a digital entity named phi that roams bsky
phi.zzstoatzz.io
feat: add post search tool, fix observation extraction, add memory inspector
- add search_posts tool: authenticated bluesky post search via bot_client
- fix extraction prompt: distinguish user-expressed interests from
bot-retrieved content (trending topics, search results)
- add scripts/memory_inspect.py: list namespaces, dump user memories,
delete rows, purge observations
- update bio and personality with search capability
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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personalities/phi.md
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personalities/phi.md
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- use pdsx tools for atproto record operations (create, list, get, update, delete any record type)
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- search memory for more context about a user when needed
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- search ATProto publications (leaflet, whitewind, offprint, etc.) via pub-search tools (prefixed with `pub_`)
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- search bluesky posts by keyword via `search_posts`
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- check what's trending on bluesky via `get_trending` (entity-level trends from coral + official trending topics)
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## how responses work
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scripts/memory_inspect.py
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scripts/memory_inspect.py
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"""Inspect and prune stored memories.
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Usage:
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uv run scripts/memory_inspect.py # list all user namespaces
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uv run scripts/memory_inspect.py USER_HANDLE # dump observations + interactions for a user
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uv run scripts/memory_inspect.py USER_HANDLE --delete ID # delete a specific row by ID
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uv run scripts/memory_inspect.py USER_HANDLE --purge-observations # delete ALL observations for a user
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"""
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import argparse
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import sys
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from turbopuffer import Turbopuffer
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from bot.config import settings
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def get_client() -> Turbopuffer:
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return Turbopuffer(api_key=settings.turbopuffer_api_key, region=settings.turbopuffer_region)
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def list_namespaces(client: Turbopuffer):
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"""List all namespaces that look like user memory."""
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prefix = "phi-users-"
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namespaces = client.namespaces()
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user_ns = [ns for ns in namespaces if ns.id.startswith(prefix)]
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if not user_ns:
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print("no user namespaces found")
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return
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print(f"found {len(user_ns)} user namespaces:\n")
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for ns in sorted(user_ns, key=lambda n: n.id):
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handle = ns.id.removeprefix(prefix).replace("_", ".")
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print(f" {handle:<40} ({ns.id})")
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def dump_user(client: Turbopuffer, handle: str):
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"""Dump all memory for a user."""
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clean = handle.replace(".", "_").replace("@", "").replace("-", "_")
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ns_name = f"phi-users-{clean}"
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ns = client.namespace(ns_name)
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try:
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response = ns.query(
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rank_by=("vector", "ANN", [0.5] * 1536),
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top_k=200,
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include_attributes=["kind", "content", "tags", "created_at"],
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)
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except Exception as e:
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if "was not found" in str(e):
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print(f"no namespace found for @{handle} ({ns_name})")
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return
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if "attribute" in str(e) and "not found" in str(e):
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# old namespace without kind/tags columns
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response = ns.query(
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rank_by=("vector", "ANN", [0.5] * 1536),
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top_k=200,
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include_attributes=True,
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)
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else:
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raise
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if not response.rows:
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print(f"no rows found for @{handle}")
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return
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observations = []
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interactions = []
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for row in response.rows:
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kind = getattr(row, "kind", "unknown")
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entry = {
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"id": row.id,
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"content": row.content,
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"tags": getattr(row, "tags", []),
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"created_at": getattr(row, "created_at", ""),
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}
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if kind == "observation":
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observations.append(entry)
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else:
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interactions.append(entry)
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if observations:
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print(f"=== observations ({len(observations)}) ===\n")
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for obs in observations:
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tags = f" [{', '.join(obs['tags'])}]" if obs["tags"] else ""
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print(f" [{obs['id']}] {obs['content']}{tags}")
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if obs["created_at"]:
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print(f" created: {obs['created_at']}")
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print()
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if interactions:
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print(f"=== interactions ({len(interactions)}) ===\n")
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for ix in interactions:
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content = ix["content"].replace("\n", "\n ")
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print(f" [{ix['id']}]")
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print(f" {content}")
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if ix["created_at"]:
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print(f" created: {ix['created_at']}")
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print()
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print(f"total: {len(observations)} observations, {len(interactions)} interactions")
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def delete_row(client: Turbopuffer, handle: str, row_id: str):
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"""Delete a specific row by ID."""
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clean = handle.replace(".", "_").replace("@", "").replace("-", "_")
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ns_name = f"phi-users-{clean}"
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ns = client.namespace(ns_name)
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ns.write(deletes=[row_id])
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print(f"deleted row {row_id} from {ns_name}")
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def purge_observations(client: Turbopuffer, handle: str):
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"""Delete all observations for a user."""
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clean = handle.replace(".", "_").replace("@", "").replace("-", "_")
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ns_name = f"phi-users-{clean}"
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ns = client.namespace(ns_name)
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try:
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response = ns.query(
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rank_by=("vector", "ANN", [0.5] * 1536),
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top_k=200,
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filters={"kind": ["Eq", "observation"]},
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include_attributes=["content"],
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)
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except Exception as e:
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if "was not found" in str(e):
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print(f"no namespace found for @{handle}")
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return
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raise
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if not response.rows:
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print(f"no observations to purge for @{handle}")
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return
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ids = [row.id for row in response.rows]
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print(f"purging {len(ids)} observations for @{handle}:")
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for row in response.rows:
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print(f" - {row.content}")
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ns.write(deletes=ids)
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print(f"\ndeleted {len(ids)} observations")
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def main():
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parser = argparse.ArgumentParser(description="Inspect and prune phi memories")
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parser.add_argument("handle", nargs="?", help="User handle to inspect")
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parser.add_argument("--delete", metavar="ID", help="Delete a specific row by ID")
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parser.add_argument("--purge-observations", action="store_true", help="Delete all observations for a user")
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args = parser.parse_args()
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client = get_client()
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if not args.handle:
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list_namespaces(client)
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return
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if args.purge_observations:
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purge_observations(client, args.handle)
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elif args.delete:
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delete_row(client, args.handle, args.delete)
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else:
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dump_user(client, args.handle)
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if __name__ == "__main__":
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main()
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src/bot/agent.py
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src/bot/agent.py
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return "\n".join(parts)
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@self.agent.tool
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async def search_posts(ctx: RunContext[dict], query: str, limit: int = 10) -> str:
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"""Search Bluesky posts by keyword. Use this to find what people are saying about a topic."""
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from bot.core.atproto_client import bot_client
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try:
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response = bot_client.client.app.bsky.feed.search_posts(
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params={"q": query, "limit": min(limit, 25), "sort": "top"}
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)
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if not response.posts:
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return f"no posts found for '{query}'"
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lines = []
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for post in response.posts:
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text = post.record.text if hasattr(post.record, "text") else ""
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handle = post.author.handle
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likes = post.like_count or 0
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lines.append(f"@{handle} ({likes} likes): {text[:200]}")
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return "\n\n".join(lines)
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except Exception as e:
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return f"search failed: {e}"
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@self.agent.tool
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async def get_trending(ctx: RunContext[dict]) -> str:
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"""Get what's currently trending on Bluesky. Returns entity-level trends from the firehose (via coral) and official Bluesky trending topics. Use this when someone asks about current events, what people are talking about, or when you want timely context."""
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parts: list[str] = []
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src/bot/core/profile_manager.py
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src/bot/core/profile_manager.py
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logger = logging.getLogger("bot.profile_manager")
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_ONLINE_SUFFIX = "\n\n🟢 memory, thread context, atproto records, publication search, trending"
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_ONLINE_SUFFIX = "\n\n🟢 memory, thread context, atproto records, publication search, post search, trending"
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_OFFLINE_SUFFIX = " • 🔴 offline"
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_ALL_SUFFIXES = [_ONLINE_SUFFIX, _OFFLINE_SUFFIX]
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src/bot/memory/namespace_memory.py
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src/bot/memory/namespace_memory.py
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EXTRACTION_SYSTEM_PROMPT = """\
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extract factual observations from this conversation exchange.
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focus on: interests, preferences, facts about the user, topics discussed, opinions expressed.
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skip: greetings, filler, things that are only meaningful in the moment.
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each observation should be a standalone fact that would be useful context in a future conversation.
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use short, lowercase tags to categorize each observation.
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if there's nothing worth extracting, return an empty list.
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extract factual observations about the USER from this conversation exchange.
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focus on things the user explicitly stated or clearly demonstrated:
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- interests they expressed (not topics the bot brought up)
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- preferences, opinions, facts about themselves
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- what they asked about and WHY (e.g. "curious about current events" not the specific events listed)
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skip:
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- greetings, filler, things only meaningful in the moment
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- content the bot retrieved or generated (trending topics, search results, etc.) — those are NOT the user's interests
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- circumstantial details from bot tool output
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each observation should be a standalone fact useful in a future conversation.
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use short, lowercase tags. return an empty list if nothing is worth extracting.
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deduplicate against the existing observations provided."""
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_extraction_agent: Agent[None, ExtractionResult] | None = None