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Part 1: Server-side changes to support agents integrated with Conversations (#671)
* Initial pass at backend changes to support agents - Add a db model for Agents, attaching them to conversations - When an agent is added to a conversation, override the system prompt to tweak the instructions - Agents can be configured with prompt modification, model specification, a profile picture, and other things - Admin-configured models will not be editable by individual users - Add unit tests to verify agent behavior. Unit tests demonstrate imperfect adherence to prompt specifications * Customize default behaviors for conversations without agents or with default agents * Use agent_id for getting correct agent * Merge migrations * Simplify some variable definitions, add additional security checks for agents * Rename agent.tuning -> agent.personality
This commit is contained in:
sabaimran
GitHub
parent
7416ca9ae1
commit
8abc8ded82
18 changed files with 527 additions and 60 deletions
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@ -21,6 +21,7 @@ from starlette.middleware.sessions import SessionMiddleware
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from starlette.requests import HTTPConnection
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from khoj.database.adapters import (
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AgentAdapters,
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ClientApplicationAdapters,
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ConversationAdapters,
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SubscriptionState,
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@ -229,11 +230,16 @@ def configure_server(
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state.SearchType = configure_search_types()
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state.search_models = configure_search(state.search_models, state.config.search_type)
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setup_default_agent()
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initialize_content(regenerate, search_type, init, user)
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except Exception as e:
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raise e
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def setup_default_agent():
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AgentAdapters.create_default_agent()
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def initialize_content(regenerate: bool, search_type: Optional[SearchType] = None, init=False, user: KhojUser = None):
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# Initialize Content from Config
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if state.search_models:
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@ -16,6 +16,7 @@ from pgvector.django import CosineDistance
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from torch import Tensor
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from khoj.database.models import (
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Agent,
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ChatModelOptions,
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ClientApplication,
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Conversation,
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@ -37,6 +38,7 @@ from khoj.database.models import (
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UserRequests,
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UserSearchModelConfig,
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)
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from khoj.processor.conversation import prompts
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from khoj.search_filter.date_filter import DateFilter
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from khoj.search_filter.file_filter import FileFilter
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from khoj.search_filter.word_filter import WordFilter
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@ -391,6 +393,58 @@ class ClientApplicationAdapters:
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return await ClientApplication.objects.filter(client_id=client_id, client_secret=client_secret).afirst()
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class AgentAdapters:
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DEFAULT_AGENT_NAME = "khoj"
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DEFAULT_AGENT_AVATAR = "https://khoj-web-bucket.s3.amazonaws.com/lamp-128.png"
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@staticmethod
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async def aget_agent_by_id(agent_id: int, user: KhojUser):
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agent = await Agent.objects.filter(id=agent_id).afirst()
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# Check if it's accessible to the user
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if agent and (agent.public or agent.creator == user):
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return agent
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return None
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@staticmethod
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def get_all_accessible_agents(user: KhojUser = None):
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return Agent.objects.filter(Q(public=True) | Q(creator=user)).distinct()
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@staticmethod
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def get_conversation_agent_by_id(agent_id: int):
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agent = Agent.objects.filter(id=agent_id).first()
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if agent == AgentAdapters.get_default_agent():
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# If the agent is set to the default agent, then return None and let the default application code be used
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return None
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return agent
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@staticmethod
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def get_default_agent():
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return Agent.objects.filter(name=AgentAdapters.DEFAULT_AGENT_NAME).first()
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@staticmethod
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def create_default_agent():
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# First delete the existing default
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Agent.objects.filter(name=AgentAdapters.DEFAULT_AGENT_NAME).delete()
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default_conversation_config = ConversationAdapters.get_default_conversation_config()
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default_personality = prompts.personality.format(current_date="placeholder")
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# The default agent is public and managed by the admin. It's handled a little differently than other agents.
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return Agent.objects.create(
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name=AgentAdapters.DEFAULT_AGENT_NAME,
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public=True,
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managed_by_admin=True,
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chat_model=default_conversation_config,
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personality=default_personality,
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tools=["*"],
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avatar=AgentAdapters.DEFAULT_AGENT_AVATAR,
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)
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@staticmethod
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async def aget_default_agent():
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return await Agent.objects.filter(name=AgentAdapters.DEFAULT_AGENT_NAME).afirst()
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class ConversationAdapters:
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@staticmethod
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def get_conversation_by_user(
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@ -431,7 +485,12 @@ class ConversationAdapters:
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return Conversation.objects.filter(id=conversation_id).first()
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@staticmethod
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async def acreate_conversation_session(user: KhojUser, client_application: ClientApplication = None):
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async def acreate_conversation_session(
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user: KhojUser, client_application: ClientApplication = None, agent_id: int = None
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):
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if agent_id:
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agent = await AgentAdapters.aget_agent_by_id(agent_id, user)
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return await Conversation.objects.acreate(user=user, client=client_application, agent=agent)
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return await Conversation.objects.acreate(user=user, client=client_application)
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@staticmethod
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@ -443,9 +502,14 @@ class ConversationAdapters:
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elif title:
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return await Conversation.objects.filter(user=user, client=client_application, title=title).afirst()
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else:
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return await (
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Conversation.objects.filter(user=user, client=client_application).order_by("-updated_at").afirst()
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) or await Conversation.objects.acreate(user=user, client=client_application)
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conversation = Conversation.objects.filter(user=user, client=client_application).order_by("-updated_at")
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if await conversation.aexists():
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return await conversation.prefetch_related("agent").afirst()
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return await (
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Conversation.objects.filter(user=user, client=client_application).order_by("-updated_at").afirst()
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) or await Conversation.objects.acreate(user=user, client=client_application)
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@staticmethod
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async def adelete_conversation_by_user(
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@ -603,9 +667,14 @@ class ConversationAdapters:
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return random.sample(all_questions, max_results)
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@staticmethod
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def get_valid_conversation_config(user: KhojUser):
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def get_valid_conversation_config(user: KhojUser, conversation: Conversation):
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offline_chat_config = ConversationAdapters.get_offline_chat_conversation_config()
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conversation_config = ConversationAdapters.get_conversation_config(user)
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if conversation.agent and conversation.agent.chat_model:
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conversation_config = conversation.agent.chat_model
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else:
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conversation_config = ConversationAdapters.get_conversation_config(user)
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if conversation_config is None:
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conversation_config = ConversationAdapters.get_default_conversation_config()
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@ -6,6 +6,7 @@ from django.contrib.auth.admin import UserAdmin
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from django.http import HttpResponse
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from khoj.database.models import (
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Agent,
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ChatModelOptions,
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ClientApplication,
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Conversation,
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@ -50,6 +51,7 @@ admin.site.register(ReflectiveQuestion)
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admin.site.register(UserSearchModelConfig)
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admin.site.register(TextToImageModelConfig)
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admin.site.register(ClientApplication)
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admin.site.register(Agent)
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@admin.register(Entry)
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@ -0,0 +1,52 @@
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# Generated by Django 4.2.10 on 2024-03-11 05:12
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import django.db.models.deletion
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from django.conf import settings
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from django.db import migrations, models
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class Migration(migrations.Migration):
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dependencies = [
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("database", "0030_conversation_slug_and_title"),
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]
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operations = [
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migrations.CreateModel(
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name="Agent",
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fields=[
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("id", models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name="ID")),
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("created_at", models.DateTimeField(auto_now_add=True)),
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("updated_at", models.DateTimeField(auto_now=True)),
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("name", models.CharField(max_length=200)),
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("tuning", models.TextField()),
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("avatar", models.URLField(blank=True, default=None, max_length=400, null=True)),
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("tools", models.JSONField(default=list)),
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("public", models.BooleanField(default=False)),
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("managed_by_admin", models.BooleanField(default=False)),
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(
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"chat_model",
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models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to="database.chatmodeloptions"),
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),
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(
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"creator",
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models.ForeignKey(
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blank=True,
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default=None,
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null=True,
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on_delete=django.db.models.deletion.CASCADE,
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to=settings.AUTH_USER_MODEL,
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),
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),
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],
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options={
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"abstract": False,
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},
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),
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migrations.AddField(
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model_name="conversation",
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name="agent",
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field=models.ForeignKey(
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blank=True, default=None, null=True, on_delete=django.db.models.deletion.SET_NULL, to="database.agent"
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),
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),
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]
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14
src/khoj/database/migrations/0032_merge_20240322_0427.py
Normal file
14
src/khoj/database/migrations/0032_merge_20240322_0427.py
Normal file
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@ -0,0 +1,14 @@
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# Generated by Django 4.2.10 on 2024-03-22 04:27
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from typing import List
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from django.db import migrations
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class Migration(migrations.Migration):
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dependencies = [
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("database", "0031_agent_conversation_agent"),
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("database", "0031_alter_googleuser_locale"),
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]
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operations: List[str] = []
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@ -0,0 +1,17 @@
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# Generated by Django 4.2.10 on 2024-03-23 16:01
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from django.db import migrations
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class Migration(migrations.Migration):
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dependencies = [
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("database", "0032_merge_20240322_0427"),
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]
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operations = [
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migrations.RenameField(
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model_name="agent",
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old_name="tuning",
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new_name="personality",
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),
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]
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@ -1,7 +1,10 @@
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import uuid
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from django.contrib.auth.models import AbstractUser
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from django.core.exceptions import ValidationError
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from django.db import models
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from django.db.models.signals import pre_save
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from django.dispatch import receiver
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from pgvector.django import VectorField
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from phonenumber_field.modelfields import PhoneNumberField
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@ -69,6 +72,37 @@ class Subscription(BaseModel):
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renewal_date = models.DateTimeField(null=True, default=None, blank=True)
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class ChatModelOptions(BaseModel):
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class ModelType(models.TextChoices):
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OPENAI = "openai"
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OFFLINE = "offline"
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max_prompt_size = models.IntegerField(default=None, null=True, blank=True)
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tokenizer = models.CharField(max_length=200, default=None, null=True, blank=True)
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chat_model = models.CharField(max_length=200, default="mistral-7b-instruct-v0.1.Q4_0.gguf")
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model_type = models.CharField(max_length=200, choices=ModelType.choices, default=ModelType.OFFLINE)
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class Agent(BaseModel):
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creator = models.ForeignKey(
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KhojUser, on_delete=models.CASCADE, default=None, null=True, blank=True
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) # Creator will only be null when the agents are managed by admin
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name = models.CharField(max_length=200)
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personality = models.TextField()
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avatar = models.URLField(max_length=400, default=None, null=True, blank=True)
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tools = models.JSONField(default=list) # List of tools the agent has access to, like online search or notes search
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public = models.BooleanField(default=False)
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managed_by_admin = models.BooleanField(default=False)
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chat_model = models.ForeignKey(ChatModelOptions, on_delete=models.CASCADE)
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@receiver(pre_save, sender=Agent)
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def check_public_name(sender, instance, **kwargs):
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if instance.public:
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if Agent.objects.filter(name=instance.name, public=True).exists():
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raise ValidationError(f"A public Agent with the name {instance.name} already exists.")
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class NotionConfig(BaseModel):
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token = models.CharField(max_length=200)
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user = models.ForeignKey(KhojUser, on_delete=models.CASCADE)
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@ -153,17 +187,6 @@ class SpeechToTextModelOptions(BaseModel):
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model_type = models.CharField(max_length=200, choices=ModelType.choices, default=ModelType.OFFLINE)
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class ChatModelOptions(BaseModel):
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class ModelType(models.TextChoices):
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OPENAI = "openai"
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OFFLINE = "offline"
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max_prompt_size = models.IntegerField(default=None, null=True, blank=True)
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tokenizer = models.CharField(max_length=200, default=None, null=True, blank=True)
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chat_model = models.CharField(max_length=200, default="mistral-7b-instruct-v0.1.Q4_0.gguf")
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model_type = models.CharField(max_length=200, choices=ModelType.choices, default=ModelType.OFFLINE)
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class UserConversationConfig(BaseModel):
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user = models.OneToOneField(KhojUser, on_delete=models.CASCADE)
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setting = models.ForeignKey(ChatModelOptions, on_delete=models.CASCADE, default=None, null=True, blank=True)
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@ -180,6 +203,7 @@ class Conversation(BaseModel):
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client = models.ForeignKey(ClientApplication, on_delete=models.CASCADE, default=None, null=True, blank=True)
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slug = models.CharField(max_length=200, default=None, null=True, blank=True)
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title = models.CharField(max_length=200, default=None, null=True, blank=True)
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agent = models.ForeignKey(Agent, on_delete=models.SET_NULL, default=None, null=True, blank=True)
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class ReflectiveQuestion(BaseModel):
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@ -6,6 +6,7 @@ from typing import Any, Iterator, List, Union
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from langchain.schema import ChatMessage
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from khoj.database.models import Agent
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from khoj.processor.conversation import prompts
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from khoj.processor.conversation.utils import (
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ThreadedGenerator,
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@ -141,6 +142,7 @@ def converse_offline(
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tokenizer_name=None,
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location_data: LocationData = None,
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user_name: str = None,
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agent: Agent = None,
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) -> Union[ThreadedGenerator, Iterator[str]]:
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"""
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Converse with user using Llama
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@ -156,6 +158,15 @@ def converse_offline(
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# Initialize Variables
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compiled_references_message = "\n\n".join({f"{item}" for item in references})
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current_date = datetime.now().strftime("%Y-%m-%d")
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if agent and agent.personality:
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system_prompt = prompts.custom_system_prompt_message_gpt4all.format(
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name=agent.name, bio=agent.personality, current_date=current_date
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)
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else:
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system_prompt = prompts.system_prompt_message_gpt4all.format(current_date=current_date)
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conversation_primer = prompts.query_prompt.format(query=user_query)
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if location_data:
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@ -185,10 +196,9 @@ def converse_offline(
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conversation_primer = f"{prompts.notes_conversation_gpt4all.format(references=compiled_references_message)}\n{conversation_primer}"
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# Setup Prompt with Primer or Conversation History
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current_date = datetime.now().strftime("%Y-%m-%d")
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messages = generate_chatml_messages_with_context(
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conversation_primer,
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prompts.system_prompt_message_gpt4all.format(current_date=current_date),
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system_prompt,
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conversation_log,
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model_name=model,
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max_prompt_size=max_prompt_size,
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@ -5,6 +5,7 @@ from typing import Optional
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from langchain.schema import ChatMessage
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from khoj.database.models import Agent
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from khoj.processor.conversation import prompts
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from khoj.processor.conversation.openai.utils import (
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chat_completion_with_backoff,
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@ -115,6 +116,7 @@ def converse(
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tokenizer_name=None,
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location_data: LocationData = None,
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user_name: str = None,
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agent: Agent = None,
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):
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"""
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Converse with user using OpenAI's ChatGPT
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@ -125,6 +127,13 @@ def converse(
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conversation_primer = prompts.query_prompt.format(query=user_query)
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if agent and agent.personality:
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system_prompt = prompts.custom_personality.format(
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name=agent.name, bio=agent.personality, current_date=current_date
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)
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else:
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system_prompt = prompts.personality.format(current_date=current_date)
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if location_data:
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location = f"{location_data.city}, {location_data.region}, {location_data.country}"
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location_prompt = prompts.user_location.format(location=location)
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@ -152,7 +161,7 @@ def converse(
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# Setup Prompt with Primer or Conversation History
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messages = generate_chatml_messages_with_context(
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conversation_primer,
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prompts.personality.format(current_date=current_date),
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system_prompt,
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conversation_log,
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model,
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max_prompt_size,
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@ -21,6 +21,24 @@ Today is {current_date} in UTC.
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""".strip()
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)
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custom_personality = PromptTemplate.from_template(
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"""
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Your are {name}, a personal agent on Khoj.
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Use your general knowledge and past conversation with the user as context to inform your responses.
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You were created by Khoj Inc. with the following capabilities:
|
||||
|
||||
- You *CAN REMEMBER ALL NOTES and PERSONAL INFORMATION FOREVER* that the user ever shares with you.
|
||||
- Users can share files and other information with you using the Khoj Desktop, Obsidian or Emacs app. They can also drag and drop their files into the chat window.
|
||||
- Say "I don't know" or "I don't understand" if you don't know what to say or if you don't know the answer to a question.
|
||||
- Ask crisp follow-up questions to get additional context, when the answer cannot be inferred from the provided notes or past conversations.
|
||||
- Sometimes the user will share personal information that needs to be remembered, like an account ID or a residential address. These can be acknowledged with a simple "Got it" or "Okay".
|
||||
|
||||
Today is {current_date} in UTC.
|
||||
|
||||
Instructions:\n{bio}
|
||||
""".strip()
|
||||
)
|
||||
|
||||
## General Conversation
|
||||
## --
|
||||
general_conversation = PromptTemplate.from_template(
|
||||
|
|
@ -61,6 +79,20 @@ Today is {current_date} in UTC.
|
|||
""".strip()
|
||||
)
|
||||
|
||||
custom_system_prompt_message_gpt4all = PromptTemplate.from_template(
|
||||
"""
|
||||
You are {name}, a personal agent on Khoj.
|
||||
- Use your general knowledge and past conversation with the user as context to inform your responses.
|
||||
- If you do not know the answer, say 'I don't know.'
|
||||
- Think step-by-step and ask questions to get the necessary information to answer the user's question.
|
||||
- Do not print verbatim Notes unless necessary.
|
||||
|
||||
Today is {current_date} in UTC.
|
||||
|
||||
Instructions:\n{bio}
|
||||
""".strip()
|
||||
)
|
||||
|
||||
system_prompt_message_extract_questions_gpt4all = f"""You are Khoj, a kind and intelligent personal assistant. When the user asks you a question, you ask follow-up questions to clarify the necessary information you need in order to answer from the user's perspective.
|
||||
- Write the question as if you can search for the answer on the user's personal notes.
|
||||
- Try to be as specific as possible. Instead of saying "they" or "it" or "he", use the name of the person or thing you are referring to. For example, instead of saying "Which store did they go to?", say "Which store did Alice and Bob go to?".
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ from fastapi.requests import Request
|
|||
from fastapi.responses import Response
|
||||
from starlette.authentication import requires
|
||||
|
||||
from khoj.configure import configure_server, initialize_content
|
||||
from khoj.configure import initialize_content
|
||||
from khoj.database.adapters import (
|
||||
ConversationAdapters,
|
||||
EntryAdapters,
|
||||
|
|
|
|||
|
|
@ -148,11 +148,12 @@ def chat_sessions(
|
|||
async def create_chat_session(
|
||||
request: Request,
|
||||
common: CommonQueryParams,
|
||||
agent_id: Optional[int] = None,
|
||||
):
|
||||
user = request.user.object
|
||||
|
||||
# Create new Conversation Session
|
||||
conversation = await ConversationAdapters.acreate_conversation_session(user, request.user.client_app)
|
||||
conversation = await ConversationAdapters.acreate_conversation_session(user, request.user.client_app, agent_id)
|
||||
|
||||
response = {"conversation_id": conversation.id}
|
||||
|
||||
|
|
@ -341,6 +342,7 @@ async def chat(
|
|||
llm_response, chat_metadata = await agenerate_chat_response(
|
||||
defiltered_query,
|
||||
meta_log,
|
||||
conversation,
|
||||
compiled_references,
|
||||
online_results,
|
||||
inferred_queries,
|
||||
|
|
|
|||
|
|
@ -10,10 +10,11 @@ import openai
|
|||
from fastapi import Depends, Header, HTTPException, Request, UploadFile
|
||||
from starlette.authentication import has_required_scope
|
||||
|
||||
from khoj.database.adapters import ConversationAdapters, EntryAdapters
|
||||
from khoj.database.adapters import AgentAdapters, ConversationAdapters, EntryAdapters
|
||||
from khoj.database.models import (
|
||||
ChatModelOptions,
|
||||
ClientApplication,
|
||||
Conversation,
|
||||
KhojUser,
|
||||
Subscription,
|
||||
TextToImageModelConfig,
|
||||
|
|
@ -364,6 +365,7 @@ async def send_message_to_model_wrapper(
|
|||
def generate_chat_response(
|
||||
q: str,
|
||||
meta_log: dict,
|
||||
conversation: Conversation,
|
||||
compiled_references: List[str] = [],
|
||||
online_results: Dict[str, Any] = {},
|
||||
inferred_queries: List[str] = [],
|
||||
|
|
@ -379,6 +381,7 @@ def generate_chat_response(
|
|||
logger.debug(f"Conversation Types: {conversation_commands}")
|
||||
|
||||
metadata = {}
|
||||
agent = AgentAdapters.get_conversation_agent_by_id(conversation.agent.id) if conversation.agent else None
|
||||
|
||||
try:
|
||||
partial_completion = partial(
|
||||
|
|
@ -393,7 +396,7 @@ def generate_chat_response(
|
|||
conversation_id=conversation_id,
|
||||
)
|
||||
|
||||
conversation_config = ConversationAdapters.get_valid_conversation_config(user)
|
||||
conversation_config = ConversationAdapters.get_valid_conversation_config(user, conversation)
|
||||
if conversation_config.model_type == "offline":
|
||||
if state.gpt4all_processor_config is None or state.gpt4all_processor_config.loaded_model is None:
|
||||
state.gpt4all_processor_config = GPT4AllProcessorModel(conversation_config.chat_model)
|
||||
|
|
@ -412,6 +415,7 @@ def generate_chat_response(
|
|||
tokenizer_name=conversation_config.tokenizer,
|
||||
location_data=location_data,
|
||||
user_name=user_name,
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
elif conversation_config.model_type == "openai":
|
||||
|
|
@ -431,6 +435,7 @@ def generate_chat_response(
|
|||
tokenizer_name=conversation_config.tokenizer,
|
||||
location_data=location_data,
|
||||
user_name=user_name,
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
metadata.update({"chat_model": conversation_config.chat_model})
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ from khoj.configure import (
|
|||
configure_search_types,
|
||||
)
|
||||
from khoj.database.models import (
|
||||
Agent,
|
||||
GithubConfig,
|
||||
GithubRepoConfig,
|
||||
KhojApiUser,
|
||||
|
|
@ -181,6 +182,28 @@ def api_user4(default_user4):
|
|||
)
|
||||
|
||||
|
||||
@pytest.mark.django_db
|
||||
@pytest.fixture
|
||||
def offline_agent():
|
||||
chat_model = ChatModelOptionsFactory()
|
||||
return Agent.objects.create(
|
||||
name="Accountant",
|
||||
chat_model=chat_model,
|
||||
personality="You are a certified CPA. You are able to tell me how much I've spent based on my notes. Regardless of what I ask, you should always respond with the total amount I've spent. ALWAYS RESPOND WITH A SUMMARY TOTAL OF HOW MUCH MONEY I HAVE SPENT.",
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.django_db
|
||||
@pytest.fixture
|
||||
def openai_agent():
|
||||
chat_model = ChatModelOptionsFactory(chat_model="gpt-3.5-turbo", model_type="openai")
|
||||
return Agent.objects.create(
|
||||
name="Accountant",
|
||||
chat_model=chat_model,
|
||||
personality="You are a certified CPA. You are able to tell me how much I've spent based on my notes. Regardless of what I ask, you should always respond with the total amount I've spent.",
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def search_models(search_config: SearchConfig):
|
||||
search_models = SearchModels()
|
||||
|
|
|
|||
|
|
@ -465,6 +465,47 @@ My sister, Aiyla is married to Tolga. They have 3 kids, Yildiz, Ali and Ahmet.""
|
|||
)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
@pytest.mark.chatquality
|
||||
def test_agent_prompt_should_be_used(loaded_model, offline_agent):
|
||||
"Chat actor should ask be tuned to think like an accountant based on the agent definition"
|
||||
# Arrange
|
||||
context = [
|
||||
f"""I went to the store and bought some bananas for 2.20""",
|
||||
f"""I went to the store and bought some apples for 1.30""",
|
||||
f"""I went to the store and bought some oranges for 6.00""",
|
||||
]
|
||||
|
||||
# Act
|
||||
response_gen = converse_offline(
|
||||
references=context, # Assume context retrieved from notes for the user_query
|
||||
user_query="What did I buy?",
|
||||
loaded_model=loaded_model,
|
||||
)
|
||||
response = "".join([response_chunk for response_chunk in response_gen])
|
||||
|
||||
# Assert that the model without the agent prompt does not include the summary of purchases
|
||||
expected_responses = ["9.50", "9.5"]
|
||||
assert all([expected_response not in response for expected_response in expected_responses]), (
|
||||
"Expected chat actor to summarize values of purchases" + response
|
||||
)
|
||||
|
||||
# Act
|
||||
response_gen = converse_offline(
|
||||
references=context, # Assume context retrieved from notes for the user_query
|
||||
user_query="What did I buy?",
|
||||
loaded_model=loaded_model,
|
||||
agent=offline_agent,
|
||||
)
|
||||
response = "".join([response_chunk for response_chunk in response_gen])
|
||||
|
||||
# Assert that the model with the agent prompt does include the summary of purchases
|
||||
expected_responses = ["9.50", "9.5"]
|
||||
assert any([expected_response in response for expected_response in expected_responses]), (
|
||||
"Expected chat actor to summarize values of purchases" + response
|
||||
)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
def test_chat_does_not_exceed_prompt_size(loaded_model):
|
||||
"Ensure chat context and response together do not exceed max prompt size for the model"
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ import pytest
|
|||
from faker import Faker
|
||||
from freezegun import freeze_time
|
||||
|
||||
from khoj.database.models import Agent, KhojUser
|
||||
from khoj.processor.conversation import prompts
|
||||
from khoj.processor.conversation.utils import message_to_log
|
||||
from khoj.routers.helpers import aget_relevant_information_sources
|
||||
|
|
@ -26,20 +27,20 @@ def generate_history(message_list):
|
|||
# Generate conversation logs
|
||||
conversation_log = {"chat": []}
|
||||
for user_message, gpt_message, context in message_list:
|
||||
conversation_log["chat"] += message_to_log(
|
||||
message_to_log(
|
||||
user_message,
|
||||
gpt_message,
|
||||
{"context": context, "intent": {"query": user_message, "inferred-queries": f'["{user_message}"]'}},
|
||||
conversation_log=conversation_log.get("chat", []),
|
||||
)
|
||||
return conversation_log
|
||||
|
||||
|
||||
def populate_chat_history(message_list, user):
|
||||
def create_conversation(message_list, user, agent=None):
|
||||
# Generate conversation logs
|
||||
conversation_log = generate_history(message_list)
|
||||
|
||||
# Update Conversation Metadata Logs in Database
|
||||
ConversationFactory(user=user, conversation_log=conversation_log)
|
||||
return ConversationFactory(user=user, conversation_log=conversation_log, agent=agent)
|
||||
|
||||
|
||||
# Tests
|
||||
|
|
@ -114,7 +115,7 @@ def test_answer_from_chat_history(client_offline_chat, default_user2):
|
|||
("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(f'/api/chat?q="What is my name?"&stream=true')
|
||||
|
|
@ -141,7 +142,7 @@ def test_answer_from_currently_retrieved_content(client_offline_chat, default_us
|
|||
["Testatron was born on 1st April 1984 in Testville."],
|
||||
),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(f'/api/chat?q="Where was Xi Li born?"')
|
||||
|
|
@ -165,7 +166,7 @@ def test_answer_from_chat_history_and_previously_retrieved_content(client_offlin
|
|||
["Testatron was born on 1st April 1984 in Testville."],
|
||||
),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(f'/api/chat?q="Where was I born?"')
|
||||
|
|
@ -191,7 +192,7 @@ def test_answer_from_chat_history_and_currently_retrieved_content(client_offline
|
|||
("Hello, my name is Xi Li. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(f'/api/chat?q="Where was I born?"')
|
||||
|
|
@ -217,7 +218,7 @@ def test_no_answer_in_chat_history_or_retrieved_content(client_offline_chat, def
|
|||
("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(f'/api/chat?q="Where was I born?"&stream=true')
|
||||
|
|
@ -238,7 +239,7 @@ def test_answer_using_general_command(client_offline_chat, default_user2):
|
|||
# Arrange
|
||||
query = urllib.parse.quote("/general Where was Xi Li born?")
|
||||
message_list = []
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(f"/api/chat?q={query}&stream=true")
|
||||
|
|
@ -256,7 +257,7 @@ def test_answer_from_retrieved_content_using_notes_command(client_offline_chat,
|
|||
# Arrange
|
||||
query = urllib.parse.quote("/notes Where was Xi Li born?")
|
||||
message_list = []
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(f"/api/chat?q={query}&stream=true")
|
||||
|
|
@ -275,7 +276,7 @@ def test_answer_using_file_filter(client_offline_chat, default_user2):
|
|||
no_answer_query = urllib.parse.quote('Where was Xi Li born? file:"Namita.markdown"')
|
||||
answer_query = urllib.parse.quote('Where was Xi Li born? file:"Xi Li.markdown"')
|
||||
message_list = []
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
no_answer_response = client_offline_chat.get(f"/api/chat?q={no_answer_query}&stream=true").content.decode("utf-8")
|
||||
|
|
@ -293,7 +294,7 @@ def test_answer_not_known_using_notes_command(client_offline_chat, default_user2
|
|||
# Arrange
|
||||
query = urllib.parse.quote("/notes Where was Testatron born?")
|
||||
message_list = []
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(f"/api/chat?q={query}&stream=true")
|
||||
|
|
@ -351,7 +352,7 @@ def test_answer_general_question_not_in_chat_history_or_retrieved_content(client
|
|||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
("Where was I born?", "You were born Testville.", []),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(
|
||||
|
|
@ -394,14 +395,14 @@ def test_ask_for_clarification_if_not_enough_context_in_question(client_offline_
|
|||
@pytest.mark.xfail(reason="Chat director not capable of answering this question yet")
|
||||
@pytest.mark.chatquality
|
||||
@pytest.mark.django_db(transaction=True)
|
||||
def test_answer_in_chat_history_beyond_lookback_window(client_offline_chat, default_user2):
|
||||
def test_answer_in_chat_history_beyond_lookback_window(client_offline_chat, default_user2: KhojUser):
|
||||
# Arrange
|
||||
message_list = [
|
||||
("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
("Where was I born?", "You were born Testville.", []),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(f'/api/chat?q="What is my name?"&stream=true')
|
||||
|
|
@ -415,13 +416,77 @@ def test_answer_in_chat_history_beyond_lookback_window(client_offline_chat, defa
|
|||
)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
@pytest.mark.chatquality
|
||||
@pytest.mark.django_db(transaction=True)
|
||||
def test_answer_in_chat_history_by_conversation_id(client_offline_chat, default_user2: KhojUser):
|
||||
# Arrange
|
||||
message_list = [
|
||||
("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
("What's my favorite color", "Your favorite color is green.", []),
|
||||
("Where was I born?", "You were born Testville.", []),
|
||||
]
|
||||
message_list2 = [
|
||||
("Hello, my name is Julia. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 14th August 1947.", []),
|
||||
("What's my favorite color", "Your favorite color is maroon.", []),
|
||||
("Where was I born?", "You were born in a potato farm.", []),
|
||||
]
|
||||
conversation = create_conversation(message_list, default_user2)
|
||||
create_conversation(message_list2, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(
|
||||
f'/api/chat?q="What is my favorite color?"&conversation_id={conversation.id}&stream=true'
|
||||
)
|
||||
response_message = response.content.decode("utf-8")
|
||||
|
||||
# Assert
|
||||
expected_responses = ["green"]
|
||||
assert response.status_code == 200
|
||||
assert any([expected_response in response_message.lower() for expected_response in expected_responses]), (
|
||||
"Expected green in response, but got: " + response_message
|
||||
)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
@pytest.mark.xfail(reason="Chat director not great at adhering to agent instructions yet")
|
||||
@pytest.mark.chatquality
|
||||
@pytest.mark.django_db(transaction=True)
|
||||
def test_answer_in_chat_history_by_conversation_id_with_agent(
|
||||
client_offline_chat, default_user2: KhojUser, offline_agent: Agent
|
||||
):
|
||||
# Arrange
|
||||
message_list = [
|
||||
("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
("What's my favorite color", "Your favorite color is green.", []),
|
||||
("Where was I born?", "You were born Testville.", []),
|
||||
("What did I buy?", "You bought an apple for 2.00, an orange for 3.00, and a potato for 8.00", []),
|
||||
]
|
||||
conversation = create_conversation(message_list, default_user2, offline_agent)
|
||||
|
||||
# Act
|
||||
query = urllib.parse.quote("/general What did I eat for breakfast?")
|
||||
response = client_offline_chat.get(f"/api/chat?q={query}&conversation_id={conversation.id}&stream=true")
|
||||
response_message = response.content.decode("utf-8")
|
||||
|
||||
# Assert that agent only responds with the summary of spending
|
||||
expected_responses = ["13.00", "13", "13.0", "thirteen"]
|
||||
assert response.status_code == 200
|
||||
assert any([expected_response in response_message.lower() for expected_response in expected_responses]), (
|
||||
"Expected green in response, but got: " + response_message
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.chatquality
|
||||
@pytest.mark.django_db(transaction=True)
|
||||
def test_answer_chat_history_very_long(client_offline_chat, default_user2):
|
||||
# Arrange
|
||||
message_list = [(" ".join([fake.paragraph() for _ in range(50)]), fake.sentence(), []) for _ in range(10)]
|
||||
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = client_offline_chat.get(f'/api/chat?q="What is my name?"&stream=true')
|
||||
|
|
@ -525,7 +590,7 @@ async def test_get_correct_tools_with_chat_history(client_offline_chat):
|
|||
),
|
||||
("What's up in New York City?", "A Pride parade has recently been held in New York City, on July 31st.", []),
|
||||
]
|
||||
chat_history = populate_chat_history(chat_log)
|
||||
chat_history = create_conversation(chat_log)
|
||||
|
||||
# Act
|
||||
tools = await aget_relevant_information_sources(user_query, chat_history)
|
||||
|
|
|
|||
|
|
@ -413,6 +413,42 @@ My sister, Aiyla is married to Tolga. They have 3 kids, Yildiz, Ali and Ahmet.""
|
|||
)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
@pytest.mark.chatquality
|
||||
def test_agent_prompt_should_be_used(openai_agent):
|
||||
"Chat actor should ask be tuned to think like an accountant based on the agent definition"
|
||||
# Arrange
|
||||
context = [
|
||||
f"""I went to the store and bought some bananas for 2.20""",
|
||||
f"""I went to the store and bought some apples for 1.30""",
|
||||
f"""I went to the store and bought some oranges for 6.00""",
|
||||
]
|
||||
expected_responses = ["9.50", "9.5"]
|
||||
|
||||
# Act
|
||||
response_gen = converse(
|
||||
references=context, # Assume context retrieved from notes for the user_query
|
||||
user_query="What did I buy?",
|
||||
api_key=api_key,
|
||||
)
|
||||
no_agent_response = "".join([response_chunk for response_chunk in response_gen])
|
||||
response_gen = converse(
|
||||
references=context, # Assume context retrieved from notes for the user_query
|
||||
user_query="What did I buy?",
|
||||
api_key=api_key,
|
||||
agent=openai_agent,
|
||||
)
|
||||
agent_response = "".join([response_chunk for response_chunk in response_gen])
|
||||
|
||||
# Assert that the model without the agent prompt does not include the summary of purchases
|
||||
assert all([expected_response not in no_agent_response for expected_response in expected_responses]), (
|
||||
"Expected chat actor to summarize values of purchases" + no_agent_response
|
||||
)
|
||||
assert any([expected_response in agent_response for expected_response in expected_responses]), (
|
||||
"Expected chat actor to summarize values of purchases" + agent_response
|
||||
)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
@pytest.mark.anyio
|
||||
@pytest.mark.django_db(transaction=True)
|
||||
|
|
|
|||
|
|
@ -5,13 +5,10 @@ from urllib.parse import quote
|
|||
import pytest
|
||||
from freezegun import freeze_time
|
||||
|
||||
from khoj.database.models import KhojUser
|
||||
from khoj.database.models import Agent, KhojUser
|
||||
from khoj.processor.conversation import prompts
|
||||
from khoj.processor.conversation.utils import message_to_log
|
||||
from khoj.routers.helpers import (
|
||||
aget_relevant_information_sources,
|
||||
aget_relevant_output_modes,
|
||||
)
|
||||
from khoj.routers.helpers import aget_relevant_information_sources
|
||||
from tests.helpers import ConversationFactory
|
||||
|
||||
# Initialize variables for tests
|
||||
|
|
@ -29,20 +26,21 @@ def generate_history(message_list):
|
|||
# Generate conversation logs
|
||||
conversation_log = {"chat": []}
|
||||
for user_message, gpt_message, context in message_list:
|
||||
conversation_log["chat"] += message_to_log(
|
||||
message_to_log(
|
||||
user_message,
|
||||
gpt_message,
|
||||
{"context": context, "intent": {"query": user_message, "inferred-queries": f'["{user_message}"]'}},
|
||||
conversation_log=conversation_log.get("chat", []),
|
||||
)
|
||||
return conversation_log
|
||||
|
||||
|
||||
def populate_chat_history(message_list, user):
|
||||
def create_conversation(message_list, user, agent=None):
|
||||
# Generate conversation logs
|
||||
conversation_log = generate_history(message_list)
|
||||
|
||||
# Update Conversation Metadata Logs in Database
|
||||
ConversationFactory(user=user, conversation_log=conversation_log)
|
||||
return ConversationFactory(user=user, conversation_log=conversation_log, agent=agent)
|
||||
|
||||
|
||||
# Tests
|
||||
|
|
@ -116,7 +114,7 @@ def test_answer_from_chat_history(chat_client, default_user2: KhojUser):
|
|||
("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = chat_client.get(f'/api/chat?q="What is my name?"&stream=true')
|
||||
|
|
@ -143,7 +141,7 @@ def test_answer_from_currently_retrieved_content(chat_client, default_user2: Kho
|
|||
["Testatron was born on 1st April 1984 in Testville."],
|
||||
),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = chat_client.get(f'/api/chat?q="Where was Xi Li born?"')
|
||||
|
|
@ -167,7 +165,7 @@ def test_answer_from_chat_history_and_previously_retrieved_content(chat_client_n
|
|||
["Testatron was born on 1st April 1984 in Testville."],
|
||||
),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = chat_client_no_background.get(f'/api/chat?q="Where was I born?"')
|
||||
|
|
@ -190,7 +188,7 @@ def test_answer_from_chat_history_and_currently_retrieved_content(chat_client, d
|
|||
("Hello, my name is Xi Li. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = chat_client.get(f'/api/chat?q="Where was I born?"')
|
||||
|
|
@ -215,7 +213,7 @@ def test_no_answer_in_chat_history_or_retrieved_content(chat_client, default_use
|
|||
("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = chat_client.get(f'/api/chat?q="Where was I born?"&stream=true')
|
||||
|
|
@ -244,7 +242,7 @@ def test_answer_using_general_command(chat_client, default_user2: KhojUser):
|
|||
# Arrange
|
||||
query = urllib.parse.quote("/general Where was Xi Li born?")
|
||||
message_list = []
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = chat_client.get(f"/api/chat?q={query}&stream=true")
|
||||
|
|
@ -262,7 +260,7 @@ def test_answer_from_retrieved_content_using_notes_command(chat_client, default_
|
|||
# Arrange
|
||||
query = urllib.parse.quote("/notes Where was Xi Li born?")
|
||||
message_list = []
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = chat_client.get(f"/api/chat?q={query}&stream=true")
|
||||
|
|
@ -280,7 +278,7 @@ def test_answer_not_known_using_notes_command(chat_client_no_background, default
|
|||
# Arrange
|
||||
query = urllib.parse.quote("/notes Where was Testatron born?")
|
||||
message_list = []
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = chat_client_no_background.get(f"/api/chat?q={query}&stream=true")
|
||||
|
|
@ -335,7 +333,7 @@ def test_answer_general_question_not_in_chat_history_or_retrieved_content(chat_c
|
|||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
("Where was I born?", "You were born Testville.", []),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = chat_client.get(f'/api/chat?q="Write a haiku about unit testing. Do not say anything else."&stream=true')
|
||||
|
|
@ -387,7 +385,7 @@ def test_answer_in_chat_history_beyond_lookback_window(chat_client, default_user
|
|||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
("Where was I born?", "You were born Testville.", []),
|
||||
]
|
||||
populate_chat_history(message_list, default_user2)
|
||||
create_conversation(message_list, default_user2)
|
||||
|
||||
# Act
|
||||
response = chat_client.get(f'/api/chat?q="What is my name?"&stream=true')
|
||||
|
|
@ -401,6 +399,68 @@ def test_answer_in_chat_history_beyond_lookback_window(chat_client, default_user
|
|||
)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
@pytest.mark.chatquality
|
||||
@pytest.mark.django_db(transaction=True)
|
||||
def test_answer_in_chat_history_by_conversation_id(chat_client, default_user2: KhojUser):
|
||||
# Arrange
|
||||
message_list = [
|
||||
("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
("What's my favorite color", "Your favorite color is green.", []),
|
||||
("Where was I born?", "You were born Testville.", []),
|
||||
]
|
||||
message_list2 = [
|
||||
("Hello, my name is Julia. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 14th August 1947.", []),
|
||||
("What's my favorite color", "Your favorite color is maroon.", []),
|
||||
("Where was I born?", "You were born in a potato farm.", []),
|
||||
]
|
||||
conversation = create_conversation(message_list, default_user2)
|
||||
create_conversation(message_list2, default_user2)
|
||||
|
||||
# Act
|
||||
query = urllib.parse.quote("/general What is my favorite color?")
|
||||
response = chat_client.get(f"/api/chat?q={query}&conversation_id={conversation.id}&stream=true")
|
||||
response_message = response.content.decode("utf-8")
|
||||
|
||||
# Assert
|
||||
expected_responses = ["green"]
|
||||
assert response.status_code == 200
|
||||
assert any([expected_response in response_message.lower() for expected_response in expected_responses]), (
|
||||
"Expected green in response, but got: " + response_message
|
||||
)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
@pytest.mark.chatquality
|
||||
@pytest.mark.django_db(transaction=True)
|
||||
def test_answer_in_chat_history_by_conversation_id_with_agent(
|
||||
chat_client, default_user2: KhojUser, openai_agent: Agent
|
||||
):
|
||||
# Arrange
|
||||
message_list = [
|
||||
("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
|
||||
("When was I born?", "You were born on 1st April 1984.", []),
|
||||
("What's my favorite color", "Your favorite color is green.", []),
|
||||
("Where was I born?", "You were born Testville.", []),
|
||||
("What did I buy?", "You bought an apple for 2.00, an orange for 3.00, and a potato for 8.00", []),
|
||||
]
|
||||
conversation = create_conversation(message_list, default_user2, openai_agent)
|
||||
|
||||
# Act
|
||||
query = urllib.parse.quote("/general What did I eat for breakfast?")
|
||||
response = chat_client.get(f"/api/chat?q={query}&conversation_id={conversation.id}&stream=true")
|
||||
response_message = response.content.decode("utf-8")
|
||||
|
||||
# Assert that agent only responds with the summary of spending
|
||||
expected_responses = ["13.00", "13", "13.0", "thirteen"]
|
||||
assert response.status_code == 200
|
||||
assert any([expected_response in response_message.lower() for expected_response in expected_responses]), (
|
||||
"Expected green in response, but got: " + response_message
|
||||
)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
@pytest.mark.django_db(transaction=True)
|
||||
@pytest.mark.chatquality
|
||||
|
|
|
|||
Loading…
Reference in a new issue