fact_meta: dict[str, dict[str, Any]],
*,
focus_tags: set[str],
avoid_tags: set[str],
count: int,
) -> list[str]:
if not fact_meta:
@ -3202,9 +3267,16 @@ def _fallback_fact_ids(
for fid, meta in fact_meta.items()
if focus_tags & set(meta.get("tags") or [])
]
if avoid_tags:
tagged = [fid for fid in tagged if not (avoid_tags & set(fact_meta.get(fid, {}).get("tags") or []))]
if tagged:
return tagged[:count]
return list(fact_meta.keys())[:count]
all_ids = list(fact_meta.keys())
if avoid_tags:
filtered = [fid for fid in all_ids if not (avoid_tags & set(fact_meta.get(fid, {}).get("tags") or []))]
if filtered:
return filtered[:count]
return all_ids[:count]
def _open_ended_select_facts(
@ -3214,6 +3286,7 @@ def _open_ended_select_facts(
fact_meta: dict[str, dict[str, Any]],
history_lines: list[str],
focus_tags: set[str],
avoid_tags: set[str],
avoid_fact_ids: list[str],
count: int,
subjective: bool,
@ -3224,11 +3297,13 @@ def _open_ended_select_facts(
if state:
state.update("selecting facts", step=step, note="picking evidence")
focus_hint = ", ".join(sorted(focus_tags)) if focus_tags else "any"
avoid_tag_hint = ", ".join(sorted(avoid_tags)) if avoid_tags else "none"
avoid_hint = ", ".join(avoid_fact_ids) if avoid_fact_ids else "none"
prompt_text = (
"Select the fact IDs that best answer the question. "
f"Pick up to {count} fact IDs. "
f"Focus tags: {focus_hint}. "
f"Avoid these tags if possible: {avoid_tag_hint}. "
f"Avoid these fact IDs: {avoid_hint}. "
"If the question is subjective, pick standout or unusual facts; "
"if objective, pick the minimal facts needed. "
@ -3248,7 +3323,18 @@ def _open_ended_select_facts(
selected.append(fid)
if len(selected) >= count:
break
seed = _fallback_fact_ids(fact_meta, focus_tags=focus_tags, count=count)
if avoid_tags:
selected = [
fid
for fid in selected
if not (avoid_tags & set(fact_meta.get(fid, {}).get("tags") or []))
] or selected
seed = _fallback_fact_ids(
fact_meta,
focus_tags=focus_tags,
avoid_tags=avoid_tags,
count=count,
)
if selected:
for fid in seed:
if fid not in selected:
@ -3483,7 +3569,7 @@ def _open_ended_multi(
if mode == "fast":
total_steps = 4
else:
total_steps = 7
total_steps = 9
if state:
state.total_steps = total_steps
@ -3503,41 +3589,25 @@ def _open_ended_multi(
focus_tags = set(interpretation.get("focus_tags") or []) or _preferred_tags_for_prompt(prompt)
if not focus_tags and subjective:
focus_tags = set(_ALLOWED_INSIGHT_TAGS)
avoid_tags = _history_focus_tags(history_lines) if (subjective or _is_followup_query(prompt)) else set()
if mode == "fast":
primary_ids = _open_ended_select_facts(
prompt,
fact_pack=fact_pack,
fact_meta=fact_meta,
history_lines=history_lines,
focus_tags=focus_tags,
avoid_tags=avoid_tags,
avoid_fact_ids=[],
count=4 if mode == "deep" else 3,
count=3,
subjective=subjective,
state=state,
step=2,
model=model,
)
alternate_ids: list[str] = []
if mode == "deep":
alternate_ids = _open_ended_select_facts(
prompt,
fact_pack=fact_pack,
fact_meta=fact_meta,
history_lines=history_lines,
focus_tags=focus_tags,
avoid_fact_ids=primary_ids,
count=4,
subjective=subjective,
state=state,
step=3,
model=model,
)
candidates: list[dict[str, Any]] = []
focus_label = interpretation.get("focus_label") or "primary angle"
step = 3 if mode == "fast" else 4
candidates.append(
_open_ended_candidate(
candidate = _open_ended_candidate(
prompt,
focus=str(focus_label),
fact_pack=fact_pack,
@ -3546,17 +3616,65 @@ def _open_ended_multi(
tone=str(tone),
allow_list=allow_list,
state=state,
step=step,
step=3,
fact_hints=primary_ids,
model=model,
)
reply = _open_ended_synthesize(
prompt,
fact_pack=fact_pack,
history_lines=history_lines,
candidates=[candidate],
subjective=subjective,
tone=str(tone),
allow_list=allow_list,
state=state,
step=4,
model=model,
critique=None,
)
step += 1
if mode == "deep" and alternate_ids:
if state:
state.update("done", step=total_steps)
return reply
angles = _open_ended_plan(
prompt,
fact_pack=fact_pack,
history_lines=history_lines,
focus_tags=focus_tags,
avoid_tags=avoid_tags,
count=5,
state=state,
step=2,
model=model,
)
if state and avoid_tags:
state.update("planning", step=2, note=f"avoiding {', '.join(sorted(avoid_tags))}")
insights = _open_ended_insights(
prompt,
fact_pack=fact_pack,
fact_meta=fact_meta,
history_lines=history_lines,
count=7,
state=state,
step=3,
model=model,
)
ranked_insights = _rank_insights(
insights,
focus_tags=focus_tags,
avoid_tags=avoid_tags,
count=3,
)
candidates: list[dict[str, Any]] = []
step = 4
for insight in ranked_insights:
candidates.append(
_open_ended_candidate(
prompt,
focus="alternate angle",
focus=insight.get("summary") or "insight",
fact_pack=fact_pack,
history_lines=history_lines,
subjective=subjective,
@ -3564,17 +3682,51 @@ def _open_ended_multi(
allow_list=allow_list,
state=state,
step=step,
fact_hints=alternate_ids,
fact_hints=insight.get("fact_ids") or [],
model=model,
)
)
step += 1
if not candidates and angles:
for angle in angles[:2]:
angle_tags = set(angle.get("tags") or []) or _tags_from_text(angle.get("focus") or "")
fact_ids = _open_ended_select_facts(
prompt,
fact_pack=fact_pack,
fact_meta=fact_meta,
history_lines=history_lines,
focus_tags=angle_tags or focus_tags,
avoid_tags=avoid_tags,
avoid_fact_ids=[],
count=4,
subjective=subjective,
state=state,
step=step,
model=model,
)
candidates.append(
_open_ended_candidate(
prompt,
focus=angle.get("focus") or "alternate angle",
fact_pack=fact_pack,
history_lines=history_lines,
subjective=subjective,
tone=str(tone),
allow_list=allow_list,
state=state,
step=step,
fact_hints=fact_ids,
model=model,
)
)
step += 1
if len(candidates) >= 2:
break
if state:
state.update("evaluating", step=step, note="ranking candidates")
selected = _select_candidates(candidates, count=1 if mode == "fast" else 2)
selected = _select_candidates(candidates, count=2)
step += 1
critique = ""
if mode == "deep":
critique = _open_ended_critique(
prompt,
fact_pack=fact_pack,
@ -3605,8 +3757,8 @@ def _open_ended_multi(
def _open_ended_total_steps(mode: str) -> int:
if mode == "fast":
return 3
return 7
return 4
return 9
def _fast_fact_lines(
@ -3656,34 +3808,14 @@ def _open_ended_fast(
history_lines: list[str],
state: ThoughtState | None = None,
) -> str:
model = _model_for_mode("fast")
if state:
state.update("selecting", step=2, note="picking key facts")
subjective = _is_subjective_query(prompt)
focus_tags = _preferred_tags_for_prompt(prompt)
if not focus_tags and subjective:
focus_tags = set(_ALLOWED_INSIGHT_TAGS)
primary_ids = _open_ended_select_facts(
return _open_ended_multi(
prompt,
fact_pack=fact_pack,
fact_lines=fact_lines,
fact_meta=fact_meta,
history_lines=history_lines,
focus_tags=focus_tags,
avoid_fact_ids=[],
count=3,
subjective=subjective,
mode="fast",
state=state,
step=2,
model=model,
)
selected_lines = _fast_fact_lines(fact_lines, fact_meta, primary_ids)
fact_pack = _fact_pack_text(selected_lines, fact_meta)
return _open_ended_fast_single(
prompt,
fact_pack=fact_pack,
history_lines=history_lines,
state=state,
model=model,
)
@ -3846,7 +3978,7 @@ class _AtlasbotHandler(BaseHTTPRequestHandler):
if cluster_query:
context = build_context(
cleaned,
allow_tools=False,
allow_tools=True,
targets=[],
inventory=inventory,
snapshot=snapshot,
@ -3860,7 +3992,7 @@ class _AtlasbotHandler(BaseHTTPRequestHandler):
workloads=workloads,
history_lines=history_lines,
mode=mode,
allow_tools=False,
allow_tools=True,
state=None,
)
else: