diff --git a/frontend/src/api/apiClient.ts b/frontend/src/api/apiClient.ts index 915226d6..08719bb4 100644 --- a/frontend/src/api/apiClient.ts +++ b/frontend/src/api/apiClient.ts @@ -289,7 +289,9 @@ const sendAssistantMessage = async ( previousResponseId?: string, ) => { try { - const response = await publicApi.post(`/v1/api/assistant`, { + // The adminApi interceptor doesn't gracefully omit the JWT token if you're not authenticated + const api = localStorage.getItem("access") ? adminApi : publicApi; + const response = await api.post(`/v1/api/assistant`, { message, previous_response_id: previousResponseId, }); diff --git a/server/api/migrations/0015_semanticsearchusage.py b/server/api/migrations/0015_semanticsearchusage.py new file mode 100644 index 00000000..0475b71f --- /dev/null +++ b/server/api/migrations/0015_semanticsearchusage.py @@ -0,0 +1,39 @@ +# Generated by Django 4.2.3 on 2025-11-26 21:02 + +from django.conf import settings +from django.db import migrations, models +import django.db.models.deletion +import uuid + + +class Migration(migrations.Migration): + + dependencies = [ + ('api', '0014_alter_medrule_rule_type'), + ] + + operations = [ + migrations.CreateModel( + name='SemanticSearchUsage', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('guid', models.UUIDField(default=uuid.uuid4, editable=False, unique=True)), + ('timestamp', models.DateTimeField(auto_now_add=True)), + ('query_text', models.TextField(blank=True, help_text='The search query text', null=True)), + ('document_name', models.TextField(blank=True, help_text='Document name filter if used', null=True)), + ('document_guid', models.UUIDField(blank=True, help_text='Document GUID filter if used', null=True)), + ('num_results_requested', models.IntegerField(default=10, help_text='Number of results requested')), + ('encoding_time', models.FloatField(help_text='Time to encode query in seconds')), + ('db_query_time', models.FloatField(help_text='Time for database query in seconds')), + ('num_results_returned', models.IntegerField(help_text='Number of results returned')), + ('min_distance', models.FloatField(blank=True, help_text='Minimum L2 distance (null if no results)', null=True)), + ('max_distance', models.FloatField(blank=True, help_text='Maximum L2 distance (null if no results)', null=True)), + ('median_distance', models.FloatField(blank=True, help_text='Median L2 distance (null if no results)', null=True)), + ('user', models.ForeignKey(blank=True, help_text='User who performed the search (null for unauthenticated users)', null=True, on_delete=django.db.models.deletion.CASCADE, related_name='semantic_searches', to=settings.AUTH_USER_MODEL)), + ], + options={ + 'ordering': ['-timestamp'], + 'indexes': [models.Index(fields=['-timestamp'], name='api_semanti_timesta_0b5730_idx'), models.Index(fields=['user', '-timestamp'], name='api_semanti_user_id_e11ecb_idx')], + }, + ), + ] diff --git a/server/api/models/model_search_usage.py b/server/api/models/model_search_usage.py new file mode 100644 index 00000000..cdc3dee6 --- /dev/null +++ b/server/api/models/model_search_usage.py @@ -0,0 +1,42 @@ +import uuid + +from django.db import models +from django.conf import settings + +class SemanticSearchUsage(models.Model): + """ + Tracks performance metrics and usage data for embedding searches. + """ + guid = models.UUIDField(unique=True, default=uuid.uuid4, editable=False) + timestamp = models.DateTimeField(auto_now_add=True) + query_text = models.TextField(blank=True, null=True, help_text="The search query text") + document_name = models.TextField(blank=True, null=True, help_text="Document name filter if used") + document_guid = models.UUIDField(blank=True, null=True, help_text="Document GUID filter if used") + num_results_requested = models.IntegerField(default=10, help_text="Number of results requested") + user = models.ForeignKey( + settings.AUTH_USER_MODEL, + on_delete=models.CASCADE, + related_name='semantic_searches', + null=True, + blank=True, + help_text="User who performed the search (null for unauthenticated users)" + ) + encoding_time = models.FloatField(help_text="Time to encode query in seconds") + db_query_time = models.FloatField(help_text="Time for database query in seconds") + num_results_returned = models.IntegerField(help_text="Number of results returned") + min_distance = models.FloatField(null=True, blank=True, help_text="Minimum L2 distance (null if no results)") + max_distance = models.FloatField(null=True, blank=True, help_text="Maximum L2 distance (null if no results)") + median_distance = models.FloatField(null=True, blank=True, help_text="Median L2 distance (null if no results)") + + + class Meta: + ordering = ['-timestamp'] + indexes = [ + models.Index(fields=['-timestamp']), + models.Index(fields=['user', '-timestamp']), + ] + + def __str__(self): + total_time = self.encoding_time + self.db_query_time + user_display = self.user.email if self.user else "Anonymous" + return f"Search by {user_display} at {self.timestamp} ({total_time:.3f}s)" diff --git a/server/api/services/embedding_services.py b/server/api/services/embedding_services.py index b50dd750..e35f7965 100644 --- a/server/api/services/embedding_services.py +++ b/server/api/services/embedding_services.py @@ -1,11 +1,15 @@ +import time +import logging +from statistics import median + from django.db.models import Q from pgvector.django import L2Distance from .sentencetTransformer_model import TransformerModel - -# Adjust import path as needed from ..models.model_embeddings import Embeddings +from ..models.model_search_usage import SemanticSearchUsage +logger = logging.getLogger(__name__) def get_closest_embeddings( user, message_data, document_name=None, guid=None, num_results=10 @@ -38,9 +42,14 @@ def get_closest_embeddings( - file_id: GUID of the source file """ + encoding_start = time.time() transformerModel = TransformerModel.get_instance().model embedding_message = transformerModel.encode(message_data) + encoding_time = time.time() - encoding_start + db_query_start = time.time() + + # Django QuerySets are lazily evaluated if user.is_authenticated: # User sees their own files + files uploaded by superusers closest_embeddings_query = ( @@ -62,7 +71,7 @@ def get_closest_embeddings( .order_by("distance") ) - # Filter by GUID if provided, otherwise filter by document name if provided + # Filtering to a document GUID takes precedence over a document name if guid: closest_embeddings_query = closest_embeddings_query.filter( upload_file__guid=guid @@ -70,10 +79,11 @@ def get_closest_embeddings( elif document_name: closest_embeddings_query = closest_embeddings_query.filter(name=document_name) - # Slice the results to limit to num_results + # Slicing is equivalent to SQL's LIMIT clause closest_embeddings_query = closest_embeddings_query[:num_results] - # Format the results to be returned + # Iterating evaluates the QuerySet and hits the database + # TODO: Research improving the query evaluation performance results = [ { "name": obj.name, @@ -86,4 +96,42 @@ def get_closest_embeddings( for obj in closest_embeddings_query ] + db_query_time = time.time() - db_query_start + + try: + # Handle user having no uploaded docs or doc filtering returning no matches + if results: + distances = [r["distance"] for r in results] + SemanticSearchUsage.objects.create( + query_text=message_data, + user=user if (user and user.is_authenticated) else None, + document_guid=guid, + document_name=document_name, + num_results_requested=num_results, + encoding_time=encoding_time, + db_query_time=db_query_time, + num_results_returned=len(results), + max_distance=max(distances), + median_distance=median(distances), + min_distance=min(distances) + ) + else: + logger.warning("Semantic search returned no results") + + SemanticSearchUsage.objects.create( + query_text=message_data, + user=user if (user and user.is_authenticated) else None, + document_guid=guid, + document_name=document_name, + num_results_requested=num_results, + encoding_time=encoding_time, + db_query_time=db_query_time, + num_results_returned=0, + max_distance=None, + median_distance=None, + min_distance=None + ) + except Exception as e: + logger.error(f"Failed to create semantic search usage database record: {e}") + return results