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pyproject.toml

··· 16 16 "pyarrow>=21.0.0", 17 17 "rich>=14.1.0", 18 18 "ruff>=0.13.2", 19 19 + "scikit-learn>=1.6.1", 19 20 "torch>=2.8.0", 20 21 "torchdata>=0.11.0", 21 22 ]

+57 -5

scripts/serving/main.py

··· 10 10 from jinja2 import Template 11 11 import numpy as np 12 12 import httpx 13 13 + from sklearn.cluster import MiniBatchKMeans 13 14 import torch 14 15 15 16 from scripts.utils import get_logger ··· 19 20 20 21 BSKY_URL = "https://public.api.bsky.app" 21 22 22 22 - for var in ["CHECKPOINT_PATH", "DID_ID_MAP_PATH"]: 23 23 + for var in ["CHECKPOINT_PATH", "DID_ID_MAP_PATH", "NUM_CLUSTERS"]: 23 24 if var not in os.environ: 24 25 raise ValueError(f"Missing required env var: {var}") 25 26 checkpoint_path = os.environ["CHECKPOINT_PATH"] 26 27 did_id_map_path = os.environ["DID_ID_MAP_PATH"] 28 28 + num_clusters = int(os.environ["NUM_CLUSTERS"]) 27 29 28 30 logger.info("Loading model from checkpoint...") 29 31 model = FollowEmbedModule.load_from_checkpoint(checkpoint_path, device="cpu") ··· 36 38 id_did_map = {value: key for key, value in did_id_map.items()} 37 39 38 40 logger.info("Building index...") 39 39 - source_index = faiss.IndexHNSWFlat(target_embed.shape[1], 32) 40 40 - source_index.add(target_embed) # type: ignore 41 41 + target_index = faiss.IndexHNSWFlat(target_embed.shape[1], 32) 42 42 + target_index.add(target_embed) # type: ignore 41 43 logger.info("Index constructed") 42 44 45 45 + logger.info("Initializing cluster model...") 46 46 + kmeans = MiniBatchKMeans( 47 47 + n_clusters=num_clusters, 48 48 + random_state=123, 49 49 + batch_size=1024, 50 50 + ) 51 51 + logger.info("Training k-means model...") 52 52 + kmeans.fit(target_embed) 53 53 + logger.info("Cluster model training complete") 54 54 + 43 55 44 56 async def get_follows(did: str) -> List[str]: 45 57 follows = [] ··· 81 93 82 94 83 95 async def get_profiles(dids: list[str]): 96 96 + # logger.info("Getting data on:\n" + "\n".join(dids)) 84 97 profiles = [] 85 98 try: 86 99 async with httpx.AsyncClient() as client: ··· 104 117 user_id = did_id_map[did] 105 118 user_vec = np.expand_dims(source_embed[user_id, :], axis=0) # (1,256) 106 119 107 107 - _, neighbors = source_index.search(user_vec, 25) # type: ignore 120 120 + _, neighbors = target_index.search(user_vec, 25) # type: ignore 108 121 neigh_dids: list[str] = [] 109 122 for neigh in neighbors[0]: 110 123 neigh_did: str = id_did_map[neigh.item()] ··· 115 128 return profiles 116 129 117 130 131 131 + async def get_cluster_recs(did: str): 132 132 + follows = await get_follows(did) 133 133 + follow_ids = [] 134 134 + for follow_did in follows: 135 135 + if follow_did in did_id_map: 136 136 + follow_ids.append(did_id_map[follow_did]) 137 137 + 138 138 + # For each follow, check centroid assignment 139 139 + followed_user_vectors = target_embed[follow_ids, :] 140 140 + centroids = kmeans.predict(followed_user_vectors) 141 141 + 142 142 + # Calculate percent weighting for top 5 centroids 143 143 + centroids, counts = np.unique(centroids, return_counts=True) 144 144 + sort_indexes = counts.argsort() 145 145 + centroids = centroids[sort_indexes[::-1]][:5] 146 146 + counts = counts[sort_indexes[::-1]][:5] 147 147 + weights = counts / counts.sum() 148 148 + 149 149 + # Get neighbors of centroids based on weighted relevance 150 150 + neighbor_dids: list[str] = [] 151 151 + samples = np.random.multinomial(25, weights) 152 152 + for idx, sample_count in enumerate(samples): 153 153 + # logger.info(f"Drawing {sample_count} samples from centroid: {centroids[idx]}") 154 154 + if sample_count < 1: 155 155 + continue 156 156 + centroid_vec = np.expand_dims( 157 157 + kmeans.cluster_centers_[centroids[idx], :], axis=0 # type: ignore 158 158 + ) 159 159 + _, neighbors = target_index.search(centroid_vec, sample_count.item()) # type: ignore 160 160 + for neigh in neighbors[0]: 161 161 + neigh_did: str = id_did_map[neigh.item()] 162 162 + if neigh_did not in follows: 163 163 + # logger.info(f"Recommending did: {neigh_did}") 164 164 + neighbor_dids.append(neigh_did) 165 165 + 166 166 + profiles = await get_profiles(neighbor_dids) 167 167 + return profiles 168 168 + 169 169 + 118 170 app = FastAPI() 119 171 120 172 with open( ··· 140 192 if did is None: 141 193 return HTTPException(status_code=500, detail="Couldn't resolve handle") 142 194 143 143 - recs = await get_recs(did) 195 195 + recs = await get_cluster_recs(did) 144 196 for rec in recs: 145 197 if "avatar" in rec: 146 198 rec["avatar"] = rec["avatar"].replace("avatar", "avatar_thumbnail")

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