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#A demo of a side functionality of Guillaume-Tell: guessing whether the question should open up a source retrieval pipeline.
#The function should return a structured answer in json with two components:
##A short analysis with reasoning.
##A boolean answer in French ("oui" or "non")
#Notice that json generation with LLM is still challenging due to unpredictable behavior.
#Some library like marginalia ensures the output will always be json compliant: https://github.com/Pleias/marginalia
#A typical exemple:
#{
# "analysis":"La question concerne un formulaire spécifique, le formulaire A36. Il est donc probable que des références encyclopédiques soient nécessaires pour fournir des informations précises sur ce formulaire.",
# "result":"oui"
#}
import sys, os
from pprint import pprint
from jinja2 import Environment, FileSystemLoader, meta
import yaml
import pandas as pd
from vllm import LLM, SamplingParams
sys.path.append(".")
os.chdir(os.path.dirname(os.path.abspath(__file__)))
#Specific function to deal with json format.
def get_llm_response(prompt_template, sampling_params):
prompts = [prompt_template]
outputs = llm.generate(prompts, sampling_params, use_tqdm = False)
generated_text = outputs[0].outputs[0].text
if generated_text[-1] != "}":
generated_text = generated_text + "}"
prompt = prompt_template + generated_text
return prompt, generated_text
if __name__ == "__main__":
with open('prompt_config.yaml') as f:
config = yaml.safe_load(f)
print("prompt format:", config.get("prompt_format"))
print(config)
print()
for prompt in config["prompts"]:
if prompt["mode"] == "analysis":
print(f'--- prompt mode: {prompt["mode"]} ---')
env = Environment(loader=FileSystemLoader("."))
template = env.get_template(prompt["template"])
source = template.environment.loader.get_source(template.environment, template.name)
variables = meta.find_undeclared_variables(env.parse(source[0]))
print("variables:", variables)
print("---")
data = {"query": "Comment obtenir le formulaire A36 ?"}
if "system_prompt" in variables:
data["system_prompt"] = prompt["system_prompt"]
rendered_template = template.render(**data)
print(rendered_template)
print("---")
llm = LLM("mistral-mfs-reference-2/mistral-mfs-reference-2")
sampling_params = SamplingParams(temperature=0.2, top_p=0.95, max_tokens=300, stop="}")
prompt, generated_text = get_llm_response(rendered_template, sampling_params)
print("Albert : ", generated_text)
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