mirror of
https://github.com/khoj-ai/khoj.git
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Extract prompts as LangChain Prompt Templates into a separate module
Improves code modularity, cleanliness. Reduces bloat in GPT.py module
This commit is contained in:
Debanjum Singh Solanky
parent
b484953bb3
commit
efcf7d1508
3 changed files with 189 additions and 126 deletions
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@ -53,6 +53,7 @@ dependencies = [
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"torch == 1.13.1",
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"uvicorn == 0.17.6",
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"aiohttp == 3.8.4",
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"langchain >= 0.0.187",
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]
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dynamic = ["version"]
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@ -5,10 +5,10 @@ from datetime import datetime
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# Internal Packages
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from khoj.utils.constants import empty_escape_sequences
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from khoj.processor.conversation import prompts
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from khoj.processor.conversation.utils import (
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chat_completion_with_backoff,
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completion_with_backoff,
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message_to_prompt,
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generate_chatml_messages_with_context,
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)
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@ -20,17 +20,9 @@ def answer(text, user_query, model, api_key=None, temperature=0.5, max_tokens=50
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"""
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Answer user query using provided text as reference with OpenAI's GPT
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"""
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# Setup Prompt based on Summary Type
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prompt = f"""
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You are a friendly, helpful personal assistant.
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Using the users notes below, answer their following question. If the answer is not contained within the notes, say "I don't know."
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# Setup Prompt from arguments
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prompt = prompts.answer.format(text=text, user_query=user_query)
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Notes:
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{text}
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Question: {user_query}
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Answer (in second person):"""
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# Get Response from GPT
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logger.debug(f"Prompt for GPT: {prompt}")
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response = completion_with_backoff(
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@ -53,19 +45,9 @@ def summarize(text, summary_type, model, user_query=None, api_key=None, temperat
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"""
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# Setup Prompt based on Summary Type
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if summary_type == "chat":
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prompt = f"""
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You are an AI. Summarize the conversation below from your perspective:
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{text}
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Summarize the conversation from the AI's first-person perspective:"""
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prompt = prompts.summarize_chat.format(text=text)
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elif summary_type == "notes":
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prompt = f"""
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Summarize the below notes about {user_query}:
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{text}
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Summarize the notes in second person perspective:"""
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prompt = prompts.summarize_notes.format(text=text, user_query=user_query)
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# Get Response from GPT
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logger.debug(f"Prompt for GPT: {prompt}")
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@ -102,63 +84,16 @@ def extract_questions(text, model="text-davinci-003", conversation_log={}, api_k
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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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prompt = f"""
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You are Khoj, an extremely smart and helpful search assistant with the ability to retrieve information from the users notes.
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- The user will provide their questions and answers to you for context.
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- Add as much context from the previous questions and answers as required into your search queries.
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- Break messages into multiple search queries when required to retrieve the relevant information.
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- Add date filters to your search queries from questions and answers when required to retrieve the relevant information.
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What searches, if any, will you need to perform to answer the users question?
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Provide search queries as a JSON list of strings
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Current Date: {today.strftime("%A, %Y-%m-%d")}
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Q: How was my trip to Cambodia?
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["How was my trip to Cambodia?"]
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A: The trip was amazing. I went to the Angkor Wat temple and it was beautiful.
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Q: Who did i visit that temple with?
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["Who did I visit the Angkor Wat Temple in Cambodia with?"]
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A: You visited the Angkor Wat Temple in Cambodia with Pablo, Namita and Xi.
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Q: What national parks did I go to last year?
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["National park I visited in {last_new_year.strftime("%Y")} dt>=\\"{last_new_year.strftime("%Y-%m-%d")}\\" dt<\\"{current_new_year.strftime("%Y-%m-%d")}\\""]
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A: You visited the Grand Canyon and Yellowstone National Park in {last_new_year.strftime("%Y")}.
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Q: How are you feeling today?
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[]
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A: I'm feeling a little bored. Helping you will hopefully make me feel better!
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Q: How many tennis balls fit in the back of a 2002 Honda Civic?
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["What is the size of a tennis ball?", "What is the trunk size of a 2002 Honda Civic?"]
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A: 1085 tennis balls will fit in the trunk of a Honda Civic
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Q: Is Bob older than Tom?
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["When was Bob born?", "What is Tom's age?"]
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A: Yes, Bob is older than Tom. As Bob was born on 1984-01-01 and Tom is 30 years old.
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Q: What is their age difference?
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["What is Bob's age?", "What is Tom's age?"]
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A: Bob is {current_new_year.year - 1984 - 30} years older than Tom. As Bob is {current_new_year.year - 1984} years old and Tom is 30 years old.
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{chat_history}
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Q: {text}
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"""
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prompt = prompts.extract_questions.format(
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current_date=today.strftime("%A, %Y-%m-%d"),
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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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)
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# Get Response from GPT
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response = completion_with_backoff(
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@ -191,31 +126,8 @@ def extract_search_type(text, model, api_key=None, temperature=0.5, max_tokens=1
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"""
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Extract search type from user query using OpenAI's GPT
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"""
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# Initialize Variables
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understand_primer = """
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Objective: Extract search type from user query and return information as JSON
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Allowed search types are listed below:
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- search-type=["notes","ledger","image","music"]
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Some examples are given below for reference:
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Q:What fiction book was I reading last week about AI starship?
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A:{ "search-type": "notes" }
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Q:Play some calm classical music?
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A:{ "search-type": "music" }
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Q:How much did I spend at Subway for dinner last time?
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A:{ "search-type": "ledger" }
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Q:What was that popular Sri lankan song that Alex had mentioned?
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A:{ "search-type": "music" }
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Q:Can you recommend a movie to watch from my notes?
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A:{ "search-type": "notes" }
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Q: When did I buy Groceries last?
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A:{ "search-type": "ledger" }
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Q:When did I go surfing last?
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A:{ "search-type": "notes" }"""
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# Setup Prompt with Understand Primer
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prompt = message_to_prompt(text, understand_primer, start_sequence="\nA:", restart_sequence="\nQ:")
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# Setup Prompt to extract search type
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prompt = prompts.search_type + f"{text}\nA:"
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if verbose > 1:
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print(f"Message -> Prompt: {text} -> {prompt}")
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@ -241,36 +153,23 @@ def converse(references, user_query, conversation_log={}, model="gpt-3.5-turbo",
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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().strftime("%Y-%m-%d")
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compiled_references = "\n\n".join({f"# {item}" for item in references})
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personality_primer = "You are Khoj, a friendly, smart and helpful personal assistant."
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conversation_primers = {
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"general": f"""
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Using your general knowledge and our past conversations as context, answer the following question.
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Current Date: {datetime.now().strftime("%Y-%m-%d")}
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Question: {user_query}
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""".strip(),
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"notes": f"""
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Using the notes and our past conversations as context, answer the following question.
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Current Date: {datetime.now().strftime("%Y-%m-%d")}
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Notes:
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{compiled_references}
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Question: {user_query}
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""".strip(),
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}
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# Get Conversation Primer appropriate to Conversation Type
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conversation_type = "general" if user_query.startswith("@general") or compiled_references.strip() == "" else "notes"
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logger.debug(f"Conversation Type: {conversation_type}")
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conversation_primer = conversation_primers.get(conversation_type)
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if conversation_type == "general":
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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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)
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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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personality_primer,
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prompts.personality.format(),
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conversation_log,
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model,
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)
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163
src/khoj/processor/conversation/prompts.py
Normal file
163
src/khoj/processor/conversation/prompts.py
Normal file
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@ -0,0 +1,163 @@
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# External Packages
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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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## General Conversation
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## --
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general_conversation = PromptTemplate.from_template(
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"""
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Using your general knowledge and our past conversations as context, answer the following question.
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Current Date: {current_date}
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Question: {query}
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""".strip()
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)
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## Notes Conversation
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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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Current Date: {current_date}
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Notes:
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{references}
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Question: {query}
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""".strip()
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)
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## Summarize Chat
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## --
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summarize_chat = PromptTemplate.from_template(
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"""
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You are an AI. Summarize the conversation below from your perspective:
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{text}
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Summarize the conversation from the AI's first-person perspective:"""
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)
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## Summarize Notes
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## --
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summarize_notes = PromptTemplate.from_template(
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"""
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Summarize the below notes about {user_query}:
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{text}
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Summarize the notes in second person perspective:"""
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)
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## Answer
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## --
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answer = PromptTemplate.from_template(
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"""
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You are a friendly, helpful personal assistant.
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Using the users notes below, answer their following question. If the answer is not contained within the notes, say "I don't know."
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Notes:
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{text}
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Question: {user_query}
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Answer (in second person):"""
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)
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## Extract Questions
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## --
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extract_questions = PromptTemplate.from_template(
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"""
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You are Khoj, an extremely smart and helpful search assistant with the ability to retrieve information from the users notes.
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- The user will provide their questions and answers to you for context.
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- Add as much context from the previous questions and answers as required into your search queries.
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- Break messages into multiple search queries when required to retrieve the relevant information.
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- Add date filters to your search queries from questions and answers when required to retrieve the relevant information.
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What searches, if any, will you need to perform to answer the users question?
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Provide search queries as a JSON list of strings
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Current Date: {current_date}
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Q: How was my trip to Cambodia?
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["How was my trip to Cambodia?"]
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A: The trip was amazing. I went to the Angkor Wat temple and it was beautiful.
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Q: Who did i visit that temple with?
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["Who did I visit the Angkor Wat Temple in Cambodia with?"]
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A: You visited the Angkor Wat Temple in Cambodia with Pablo, Namita and Xi.
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Q: What national parks did I go to last year?
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["National park I visited in {last_new_year} dt>=\\"{last_new_year_date}\\" dt<\\"{current_new_year_date}\\""]
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A: You visited the Grand Canyon and Yellowstone National Park in {last_new_year}.
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Q: How are you feeling today?
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[]
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A: I'm feeling a little bored. Helping you will hopefully make me feel better!
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Q: How many tennis balls fit in the back of a 2002 Honda Civic?
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["What is the size of a tennis ball?", "What is the trunk size of a 2002 Honda Civic?"]
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A: 1085 tennis balls will fit in the trunk of a Honda Civic
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Q: Is Bob older than Tom?
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["When was Bob born?", "What is Tom's age?"]
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A: Yes, Bob is older than Tom. As Bob was born on 1984-01-01 and Tom is 30 years old.
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Q: What is their age difference?
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["What is Bob's age?", "What is Tom's age?"]
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A: Bob is {bob_tom_age_difference} years older than Tom. As Bob is {bob_age} years old and Tom is 30 years old.
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{chat_history}
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Q: {text}
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"""
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)
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## Extract Search Type
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## --
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search_type = """
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Objective: Extract search type from user query and return information as JSON
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Allowed search types are listed below:
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- search-type=["notes","ledger","image","music"]
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Some examples are given below for reference:
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Q:What fiction book was I reading last week about AI starship?
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A:{ "search-type": "notes" }
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Q:Play some calm classical music?
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A:{ "search-type": "music" }
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Q:How much did I spend at Subway for dinner last time?
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A:{ "search-type": "ledger" }
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Q:What was that popular Sri lankan song that Alex had mentioned?
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A:{ "search-type": "music" }
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Q:Can you recommend a movie to watch from my notes?
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A:{ "search-type": "notes" }
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Q:When did I buy Groceries last?
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A:{ "search-type": "ledger" }
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Q:When did I go surfing last?
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A:{ "search-type": "notes" }
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Q:"""
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