mirror of
https://github.com/khoj-ai/khoj.git
synced 2024-11-23 23:48:56 +01:00
Move remaining chat actors to use OpenAI chat models
- Deprecate the unused beta /answer and /search type identification endpoints and associated GPT functions - Update extract_questions to use GPT4 - Update summarize method to default to GPT-3.5 - Update date filter to support quoting values in single quotes too. So now both dt>'2023-04-01' and dt>"2023-04-01" should work - Remove "model" field from chat settings on the web interface
This commit is contained in:
Debanjum
GitHub
commit
4b79d8216f
10 changed files with 49 additions and 174 deletions
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@ -205,7 +205,7 @@ def configure_conversation_processor(conversation_processor_config):
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else:
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# Initialize Conversation Logs
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conversation_processor.meta_log = {}
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conversation_processor.chat_session = ""
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conversation_processor.chat_session = []
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return conversation_processor
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@ -225,9 +225,9 @@ def save_chat_session():
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chat_session = state.processor_config.conversation.chat_session
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openai_api_key = state.processor_config.conversation.openai_api_key
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conversation_log = state.processor_config.conversation.meta_log
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model = state.processor_config.conversation.model
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chat_model = state.processor_config.conversation.chat_model
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session = {
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"summary": summarize(chat_session, summary_type="chat", model=model, api_key=openai_api_key),
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"summary": summarize(chat_session, model=chat_model, api_key=openai_api_key),
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"session-start": conversation_log.get("session", [{"session-end": 0}])[-1]["session-end"],
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"session-end": len(conversation_log["chat"]),
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}
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@ -242,7 +242,7 @@ def save_chat_session():
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with open(conversation_logfile, "w+", encoding="utf-8") as logfile:
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json.dump(conversation_log, logfile, indent=2)
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state.processor_config.conversation.chat_session = None
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state.processor_config.conversation.chat_session = []
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logger.info("📩 Saved current chat session to conversation logs")
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@ -16,9 +16,17 @@
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<input type="text" id="openai-api-key" name="openai-api-key" value="{{ current_config['openai_api_key'] }}">
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</td>
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</tr>
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<tr>
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<td>
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<label for="chat-model">Chat Model</label>
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</td>
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<td>
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<input type="text" id="chat-model" name="chat-model" value="{{ current_config['chat_model'] }}">
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</td>
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</tr>
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</table>
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<table >
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<tr style="display: none;">
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<table style="display: none;">
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<tr>
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<td>
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<label for="conversation-logfile">Conversation Logfile</label>
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</td>
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@ -34,14 +42,6 @@
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<input type="text" id="model" name="model" value="{{ current_config['model'] }}">
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</td>
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</tr>
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<tr>
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<td>
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<label for="chat-model">Chat Model</label>
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</td>
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<td>
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<input type="text" id="chat-model" name="chat-model" value="{{ current_config['chat_model'] }}">
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</td>
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</tr>
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</table>
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<div class="section">
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<div id="success" style="display: none;" ></div>
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@ -1,9 +1,11 @@
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# Standard Packages
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import json
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import logging
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from datetime import datetime
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from typing import Optional
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# External Packages
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from langchain.schema import ChatMessage
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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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@ -17,44 +19,16 @@ from khoj.processor.conversation.utils import (
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logger = logging.getLogger(__name__)
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def answer(text, user_query, model, api_key=None, temperature=0.5, max_tokens=500):
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def summarize(session, model, api_key=None, temperature=0.5, max_tokens=200):
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"""
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Answer user query using provided text as reference with OpenAI's GPT
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Summarize conversation session using the specified OpenAI chat model
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"""
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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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messages = [ChatMessage(content=prompts.summarize_chat.format(), role="system")] + session
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# Get Response from GPT
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logger.debug(f"Prompt for GPT: {prompt}")
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logger.debug(f"Prompt for GPT: {messages}")
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response = completion_with_backoff(
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prompt=prompt,
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model_name=model,
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temperature=temperature,
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max_tokens=max_tokens,
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model_kwargs={"stop": ['"""']},
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openai_api_key=api_key,
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)
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# Extract, Clean Message from GPT's Response
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return str(response).replace("\n\n", "")
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def summarize(text, summary_type, model, user_query=None, api_key=None, temperature=0.5, max_tokens=200):
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"""
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Summarize user input using OpenAI's GPT
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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 = prompts.summarize_chat.format(text=text)
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elif summary_type == "notes":
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prompt = prompts.summarize_notes.format(text=text, user_query=user_query)
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else:
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raise ValueError(f"Invalid summary type: {summary_type}")
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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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prompt=prompt,
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messages=messages,
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model_name=model,
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temperature=temperature,
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max_tokens=max_tokens,
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@ -64,11 +38,11 @@ def summarize(text, summary_type, model, user_query=None, api_key=None, temperat
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)
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# Extract, Clean Message from GPT's Response
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return str(response).replace("\n\n", "")
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return str(response.content).replace("\n\n", "")
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def extract_questions(
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text, model: Optional[str] = "text-davinci-003", conversation_log={}, api_key=None, temperature=0, max_tokens=100
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text, model: Optional[str] = "gpt-4", conversation_log={}, api_key=None, temperature=0, max_tokens=100
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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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@ -97,10 +71,11 @@ def extract_questions(
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chat_history=chat_history,
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text=text,
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)
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messages = [ChatMessage(content=prompt, role="assistant")]
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# Get Response from GPT
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response = completion_with_backoff(
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prompt=prompt,
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messages=messages,
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model_name=model,
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temperature=temperature,
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max_tokens=max_tokens,
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@ -111,7 +86,7 @@ def extract_questions(
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# Extract, Clean Message from GPT's Response
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try:
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questions = (
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response.strip(empty_escape_sequences)
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response.content.strip(empty_escape_sequences)
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.replace("['", '["')
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.replace("']", '"]')
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.replace("', '", '", "')
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@ -126,31 +101,6 @@ def extract_questions(
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return questions
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def extract_search_type(text, model, api_key=None, temperature=0.5, max_tokens=100, verbose=0):
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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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# 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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# 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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prompt=prompt,
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model_name=model,
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temperature=temperature,
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max_tokens=max_tokens,
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frequency_penalty=0.2,
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model_kwargs={"stop": ["\n"]},
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openai_api_key=api_key,
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)
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# Extract, Clean Message from GPT's Response
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return json.loads(response.strip(empty_escape_sequences))
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def converse(
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references,
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user_query,
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@ -37,12 +37,7 @@ Question: {query}
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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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f"{personality.format()} Summarize the conversation from your first person perspective"
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)
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@ -102,7 +97,7 @@ 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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["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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@ -9,7 +9,6 @@ import json
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# External Packages
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from langchain.chat_models import ChatOpenAI
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from langchain.llms import OpenAI
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from langchain.schema import ChatMessage
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from langchain.callbacks.base import BaseCallbackManager
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@ -89,13 +88,11 @@ class StreamingChatCallbackHandler(StreamingStdOutCallbackHandler):
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reraise=True,
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)
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def completion_with_backoff(**kwargs):
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prompt = kwargs.pop("prompt")
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if "api_key" in kwargs:
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kwargs["openai_api_key"] = kwargs.get("api_key")
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else:
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messages = kwargs.pop("messages")
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if not "openai_api_key" in kwargs:
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kwargs["openai_api_key"] = os.getenv("OPENAI_API_KEY")
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llm = OpenAI(**kwargs, request_timeout=20, max_retries=1)
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return llm(prompt)
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llm = ChatOpenAI(**kwargs, request_timeout=20, max_retries=1)
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return llm(messages=messages)
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@retry(
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@ -126,11 +123,12 @@ def llm_thread(g, messages, model_name, temperature, openai_api_key=None):
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streaming=True,
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verbose=True,
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callback_manager=BaseCallbackManager([callback_handler]),
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model_name=model_name,
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model_name=model_name, # type: ignore
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temperature=temperature,
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openai_api_key=openai_api_key or os.getenv("OPENAI_API_KEY"),
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request_timeout=20,
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max_retries=1,
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client=None,
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)
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chat(messages=messages)
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@ -196,15 +194,6 @@ def reciprocal_conversation_to_chatml(message_pair):
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return [ChatMessage(content=message, role=role) for message, role in zip(message_pair, ["user", "assistant"])]
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def message_to_prompt(
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user_message, conversation_history="", gpt_message=None, start_sequence="\nAI:", restart_sequence="\nHuman:"
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):
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"""Create prompt for GPT from messages and conversation history"""
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gpt_message = f" {gpt_message}" if gpt_message else ""
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return f"{conversation_history}{restart_sequence} {user_message}{start_sequence}{gpt_message}"
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def message_to_log(user_message, gpt_message, user_message_metadata={}, khoj_message_metadata={}, conversation_log=[]):
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"""Create json logs from messages, metadata for conversation log"""
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default_khoj_message_metadata = {
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@ -15,7 +15,7 @@ from sentence_transformers import util
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# Internal Packages
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from khoj.configure import configure_processor, configure_search
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from khoj.processor.conversation.gpt import converse, extract_questions
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from khoj.processor.conversation.utils import message_to_log, message_to_prompt
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from khoj.processor.conversation.utils import message_to_log, reciprocal_conversation_to_chatml
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from khoj.search_type import image_search, text_search
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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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@ -448,10 +448,9 @@ async def chat(
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user_message_time: str,
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compiled_references: List[str],
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inferred_queries: List[str],
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chat_session: str,
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meta_log,
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):
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state.processor_config.conversation.chat_session = message_to_prompt(q, chat_session, gpt_message=gpt_response)
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state.processor_config.conversation.chat_session += reciprocal_conversation_to_chatml([q, gpt_response])
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state.processor_config.conversation.meta_log["chat"] = message_to_log(
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q,
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gpt_response,
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@ -470,7 +469,6 @@ async def chat(
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)
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# Load Conversation History
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chat_session = state.processor_config.conversation.chat_session
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meta_log = state.processor_config.conversation.meta_log
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# If user query is empty, return nothing
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@ -479,7 +477,6 @@ async def chat(
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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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chat_model = state.processor_config.conversation.chat_model
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user_message_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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conversation_type = "general" if q.startswith("@general") else "notes"
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@ -489,7 +486,7 @@ async def chat(
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if conversation_type == "notes":
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# Infer search queries from user message
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with timer("Extracting search queries took", logger):
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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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with timer("Searching knowledge base took", logger):
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@ -525,7 +522,6 @@ async def chat(
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user_message_time=user_message_time,
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compiled_references=compiled_references,
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inferred_queries=inferred_queries,
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chat_session=chat_session,
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meta_log=meta_log,
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)
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|
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@ -1,64 +1,9 @@
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# Standard Packages
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import logging
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from typing import Optional
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# External Packages
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from fastapi import APIRouter
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# Internal Packages
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from khoj.routers.api import search
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from khoj.processor.conversation.gpt import (
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answer,
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extract_search_type,
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)
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from khoj.utils.state import SearchType
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from khoj.utils.helpers import get_from_dict
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from khoj.utils import state
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# Initialize Router
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api_beta = APIRouter()
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logger = logging.getLogger(__name__)
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# Create Routes
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@api_beta.get("/search")
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def search_beta(q: str, n: Optional[int] = 1):
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# Initialize Variables
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model = state.processor_config.conversation.model
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api_key = state.processor_config.conversation.openai_api_key
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# Extract Search Type using GPT
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try:
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metadata = extract_search_type(q, model=model, api_key=api_key, verbose=state.verbose)
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search_type = get_from_dict(metadata, "search-type")
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except Exception as e:
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return {"status": "error", "result": [str(e)], "type": None}
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# Search
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search_results = search(q, n=n, t=SearchType(search_type))
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# Return response
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return {"status": "ok", "result": search_results, "type": search_type}
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@api_beta.get("/answer")
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def answer_beta(q: str):
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# Initialize Variables
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model = state.processor_config.conversation.model
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api_key = state.processor_config.conversation.openai_api_key
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# Collate context for GPT
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result_list = search(q, n=2, r=True, score_threshold=0, dedupe=False)
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collated_result = "\n\n".join([f"# {item.additional['compiled']}" for item in result_list])
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logger.debug(f"Reference Context:\n{collated_result}")
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# Make GPT respond to user query using provided context
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try:
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gpt_response = answer(collated_result, user_query=q, model=model, api_key=api_key)
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status = "ok"
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except Exception as e:
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gpt_response = str(e)
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status = "error"
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return {"status": status, "response": gpt_response}
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|
|
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@ -23,7 +23,7 @@ class DateFilter(BaseFilter):
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# - dt>="yesterday" dt<"tomorrow"
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# - dt>="last week"
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# - dt:"2 years ago"
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date_regex = r"dt([:><=]{1,2})\"(.*?)\""
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date_regex = r"dt([:><=]{1,2})[\"'](.*?)[\"']"
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def __init__(self, entry_key="raw"):
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self.entry_key = entry_key
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|
|
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|
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@ -72,7 +72,7 @@ class ConversationProcessorConfigModel:
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self.model = processor_config.model
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self.chat_model = processor_config.chat_model
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self.conversation_logfile = Path(processor_config.conversation_logfile)
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self.chat_session = ""
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self.chat_session: List[str] = []
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self.meta_log: dict = {}
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|
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|
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@ -33,9 +33,9 @@ def test_extract_question_with_date_filter_from_relative_day():
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# Assert
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expected_responses = [
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('dt="1984-04-01"', ""),
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('dt>="1984-04-01"', 'dt<"1984-04-02"'),
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('dt>"1984-03-31"', 'dt<"1984-04-02"'),
|
||||
("dt='1984-04-01'", ""),
|
||||
("dt>='1984-04-01'", "dt<'1984-04-02'"),
|
||||
("dt>'1984-03-31'", "dt<'1984-04-02'"),
|
||||
]
|
||||
assert len(response) == 1
|
||||
assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
|
||||
|
|
@ -51,7 +51,7 @@ def test_extract_question_with_date_filter_from_relative_month():
|
|||
response = extract_questions("Which countries did I visit last month?")
|
||||
|
||||
# Assert
|
||||
expected_responses = [('dt>="1984-03-01"', 'dt<"1984-04-01"'), ('dt>="1984-03-01"', 'dt<="1984-03-31"')]
|
||||
expected_responses = [("dt>='1984-03-01'", "dt<'1984-04-01'"), ("dt>='1984-03-01'", "dt<='1984-03-31'")]
|
||||
assert len(response) == 1
|
||||
assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
|
||||
"Expected date filter to limit to March 1984 in response but got: " + response[0]
|
||||
|
|
@ -67,9 +67,9 @@ def test_extract_question_with_date_filter_from_relative_year():
|
|||
|
||||
# Assert
|
||||
expected_responses = [
|
||||
('dt>="1984-01-01"', ""),
|
||||
('dt>="1984-01-01"', 'dt<"1985-01-01"'),
|
||||
('dt>="1984-01-01"', 'dt<="1984-12-31"'),
|
||||
("dt>='1984-01-01'", ""),
|
||||
("dt>='1984-01-01'", "dt<'1985-01-01'"),
|
||||
("dt>='1984-01-01'", "dt<='1984-12-31'"),
|
||||
]
|
||||
assert len(response) == 1
|
||||
assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
|
||||
|
|
@ -172,8 +172,8 @@ def test_generate_search_query_with_date_and_context_from_chat_history():
|
|||
|
||||
# Assert
|
||||
expected_responses = [
|
||||
('dt>="2000-04-01"', 'dt<"2000-05-01"'),
|
||||
('dt>="2000-04-01"', 'dt<="2000-04-31"'),
|
||||
("dt>='2000-04-01'", "dt<'2000-05-01'"),
|
||||
("dt>='2000-04-01'", "dt<='2000-04-30'"),
|
||||
]
|
||||
assert len(response) == 1
|
||||
assert "Masai Mara" in response[0]
|
||||
|
|
|
|||
Loading…
Reference in a new issue