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Merge branch 'master' of github.com:khoj-ai/khoj into features/customize-chat-with-agents

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sabaimran 2024-03-15 12:13:28 +05:30
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2
documentation/docs/get-started/setup.mdx
View file

@ -175,7 +175,7 @@ To use the desktop client, you need to go to your Khoj server's settings page (h
1. Go to http://localhost:42110/server/admin and login with your admin credentials.
1. Go to [OpenAI settings](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/) in the server admin settings to add an OpenAI processor conversation config. This is where you set your API key. Alternatively, you can go to the [offline chat settings](http://localhost:42110/server/admin/database/offlinechatprocessorconversationconfig/) and simply create a new setting with `Enabled` set to `True`.
2. Go to the ChatModelOptions if you want to add additional models for chat.
- Set the `chat-model` field to a supported chat model[^1] of your choice. For example, you can specify `gpt-4` if you're using OpenAI or `mistral-7b-instruct-v0.1.Q4_0.gguf` if you're using offline chat.
- Set the `chat-model` field to a supported chat model[^1] of your choice. For example, you can specify `gpt-4-turbo-preview` if you're using OpenAI or `mistral-7b-instruct-v0.1.Q4_0.gguf` if you're using offline chat.
- Make sure to set the `model-type` field to `OpenAI` or `Offline` respectively.
- The `tokenizer` and `max-prompt-size` fields are optional. Set them only when using a non-standard model (i.e not mistral, gpt or llama2 model).
1. Select files and folders to index [using the desktop client](/get-started/setup#2-download-the-desktop-client). When you click 'Save', the files will be sent to your server for indexing.

2
documentation/docs/miscellaneous/advanced.md
View file

@ -35,7 +35,7 @@ Use structured query syntax to filter entries from your knowledge based used by
Use this if you want to use non-standard, open or commercial, local or hosted LLM models for Khoj chat
1. Setup your desired chat LLM by installing an OpenAI compatible LLM API Server like [LiteLLM](https://docs.litellm.ai/docs/proxy/quick_start), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#openai-compatible-web-server)
2. Set environment variable `OPENAI_API_BASE="<url-of-your-llm-server>"` before starting Khoj
3. Add ChatModelOptions with `model-type` `OpenAI`, and `chat-model` to anything (e.g `gpt-4`) during [Config](/get-started/setup#3-configure)
3. Add ChatModelOptions with `model-type` `OpenAI`, and `chat-model` to anything (e.g `gpt-3.5-turbo`) during [Config](/get-started/setup#3-configure)
- *(Optional)* Set the `tokenizer` and `max-prompt-size` relevant to the actual chat model you're using
#### Sample Setup using LiteLLM and Mistral API

2
manifest.json
View file

@ -1,7 +1,7 @@
{
"id": "khoj",
"name": "Khoj",
"version": "1.6.2",
"version": "1.7.0",
"minAppVersion": "0.15.0",
"description": "An AI copilot for your Second Brain",
"author": "Khoj Inc.",

11
pyproject.toml
View file

@ -36,7 +36,7 @@ classifiers = [
"Topic :: Text Processing :: Linguistic",
]
dependencies = [
"bs4 >= 0.0.1",
"beautifulsoup4 ~= 4.12.3",
"dateparser >= 1.1.1",
"defusedxml == 0.7.1",
"fastapi >= 0.104.1",
@ -58,7 +58,6 @@ dependencies = [
"langchain <= 0.2.0",
"langchain-openai >= 0.0.5",
"requests >= 2.26.0",
"bs4 >= 0.0.1",
"anyio == 3.7.1",
"pymupdf >= 1.23.5",
"django == 4.2.10",
@ -76,14 +75,14 @@ dependencies = [
"openai-whisper >= 20231117",
"django-phonenumber-field == 7.3.0",
"phonenumbers == 8.13.27",
"markdownify ~= 0.11.6",
]
dynamic = ["version"]
[project.urls]
Homepage = "https://github.com/khoj-ai/khoj#readme"
Issues = "https://github.com/khoj-ai/khoj/issues"
Discussions = "https://github.com/khoj-ai/khoj/discussions"
Releases = "https://github.com/khoj-ai/khoj/releases"
Homepage = "https://khoj.dev"
Documentation = "https://docs.khoj.dev"
Code = "https://github.com/khoj-ai/khoj"
[project.scripts]
khoj = "khoj.main:run"

23
src/interface/desktop/chat.html
View file

@ -357,15 +357,16 @@
let numReferences = 0;
if (Array.isArray(references)) {
numReferences = references.length;
if (references.hasOwnProperty("notes")) {
numReferences += references["notes"].length;
references.forEach((reference, index) => {
references["notes"].forEach((reference, index) => {
let polishedReference = generateReference(reference, index);
referenceSection.appendChild(polishedReference);
});
} else {
numReferences += processOnlineReferences(referenceSection, references);
}
if (references.hasOwnProperty("online")){
numReferences += processOnlineReferences(referenceSection, references["online"]);
}
let referenceExpandButton = document.createElement('button');
@ -511,7 +512,7 @@
// Handle streamed response of type text/event-stream or text/plain
const reader = response.body.getReader();
const decoder = new TextDecoder();
let references = null;
let references = {};
readStream();
@ -519,8 +520,8 @@
reader.read().then(({ done, value }) => {
if (done) {
// Append any references after all the data has been streamed
if (references != null) {
newResponseText.appendChild(references);
if (references != {}) {
newResponseText.appendChild(createReferenceSection(references));
}
document.getElementById("chat-body").scrollTop = document.getElementById("chat-body").scrollHeight;
document.getElementById("chat-input").removeAttribute("disabled");
@ -538,7 +539,11 @@
const rawReference = chunk.split("### compiled references:")[1];
const rawReferenceAsJson = JSON.parse(rawReference);
references = createReferenceSection(rawReferenceAsJson);
if (rawReferenceAsJson instanceof Array) {
references["notes"] = rawReferenceAsJson;
} else if (typeof rawReferenceAsJson === "object" && rawReferenceAsJson !== null) {
references["online"] = rawReferenceAsJson;
}
readStream();
} else {
// Display response from Khoj

6
src/interface/desktop/main.js
View file

@ -384,10 +384,10 @@ const createWindow = (tab = 'chat.html') => {
// Open external links in link handler registered on OS (e.g. browser)
win.webContents.setWindowOpenHandler(async ({ url }) => {
const shouldOpen = { response: 0 };
let shouldOpen = { response: 0 };
if (!url.startsWith('http://')) {
// Confirm before opening non-HTTP links
if (!url.startsWith(store.get('hostURL'))) {
// Confirm before opening external links
const confirmNotice = `Do you want to open this link? It will be handled by an external application.\n\n${url}`;
shouldOpen = await dialog.showMessageBox({
type: 'question',

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

@ -1,6 +1,6 @@
{
"name": "Khoj",
"version": "1.6.2",
"version": "1.7.0",
"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: 1.6.2
;; Version: 1.7.0
;; 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": "1.6.2",
"version": "1.7.0",
"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": "1.6.2",
"version": "1.7.0",
"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

@ -38,5 +38,6 @@
"1.5.1": "0.15.0",
"1.6.0": "0.15.0",
"1.6.1": "0.15.0",
"1.6.2": "0.15.0"
"1.6.2": "0.15.0",
"1.7.0": "0.15.0"
}

23
src/khoj/interface/web/chat.html
View file

@ -368,15 +368,16 @@ To get started, just start typing below. You can also type / to see a list of co
let numReferences = 0;
if (Array.isArray(references)) {
numReferences = references.length;
if (references.hasOwnProperty("notes")) {
numReferences += references["notes"].length;
references.forEach((reference, index) => {
references["notes"].forEach((reference, index) => {
let polishedReference = generateReference(reference, index);
referenceSection.appendChild(polishedReference);
});
} else {
numReferences += processOnlineReferences(referenceSection, references);
}
if (references.hasOwnProperty("online")) {
numReferences += processOnlineReferences(referenceSection, references["online"]);
}
let referenceExpandButton = document.createElement('button');
@ -518,7 +519,7 @@ To get started, just start typing below. You can also type / to see a list of co
// Handle streamed response of type text/event-stream or text/plain
const reader = response.body.getReader();
const decoder = new TextDecoder();
let references = null;
let references = {};
readStream();
@ -526,8 +527,8 @@ To get started, just start typing below. You can also type / to see a list of co
reader.read().then(({ done, value }) => {
if (done) {
// Append any references after all the data has been streamed
if (references != null) {
newResponseText.appendChild(references);
if (references != {}) {
newResponseText.appendChild(createReferenceSection(references));
}
document.getElementById("chat-body").scrollTop = document.getElementById("chat-body").scrollHeight;
document.getElementById("chat-input").removeAttribute("disabled");
@ -545,7 +546,11 @@ To get started, just start typing below. You can also type / to see a list of co
const rawReference = chunk.split("### compiled references:")[1];
const rawReferenceAsJson = JSON.parse(rawReference);
references = createReferenceSection(rawReferenceAsJson);
if (rawReferenceAsJson instanceof Array) {
references["notes"] = rawReferenceAsJson;
} else if (typeof rawReferenceAsJson === "object" && rawReferenceAsJson !== null) {
references["online"] = rawReferenceAsJson;
}
readStream();
} else {
// Display response from Khoj

2
src/khoj/processor/conversation/offline/chat_model.py
View file

@ -258,7 +258,7 @@ def llm_thread(g, messages: List[ChatMessage], model: Any):
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:

31
src/khoj/processor/conversation/openai/gpt.py
View file

@ -12,7 +12,6 @@ from khoj.processor.conversation.openai.utils import (
completion_with_backoff,
)
from khoj.processor.conversation.utils import generate_chatml_messages_with_context
from khoj.utils.constants import empty_escape_sequences
from khoj.utils.helpers import ConversationCommand, is_none_or_empty
from khoj.utils.rawconfig import LocationData
@ -21,7 +20,7 @@ logger = logging.getLogger(__name__)
def extract_questions(
text,
model: Optional[str] = "gpt-4",
model: Optional[str] = "gpt-4-turbo-preview",
conversation_log={},
api_key=None,
temperature=0,
@ -36,9 +35,9 @@ def extract_questions(
# Extract Past User Message and Inferred Questions from Conversation Log
chat_history = "".join(
[
f'Q: {chat["intent"]["query"]}\n\n{chat["intent"].get("inferred-queries") or list([chat["intent"]["query"]])}\n\n{chat["message"]}\n\n'
f'Q: {chat["intent"]["query"]}\nKhoj: {{"queries": {chat["intent"].get("inferred-queries") or list([chat["intent"]["query"]])}}}\nA: {chat["message"]}\n\n'
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")
]
)
@ -67,7 +66,7 @@ def extract_questions(
model_name=model,
temperature=temperature,
max_tokens=max_tokens,
model_kwargs={"stop": ["A: ", "\n"]},
model_kwargs={"stop": ["A: ", "\n"], "response_format": {"type": "json_object"}},
openai_api_key=api_key,
)
@ -75,8 +74,8 @@ def extract_questions(
try:
response = response.strip()
response = json.loads(response)
response = [q.strip() for q in response if q.strip()]
if not isinstance(response, list) or not response or len(response) == 0:
response = [q.strip() for q in response["queries"] if q.strip()]
if not isinstance(response, list) or not response:
logger.error(f"Invalid response for constructing subqueries: {response}")
return [text]
return response
@ -88,11 +87,7 @@ def extract_questions(
return questions
def send_message_to_model(
messages,
api_key,
model,
):
def send_message_to_model(messages, api_key, model, response_type="text"):
"""
Send message to model
"""
@ -102,6 +97,7 @@ def send_message_to_model(
messages=messages,
model=model,
openai_api_key=api_key,
model_kwargs={"response_format": {"type": response_type}},
)
@ -155,14 +151,11 @@ def converse(
return iter([prompts.no_online_results_found.format()])
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"]
conversation_primer = f"{prompts.online_search_conversation.format(online_results=str(simplified_online_results))}\n{conversation_primer}"
conversation_primer = (
f"{prompts.online_search_conversation.format(online_results=str(online_results))}\n{conversation_primer}"
)
if not is_none_or_empty(compiled_references):
conversation_primer = f"{prompts.notes_conversation.format(query=user_query, references=compiled_references)}\n{conversation_primer}"
conversation_primer = f"{prompts.notes_conversation.format(query=user_query, references=compiled_references)}\n\n{conversation_primer}"
# Setup Prompt with Primer or Conversation History
messages = generate_chatml_messages_with_context(

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

@ -43,7 +43,7 @@ class StreamingChatCallbackHandler(StreamingStdOutCallbackHandler):
before_sleep=before_sleep_log(logger, logging.DEBUG),
reraise=True,
)
def completion_with_backoff(**kwargs):
def completion_with_backoff(**kwargs) -> str:
messages = kwargs.pop("messages")
if not "openai_api_key" in kwargs:
kwargs["openai_api_key"] = os.getenv("OPENAI_API_KEY")

150
src/khoj/processor/conversation/prompts.py
View file

@ -136,8 +136,6 @@ Ask crisp follow-up questions to get additional context, when a helpful response
Notes:
{references}
Query: {query}
""".strip()
)
@ -249,69 +247,52 @@ Use these notes from the user's previous conversations to provide a response:
extract_questions = PromptTemplate.from_template(
"""
You are Khoj, an extremely smart and helpful search assistant with the ability to retrieve information from the user's notes.
- The user will provide their questions and answers to you for context.
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.
- 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.
What searches, if any, will you need to perform to answer the users question?
Provide search queries as a JSON list of strings
What searches will you need to perform to answer the users question? Respond with search queries as list of strings in a JSON object.
Current Date: {current_date}
User's Location: {location}
Q: How was my trip to Cambodia?
["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.
Q: Who did i visit that temple with?
["Who did I visit the Angkor Wat Temple in Cambodia with?"]
Khoj: {{"queries": ["Who did I visit the Angkor Wat Temple in Cambodia with?"]}}
A: You visited the Angkor Wat Temple in Cambodia with Pablo, Namita and Xi.
Q: What national parks did I go to last year?
["National park I visited in {last_new_year} dt>='{last_new_year_date}' dt<'{current_new_year_date}'"]
Khoj: {{"queries": ["National park I visited in {last_new_year} dt>='{last_new_year_date}' dt<'{current_new_year_date}'"]}}
A: You visited the Grand Canyon and Yellowstone National Park in {last_new_year}.
Q: How are you feeling today?
[]
A: I'm feeling a little bored. Helping you will hopefully make me feel better!
Q: How can you help me?
Khoj: {{"queries": ["Social relationships", "Physical and mental health", "Education and career", "Personal life goals and habits"]}}
A: I can help you live healthier and happier across work and personal life
Q: How many tennis balls fit in the back of a 2002 Honda Civic?
["What is the size of a tennis ball?", "What is the trunk size 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: Is Bob older than Tom?
["When was Bob born?", "What is Tom's age?"]
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.
Q: What is their age difference?
["What is Bob's age?", "What is Tom's age?"]
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?
["Note from {yesterday_date} dt>='{yesterday_date}' dt<'{current_date}'"]
A: Yesterday's note contains the following information: ...
Khoj: {{"queries": ["Note from {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}
Q: {text}
"""
Khoj:
""".strip()
)
system_prompt_extract_relevant_information = """As a professional analyst, create a comprehensive report of the most relevant information from a web page in response to a user's query. The text provided is directly from within the web page. The report you create should be multiple paragraphs, and it should represent the content of the website. Tell the user exactly what the website says in response to their query, while adhering to these guidelines:
@ -371,7 +352,12 @@ Khoj:
pick_relevant_information_collection_tools = PromptTemplate.from_template(
"""
You are Khoj, a smart and helpful personal assistant. You have access to a variety of data sources to help you answer the user's question. You can use the data sources listed below to collect more relevant information. You can use any combination of these data sources to answer the user's question. Tell me which data sources you would like to use to answer the user's question.
You are Khoj, an extremely smart and helpful search assistant.
- You have access to a variety of data sources to help you answer the user's question
- You can use the data sources listed below to collect more relevant information
- You can use any combination of these data sources to answer the user's question
Which of the data sources listed below you would use to answer the user's question?
{tools}
@ -383,7 +369,7 @@ User: I'm thinking of moving to a new city. I'm trying to decide between New Yor
AI: Moving to a new city can be challenging. Both New York and San Francisco are great cities to live in. New York is known for its diverse culture and San Francisco is known for its tech scene.
Q: What is the population of each of those cities?
Khoj: ["online"]
Khoj: {{"source": ["online"]}}
Example:
Chat History:
@ -391,23 +377,32 @@ User: I'm thinking of my next vacation idea. Ideally, I want to see something ne
AI: Excellent! Taking a vacation is a great way to relax and recharge.
Q: Where did Grandma grow up?
Khoj: ["notes"]
Khoj: {{"source": ["notes"]}}
Example:
Chat History:
Q: What's the latest news with the first company I worked for?
Khoj: ["notes", "online"]
Q: What can you do for me?
Khoj: {{"source": ["notes", "online"]}}
Example:
Chat History:
User: Good morning
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: I want to start a new hobby. I'm thinking of learning to play the guitar.
AI: Learning to play the guitar is a great hobby. It can be a lot of fun and a great way to express yourself.
Q: Who is Sandra?
Khoj: ["default"]
Q: What is the first element of the periodic table?
Khoj: {{"source": ["general"]}}
Now it's your turn to pick the tools you would like to use to answer the user's question. Provide your response as a list of strings.
Now it's your turn to pick the data sources you would like to use to answer the user's question. Respond with data sources as a list of strings in a JSON object.
Chat History:
{chat_history}
@ -419,76 +414,71 @@ Khoj:
online_search_conversation_subqueries = PromptTemplate.from_template(
"""
You are Khoj, an extremely smart and helpful search assistant. You are tasked with constructing **up to three** search queries for Google to answer the user's question.
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.
- You will receive the conversation history as context.
- 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.
- Use site: and after: google search operators when appropriate
- You have access to the the whole internet to retrieve information.
- Official, up-to-date information about you, Khoj, is available at site:khoj.dev
What Google searches, if any, will you need to perform to answer the user's question?
Provide search queries as a list of strings
Provide search queries as a JSON list of strings
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.
Khoj: 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.
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: Posts about vector databases on Hacker News
A: ["site:"news.ycombinator.com vector database"]
Q: Summarize posts about vector databases on Hacker News since Feb 2024
Khoj: {{"queries": ["site:news.ycombinator.com after:2024/02/01 vector database"]}}
History:
User: I'm currently living in New York but I'm thinking about moving to San Francisco.
Khoj: 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 a lot of great restaurants and museums.
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 weather like in those cities?
A: ["weather in new york", "weather in san francisco"]
Q: What is the climate like in those cities?
Khoj: {{"queries": ["climate in new york city", "climate in san francisco"]}}
History:
User: I'm thinking of my next vacation idea. Ideally, I want to see something new and exciting.
Khoj: You could time your next trip with the next lunar eclipse, as that would be a novel experience.
AI: Hey, how is it going?
User: Going well. Ananya is in town tonight!
AI: Oh that's awesome! What are your plans for the evening?
Q: When is the next one?
A: ["next lunar eclipse"]
Q: She wants to see a movie. Any decent sci-fi movies playing at the local theater?
Khoj: {{"queries": ["new sci-fi movies in theaters near {location}"]}}
History:
User: Can I chat with you over WhatsApp?
AI: Yes, you can chat with me using WhatsApp.
Q: How
Khoj: {{"queries": ["site:khoj.dev chat with Khoj on Whatsapp"]}}
History:
Q: How do I share my files with you?
Khoj: {{"queries": ["site:khoj.dev sync files with Khoj"]}}
History:
User: I need to transport a lot of oranges to the moon. Are there any rockets that can fit a lot of oranges?
Khoj: NASA's Saturn V rocket frequently makes lunar trips and has a large cargo capacity.
AI: NASA's Saturn V rocket frequently makes lunar trips and has a large cargo capacity.
Q: How many oranges would fit in NASA's Saturn V rocket?
A: ["volume of an orange", "volume of saturn v rocket"]
Khoj: {{"queries": ["volume of an orange", "volume of saturn v rocket"]}}
Now it's your turn to construct a search query for Google to answer the user's question.
History:
{chat_history}
Q: {query}
A:
"""
Khoj:
""".strip()
)
## Extract Search Type
## --
search_type = """
Objective: Extract search type from user query and return information as JSON
Allowed search types are listed below:
- search-type=["notes", "image", "pdf"]
Some examples are given below for reference:
Q:What fiction book was I reading last week about AI starship?
A:{ "search-type": "notes" }
Q: What did the lease say about early termination
A: { "search-type": "pdf" }
Q:Can you recommend a movie to watch from my notes?
A:{ "search-type": "notes" }
Q:When did I go surfing last?
A:{ "search-type": "notes" }
Q:"""
# System messages to user
# --
help_message = PromptTemplate.from_template(

7
src/khoj/processor/conversation/utils.py
View file

@ -16,11 +16,10 @@ 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-4": 7000,
"gpt-4-1106-preview": 7000,
"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,
"gpt-3.5-turbo-16k": 15000,
"mistral-7b-instruct-v0.1.Q4_0.gguf": 1548,
}
model_to_tokenizer = {
@ -64,7 +63,7 @@ class ThreadedGenerator:
def close(self):
if self.compiled_references and len(self.compiled_references) > 0:
self.queue.put(f"### compiled references:{json.dumps(self.compiled_references)}")
elif self.online_results and len(self.online_results) > 0:
if self.online_results and len(self.online_results) > 0:
self.queue.put(f"### compiled references:{json.dumps(self.online_results)}")
self.queue.put(StopIteration)

155
src/khoj/processor/tools/online_search.py
View file

@ -1,116 +1,131 @@
import asyncio
import json
import logging
import os
from typing import Dict, List, Union
from typing import Dict, Tuple, Union
import aiohttp
import requests
from bs4 import BeautifulSoup
from markdownify import markdownify
from khoj.routers.helpers import extract_relevant_info, generate_online_subqueries
from khoj.utils.helpers import is_none_or_empty
from khoj.utils.helpers import is_none_or_empty, timer
from khoj.utils.rawconfig import LocationData
logger = logging.getLogger(__name__)
SERPER_DEV_API_KEY = os.getenv("SERPER_DEV_API_KEY")
OLOSTEP_API_KEY = os.getenv("OLOSTEP_API_KEY")
SERPER_DEV_URL = "https://google.serper.dev/search"
OLOSTEP_API_KEY = os.getenv("OLOSTEP_API_KEY")
OLOSTEP_API_URL = "https://agent.olostep.com/olostep-p2p-incomingAPI"
OLOSTEP_QUERY_PARAMS = {
"timeout": 35, # seconds
"waitBeforeScraping": 1, # seconds
"saveHtml": False,
"saveMarkdown": True,
"saveHtml": "False",
"saveMarkdown": "True",
"removeCSSselectors": "default",
"htmlTransformer": "none",
"removeImages": True,
"fastLane": True,
"removeImages": "True",
"fastLane": "True",
# Similar to Stripe's API, the expand parameters avoid the need to make a second API call
# to retrieve the dataset (from the dataset API) if you only need the markdown or html.
"expandMarkdown": True,
"expandHtml": False,
"expandMarkdown": "True",
"expandHtml": "False",
}
MAX_WEBPAGES_TO_READ = 1
async def search_with_google(query: str, conversation_history: dict, location: LocationData):
def _search_with_google(subquery: str):
payload = json.dumps(
{
"q": subquery,
}
)
headers = {"X-API-KEY": SERPER_DEV_API_KEY, "Content-Type": "application/json"}
response = requests.request("POST", SERPER_DEV_URL, headers=headers, data=payload)
if response.status_code != 200:
logger.error(response.text)
return {}
json_response = response.json()
sub_response_dict = {}
sub_response_dict["knowledgeGraph"] = json_response.get("knowledgeGraph", {})
sub_response_dict["organic"] = json_response.get("organic", [])
sub_response_dict["answerBox"] = json_response.get("answerBox", [])
sub_response_dict["peopleAlsoAsk"] = json_response.get("peopleAlsoAsk", [])
return sub_response_dict
async def search_online(query: str, conversation_history: dict, location: LocationData):
if SERPER_DEV_API_KEY is None:
logger.warn("SERPER_DEV_API_KEY is not set")
return {}
# Breakdown the query into subqueries to get the correct answer
subqueries = await generate_online_subqueries(query, conversation_history, location)
response_dict = {}
for subquery in subqueries:
logger.info(f"Searching with Google for '{subquery}'")
response_dict[subquery] = _search_with_google(subquery)
response_dict[subquery] = search_with_google(subquery)
extracted_content: Dict[str, List] = {}
if is_none_or_empty(OLOSTEP_API_KEY):
logger.warning("OLOSTEP_API_KEY is not set. Skipping web scraping.")
return response_dict
# Gather distinct web pages from organic search results of each subquery without an instant answer
webpage_links = {
result["link"]
for subquery in response_dict
for result in response_dict[subquery].get("organic", [])[:MAX_WEBPAGES_TO_READ]
if "answerBox" not in response_dict[subquery]
}
for subquery in response_dict:
# If a high quality answer is not found, search the web pages of the first 3 organic results
if is_none_or_empty(response_dict[subquery].get("answerBox")):
extracted_content[subquery] = []
for result in response_dict[subquery].get("organic")[:1]:
logger.info(f"Searching web page of '{result['link']}'")
try:
extracted_content[subquery].append(search_with_olostep(result["link"]).strip())
except Exception as e:
logger.error(f"Error while searching web page of '{result['link']}': {e}", exc_info=True)
continue
extracted_relevant_content = await extract_relevant_info(subquery, extracted_content)
response_dict[subquery]["extracted_content"] = extracted_relevant_content
# 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)
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
return response_dict
def search_with_olostep(web_url: str) -> str:
if OLOSTEP_API_KEY is None:
raise ValueError("OLOSTEP_API_KEY is not set")
def search_with_google(subquery: str):
payload = json.dumps({"q": subquery})
headers = {"X-API-KEY": SERPER_DEV_API_KEY, "Content-Type": "application/json"}
response = requests.request("POST", SERPER_DEV_URL, headers=headers, data=payload)
if response.status_code != 200:
logger.error(response.text)
return {}
json_response = response.json()
extraction_fields = ["organic", "answerBox", "peopleAlsoAsk", "knowledgeGraph"]
extracted_search_result = {
field: json_response[field] for field in extraction_fields if not is_none_or_empty(json_response.get(field))
}
return extracted_search_result
async def read_webpage_and_extract_content(subquery: str, url: str) -> Tuple[str, Union[None, 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)
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
except Exception as e:
logger.error(f"Failed to read web page at '{url}' with {e}")
return subquery, None
async def read_webpage(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",
}
async with aiohttp.ClientSession() as session:
async with session.get(web_url, headers=headers, timeout=30) as response:
response.raise_for_status()
html = await response.text()
parsed_html = BeautifulSoup(html, "html.parser")
body = parsed_html.body.get_text(separator="\n", strip=True)
return markdownify(body)
async def read_webpage_with_olostep(web_url: str) -> str:
headers = {"Authorization": f"Bearer {OLOSTEP_API_KEY}"}
web_scraping_params: Dict[str, Union[str, int, bool]] = OLOSTEP_QUERY_PARAMS.copy() # type: ignore
web_scraping_params["url"] = web_url
try:
response = requests.request("GET", OLOSTEP_API_URL, params=web_scraping_params, headers=headers)
if response.status_code != 200:
logger.error(response, exc_info=True)
return None
except Exception as e:
logger.error(f"Error while searching with Olostep: {e}", exc_info=True)
return None
return response.json()["markdown_content"]
async with aiohttp.ClientSession() as session:
async with session.get(OLOSTEP_API_URL, params=web_scraping_params, headers=headers) as response:
response.raise_for_status()
response_json = await response.json()
return response_json["markdown_content"]

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

@ -14,7 +14,7 @@ from khoj.database.adapters import ConversationAdapters, EntryAdapters, aget_use
from khoj.database.models import KhojUser
from khoj.processor.conversation.prompts import help_message, no_entries_found
from khoj.processor.conversation.utils import save_to_conversation_log
from khoj.processor.tools.online_search import search_with_google
from khoj.processor.tools.online_search import search_online
from khoj.routers.api import extract_references_and_questions
from khoj.routers.helpers import (
ApiUserRateLimiter,
@ -292,7 +292,7 @@ async def chat(
if ConversationCommand.Online in conversation_commands:
try:
online_results = await search_with_google(defiltered_query, meta_log, location)
online_results = await search_online(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 🌐"]),

37
src/khoj/routers/helpers.py
View file

@ -113,15 +113,15 @@ def update_telemetry_state(
]
def construct_chat_history(conversation_history: dict, n: int = 4) -> str:
def construct_chat_history(conversation_history: dict, n: int = 4, agent_name="AI") -> str:
chat_history = ""
for chat in conversation_history.get("chat", [])[-n:]:
if chat["by"] == "khoj" and chat["intent"].get("type") == "remember":
chat_history += f"User: {chat['intent']['query']}\n"
chat_history += f"Khoj: {chat['message']}\n"
chat_history += f"{agent_name}: {chat['message']}\n"
elif chat["by"] == "khoj" and ("text-to-image" in chat["intent"].get("type")):
chat_history += f"User: {chat['intent']['query']}\n"
chat_history += f"Khoj: [generated image redacted for space]\n"
chat_history += f"{agent_name}: [generated image redacted for space]\n"
return chat_history
@ -154,24 +154,26 @@ async def aget_relevant_information_sources(query: str, conversation_history: di
"""
tool_options = dict()
tool_options_str = ""
for tool, description in tool_descriptions_for_llm.items():
tool_options[tool.value] = description
tool_options_str += f'- "{tool.value}": "{description}"\n'
chat_history = construct_chat_history(conversation_history)
relevant_tools_prompt = prompts.pick_relevant_information_collection_tools.format(
query=query,
tools=str(tool_options),
tools=tool_options_str,
chat_history=chat_history,
)
response = await send_message_to_model_wrapper(relevant_tools_prompt)
response = await send_message_to_model_wrapper(relevant_tools_prompt, response_type="json_object")
try:
response = response.strip()
response = json.loads(response)
response = [q.strip() for q in response if q.strip()]
response = [q.strip() for q in response["source"] if q.strip()]
if not isinstance(response, list) or not response or len(response) == 0:
logger.error(f"Invalid response for determining relevant tools: {response}")
return tool_options
@ -196,15 +198,17 @@ async def aget_relevant_output_modes(query: str, conversation_history: dict):
"""
mode_options = dict()
mode_options_str = ""
for mode, description in mode_descriptions_for_llm.items():
mode_options[mode.value] = description
mode_options_str += f'- "{mode.value}": "{description}"\n'
chat_history = construct_chat_history(conversation_history)
relevant_mode_prompt = prompts.pick_relevant_output_mode.format(
query=query,
modes=str(mode_options),
modes=mode_options_str,
chat_history=chat_history,
)
@ -241,13 +245,13 @@ async def generate_online_subqueries(q: str, conversation_history: dict, locatio
location=location,
)
response = await send_message_to_model_wrapper(online_queries_prompt)
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:
response = response.strip()
response = json.loads(response)
response = [q.strip() for q in response if q.strip()]
response = [q.strip() for q in response["queries"] if q.strip()]
if not isinstance(response, list) or not response or len(response) == 0:
logger.error(f"Invalid response for constructing subqueries: {response}. Returning original query: {q}")
return [q]
@ -257,15 +261,17 @@ async def generate_online_subqueries(q: str, conversation_history: dict, locatio
return [q]
async def extract_relevant_info(q: str, corpus: dict) -> List[str]:
async def extract_relevant_info(q: str, corpus: str) -> Union[str, None]:
"""
Given a target corpus, extract the most relevant info given a query
Extract relevant information for a given query from the target corpus
"""
key = list(corpus.keys())[0]
if is_none_or_empty(corpus) or is_none_or_empty(q):
return None
extract_relevant_information = prompts.extract_relevant_information.format(
query=q,
corpus=corpus[key],
corpus=corpus.strip(),
)
response = await send_message_to_model_wrapper(
@ -319,6 +325,7 @@ async def generate_better_image_prompt(
async def send_message_to_model_wrapper(
message: str,
system_message: str = "",
response_type: str = "text",
):
conversation_config: ChatModelOptions = await ConversationAdapters.aget_default_conversation_config()
@ -347,9 +354,7 @@ async def send_message_to_model_wrapper(
api_key = openai_chat_config.api_key
chat_model = conversation_config.chat_model
openai_response = send_message_to_model(
messages=truncated_messages,
api_key=api_key,
model=chat_model,
messages=truncated_messages, api_key=api_key, model=chat_model, response_type=response_type
)
return openai_response

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

@ -7,7 +7,7 @@ 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_online_chat_model = "gpt-4"
default_online_chat_model = "gpt-4-turbo-preview"
empty_config = {
"search-type": {

8
src/khoj/utils/helpers.py
View file

@ -277,16 +277,16 @@ command_descriptions = {
ConversationCommand.General: "Only talk about information that relies on Khoj's general knowledge, not your personal knowledge base.",
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: "Look up information on the internet.",
ConversationCommand.Online: "Search for information on the internet.",
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: "Use this if there might be a mix of general and personal knowledge in the question, or if you don't entirely understand the query.",
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.Notes: "Use this when you would like to use the user's personal knowledge base to answer the question. This is especially helpful if the query seems to be missing context.",
ConversationCommand.Online: "Use this when you would like to look up information on the internet",
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**",
}
mode_descriptions_for_llm = {

24
tests/test_helpers.py
View file

@ -7,7 +7,7 @@ import pytest
from scipy.stats import linregress
from khoj.processor.embeddings import EmbeddingsModel
from khoj.processor.tools.online_search import search_with_olostep
from khoj.processor.tools.online_search import read_webpage, read_webpage_with_olostep
from khoj.utils import helpers
@ -84,13 +84,29 @@ def test_encode_docs_memory_leak():
assert slope < 2, f"Memory leak suspected on {device}. Memory usage increased at ~{slope:.2f} MB per iteration"
@pytest.mark.skipif(os.getenv("OLOSTEP_API_KEY") is None, reason="OLOSTEP_API_KEY is not set")
def test_olostep_api():
@pytest.mark.asyncio
async def test_reading_webpage():
# Arrange
website = "https://en.wikipedia.org/wiki/Great_Chicago_Fire"
# Act
response = search_with_olostep(website)
response = await read_webpage(website)
# Assert
assert (
"An alarm sent from the area near the fire also failed to register at the courthouse where the fire watchmen were"
in response
)
@pytest.mark.skipif(os.getenv("OLOSTEP_API_KEY") is None, reason="OLOSTEP_API_KEY is not set")
@pytest.mark.asyncio
async def test_reading_webpage_with_olostep():
# Arrange
website = "https://en.wikipedia.org/wiki/Great_Chicago_Fire"
# Act
response = await read_webpage_with_olostep(website)
# Assert
assert (

107
tests/test_openai_chat_actors.py
View file

@ -7,7 +7,12 @@ from freezegun import freeze_time
from khoj.processor.conversation.openai.gpt import converse, extract_questions
from khoj.processor.conversation.utils import message_to_log
from khoj.routers.helpers import aget_relevant_output_modes
from khoj.routers.helpers import (
aget_relevant_information_sources,
aget_relevant_output_modes,
generate_online_subqueries,
)
from khoj.utils.helpers import ConversationCommand
# Initialize variables for tests
api_key = os.getenv("OPENAI_API_KEY")
@ -154,33 +159,6 @@ def test_generate_search_query_using_question_and_answer_from_chat_history():
assert "Leia" in response[0] and "Luke" in response[0]
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_generate_search_query_with_date_and_context_from_chat_history():
# Arrange
message_list = [
("When did I visit Masai Mara?", "You visited Masai Mara in April 2000", []),
]
# Act
response = extract_questions(
"What was the Pizza place we ate at over there?", conversation_log=populate_chat_history(message_list)
)
# Assert
expected_responses = [
("dt>='2000-04-01'", "dt<'2000-05-01'"),
("dt>='2000-04-01'", "dt<='2000-04-30'"),
('dt>="2000-04-01"', 'dt<"2000-05-01"'),
('dt>="2000-04-01"', 'dt<="2000-04-30"'),
]
assert len(response) == 1
assert "Masai Mara" in response[0]
assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
"Expected date filter to limit to April 2000 in response but got: " + response[0]
)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_chat_with_no_chat_history_or_retrieved_content():
@ -391,7 +369,7 @@ def test_answer_general_question_not_in_chat_history_or_retrieved_content():
# Act
response_gen = converse(
references=[], # Assume no context retrieved from notes for the user_query
user_query="Write a haiku about unit testing in 3 lines",
user_query="Write a haiku about unit testing in 3 lines. Do not say anything else",
conversation_log=populate_chat_history(message_list),
api_key=api_key,
)
@ -471,6 +449,47 @@ 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):
# Arrange
user_query = "Share popular posts on r/worldnews this month"
# Act
responses = await generate_online_subqueries(user_query, {}, None)
# Assert
assert any(
["reddit.com/r/worldnews" in response for response in responses]
), "Expected a search query to include site:reddit.com but got: " + str(responses)
assert any(
["site:reddit.com" in response for response in responses]
), "Expected a search query to include site:reddit.com but got: " + str(responses)
assert any(
["after:2024/04/01" in response for response in responses]
), "Expected a search query to include after:2024/04/01 but got: " + str(responses)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
async def test_websearch_khoj_website_for_info_about_khoj(chat_client):
# Arrange
user_query = "Do you support image search?"
# Act
responses = await generate_online_subqueries(user_query, {}, None)
# Assert
assert any(
["site:khoj.dev" in response for response in responses]
), "Expected search query to include site:khoj.dev but got: " + str(responses)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
@ -490,7 +509,7 @@ async def test_use_default_response_mode(chat_client):
@pytest.mark.django_db(transaction=True)
async def test_use_image_response_mode(chat_client):
# Arrange
user_query = "Paint a picture of the scenery in Timbuktu in the winter"
user_query = "Paint a scenery in Timbuktu in the winter"
# Act
mode = await aget_relevant_output_modes(user_query, {})
@ -499,6 +518,34 @@ async def test_use_image_response_mode(chat_client):
assert mode.value == "image"
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
async def test_select_data_sources_actor_chooses_to_search_notes(chat_client):
# Arrange
user_query = "Where did I learn to swim?"
# Act
conversation_commands = await aget_relevant_information_sources(user_query, {})
# Assert
assert ConversationCommand.Notes in conversation_commands
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
async def test_select_data_sources_actor_chooses_to_search_online(chat_client):
# Arrange
user_query = "Where is the nearest hospital?"
# Act
conversation_commands = await aget_relevant_information_sources(user_query, {})
# Assert
assert ConversationCommand.Online in conversation_commands
# Helpers
# ----------------------------------------------------------------------------------------------------
def populate_chat_history(message_list):

50
tests/test_openai_chat_director.py
View file

@ -220,9 +220,17 @@ def test_no_answer_in_chat_history_or_retrieved_content(chat_client, default_use
response_message = response.content.decode("utf-8")
# Assert
expected_responses = ["don't know", "do not know", "no information", "do not have", "don't have"]
expected_responses = [
"don't know",
"do not know",
"no information",
"do not have",
"don't have",
"where were you born?",
]
assert response.status_code == 200
assert any([expected_response in response_message for expected_response in expected_responses]), (
assert any([expected_response in response_message.lower() for expected_response in expected_responses]), (
"Expected chat director to say they don't know in response, but got: " + response_message
)
@ -328,10 +336,8 @@ def test_answer_general_question_not_in_chat_history_or_retrieved_content(chat_c
create_conversation(message_list, default_user2)
# Act
response = chat_client.get(
f'/api/chat?q=""Write a haiku about unit testing. Do not say anything else."&stream=true'
)
response_message = response.content.decode("utf-8")
response = chat_client.get(f'/api/chat?q="Write a haiku about unit testing. Do not say anything else."&stream=true')
response_message = response.content.decode("utf-8").split("### compiled references")[0]
# Assert
expected_responses = ["test", "Test"]
@ -348,8 +354,8 @@ def test_answer_general_question_not_in_chat_history_or_retrieved_content(chat_c
def test_ask_for_clarification_if_not_enough_context_in_question(chat_client_no_background):
# Act
response = chat_client_no_background.get(f'/api/chat?q="What is the name of Namitas older son"&stream=true')
response_message = response.content.decode("utf-8")
response = chat_client_no_background.get(f'/api/chat?q="What is the name of Namitas older son?"&stream=true')
response_message = response.content.decode("utf-8").split("### compiled references")[0].lower()
# Assert
expected_responses = [
@ -359,9 +365,11 @@ def test_ask_for_clarification_if_not_enough_context_in_question(chat_client_no_
"the birth order",
"provide more context",
"provide me with more context",
"don't have that",
"haven't provided me",
]
assert response.status_code == 200
assert any([expected_response in response_message.lower() for expected_response in expected_responses]), (
assert any([expected_response in response_message for expected_response in expected_responses]), (
"Expected chat director to ask for clarification in response, but got: " + response_message
)
@ -459,13 +467,18 @@ def test_answer_in_chat_history_by_conversation_id_with_agent(
def test_answer_requires_multiple_independent_searches(chat_client):
"Chat director should be able to answer by doing multiple independent searches for required information"
# Act
response = chat_client.get(f'/api/chat?q="Is Xi older than Namita?"&stream=true')
response_message = response.content.decode("utf-8")
response = chat_client.get(f'/api/chat?q="Is Xi older than Namita? Just the older persons full name"&stream=true')
response_message = response.content.decode("utf-8").split("### compiled references")[0].lower()
# Assert
expected_responses = ["he is older than namita", "xi is older than namita", "xi li is older than namita"]
only_full_name_check = "xi li" in response_message and "namita" not in response_message
comparative_statement_check = any(
[expected_response in response_message for expected_response in expected_responses]
)
assert response.status_code == 200
assert any([expected_response in response_message.lower() for expected_response in expected_responses]), (
assert only_full_name_check or comparative_statement_check, (
"Expected Xi is older than Namita, but got: " + response_message
)
@ -475,15 +488,22 @@ def test_answer_requires_multiple_independent_searches(chat_client):
def test_answer_using_file_filter(chat_client):
"Chat should be able to use search filters in the query"
# Act
query = urllib.parse.quote('Is Xi older than Namita? file:"Namita.markdown" file:"Xi Li.markdown"')
query = urllib.parse.quote(
'Is Xi older than Namita? Just say the older persons full name. file:"Namita.markdown" file:"Xi Li.markdown"'
)
response = chat_client.get(f"/api/chat?q={query}&stream=true")
response_message = response.content.decode("utf-8")
response_message = response.content.decode("utf-8").split("### compiled references")[0].lower()
# Assert
expected_responses = ["he is older than namita", "xi is older than namita", "xi li is older than namita"]
only_full_name_check = "xi li" in response_message and "namita" not in response_message
comparative_statement_check = any(
[expected_response in response_message for expected_response in expected_responses]
)
assert response.status_code == 200
assert any([expected_response in response_message.lower() for expected_response in expected_responses]), (
assert only_full_name_check or comparative_statement_check, (
"Expected Xi is older than Namita, but got: " + response_message
)

3
versions.json
View file

@ -38,5 +38,6 @@
"1.5.1": "0.15.0",
"1.6.0": "0.15.0",
"1.6.1": "0.15.0",
"1.6.2": "0.15.0"
"1.6.2": "0.15.0",
"1.7.0": "0.15.0"
}