my prefect server setup
prefect-metrics.waow.tech
python
orchestration
resolve bsky profiles in compact flow to prevent name hallucination
the compaction LLM was guessing user names from context — it hallucinated
"zoë" for a user named nate. now fetches display name and bio from the
public bluesky API and includes them in the synthesis prompt.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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flows/compact.py
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flows/compact.py
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from datetime import datetime, timedelta, timezone
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import duckdb
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import httpx
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import turbopuffer
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from openai import OpenAI
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from pydantic_ai import Agent
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uncertainty. if the relationship is thin, say so — a thin summary beats
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a fabricated one. include concrete details (projects, interests, topics)
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not just vibes.
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IMPORTANT: use ONLY facts present in the provided data. the user's
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bluesky profile (handle, display name, bio) is included — use that for
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their name and identity. never guess or infer names.
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"""
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···
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return "\n\n".join(lines)
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@task
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def resolve_bsky_profile(handle: str) -> dict | None:
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"""Fetch display name and bio from the public Bluesky API."""
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try:
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resp = httpx.get(
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"https://public.api.bsky.app/xrpc/app.bsky.actor.getProfile",
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params={"actor": handle},
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timeout=10,
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)
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resp.raise_for_status()
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data = resp.json()
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return {
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"handle": data.get("handle", handle),
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"display_name": data.get("displayName", ""),
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"bio": data.get("description", ""),
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}
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except Exception:
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return None
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def _format_stats(profile: dict) -> str:
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tags = ", ".join(profile.get("top_tags") or [])
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return (
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observations_text: str,
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interactions_text: str,
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api_key: str,
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bsky_profile: dict | None = None,
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) -> str:
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"""LLM synthesis of a relationship summary. Cached by observations hash."""
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model = AnthropicModel("claude-haiku-4-5", provider=AnthropicProvider(api_key=api_key))
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agent = Agent(model, system_prompt=SYSTEM_PROMPT, name="phi-compactor")
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profile_section = f"handle: @{handle}\n"
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if bsky_profile:
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if bsky_profile.get("display_name"):
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profile_section += f"display name: {bsky_profile['display_name']}\n"
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if bsky_profile.get("bio"):
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profile_section += f"bio: {bsky_profile['bio']}\n"
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prompt = (
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f"user: @{handle}\n"
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f"user profile:\n{profile_section}\n"
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f"stats: {stats_text}\n\n"
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f"observations:\n{observations_text}\n\n"
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f"recent interactions:\n{interactions_text}"
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for profile in profiles:
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handle = profile["handle"]
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bsky_profile = resolve_bsky_profile(handle)
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obs_text = load_user_observations(snap_path, handle)
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ix_text = load_user_interactions(snap_path, handle)
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stats_text = _format_stats(profile)
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summary = await synthesize_summary(
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handle, stats_text, obs_text, ix_text, anthropic_key,
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bsky_profile=bsky_profile,
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)
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write_summary_to_turbopuffer(tpuf_key, openai_key, handle, summary)
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logger.info(f"@{handle}: compacted {profile['observation_count']} observations")