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https://github.com/khoj-ai/khoj.git
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Merge branch 'master' into features/advanced-reasoning
This commit is contained in:
Debanjum
commit
50ffd7f199
12 changed files with 116 additions and 96 deletions
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@ -394,6 +394,11 @@ export const ChatInputArea = forwardRef<HTMLTextAreaElement, ChatInputProps>((pr
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<PopoverContent
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onOpenAutoFocus={(e) => e.preventDefault()}
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className={`${props.isMobileWidth ? "w-[100vw]" : "w-full"} rounded-md`}
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side="top"
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align="center"
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/* Offset below text area on home page (i.e where conversationId is unset) */
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sideOffset={props.conversationId ? 0 : 80}
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alignOffset={0}
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>
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<Command className="max-w-full">
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<CommandInput
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@ -362,7 +362,7 @@ function ChatBodyData(props: ChatBodyDataProps) {
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className={`${selectedAgent === agents[index].slug ? convertColorToBorderClass(agents[index].color) : "border-muted text-muted-foreground"} hover:cursor-pointer`}
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>
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<CardTitle
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className="text-center text-xs font-medium flex justify-center items-center px-1.5 py-1"
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className="text-center text-xs font-medium flex justify-center items-center whitespace-nowrap px-1.5 py-1"
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onDoubleClick={() =>
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openAgentEditCard(agents[index].slug)
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}
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@ -19,6 +19,8 @@ from khoj.processor.embeddings import EmbeddingsModel
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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BATCH_SIZE = 1000 # Define an appropriate batch size
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class Command(BaseCommand):
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help = "Convert all existing Entry objects to use a new default Search model."
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@ -42,17 +44,19 @@ class Command(BaseCommand):
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def handle(self, *args, **options):
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@transaction.atomic
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def regenerate_entries(entry_filter: Q, embeddings_model: EmbeddingsModel, search_model: SearchModelConfig):
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entries = Entry.objects.filter(entry_filter).all()
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total_entries = Entry.objects.filter(entry_filter).count()
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for start in tqdm(range(0, total_entries, BATCH_SIZE)):
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end = start + BATCH_SIZE
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entries = Entry.objects.filter(entry_filter)[start:end]
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compiled_entries = [entry.compiled for entry in entries]
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updated_entries: List[Entry] = []
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try:
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embeddings = embeddings_model.embed_documents(compiled_entries)
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except Exception as e:
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logger.error(f"Error embedding documents: {e}")
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return
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for i, entry in enumerate(tqdm(entries)):
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for i, entry in enumerate(entries):
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entry.embeddings = embeddings[i]
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entry.search_model_id = search_model.id
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updated_entries.append(entry)
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@ -152,9 +152,7 @@ def converse_anthropic(
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"""
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# Initialize Variables
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current_date = datetime.now()
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compiled_references = "\n\n".join({f"# {item}" for item in references})
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conversation_primer = prompts.query_prompt.format(query=user_query)
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compiled_references = "\n\n".join({f"# File: {item['file']}\n## {item['compiled']}\n" for item in references})
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if agent and agent.personality:
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system_prompt = prompts.custom_personality.format(
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@ -185,20 +183,19 @@ def converse_anthropic(
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completion_func(chat_response=prompts.no_online_results_found.format())
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return iter([prompts.no_online_results_found.format()])
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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conversation_primer = (
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f"{prompts.code_executed_context.format(code_results=str(code_results))}\n{conversation_primer}"
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)
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if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
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conversation_primer = (
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f"{prompts.online_search_conversation.format(online_results=str(online_results))}\n{conversation_primer}"
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)
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context_message = ""
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if not is_none_or_empty(compiled_references):
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conversation_primer = f"{prompts.notes_conversation.format(query=user_query, references=compiled_references)}\n\n{conversation_primer}"
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context_message = f"{prompts.notes_conversation.format(query=user_query, references=compiled_references)}\n\n"
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if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
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context_message += f"{prompts.online_search_conversation.format(online_results=str(online_results))}\n\n"
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
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context_message = context_message.strip()
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# Setup Prompt with Primer or Conversation History
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messages = generate_chatml_messages_with_context(
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conversation_primer,
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user_query,
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context_message=context_message,
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conversation_log=conversation_log,
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model_name=model,
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max_prompt_size=max_prompt_size,
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@ -157,9 +157,7 @@ def converse_gemini(
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"""
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# Initialize Variables
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current_date = datetime.now()
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compiled_references = "\n\n".join({f"# {item}" for item in references})
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conversation_primer = prompts.query_prompt.format(query=user_query)
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compiled_references = "\n\n".join({f"# File: {item['file']}\n## {item['compiled']}\n" for item in references})
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if agent and agent.personality:
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system_prompt = prompts.custom_personality.format(
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@ -191,20 +189,19 @@ def converse_gemini(
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completion_func(chat_response=prompts.no_online_results_found.format())
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return iter([prompts.no_online_results_found.format()])
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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conversation_primer = (
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f"{prompts.code_executed_context.format(code_results=str(code_results))}\n{conversation_primer}"
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)
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if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
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conversation_primer = (
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f"{prompts.online_search_conversation.format(online_results=str(online_results))}\n{conversation_primer}"
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)
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context_message = ""
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if not is_none_or_empty(compiled_references):
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conversation_primer = f"{prompts.notes_conversation.format(query=user_query, references=compiled_references)}\n\n{conversation_primer}"
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context_message = f"{prompts.notes_conversation.format(query=user_query, references=compiled_references)}\n\n"
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if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
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context_message += f"{prompts.online_search_conversation.format(online_results=str(online_results))}\n\n"
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
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context_message = context_message.strip()
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# Setup Prompt with Primer or Conversation History
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messages = generate_chatml_messages_with_context(
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conversation_primer,
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user_query,
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context_message=context_message,
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conversation_log=conversation_log,
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model_name=model,
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max_prompt_size=max_prompt_size,
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@ -158,7 +158,7 @@ def converse_offline(
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# Initialize Variables
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assert loaded_model is None or isinstance(loaded_model, Llama), "loaded_model must be of type Llama, if configured"
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offline_chat_model = loaded_model or download_model(model, max_tokens=max_prompt_size)
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compiled_references_message = "\n\n".join({f"{item['compiled']}" for item in references})
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compiled_references = "\n\n".join({f"# File: {item['file']}\n## {item['compiled']}\n" for item in references})
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tracer["chat_model"] = model
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current_date = datetime.now()
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@ -176,8 +176,6 @@ def converse_offline(
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day_of_week=current_date.strftime("%A"),
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)
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conversation_primer = prompts.query_prompt.format(query=user_query)
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if location_data:
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location_prompt = prompts.user_location.format(location=f"{location_data}")
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system_prompt = f"{system_prompt}\n{location_prompt}"
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@ -187,31 +185,34 @@ def converse_offline(
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system_prompt = f"{system_prompt}\n{user_name_prompt}"
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# Get Conversation Primer appropriate to Conversation Type
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if conversation_commands == [ConversationCommand.Notes] and is_none_or_empty(compiled_references_message):
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if conversation_commands == [ConversationCommand.Notes] and is_none_or_empty(compiled_references):
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return iter([prompts.no_notes_found.format()])
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elif conversation_commands == [ConversationCommand.Online] and is_none_or_empty(online_results):
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completion_func(chat_response=prompts.no_online_results_found.format())
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return iter([prompts.no_online_results_found.format()])
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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conversation_primer = (
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f"{prompts.code_executed_context.format(code_results=str(code_results))}\n{conversation_primer}"
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)
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if ConversationCommand.Online in conversation_commands:
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context_message = ""
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if not is_none_or_empty(compiled_references):
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context_message = f"{prompts.notes_conversation_offline.format(references=compiled_references)}\n\n"
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if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
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simplified_online_results = online_results.copy()
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for result in online_results:
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if online_results[result].get("webpages"):
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simplified_online_results[result] = online_results[result]["webpages"]
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conversation_primer = f"{prompts.online_search_conversation_offline.format(online_results=str(simplified_online_results))}\n{conversation_primer}"
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if not is_none_or_empty(compiled_references_message):
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conversation_primer = f"{prompts.notes_conversation_offline.format(references=compiled_references_message)}\n\n{conversation_primer}"
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context_message += (
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f"{prompts.online_search_conversation_offline.format(online_results=str(simplified_online_results))}\n\n"
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)
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
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context_message = context_message.strip()
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# Setup Prompt with Primer or Conversation History
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messages = generate_chatml_messages_with_context(
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conversation_primer,
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user_query,
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system_prompt,
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conversation_log,
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context_message=context_message,
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model_name=model,
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loaded_model=offline_chat_model,
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max_prompt_size=max_prompt_size,
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@ -155,9 +155,7 @@ def converse(
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"""
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# Initialize Variables
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current_date = datetime.now()
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compiled_references = "\n\n".join({f"# {item['compiled']}" for item in references})
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conversation_primer = prompts.query_prompt.format(query=user_query)
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compiled_references = "\n\n".join({f"# File: {item['file']}\n## {item['compiled']}\n" for item in references})
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if agent and agent.personality:
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system_prompt = prompts.custom_personality.format(
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@ -188,22 +186,21 @@ def converse(
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completion_func(chat_response=prompts.no_online_results_found.format())
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return iter([prompts.no_online_results_found.format()])
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if not is_none_or_empty(code_results):
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conversation_primer = (
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f"{prompts.code_executed_context.format(code_results=str(code_results))}\n{conversation_primer}"
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)
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if not is_none_or_empty(online_results):
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conversation_primer = (
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f"{prompts.online_search_conversation.format(online_results=str(online_results))}\n{conversation_primer}"
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)
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context_message = ""
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if not is_none_or_empty(compiled_references):
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conversation_primer = f"{prompts.notes_conversation.format(query=user_query, references=compiled_references)}\n\n{conversation_primer}"
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context_message = f"{prompts.notes_conversation.format(references=compiled_references)}\n\n"
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if not is_none_or_empty(online_results):
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context_message += f"{prompts.online_search_conversation.format(online_results=str(online_results))}\n\n"
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if not is_none_or_empty(code_results):
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context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
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context_message = context_message.strip()
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# Setup Prompt with Primer or Conversation History
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messages = generate_chatml_messages_with_context(
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conversation_primer,
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user_query,
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system_prompt,
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conversation_log,
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context_message=context_message,
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model_name=model,
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max_prompt_size=max_prompt_size,
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tokenizer_name=tokenizer_name,
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|
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@ -118,6 +118,7 @@ Use my personal notes and our past conversations to inform your response.
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Ask crisp follow-up questions to get additional context, when a helpful response cannot be provided from the provided notes or past conversations.
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User's Notes:
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-----
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{references}
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""".strip()
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)
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@ -127,6 +128,7 @@ notes_conversation_offline = PromptTemplate.from_template(
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Use my personal notes and our past conversations to inform your response.
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User's Notes:
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-----
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{references}
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""".strip()
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)
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@ -328,6 +330,7 @@ Use this up-to-date information from the internet to inform your response.
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Ask crisp follow-up questions to get additional context, when a helpful response cannot be provided from the online data or past conversations.
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Information from the internet:
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-----
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{online_results}
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""".strip()
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)
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@ -337,6 +340,7 @@ online_search_conversation_offline = PromptTemplate.from_template(
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Use this up-to-date information from the internet to inform your response.
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Information from the internet:
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-----
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{online_results}
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""".strip()
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)
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|
|
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@ -22,6 +22,7 @@ from transformers import AutoTokenizer
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from khoj.database.adapters import ConversationAdapters, ais_user_subscribed
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from khoj.database.models import ChatModelOptions, ClientApplication, KhojUser
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from khoj.processor.conversation import prompts
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from khoj.processor.conversation.offline.utils import download_model, infer_max_tokens
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from khoj.search_filter.date_filter import DateFilter
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from khoj.search_filter.file_filter import FileFilter
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@ -259,8 +260,9 @@ def generate_chatml_messages_with_context(
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query_images=None,
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vision_enabled=False,
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model_type="",
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context_message="",
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):
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"""Generate messages for ChatGPT with context from previous conversation"""
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"""Generate chat messages with appropriate context from previous conversation to send to the chat model"""
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# Set max prompt size from user config or based on pre-configured for model and machine specs
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if not max_prompt_size:
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if loaded_model:
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|
|
@ -274,21 +276,27 @@ def generate_chatml_messages_with_context(
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# Extract Chat History for Context
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chatml_messages: List[ChatMessage] = []
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for chat in conversation_log.get("chat", []):
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message_notes = f'\n\n Notes:\n{chat.get("context")}' if chat.get("context") else "\n"
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message_context = ""
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if chat["by"] == "khoj" and "excalidraw" in chat["intent"].get("type", ""):
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message_context += chat.get("intent").get("inferred-queries")[0]
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if not is_none_or_empty(chat.get("context")):
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references = "\n\n".join(
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{f"# File: {item['file']}\n## {item['compiled']}\n" for item in chat.get("context") or []}
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)
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message_context += f"{prompts.notes_conversation.format(references=references)}\n\n"
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if not is_none_or_empty(chat.get("onlineContext")):
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message_context += f"{prompts.online_search_conversation.format(online_results=chat.get('onlineContext'))}"
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if not is_none_or_empty(message_context):
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reconstructed_context_message = ChatMessage(content=message_context, role="user")
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chatml_messages.insert(0, reconstructed_context_message)
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role = "user" if chat["by"] == "you" else "assistant"
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if chat["by"] == "khoj" and "excalidraw" in chat["intent"].get("type"):
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message_content = chat.get("intent").get("inferred-queries")[0] + message_notes
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else:
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message_content = chat["message"] + message_notes
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message_content = construct_structured_message(message_content, chat.get("images"), model_type, vision_enabled)
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message_content = construct_structured_message(chat["message"], chat.get("images"), model_type, vision_enabled)
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reconstructed_message = ChatMessage(content=message_content, role=role)
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chatml_messages.insert(0, reconstructed_message)
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if len(chatml_messages) >= 2 * lookback_turns:
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if len(chatml_messages) >= 3 * lookback_turns:
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break
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messages = []
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||||
|
|
@ -299,6 +307,8 @@ def generate_chatml_messages_with_context(
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role="user",
|
||||
)
|
||||
)
|
||||
if not is_none_or_empty(context_message):
|
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messages.append(ChatMessage(content=context_message, role="user"))
|
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if len(chatml_messages) > 0:
|
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messages += chatml_messages
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if not is_none_or_empty(system_message):
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|
|
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|||
|
|
@ -120,7 +120,7 @@ def add_files_filter(request: Request, filter: FilesFilterRequest):
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file_filters = ConversationAdapters.add_files_to_filter(request.user.object, conversation_id, files_filter)
|
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return Response(content=json.dumps(file_filters), media_type="application/json", status_code=200)
|
||||
except Exception as e:
|
||||
logger.error(f"Error adding file filter {filter.filename}: {e}", exc_info=True)
|
||||
logger.error(f"Error adding file filter {filter.filenames}: {e}", exc_info=True)
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raise HTTPException(status_code=422, detail=str(e))
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -214,7 +214,7 @@ def test_answer_from_chat_history_and_previously_retrieved_content():
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|||
(
|
||||
"When was I born?",
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||||
"You were born on 1st April 1984.",
|
||||
["Testatron was born on 1st April 1984 in Testville."],
|
||||
[{"compiled": "Testatron was born on 1st April 1984 in Testville.", "file": "birth.org"}],
|
||||
),
|
||||
]
|
||||
|
||||
|
|
@ -415,15 +415,18 @@ def test_ask_for_clarification_if_not_enough_context_in_question():
|
|||
context = [
|
||||
{
|
||||
"compiled": f"""# Ramya
|
||||
My sister, Ramya, is married to Kali Devi. They have 2 kids, Ravi and Rani."""
|
||||
My sister, Ramya, is married to Kali Devi. They have 2 kids, Ravi and Rani.""",
|
||||
"file": "Family.md",
|
||||
},
|
||||
{
|
||||
"compiled": f"""# Fang
|
||||
My sister, Fang Liu is married to Xi Li. They have 1 kid, Xiao Li."""
|
||||
My sister, Fang Liu is married to Xi Li. They have 1 kid, Xiao Li.""",
|
||||
"file": "Family.md",
|
||||
},
|
||||
{
|
||||
"compiled": f"""# Aiyla
|
||||
My sister, Aiyla is married to Tolga. They have 3 kids, Yildiz, Ali and Ahmet."""
|
||||
My sister, Aiyla is married to Tolga. They have 3 kids, Yildiz, Ali and Ahmet.""",
|
||||
"file": "Family.md",
|
||||
},
|
||||
]
|
||||
|
||||
|
|
@ -608,9 +611,11 @@ async def test_infer_webpage_urls_actor_extracts_correct_links(chat_client, defa
|
|||
),
|
||||
],
|
||||
)
|
||||
async def test_infer_task_scheduling_request(chat_client, user_query, expected_crontime, expected_qs, unexpected_qs):
|
||||
async def test_infer_task_scheduling_request(
|
||||
chat_client, user_query, expected_crontime, expected_qs, unexpected_qs, default_user2
|
||||
):
|
||||
# Act
|
||||
crontime, inferred_query, _ = await schedule_query(user_query, {})
|
||||
crontime, inferred_query, _ = await schedule_query(user_query, {}, default_user2)
|
||||
inferred_query = inferred_query.lower()
|
||||
|
||||
# Assert
|
||||
|
|
@ -630,7 +635,7 @@ async def test_infer_task_scheduling_request(chat_client, user_query, expected_c
|
|||
"scheduling_query, executing_query, generated_response, expected_should_notify",
|
||||
[
|
||||
(
|
||||
"Notify me if it is going to rain tomorrow?",
|
||||
"Notify me only if it is going to rain tomorrow?",
|
||||
"What's the weather forecast for tomorrow?",
|
||||
"It is sunny and warm tomorrow.",
|
||||
False,
|
||||
|
|
@ -656,10 +661,10 @@ async def test_infer_task_scheduling_request(chat_client, user_query, expected_c
|
|||
],
|
||||
)
|
||||
def test_decision_on_when_to_notify_scheduled_task_results(
|
||||
chat_client, scheduling_query, executing_query, generated_response, expected_should_notify
|
||||
chat_client, default_user2, scheduling_query, executing_query, generated_response, expected_should_notify
|
||||
):
|
||||
# Act
|
||||
generated_should_notify = should_notify(scheduling_query, executing_query, generated_response)
|
||||
generated_should_notify = should_notify(scheduling_query, executing_query, generated_response, default_user2)
|
||||
|
||||
# Assert
|
||||
assert generated_should_notify == expected_should_notify
|
||||
|
|
|
|||
|
|
@ -307,7 +307,7 @@ def test_summarize_one_file(chat_client, default_user2: KhojUser):
|
|||
json={"filename": summarization_file, "conversation_id": str(conversation.id)},
|
||||
)
|
||||
query = "/summarize"
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": conversation.id})
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": str(conversation.id)})
|
||||
response_message = response.json()["response"]
|
||||
# Assert
|
||||
assert response_message != ""
|
||||
|
|
@ -339,7 +339,7 @@ def test_summarize_extra_text(chat_client, default_user2: KhojUser):
|
|||
json={"filename": summarization_file, "conversation_id": str(conversation.id)},
|
||||
)
|
||||
query = "/summarize tell me about Xiu"
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": conversation.id})
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": str(conversation.id)})
|
||||
response_message = response.json()["response"]
|
||||
# Assert
|
||||
assert response_message != ""
|
||||
|
|
@ -367,7 +367,7 @@ def test_summarize_multiple_files(chat_client, default_user2: KhojUser):
|
|||
)
|
||||
|
||||
query = "/summarize"
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": conversation.id})
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": str(conversation.id)})
|
||||
response_message = response.json()["response"]
|
||||
|
||||
# Assert
|
||||
|
|
@ -383,7 +383,7 @@ def test_summarize_no_files(chat_client, default_user2: KhojUser):
|
|||
|
||||
# Act
|
||||
query = "/summarize"
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": conversation.id})
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": str(conversation.id)})
|
||||
response_message = response.json()["response"]
|
||||
|
||||
# Assert
|
||||
|
|
@ -418,11 +418,11 @@ def test_summarize_different_conversation(chat_client, default_user2: KhojUser):
|
|||
|
||||
# Act
|
||||
query = "/summarize"
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": conversation2.id})
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": str(conversation2.id)})
|
||||
response_message_conv2 = response.json()["response"]
|
||||
|
||||
# now make sure that the file filter is still in conversation 1
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": conversation1.id})
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": str(conversation1.id)})
|
||||
response_message_conv1 = response.json()["response"]
|
||||
|
||||
# Assert
|
||||
|
|
@ -449,7 +449,7 @@ def test_summarize_nonexistant_file(chat_client, default_user2: KhojUser):
|
|||
json={"filename": "imaginary.markdown", "conversation_id": str(conversation.id)},
|
||||
)
|
||||
query = urllib.parse.quote("/summarize")
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": conversation.id})
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": str(conversation.id)})
|
||||
response_message = response.json()["response"]
|
||||
# Assert
|
||||
assert response_message == "No files selected for summarization. Please add files using the section on the left."
|
||||
|
|
@ -481,7 +481,7 @@ def test_summarize_diff_user_file(chat_client, default_user: KhojUser, pdf_confi
|
|||
|
||||
# Act
|
||||
query = "/summarize"
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": conversation.id})
|
||||
response = chat_client.post(f"/api/chat", json={"q": query, "conversation_id": str(conversation.id)})
|
||||
response_message = response.json()["response"]
|
||||
|
||||
# Assert
|
||||
|
|
|
|||
Loading…
Reference in a new issue