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https://github.com/khoaliber/khoj.git
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86ffd7a7a2
Use placeholder for newline in json object values until json parsed and values extracted. This is useful when research mode models outputs multi-line codeblocks in queries etc.
226 lines
7.8 KiB
Python
226 lines
7.8 KiB
Python
import json
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import logging
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from datetime import datetime, timedelta
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from typing import Dict, Optional
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from langchain.schema import ChatMessage
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from khoj.database.models import Agent, ChatModelOptions, KhojUser
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from khoj.processor.conversation import prompts
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from khoj.processor.conversation.openai.utils import (
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chat_completion_with_backoff,
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completion_with_backoff,
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)
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from khoj.processor.conversation.utils import (
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clean_json,
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construct_structured_message,
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generate_chatml_messages_with_context,
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)
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from khoj.utils.helpers import ConversationCommand, is_none_or_empty
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from khoj.utils.rawconfig import LocationData
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from khoj.utils.yaml import yaml_dump
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logger = logging.getLogger(__name__)
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def extract_questions(
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text,
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model: Optional[str] = "gpt-4o-mini",
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conversation_log={},
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api_key=None,
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api_base_url=None,
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location_data: LocationData = None,
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user: KhojUser = None,
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query_images: Optional[list[str]] = None,
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vision_enabled: bool = False,
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personality_context: Optional[str] = None,
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tracer: dict = {},
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):
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"""
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Infer search queries to retrieve relevant notes to answer user query
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"""
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location = f"{location_data}" if location_data else "Unknown"
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username = prompts.user_name.format(name=user.get_full_name()) if user and user.get_full_name() else ""
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# Extract Past User Message and Inferred Questions from Conversation Log
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chat_history = "".join(
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[
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f'Q: {chat["intent"]["query"]}\nKhoj: {{"queries": {chat["intent"].get("inferred-queries") or list([chat["intent"]["query"]])}}}\nA: {chat["message"]}\n\n'
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for chat in conversation_log.get("chat", [])[-4:]
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if chat["by"] == "khoj" and "to-image" not in chat["intent"].get("type")
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]
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)
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# Get dates relative to today for prompt creation
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today = datetime.today()
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current_new_year = today.replace(month=1, day=1)
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last_new_year = current_new_year.replace(year=today.year - 1)
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temperature = 0.7
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prompt = prompts.extract_questions.format(
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current_date=today.strftime("%Y-%m-%d"),
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day_of_week=today.strftime("%A"),
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current_month=today.strftime("%Y-%m"),
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last_new_year=last_new_year.strftime("%Y"),
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last_new_year_date=last_new_year.strftime("%Y-%m-%d"),
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current_new_year_date=current_new_year.strftime("%Y-%m-%d"),
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bob_tom_age_difference={current_new_year.year - 1984 - 30},
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bob_age={current_new_year.year - 1984},
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chat_history=chat_history,
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text=text,
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yesterday_date=(today - timedelta(days=1)).strftime("%Y-%m-%d"),
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location=location,
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username=username,
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personality_context=personality_context,
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)
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prompt = construct_structured_message(
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message=prompt,
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images=query_images,
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model_type=ChatModelOptions.ModelType.OPENAI,
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vision_enabled=vision_enabled,
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)
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messages = [ChatMessage(content=prompt, role="user")]
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response = send_message_to_model(
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messages,
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api_key,
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model,
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response_type="json_object",
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api_base_url=api_base_url,
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temperature=temperature,
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tracer=tracer,
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)
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# Extract, Clean Message from GPT's Response
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try:
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response = clean_json(response)
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response = json.loads(response)
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response = [q.strip() for q in response["queries"] if q.strip()]
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if not isinstance(response, list) or not response:
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logger.error(f"Invalid response for constructing subqueries: {response}")
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return [text]
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return response
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except:
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logger.warning(f"GPT returned invalid JSON. Falling back to using user message as search query.\n{response}")
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questions = [text]
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logger.debug(f"Extracted Questions by GPT: {questions}")
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return questions
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def send_message_to_model(
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messages, api_key, model, response_type="text", api_base_url=None, temperature=0, tracer: dict = {}
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):
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"""
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Send message to model
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"""
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# Get Response from GPT
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return completion_with_backoff(
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messages=messages,
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model=model,
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openai_api_key=api_key,
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temperature=temperature,
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api_base_url=api_base_url,
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model_kwargs={"response_format": {"type": response_type}},
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tracer=tracer,
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)
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def converse(
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references,
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user_query,
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online_results: Optional[Dict[str, Dict]] = None,
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code_results: Optional[Dict[str, Dict]] = None,
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conversation_log={},
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model: str = "gpt-4o-mini",
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api_key: Optional[str] = None,
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api_base_url: Optional[str] = None,
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temperature: float = 0.2,
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completion_func=None,
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conversation_commands=[ConversationCommand.Default],
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max_prompt_size=None,
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tokenizer_name=None,
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location_data: LocationData = None,
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user_name: str = None,
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agent: Agent = None,
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query_images: Optional[list[str]] = None,
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vision_available: bool = False,
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tracer: dict = {},
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):
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"""
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Converse with user using OpenAI's ChatGPT
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"""
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# Initialize Variables
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current_date = datetime.now()
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if agent and agent.personality:
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system_prompt = prompts.custom_personality.format(
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name=agent.name,
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bio=agent.personality,
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current_date=current_date.strftime("%Y-%m-%d"),
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day_of_week=current_date.strftime("%A"),
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)
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else:
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system_prompt = prompts.personality.format(
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current_date=current_date.strftime("%Y-%m-%d"),
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day_of_week=current_date.strftime("%A"),
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)
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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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if user_name:
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user_name_prompt = prompts.user_name.format(name=user_name)
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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(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_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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context_message = ""
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if not is_none_or_empty(references):
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context_message = f"{prompts.notes_conversation.format(references=yaml_dump(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=yaml_dump(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=yaml_dump(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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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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query_images=query_images,
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vision_enabled=vision_available,
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model_type=ChatModelOptions.ModelType.OPENAI,
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)
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truncated_messages = "\n".join({f"{message.content[:70]}..." for message in messages})
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logger.debug(f"Conversation Context for GPT: {truncated_messages}")
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# Get Response from GPT
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return chat_completion_with_backoff(
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messages=messages,
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compiled_references=references,
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online_results=online_results,
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model_name=model,
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temperature=temperature,
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openai_api_key=api_key,
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api_base_url=api_base_url,
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completion_func=completion_func,
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model_kwargs={"stop": ["Notes:\n["]},
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tracer=tracer,
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)
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