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29cdc9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import os
import json
import torch
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from core.transformer_wrapper import SovereignTransformer
class LocalRetrievalEngine:
def __init__(self, cache_dir="storage/knowledge"):
self.cache_dir = cache_dir
self.manifest_path = os.path.join(self.cache_dir, "chunks_manifest.json")
self.vector_path = os.path.join(self.cache_dir, "vectors_cache.pt")
# Align query encoding with the new generative tier
self.embedder = SovereignTransformer(model_name="facebook/opt-125m")
def _load_memory_vault(self):
if not os.path.exists(self.manifest_path) or not os.path.exists(self.vector_path):
return None, None
with open(self.manifest_path, 'r') as f:
manifest = json.load(f)
vectors = torch.load(self.vector_path, map_location='cpu')
return manifest, vectors
def query(self, query_text, top_k=3, temporal_ceiling=None):
manifest, db_vectors = self._load_memory_vault()
if manifest is None or db_vectors is None or len(manifest) == 0:
return []
# Generate query vector directly from the LLM hidden state
q_vec = self.embedder.encode(query_text).unsqueeze(0)
# Pure localized cosine similarity via matrix multiplication
similarities = torch.mm(q_vec, db_vectors.transpose(0, 1)).squeeze(0)
top_k = min(top_k, len(manifest))
scores, indices = torch.topk(similarities, top_k)
results = []
for score, idx in zip(scores.tolist(), indices.tolist()):
node = manifest[idx]
if temporal_ceiling and node.get('timestamp', float('inf')) > temporal_ceiling:
continue
node_copy = dict(node)
node_copy['alignment_score'] = score
results.append(node_copy)
return results
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