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| from __future__ import annotations
import os
import re
#import pandas
import numpy as np
import sys
sys.path = ["src"] + sys.path
#from datasets import load_dataset
from googlesearch import search
from langchain_chroma import Chroma
from langchain_community.chat_models import ChatOllama
from langchain_community.document_loaders import PyMuPDFLoader
from langchain_community.embeddings.ollama import OllamaEmbeddings
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.vectorstores import VectorStore
from langchain_text_splitters import CharacterTextSplitter
from pathlib import Path
from typing import List, TypedDict, Optional
from api.config import OLLAMA_URL
from api.services.llm._chat import BaseMessage, HumanMessage, AIMessage, SourcedAIMessage, SystemMessage, DetectbotChat
def output_parser_test(ratings, moy):
"""
uniquement pour des tests
Ne pas importer !!!
"""
notes = []
for rating in ratings:
pos = rating.find("/10")
if pos < 2:
pos = rating.find(" out of 10")
if pos >= 2 and rating[pos-1] <= '9' and rating[pos - 1]>='0':
notes.append(rating[pos-2:pos])
else:
notes.append("00")
notes_processed = [10 if notes[item]=='10' else int(notes[item][1]) for item in range(len(notes))]
for i in range(len(notes)):
moy[i].append(notes_processed[i])
class ReWOO_args:
# doit contenir les informations nécessaires à la création de l'agent avant l'attribution de la tâche
# ajouter des éléments sur la DocSearch
planner_prompt = """For the following task, make plans that can solve the problem step by step. For each plan, indicate \
which external tool together with tool input to retrieve evidence. You can store the evidence into a \
variable #E that can be called by later tools. (Plan, #E1, Plan, #E2, Plan, ...)
Tools can be one of the following:
(1) Google[input]: Worker that searches results from Google. Useful when you need to find short
and succinct answers about a specific topic. The input should be a search query.
(2) LLM[input]: A pretrained LLM like yourself. Useful when you need to act with general
world knowledge and common sense. Prioritize it when you are confident in solving the problem
yourself. Input can be any instruction.
(3) DocSearch[input]: Research in documents. Useful when you need to find precise knowledge on
a specific subject. The input should be a query
For example,
Task: Thomas, Toby, and Rebecca worked a total of 157 hours in one week. Thomas worked x
hours. Toby worked 10 hours less than twice what Thomas worked, and Rebecca worked 8 hours
less than Toby. How many hours did Rebecca work?
Plan: Given Thomas worked x hours, translate the problem into algebraic expressions and solve
with Wolfram Alpha. #E1 = WolframAlpha[Solve x + (2x − 10) + ((2x − 10) − 8) = 157]
Plan: Find out the number of hours Thomas worked. #E2 = LLM[What is x, given #E1]
Plan: Calculate the number of hours Rebecca worked. #E3 = Calculator[(2 ∗ #E2 − 10) − 8]
Begin!
Describe your plans with rich details. Each Plan should be followed by no number and only one #E.
Task: {task}"""
solver_prompt = """Solve the following task or problem. To solve the problem, we have made step-by-step Plan and \
retrieved corresponding Evidence to each Plan. Use them with caution since long evidence might \
contain irrelevant information.
{plan}
Now solve the question or task according to provided Evidence above. Respond with the answer
directly with no extra words.
Task: {task}
Response:"""
planner_llm_model = "mistral"
worker_llm_model = "mistral"
solver_llm_model = "mistral"
embedding_fn=OllamaEmbeddings(base_url=OLLAMA_URL,model='nomic-embed-text')
docpaths : List[str] = ["C:\\Users\\JaufreHACQUARD\\Documents\\detebot_api\\test\\test_data\\guide_myndea.pdf"]
task : str
def chargeDocs(docpaths:List[str], embedding) -> Optional[VectorStore]:
"""doit charger les documents dont les paths sont en input"""
data = []
for document in docpaths:
loader = PyMuPDFLoader(document)
data += loader.load()
splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
docs=splitter.split_documents(data)
docs : List[Document] = [Document(page_content=doc.page_content,metadata={"document_id":doc.metadata['source'].split("/")[-1],"detail":f"page {doc.metadata['page']}"}) for doc in docs]
return Chroma.from_documents(documents = docs, embedding=embedding)
class ReWOO(TypedDict):
task: str
plan_string: str
steps: List
results: dict
result: str
class ReWOO_Agent:
"""créer une méthode de génération"""
planner : ChatPromptTemplate | ChatOllama
worker_llm: ChatOllama
solver: ChatPromptTemplate | ChatOllama
state : ReWOO
vector_store: Optional[VectorStore]
def __init__(self, params: ReWOO_args) -> None:
self.planner = ChatPromptTemplate.from_messages([("user", params.planner_prompt)])|ChatOllama(base_url=OLLAMA_URL, model=params.planner_llm_model)
self.worker_llm = ChatOllama(base_url=OLLAMA_URL, model=params.worker_llm_model)
self.solver = ChatPromptTemplate.from_messages([("user", params.solver_prompt)])|ChatOllama(base_url=OLLAMA_URL, model=params.solver_llm_model)
self.vector_store = chargeDocs(params.docpaths, params.embedding_fn)
def lancement(self, task:str):
self.state = {"task":task}
result = self.planner.invoke({"task": task})
regex_pattern = r"Plan:\s*(.+)\s*(#E\d+)\s*=\s*(\w+)\s*\[([^\]]+)\]"
matches = re.findall(regex_pattern, result.content)
self.state["plan_string"] = result.content
self.state["steps"] = matches
self.state["results"] = {}
def _get_current_task(self):
if "results" not in self.state or self.state["results"] is None:
return 1
if len(self.state["results"]) == len(self.state["steps"]):
return None
else:
return len(self.state["results"]) + 1
def tool_execution(self):
"""Worker node that executes the tools of a given plan."""
_step = self._get_current_task()
_, step_name, tool, tool_input = self.state["steps"][_step - 1]
for k, v in self.state["results"].items():
tool_input = tool_input.replace(k, v)
if tool == "Google":
search_results = search(tool_input, advanced=True)
result = [(search_result.url, search_result.description) for search_result in search_results]
elif tool == "LLM":
result = self.worker_llm.invoke(tool_input)
elif tool == "DocSearch":
result = self.vector_store.similarity_search_with_relevance_scores(tool_input)
else:
raise ValueError
self.state["results"][step_name] = str(result)
def solve(self):
plan = ""
for _plan, step_name, tool, tool_input in self.state["steps"]:
_results = (self.state["results"] or {}) if "results" in self.state else {}
for k, v in _results.items():
tool_input = tool_input.replace(k, v)
step_name = step_name.replace(k, v)
plan += f"Plan: {_plan}\n{step_name} = {tool}[{tool_input}]"
result = self.solver.invoke({"plan":plan, "task":self.state["task"]})
self.state["result"] = result
def _route(self):
_step = self._get_current_task()
if _step is None:
# We have executed all tasks
return "solve"
else:
# We are still executing tasks, loop back to the "tool" node
return "tool"
def usage(self, task):
self.lancement(task=task)
while self._route() == "tool":
self.tool_execution()
self.solve()
return self.state
def compare(args : ReWOO_args):
agent1 = ReWOO_Agent(params=args)
agent2 = DetectbotChat(ChatOllama(base_url=OLLAMA_URL, model=args.worker_llm_model), vector_store=agent1.vector_store)
eval_data=load_dataset('json',data_files=f"C:\\Users\\JaufreHACQUARD\\Documents\\detebot_api\\notebooks\\ds.jsonl")
moy1 = [[] for i in range(0, len(eval_data["train"]), 5)]
moy2 = [[] for i in range(0, len(eval_data["train"]), 5)]
ds = {"question" : [eval_data['train'][i]['question'] for i in range(len(eval_data["train"]))]}
try:
for i in range(3):
answer_list1 = []
answer_list2 = []
for index in range(0, len(eval_data["train"]), 5) :
question = eval_data['train'][index]['question']
chat = agent2.chat_with_rag(question)
response= {"response":chat.chat_list[3].content}
answer_list2.append(response)
response = {"response":agent1.usage(task=question)["result"].content}
answer_list1.append(response)
print('generating output ', i, index) #~1min / ligne
rating_list1 = []
rating_list2 = []
for index in range(len(answer_list2)) :
chat=(ChatOllama(base_url=OLLAMA_URL,model="mistral:latest"))
prompt = ChatPromptTemplate.from_template("""Down below is the LLM output from a QA-type chatbot. This response is made up of a prompt, a question and documents
useful to answer the question. The answer sequence to analyze should start with the tag <ANSWER> :
{LLM_output}. Compare this sequence to the true answer given here : {true_answer} . Rate the LLM_output out of 10 based with the on its resemblance to the true_answer in the format X/10.""")
chain = prompt | chat | StrOutputParser()
TRUE_ANSWER = eval_data['train'][index * 5]['cot_answer']
eval_response=chain.invoke({"LLM_output" : {answer_list2[index]["response"]}, 'true_answer' : {TRUE_ANSWER} })
rating_list2.append(eval_response)
eval_response=chain.invoke({"LLM_output" : {answer_list1[index]["response"]}, 'true_answer' : {TRUE_ANSWER} })
rating_list1.append(eval_response)
output_parser_test(rating_list2, moy2)
output_parser_test(rating_list1, moy1)
except PermissionError:
print("nb_turn trop grand, veuillez bien diminuer")
l = []
for k in range(len(rating_list2)):
moyenne_question_naif = np.mean(np.asarray(moy2[k]))
variance_question_naif = np.var(np.asarray(moy2[k]))
variance_question_ReWOO = np.var(np.asarray(moy1[k]))
moyenne_question_ReWOO = np.mean(np.asarray(moy1[k]))
l.append([moyenne_question_naif, variance_question_naif, moyenne_question_ReWOO, variance_question_ReWOO])
#return pandas.DataFrame(data = l, columns = ["moyenne_naif", "variance_naif", "moyenne_ReWOO", "variance_ReWOO"], index = [ds["question"][i] for i in range(0, len(ds["question"]), 5)])
if __name__ == "__main__":
args2 = ReWOO_args()
args2.docpaths = [
"C:\\Users\\JaufreHACQUARD\\Documents\\detebot_api\\test\\test_data\\guide_myndea.pdf",
"C:\\Users\\JaufreHACQUARD\\Documents\\detebot_api\\Détails.pdf",
"C:\\Users\\JaufreHACQUARD\\Documents\\detebot_api\\Kober_affect.pdf",
"C:\\Users\\JaufreHACQUARD\\Documents\\detebot_api\\Kober-grille-orientation.pdf"
]
res = compare(args=args2)
#res.to_csv(Path("C:\\Users\\JaufreHACQUARD\\Documents\\detebot_api\\notebooks/question_evals.csv"), sep=';') |
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