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Rebase with matser

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This commit is contained in:
sabaimran
2024-04-02 16:16:06 +05:30
parent 4deb849fb1 3c3e48b18c
commit 47fc7e1ce6
61 changed files with 832 additions and 481 deletions
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11
src/interface/desktop/chat.html
View File

@@ -87,7 +87,7 @@
function generateOnlineReference(reference, index) {
// Generate HTML for Chat Reference
let title = reference.title;
let title = reference.title || reference.link;
let link = reference.link;
let snippet = reference.snippet;
let question = reference.question;
@@ -191,6 +191,15 @@
referenceSection.appendChild(polishedReference);
}
}
if (onlineReference.webpages && onlineReference.webpages.length > 0) {
numOnlineReferences += onlineReference.webpages.length;
for (let index in onlineReference.webpages) {
let reference = onlineReference.webpages[index];
let polishedReference = generateOnlineReference(reference, index);
referenceSection.appendChild(polishedReference);
}
}
}
return numOnlineReferences;

2
src/interface/desktop/package.json
View File

@@ -1,6 +1,6 @@
{
"name": "Khoj",
"version": "1.7.0",
"version": "1.8.0",
"description": "An AI copilot for your Second Brain",
"author": "Saba Imran, Debanjum Singh Solanky <team@khoj.dev>",
"license": "GPL-3.0-or-later",

2
src/interface/emacs/khoj.el
View File

@@ -6,7 +6,7 @@
;; Saba Imran <saba@khoj.dev>
;; Description: An AI copilot for your Second Brain
;; Keywords: search, chat, org-mode, outlines, markdown, pdf, image
;; Version: 1.7.0
;; Version: 1.8.0
;; Package-Requires: ((emacs "27.1") (transient "0.3.0") (dash "2.19.1"))
;; URL: https://github.com/khoj-ai/khoj/tree/master/src/interface/emacs

2
src/interface/obsidian/manifest.json
View File

@@ -1,7 +1,7 @@
{
"id": "khoj",
"name": "Khoj",
"version": "1.7.0",
"version": "1.8.0",
"minAppVersion": "0.15.0",
"description": "An AI copilot for your Second Brain",
"author": "Khoj Inc.",

2
src/interface/obsidian/package.json
View File

@@ -1,6 +1,6 @@
{
"name": "Khoj",
"version": "1.7.0",
"version": "1.8.0",
"description": "An AI copilot for your Second Brain",
"author": "Debanjum Singh Solanky, Saba Imran <team@khoj.dev>",
"license": "GPL-3.0-or-later",

3
src/interface/obsidian/versions.json
View File

@@ -39,5 +39,6 @@
"1.6.0": "0.15.0",
"1.6.1": "0.15.0",
"1.6.2": "0.15.0",
"1.7.0": "0.15.0"
"1.7.0": "0.15.0",
"1.8.0": "0.15.0"
}

77
src/khoj/database/adapters/__init__.py
View File

@@ -43,7 +43,7 @@ from khoj.search_filter.date_filter import DateFilter
from khoj.search_filter.file_filter import FileFilter
from khoj.search_filter.word_filter import WordFilter
from khoj.utils import state
from khoj.utils.config import GPT4AllProcessorModel
from khoj.utils.config import OfflineChatProcessorModel
from khoj.utils.helpers import generate_random_name, is_none_or_empty
@@ -399,32 +399,26 @@ class AgentAdapters:
DEFAULT_AGENT_SLUG = "khoj"
@staticmethod
async def aget_agent_by_id(agent_id: int):
return await Agent.objects.filter(id=agent_id).afirst()
@staticmethod
async def aget_agent_by_slug(agent_slug: str):
return await Agent.objects.filter(slug__iexact=agent_slug.lower()).afirst()
async def aget_agent_by_slug(agent_slug: str, user: KhojUser):
return await Agent.objects.filter(
(Q(slug__iexact=agent_slug.lower())) & (Q(public=True) | Q(creator=user))
).afirst()
@staticmethod
def get_agent_by_slug(slug: str, user: KhojUser = None):
agent = Agent.objects.filter(slug=slug).first()
# Check if agent is public or created by the user
if agent and (agent.public or agent.creator == user):
return agent
return None
if user:
return Agent.objects.filter((Q(slug__iexact=slug.lower())) & (Q(public=True) | Q(creator=user))).first()
return Agent.objects.filter(slug__iexact=slug.lower(), public=True).first()
@staticmethod
def get_all_accessible_agents(user: KhojUser = None):
return Agent.objects.filter(Q(public=True) | Q(creator=user)).distinct().order_by("created_at")
if user:
return Agent.objects.filter(Q(public=True) | Q(creator=user)).distinct().order_by("created_at")
return Agent.objects.filter(public=True).order_by("created_at")
@staticmethod
async def aget_all_accessible_agents(user: KhojUser = None) -> List[Agent]:
get_all_accessible_agents = sync_to_async(
lambda: Agent.objects.filter(Q(public=True) | Q(creator=user)).distinct().order_by("created_at").all(),
thread_sensitive=True,
)
agents = await get_all_accessible_agents()
agents = await sync_to_async(AgentAdapters.get_all_accessible_agents)(user)
return await sync_to_async(list)(agents)
@staticmethod
@@ -444,26 +438,29 @@ class AgentAdapters:
default_conversation_config = ConversationAdapters.get_default_conversation_config()
default_personality = prompts.personality.format(current_date="placeholder")
if Agent.objects.filter(name=AgentAdapters.DEFAULT_AGENT_NAME).exists():
agent = Agent.objects.filter(name=AgentAdapters.DEFAULT_AGENT_NAME).first()
agent.tuning = default_personality
agent = Agent.objects.filter(name=AgentAdapters.DEFAULT_AGENT_NAME).first()
if agent:
agent.personality = default_personality
agent.chat_model = default_conversation_config
agent.slug = AgentAdapters.DEFAULT_AGENT_SLUG
agent.name = AgentAdapters.DEFAULT_AGENT_NAME
agent.save()
return agent
else:
# The default agent is public and managed by the admin. It's handled a little differently than other agents.
agent = Agent.objects.create(
name=AgentAdapters.DEFAULT_AGENT_NAME,
public=True,
managed_by_admin=True,
chat_model=default_conversation_config,
personality=default_personality,
tools=["*"],
avatar=AgentAdapters.DEFAULT_AGENT_AVATAR,
slug=AgentAdapters.DEFAULT_AGENT_SLUG,
)
Conversation.objects.filter(agent=None).update(agent=agent)
# The default agent is public and managed by the admin. It's handled a little differently than other agents.
return Agent.objects.create(
name=AgentAdapters.DEFAULT_AGENT_NAME,
public=True,
managed_by_admin=True,
chat_model=default_conversation_config,
tuning=default_personality,
tools=["*"],
avatar=AgentAdapters.DEFAULT_AGENT_AVATAR,
slug=AgentAdapters.DEFAULT_AGENT_SLUG,
)
return agent
@staticmethod
async def aget_default_agent():
@@ -482,9 +479,10 @@ class ConversationAdapters:
.first()
)
else:
agent = AgentAdapters.get_default_agent()
conversation = (
Conversation.objects.filter(user=user, client=client_application).order_by("-updated_at").first()
) or Conversation.objects.create(user=user, client=client_application)
) or Conversation.objects.create(user=user, client=client_application, agent=agent)
return conversation
@@ -514,11 +512,12 @@ class ConversationAdapters:
user: KhojUser, client_application: ClientApplication = None, agent_slug: str = None
):
if agent_slug:
agent = await AgentAdapters.aget_agent_by_slug(agent_slug)
agent = await AgentAdapters.aget_agent_by_slug(agent_slug, user)
if agent is None:
raise HTTPException(status_code=400, detail="Invalid agent id")
raise HTTPException(status_code=400, detail="No such agent currently exists.")
return await Conversation.objects.acreate(user=user, client=client_application, agent=agent)
return await Conversation.objects.acreate(user=user, client=client_application)
agent = await AgentAdapters.aget_default_agent()
return await Conversation.objects.acreate(user=user, client=client_application, agent=agent)
@staticmethod
async def aget_conversation_by_user(
@@ -706,8 +705,8 @@ class ConversationAdapters:
conversation_config = ConversationAdapters.get_default_conversation_config()
if offline_chat_config and offline_chat_config.enabled and 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)
if state.offline_chat_processor_config is None or state.offline_chat_processor_config.loaded_model is None:
state.offline_chat_processor_config = OfflineChatProcessorModel(conversation_config.chat_model)
return conversation_config

2
src/khoj/database/migrations/0031_agent_conversation_agent.py
View File

@@ -23,7 +23,7 @@ class Migration(migrations.Migration):
("tools", models.JSONField(default=list)),
("public", models.BooleanField(default=False)),
("managed_by_admin", models.BooleanField(default=False)),
("slug", models.CharField(blank=True, default=None, max_length=200, null=True)),
("slug", models.CharField(max_length=200)),
(
"chat_model",
models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to="database.chatmodeloptions"),

13
src/khoj/database/migrations/0031_alter_googleuser_locale.py
View File

@@ -3,6 +3,18 @@
from django.db import migrations, models
def set_default_locale(apps, schema_editor):
return
def reverse_set_default_locale(apps, schema_editor):
GoogleUser = apps.get_model("database", "GoogleUser")
for user in GoogleUser.objects.all():
if not user.locale:
user.locale = "en"
user.save()
class Migration(migrations.Migration):
dependencies = [
("database", "0030_conversation_slug_and_title"),
@@ -14,4 +26,5 @@ class Migration(migrations.Migration):
name="locale",
field=models.CharField(blank=True, default=None, max_length=200, null=True),
),
migrations.RunPython(set_default_locale, reverse_set_default_locale),
]

17
src/khoj/database/migrations/0033_rename_tuning_agent_personality.py Normal file
View File

@@ -0,0 +1,17 @@
# Generated by Django 4.2.10 on 2024-03-23 16:01
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
("database", "0032_merge_20240322_0427"),
]
operations = [
migrations.RenameField(
model_name="agent",
old_name="tuning",
new_name="personality",
),
]

15
src/khoj/database/models/__init__.py
View File

@@ -80,20 +80,22 @@ class ChatModelOptions(BaseModel):
max_prompt_size = models.IntegerField(default=None, null=True, blank=True)
tokenizer = models.CharField(max_length=200, default=None, null=True, blank=True)
chat_model = models.CharField(max_length=200, default="mistral-7b-instruct-v0.1.Q4_0.gguf")
chat_model = models.CharField(max_length=200, default="NousResearch/Hermes-2-Pro-Mistral-7B-GGUF")
model_type = models.CharField(max_length=200, choices=ModelType.choices, default=ModelType.OFFLINE)
class Agent(BaseModel):
creator = models.ForeignKey(KhojUser, on_delete=models.CASCADE, default=None, null=True, blank=True)
creator = models.ForeignKey(
KhojUser, on_delete=models.CASCADE, default=None, null=True, blank=True
) # Creator will only be null when the agents are managed by admin
name = models.CharField(max_length=200)
tuning = models.TextField()
personality = models.TextField()
avatar = models.URLField(max_length=400, default=None, null=True, blank=True)
tools = models.JSONField(default=list) # List of tools the agent has access to, like online search or notes search
public = models.BooleanField(default=False)
managed_by_admin = models.BooleanField(default=False)
chat_model = models.ForeignKey(ChatModelOptions, on_delete=models.CASCADE)
slug = models.CharField(max_length=200, default=None, null=True, blank=True)
slug = models.CharField(max_length=200)
@receiver(pre_save, sender=Agent)
@@ -108,7 +110,10 @@ def verify_agent(sender, instance, **kwargs):
slug = instance.name.lower().replace(" ", "-")
observed_random_numbers = set()
while Agent.objects.filter(slug=slug).exists():
random_number = choice([i for i in range(0, 10000) if i not in observed_random_numbers])
try:
random_number = choice([i for i in range(0, 1000) if i not in observed_random_numbers])
except IndexError:
raise ValidationError("Unable to generate a unique slug for the Agent. Please try again later.")
observed_random_numbers.add(random_number)
slug = f"{slug}-{random_number}"
instance.slug = slug

8
src/khoj/interface/web/agent.html
View File

@@ -24,9 +24,9 @@
<img id="agent-avatar" src="{{ agent.avatar }}" alt="Agent Avatar">
<input type="text" id="agent-name-input" value="{{ agent.name }}" {% if agent.creator_not_self %} disabled {% endif %}>
</div>
<div id="agent-instructions">Instructions</div>
<div id="agent-instructions">Personality</div>
<div id="agent-tuning">
<p>{{ agent.tuning }}</p>
<p>{{ agent.personality }}</p>
</div>
<div class="divider"></div>
<div id="agent-public">
@@ -256,8 +256,8 @@
}
</style>
<script>
async function openChat(agentId) {
let response = await fetch(`/api/chat/sessions?agent_slug=${agentId}`, { method: "POST" });
async function openChat(agentSlug) {
let response = await fetch(`/api/chat/sessions?agent_slug=${agentSlug}`, { method: "POST" });
let data = await response.json();
if (response.status == 200) {
window.location.href = "/";

14
src/khoj/interface/web/agents.html
View File

@@ -36,7 +36,7 @@
<a href="/agent/{{ agent.slug }}">
<h2>{{ agent.name }}</h2>
</a>
<p>{{ agent.tuning }}</p>
<p>{{ agent.personality }}</p>
</div>
<div class="agent-info">
<button onclick="openChat('{{ agent.slug }}')">Talk</button>
@@ -69,7 +69,7 @@
}
.agent-info p {
height: 50px; /* Adjust this value as needed */
height: 50px;
overflow: auto;
margin: 0px;
}
@@ -84,8 +84,10 @@
}
div.agent img {
width: 50px;
width: 78px;
height: 78px;
border-radius: 50%;
object-fit: cover;
}
div.agent a {
@@ -157,7 +159,7 @@
background-color: var(--frosted-background-color);
box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
border-radius: 8px;
width: 50%;
width: 75%;
margin-right: auto;
margin-left: auto;
}
@@ -186,8 +188,8 @@
}
</style>
<script>
async function openChat(agentId) {
let response = await fetch(`/api/chat/sessions?agent_slug=${agentId}`, { method: "POST" });
async function openChat(agentSlug) {
let response = await fetch(`/api/chat/sessions?agent_slug=${agentSlug}`, { method: "POST" });
let data = await response.json();
if (response.status == 200) {
window.location.href = "/";

19
src/khoj/interface/web/chat.html
View File

@@ -5,6 +5,7 @@
<title>Khoj - Chat</title>
<link rel="icon" type="image/png" sizes="128x128" href="/static/assets/icons/favicon-128x128.png?v={{ khoj_version }}">
<link rel="apple-touch-icon" href="/static/assets/icons/favicon-128x128.png?v={{ khoj_version }}">
<link rel="manifest" href="/static/khoj.webmanifest?v={{ khoj_version }}">
<link rel="stylesheet" href="/static/assets/khoj.css?v={{ khoj_version }}">
</head>
@@ -19,7 +20,13 @@ Hi, I am Khoj, your open, personal AI 👋🏽. I can:
- 🌄 Generate images based on your messages
- 🔎 Search the web for answers to your questions
- 🎙️ Listen to your audio messages (use the mic by the input box to speak your message)
<<<<<<< HEAD
- 📚 Understand files you drag & drop here
||||||| 7416ca9a
=======
- 📚 Understand files you drag & drop here
- 👩🏾‍🚀 Be tuned to your conversation needs via [agents](./agents)
>>>>>>> 3c3e48b18c87a42e3b69af5dbfb86b5c8b68effa
Get the Khoj [Desktop](https://khoj.dev/downloads), [Obsidian](https://docs.khoj.dev/clients/obsidian#setup), [Emacs](https://docs.khoj.dev/clients/emacs#setup) apps to search, chat with your 🖥️ computer docs. You can manage all the files you've shared with me at any time by going to [your settings](/config/content-source/computer/).
@@ -111,7 +118,7 @@ To get started, just start typing below. You can also type / to see a list of co
function generateOnlineReference(reference, index) {
// Generate HTML for Chat Reference
let title = reference.title;
let title = reference.title || reference.link;
let link = reference.link;
let snippet = reference.snippet;
let question = reference.question;
@@ -214,6 +221,15 @@ To get started, just start typing below. You can also type / to see a list of co
referenceSection.appendChild(polishedReference);
}
}
if (onlineReference.webpages && onlineReference.webpages.length > 0) {
numOnlineReferences += onlineReference.webpages.length;
for (let index in onlineReference.webpages) {
let reference = onlineReference.webpages[index];
let polishedReference = generateOnlineReference(reference, index);
referenceSection.appendChild(polishedReference);
}
}
}
return numOnlineReferences;
@@ -2452,6 +2468,7 @@ To get started, just start typing below. You can also type / to see a list of co
width: 100%; /* Full width */
height: 100%; /* Full height */
background-color: rgba(0,0,0,0.4); /* Black w/ opacity */
margin: 0px;
}
.modal-content {

1
src/khoj/interface/web/search.html
View File

@@ -5,6 +5,7 @@
<title>Khoj - Search</title>
<link rel="icon" type="image/png" sizes="128x128" href="/static/assets/icons/favicon-128x128.png?v={{ khoj_version }}">
<link rel="apple-touch-icon" href="/static/assets/icons/favicon-128x128.png?v={{ khoj_version }}">
<link rel="manifest" href="/static/khoj.webmanifest?v={{ khoj_version }}">
<link rel="stylesheet" href="/static/assets/khoj.css?v={{ khoj_version }}">
</head>

4
src/khoj/interface/web/utils.html
View File

@@ -4,9 +4,9 @@
<img class="khoj-logo" src="/static/assets/icons/khoj-logo-sideways-500.png?v={{ khoj_version }}" alt="Khoj"></img>
</a>
<nav class="khoj-nav">
<a id="agents-nav" class="khoj-nav" href="/agents">Agents</a>
{% if has_documents %}
<a id="chat-nav" class="khoj-nav" href="/chat">💬 Chat</a>
<a id="search-nav" class="khoj-nav" href="/search">🔎 Search</a>
<a id="search-nav" class="khoj-nav" href="/search">Search</a>
{% endif %}
<!-- Dropdown Menu -->
{% if username %}

4
src/khoj/main.py
View File

@@ -160,7 +160,9 @@ def start_server(app, host=None, port=None, socket=None):
if socket:
uvicorn.run(app, proxy_headers=True, uds=socket, log_level="debug", use_colors=True, log_config=None)
else:
uvicorn.run(app, host=host, port=port, log_level="debug", use_colors=True, log_config=None)
uvicorn.run(
app, host=host, port=port, log_level="debug", use_colors=True, log_config=None, timeout_keep_alive=60
)
logger.info("🌒 Stopping Khoj")

71
src/khoj/migrations/migrate_offline_chat_default_model_2.py Normal file
View File

@@ -0,0 +1,71 @@
"""
Current format of khoj.yml
---
app:
...
content-type:
...
processor:
conversation:
offline-chat:
enable-offline-chat: false
chat-model: mistral-7b-instruct-v0.1.Q4_0.gguf
...
search-type:
...
New format of khoj.yml
---
app:
...
content-type:
...
processor:
conversation:
offline-chat:
enable-offline-chat: false
chat-model: NousResearch/Hermes-2-Pro-Mistral-7B-GGUF
...
search-type:
...
"""
import logging
from packaging import version
from khoj.utils.yaml import load_config_from_file, save_config_to_file
logger = logging.getLogger(__name__)
def migrate_offline_chat_default_model(args):
schema_version = "1.7.0"
raw_config = load_config_from_file(args.config_file)
previous_version = raw_config.get("version")
if "processor" not in raw_config:
return args
if raw_config["processor"] is None:
return args
if "conversation" not in raw_config["processor"]:
return args
if "offline-chat" not in raw_config["processor"]["conversation"]:
return args
if "chat-model" not in raw_config["processor"]["conversation"]["offline-chat"]:
return args
if previous_version is None or version.parse(previous_version) < version.parse(schema_version):
logger.info(
f"Upgrading config schema to {schema_version} from {previous_version} to change default (offline) chat model to mistral GGUF"
)
raw_config["version"] = schema_version
# Update offline chat model to use Nous Research's Hermes-2-Pro GGUF in path format suitable for llama-cpp
offline_chat_model = raw_config["processor"]["conversation"]["offline-chat"]["chat-model"]
if offline_chat_model == "mistral-7b-instruct-v0.1.Q4_0.gguf":
raw_config["processor"]["conversation"]["offline-chat"][
"chat-model"
] = "NousResearch/Hermes-2-Pro-Mistral-7B-GGUF"
save_config_to_file(raw_config, args.config_file)
return args

157
src/khoj/processor/conversation/offline/chat_model.py
View File

@@ -1,13 +1,15 @@
import json
import logging
from collections import deque
from datetime import datetime
from datetime import datetime, timedelta
from threading import Thread
from typing import Any, Iterator, List, Union
from langchain.schema import ChatMessage
from llama_cpp import Llama
from khoj.database.models import Agent
from khoj.processor.conversation import prompts
from khoj.processor.conversation.offline.utils import download_model
from khoj.processor.conversation.utils import (
ThreadedGenerator,
generate_chatml_messages_with_context,
@@ -22,7 +24,7 @@ logger = logging.getLogger(__name__)
def extract_questions_offline(
text: str,
model: str = "mistral-7b-instruct-v0.1.Q4_0.gguf",
model: str = "NousResearch/Hermes-2-Pro-Mistral-7B-GGUF",
loaded_model: Union[Any, None] = None,
conversation_log={},
use_history: bool = True,
@@ -32,22 +34,14 @@ def extract_questions_offline(
"""
Infer search queries to retrieve relevant notes to answer user query
"""
try:
from gpt4all import GPT4All
except ModuleNotFoundError as e:
logger.info("There was an error importing GPT4All. Please run pip install gpt4all in order to install it.")
raise e
# Assert that loaded_model is either None or of type GPT4All
assert loaded_model is None or isinstance(loaded_model, GPT4All), "loaded_model must be of type GPT4All or None"
all_questions = text.split("? ")
all_questions = [q + "?" for q in all_questions[:-1]] + [all_questions[-1]]
if not should_extract_questions:
return all_questions
gpt4all_model = loaded_model or GPT4All(model)
assert loaded_model is None or isinstance(loaded_model, Llama), "loaded_model must be of type Llama, if configured"
offline_chat_model = loaded_model or download_model(model)
location = f"{location_data.city}, {location_data.region}, {location_data.country}" if location_data else "Unknown"
@@ -56,37 +50,36 @@ def extract_questions_offline(
if use_history:
for chat in conversation_log.get("chat", [])[-4:]:
if chat["by"] == "khoj" and chat["intent"].get("type") != "text-to-image":
if chat["by"] == "khoj" and "text-to-image" not in chat["intent"].get("type"):
chat_history += f"Q: {chat['intent']['query']}\n"
chat_history += f"A: {chat['message']}\n"
chat_history += f"Khoj: {chat['message']}\n\n"
current_date = datetime.now().strftime("%Y-%m-%d")
last_year = datetime.now().year - 1
last_christmas_date = f"{last_year}-12-25"
next_christmas_date = f"{datetime.now().year}-12-25"
system_prompt = prompts.system_prompt_extract_questions_gpt4all.format(
message=(prompts.system_prompt_message_extract_questions_gpt4all)
)
example_questions = prompts.extract_questions_gpt4all_sample.format(
today = datetime.today()
yesterday = (today - timedelta(days=1)).strftime("%Y-%m-%d")
last_year = today.year - 1
example_questions = prompts.extract_questions_offline.format(
query=text,
chat_history=chat_history,
current_date=current_date,
current_date=today.strftime("%Y-%m-%d"),
yesterday_date=yesterday,
last_year=last_year,
last_christmas_date=last_christmas_date,
next_christmas_date=next_christmas_date,
this_year=today.year,
location=location,
)
message = system_prompt + example_questions
messages = generate_chatml_messages_with_context(
example_questions, model_name=model, loaded_model=offline_chat_model
)
state.chat_lock.acquire()
try:
response = gpt4all_model.generate(message, max_tokens=200, top_k=2, temp=0, n_batch=512)
response = send_message_to_model_offline(messages, loaded_model=offline_chat_model)
finally:
state.chat_lock.release()
# Extract, Clean Message from GPT's Response
try:
# This will expect to be a list with a single string with a list of questions
questions = (
questions_str = (
str(response)
.strip(empty_escape_sequences)
.replace("['", '["')
@@ -94,11 +87,8 @@ def extract_questions_offline(
.replace("</s>", "")
.replace("']", '"]')
.replace("', '", '", "')
.replace('["', "")
.replace('"]', "")
.split("? ")
)
questions = [q + "?" for q in questions[:-1]] + [questions[-1]]
questions: List[str] = json.loads(questions_str)
questions = filter_questions(questions)
except:
logger.warning(f"Llama returned invalid JSON. Falling back to using user message as search query.\n{response}")
@@ -121,12 +111,12 @@ def filter_questions(questions: List[str]):
"do not know",
"do not understand",
]
filtered_questions = []
filtered_questions = set()
for q in questions:
if not any([word in q.lower() for word in hint_words]) and not is_none_or_empty(q):
filtered_questions.append(q)
filtered_questions.add(q)
return filtered_questions
return list(filtered_questions)
def converse_offline(
@@ -134,7 +124,7 @@ def converse_offline(
references=[],
online_results=[],
conversation_log={},
model: str = "mistral-7b-instruct-v0.1.Q4_0.gguf",
model: str = "NousResearch/Hermes-2-Pro-Mistral-7B-GGUF",
loaded_model: Union[Any, None] = None,
completion_func=None,
conversation_commands=[ConversationCommand.Default],
@@ -147,26 +137,19 @@ def converse_offline(
"""
Converse with user using Llama
"""
try:
from gpt4all import GPT4All
except ModuleNotFoundError as e:
logger.info("There was an error importing GPT4All. Please run pip install gpt4all in order to install it.")
raise e
assert loaded_model is None or isinstance(loaded_model, GPT4All), "loaded_model must be of type GPT4All or None"
gpt4all_model = loaded_model or GPT4All(model)
# Initialize Variables
assert loaded_model is None or isinstance(loaded_model, Llama), "loaded_model must be of type Llama, if configured"
offline_chat_model = loaded_model or download_model(model)
compiled_references_message = "\n\n".join({f"{item}" for item in references})
system_prompt = ""
current_date = datetime.now().strftime("%Y-%m-%d")
if agent and agent.tuning:
system_prompt = prompts.custom_system_prompt_message_gpt4all.format(
name=agent.name, bio=agent.tuning, current_date=current_date
if agent and agent.personality:
system_prompt = prompts.custom_system_prompt_offline_chat.format(
name=agent.name, bio=agent.personality, current_date=current_date
)
else:
system_prompt = prompts.system_prompt_message_gpt4all.format(current_date=current_date)
system_prompt = prompts.system_prompt_offline_chat.format(current_date=current_date)
conversation_primer = prompts.query_prompt.format(query=user_query)
@@ -189,12 +172,12 @@ def converse_offline(
if ConversationCommand.Online in conversation_commands:
simplified_online_results = online_results.copy()
for result in online_results:
if online_results[result].get("extracted_content"):
simplified_online_results[result] = online_results[result]["extracted_content"]
if online_results[result].get("webpages"):
simplified_online_results[result] = online_results[result]["webpages"]
conversation_primer = f"{prompts.online_search_conversation.format(online_results=str(simplified_online_results))}\n{conversation_primer}"
if not is_none_or_empty(compiled_references_message):
conversation_primer = f"{prompts.notes_conversation_gpt4all.format(references=compiled_references_message)}\n{conversation_primer}"
conversation_primer = f"{prompts.notes_conversation_offline.format(references=compiled_references_message)}\n{conversation_primer}"
# Setup Prompt with Primer or Conversation History
messages = generate_chatml_messages_with_context(
@@ -202,72 +185,44 @@ def converse_offline(
system_prompt,
conversation_log,
model_name=model,
loaded_model=offline_chat_model,
max_prompt_size=max_prompt_size,
tokenizer_name=tokenizer_name,
)
g = ThreadedGenerator(references, online_results, completion_func=completion_func)
t = Thread(target=llm_thread, args=(g, messages, gpt4all_model))
t = Thread(target=llm_thread, args=(g, messages, offline_chat_model))
t.start()
return g
def llm_thread(g, messages: List[ChatMessage], model: Any):
user_message = messages[-1]
system_message = messages[0]
conversation_history = messages[1:-1]
formatted_messages = [
prompts.khoj_message_gpt4all.format(message=message.content)
if message.role == "assistant"
else prompts.user_message_gpt4all.format(message=message.content)
for message in conversation_history
]
stop_phrases = ["<s>", "INST]", "Notes:"]
chat_history = "".join(formatted_messages)
templated_system_message = prompts.system_prompt_gpt4all.format(message=system_message.content)
templated_user_message = prompts.user_message_gpt4all.format(message=user_message.content)
prompted_message = templated_system_message + chat_history + templated_user_message
response_queue: deque[str] = deque(maxlen=3) # Create a response queue with a maximum length of 3
hit_stop_phrase = False
state.chat_lock.acquire()
response_iterator = send_message_to_model_offline(prompted_message, loaded_model=model, streaming=True)
try:
response_iterator = send_message_to_model_offline(
messages, loaded_model=model, stop=stop_phrases, streaming=True
)
for response in response_iterator:
response_queue.append(response)
hit_stop_phrase = any(stop_phrase in "".join(response_queue) for stop_phrase in stop_phrases)
if hit_stop_phrase:
logger.debug(f"Stop response as hit stop phrase: {''.join(response_queue)}")
break
# Start streaming the response at a lag once the queue is full
# This allows stop word testing before sending the response
if len(response_queue) == response_queue.maxlen:
g.send(response_queue[0])
g.send(response["choices"][0]["delta"].get("content", ""))
finally:
if not hit_stop_phrase:
if len(response_queue) == response_queue.maxlen:
# remove already sent reponse chunk
response_queue.popleft()
# send the remaining response
g.send("".join(response_queue))
state.chat_lock.release()
g.close()
def send_message_to_model_offline(
message, loaded_model=None, model="mistral-7b-instruct-v0.1.Q4_0.gguf", streaming=False, system_message=""
) -> str:
try:
from gpt4all import GPT4All
except ModuleNotFoundError as e:
logger.info("There was an error importing GPT4All. Please run pip install gpt4all in order to install it.")
raise e
assert loaded_model is None or isinstance(loaded_model, GPT4All), "loaded_model must be of type GPT4All or None"
gpt4all_model = loaded_model or GPT4All(model)
return gpt4all_model.generate(
system_message + message, max_tokens=200, top_k=2, temp=0, n_batch=512, streaming=streaming
)
messages: List[ChatMessage],
loaded_model=None,
model="NousResearch/Hermes-2-Pro-Mistral-7B-GGUF",
streaming=False,
stop=[],
):
assert loaded_model is None or isinstance(loaded_model, Llama), "loaded_model must be of type Llama, if configured"
offline_chat_model = loaded_model or download_model(model)
messages_dict = [{"role": message.role, "content": message.content} for message in messages]
response = offline_chat_model.create_chat_completion(messages_dict, stop=stop, stream=streaming)
if streaming:
return response
else:
return response["choices"][0]["message"].get("content", "")

69
src/khoj/processor/conversation/offline/utils.py
View File

@@ -1,43 +1,54 @@
import glob
import logging
import os
from huggingface_hub.constants import HF_HUB_CACHE
from khoj.utils import state
logger = logging.getLogger(__name__)
def download_model(model_name: str):
try:
import gpt4all
except ModuleNotFoundError as e:
logger.info("There was an error importing GPT4All. Please run pip install gpt4all in order to install it.")
raise e
def download_model(repo_id: str, filename: str = "*Q4_K_M.gguf"):
from llama_cpp.llama import Llama
# Initialize Model Parameters. Use n_ctx=0 to get context size from the model
kwargs = {"n_threads": 4, "n_ctx": 0, "verbose": False}
# Decide whether to load model to GPU or CPU
chat_model_config = None
device = "gpu" if state.chat_on_gpu and state.device != "cpu" else "cpu"
kwargs["n_gpu_layers"] = -1 if device == "gpu" else 0
# Check if the model is already downloaded
model_path = load_model_from_cache(repo_id, filename)
chat_model = None
try:
# Download the chat model and its config
chat_model_config = gpt4all.GPT4All.retrieve_model(model_name=model_name, allow_download=True)
# Try load chat model to GPU if:
# 1. Loading chat model to GPU isn't disabled via CLI and
# 2. Machine has GPU
# 3. GPU has enough free memory to load the chat model with max context length of 4096
device = (
"gpu"
if state.chat_on_gpu and gpt4all.pyllmodel.LLModel().list_gpu(chat_model_config["path"], 4096)
else "cpu"
)
except ValueError:
device = "cpu"
except Exception as e:
if chat_model_config is None:
device = "cpu" # Fallback to CPU as can't determine if GPU has enough memory
logger.debug(f"Unable to download model config from gpt4all website: {e}")
if model_path:
chat_model = Llama(model_path, **kwargs)
else:
raise e
Llama.from_pretrained(repo_id=repo_id, filename=filename, **kwargs)
except:
# Load model on CPU if GPU is not available
kwargs["n_gpu_layers"], device = 0, "cpu"
if model_path:
chat_model = Llama(model_path, **kwargs)
else:
chat_model = Llama.from_pretrained(repo_id=repo_id, filename=filename, **kwargs)
# Now load the downloaded chat model onto appropriate device
chat_model = gpt4all.GPT4All(model_name=model_name, n_ctx=4096, device=device, allow_download=False)
logger.debug(f"Loaded chat model to {device.upper()}.")
logger.debug(f"{'Loaded' if model_path else 'Downloaded'} chat model to {device.upper()}")
return chat_model
def load_model_from_cache(repo_id: str, filename: str, repo_type="models"):
# Construct the path to the model file in the cache directory
repo_org, repo_name = repo_id.split("/")
object_id = "--".join([repo_type, repo_org, repo_name])
model_path = os.path.sep.join([HF_HUB_CACHE, object_id, "snapshots", "**", filename])
# Check if the model file exists
paths = glob.glob(model_path)
if paths:
return paths[0]
else:
return None

16
src/khoj/processor/conversation/openai/gpt.py
View File

@@ -1,7 +1,7 @@
import json
import logging
from datetime import datetime, timedelta
from typing import Optional
from typing import Dict, Optional
from langchain.schema import ChatMessage
@@ -105,7 +105,7 @@ def send_message_to_model(messages, api_key, model, response_type="text"):
def converse(
references,
user_query,
online_results: Optional[dict] = None,
online_results: Optional[Dict[str, Dict]] = None,
conversation_log={},
model: str = "gpt-3.5-turbo",
api_key: Optional[str] = None,
@@ -127,10 +127,10 @@ def converse(
conversation_primer = prompts.query_prompt.format(query=user_query)
system_prompt = ""
if agent and agent.tuning:
system_prompt = prompts.custom_personality.format(name=agent.name, bio=agent.tuning, current_date=current_date)
if agent and agent.personality:
system_prompt = prompts.custom_personality.format(
name=agent.name, bio=agent.personality, current_date=current_date
)
else:
system_prompt = prompts.personality.format(current_date=current_date)
@@ -151,7 +151,7 @@ def converse(
completion_func(chat_response=prompts.no_online_results_found.format())
return iter([prompts.no_online_results_found.format()])
if ConversationCommand.Online in conversation_commands:
if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
conversation_primer = (
f"{prompts.online_search_conversation.format(online_results=str(online_results))}\n{conversation_primer}"
)
@@ -167,7 +167,7 @@ def converse(
max_prompt_size,
tokenizer_name,
)
truncated_messages = "\n".join({f"{message.content[:40]}..." for message in messages})
truncated_messages = "\n".join({f"{message.content[:70]}..." for message in messages})
logger.debug(f"Conversation Context for GPT: {truncated_messages}")
# Get Response from GPT

5
src/khoj/processor/conversation/openai/utils.py
View File

@@ -101,8 +101,3 @@ def llm_thread(g, messages, model_name, temperature, openai_api_key=None, model_
chat(messages=messages)
g.close()
def extract_summaries(metadata):
"""Extract summaries from metadata"""
return "".join([f'\n{session["summary"]}' for session in metadata])

196
src/khoj/processor/conversation/prompts.py
View File

@@ -10,7 +10,7 @@ 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.
- You can generate images, look-up information from the internet, and answer questions based on the user's notes.
- You *CAN* generate images, look-up real-time information from the internet, and answer questions based on the user's notes.
- You cannot set reminders.
- 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.
@@ -65,9 +65,9 @@ no_entries_found = PromptTemplate.from_template(
""".strip()
)
## Conversation Prompts for GPT4All Models
## Conversation Prompts for Offline Chat Models
## --
system_prompt_message_gpt4all = PromptTemplate.from_template(
system_prompt_offline_chat = PromptTemplate.from_template(
"""
You are Khoj, a smart, inquisitive and helpful personal assistant.
- Use your general knowledge and past conversation with the user as context to inform your responses.
@@ -79,7 +79,7 @@ Today is {current_date} in UTC.
""".strip()
)
custom_system_prompt_message_gpt4all = PromptTemplate.from_template(
custom_system_prompt_offline_chat = 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.
@@ -93,40 +93,6 @@ 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?".
- Add as much context from the previous questions and notes as required into your search queries.
- Provide search queries as a list of questions
What follow-up questions, if any, will you need to ask to answer the user's question?
"""
system_prompt_gpt4all = PromptTemplate.from_template(
"""
<s>[INST] <<SYS>>
{message}
<</SYS>>Hi there! [/INST] Hello! How can I help you today? </s>"""
)
system_prompt_extract_questions_gpt4all = PromptTemplate.from_template(
"""
<s>[INST] <<SYS>>
{message}
<</SYS>>[/INST]</s>"""
)
user_message_gpt4all = PromptTemplate.from_template(
"""
<s>[INST] {message} [/INST]
""".strip()
)
khoj_message_gpt4all = PromptTemplate.from_template(
"""
{message}</s>
""".strip()
)
## Notes Conversation
## --
notes_conversation = PromptTemplate.from_template(
@@ -139,7 +105,7 @@ Notes:
""".strip()
)
notes_conversation_gpt4all = PromptTemplate.from_template(
notes_conversation_offline = PromptTemplate.from_template(
"""
User's Notes:
{references}
@@ -178,7 +144,8 @@ online_search_conversation = PromptTemplate.from_template(
Use this up-to-date information from the internet to inform your response.
Ask crisp follow-up questions to get additional context, when a helpful response cannot be provided from the online data or past conversations.
Information from the internet: {online_results}
Information from the internet:
{online_results}
""".strip()
)
@@ -190,58 +157,50 @@ Query: {query}""".strip()
)
## Summarize Notes
## --
summarize_notes = PromptTemplate.from_template(
"""
Summarize the below notes about {user_query}:
{text}
Summarize the notes in second person perspective:"""
)
## Answer
## --
answer = PromptTemplate.from_template(
"""
You are a friendly, helpful personal assistant.
Using the users notes below, answer their following question. If the answer is not contained within the notes, say "I don't know."
Notes:
{text}
Question: {user_query}
Answer (in second person):"""
)
## Extract Questions
## --
extract_questions_gpt4all_sample = PromptTemplate.from_template(
extract_questions_offline = PromptTemplate.from_template(
"""
<s>[INST] <<SYS>>Current Date: {current_date}. User's Location: {location}<</SYS>> [/INST]</s>
<s>[INST] How was my trip to Cambodia? [/INST]
How was my trip to Cambodia?</s>
<s>[INST] Who did I visit the temple with on that trip? [/INST]
Who did I visit the temple with in Cambodia?</s>
<s>[INST] How should I take care of my plants? [/INST]
What kind of plants do I have? What issues do my plants have?</s>
<s>[INST] How many tennis balls fit in the back of a 2002 Honda Civic? [/INST]
What is the size of a tennis ball? What is the trunk size of a 2002 Honda Civic?</s>
<s>[INST] What did I do for Christmas last year? [/INST]
What did I do for Christmas {last_year} dt>='{last_christmas_date}' dt<'{next_christmas_date}'</s>
<s>[INST] How are you feeling today? [/INST]</s>
<s>[INST] Is Alice older than Bob? [/INST]
When was Alice born? What is Bob's age?</s>
<s>[INST] <<SYS>>
Use these notes from the user's previous conversations to provide a response:
You are Khoj, an extremely smart and helpful search assistant with the ability to retrieve information from the user's notes. Construct search queries to retrieve relevant information to answer the user's question.
- You will be provided past questions(Q) and answers(A) for context.
- Try to be as specific as possible. Instead of saying "they" or "it" or "he", use proper nouns like name of the person or thing you are referring to.
- Add as much context from the previous questions and answers as required into your search queries.
- Break messages into multiple search queries when required to retrieve the relevant information.
- Add date filters to your search queries from questions and answers when required to retrieve the relevant information.
Current Date: {current_date}
User's Location: {location}
Examples:
Q: How was my trip to Cambodia?
Khoj: ["How was my trip to Cambodia?"]
Q: Who did I visit the temple with on that trip?
Khoj: ["Who did I visit the temple with in Cambodia?"]
Q: Which of them is older?
Khoj: ["When was Alice born?", "What is Bob's age?"]
Q: Where did John say he was? He mentioned it in our call last week.
Khoj: ["Where is John? dt>='{last_year}-12-25' dt<'{last_year}-12-26'", "John's location in call notes"]
Q: How can you help me?
Khoj: ["Social relationships", "Physical and mental health", "Education and career", "Personal life goals and habits"]
Q: What did I do for Christmas last year?
Khoj: ["What did I do for Christmas {last_year} dt>='{last_year}-12-25' dt<'{last_year}-12-26'"]
Q: How should I take care of my plants?
Khoj: ["What kind of plants do I have?", "What issues do my plants have?"]
Q: Who all did I meet here yesterday?
Khoj: ["Met in {location} on {yesterday_date} dt>='{yesterday_date}' dt<'{current_date}'"]
Chat History:
{chat_history}
<</SYS>> [/INST]</s>
<s>[INST] {query} [/INST]
"""
What searches will you perform to answer the following question, using the chat history as reference? Respond with relevant search queries as list of strings.
Q: {query}
""".strip()
)
@@ -259,7 +218,7 @@ User's Location: {location}
Q: How was my trip to Cambodia?
Khoj: {{"queries": ["How was my trip to Cambodia?"]}}
A: The trip was amazing. I went to the Angkor Wat temple and it was beautiful.
A: The trip was amazing. You went to the Angkor Wat temple and it was beautiful.
Q: Who did i visit that temple with?
Khoj: {{"queries": ["Who did I visit the Angkor Wat Temple in Cambodia with?"]}}
@@ -285,8 +244,8 @@ Q: What is their age difference?
Khoj: {{"queries": ["What is Bob's age?", "What is Tom's age?"]}}
A: Bob is {bob_tom_age_difference} years older than Tom. As Bob is {bob_age} years old and Tom is 30 years old.
Q: What does yesterday's note say?
Khoj: {{"queries": ["Note from {yesterday_date} dt>='{yesterday_date}' dt<'{current_date}'"]}}
Q: Who all did I meet here yesterday?
Khoj: {{"queries": ["Met in {location} on {yesterday_date} dt>='{yesterday_date}' dt<'{current_date}'"]}}
A: Yesterday's note mentions your visit to your local beach with Ram and Shyam.
{chat_history}
@@ -312,7 +271,7 @@ Target Query: {query}
Web Pages:
{corpus}
Collate the relevant information from the website to answer the target query.
Collate only relevant information from the website to answer the target query.
""".strip()
)
@@ -394,6 +353,14 @@ AI: Good morning! How can I help you today?
Q: How can I share my files with Khoj?
Khoj: {{"source": ["default", "online"]}}
Example:
Chat History:
User: What is the first element in the periodic table?
AI: The first element in the periodic table is Hydrogen.
Q: Summarize this article https://en.wikipedia.org/wiki/Hydrogen
Khoj: {{"source": ["webpage"]}}
Example:
Chat History:
User: I want to start a new hobby. I'm thinking of learning to play the guitar.
@@ -412,6 +379,50 @@ Khoj:
""".strip()
)
infer_webpages_to_read = PromptTemplate.from_template(
"""
You are Khoj, an advanced web page reading assistant. You are to construct **up to three, valid** webpage urls to read before answering the user's question.
- You will receive the conversation history as context.
- Add as much context from the previous questions and answers as required to construct the webpage urls.
- Use multiple web page urls if required to retrieve the relevant information.
- You have access to the the whole internet to retrieve information.
Which webpages will you need to read to answer the user's question?
Provide web page links as a list of strings in a JSON object.
Current Date: {current_date}
User's Location: {location}
Here are some examples:
History:
User: I like to use Hacker News to get my tech news.
AI: Hacker News is an online forum for sharing and discussing the latest tech news. It is a great place to learn about new technologies and startups.
Q: Summarize this post about vector database on Hacker News, https://news.ycombinator.com/item?id=12345
Khoj: {{"links": ["https://news.ycombinator.com/item?id=12345"]}}
History:
User: I'm currently living in New York but I'm thinking about moving to San Francisco.
AI: New York is a great city to live in. It has a lot of great restaurants and museums. San Francisco is also a great city to live in. It has good access to nature and a great tech scene.
Q: What is the climate like in those cities?
Khoj: {{"links": ["https://en.wikipedia.org/wiki/New_York_City", "https://en.wikipedia.org/wiki/San_Francisco"]}}
History:
User: Hey, how is it going?
AI: Not too bad. How can I help you today?
Q: What's the latest news on r/worldnews?
Khoj: {{"links": ["https://www.reddit.com/r/worldnews/"]}}
Now it's your turn to share actual webpage urls you'd like to read to answer the user's question.
History:
{chat_history}
Q: {query}
Khoj:
""".strip()
)
online_search_conversation_subqueries = PromptTemplate.from_template(
"""
You are Khoj, an advanced google search assistant. You are tasked with constructing **up to three** google search queries to answer the user's question.
@@ -490,7 +501,6 @@ help_message = PromptTemplate.from_template(
- **/image**: Generate an image based on your message.
- **/help**: Show this help message.
You are using the **{model}** model on the **{device}**.
**version**: {version}
""".strip()

54
src/khoj/processor/conversation/utils.py
View File

@@ -3,29 +3,28 @@ import logging
import queue
from datetime import datetime
from time import perf_counter
from typing import Any, Dict, List
from typing import Any, Dict, List, Optional
import tiktoken
from langchain.schema import ChatMessage
from llama_cpp.llama import Llama
from transformers import AutoTokenizer
from khoj.database.adapters import ConversationAdapters
from khoj.database.models import ClientApplication, KhojUser
from khoj.processor.conversation.offline.utils import download_model
from khoj.utils.helpers import is_none_or_empty, merge_dicts
logger = logging.getLogger(__name__)
model_to_prompt_size = {
"gpt-3.5-turbo": 3000,
"gpt-3.5-turbo-0125": 3000,
"gpt-4-0125-preview": 7000,
"gpt-4-turbo-preview": 7000,
"llama-2-7b-chat.ggmlv3.q4_0.bin": 1548,
"mistral-7b-instruct-v0.1.Q4_0.gguf": 1548,
}
model_to_tokenizer = {
"llama-2-7b-chat.ggmlv3.q4_0.bin": "hf-internal-testing/llama-tokenizer",
"mistral-7b-instruct-v0.1.Q4_0.gguf": "mistralai/Mistral-7B-Instruct-v0.1",
"gpt-3.5-turbo": 12000,
"gpt-3.5-turbo-0125": 12000,
"gpt-4-0125-preview": 20000,
"gpt-4-turbo-preview": 20000,
"TheBloke/Mistral-7B-Instruct-v0.2-GGUF": 3500,
"NousResearch/Hermes-2-Pro-Mistral-7B-GGUF": 3500,
}
model_to_tokenizer: Dict[str, str] = {}
class ThreadedGenerator:
@@ -134,9 +133,10 @@ Khoj: "{inferred_queries if ("text-to-image" in intent_type) else chat_response}
def generate_chatml_messages_with_context(
user_message,
system_message,
system_message=None,
conversation_log={},
model_name="gpt-3.5-turbo",
loaded_model: Optional[Llama] = None,
max_prompt_size=None,
tokenizer_name=None,
):
@@ -159,7 +159,7 @@ def generate_chatml_messages_with_context(
chat_notes = f'\n\n Notes:\n{chat.get("context")}' if chat.get("context") else "\n"
chat_logs += [chat["message"] + chat_notes]
rest_backnforths = []
rest_backnforths: List[ChatMessage] = []
# Extract in reverse chronological order
for user_msg, assistant_msg in zip(chat_logs[-2::-2], chat_logs[::-2]):
if len(rest_backnforths) >= 2 * lookback_turns:
@@ -176,22 +176,31 @@ def generate_chatml_messages_with_context(
messages.append(ChatMessage(content=system_message, role="system"))
# Truncate oldest messages from conversation history until under max supported prompt size by model
messages = truncate_messages(messages, max_prompt_size, model_name, tokenizer_name)
messages = truncate_messages(messages, max_prompt_size, model_name, loaded_model, tokenizer_name)
# Return message in chronological order
return messages[::-1]
def truncate_messages(
messages: list[ChatMessage], max_prompt_size, model_name: str, tokenizer_name=None
messages: list[ChatMessage],
max_prompt_size,
model_name: str,
loaded_model: Optional[Llama] = None,
tokenizer_name=None,
) -> list[ChatMessage]:
"""Truncate messages to fit within max prompt size supported by model"""
try:
if model_name.startswith("gpt-"):
if loaded_model:
encoder = loaded_model.tokenizer()
elif model_name.startswith("gpt-"):
encoder = tiktoken.encoding_for_model(model_name)
else:
encoder = AutoTokenizer.from_pretrained(tokenizer_name or model_to_tokenizer[model_name])
try:
encoder = download_model(model_name).tokenizer()
except:
encoder = AutoTokenizer.from_pretrained(tokenizer_name or model_to_tokenizer[model_name])
except:
default_tokenizer = "hf-internal-testing/llama-tokenizer"
encoder = AutoTokenizer.from_pretrained(default_tokenizer)
@@ -223,12 +232,17 @@ def truncate_messages(
original_question = "\n".join(messages[0].content.split("\n")[-1:]) if type(messages[0].content) == str else ""
original_question = f"\n{original_question}"
original_question_tokens = len(encoder.encode(original_question))
remaining_tokens = max_prompt_size - original_question_tokens - system_message_tokens
truncated_message = encoder.decode(encoder.encode(current_message)[:remaining_tokens]).strip()
remaining_tokens = max_prompt_size - system_message_tokens
if remaining_tokens > original_question_tokens:
remaining_tokens -= original_question_tokens
truncated_message = encoder.decode(encoder.encode(current_message)[:remaining_tokens]).strip()
messages = [ChatMessage(content=truncated_message + original_question, role=messages[0].role)]
else:
truncated_message = encoder.decode(encoder.encode(original_question)[:remaining_tokens]).strip()
messages = [ChatMessage(content=truncated_message, role=messages[0].role)]
logger.debug(
f"Truncate current message to fit within max prompt size of {max_prompt_size} supported by {model_name} model:\n {truncated_message}"
)
messages = [ChatMessage(content=truncated_message + original_question, role=messages[0].role)]
return messages + [system_message] if system_message else messages

56
src/khoj/processor/tools/online_search.py
View File

@@ -2,6 +2,7 @@ import asyncio
import json
import logging
import os
from collections import defaultdict
from typing import Dict, Tuple, Union
import aiohttp
@@ -9,7 +10,11 @@ import requests
from bs4 import BeautifulSoup
from markdownify import markdownify
from khoj.routers.helpers import extract_relevant_info, generate_online_subqueries
from khoj.routers.helpers import (
extract_relevant_info,
generate_online_subqueries,
infer_webpage_urls,
)
from khoj.utils.helpers import is_none_or_empty, timer
from khoj.utils.rawconfig import LocationData
@@ -38,7 +43,7 @@ MAX_WEBPAGES_TO_READ = 1
async def search_online(query: str, conversation_history: dict, location: LocationData):
if SERPER_DEV_API_KEY is None:
if not online_search_enabled():
logger.warn("SERPER_DEV_API_KEY is not set")
return {}
@@ -52,24 +57,21 @@ async def search_online(query: str, conversation_history: dict, location: Locati
# Gather distinct web pages from organic search results of each subquery without an instant answer
webpage_links = {
result["link"]
organic["link"]: subquery
for subquery in response_dict
for result in response_dict[subquery].get("organic", [])[:MAX_WEBPAGES_TO_READ]
for organic in response_dict[subquery].get("organic", [])[:MAX_WEBPAGES_TO_READ]
if "answerBox" not in response_dict[subquery]
}
# Read, extract relevant info from the retrieved web pages
tasks = []
for webpage_link in webpage_links:
logger.info(f"Reading web page at '{webpage_link}'")
task = read_webpage_and_extract_content(subquery, webpage_link)
tasks.append(task)
logger.info(f"Reading web pages at: {webpage_links.keys()}")
tasks = [read_webpage_and_extract_content(subquery, link) for link, subquery in webpage_links.items()]
results = await asyncio.gather(*tasks)
# Collect extracted info from the retrieved web pages
for subquery, extracted_webpage_content in results:
if extracted_webpage_content is not None:
response_dict[subquery]["extracted_content"] = extracted_webpage_content
for subquery, webpage_extract, url in results:
if webpage_extract is not None:
response_dict[subquery]["webpages"] = {"link": url, "snippet": webpage_extract}
return response_dict
@@ -93,19 +95,35 @@ def search_with_google(subquery: str):
return extracted_search_result
async def read_webpage_and_extract_content(subquery: str, url: str) -> Tuple[str, Union[None, str]]:
async def read_webpages(query: str, conversation_history: dict, location: LocationData):
"Infer web pages to read from the query and extract relevant information from them"
logger.info(f"Inferring web pages to read")
urls = await infer_webpage_urls(query, conversation_history, location)
logger.info(f"Reading web pages at: {urls}")
tasks = [read_webpage_and_extract_content(query, url) for url in urls]
results = await asyncio.gather(*tasks)
response: Dict[str, Dict] = defaultdict(dict)
response[query]["webpages"] = [
{"query": q, "link": url, "snippet": web_extract} for q, web_extract, url in results if web_extract is not None
]
return response
async def read_webpage_and_extract_content(subquery: str, url: str) -> Tuple[str, Union[None, str], str]:
try:
with timer(f"Reading web page at '{url}' took", logger):
content = await read_webpage_with_olostep(url) if OLOSTEP_API_KEY else await read_webpage(url)
content = await read_webpage_with_olostep(url) if OLOSTEP_API_KEY else await read_webpage_at_url(url)
with timer(f"Extracting relevant information from web page at '{url}' took", logger):
extracted_info = await extract_relevant_info(subquery, content)
return subquery, extracted_info
return subquery, extracted_info, url
except Exception as e:
logger.error(f"Failed to read web page at '{url}' with {e}")
return subquery, None
return subquery, None, url
async def read_webpage(web_url: str) -> str:
async def read_webpage_at_url(web_url: str) -> str:
headers = {
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",
}
@@ -129,3 +147,7 @@ async def read_webpage_with_olostep(web_url: str) -> str:
response.raise_for_status()
response_json = await response.json()
return response_json["markdown_content"]
def online_search_enabled():
return SERPER_DEV_API_KEY is not None

10
src/khoj/routers/api.py
View File

@@ -35,7 +35,7 @@ from khoj.search_filter.file_filter import FileFilter
from khoj.search_filter.word_filter import WordFilter
from khoj.search_type import image_search, text_search
from khoj.utils import constants, state
from khoj.utils.config import GPT4AllProcessorModel
from khoj.utils.config import OfflineChatProcessorModel
from khoj.utils.helpers import ConversationCommand, timer
from khoj.utils.rawconfig import LocationData, SearchResponse
from khoj.utils.state import SearchType
@@ -341,16 +341,16 @@ async def extract_references_and_questions(
using_offline_chat = True
default_offline_llm = await ConversationAdapters.get_default_offline_llm()
chat_model = default_offline_llm.chat_model
if state.gpt4all_processor_config is None:
state.gpt4all_processor_config = GPT4AllProcessorModel(chat_model=chat_model)
if state.offline_chat_processor_config is None:
state.offline_chat_processor_config = OfflineChatProcessorModel(chat_model=chat_model)
loaded_model = state.gpt4all_processor_config.loaded_model
loaded_model = state.offline_chat_processor_config.loaded_model
inferred_queries = extract_questions_offline(
defiltered_query,
loaded_model=loaded_model,
conversation_log=meta_log,
should_extract_questions=False,
should_extract_questions=True,
location_data=location_data,
)
elif conversation_config and conversation_config.model_type == ChatModelOptions.ModelType.OPENAI:

4
src/khoj/routers/api_agents.py
View File

@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
api_agents = APIRouter()
@api_agents.get("/", response_class=Response)
@api_agents.get("", response_class=Response)
async def all_agents(
request: Request,
common: CommonQueryParams,
@@ -30,7 +30,7 @@ async def all_agents(
"slug": agent.slug,
"avatar": agent.avatar,
"name": agent.name,
"tuning": agent.tuning,
"personality": agent.personality,
"public": agent.public,
"creator": agent.creator.username if agent.creator else None,
"managed_by_admin": agent.managed_by_admin,

49
src/khoj/routers/api_chat.py
View File

@@ -14,9 +14,17 @@ from websockets import ConnectionClosedOK
from khoj.database.adapters import ConversationAdapters, EntryAdapters, aget_user_name
from khoj.database.models import KhojUser
from khoj.processor.conversation.prompts import help_message, no_entries_found
from khoj.processor.conversation.prompts import (
help_message,
no_entries_found,
no_notes_found,
)
from khoj.processor.conversation.utils import save_to_conversation_log
from khoj.processor.tools.online_search import search_online
from khoj.processor.tools.online_search import (
online_search_enabled,
read_webpages,
search_online,
)
from khoj.routers.api import extract_references_and_questions
from khoj.routers.helpers import (
ApiUserRateLimiter,
@@ -172,10 +180,15 @@ async def create_chat_session(
response = {"conversation_id": conversation.id}
conversation_metadata = {
"agent": agent_slug,
}
update_telemetry_state(
request=request,
telemetry_type="api",
api="create_chat_sessions",
metadata=conversation_metadata,
**common.__dict__,
)
@@ -469,6 +482,7 @@ async def chat(
) -> Response:
user: KhojUser = request.user.object
q = unquote(q)
logger.info(f"Chat request by {user.username}: {q}")
await is_ready_to_chat(user)
conversation_commands = [get_conversation_command(query=q, any_references=True)]
@@ -511,7 +525,7 @@ async def chat(
compiled_references, inferred_queries, defiltered_query = await extract_references_and_questions(
request, common, meta_log, q, (n or 5), (d or math.inf), conversation_commands, location
)
online_results: Dict = dict()
online_results: Dict[str, Dict] = {}
if conversation_commands == [ConversationCommand.Notes] and not await EntryAdapters.auser_has_entries(user):
no_entries_found_format = no_entries_found.format()
@@ -521,17 +535,35 @@ async def chat(
response_obj = {"response": no_entries_found_format}
return Response(content=json.dumps(response_obj), media_type="text/plain", status_code=200)
if conversation_commands == [ConversationCommand.Notes] and is_none_or_empty(compiled_references):
no_notes_found_format = no_notes_found.format()
if stream:
return StreamingResponse(iter([no_notes_found_format]), media_type="text/event-stream", status_code=200)
else:
response_obj = {"response": no_notes_found_format}
return Response(content=json.dumps(response_obj), media_type="text/plain", status_code=200)
if ConversationCommand.Notes in conversation_commands and is_none_or_empty(compiled_references):
conversation_commands.remove(ConversationCommand.Notes)
if ConversationCommand.Online in conversation_commands:
if not online_search_enabled():
conversation_commands.remove(ConversationCommand.Online)
# If online search is not enabled, try to read webpages directly
if ConversationCommand.Webpage not in conversation_commands:
conversation_commands.append(ConversationCommand.Webpage)
else:
try:
online_results = await search_online(defiltered_query, meta_log, location)
except ValueError as e:
logger.warning(f"Error searching online: {e}. Attempting to respond without online results")
if ConversationCommand.Webpage in conversation_commands:
try:
online_results = await search_online(defiltered_query, meta_log, location)
online_results = await read_webpages(defiltered_query, meta_log, location)
except ValueError as e:
return StreamingResponse(
iter(["Please set your SERPER_DEV_API_KEY to get started with online searches 🌐"]),
media_type="text/event-stream",
status_code=200,
logger.warning(
f"Error directly reading webpages: {e}. Attempting to respond without online results", exc_info=True
)
if ConversationCommand.Image in conversation_commands:
@@ -586,6 +618,7 @@ async def chat(
cmd_set = set([cmd.value for cmd in conversation_commands])
chat_metadata["conversation_command"] = cmd_set
chat_metadata["agent"] = conversation.agent.slug if conversation.agent else None
update_telemetry_state(
request=request,

99
src/khoj/routers/helpers.py
View File

@@ -33,10 +33,11 @@ from khoj.processor.conversation.utils import (
)
from khoj.routers.storage import upload_image
from khoj.utils import state
from khoj.utils.config import GPT4AllProcessorModel
from khoj.utils.config import OfflineChatProcessorModel
from khoj.utils.helpers import (
ConversationCommand,
is_none_or_empty,
is_valid_url,
log_telemetry,
mode_descriptions_for_llm,
timer,
@@ -68,9 +69,9 @@ async def is_ready_to_chat(user: KhojUser):
if has_offline_config and user_conversation_config and user_conversation_config.model_type == "offline":
chat_model = user_conversation_config.chat_model
if state.gpt4all_processor_config is None:
if state.offline_chat_processor_config is None:
logger.info("Loading Offline Chat Model...")
state.gpt4all_processor_config = GPT4AllProcessorModel(chat_model=chat_model)
state.offline_chat_processor_config = OfflineChatProcessorModel(chat_model=chat_model)
return True
ready = has_openai_config or has_offline_config
@@ -168,7 +169,8 @@ async def aget_relevant_information_sources(query: str, conversation_history: di
chat_history=chat_history,
)
response = await send_message_to_model_wrapper(relevant_tools_prompt, response_type="json_object")
with timer("Chat actor: Infer information sources to refer", logger):
response = await send_message_to_model_wrapper(relevant_tools_prompt, response_type="json_object")
try:
response = response.strip()
@@ -212,7 +214,8 @@ async def aget_relevant_output_modes(query: str, conversation_history: dict):
chat_history=chat_history,
)
response = await send_message_to_model_wrapper(relevant_mode_prompt)
with timer("Chat actor: Infer output mode for chat response", logger):
response = await send_message_to_model_wrapper(relevant_mode_prompt)
try:
response = response.strip()
@@ -230,6 +233,36 @@ async def aget_relevant_output_modes(query: str, conversation_history: dict):
return ConversationCommand.Default
async def infer_webpage_urls(q: str, conversation_history: dict, location_data: LocationData) -> List[str]:
"""
Infer webpage links from the given query
"""
location = f"{location_data.city}, {location_data.region}, {location_data.country}" if location_data else "Unknown"
chat_history = construct_chat_history(conversation_history)
utc_date = datetime.utcnow().strftime("%Y-%m-%d")
online_queries_prompt = prompts.infer_webpages_to_read.format(
current_date=utc_date,
query=q,
chat_history=chat_history,
location=location,
)
with timer("Chat actor: Infer webpage urls to read", logger):
response = await send_message_to_model_wrapper(online_queries_prompt, response_type="json_object")
# Validate that the response is a non-empty, JSON-serializable list of URLs
try:
response = response.strip()
urls = json.loads(response)
valid_unique_urls = {str(url).strip() for url in urls["links"] if is_valid_url(url)}
if is_none_or_empty(valid_unique_urls):
raise ValueError(f"Invalid list of urls: {response}")
return list(valid_unique_urls)
except Exception:
raise ValueError(f"Invalid list of urls: {response}")
async def generate_online_subqueries(q: str, conversation_history: dict, location_data: LocationData) -> List[str]:
"""
Generate subqueries from the given query
@@ -245,7 +278,8 @@ async def generate_online_subqueries(q: str, conversation_history: dict, locatio
location=location,
)
response = await send_message_to_model_wrapper(online_queries_prompt, response_type="json_object")
with timer("Chat actor: Generate online search subqueries", logger):
response = await send_message_to_model_wrapper(online_queries_prompt, response_type="json_object")
# Validate that the response is a non-empty, JSON-serializable list
try:
@@ -274,9 +308,10 @@ async def extract_relevant_info(q: str, corpus: str) -> Union[str, None]:
corpus=corpus.strip(),
)
response = await send_message_to_model_wrapper(
extract_relevant_information, prompts.system_prompt_extract_relevant_information
)
with timer("Chat actor: Extract relevant information from data", logger):
response = await send_message_to_model_wrapper(
extract_relevant_information, prompts.system_prompt_extract_relevant_information
)
return response.strip()
@@ -292,10 +327,13 @@ async def generate_better_image_prompt(
Generate a better image prompt from the given query
"""
location = f"{location_data.city}, {location_data.region}, {location_data.country}" if location_data else "Unknown"
today_date = datetime.now(tz=timezone.utc).strftime("%Y-%m-%d")
location_prompt = prompts.user_location.format(location=location)
if location_data:
location = f"{location_data.city}, {location_data.region}, {location_data.country}"
location_prompt = prompts.user_location.format(location=location)
else:
location_prompt = "Unknown"
user_references = "\n\n".join([f"# {item}" for item in note_references])
@@ -305,8 +343,8 @@ async def generate_better_image_prompt(
for result in online_results:
if online_results[result].get("answerBox"):
simplified_online_results[result] = online_results[result]["answerBox"]
elif online_results[result].get("extracted_content"):
simplified_online_results[result] = online_results[result]["extracted_content"]
elif online_results[result].get("webpages"):
simplified_online_results[result] = online_results[result]["webpages"]
image_prompt = prompts.image_generation_improve_prompt.format(
query=q,
@@ -317,7 +355,8 @@ async def generate_better_image_prompt(
online_results=simplified_online_results,
)
response = await send_message_to_model_wrapper(image_prompt)
with timer("Chat actor: Generate contextual image prompt", logger):
response = await send_message_to_model_wrapper(image_prompt)
return response.strip()
@@ -332,27 +371,31 @@ async def send_message_to_model_wrapper(
if conversation_config is None:
raise HTTPException(status_code=500, detail="Contact the server administrator to set a default chat model.")
truncated_messages = generate_chatml_messages_with_context(
user_message=message, system_message=system_message, model_name=conversation_config.chat_model
)
chat_model = conversation_config.chat_model
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)
if state.offline_chat_processor_config is None or state.offline_chat_processor_config.loaded_model is None:
state.offline_chat_processor_config = OfflineChatProcessorModel(chat_model)
loaded_model = state.offline_chat_processor_config.loaded_model
truncated_messages = generate_chatml_messages_with_context(
user_message=message, system_message=system_message, model_name=chat_model, loaded_model=loaded_model
)
loaded_model = state.gpt4all_processor_config.loaded_model
return send_message_to_model_offline(
message=truncated_messages[-1].content,
messages=truncated_messages,
loaded_model=loaded_model,
model=conversation_config.chat_model,
model=chat_model,
streaming=False,
system_message=truncated_messages[0].content,
)
elif conversation_config.model_type == "openai":
openai_chat_config = await ConversationAdapters.aget_openai_conversation_config()
api_key = openai_chat_config.api_key
chat_model = conversation_config.chat_model
truncated_messages = generate_chatml_messages_with_context(
user_message=message, system_message=system_message, model_name=chat_model
)
openai_response = send_message_to_model(
messages=truncated_messages, api_key=api_key, model=chat_model, response_type=response_type
)
@@ -367,7 +410,7 @@ def generate_chat_response(
meta_log: dict,
conversation: Conversation,
compiled_references: List[str] = [],
online_results: Dict[str, Any] = {},
online_results: Dict[str, Dict] = {},
inferred_queries: List[str] = [],
conversation_commands: List[ConversationCommand] = [ConversationCommand.Default],
user: KhojUser = None,
@@ -398,10 +441,10 @@ def generate_chat_response(
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)
if state.offline_chat_processor_config is None or state.offline_chat_processor_config.loaded_model is None:
state.offline_chat_processor_config = OfflineChatProcessorModel(conversation_config.chat_model)
loaded_model = state.gpt4all_processor_config.loaded_model
loaded_model = state.offline_chat_processor_config.loaded_model
chat_response = converse_offline(
references=compiled_references,
online_results=online_results,

4
src/khoj/routers/web_client.py
View File

@@ -142,7 +142,7 @@ def agents_page(request: Request):
"slug": agent.slug,
"avatar": agent.avatar,
"name": agent.name,
"tuning": agent.tuning,
"personality": agent.personality,
"public": agent.public,
"creator": agent.creator.username if agent.creator else None,
"managed_by_admin": agent.managed_by_admin,
@@ -186,7 +186,7 @@ def agent_page(request: Request, agent_slug: str):
"slug": agent.slug,
"avatar": agent.avatar,
"name": agent.name,
"tuning": agent.tuning,
"personality": agent.personality,
"public": agent.public,
"creator": agent.creator.username if agent.creator else None,
"managed_by_admin": agent.managed_by_admin,

9
src/khoj/utils/config.py
View File

@@ -70,15 +70,12 @@ class SearchModels:
@dataclass
class GPT4AllProcessorConfig:
class OfflineChatProcessorConfig:
loaded_model: Union[Any, None] = None
class GPT4AllProcessorModel:
def __init__(
self,
chat_model: str = "mistral-7b-instruct-v0.1.Q4_0.gguf",
):
class OfflineChatProcessorModel:
def __init__(self, chat_model: str = "NousResearch/Hermes-2-Pro-Mistral-7B-GGUF"):
self.chat_model = chat_model
self.loaded_model = None
try:

2
src/khoj/utils/constants.py
View File

@@ -6,7 +6,7 @@ empty_escape_sequences = "\n|\r|\t| "
app_env_filepath = "~/.khoj/env"
telemetry_server = "https://khoj.beta.haletic.com/v1/telemetry"
content_directory = "~/.khoj/content/"
default_offline_chat_model = "mistral-7b-instruct-v0.1.Q4_0.gguf"
default_offline_chat_model = "NousResearch/Hermes-2-Pro-Mistral-7B-GGUF"
default_online_chat_model = "gpt-4-turbo-preview"
empty_config = {

15
src/khoj/utils/helpers.py
View File

@@ -15,6 +15,7 @@ from os import path
from pathlib import Path
from time import perf_counter
from typing import TYPE_CHECKING, Optional, Union
from urllib.parse import urlparse
import torch
from asgiref.sync import sync_to_async
@@ -270,6 +271,7 @@ class ConversationCommand(str, Enum):
Notes = "notes"
Help = "help"
Online = "online"
Webpage = "webpage"
Image = "image"
@@ -278,15 +280,17 @@ command_descriptions = {
ConversationCommand.Notes: "Only talk about information that is available in your knowledge base.",
ConversationCommand.Default: "The default command when no command specified. It intelligently auto-switches between general and notes mode.",
ConversationCommand.Online: "Search for information on the internet.",
ConversationCommand.Webpage: "Get information from webpage links provided by you.",
ConversationCommand.Image: "Generate images by describing your imagination in words.",
ConversationCommand.Help: "Display a help message with all available commands and other metadata.",
}
tool_descriptions_for_llm = {
ConversationCommand.Default: "To use a mix of your internal knowledge and the user's personal knowledge, or if you don't entirely understand the query.",
ConversationCommand.General: "Use this when you can answer the question without any outside information or personal knowledge",
ConversationCommand.General: "To use when you can answer the question without any outside information or personal knowledge",
ConversationCommand.Notes: "To search the user's personal knowledge base. Especially helpful if the question expects context from the user's notes or documents.",
ConversationCommand.Online: "To search for the latest, up-to-date information from the internet. Note: **Questions about Khoj should always use this data source**",
ConversationCommand.Webpage: "To use if the user has directly provided the webpage urls or you are certain of the webpage urls to read.",
}
mode_descriptions_for_llm = {
@@ -340,3 +344,12 @@ def in_debug_mode():
"""Check if Khoj is running in debug mode.
Set KHOJ_DEBUG environment variable to true to enable debug mode."""
return is_env_var_true("KHOJ_DEBUG")
def is_valid_url(url: str) -> bool:
"""Check if a string is a valid URL"""
try:
result = urlparse(url.strip())
return all([result.scheme, result.netloc])
except:
return False

6
src/khoj/utils/initialization.py
View File

@@ -32,17 +32,13 @@ def initialization():
)
try:
# Note: gpt4all package is not available on all devices.
# So ensure gpt4all package is installed before continuing this step.
import gpt4all
use_offline_model = input("Use offline chat model? (y/n): ")
if use_offline_model == "y":
logger.info("🗣️ Setting up offline chat model")
OfflineChatProcessorConversationConfig.objects.create(enabled=True)
offline_chat_model = input(
f"Enter the offline chat model you want to use, See GPT4All for supported models (default: {default_offline_chat_model}): "
f"Enter the offline chat model you want to use. See HuggingFace for available GGUF models (default: {default_offline_chat_model}): "
)
if offline_chat_model == "":
ChatModelOptions.objects.create(

2
src/khoj/utils/rawconfig.py
View File

@@ -91,7 +91,7 @@ class OpenAIProcessorConfig(ConfigBase):
class OfflineChatProcessorConfig(ConfigBase):
enable_offline_chat: Optional[bool] = False
chat_model: Optional[str] = "mistral-7b-instruct-v0.1.Q4_0.gguf"
chat_model: Optional[str] = "NousResearch/Hermes-2-Pro-Mistral-7B-GGUF"
class ConversationProcessorConfig(ConfigBase):

4
src/khoj/utils/state.py
View File

@@ -9,7 +9,7 @@ from whisper import Whisper
from khoj.processor.embeddings import CrossEncoderModel, EmbeddingsModel
from khoj.utils import config as utils_config
from khoj.utils.config import ContentIndex, GPT4AllProcessorModel, SearchModels
from khoj.utils.config import ContentIndex, OfflineChatProcessorModel, SearchModels
from khoj.utils.helpers import LRU, get_device
from khoj.utils.rawconfig import FullConfig
@@ -20,7 +20,7 @@ embeddings_model: Dict[str, EmbeddingsModel] = None
cross_encoder_model: Dict[str, CrossEncoderModel] = None
content_index = ContentIndex()
openai_client: OpenAI = None
gpt4all_processor_config: GPT4AllProcessorModel = None
offline_chat_processor_config: OfflineChatProcessorModel = None
whisper_model: Whisper = None
config_file: Path = None
verbose: int = 0