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https://github.com/khoj-ai/khoj.git
synced 2024-11-23 23:48:56 +01:00
Add Default chat command. Make Khoj ask clarifying questions (#468)
- Make Khoj ask clarifying questions when answer not in provided context - Add default conversation command to auto switch b/w general, notes modes - Show filtered list of commands available with the currently input text - Use general prompt when no references found and not in Notes mode - Test general and notes slash commands in offline chat director tests
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commit
150105505b
10 changed files with 92 additions and 30 deletions
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@ -168,12 +168,15 @@
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function onChatInput() {
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let chatInput = document.getElementById("chat-input");
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if (chatInput.value === "/") {
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if (chatInput.value.startsWith("/") && chatInput.value.split(" ").length === 1) {
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let chatTooltip = document.getElementById("chat-tooltip");
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chatTooltip.style.display = "block";
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let helpText = "<div>";
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const command = chatInput.value.split(" ")[0].substring(1);
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for (let key in chatOptions) {
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helpText += "<b>/" + key + "</b>: " + chatOptions[key] + "<br>";
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if (!!!command || key.startsWith(command)) {
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helpText += "<b>/" + key + "</b>: " + chatOptions[key] + "<br>";
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}
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}
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chatTooltip.innerHTML = helpText;
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} else if (chatInput.value.startsWith("/")) {
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@ -514,6 +517,7 @@
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div#chat-tooltip {
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text-align: left;
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font-size: medium;
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}
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@keyframes gradient {
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@ -287,7 +287,7 @@
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</div>
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<!--Add Text Box To Enter Query, Trigger Incremental Search OnChange -->
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<input type="text" id="query" class="option" onkeyup=incrementalSearch(event) autofocus="autofocus" placeholder="Search directly from your knowledge base">
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<input type="text" id="query" class="option" onkeyup=incrementalSearch(event) autofocus="autofocus" placeholder="Search your knowledge base using natural language">
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<div id="options">
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<!--Add Dropdown to Select Query Type -->
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@ -119,7 +119,7 @@ def converse_offline(
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model: str = "llama-2-7b-chat.ggmlv3.q4_K_S.bin",
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loaded_model: Union[GPT4All, None] = None,
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completion_func=None,
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conversation_command=ConversationCommand.Notes,
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conversation_command=ConversationCommand.Default,
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) -> Union[ThreadedGenerator, Iterator[str]]:
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"""
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Converse with user using Llama
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@ -129,10 +129,10 @@ def converse_offline(
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compiled_references_message = "\n\n".join({f"{item}" for item in references})
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# Get Conversation Primer appropriate to Conversation Type
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if conversation_command == ConversationCommand.General:
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conversation_primer = user_query
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elif conversation_command == ConversationCommand.Notes and is_none_or_empty(compiled_references_message):
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if conversation_command == ConversationCommand.Notes and is_none_or_empty(compiled_references_message):
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return iter([prompts.no_notes_found.format()])
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elif conversation_command == ConversationCommand.General or is_none_or_empty(compiled_references_message):
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conversation_primer = user_query
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else:
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conversation_primer = prompts.notes_conversation_llamav2.format(
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query=user_query, references=compiled_references_message
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@ -109,7 +109,7 @@ def converse(
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api_key: Optional[str] = None,
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temperature: float = 0.2,
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completion_func=None,
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conversation_command=ConversationCommand.Notes,
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conversation_command=ConversationCommand.Default,
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):
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"""
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Converse with user using OpenAI's ChatGPT
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@ -119,11 +119,11 @@ def converse(
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compiled_references = "\n\n".join({f"# {item}" for item in references})
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# Get Conversation Primer appropriate to Conversation Type
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if conversation_command == ConversationCommand.General:
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conversation_primer = prompts.general_conversation.format(current_date=current_date, query=user_query)
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elif conversation_command == ConversationCommand.Notes and is_none_or_empty(compiled_references):
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if conversation_command == ConversationCommand.Notes and is_none_or_empty(compiled_references):
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completion_func(chat_response=prompts.no_notes_found.format())
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return iter([prompts.no_notes_found.format()])
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elif conversation_command == ConversationCommand.General or is_none_or_empty(compiled_references):
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conversation_primer = prompts.general_conversation.format(current_date=current_date, query=user_query)
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else:
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conversation_primer = prompts.notes_conversation.format(
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current_date=current_date, query=user_query, references=compiled_references
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@ -4,7 +4,7 @@ from langchain.prompts import PromptTemplate
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## Personality
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## --
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personality = PromptTemplate.from_template("You are Khoj, a friendly, smart and helpful personal assistant.")
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personality = PromptTemplate.from_template("You are Khoj, a smart, inquisitive and helpful personal assistant.")
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## General Conversation
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@ -77,7 +77,9 @@ conversation_llamav2 = PromptTemplate.from_template(
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## --
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notes_conversation = PromptTemplate.from_template(
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"""
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Using the notes and our past conversations as context, answer the following question.
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Using my personal notes and our past conversations as context, answer the following question.
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Ask crisp follow-up questions to get additional context, when the answer cannot be inferred from the provided notes or past conversations.
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These questions should end with a question mark.
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Current Date: {current_date}
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Notes:
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@ -236,9 +238,10 @@ Q:"""
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# --
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help_message = PromptTemplate.from_template(
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"""
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**/notes**: Chat using the information in your knowledge base.
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**/general**: Chat using just Khoj's general knowledge. This will not search against your notes.
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**/default**: Chat using your knowledge base and Khoj's general knowledge for context.
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**/help**: Show this help message.
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**/notes**: Chat using the information in your knowledge base. This is the default method.
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**/general**: Chat using general knowledge with the LLM. This will not search against your notes.
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You are using the **{model}** model.
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**version**: {version}
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@ -705,7 +705,7 @@ async def chat(
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compiled_references, inferred_queries = await extract_references_and_questions(
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request, q, (n or 5), conversation_command
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)
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conversation_command = get_conversation_command(query=q, any_references=is_none_or_empty(compiled_references))
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conversation_command = get_conversation_command(query=q, any_references=not is_none_or_empty(compiled_references))
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if conversation_command == ConversationCommand.Help:
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model_type = "offline" if state.processor_config.conversation.enable_offline_chat else "openai"
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formatted_help = help_message.format(model=model_type, version=state.khoj_version)
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@ -755,7 +755,7 @@ async def extract_references_and_questions(
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request: Request,
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q: str,
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n: int,
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conversation_type: ConversationCommand = ConversationCommand.Notes,
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conversation_type: ConversationCommand = ConversationCommand.Default,
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):
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# Load Conversation History
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meta_log = state.processor_config.conversation.meta_log
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@ -60,15 +60,15 @@ def update_telemetry_state(
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def get_conversation_command(query: str, any_references: bool = False) -> ConversationCommand:
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if query.startswith("/notes"):
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return ConversationCommand.Notes
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elif query.startswith("/general"):
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return ConversationCommand.General
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elif query.startswith("/help"):
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return ConversationCommand.Help
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elif query.startswith("/general"):
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return ConversationCommand.General
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# If no relevant notes found for the given query
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elif not any_references:
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return ConversationCommand.General
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else:
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return ConversationCommand.Notes
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return ConversationCommand.Default
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def generate_chat_response(
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@ -76,7 +76,7 @@ def generate_chat_response(
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meta_log: dict,
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compiled_references: List[str] = [],
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inferred_queries: List[str] = [],
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conversation_command: ConversationCommand = ConversationCommand.Notes,
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conversation_command: ConversationCommand = ConversationCommand.Default,
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) -> Union[ThreadedGenerator, Iterator[str]]:
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def _save_to_conversation_log(
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q: str,
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@ -214,13 +214,15 @@ def log_telemetry(
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class ConversationCommand(str, Enum):
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Default = "default"
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General = "general"
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Notes = "notes"
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Help = "help"
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command_descriptions = {
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ConversationCommand.General: "This command allows you to search talk with the LLM without including context from your knowledge base.",
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ConversationCommand.Notes: "This command allows you to search talk with the LLM while including context from your knowledge base.",
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ConversationCommand.Help: "This command displays a help message with all available commands and other metadata.",
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ConversationCommand.General: "Only talk about information that relies on Khoj's general knowledge, not your personal knowledge base.",
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ConversationCommand.Notes: "Only talk about information that is available in your knowledge base.",
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ConversationCommand.Default: "The default command when no command specified. It intelligently auto-switches between general and notes mode.",
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ConversationCommand.Help: "Display a help message with all available commands and other metadata.",
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}
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@ -1,10 +1,13 @@
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# Standard Packages
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import urllib.parse
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# External Packages
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import pytest
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from freezegun import freeze_time
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from faker import Faker
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# Internal Packages
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from khoj.processor.conversation import prompts
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from khoj.processor.conversation.utils import message_to_log
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from khoj.utils import state
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@ -172,6 +175,57 @@ def test_no_answer_in_chat_history_or_retrieved_content(client_offline_chat):
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)
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_answer_using_general_command(client_offline_chat):
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# Arrange
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query = urllib.parse.quote("/general Where was Xi Li born?")
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message_list = []
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populate_chat_history(message_list)
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# Act
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response = client_offline_chat.get(f"/api/chat?q={query}&stream=true")
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response_message = response.content.decode("utf-8")
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# Assert
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assert response.status_code == 200
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assert "Fujiang" not in response_message
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_answer_from_retrieved_content_using_notes_command(client_offline_chat):
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# Arrange
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query = urllib.parse.quote("/notes Where was Xi Li born?")
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message_list = []
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populate_chat_history(message_list)
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# Act
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response = client_offline_chat.get(f"/api/chat?q={query}&stream=true")
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response_message = response.content.decode("utf-8")
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# Assert
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assert response.status_code == 200
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assert "Fujiang" in response_message
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_answer_not_known_using_notes_command(client_offline_chat):
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# Arrange
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query = urllib.parse.quote("/notes Where was Testatron born?")
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message_list = []
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populate_chat_history(message_list)
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# Act
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response = client_offline_chat.get(f"/api/chat?q={query}&stream=true")
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response_message = response.content.decode("utf-8")
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# Assert
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assert response.status_code == 200
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assert response_message == prompts.no_notes_found.format()
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.xfail(AssertionError, reason="Chat director not capable of answering time aware questions yet")
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@pytest.mark.chatquality
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@ -280,7 +280,6 @@ def test_answer_general_question_not_in_chat_history_or_retrieved_content(chat_c
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.xfail(reason="Chat director not consistently capable of asking for clarification yet.")
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@pytest.mark.chatquality
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def test_ask_for_clarification_if_not_enough_context_in_question(chat_client):
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# Act
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@ -289,10 +288,10 @@ def test_ask_for_clarification_if_not_enough_context_in_question(chat_client):
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# Assert
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expected_responses = [
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"which of them is the older",
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"which one is older",
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"which of them is older",
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"which one is the older",
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"which of them",
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"which one is",
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"which of namita's sons",
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"the birth order",
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]
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assert response.status_code == 200
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assert any([expected_response in response_message.lower() for expected_response in expected_responses]), (
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