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import numpy as np
from sentence_transformers import SentenceTransformer
class ContextGraph:
def __init__(self):
self.model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
def encode(self, text):
return self.model.encode(text)
def connect(self, memory_texts, threshold=0.35):
"""
Create relationships between memory nodes based on vector similarity.
"""
vectors = [self.encode(t) for t in memory_texts]
relationships = []
for i, vec1 in enumerate(vectors):
for j, vec2 in enumerate(vectors):
if i != j:
similarity = np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
if similarity >= threshold:
relationships.append((memory_texts[i], memory_texts[j], float(similarity)))
return relationships
def score_context(self, query, memories):
"""
Score stored memories against the new query.
"""
query_vec = self.encode(query)
scored = []
for mem, vec in memories:
score = np.dot(query_vec, vec) / (np.linalg.norm(query_vec) * np.linalg.norm(vec))
scored.append((mem, float(score)))
return sorted(scored, key=lambda x: x[1], reverse=True) |