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Code Issues Projects Releases Packages Wiki Activity Actions 5

Improve Document, Online Search to Answer Vague or Meta Questions (#870)

Browse source 
- Major
  - Improve doc search actor performance on vague, random or meta questions
  - Pass user's name to document and online search actors prompts

- Minor
  - Fix and improve openai chat actor tests
  - Remove unused max tokns arg to extract qs func of doc search actor
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Debanjum 2024-08-16 06:46:13 -07:00 committed by GitHub
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10 changed files with 96 additions and 31 deletions
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8
src/khoj/processor/conversation/anthropic/anthropic_chat.py
View file

@ -6,7 +6,7 @@ from typing import Dict, Optional
from langchain.schema import ChatMessage
from khoj.database.models import Agent
from khoj.database.models import Agent, KhojUser
from khoj.processor.conversation import prompts
from khoj.processor.conversation.anthropic.utils import (
anthropic_chat_completion_with_backoff,
@ -24,14 +24,16 @@ def extract_questions_anthropic(
model: Optional[str] = "claude-instant-1.2",
conversation_log={},
api_key=None,
temperature=0,
temperature=0.7,
location_data: LocationData = None,
user: KhojUser = None,
):
"""
Infer search queries to retrieve relevant notes to answer user query
"""
# Extract Past User Message and Inferred Questions from Conversation Log
location = f"{location_data.city}, {location_data.region}, {location_data.country}" if location_data else "Unknown"
username = prompts.user_name.format(name=user.get_full_name()) if user and user.get_full_name() else ""
# Extract Past User Message and Inferred Questions from Conversation Log
chat_history = "".join(
@ -50,11 +52,13 @@ def extract_questions_anthropic(
system_prompt = prompts.extract_questions_anthropic_system_prompt.format(
current_date=today.strftime("%Y-%m-%d"),
day_of_week=today.strftime("%A"),
current_month=today.strftime("%Y-%m"),
last_new_year=last_new_year.strftime("%Y"),
last_new_year_date=last_new_year.strftime("%Y-%m-%d"),
current_new_year_date=current_new_year.strftime("%Y-%m-%d"),
yesterday_date=(today - timedelta(days=1)).strftime("%Y-%m-%d"),
location=location,
username=username,
)
prompt = prompts.extract_questions_anthropic_user_message.format(

18
src/khoj/processor/conversation/offline/chat_model.py
View file

@ -7,7 +7,7 @@ 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.database.models import Agent, KhojUser
from khoj.processor.conversation import prompts
from khoj.processor.conversation.offline.utils import download_model
from khoj.processor.conversation.utils import (
@ -30,7 +30,9 @@ def extract_questions_offline(
use_history: bool = True,
should_extract_questions: bool = True,
location_data: LocationData = None,
user: KhojUser = None,
max_prompt_size: int = None,
temperature: float = 0.7,
) -> List[str]:
"""
Infer search queries to retrieve relevant notes to answer user query
@ -45,6 +47,7 @@ def extract_questions_offline(
offline_chat_model = loaded_model or download_model(model, max_tokens=max_prompt_size)
location = f"{location_data.city}, {location_data.region}, {location_data.country}" if location_data else "Unknown"
username = prompts.user_name.format(name=user.get_full_name()) if user and user.get_full_name() else ""
# Extract Past User Message and Inferred Questions from Conversation Log
chat_history = ""
@ -64,10 +67,12 @@ def extract_questions_offline(
chat_history=chat_history,
current_date=today.strftime("%Y-%m-%d"),
day_of_week=today.strftime("%A"),
current_month=today.strftime("%Y-%m"),
yesterday_date=yesterday,
last_year=last_year,
this_year=today.year,
location=location,
username=username,
)
messages = generate_chatml_messages_with_context(
@ -77,7 +82,11 @@ def extract_questions_offline(
state.chat_lock.acquire()
try:
response = send_message_to_model_offline(
messages, loaded_model=offline_chat_model, model=model, max_prompt_size=max_prompt_size
messages,
loaded_model=offline_chat_model,
model=model,
max_prompt_size=max_prompt_size,
temperature=temperature,
)
finally:
state.chat_lock.release()
@ -229,6 +238,7 @@ def send_message_to_model_offline(
messages: List[ChatMessage],
loaded_model=None,
model="NousResearch/Hermes-2-Pro-Mistral-7B-GGUF",
temperature: float = 0.2,
streaming=False,
stop=[],
max_prompt_size: int = None,
@ -236,7 +246,9 @@ def send_message_to_model_offline(
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, max_tokens=max_prompt_size)
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)
response = offline_chat_model.create_chat_completion(
messages_dict, stop=stop, stream=streaming, temperature=temperature
)
if streaming:
return response
else:

9
src/khoj/processor/conversation/openai/gpt.py
View file

@ -5,7 +5,7 @@ from typing import Dict, Optional
from langchain.schema import ChatMessage
from khoj.database.models import Agent
from khoj.database.models import Agent, KhojUser
from khoj.processor.conversation import prompts
from khoj.processor.conversation.openai.utils import (
chat_completion_with_backoff,
@ -24,14 +24,15 @@ def extract_questions(
conversation_log={},
api_key=None,
api_base_url=None,
temperature=0,
max_tokens=100,
temperature=0.7,
location_data: LocationData = None,
user: KhojUser = None,
):
"""
Infer search queries to retrieve relevant notes to answer user query
"""
location = f"{location_data.city}, {location_data.region}, {location_data.country}" if location_data else "Unknown"
username = prompts.user_name.format(name=user.get_full_name()) if user and user.get_full_name() else ""
# Extract Past User Message and Inferred Questions from Conversation Log
chat_history = "".join(
@ -50,6 +51,7 @@ def extract_questions(
prompt = prompts.extract_questions.format(
current_date=today.strftime("%Y-%m-%d"),
day_of_week=today.strftime("%A"),
current_month=today.strftime("%Y-%m"),
last_new_year=last_new_year.strftime("%Y"),
last_new_year_date=last_new_year.strftime("%Y-%m-%d"),
current_new_year_date=current_new_year.strftime("%Y-%m-%d"),
@ -59,6 +61,7 @@ def extract_questions(
text=text,
yesterday_date=(today - timedelta(days=1)).strftime("%Y-%m-%d"),
location=location,
username=username,
)
messages = [ChatMessage(content=prompt, role="user")]

2
src/khoj/processor/conversation/openai/utils.py
View file

@ -36,7 +36,7 @@ def completion_with_backoff(
messages, model, temperature=0, openai_api_key=None, api_base_url=None, model_kwargs=None
) -> str:
client_key = f"{openai_api_key}--{api_base_url}"
client: openai.OpenAI = openai_clients.get(client_key)
client: openai.OpenAI | None = openai_clients.get(client_key)
if not client:
client = openai.OpenAI(
api_key=openai_api_key,

20
src/khoj/processor/conversation/prompts.py
View file

@ -208,10 +208,12 @@ Construct search queries to retrieve relevant information to answer the user's q
- 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.
- When asked a meta, vague or random questions, search for a variety of broad topics to answer the user's question.
- Share relevant search queries as a JSON list of strings. Do not say anything else.
Current Date: {day_of_week}, {current_date}
User's Location: {location}
{username}
Examples:
Q: How was my trip to Cambodia?
@ -238,6 +240,9 @@ 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}'"]
Q: Share some random, interesting experiences from this month
Khoj: ["Exciting travel adventures from {current_month}", "Fun social events dt>='{current_month}-01' dt<'{current_date}'", "Intense emotional experiences in {current_month}"]
Chat History:
{chat_history}
What searches will you perform to answer the following question, using the chat history as reference? Respond only with relevant search queries as a valid JSON list of strings.
@ -254,10 +259,12 @@ Construct search queries to retrieve relevant information to answer the user's q
- 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.
- When asked a meta, vague or random questions, search for a variety of broad topics to answer the user's question.
What searches will you perform to answer the users question? Respond with search queries as list of strings in a JSON object.
Current Date: {day_of_week}, {current_date}
User's Location: {location}
{username}
Q: How was my trip to Cambodia?
Khoj: {{"queries": ["How was my trip to Cambodia?"]}}
@ -279,6 +286,10 @@ Q: How many tennis balls fit in the back of a 2002 Honda Civic?
Khoj: {{"queries": ["What is the size of a tennis ball?", "What is the trunk size of a 2002 Honda Civic?"]}}
A: 1085 tennis balls will fit in the trunk of a Honda Civic
Q: Share some random, interesting experiences from this month
Khoj: {{"queries": ["Exciting travel adventures from {current_month}", "Fun social events dt>='{current_month}-01' dt<'{current_date}'", "Intense emotional experiences in {current_month}"]}}
A: You had a great time at the local beach with your friends, attended a music concert and had a deep conversation with your friend, Khalid.
Q: Is Bob older than Tom?
Khoj: {{"queries": ["When was Bob born?", "What is Tom's age?"]}}
A: Yes, Bob is older than Tom. As Bob was born on 1984-01-01 and Tom is 30 years old.
@ -305,11 +316,13 @@ Construct search queries to retrieve relevant information to answer the user's q
- 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.
- When asked a meta, vague or random questions, search for a variety of broad topics to answer the user's question.
What searches will you perform to answer the users question? Respond with a JSON object with the key "queries" mapping to a list of searches you would perform on the user's knowledge base. Just return the queries and nothing else.
Current Date: {day_of_week}, {current_date}
User's Location: {location}
{username}
Here are some examples of how you can construct search queries to answer the user's question:
@ -328,6 +341,11 @@ A: I can help you live healthier and happier across work and personal life
User: Who all did I meet here yesterday?
Assistant: {{"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.
User: Share some random, interesting experiences from this month
Assistant: {{"queries": ["Exciting travel adventures from {current_month}", "Fun social events dt>='{current_month}-01' dt<'{current_date}'", "Intense emotional experiences in {current_month}"]}}
A: You had a great time at the local beach with your friends, attended a music concert and had a deep conversation with your friend, Khalid.
""".strip()
)
@ -525,6 +543,7 @@ 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}
{username}
Here are some examples:
History:
@ -571,6 +590,7 @@ What Google searches, if any, will you need to perform to answer the user's ques
Provide search queries as a list of strings in a JSON object. Do not wrap the json in a codeblock.
Current Date: {current_date}
User's Location: {location}
{username}
Here are some examples:
History:

12
src/khoj/processor/tools/online_search.py
View file

@ -10,6 +10,7 @@ import aiohttp
from bs4 import BeautifulSoup
from markdownify import markdownify
from khoj.database.models import KhojUser
from khoj.routers.helpers import (
ChatEvent,
extract_relevant_info,
@ -51,6 +52,7 @@ async def search_online(
query: str,
conversation_history: dict,
location: LocationData,
user: KhojUser,
send_status_func: Optional[Callable] = None,
custom_filters: List[str] = [],
):
@ -61,7 +63,7 @@ async def search_online(
return
# Breakdown the query into subqueries to get the correct answer
subqueries = await generate_online_subqueries(query, conversation_history, location)
subqueries = await generate_online_subqueries(query, conversation_history, location, user)
response_dict = {}
if subqueries:
@ -126,14 +128,18 @@ async def search_with_google(query: str) -> Tuple[str, Dict[str, List[Dict]]]:
async def read_webpages(
query: str, conversation_history: dict, location: LocationData, send_status_func: Optional[Callable] = None
query: str,
conversation_history: dict,
location: LocationData,
user: KhojUser,
send_status_func: Optional[Callable] = None,
):
"Infer web pages to read from the query and extract relevant information from them"
logger.info(f"Inferring web pages to read")
if send_status_func:
async for event in send_status_func(f"**Inferring web pages to read**"):
yield {ChatEvent.STATUS: event}
urls = await infer_webpage_urls(query, conversation_history, location)
urls = await infer_webpage_urls(query, conversation_history, location, user)
logger.info(f"Reading web pages at: {urls}")
if send_status_func:

4
src/khoj/routers/api.py
View file

@ -388,6 +388,7 @@ async def extract_references_and_questions(
conversation_log=meta_log,
should_extract_questions=True,
location_data=location_data,
user=user,
max_prompt_size=conversation_config.max_prompt_size,
)
elif conversation_config.model_type == ChatModelOptions.ModelType.OPENAI:
@ -402,7 +403,7 @@ async def extract_references_and_questions(
api_base_url=base_url,
conversation_log=meta_log,
location_data=location_data,
max_tokens=conversation_config.max_prompt_size,
user=user,
)
elif conversation_config.model_type == ChatModelOptions.ModelType.ANTHROPIC:
api_key = conversation_config.openai_config.api_key
@ -413,6 +414,7 @@ async def extract_references_and_questions(
api_key=api_key,
conversation_log=meta_log,
location_data=location_data,
user=user,
)
# Collate search results as context for GPT

4
src/khoj/routers/api_chat.py
View file

@ -792,7 +792,7 @@ async def chat(
if ConversationCommand.Online in conversation_commands:
try:
async for result in search_online(
defiltered_query, meta_log, location, partial(send_event, ChatEvent.STATUS), custom_filters
defiltered_query, meta_log, location, user, partial(send_event, ChatEvent.STATUS), custom_filters
):
if isinstance(result, dict) and ChatEvent.STATUS in result:
yield result[ChatEvent.STATUS]
@ -809,7 +809,7 @@ async def chat(
if ConversationCommand.Webpage in conversation_commands:
try:
async for result in read_webpages(
defiltered_query, meta_log, location, partial(send_event, ChatEvent.STATUS)
defiltered_query, meta_log, location, user, partial(send_event, ChatEvent.STATUS)
):
if isinstance(result, dict) and ChatEvent.STATUS in result:
yield result[ChatEvent.STATUS]

12
src/khoj/routers/helpers.py
View file

@ -340,11 +340,14 @@ async def aget_relevant_output_modes(query: str, conversation_history: dict, is_
return ConversationCommand.Text
async def infer_webpage_urls(q: str, conversation_history: dict, location_data: LocationData) -> List[str]:
async def infer_webpage_urls(
q: str, conversation_history: dict, location_data: LocationData, user: KhojUser
) -> 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"
username = prompts.user_name.format(name=user.get_full_name()) if user.get_full_name() else ""
chat_history = construct_chat_history(conversation_history)
utc_date = datetime.utcnow().strftime("%Y-%m-%d")
@ -353,6 +356,7 @@ async def infer_webpage_urls(q: str, conversation_history: dict, location_data:
query=q,
chat_history=chat_history,
location=location,
username=username,
)
with timer("Chat actor: Infer webpage urls to read", logger):
@ -370,11 +374,14 @@ async def infer_webpage_urls(q: str, conversation_history: dict, location_data:
raise ValueError(f"Invalid list of urls: {response}")
async def generate_online_subqueries(q: str, conversation_history: dict, location_data: LocationData) -> List[str]:
async def generate_online_subqueries(
q: str, conversation_history: dict, location_data: LocationData, user: KhojUser
) -> List[str]:
"""
Generate subqueries from the given query
"""
location = f"{location_data.city}, {location_data.region}, {location_data.country}" if location_data else "Unknown"
username = prompts.user_name.format(name=user.get_full_name()) if user.get_full_name() else ""
chat_history = construct_chat_history(conversation_history)
utc_date = datetime.utcnow().strftime("%Y-%m-%d")
@ -383,6 +390,7 @@ async def generate_online_subqueries(q: str, conversation_history: dict, locatio
query=q,
chat_history=chat_history,
location=location,
username=username,
)
with timer("Chat actor: Generate online search subqueries", logger):

38
tests/test_openai_chat_actors.py
View file

@ -17,6 +17,7 @@ from khoj.routers.helpers import (
)
from khoj.utils.helpers import ConversationCommand
from khoj.utils.rawconfig import LocationData
from tests.conftest import default_user2
# Initialize variables for tests
api_key = os.getenv("OPENAI_API_KEY")
@ -412,18 +413,23 @@ def test_answer_general_question_not_in_chat_history_or_retrieved_content():
# ----------------------------------------------------------------------------------------------------
@pytest.mark.xfail(reason="Chat actor not consistently capable of asking for clarification yet.")
@pytest.mark.chatquality
def test_ask_for_clarification_if_not_enough_context_in_question():
"Chat actor should ask for clarification if question cannot be answered unambiguously with the provided context"
# Arrange
context = [
f"""# Ramya
My sister, Ramya, is married to Kali Devi. They have 2 kids, Ravi and Rani.""",
f"""# Fang
My sister, Fang Liu is married to Xi Li. They have 1 kid, Xiao Li.""",
f"""# Aiyla
My sister, Aiyla is married to Tolga. They have 3 kids, Yildiz, Ali and Ahmet.""",
{
"compiled": f"""# Ramya
My sister, Ramya, is married to Kali Devi. They have 2 kids, Ravi and Rani."""
},
{
"compiled": f"""# Fang
My sister, Fang Liu is married to Xi Li. They have 1 kid, Xiao Li."""
},
{
"compiled": f"""# Aiyla
My sister, Aiyla is married to Tolga. They have 3 kids, Yildiz, Ali and Ahmet."""
},
]
# Act
@ -481,12 +487,12 @@ def test_agent_prompt_should_be_used(openai_agent):
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
@freeze_time("2024-04-04", ignore=["transformers"])
async def test_websearch_with_operators(chat_client):
async def test_websearch_with_operators(chat_client, default_user2):
# Arrange
user_query = "Share popular posts on r/worldnews this month"
# Act
responses = await generate_online_subqueries(user_query, {}, None)
responses = await generate_online_subqueries(user_query, {}, None, default_user2)
# Assert
assert any(
@ -501,12 +507,12 @@ async def test_websearch_with_operators(chat_client):
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
async def test_websearch_khoj_website_for_info_about_khoj(chat_client):
async def test_websearch_khoj_website_for_info_about_khoj(chat_client, default_user2):
# Arrange
user_query = "Do you support image search?"
# Act
responses = await generate_online_subqueries(user_query, {}, None)
responses = await generate_online_subqueries(user_query, {}, None, default_user2)
# Assert
assert any(
@ -558,12 +564,12 @@ async def test_select_data_sources_actor_chooses_to_search_notes(
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
async def test_infer_webpage_urls_actor_extracts_correct_links(chat_client):
async def test_infer_webpage_urls_actor_extracts_correct_links(chat_client, default_user2):
# Arrange
user_query = "Summarize the wikipedia page on the history of the internet"
# Act
urls = await infer_webpage_urls(user_query, {}, None)
urls = await infer_webpage_urls(user_query, {}, None, default_user2)
# Assert
assert "https://en.wikipedia.org/wiki/History_of_the_Internet" in urls
@ -667,6 +673,10 @@ def populate_chat_history(message_list):
conversation_log["chat"] += message_to_log(
user_message,
gpt_message,
{"context": context, "intent": {"query": user_message, "inferred-queries": f'["{user_message}"]'}},
khoj_message_metadata={
"context": context,
"intent": {"query": user_message, "inferred-queries": f'["{user_message}"]'},
},
conversation_log=[],
)
return conversation_log