Signals & Auto-Indexing

Django RAGKit includes built-in Django signals that eliminate manual vectorization workflows. Whenever knowledge entries are created or updated, their vector representations are automatically computed and indexed.

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Implementation Details

The signal receivers are registered in django_ragkit/signals.py and connected when the Django app initializes.

1. Automatic Creation (post_save)

When a new QuestionAnswer instance is saved for the first time:

@receiver(post_save, sender=QuestionAnswer)
def create_embedding(sender, instance, created, **kwargs):
    if created:
        qa_create_and_save_embed(instance)

1. Checks if created is True.
2. Sends instance.question to the configured embedding provider.
3. Inserts a new row in QAEmbedding linked to this QuestionAnswer.
4. The PostgreSQL HNSW index automatically incorporates the new vector.

2. Automatic Updates (pre_save)

When an existing QuestionAnswer instance is modified:

@receiver(pre_save, sender=QuestionAnswer)
def update_embedding(sender, instance, **kwargs):
    if not instance.pk:
        return
    old = QuestionAnswer.objects.get(pk=instance.pk)
    if old.question != instance.question:
        vector = qa_create_embed(instance.question)
        QAEmbedding.objects.filter(QA_foreign_key=instance).update(vector=vector)

1. Inspects the previous database state using instance.pk.
2. Compares old.question against the incoming instance.question.
3. If the question string was changed:
4. Recalculates the vector embedding.
5. Updates the associated QAEmbedding row in-place.
6. If only the answer was modified, the embedding calculation is skipped, saving network bandwidth and API costs.

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App Initialization

To ensure signals are properly registered, django_ragkit/apps.py imports them in the ready() lifecycle method:

apps.py

from django.apps import AppConfig

class DjangoRagkitConfig(AppConfig):
    default_auto_field = 'django.db.models.BigAutoField'
    name = 'django_ragkit'

    def ready(self):
        import django_ragkit.signals