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utils.py
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utils.py
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import json
import logging
import queue
from datetime import datetime
from time import perf_counter
from typing import Any, Dict, List
import tiktoken
from langchain.schema import ChatMessage
from transformers import AutoTokenizer
from khoj.database.adapters import ConversationAdapters
from khoj.database.models import ClientApplication, KhojUser
from khoj.utils.helpers import is_none_or_empty, merge_dicts
logger = logging.getLogger(__name__)
model_to_prompt_size = {
"gpt-3.5-turbo": 3000,
"gpt-4": 7000,
"gpt-4-1106-preview": 7000,
"gpt-4-turbo-preview": 7000,
"llama-2-7b-chat.ggmlv3.q4_0.bin": 1548,
"gpt-3.5-turbo-16k": 15000,
"mistral-7b-instruct-v0.1.Q4_0.gguf": 1548,
}
model_to_tokenizer = {
"llama-2-7b-chat.ggmlv3.q4_0.bin": "hf-internal-testing/llama-tokenizer",
"mistral-7b-instruct-v0.1.Q4_0.gguf": "mistralai/Mistral-7B-Instruct-v0.1",
}
class ThreadedGenerator:
def __init__(self, compiled_references, online_results, completion_func=None):
self.queue = queue.Queue()
self.compiled_references = compiled_references
self.online_results = online_results
self.completion_func = completion_func
self.response = ""
self.start_time = perf_counter()
def __iter__(self):
return self
def __next__(self):
item = self.queue.get()
if item is StopIteration:
time_to_response = perf_counter() - self.start_time
logger.info(f"Chat streaming took: {time_to_response:.3f} seconds")
if self.completion_func:
# The completion func effectively acts as a callback.
# It adds the aggregated response to the conversation history.
self.completion_func(chat_response=self.response)
raise StopIteration
return item
def send(self, data):
if self.response == "":
time_to_first_response = perf_counter() - self.start_time
logger.info(f"First response took: {time_to_first_response:.3f} seconds")
self.response += data
self.queue.put(data)
def close(self):
if self.compiled_references and len(self.compiled_references) > 0:
self.queue.put(f"### compiled references:{json.dumps(self.compiled_references)}")
if self.online_results and len(self.online_results) > 0:
self.queue.put(f"### compiled references:{json.dumps(self.online_results)}")
self.queue.put(StopIteration)
def message_to_log(
user_message, chat_response, user_message_metadata={}, khoj_message_metadata={}, conversation_log=[]
):
"""Create json logs from messages, metadata for conversation log"""
default_khoj_message_metadata = {
"intent": {"type": "remember", "memory-type": "notes", "query": user_message},
"trigger-emotion": "calm",
}
khoj_response_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
# Create json log from Human's message
human_log = merge_dicts({"message": user_message, "by": "you"}, user_message_metadata)
# Create json log from GPT's response
khoj_log = merge_dicts(khoj_message_metadata, default_khoj_message_metadata)
khoj_log = merge_dicts({"message": chat_response, "by": "khoj", "created": khoj_response_time}, khoj_log)
conversation_log.extend([human_log, khoj_log])
return conversation_log
def save_to_conversation_log(
q: str,
chat_response: str,
user: KhojUser,
meta_log: Dict,
user_message_time: str = None,
compiled_references: List[str] = [],
online_results: Dict[str, Any] = {},
inferred_queries: List[str] = [],
intent_type: str = "remember",
client_application: ClientApplication = None,
conversation_id: int = None,
):
user_message_time = user_message_time or datetime.now().strftime("%Y-%m-%d %H:%M:%S")
updated_conversation = message_to_log(
user_message=q,
chat_response=chat_response,
user_message_metadata={"created": user_message_time},
khoj_message_metadata={
"context": compiled_references,
"intent": {"inferred-queries": inferred_queries, "type": intent_type},
"onlineContext": online_results,
},
conversation_log=meta_log.get("chat", []),
)
ConversationAdapters.save_conversation(
user,
{"chat": updated_conversation},
client_application=client_application,
conversation_id=conversation_id,
user_message=q,
)
logger.info(
f"""
Saved Conversation Turn
You ({user.username}): "{q}"
Khoj: "{inferred_queries if ("text-to-image" in intent_type) else chat_response}"
""".strip()
)
def generate_chatml_messages_with_context(
user_message,
system_message,
conversation_log={},
model_name="gpt-3.5-turbo",
max_prompt_size=None,
tokenizer_name=None,
):
"""Generate messages for ChatGPT with context from previous conversation"""
# Set max prompt size from user config, pre-configured for model or to default prompt size
try:
max_prompt_size = max_prompt_size or model_to_prompt_size[model_name]
except:
max_prompt_size = 2000
logger.warning(
f"Fallback to default prompt size: {max_prompt_size}.\nConfigure max_prompt_size for unsupported model: {model_name} in Khoj settings to longer context window."
)
# Scale lookback turns proportional to max prompt size supported by model
lookback_turns = max_prompt_size // 750
# Extract Chat History for Context
chat_logs = []
for chat in conversation_log.get("chat", []):
chat_notes = f'\n\n Notes:\n{chat.get("context")}' if chat.get("context") else "\n"
chat_logs += [chat["message"] + chat_notes]
rest_backnforths = []
# Extract in reverse chronological order
for user_msg, assistant_msg in zip(chat_logs[-2::-2], chat_logs[::-2]):
if len(rest_backnforths) >= 2 * lookback_turns:
break
rest_backnforths += reciprocal_conversation_to_chatml([user_msg, assistant_msg])[::-1]
# Format user and system messages to chatml format
messages = []
if not is_none_or_empty(user_message):
messages.append(ChatMessage(content=user_message, role="user"))
if len(rest_backnforths) > 0:
messages += rest_backnforths
if not is_none_or_empty(system_message):
messages.append(ChatMessage(content=system_message, role="system"))
# Truncate oldest messages from conversation history until under max supported prompt size by model
messages = truncate_messages(messages, max_prompt_size, model_name, tokenizer_name)
# Return message in chronological order
return messages[::-1]
def truncate_messages(
messages: list[ChatMessage], max_prompt_size, model_name: str, tokenizer_name=None
) -> list[ChatMessage]:
"""Truncate messages to fit within max prompt size supported by model"""
try:
if model_name.startswith("gpt-"):
encoder = tiktoken.encoding_for_model(model_name)
else:
encoder = AutoTokenizer.from_pretrained(tokenizer_name or model_to_tokenizer[model_name])
except:
default_tokenizer = "hf-internal-testing/llama-tokenizer"
encoder = AutoTokenizer.from_pretrained(default_tokenizer)
logger.warning(
f"Fallback to default chat model tokenizer: {default_tokenizer}.\nConfigure tokenizer for unsupported model: {model_name} in Khoj settings to improve context stuffing."
)
system_message = messages.pop()
assert type(system_message.content) == str
system_message_tokens = len(encoder.encode(system_message.content))
tokens = sum([len(encoder.encode(message.content)) for message in messages if type(message.content) == str])
while (tokens + system_message_tokens) > max_prompt_size and len(messages) > 1:
messages.pop()
assert type(system_message.content) == str
tokens = sum([len(encoder.encode(message.content)) for message in messages if type(message.content) == str])
# Truncate current message if still over max supported prompt size by model
if (tokens + system_message_tokens) > max_prompt_size:
assert type(system_message.content) == str
current_message = "\n".join(messages[0].content.split("\n")[:-1]) if type(messages[0].content) == str else ""
original_question = "\n".join(messages[0].content.split("\n")[-1:]) if type(messages[0].content) == str else ""
original_question = f"\n{original_question}"
original_question_tokens = len(encoder.encode(original_question))
remaining_tokens = max_prompt_size - original_question_tokens - system_message_tokens
truncated_message = encoder.decode(encoder.encode(current_message)[:remaining_tokens]).strip()
logger.debug(
f"Truncate current message to fit within max prompt size of {max_prompt_size} supported by {model_name} model:\n {truncated_message}"
)
messages = [ChatMessage(content=truncated_message + original_question, role=messages[0].role)]
return messages + [system_message]
def reciprocal_conversation_to_chatml(message_pair):
"""Convert a single back and forth between user and assistant to chatml format"""
return [ChatMessage(content=message, role=role) for message, role in zip(message_pair, ["user", "assistant"])]