import os, json, gc
import time
import torch
import transformers
import random
from transformers import BitsAndBytesConfig#, AutoModelForCausalLM, AutoTokenizer
from langchain.output_parsers import RetryWithErrorOutputParser
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from langchain_experimental.llms import JsonFormer
from langchain.tools import tool
# from langchain_community.llms import CTransformers
# from ctransformers import AutoModelForCausalLM, AutoConfig, Config
from langchain_community.llms import LlamaCpp
# from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.base import BaseCallbackHandler
from huggingface_hub import hf_hub_download
from vouchervision.utils_LLM import SystemLoadMonitor, count_tokens, save_individual_prompt
from vouchervision.utils_LLM_JSON_validation import validate_and_align_JSON_keys_with_template
from vouchervision.utils_taxonomy_WFO import validate_taxonomy_WFO
from vouchervision.utils_geolocate_HERE import validate_coordinates_here
from vouchervision.tool_wikipedia import WikipediaLinks
class LocalCPUMistralHandler:
RETRY_DELAY = 2 # Wait 2 seconds before retrying
MAX_RETRIES = 5 # Maximum number of retries
STARTING_TEMP = 0.1
TOKENIZER_NAME = None
VENDOR = 'mistral'
SEED = 2023
def __init__(self, logger, model_name, JSON_dict_structure):
self.logger = logger
self.monitor = SystemLoadMonitor(logger)
self.has_GPU = torch.cuda.is_available()
self.JSON_dict_structure = JSON_dict_structure
self.model_file = None
self.model_name = model_name
# https://medium.com/@scholarly360/mistral-7b-complete-guide-on-colab-129fa5e9a04d
self.model_name = "Mistral-7B-Instruct-v0.2-GGUF" #huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GGUF mistral-7b-instruct-v0.2.Q4_K_M.gguf --local-dir /home/brlab/.cache --local-dir-use-symlinks False
self.model_id = f"TheBloke/{self.model_name}"
name_parts = self.model_name.split('-')
if self.model_name == "Mistral-7B-Instruct-v0.2-GGUF":
self.model_file = 'mistral-7b-instruct-v0.2.Q4_K_M.gguf'
self.model_path = hf_hub_download(repo_id=self.model_id,
filename=self.model_file,
repo_type="model")
else:
raise f"Unsupported GGUF model name"
# self.model_id = f"mistralai/{self.model_name}"
self.gpu_usage = {'max_load': 0, 'max_memory_usage': 0, 'monitoring': True}
self.starting_temp = float(self.STARTING_TEMP)
self.temp_increment = float(0.2)
self.adjust_temp = self.starting_temp
system_prompt = "You are a helpful AI assistant who answers queries with JSON objects and no explanations."
template = """
[INST]{}[/INST]
[INST]{}[/INST]
""".format(system_prompt, "{query}")
# Create a prompt from the template so we can use it with Langchain
self.prompt = PromptTemplate(template=template, input_variables=["query"])
# Set up a parser
self.parser = JsonOutputParser()
self._set_config()
# def _clear_VRAM(self):
# # Clear CUDA cache if it's being used
# if self.has_GPU:
# self.local_model = None
# del self.local_model
# gc.collect() # Explicitly invoke garbage collector
# torch.cuda.empty_cache()
# else:
# self.local_model = None
# del self.local_model
# gc.collect() # Explicitly invoke garbage collector
def _set_config(self):
# self._clear_VRAM()
self.config = {'max_new_tokens': 1024,
'temperature': self.starting_temp,
'seed': self.SEED,
'top_p': 1,
'top_k': 40,
'n_ctx': 4096,
'do_sample': True,
}
self._build_model_chain_parser()
def _adjust_config(self):
new_temp = self.adjust_temp + self.temp_increment
self.json_report.set_text(text_main=f'Incrementing temperature from {self.adjust_temp} to {new_temp}')
self.logger.info(f'Incrementing temperature from {self.adjust_temp} to {new_temp}')
self.adjust_temp += self.temp_increment
self.config['temperature'] = self.adjust_temp
def _reset_config(self):
self.json_report.set_text(text_main=f'Resetting temperature from {self.adjust_temp} to {self.starting_temp}')
self.logger.info(f'Resetting temperature from {self.adjust_temp} to {self.starting_temp}')
self.adjust_temp = self.starting_temp
self.config['temperature'] = self.starting_temp
def _build_model_chain_parser(self):
self.local_model = LlamaCpp(
model_path=self.model_path,
max_tokens=self.config.get('max_new_tokens'),
top_p=self.config.get('top_p'),
# callback_manager=callback_manager,
# n_gpu_layers=1,
# n_batch=512,
n_ctx=self.config.get('n_ctx'),
stop=["[INST]"],
verbose=False,
streaming=False,
)
# Set up the retry parser with the runnable
self.retry_parser = RetryWithErrorOutputParser.from_llm(parser=self.parser, llm=self.local_model, max_retries=self.MAX_RETRIES)
# Create an llm chain with LLM and prompt
self.chain = self.prompt | self.local_model
def call_llm_local_cpu_MistralAI(self, prompt_template, json_report, paths):
_____, ____, _, __, ___, json_file_path_wiki, txt_file_path_ind_prompt = paths
self.json_report = json_report
self.json_report.set_text(text_main=f'Sending request to {self.model_name}')
self.monitor.start_monitoring_usage()
nt_in = 0
nt_out = 0
ind = 0
while ind < self.MAX_RETRIES:
ind += 1
try:
### BELOW IS BASIC MISTRAL CALL
# mistral_prompt = f"[INST] {prompt_template} [/INST]"
# results = self.local_model(mistral_prompt, temperature = 0.7,
# repetition_penalty = 1.15,
# max_new_tokens = 2048)
# print(results)
model_kwargs = {"temperature": self.adjust_temp}
# Invoke the chain to generate prompt text
results = self.chain.invoke({"query": prompt_template, "model_kwargs": model_kwargs})
# Use retry_parser to parse the response with retry logic
output = self.retry_parser.parse_with_prompt(results, prompt_value=prompt_template)
if output is None:
self.logger.error(f'[Attempt {ind}] Failed to extract JSON from:\n{results}')
self._adjust_config()
else:
nt_in = count_tokens(prompt_template, self.VENDOR, self.TOKENIZER_NAME)
nt_out = count_tokens(results, self.VENDOR, self.TOKENIZER_NAME)
output = validate_and_align_JSON_keys_with_template(output, self.JSON_dict_structure)
if output is None:
self.logger.error(f'[Attempt {ind}] Failed to extract JSON from:\n{results}')
self._adjust_config()
else:
self.monitor.stop_inference_timer() # Starts tool timer too
json_report.set_text(text_main=f'Working on WFO, Geolocation, Links')
output, WFO_record = validate_taxonomy_WFO(output, replace_if_success_wfo=False) ###################################### make this configurable
output, GEO_record = validate_coordinates_here(output, replace_if_success_geo=False) ###################################### make this configurable
Wiki = WikipediaLinks(json_file_path_wiki)
Wiki.gather_wikipedia_results(output)
save_individual_prompt(Wiki.sanitize(prompt_template), txt_file_path_ind_prompt)
self.logger.info(f"Formatted JSON:\n{json.dumps(output,indent=4)}")
usage_report = self.monitor.stop_monitoring_report_usage()
if self.adjust_temp != self.starting_temp:
self._reset_config()
json_report.set_text(text_main=f'LLM call successful')
return output, nt_in, nt_out, WFO_record, GEO_record, usage_report
except Exception as e:
self.logger.error(f'{e}')
self._adjust_config()
self.logger.info(f"Failed to extract valid JSON after [{ind}] attempts")
self.json_report.set_text(text_main=f'Failed to extract valid JSON after [{ind}] attempts')
usage_report = self.monitor.stop_monitoring_report_usage()
self._reset_config()
json_report.set_text(text_main=f'LLM call failed')
return None, nt_in, nt_out, None, None, usage_report