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Extract search queries from users message using ChatGPT as Search Actor
- Reasons - ChatGPT should be better at following instructions than GPT - At 1/10th the cost, it's much cheaper than using older GPT models
This commit is contained in:
Debanjum Singh Solanky
parent
939d7731da
commit
e28526bbc9
2 changed files with 72 additions and 59 deletions
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@ -9,7 +9,11 @@ import openai
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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.utils import message_to_prompt, generate_chatml_messages_with_context
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from khoj.processor.conversation.utils import (
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message_to_prompt,
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message_to_chatml,
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generate_chatml_messages_with_context,
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)
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logger = logging.getLogger(__name__)
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@ -78,94 +82,104 @@ Summarize the notes in second person perspective:"""
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return str(story).replace("\n\n", "")
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def extract_questions(
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text, model="text-davinci-003", conversation_log={}, api_key=None, temperature=0, max_tokens=100
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):
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def extract_questions(message, model="gpt-3.5-turbo", conversation_log={}, api_key=None, temperature=0):
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"""
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Infer search queries to retrieve relevant notes to answer user query
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Infer search queries to retrieve relevant notes to respond to user's message
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"""
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# Initialize Variables
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openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
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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"]}\n\n{chat["intent"].get("inferred-queries") or list([chat["intent"]["query"]])}\n\n{chat["message"]}\n\n'
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for chat in conversation_log.get("chat", [])[-4:]
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if chat["by"] == "khoj"
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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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prompt = f"""
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personality_primer = 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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The user will provide their questions and answers to you for context.
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You can:
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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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Current Date: {today.strftime("%H:%M %A %Y-%m-%d")}"""
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Q: How was my trip to Cambodia?
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# Extract Past User Message and Inferred Questions from Conversation Log
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# fmt: off
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messages = [
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# Add system message to prime GPT for the task
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message_to_chatml(personality_primer, role="system"),
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["How was my trip to Cambodia?"]
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# Add example user message, inferred question and answer triplets to explain the task
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message_to_chatml("Q: How was my trip to Cambodia?", role="user"),
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message_to_chatml('["How was my trip to Cambodia?"]', role="assistant"),
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message_to_chatml("A: The trip was amazing. I went to the Angkor Wat temple in August 2017 and it was beautiful.", role="user"),
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A: The trip was amazing. I went to the Angkor Wat temple and it was beautiful.
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message_to_chatml("Q: Who did I visit that temple with?", role="user"),
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message_to_chatml('["Who did I visit the Angkor Wat Temple in Cambodia with? dt>=\\"2017-08-01\\" dt<\\"2017-09-01\\""]', role="assistant"),
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message_to_chatml("A: You visited the Angkor Wat Temple in Cambodia with Pablo, Namita and Xi.", role="user"),
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Q: Who did i visit that temple with?
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message_to_chatml("Q: What national parks did I go to last year?", role="user"),
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message_to_chatml(
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f'["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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role="assistant",
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),
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message_to_chatml(
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f'A: You visited the Grand Canyon and Yellowstone National Park in {last_new_year.strftime("%Y")}.',
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role="user",
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),
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["Who did I visit the Angkor Wat Temple in Cambodia with?"]
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message_to_chatml("Q: How are you feeling?", role="user"),
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message_to_chatml("[]", role="assistant"),
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message_to_chatml("A: I'm feeling a little bored. Helping you will hopefully make me feel better!", role="user"),
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A: You visited the Angkor Wat Temple in Cambodia with Pablo, Namita and Xi.
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message_to_chatml("Q: How many tennis balls fit in the back of a 2002 Honda Civic?", role="user"),
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message_to_chatml('["What is the size of a tennis ball?", "What is the trunk size of a 2002 Honda Civic?"]', role="assistant"),
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message_to_chatml("A: 1085 tennis balls will fit in the trunk of a Honda Civic", role="user"),
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Q: What national parks did I go to last year?
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message_to_chatml("Q: Is Bob older than Tom?", role="user"),
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message_to_chatml('["When was Bob born?", "What is Tom\'s age?"]', role="assistant"),
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message_to_chatml("A: Yes, Bob is older than Tom. As Bob was born on 1984-01-01 and Tom is 30 years old.", role="user"),
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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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message_to_chatml("Q: What is their age difference?", role="user"),
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message_to_chatml('["What is Bob\'s age?", "What is Tom\'s age?"]', role="assistant"),
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message_to_chatml(
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f"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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role="user",
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),
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]
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# fmt: on
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A: You visited the Grand Canyon and Yellowstone National Park in {last_new_year.strftime("%Y")}.
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# Add last few user messages, inferred queries and answer triplets from actual conversation for context
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for chat in conversation_log.get("chat", [])[-4:]:
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if chat["by"] == "khoj":
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queries = (
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chat["intent"]["inferred-queries"]
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if chat["intent"].get("inferred-queries", "[]") != "[]"
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else [chat["intent"]["query"]]
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)
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messages.extend(
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[
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message_to_chatml(f'Q: {chat["intent"]["query"]}', role="user"),
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message_to_chatml(f"{queries}", role="assistant"),
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message_to_chatml(f'{chat["message"]}', role="user"),
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]
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)
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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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# Finally add current user message for which to infer search queries to ChatML message list
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messages.append(message_to_chatml(f"Q: {message}", role="user"))
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# Get Response from GPT
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response = openai.Completion.create(
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prompt=prompt, model=model, temperature=temperature, max_tokens=max_tokens, stop=["A: ", "\n"]
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response = openai.ChatCompletion.create(
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messages=messages,
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model=model,
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temperature=temperature,
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)
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# Extract, Clean Message from GPT's Response
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response_text = response["choices"][0]["text"]
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response_text = response["choices"][0]["message"]["content"]
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try:
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questions = json.loads(
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# Clean response to increase likelihood of valid JSON. E.g replace ' with " to enclose strings
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@ -191,7 +191,6 @@ def update(t: Optional[SearchType] = None, force: Optional[bool] = False):
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def chat(q: Optional[str] = None):
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# Initialize Variables
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api_key = state.processor_config.conversation.openai_api_key
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model = state.processor_config.conversation.model
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# Load Conversation History
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chat_session = state.processor_config.conversation.chat_session
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@ -205,7 +204,7 @@ def chat(q: Optional[str] = None):
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return {"status": "ok", "response": []}
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# Infer search queries from user message
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inferred_queries = extract_questions(q, model=model, api_key=api_key, conversation_log=meta_log)
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inferred_queries = extract_questions(q, api_key=api_key, conversation_log=meta_log)
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# Collate search results as context for GPT
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result_list = []
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