add posts, profiles, and avatars

config.py

···
 class Config(BaseSettings):
     qdrant_url: str = "http://localhost:6333"
     qdrant_profile_collection_name: str = "bluesky_profile"
+    qdrant_avatar_collection_name: str = "bluesky_avatar"
+    qdrant_post_collection_name: str = "bluesky_post"
 
     embedding_model: str = "all-MiniLM-L6-v2"
     embedding_device: str = "cuda"
···
     kafka_topic: str = "firehose_events"
     kafka_group_id: str = "skyembed"
     kafka_auto_offset_reset: str = "latest"
+
+    retina_host: str = "http://localhost:7075"
 
     model_config = SettingsConfigDict(
         env_file=".env",

consumer.py

···
-import logging
-
-logger = logging.getLogger(__name__)
-
-shutdown_requested = False
-
-
-
-
-class Consumer:

database.py

···
+from dataclasses import dataclass
+import logging
+from typing import List, Optional
+import uuid
+
+from config import CONFIG
+from qdrant_client import QdrantClient
+from qdrant_client.models import (
+    Distance,
+    FieldCondition,
+    Filter,
+    MatchValue,
+    Payload,
+    PayloadSchemaType,
+    PointStruct,
+    VectorParams,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Result:
+    did: str
+    payload: Optional[Payload]
+    score: Optional[float]
+
+
+@dataclass
+class ResultWithVector(Result):
+    vector: PointStruct
+
+
+class QdrantService:
+    def __init__(self) -> None:
+        self.client = None
+
+    def initialized(self):
+        return self.client is not None
+
+    def initialize(self) -> None:
+        logger.info(f"Connecting to Qdrant at {CONFIG.qdrant_url}")
+
+        self.client = QdrantClient(
+            url=CONFIG.qdrant_url,
+        )
+
+        self.profile_collection_name = CONFIG.qdrant_profile_collection_name
+        self.avatar_collection_name = CONFIG.qdrant_avatar_collection_name
+        self.post_collection_name = CONFIG.qdrant_post_collection_name
+        self._ensure_collections_exist()
+
+    def _ensure_collections_exist(self):
+        profile_coll_exists = self.client.collection_exists(
+            self.profile_collection_name
+        )
+        avatar_coll_exists = self.client.collection_exists(self.avatar_collection_name)
+        post_coll_exists = self.client.collection_exists(self.post_collection_name)
+
+        if not profile_coll_exists:
+            logger.info(f"Creating profile collection: {self.profile_collection_name}")
+            self.client.create_collection(
+                collection_name=self.profile_collection_name,
+                vectors_config=VectorParams(
+                    size=CONFIG.embedding_size,
+                    distance=Distance.COSINE,
+                ),
+            )
+            self.client.create_payload_index(
+                collection_name=self.profile_collection_name,
+                field_name="did",
+                field_schema=PayloadSchemaType.KEYWORD,
+            )
+            logger.info("Collection created successfully")
+
+        if not avatar_coll_exists:
+            logger.info(f"Creating avatar collection: {self.avatar_collection_name}")
+            self.client.create_collection(
+                collection_name=self.avatar_collection_name,
+                vectors_config=VectorParams(
+                    # PDQ vectors have a size of 256
+                    size=256,
+                    # Qdrant doesn't support hamming distance, so we'll use euclidian distance and
+                    # use the square root of the selected max distance for lookups
+                    distance=Distance.EUCLID,
+                ),
+            )
+            self.client.create_payload_index(
+                collection_name=self.avatar_collection_name,
+                field_name="did",
+                field_schema=PayloadSchemaType.KEYWORD,
+            )
+
+        if not post_coll_exists:
+            logger.info(f"Creating post collection: {self.post_collection_name}")
+            self.client.create_collection(
+                collection_name=self.post_collection_name,
+                vectors_config=VectorParams(
+                    size=CONFIG.embedding_size,
+                    distance=Distance.COSINE,
+                ),
+            )
+            self.client.create_payload_index(
+                collection_name=self.post_collection_name,
+                field_name="uri",
+                field_schema=PayloadSchemaType.KEYWORD,
+            )
+            logger.info("Collection created successfully")
+
+    def upsert_profile(self, did: str, description: str, vector: List[float]):
+        try:
+            payload = {
+                "did": did,
+                "description": description,
+            }
+
+            existing = self.client.scroll(
+                collection_name=self.profile_collection_name,
+                scroll_filter=Filter(
+                    must=[FieldCondition(key="did", match=MatchValue(value=did))]
+                ),
+            )
+
+            if existing and existing[0] and len(existing[0]) > 0:
+                point_id = existing[0][0].id
+            else:
+                point_id = str(uuid.uuid4())
+
+            point = PointStruct(
+                id=point_id,
+                vector=vector,
+                payload=payload,
+            )
+
+            self.client.upsert(
+                collection_name=self.profile_collection_name,
+                points=[point],
+            )
+
+            return True
+        except Exception as e:
+            logger.error(f"Error upserting profile: {e}")
+            return False
+
+    def upsert_avatar(self, did: str, cid: str, vector: List[float]):
+        try:
+            payload = {
+                "did": did,
+                "cid": cid,
+            }
+
+            existing = self.client.scroll(
+                collection_name=self.avatar_collection_name,
+                scroll_filter=Filter(
+                    must=[FieldCondition(key="did", match=MatchValue(value=did))]
+                ),
+            )
+
+            if existing and existing[0] and len(existing[0]) > 0:
+                point_id = existing[0][0].id
+            else:
+                point_id = str(uuid.uuid4())
+
+            point = PointStruct(
+                id=point_id,
+                vector=vector,
+                payload=payload,
+            )
+
+            self.client.upsert(
+                collection_name=self.avatar_collection_name,
+                points=[point],
+            )
+
+            return True
+        except Exception as e:
+            logger.error(f"Error upserting avatar: {e}")
+            return False
+
+    def upsert_post(self, did: str, uri: str, text: str, vector: List[float]):
+        try:
+            payload = {
+                "did": did,
+                "uri": uri,
+                "text": text,
+            }
+
+            # we don't care about upserting these
+            point_id = str(uuid.uuid4())
+
+            point = PointStruct(
+                id=point_id,
+                vector=vector,
+                payload=payload,
+            )
+
+            self.client.upsert(
+                collection_name=self.post_collection_name,
+                points=[point],
+            )
+
+            return True
+        except Exception as e:
+            logger.error(f"Error upserting post: {e}")
+            return False
+
+    def search_similar(
+        self,
+        collection_name: str,
+        query_vector: List[float],
+        limit: int = 10,
+        score_threshold: Optional[float] = None,
+        filter_conditions: Optional[Filter] = None,
+    ) -> Optional[List[Result]]:
+        try:
+            results = self.client.query_points(
+                collection_name=collection_name,
+                query=query_vector,
+                query_filter=filter_conditions,
+                limit=limit,
+                score_threshold=score_threshold,
+                with_payload=True,
+            ).points
+
+            return [
+                Result(
+                    did=hit.payload.get("did"),
+                    payload=hit.payload,
+                    score=hit.score,
+                )
+                for hit in results
+            ]
+        except Exception as e:
+            logger.error(f"Error searching for similar vectors: {e}")
+
+    def get_profile_by_did(self, did: str) -> Optional[ResultWithVector]:
+        result = self.client.scroll(
+            collection_name=self.profile_collection_name,
+            scroll_filter=Filter(
+                must=[FieldCondition(key="did", match=MatchValue(value=did))]
+            ),
+        )
+
+        if result and result[0] and len(result[0]) > 0:
+            point = result[0][0]
+            return ResultWithVector(
+                did=point.payload["did"],
+                payload=point.payload,
+                vector=point.vector,
+                score=1.0,
+            )
+
+
+QDRANT_SERVICE = QdrantService()

docker-compose.yaml

···
+services:
+  qdrant:
+    image: qdrant/qdrant:latest
+    ports:
+      - "6333:6333"
+      - "6334:6334"
+    volumes:
+      - qdrant_storage:/qdrant/storage
+    environment:
+      - QDRANT_HOST=0.0.0.0
+    restart: unless-stopped
+
+  retina:
+    image: ghcr.io/haileyok/osprey-atproto/retina:main-3604991
+    ports:
+      - "7075:8080"
+    restart: unless-stopped
+
+volumes:
+  qdrant_storage:

embedder.py

···
+import logging
+from typing import List
+
+from sentence_transformers import SentenceTransformer
+import torch
+
+from config import CONFIG
+
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingService:
+    def __init__(self) -> None:
+        self.model = None
+
+    def initialized(self):
+        return self.model is not None
+
+    def initialize(self) -> None:
+        device = CONFIG.embedding_device
+
+        if device == "cuda" and not torch.cuda.is_available():
+            device = "cpu"
+            logger.warning("CUDA requested but not availaable, falling back to CPU")
+
+        if device == "cuda":
+            logger.info("Using CUDA")
+        else:
+            logger.info("Using CPU")
+
+        self.model = SentenceTransformer(CONFIG.embedding_model, device=device)
+
+    def encode(self, text: str) -> List[float]:
+        if not text or not text.strip():
+            return [0.0] * CONFIG.embedding_size
+
+        vector = self.model.encode(text, convert_to_numpy=True)
+        return vector.tolist()
+
+
+EMBEDDING_SERVICE = EmbeddingService()

indexer.py

···
 import logging
 
+from database import QDRANT_SERVICE
+from embedder import EMBEDDING_SERVICE
+from retina import RETINA_CLIENT, binary_to_float_vector, hex_to_binary
 from models import AtKafkaEvent
 
 
···
         if not event.operation:
             return
 
-        if event.operation.collection != "app.bsky.actor.profile":
+        if event.operation.action == "delete":
             return
 
-        logger.info(f"{event}")
+        if event.operation.collection == "app.bsky.actor.profile":
+            self._handle_profile(event)
+        elif event.operation.collection == "app.bsky.feed.post":
+            self._handle_post(event)
+
+    def _handle_profile(self, event: AtKafkaEvent):
+        record = event.operation.record
+
+        description = record.get("description")
+        if not isinstance(description, str):
+            return
+
+        if not description or not description.strip():
+            return
+
+        vector = EMBEDDING_SERVICE.encode(description)
+
+        QDRANT_SERVICE.upsert_profile(
+            did=event.did, description=description, vector=vector
+        )
+
+        try:
+            avatar = record.get("avatar")
+
+            if not isinstance(avatar, dict):
+                return
+
+            ref = avatar.get("ref")
+
+            if not isinstance(ref, dict):
+                return
+
+            link = ref.get("$link")
+            if not isinstance(link, str):
+                return
+
+            resp = RETINA_CLIENT.get_image_hash(event.did, link)
+        except Exception as e:
+            logger.error(f"Failed to get avatar hash: {e}")
+            return
+
+        if resp.quality_too_low:
+            logger.info("avatar quality was too low")
+            return
+
+        if not resp.hash:
+            logger.error("no hash in response")
+            return
+
+        bin = hex_to_binary(resp.hash)
+        vector = binary_to_float_vector(bin)
+
+        QDRANT_SERVICE.upsert_avatar(
+            did=event.did,
+            cid=link,
+            vector=vector,
+        )
+
+    def _handle_post(self, event: AtKafkaEvent):
+        text = event.operation.record.get("text")
+        if not isinstance(text, str):
+            return
+
+        if not text or not text.strip():
+            return
+
+        vector = EMBEDDING_SERVICE.encode(text)
+
+        QDRANT_SERVICE.upsert_post(
+            did=event.did,
+            uri=f"at://{event.did}/app.bsky.feed.post/{event.operation.rkey}",
+            text=text,
+            vector=vector,
+        )

main.py

···
 
 import click
 from aiokafka import AIOKafkaConsumer
-from aiokafka.errors import KafkaError
 
 from config import CONFIG
+from database import QDRANT_SERVICE
+from embedder import EMBEDDING_SERVICE
 from indexer import Indexer
 from models import AtKafkaEvent
 
···
 ):
     signal.signal(signal.SIGINT, signal_handler)
     signal.signal(signal.SIGTERM, signal_handler)
+
+    EMBEDDING_SERVICE.initialize()
+
+    QDRANT_SERVICE.initialize()
 
     try:
         asyncio.run(

pyproject.toml

···
     "pydantic>=2.12.5",
     "pydantic-settings>=2.12.0",
     "python-snappy>=0.7.3",
+    "qdrant-client>=1.16.2",
+    "requests>=2.32.5",
+    "rich>=14.2.0",
+    "sentence-transformers>=5.2.0",
+    "torch>=2.9.1",
+    "transformers>=4.57.3",
 ]

retina.py

···
+from typing import List, Optional
+from pydantic import BaseModel, Field
+from config import CONFIG
+
+import requests
+
+
+class RetinaResult(BaseModel):
+    hash: Optional[str]
+    binary: Optional[str]
+    quality_too_low: Optional[bool] = Field(alias="qualityTooLow")
+
+
+class RetinaClient:
+    def __init__(self) -> None:
+        self._retina_host = CONFIG.retina_host
+
+    def get_image_hash(self, did: str, cid: str):
+        resp = requests.post(
+            f"{self._retina_host}/api/hash",
+            json={
+                "did": did,
+                "cid": cid,
+            },
+        )
+        resp.raise_for_status()
+
+        return RetinaResult.model_validate_json(resp.text)
+
+
+RETINA_CLIENT = RetinaClient()
+
+
+def hex_to_binary(hex: str):
+    return bytes.fromhex(hex)
+
+
+def binary_to_float_vector(bin: bytes):
+    vector_data: List[float] = []
+    for byte in bin:
+        for j in range(8):
+            if (byte >> (7 - j)) & 1 == 1:
+                vector_data.append(1.0)
+            else:
+                vector_data.append(0.0)
+    return vector_data

search.py

···
+import logging
+import sys
+from typing import List
+import click
+from rich.console import Console
+from rich.table import Table
+from rich.panel import Panel
+from rich import box
+
+from config import CONFIG
+from database import QDRANT_SERVICE, Result
+from embedder import EMBEDDING_SERVICE
+
+
+logging.basicConfig(
+    level=logging.INFO,
+    format=logging.BASIC_FORMAT,
+)
+
+logger = logging.getLogger(__name__)
+
+console = Console()
+
+
+def display_results(
+    type: str, query: str, results: List[Result], show_more: bool = False
+):
+    """
+    A lil guy that turns the results into a table for viewing
+    """
+
+    if not results:
+        console.print("[yellow]No similar profiles found.[/yellow]")
+        return
+
+    console.print(Panel(f"[bold blue]Query: {query}[/bold blue]", box=box.ROUNDED))
+    console.print()
+
+    table = Table(
+        title=f"Found {len(results)} similar results",
+        box=box.ROUNDED,
+        header_style="bold magenta",
+        expand=True,
+        show_header=True,
+        show_lines=True,
+    )
+
+    table.add_column("#", style="dim", width=4)
+    table.add_column("DID", style="cyan", width=35)
+    table.add_column("Similarity", justify="right", style="green", width=10)
+
+    if show_more:
+        table.add_column("More", style="white", overflow="fold")
+
+    for idx, result in enumerate(results, 1):
+        similarity_percent = f"{result.score or 0.0 * 100:.4f}%"
+
+        row: List[str] = [
+            str(idx),
+            result.did,
+            similarity_percent,
+        ]
+
+        if show_more and result.payload is not None:
+            more = None
+            if type == "profile":
+                more = result.payload.get("description")
+
+            if more is not None:
+                row.append(more)
+
+        table.add_row(*row)
+
+    console.print(table)
+    console.print()
+
+
[e-mail]()
+def main():
+    pass
+
+
[e-mail]()
[e-mail]("query", required=False)
[e-mail](
+    "--type",
+    default="profile",
+    show_default=True,
+)
[e-mail](
+    "--did",
+)
[e-mail](
+    "--limit",
+    default=10,
+    show_default=True,
+)
[e-mail](
+    "--threshold",
+    default=0.7,
+    type=float,
+    show_default=True,
+)
[e-mail](
+    "--show-more",
+    is_flag=True,
+    default=False,
+    show_default=True,
+)
+def search(
+    query: str,
+    type: str,
+    did: str,
+    limit: int,
+    threshold: float,
+    show_more: bool,
+):
+    # TODO: would be nice if these were flags instead
+    if type != "profile":
+        raise Exception("invalid type")
+
+    QDRANT_SERVICE.initialize()
+
+    try:
+        if type == "profile":
+            if not query:
+                console.print("[cyan]Looking up profile...[/cyan]")
+                profile = QDRANT_SERVICE.get_profile_by_did(did)
+
+                if not profile:
+                    console.print(f"[red]Profile not found: {did}[/red]")
+                    sys.exit(1)
+
+                description = profile.payload.get("description")
+                query_vector = profile.vector
+
+                console.print("[green]Found profile[/green]")
+            else:
+                EMBEDDING_SERVICE.initialize()
+
+                description = query
+                query_vector = EMBEDDING_SERVICE.encode(query)
+
+            console.print("[cyan]Looking up similar profiles...[/cyan]")
+
+            results = QDRANT_SERVICE.search_similar(
+                collection_name=CONFIG.qdrant_profile_collection_name,
+                query_vector=query_vector,
+                limit=limit,
+                score_threshold=threshold,
+            )
+
+            display_results(type, description, results, show_more)
+
+    except Exception as e:
+        console.print(f"[red]Error: {e}[/red]")
+        logger.error(f"Search error: {e}", exc_info=True)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

uv.lock

···
 version = 1
 revision = 3
 requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version == '3.13.*'",
+    "python_full_version < '3.13'",
+]
 
 [[package]]
 name = "aiokafka"
···
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+]
+
+[[package]]
+name = "anyio"
+version = "4.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
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 ]
 
 [[package]]
···
 ]
 
 [[package]]
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