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67 changes: 67 additions & 0 deletions src/khoj/database/adapters/vector_store.py
Original file line number Diff line number Diff line change
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import math
from typing import Optional, List

from django.db.models import QuerySet, Q
from pgvector.django import CosineDistance
from torch import Tensor

from khoj.database.models import KhojUser, Agent, UserMemory


class PgVectorStore:
"\""
A PostgreSQL/pgvector backed store for managing and querying vector embeddings.
Provides a standardized interface for interacting with vector fields.
"\""

def __init__(self, user: KhojUser, agent: Optional[Agent] = None):
"\""
Initialize the vector store for a specific user and optionally a specific agent.
"\""
self.user = user
self.agent = agent

def add(self, raw_text: str, embedding: Tensor, search_model=None) -> UserMemory:
"\""
Add a new memory with its embedding to the store.
"\""
memory = UserMemory.objects.create(
user=self.user,
agent=self.agent,
raw=raw_text,
embeddings=embedding,
search_model=search_model,
)
return memory

def search(
self, query_embedding: Tensor, top_k: int = 10, max_distance: float = math.inf
) -> QuerySet[UserMemory]:
"\""
Search the vector store for the closest embeddings using Cosine Distance.
"\""
# Base filter by user
owner_filter = Q(user=self.user)

# If agent is specified, narrow it down
if self.agent:
owner_filter &= Q(agent=self.agent)

relevant_memories = (
UserMemory.objects.filter(owner_filter)
.annotate(distance=CosineDistance("embeddings", query_embedding))
.filter(distance__lte=max_distance)
.order_by("distance")
)

return relevant_memories[:top_k]

def delete(self, memory_id: int) -> bool:
"\""
Delete a specific memory from the store by ID.
Returns True if deleted, False if not found or unauthorized.
"\""
deleted_count, _ = UserMemory.objects.filter(
id=memory_id, user=self.user
).delete()
return deleted_count > 0