.dockerignore

@@ -1,2 +0,0 @@
-./qavanin-faiss
-./llm-answer

.gitignore

@@ -1,5 +1,162 @@
+# ---> Python
+# Byte-compiled / optimized / DLL files
 __pycache__/
-qavanin-faiss/faiss_index_qavanin_285k_metadata.json
+*.py[cod]
-qavanin-faiss/faiss_index_qavanin_285k.index
+*$py.class
-.vscode
+
-.gitignore
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+

README.md

@@ -1,2 +1,3 @@
-# Qavanin Chatbot
+# rag_qavanin_api
 
+a chatbot based on optimized rag and reranker approch on law sections

chatbot.py

@@ -1,586 +0,0 @@
-import json
-import os
-import numpy as np
-import torch
-import faiss
-from typing import List, Tuple
-from sentence_transformers import SentenceTransformer
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-import datetime
-import re
-import random
-from fastapi.middleware.cors import CORSMiddleware
-from embedder_sbert_qavanin_285k import PersianVectorAnalyzer
-#from normalizer import cleaning
-from fastapi import FastAPI ,Header
-from pydantic import BaseModel
-# LLM Libs
-from openai import OpenAI
-from langchain_openai import ChatOpenAI  # pip install -U langchain_openai
-import requests
-
-today = f'{datetime.datetime.now().year}{datetime.datetime.now().month}{datetime.datetime.now().day}'
-
-chatbot = FastAPI()
-origins = ["*"]
-
-chatbot.add_middleware(
-    CORSMiddleware,
-    allow_origins=origins,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# -------------------
-# مدل‌ها و مسیر دادهsrc/app/qavanin-faiss/faiss_index_qavanin_285k_metadata.json
-# -------------------/src/app/qavanin-faiss
-EMBED_MODEL = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
-RERANKER_MODEL = "BAAI/bge-reranker-v2-m3"
-FAISS_INDEX_PATH = "/src/app/qavanin-faiss/faiss_index_qavanin_285k.index"
-FAISS_METADATA_PATH = "/src/app/qavanin-faiss/faiss_index_qavanin_285k_metadata.json"
-
-RERANK_BATCH = int(os.environ.get("RERANK_BATCH", 256))
-# print(f'RERANK_BATCH: {RERANK_BATCH}')
-
-def get_key():
-    key = 'aa-fdh9d847ANcBxQCBTZD5hrrAdl0UrPEnJOScYmOncrkagYPf'
-    return key
-
-def load_faiss_index(index_path: str, metadata_path: str):
-    """بارگذاری ایندکس FAISS و متادیتا (لیست جملات + عناوین)."""
-    index = faiss.read_index(index_path)
-
-    with open(metadata_path, "r", encoding="utf-8") as f:
-        metadata = json.load(f)
-
-    content_list, ids, prefix_list  = [], [], []
-    for item in metadata:
-        content_list.append(item["content"])
-        ids.append(item["id"])
-        prefix_list.append(item["prefix"])
-
-    return content_list, ids, prefix_list, index
-
-def get_client():
-    url = "https://api.avalai.ir/v1"
-    # key = 'aa-4tvAEazUBovEN1i7i7tdl1PR93OaWXs6hMflR4oQbIIA4K7Z'
-    
-    
-    client = OpenAI(
-    api_key= get_key(),  # با کلید واقعی خود جایگزین کنید
-    base_url= url,  # آدرس پایه
-        )
-    
-    return client
-
-def llm_base_request(query):
-    # model = 'cf.gemma-3-12b-it'
-    model = 'gpt-4o-mini'
-    prompt = f'برای متن {query} زیر، عنوانی کوتاه که بین 3 تا 6 کلمه داشته باشد، انتخاب کن. غیر از عنوان، به هیچ وجه توضیح اضافه ای در قبل یا بعد آن اضافه نکن.'
-    client = get_client()
-    try:
-        messages.append({"role": "user", "content": prompt})
-        response = client.chat.completions.create(
-        messages = messages,
-        model= model)  # "gpt-4o", "gpt-4o-mini", "deepseek-chat" , "gemini-2.0-flash", gemini-2.5-flash-lite
-        # gpt-4o                    : 500
-        # gpt-4o-mini           : 34
-        # deepseek-chat:      : 150
-        # gemini-2.0-flash   : error
-        # cf.gemma-3-12b-it : 1
-        # gemini-2.5-flash-lite : 35 خیلی خوب
-        
-        answer = response.choices[0].message.content
-        # پاسخ را هم به سابقه اضافه می‌کنیم
-        messages.append({"role": "assistant", "content": answer})
-       
-        
-    except Exception as error:
-        with open('./llm-answer/error-in-llm.txt', mode='a+', encoding='utf-8') as file:
-            error_message = f'\n\nquery: {query.strip()}\nerror:{error} \n-------------------------------\n'
-            file.write(error_message)
-            
-        return ''
-   
-    return answer
-
-def llm_request(query, model):
-    
-    if query == '':
-        return 'لطفا متن سوال را وارد نمائید'
-    
-    client = get_client()
-    determine_refrence = """شناسه هر ماده قانون در ابتدای آن و با فرمت "id: {idvalue}" آمده است که id-value همان شناسه ماده است. بازای هربخش از پاسخی که تولید می شود، ضروری است شناسه ماده ای که در تدوین پاسخ از آن استفاده شده در انتهای پاراگراف یا جمله مربوطه با فرمت {idvalue} اضافه شود. همیشه idvalue با رشته "qs" شروع می شود"""
-    try:
-        messages.append({"role": "user", "content": query})
-        messages.append({"role": "user", "content": determine_refrence})
-        response = client.chat.completions.create(
-        messages = messages,
-        model= model)  # "gpt-4o", "gpt-4o-mini", "deepseek-chat" , "gemini-2.0-flash", gemini-2.5-flash-lite
-        # gpt-4o                    : 500
-        # gpt-4o-mini           : 34
-        # deepseek-chat:      : 150
-        # gemini-2.0-flash   : error
-        # cf.gemma-3-12b-it : 1
-        # gemini-2.5-flash-lite : 35 خیلی خوب
-        
-        answer = response.choices[0].message.content
-        # پاسخ را هم به سابقه اضافه می‌کنیم
-        messages.append({"role": "assistant", "content": answer})
-       
-        
-    except Exception as error:
-        with open('./llm-answer/error-in-llm.txt', mode='a+', encoding='utf-8') as file:
-            error_message = f'\n\nquery: {query.strip()}\nerror:{error} \n-------------------------------\n'
-            file.write(error_message)
-            
-        return 'با عرض پوزش؛ متاسفانه خطایی رخ داده است. لطفا لحظاتی دیگر دوباره تلاش نمائید'
-   
-    return answer
-
-class HybridRetrieverReranker:
-    __slots__ = (
-        "device", "content_list", "ids", "prefix_list", "N", "embedder", "faiss_index",
-        "vectorizer", "tfidf_matrix", "tokenizer", "reranker", "dense_alpha"
-    )
-
-    def __init__(self, content_list: List[str],ids: List[str], prefix_list: List[str], faiss_index,
-                 dense_alpha: float = 0.6, device: str = None):
-
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = device
-
-        self.content_list = content_list
-        self.ids = ids
-        self.prefix_list = prefix_list
-        self.faiss_index = faiss_index
-        self.N = len(content_list)
-
-        # Dense
-        self.embedder = SentenceTransformer(EMBED_MODEL,cache_folder='/src/MODELS', device=self.device)
-        #self.embedder = SentenceTransformer(EMBED_MODEL, device=self.device)
-
-        # Sparse (مثل قبل برای حفظ خروجی)
-        self.vectorizer = TfidfVectorizer(
-            analyzer="word",
-            ngram_range=(1, 2),
-            token_pattern=r"(?u)\b[\w\u0600-\u06FF]{2,}\b",
-        )
-        self.tfidf_matrix = self.vectorizer.fit_transform(self.content_list)
-
-        # Reranker
-        self.tokenizer = AutoTokenizer.from_pretrained(RERANKER_MODEL,cache_dir='/src/MODELS', use_fast=True)
-        self.reranker = AutoModelForSequenceClassification.from_pretrained(
-            RERANKER_MODEL
-        ).to(self.device)
-        # self.reranker = AutoModelForSeq2SeqLM.from_pretrained(RERANKER_MODEL).to(self.device)
-        # self.reranker.eval()
-
-        self.dense_alpha = float(dense_alpha)
-
-    # --- Dense (FAISS) ---
-    def dense_retrieve(self, query: str, top_k: int):
-        if top_k <= 0:
-            return [], np.array([], dtype=np.float32)
-
-        q_emb = self.embedder.encode(query, convert_to_numpy=True).astype(np.float32)
-        D, I = self.faiss_index.search(np.expand_dims(q_emb, axis=0), top_k)
-
-        return I[0].tolist(), D[0]
-
-    # --- Sparse ---
-    def sparse_retrieve(self, query: str, top_k: int):
-        if top_k <= 0:
-            return [], np.array([], dtype=np.float32)
-        k = min(top_k, self.N)
-        q_vec = self.vectorizer.transform([query])
-        sims = cosine_similarity(q_vec, self.tfidf_matrix).ravel()
-        idx = np.argpartition(-sims, kth=k-1)[:k]
-        idx = idx[np.argsort(-sims[idx], kind="mergesort")]
-        return idx.tolist(), sims[idx]
-
-    # --- Utils ---
-    @staticmethod
-    def _minmax_norm(arr: np.ndarray) -> np.ndarray:
-        if arr.size == 0:
-            return arr
-        a_min = arr.min()
-        a_max = arr.max()
-        rng = a_max - a_min
-        if rng < 1e-12:
-            return np.zeros_like(arr)
-        return (arr - a_min) / rng
-
-    def fuse(self, d_idx, d_scores, s_idx, s_scores, top_k=50, k_rrf=60):
-        """
-        ادغام نتایج دو retriever (dense و sparse) با استفاده از Reciprocal Rank Fusion (RRF)
-
-        Args:
-            d_idx (list or np.ndarray): ایندکس‌های نتایج dense retriever
-            d_scores (list or np.ndarray): نمرات dense retriever
-            s_idx (list or np.ndarray): ایندکس‌های نتایج sparse retriever
-            s_scores (list or np.ndarray): نمرات sparse retriever
-            top_k (int): تعداد نتایج نهایی
-            k_rrf (int): ثابت در فرمول RRF برای کاهش تأثیر رتبه‌های پایین‌تر
-
-        Returns:
-            list: لیست ایندکس‌های ادغام‌شده به ترتیب نمره
-        """
-        combined = {}
-
-        # dense retriever
-        for rank, idx in enumerate(d_idx):
-            score = 1.0 / (k_rrf + rank)
-            combined[idx] = combined.get(idx, 0) + score
-
-        # sparse retriever
-        for rank, idx in enumerate(s_idx):
-            score = 1.0 / (k_rrf + rank)
-            combined[idx] = combined.get(idx, 0) + score
-
-        # مرتب‌سازی نهایی
-        sorted_items = sorted(combined.items(), key=lambda x: x[1], reverse=True)
-        cand_idx = [item[0] for item in sorted_items[:top_k]]
-
-        return cand_idx
-   
-    def rerank(self, query: str, candidate_indices: List[int], passages: List[str], final_k: int) -> List[Tuple[int, float]]:
-        """
-        Rerank candidate passages using a cross-encoder (e.g., MonoT5, MiniLM, etc.).
-
-        Args:
-            query (str): پرسش کاربر
-            candidate_indices (List[int]): ایندکس‌های کاندیدا (از retriever)
-            passages (List[str]): کل جملات/پاراگراف‌ها
-            final_k (int): تعداد نتایج نهایی
-
-        Returns:
-            List[Tuple[int, float]]: لیستی از (ایندکس، امتیاز) برای بهترین نتایج
-        """
-        if final_k <= 0 or not candidate_indices:
-            return []
-
-        # آماده‌سازی جفت‌های (query, passage)
-        texts = [query] * len(candidate_indices)
-        pairs = passages
-
-        scores: List[float] = []
-
-        def _iter_batches(max_bs: int):
-            bs = max_bs
-            while bs >= 16:  # حداقل batch_size
-                try:
-                    with torch.inference_mode():
-                        for start in range(0, len(pairs), bs):
-                            batch_texts = texts[start:start + bs]
-                            batch_pairs = pairs[start:start + bs]
-                            inputs = self.tokenizer(
-                                batch_texts,
-                                batch_pairs,
-                                padding=True,
-                                truncation=True,
-                                max_length=512,
-                                return_tensors="pt",
-                            ).to(self.device)
-
-                            logits = self.reranker(**inputs).logits.view(-1)
-                            scores.extend(logits.detach().cpu().tolist())
-                    return True
-                except torch.cuda.OutOfMemoryError:
-                    if torch.cuda.is_available():
-                        torch.cuda.empty_cache()
-                    bs //= 2
-            return False
-
-        # اجرای reranking
-        success = _iter_batches(max_bs=64)
-        if not success:
-            raise RuntimeError("Reranker failed due to CUDA OOM, even with small batch size.")
-
-        # مرتب‌سازی نتایج بر اساس نمره
-        reranked = sorted(
-            zip(candidate_indices, scores),
-            key=lambda x: x[1],
-            reverse=True
-        )[:final_k]
-
-        return reranked
-    
-    def get_passages(self, cand_idx, content_list):
-        passages = []
-        for idx in cand_idx:
-            passages.append(content_list[idx])
-            
-        return passages
-        
-    # --- Search (بدون تغییر) ---
-    def search(self, query: str, content_list, topk_dense=50, topk_sparse=50,
-               pre_rerank_k=50, final_k=10):
-        d_idx, d_scores = self.dense_retrieve(query, topk_dense)
-        s_idx, s_scores = self.sparse_retrieve(query, topk_sparse)
-        cand_idx = self.fuse(d_idx, d_scores, s_idx, s_scores, pre_rerank_k)
-        passages = self.get_passages(cand_idx, content_list)
-        reranked = self.rerank(query, cand_idx, passages, final_k)
-
-        return [{"idx": i, "content": self.content_list[i],"prefix": self.prefix_list[i],  "rerank_score": score}
-                for i, score in reranked]
-
-def single_query(query: str):
-
-   # query = cleaning(query)
-    retrived_sections_ids = []
-    retrived_sections = pipe.search(query, content_list, topk_dense=30, topk_sparse=30, pre_rerank_k=30, final_k=10)
-    final_similars = ''
-    for i, row in enumerate(retrived_sections, 1):
-        id_value = '{' + str(ids[row['idx']]) + '}'
-        result = f"id: {id_value} \n{row['prefix']} {row['content']}\n"
-        retrived_sections_ids.append(ids[row['idx']])
-        final_similars += ''.join(result)
-    
-    return final_similars, retrived_sections_ids
-
-def find_refrences(llm_answer: str) -> List[str]:
-    """
-    شناسایی شناسه هایی که مدل زبانی، برای تهیه پاسخ از آنها استفاده کرده است
-    
-    Args:
-        llm_answer(str): متنی که مدل زبانی تولید کرده است
-        
-    Returns:
-        refrence_ids(List[str]): لیستی از شناسه های تشخیص داده شده
-    """
-    pattern = r"\{[^\}]+\}"
-    refrence_ids = re.findall(pattern, llm_answer)
-    new_refrences_ids = []
-    for itm in refrence_ids:
-        refrence = itm.lstrip('{')
-        refrence = refrence.lstrip('}')
-        new_refrences_ids.append(refrence)
-    # refrence_ids = [item.lstrip('{').rstrip('}') for item in refrence_ids]
-    return refrence_ids
-
-def replace_refrences(llm_answer: str, refrences_list:List[str]) -> List[str]:
-    """
-    شناسایی شناسه هایی که مدل زبانی، برای تهیه پاسخ از آنها استفاده کرده است
-    
-    Args:
-        llm_answer(str): متنی که مدل زبانی تولید کرده است
-        refrences_list(List[str]): لیست شناسه ماده های مورد استفاده در پاسخ مدل زبانی
-    Returns:
-        llm_answer(str), : متن بازسازی شده پاسخ مدل زبانی که شناسه ماده های مورد استفاده در آن، اصلاح شده است
-    """
-    refrences = ''
-    for index, ref in enumerate(refrences_list,1):
-            # breakpoint()
-            llm_answer = llm_answer.replace(ref, f'[{index}]')
-            # id = ref.lstrip('{')
-            # id = id.rstrip('}')
-            # refrences += ''.join(f'[{index}] https://majles.tavasi.ir/entity/detail/view/qsection/{id}\n')
-        
-    # llm_answer = f'{llm_answer}\n\nمنابع پاسخ‌:\n{refrences.strip()}'
-    return llm_answer.strip()
-
-# load basic items
-content_list, ids, prefix_list, faiss_index = load_faiss_index(FAISS_INDEX_PATH, FAISS_METADATA_PATH)
-pipe = HybridRetrieverReranker(content_list, ids, prefix_list, faiss_index, dense_alpha=0.6)
-# query preprocess and normalize
-normalizer_obj = PersianVectorAnalyzer()
-
-messages = [
-        {"role": "system", "content": "تو یک دستیار خبره در زمینه حقوق و قوانین مرتبط به آن هستی و می توانی متون حقوقی را به صورت دقیق توضیح بدهی . پاسخ ها باید الزاما به زبان فارسی باشد. پاسخ ها فقط از متون قانونی که در پرامپت وجود دارد استخراج شود."},
-]
-
-models = ["gemini-2.5-flash-lite", "gpt-4o-mini"]
-
-def save_result(chat_obj: object) -> bool:
-    # index result in elastic
-    pass
-
-def run_chatbot(query:str, chat_id:str):
-    prompt_status = True
-    status_text = 'لطفا متن سوال را وارد نمائید'
-    if query == '':
-        prompt_status = False
-    
-    start_time = (datetime.datetime.now())
-
-    # در صورتی که وضعیت پرامپت معتبر باشد، وارد فرایند شو
-    if prompt_status:
-        result_passages_text, result_passages_ids = single_query(query)
-        end_retrive = datetime.datetime.now()
-        print('-'*40)
-        retrive_duration = (end_retrive - start_time).total_seconds()
-        print(f'retrive duration: {str(retrive_duration)}')
-
-        prompt = f'برای پرسش "{query}" از میان مواد قانونی "{result_passages_text}" .پاسخ مناسب و دقیق را استخراج کن. درصورتی که مطلبی مرتبط با پرسش در متن پیدا نشد، فقط پاسخ بده: "متاسفانه در منابع، پاسخی پیدا نشد!"'
-
-        llm_model = ''
-        for model in models:
-            try:
-                llm_model = model
-                llm_answer = llm_request(prompt, model)
-            except Exception as error:
-                    error = f'model: {model} \n{error}\n\n'
-                    prompt_status = False
-                    status_text = 'با عرض پوزش، سرویس موقتا در دسترس نیست. لطفا دقایقی دیگر دوباره تلاش نمائید!'
-                
-    else:
-        chat_obj = {
-        'id' : chat_id, # str
-        'title' : '', # str
-        'user_id' : '',
-        'user_query' : query, # str
-        'model_key' : llm_model, # str
-        'retrived_passage' : result_passages_text, # str
-        'retrived_ref_ids' : result_passages_ids, # list[obj]
-        'prompt_type' : 'question-answer', # str
-        'retrived_duration' : retrive_duration, # str
-        'llm_duration' : '0', # str
-        'full_duration' : '0', # str
-        'time_create' : str(start_time), # str
-        'used_ref_ids' : [], # list[str]
-        'prompt_answer' : '', # str
-        'status_text' : status_text,
-        'status' : prompt_status, # or False # bool
-        }
-        
-        # آبجکت ایجاد شده با بازگردان
-        return chat_obj, status_text
-            
-    llm_answer_duration = (datetime.datetime.now() - end_retrive).total_seconds()
-    print(f'llm answer duration: {str(llm_answer_duration)}')
-
-    used_refrences_in_answer = find_refrences(llm_answer)
-    llm_answer = replace_refrences(llm_answer, used_refrences_in_answer)
-            
-    full_prompt_duration = (datetime.datetime.now() - start_time).total_seconds()
-    print(f'full prompt duration: {full_prompt_duration}')
-    print('~'*40)
-    
-    status_text ='پاسخ با موفقیت ایجاد شد'
-
-    title = llm_base_request(query)
-    if title == '':
-        title = query[0:15]
-        
-    chat_obj = {
-        'id' : chat_id, # str
-        'title' : title, # str
-        'user_id' : '',
-        'user_query' : query, # str
-        'model_key' : llm_model, # str
-        'retrived_passage' : result_passages_text, # str
-        'retrived_ref_ids' : result_passages_ids, # list[obj]
-        'prompt_type' : 'question-answer', # str
-        'retrived_duration' : retrive_duration, # str
-        'llm_duration' : llm_answer_duration, # str
-        'full_duration' : full_prompt_duration, # str
-        'time_create' : str(start_time), # str
-        'used_ref_ids' : used_refrences_in_answer, # list[str]
-        'prompt_answer' : llm_answer, # str
-        'status_text' : status_text, # str
-        'status' : True, # or False # bool
-    }
-    prev_chat_data = []    
-    with open('./llm-answer/chat-messages.json', mode='r', encoding='utf-8') as file:
-            prev_chat_data = json.load(file)
-            prev_chat_data.append(chat_obj)
-                
-    with open('./llm-answer/chat-messages.json', mode='w', encoding='utf-8') as output:
-        json.dump(prev_chat_data, output, ensure_ascii=False, indent=2)
-    
-    # save_result(chat_obj)
-    
-    # ایجاد آبجکت بازگشتی به فرانت  
-    # chat_obj.pop('retrived_passage')
-    # chat_obj.pop('prompt_type')
-    
-    return chat_obj
-
-@chatbot.post("/credit_refresh")
-def credit_refresh():
-    """
-    Returns remained credit
-    """
-    url = "https://api.avalai.ir/user/credit"
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {get_key()}"
-    }
-    remained_credit = requests.get(url, headers=headers)
-    
-    with open('./llm-answer/credit.txt','w') as file:
-        file.write(str(remained_credit.json()['remaining_irt']))
-    return str(remained_credit.json()['remaining_irt'])
-
-def create_chat_id():
-    date = str((datetime.datetime.now())).replace(' ','-').replace(':','').replace('.','-')
-    print('date  ', date )
-    chat_id = f'{date}-{random.randint(100000, 999999)}'
-    print('chat_id  ', chat_id )
-    return chat_id
-
-print('#'*19)
-print('-Chatbot is Ready!!!!!-')
-print('#'*19)
-
-# تعریف مدل داده‌ها برای درخواست‌های API
-class Query(BaseModel):
-    query: str
-# مسیر API برای اجرا کردن run_chatbot
-@chatbot.post("/run_chatbot")
-def run_chat(query: Query):
-    print('query  ', query )
-    chat_id = create_chat_id()
-    print('query.query  ', query.query )
-    answer = run_chatbot(query.query, chat_id)
-    credit_refresh()
-
-    return {"answer": answer}
-
-# uvicorn src.app:app --reload
-
-if __name__ == "__main__":
-    
-    # query = 'در قانون حمایت از خانواده و جوانی جمعیت چه خدماتی در نظر گرفته شده است؟'
-    while True:
-        query = input('enter your qustion:')
-        if query == '':
-            print('لطفا متن سوال را وارد نمائید')
-            continue
-        start = (datetime.datetime.now())
-        # result = test_dataset()
-        result = single_query(query)
-        end_retrive = datetime.datetime.now()
-        print('-'*40)
-        print(f'retrive duration: {(end_retrive - start).total_seconds()}')
-        
-        prompt = f'برای پرسش "{query}" از میان مواد قانونی "{result}" .پاسخ مناسب و دقیق را استخراج کن. درصورتی که مطلبی مرتبط با پرسش در متن پیدا نشد، فقط پاسخ بده: "متاسفانه در منابع، پاسخی پیدا نشد!"'
-        llm_answer = llm_request(prompt)
-        
-        print('-'*40)
-        print(f'llm duration: {(datetime.datetime.now() - end_retrive).total_seconds()}')
-
-        refrences = ''
-        recognized_refrences = find_refrences(llm_answer)
-        llm_answer = replace_refrences(llm_answer, recognized_refrences)
-        
-        with open('./llm-answer/result.txt', mode='a+', encoding='utf-8') as file:
-            result_message = f'متن پرامپت: {query.strip()}\n\nپاسخ: {llm_answer} \n----------------------------------------------------------\n'
-            file.write(result_message)  
-            
-        with open('./llm-answer/passages.txt', mode='a+', encoding='utf-8') as file:
-            result_message = f'متن پرامپت: {query.strip()}\n\مواد مشابه: {result} \n----------------------------------------------------------\n'
-            file.write(result_message)
-            
-    
-        
-        print('----------------------------------------------------------')
-        print(f'full duration: {(datetime.datetime.now() - start).total_seconds()}')
-        print('----------------------------------------------------------')
-        print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
-        

convert_qavanin_json_to_faiss.py

@@ -1,72 +0,0 @@
-import json
-import numpy as np
-import faiss
-import os
-
-def create_faiss_index_from_json(json_file_path, faiss_index_path, metadata_file_path):
-    print(f'try to read {json_file_path} ...')
-    # --- 1. بارگذاری داده‌ها از JSON ---
-    with open(json_file_path, 'r', encoding='utf-8') as f:
-        data = json.load(f)
-    print(f'file reading finished')
-
-    # فرض بر این است که هر عنصر شامل فیلدهای زیر است:
-    # {
-    #   "speech_title": "title",
-    #   "sentence": "متن جمله",
-    #   "embeddings": [0.12, 0.34, ...]
-    # }
-
-    sentences = []
-    titles = []
-    embeddings_list = []
-    prefix_list = []
-    for k, item in data.items():
-        sentences.append(item['content'])
-        titles.append(item['id'])
-        embeddings_list.append(item['embeddings'])
-        prefix_list.append(item['section-prefix'])
- 
-    embeddings = np.array(embeddings_list).astype('float32')  # ابعاد: (n, d)
-    dimension = embeddings.shape[1]
-
-    print(f"Loaded {len(embeddings)} embeddings with dimension {dimension}")
-
-    # --- 2. ایجاد ایندکس FAISS برای GPU ---
-    # اگر فقط CPU دارید، از faiss.IndexFlatL2 استفاده کنید.
-    # اگر GPU دارید، ابتدا ایندکس را روی CPU ایجاد و سپس به GPU انتقال دهید.
-    cpu_index = faiss.IndexFlatL2(dimension)  # معیار فاصله L2 (Euclidean)
-
-    # انتقال ایندکس به GPU
-    if faiss.get_num_gpus() > 0:
-        print("Using GPU for FAISS index...")
-        res = faiss.StandardGpuResources()
-        gpu_index = faiss.index_cpu_to_gpu(res, 0, cpu_index)
-    else:
-        print("GPU not available, using CPU.")
-        gpu_index = cpu_index
-
-    # --- 3. افزودن داده‌ها به ایندکس ---
-    gpu_index.add(embeddings)
-    print(f"Total vectors indexed: {gpu_index.ntotal}")
-
-    # --- 4. ذخیره ایندکس به فایل ---
-    # برای ذخیره باید به CPU منتقل شود
-    final_index = faiss.index_gpu_to_cpu(gpu_index) if isinstance(gpu_index, faiss.Index) and faiss.get_num_gpus() > 0 else gpu_index
-    os.makedirs(os.path.dirname(faiss_index_path), exist_ok=True)
-    faiss.write_index(final_index, faiss_index_path)
-    print(f"FAISS index saved to {faiss_index_path}")
-
-    # --- 5. ذخیره متادیتا (برای نگاشت نتایج جستجو) ---
-    metadata = [{"id": id, "content": c, 'prefix': p} for id, c, p in zip(titles, sentences,prefix_list)]
-    with open(metadata_file_path, 'w', encoding='utf-8') as f:
-        json.dump(metadata, f, ensure_ascii=False, indent=2)
-    print(f"Metadata saved to {metadata_file_path}")
-
-if __name__ == '__main__':
-    # استفاده از متد
-    json_file_path = './majles-output/sections-vec-285k.json'
-    faiss_index_path = './qavanin-faiss/faiss_index_qavanin_285k.index'
-    metadata_file_path = './qavanin-faiss/faiss_index_qavanin_285k_metadata.json'
-
-    create_faiss_index_from_json(json_file_path, faiss_index_path, metadata_file_path)

dockerfile

@@ -1,11 +0,0 @@
-FROM qchat_base:1.0.0
-RUN pip install uvicorn[standard]
-
-WORKDIR /src/app 
-
-COPY . /src/app
-
-EXPOSE 80
-
-CMD [ "uvicorn","chatbot:chatbot","--reload","--port","80","--host=0.0.0.0"]
-

elastic_helper.py

@@ -1,677 +0,0 @@
-import zipfile
-import sys
-import os
-import json
-from time import sleep
-from elasticsearch7 import Elasticsearch,helpers
-
-class ElasticHelper():
-    
-    counter = 0
-    total = 0
-    id = ""
-    path_mappings = os.getcwd() + '/repo/_other/'
-    
-    def __init__(self, es_url="http://127.0.0.1:6900", es_pass="", es_user="elastic", path_mappings = ""):
-
-        if path_mappings : 
-           self.path_mappings = path_mappings
-
-        if es_pass == '' :
-            self.es = Elasticsearch(es_url)
-        else:
-            self.es = Elasticsearch(
-                es_url,
-                http_auth=(es_user, es_pass),
-            )
-        
-        # print(es_url)
-        # print(self.es)
-
-        self.success_connect = False
-        for a in range(0,10):
-            try :
-                if not self.es.ping():
-                    print('elastic not ping, sleep 30 s   : ', a)
-                    sleep(5)
-                    continue
-                else:
-                    self.success_connect = True
-                    break
-
-            except Exception as e:
-                break
-        if not self.success_connect :
-            print('******','not access to elastic service')
-            return
-
-
-        self.counter = 0
-        self.total = 0
-        self.id = ""
-
-
-    def get_doctument(self, index_name, id):
-        res = self.es.get(index=index_name, id=id)
-        return res
-    
-    def exist_doctument(self, index_name, id):
-        res = self.es.exists(index=index_name, id=id)
-        return res
-
-    def update_index_doc(self, is_update_state, index_name_o, eid, data):
-        if is_update_state:
-            resp = self.es.update(index=index_name_o, id=eid, doc=data)
-            # resp = self.es.update(index=index_name_o, id=eid, body={'doc':data})
-        else:
-            resp = self.es.index(index=index_name_o, id=eid, document=data)
-        return resp    
-
-    
-    def exportToJsonForAI(self, path_back, index_name, out_name= '', body={}, fields=[]) :
-        print('*' * 50, ' start backup -->', index_name)
-        self.counter = 0
-        sid = None
-
-        out = out_name
-        if out_name == '' :
-           out = index_name 
-
-        fout = open( path_back + "/"+ out + '.json', 'a+' , encoding='utf-8')
-
-        s_res = self.es.search(
-            index=index_name,
-            scroll='5m',
-            size=1000,
-            body=body
-        )
-        self.total = s_res["hits"]["total"]['value']
-
-        print('start index = %s' % index_name)
-        print('total = %d' % self.total)
-
-        sid = s_res['_scroll_id']
-        scroll_size = len(s_res['hits']['hits'])
-        file_count = 1
-        out_json = []
-        while scroll_size > 0:
-            "Scrolling..."
-            self.counter += scroll_size
-            print("progress -> %.2f %%" % ((self.counter / self.total)*100))
-            #############################
-            for item in s_res['hits']['hits']:                
-
-                if fields :
-                   item2={}
-                   item2['id']=item['_id']
-                   for kf in  fields :
-                       #print(kf)
-                       if kf in item['_source'] :
-                          # print(item['_source'][kf])
-                          item2[kf] = item['_source'][kf]
-                       #exit()
-                else :
-                    item2=item        
-
-                out_json.append(item2)
-
-
-            s_res = self.es.scroll(scroll_id=sid, scroll='2m', request_timeout=100000)
-            sid = s_res['_scroll_id']
-            scroll_size = len(s_res['hits']['hits'])
-
-        sid = None
-        text = json.dumps(out_json, ensure_ascii=False) 
-        fout.write(text)
-
-        ##############################
-
-    def backupIndexToZipfile(self, path_back, index_name, out_name= '', body={}, byzip = True, fields=[], noFields=[]) :
-        print('*' * 50, ' start backup -->', index_name)
-        self.counter = 0
-        sid = None 
-
-        out = out_name
-        if out_name == '' :
-           out = index_name 
-
-
-        if body == {} :
-            s_res = self.es.search(
-                index=index_name,
-                scroll='5m',
-                size=1000
-            )
-        else:
-            s_res = self.es.search(
-                index=index_name,
-                scroll='5m',
-                size=1000,
-                body=body
-            )
-    
-        self.total = s_res["hits"]["total"]['value']
-        if self.total == 0 :
-           print('total index_name by query = %d' % self.total)
-           return False
-
-        if byzip:
-            fout = zipfile.ZipFile(path_back + "/"+ out + '.zip', 'w')
-        else:    
-            fout = open( path_back + "/"+ out + '.json', 'a+' , encoding='utf-8')
-
-
-        print('start index = %s' % index_name)
-        print('total = %d' % self.total)
-
-        sid = s_res['_scroll_id']
-        scroll_size = len(s_res['hits']['hits'])
-        file_count = 1
-        while scroll_size > 0:
-            "Scrolling..."
-            self.counter += scroll_size
-            print("progress -> %.2f %%" % ((self.counter / self.total)*100))
-            #############################
-            out_json = []
-            for item in s_res['hits']['hits']:                
-                if fields :
-                   item2={}
-                   item2['id']=item['_id']
-                   item2['_source']={}
-                   for kf in  fields :
-                       if kf in item['_source'] :
-                          item2['_source'][kf] = item['_source'][kf]
-                else :
-                    item2=item   
-
-                if noFields :
-                    for kf in  noFields :
-                       if kf in item2['_source']:
-                          del item2['_source'][kf]
-                          
-
-                out_json.append(item2)
-
-
-            text = json.dumps(out_json, ensure_ascii=False) 
-            out_json = []
-            if byzip:
-                filename = out + str(file_count) + '.json'    
-                file_count +=1
-                fout.writestr(filename, text.encode('utf-8'), zipfile.ZIP_DEFLATED )
-            else:    
-                fout.write(text)
-
-            ##############################
-            s_res = self.es.scroll(scroll_id=sid, scroll='2m', request_timeout=100000)
-            sid = s_res['_scroll_id']
-            scroll_size = len(s_res['hits']['hits'])
-        sid = None    
-        fout.close()
-
-
-    def restorFileToElastic(self, path_back, index_name, app_key = '', queryDelete = True, map_name='') :
-        if not os.path.exists(path_back) : 
-            print(' **** error *** path not exist: ', path_back)  
-            return False
-
-        file_path = path_back + '/' + index_name + '.zip'
-        if not os.path.exists(file_path ) :
-            return False
-
-        if queryDelete :
-            # اگر وجود داشته باشد، از کاربر برای حذفش سوال میکند
-            if  self.deleteIndex(index_name) :
-                self.createIndex(index_name, app_key, map_name)
-                self.zipFileToElastic(file_path, index_name)
-        else : # اگر وجود داشته باشد پرش می کند و کاری نمیکند
-            self.createIndex(index_name, app_key, map_name)
-            self.zipFileToElastic(file_path, index_name) 
-
-    def restorFileToElastic2(self, path_file, index_name, app_key = '', queryDelete = True, map_name='') :
-        if not os.path.exists(path_file) : 
-            print(' **** error *** path not exist: ', path_file)  
-            return False
-
-        file_path = path_file
-        if not os.path.exists(file_path ) :
-            return False
-
-        if queryDelete :
-            # اگر وجود داشته باشد، از کاربر برای حذفش سوال میکند
-            if  self.deleteIndex(index_name) :
-                self.createIndex(index_name, app_key, map_name)
-                self.zipFileToElastic(file_path, index_name)
-        else : # اگر وجود داشته باشد پرش می کند و کاری نمیکند
-            self.createIndex(index_name, app_key, map_name)
-            self.zipFileToElastic(file_path, index_name) 
-
-
-    def renameElasticIndex(self, index_name_i, index_name_o, app_key = '', map_name='') :
-
-        if self.createIndex(index_name_o, app_key, map_name) :
-           res = self.es.reindex(
-                    body={
-                        "source": {"index": index_name_i},
-                        "dest": {"index": index_name_o}
-                    },
-                    wait_for_completion=False)
-           
-           print(type(res))
-           print(res)
-
-           taskid = res["task"] if res["task"] else ""
-           #tasks = client.TasksClient(self.es)
-           tasks = self.es.tasks
-           while True :               
-               res = tasks.get(task_id = taskid)
-               if res["completed"] :
-                  break
-
-               # print( res["task"])
-               print( '----', index_name_o, '  imported : ', res["task"]["status"]["total"] , ' / ', res["task"]["status"]["created"])
-               sleep(1)
-           print( '----', index_name_o, '  complated')
-
-
-    def deleteIndex(self, index_name) :
-        if not self.es.indices.exists(index=index_name) :
-            print(' ' * 10, " for delete NOT exist index :", index_name )
-            return True
-
-        question = 'Is DELETE elastic index (' + index_name +') ? ' 
-        if self.query_yes_no(question) :
-           self.es.indices.delete(index = index_name)  
-           print('%' * 10 , "  Finish DELETE  index :", index_name )
-           return True
-        else :
-            return False   
-
-    def query_yes_no(self, question, default="no"):
-        valid = { "yes": True, "y": True, "ye": True, "no": False, "n": False }
-        if default is None:
-            prompt = " [y/n] "
-        elif default == "yes":
-            prompt = " [Y/n] "
-        elif default == "no":
-            prompt = " [y/N] " 
-        else:
-            raise ValueError("invalid default answer: '%s'" % default)
-
-        while True:
-            print('%'*10, '  quistion ', '%'*10 , '\n')
-            sys.stdout.write(question + prompt) 
-            choice = input().lower()
-            if default is not None and choice == "":
-                return valid[default]
-            elif choice in valid:
-                return valid[choice]
-            else:
-                sys.stdout.write("لطفا یکی از موارد روبرو را وارد کنید : 'yes' or 'no' " "(or 'y' or 'n').\n") 
-
-    def createIndexIfNotExist(self, index_name_o, mapping_o=""):
-        try:
-            if not self.es.indices.exists(index=index_name_o):
-                response = self.es.indices.create(index=index_name_o, body=mapping_o)
-                # print out the response:
-                print("create index response:", response)
-        except:
-            print("....... index exist ! ... not created")
-
-
-    def createIndex(self, index_name, app_key='', map_name=''):
-
-        path_base = self.path_mappings
-        path_mapping1 =  path_base + 'general/'
-        if app_key == '' :
-           app_key = 'tavasi'
-        path_mapping2 =  path_base + app_key + '/'
-
-
-        if map_name == '':
-            map_name = index_name
-            
-        if self.es.indices.exists(index=index_name) :
-            print("============== exist index :", index_name )
-            return True  
-
-        if map_name == 'mj_rg_section' or map_name == 'semantic_search' :
-            map_name = 'mj_qa_section'
-        elif map_name[-3]=='_ai':
-            map_name=[0-len(map_name)-3]
-            print(map_name)
-    
-        mapping_file_path = path_mapping1 + map_name + '.json'
-        print("mapping_file_path : " , mapping_file_path)
-        if not os.path.isfile(mapping_file_path):
-            if not os.path.isfile(mapping_file_path):
-                mapping_file_path = path_mapping2 + map_name + '.json'
-
-        print("mapping_file_path : " , mapping_file_path)
-
-        # Create Index With Mapping
-        if os.path.isfile(mapping_file_path):
-            mapping_file = open( mapping_file_path,'r', encoding='utf-8' )
-            mapping_file_read = mapping_file.read()
-            mapping_data = json.loads(mapping_file_read)
-            mapping_file.close()   
-            if self.es.indices.exists(index=index_name) :
-                print("============== exist index :", index_name )
-            else :                   
-                self.es.indices.create(index = index_name , body = mapping_data) 
-            return True   
-        else:       
-            print('*** error not find maping file elastic : *******',  mapping_file_path)     
-            return False
-
-
-    def updateBulkList(self, listData, index_name):
-        chunk_size=1000
-        raise_on_error=False
-        raise_on_exception=False
-        stats_only=True
-        yield_ok = False
-
-        actions=[]
-        for item in listData:
-            actions.append({
-                            "_op_type": "update", 
-                            "_index": index_name,
-                            "_id"   : item['_id'],
-                            "doc": item['_source']
-                        } 
-            )                                       
-        helpers.bulk(self.es, actions, chunk_size, raise_on_error, raise_on_exception, stats_only, yield_ok )
-
-    def importBulkList(self, listData, index_name):
-        chunk_size=100000
-        raise_on_error=False
-        raise_on_exception=False
-        stats_only=True
-        yield_ok = False
-
-        for item in listData:
-            actions = [{
-                            "_op_type": "index", 
-                            "_index": index_name,
-                            "_id"   : item['_id'],
-                            "_source": item['_source']
-                        } 
-                    ]                                             
-            helpers.bulk(self.es, actions, chunk_size, raise_on_error, raise_on_exception, stats_only, yield_ok )
-
-
-    def importJsonDataToElastic(self, jsonData, index_name, fields=[]):
-        chunk_size=1000
-        raise_on_error=False
-        raise_on_exception=False
-        stats_only=True
-        yield_ok = False
-
-        actions=[]
-
-        for item in jsonData:
-            id = item['_id'] if item['_id'] else item['id']
-            source = item['_source']
-            if fields :
-                source = {}
-                for col in fields :
-                    if col in item['_source'] :
-                      source[col] = item['_source']
-
-
-            actions.append({
-                            "_op_type": "index", 
-                            "_index": index_name,
-                            "_id"   : id,
-                            "_source": source
-                        }) 
-        helpers.bulk(self.es, actions, chunk_size, raise_on_error, raise_on_exception, stats_only, yield_ok )
-                      
-
-    def fileToElastic(self, file_path, index_name, limit_pack = -1, fields=[]):
-            if not os.path.exists(file_path):
-                print("file zip:" , file_path , " not exist")
-                return
-            print("index:" , index_name , '=>' , file_path ) 
-            self.counter = 0
-            with open(file_path) as file:  
-                data = json.loads(file.read())
-                self.importJsonDataToElastic(data, index_name, fields)
-
-            self.es.indices.refresh(index=index_name)
-            print(self.es.cat.count(index=index_name, format="json"))
-                                    
-    def zipFileToElastic(self, file_path, index_name, limit_pack = -1, fields=[]):
-            if not os.path.exists(file_path):
-                print("file zip:" , file_path , " not exist for imort to elastic : ", index_name )
-                return
-
-            fileNo = 0
-            with zipfile.ZipFile(file_path, 'r') as zObject:
-                fileNo +=1
-                print("="*10, " zip fileNo: " , fileNo ,"  - ( ", index_name," ) | File Numbers:" ,len(zObject.namelist()) , "=" * 10)
-
-                packNo = 0   
-                self.counter = 0
-                for filename in zObject.namelist(): 
-                    packNo += 1
-                    if limit_pack != -1 :
-                        if packNo > limit_pack :
-                            print('limit_data  ', index_name, '  ', limit_pack)
-                            break
-
-                    print("index:" , index_name , '=>' , filename ) 
-                    with zObject.open(filename) as file:  
-                        data = json.loads(file.read())
-                        self.importJsonDataToElastic(data, index_name, fields)
-      
-                self.es.indices.refresh(index=index_name)
-                print(self.es.cat.count(index=index_name, format="json"))
-                print(" END Of Import to elastic ", index_name ,"\n")
-        
-
-    def iterateJsonFile(self, file_path, isZip=True, limit_pack = -1):
-            if not os.path.exists(file_path):
-                print("file zip:" , file_path , " not exist  iterateJsonFile " )
-                return
-
-            if isZip :
-                fileNo = 0
-                with zipfile.ZipFile(file_path, 'r') as zObject:
-                    fileNo +=1
-                    print("="*10, " zip fileNo: " , fileNo ,"  iterateJsonFile - | File Numbers:" ,len(zObject.namelist()) , "=" * 10)
-
-                    packNo = 0   
-                    self.counter = 0
-                    for filename in zObject.namelist(): 
-                        packNo += 1
-                        if limit_pack != -1 :
-                            if packNo > limit_pack :
-                                print('limit_data  iterateJsonFile   ', limit_pack)
-                                break
-
-                        print("index iterateJsonFile :", '=>' , filename ) 
-                        with zObject.open(filename) as file:  
-                            data = json.loads(file.read())
-                            # Yield each entry
-                            # yield data
-                            yield from ({"source": hit["_source"], "id": hit["_id"]} for hit in data)
-            else :
-                with open(filename, 'r', encoding='utf-8') as file:  
-                    data = json.loads(file.read())
-                    # Yield each entry
-                    # yield from (hit for hit in data)
-                    #return data
-                    yield from ({"source": hit["_source"], "id": hit["_id"]} for hit in data)
-    
-
-    def es_iterate_all_documents(self, index, body="", pagesize=250, scroll_timeout="25m", **kwargs):
-        """
-        Helper to iterate ALL values from a single index
-        Yields all the documents.
-        """
-        is_first = True
-        while True:
-            # Scroll next
-            if is_first:  # Initialize scroll
-                # result = self.es.search(index=index, scroll="2m", **kwargs, body={
-                #     "size": pagesize
-                # })
-                if body : 
-                    result = self.es.search(
-                        index=index,
-                        scroll=scroll_timeout,
-                        **kwargs,
-                        size=pagesize,
-                        body=body
-                    )
-                else :
-                    result = self.es.search(
-                        index=index,
-                        scroll=scroll_timeout,
-                        **kwargs,
-                        size=pagesize
-                    )
-
-                self.total = result["hits"]["total"]["value"]
-                if self.total > 0:
-                    print("total = %d" % self.total)
-                is_first = False
-            else:
-                # result = es.scroll(body={
-                #     "scroll_id": scroll_id,
-                #     "scroll": scroll_timeout
-                # })
-                result = self.es.scroll(scroll_id=scroll_id, scroll=scroll_timeout)
-               
-            scroll_id = result["_scroll_id"]
-            hits = result["hits"]["hits"]
-            self.counter += len(hits)
-            if self.total > 0 :
-                print("progress -> %.2f %%" % ((self.counter / self.total) * 100))
-            # Stop after no more docs
-            if not hits:
-                break
-            # Yield each entry
-            yield from ({"source": hit["_source"], "id": hit["_id"]} for hit in hits)
-
-
-    def moveCustomFileds(self, index_name_i, index_name_o, fields=[], renameFileds={}):
-        try:
-            body = {}
-            list = []
-            try:
-                list = self.es_iterate_all_documents(index_name_i)
-            except Exception as e:
-                print(e)
-
-            count = 0
-            for mentry in list:
-                count += 1
-               
-                entry = mentry["source"]
-                id = mentry["id"]
-                # print(id)
-                eid = id
-
-                if (count % 100) == 0 :   
-                    print("%s -> %.2f " % (id , (count / self.total) if self.total > 0 else 0))
-
-                data_filled = False
-                data = {}
-                for col in fields:
-
-                    if '.' in col :
-                        cols = col.split('.')
-                        subsource = entry 
-                        for sub in cols :
-                            dCol = subsource.get(sub, None)
-                            if dCol :
-                               subsource = dCol
-                            else :
-                                break
-                    else : 
-                        dCol = entry.get(col, None)
-
-                    if dCol is None:
-                        continue
-
-                    if col in renameFileds :                       
-                        data[renameFileds[col]] = dCol    
-                    else:
-                        data[col] = dCol    
-
-                    data_filled = True
-
-                if not data_filled :
-                    continue
-
-                try:
-                    resp = self.update_index_doc(True, index_name_o, eid, data)
-                except Exception as e:
-                    print(e)
-                    # save_error(id, e)
-
-        except Exception as e:
-            # print("1111")
-            print(e)
-
-            # save_error(id, e)
-
-    def mappingIndex(self, index_name_i):
-        # فقط از طریق کیبانا میشه تغییر مپ داد 
-        
-        #  با پایتون نمیشه 
-        # باید ایندکس جدیدی با مپ مطلوب ایجاد کرد و رایندکس کرد
-        pass
-
-    def updateByQueryIndex(self, index_name_i, body):
-        ## sample
-        # body = {
-        #         "script": {
-        #             "inline": "ctx._source.Device='Test'",
-        #             "lang": "painless"
-        #         },
-        #         "query": {
-        #             "match": {
-        #                 "Device": "Boiler"
-        #             }
-        #         }
-        #     }
-        try:
-            self.es.update_by_query(body=body, index=index_name_i)
-        
-        except Exception as e:
-            print(e)
-            # save_error(id, e)    
-
-
-    def deleteByQueryIndex(self, index_name_i, body):
-        ## sample
-        # body = {
-        #         "query": {
-        #             "match": {
-        #                 "Device": "Boiler"
-        #             }
-        #         }
-        #     }
-        try:
-            self.es.delete_by_query(index=index_name_i, body=body )
-        
-        except Exception as e:
-            print(e)
-            # save_error(id, e)   
-
-    def delete_by_ids(self, index_name_i, ids):
-        try:
-            # ids = ['test1', 'test2', 'test3'] 
-
-            query = {"query": {"terms": {"_id": ids}}}
-            res = self.es.delete_by_query(index=index_name_i, body=query)
-            print(res)                    
-            
-        except Exception as e:
-            print(e)
-            # save_error(id, e)   
-

embedder_sbert_qavanin_285k.py

@@ -1,681 +0,0 @@
-# !pip install hazm
-# !pip install transformers==4.26.0
-# !pip install --upgrade numpy
-# !pip install --upgrade sentence-transformers
-"""
-Persian Sentence Processing and Vector Analysis
-==============================================
-
-This script processes Persian sentences from a JSON file and performs:
-1. Word extraction and preprocessing
-2. Vector representation using multilingual transformer
-3. Similarity analysis for key words
-4. Dimensionality reduction to 3D
-5. 3D visualization with Persian labels
-
-Author: NLP Expert Assistant
-"""
-import json
-import re
-import numpy as np
-import pandas as pd
-from typing import List, Dict, Tuple, Set
-from collections import Counter
-import logging
-from pathlib import Path
-
-# NLP and ML libraries
-from sentence_transformers import SentenceTransformer
-from transformers import AutoTokenizer
-from sklearn.decomposition import PCA
-from sklearn.manifold import TSNE
-from sklearn.metrics.pairwise import cosine_similarity
-#from normalizer import cleaning
-try:
-    from elastic_helper import ElasticHelper
-except Exception as error:
-    eee = error
-    pass
-# Visualization libraries
-# import matplotlib.pyplot as plt
-# import plotly.graph_objects as go
-# import plotly.express as px
-# from plotly.subplots import make_subplots
-
-# Persian text processing
-# import hazm
-# from hazm import Normalizer, word_tokenize, POSTagger
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-
-
-
-class PersianVectorAnalyzer:
-    """
-    A comprehensive class for Persian text processing and vector analysis.
-    """
-
-    def __init__(self, model_name: str = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"):
-        """
-        Initialize the analyzer with the specified model.
-
-        Args:
-            model_name: The sentence transformer model to use
-        """
-        self.model_name = model_name
-        self.model = None
-        #self.normalizer = Normalizer()
-        self.stop_words = self._load_persian_stop_words()
-        self.key_words = [
-            "خدا", "بنده", "جهاد", "ولی", "زکات",
-            "نماز", "صبر", "عبادت", "ولایت", "خلافت","پیامبر"
-        ]
-
-        logger.info(f"Initializing Persian Vector Analyzer with model: {model_name}")
-
-    def _load_persian_stop_words(self) -> Set[str]:
-        """
-        Load Persian stop words.
-
-        Returns:
-            Set of Persian stop words
-        """
-        # Common Persian stop words
-        stop_words = {
-            'و', 'در', 'به', 'از', 'که', 'این', 'آن', 'با', 'برای', 'تا',
-            'را', 'هم', 'یا', 'اما', 'اگر', 'چون', 'چرا', 'چگونه', 'کجا',
-            'چه', 'کی', 'چند', 'چقدر', 'همه', 'هیچ', 'بعضی', 'هر', 'همه',
-            'خود', 'خویش', 'ما', 'شما', 'آنها', 'ایشان', 'اینها', 'آنها',
-            'من', 'تو', 'او', 'ما', 'شما', 'آنها', 'ایشان', 'اینها',
-            'است', 'هست', 'بود', 'شد', 'می', 'باید', 'خواهد', 'دارد',
-            'کرد', 'شد', 'بود', 'هست', 'است', 'می‌شود', 'می‌کند',
-            'یک', 'دو', 'سه', 'چهار', 'پنج', 'شش', 'هفت', 'هشت', 'نه', 'ده',
-            'اول', 'دوم', 'سوم', 'چهارم', 'پنجم', 'ششم', 'هفتم', 'هشتم', 'نهم', 'دهم',
-            'سال', 'ماه', 'روز', 'هفته', 'ساعت', 'دقیقه', 'ثانیه','پس'
-            'بله', 'نه', 'آری', 'خیر', 'بلی', 'نخیر',
-            'حالا', 'الان', 'امروز', 'دیروز', 'فردا', 'هفته', 'ماه', 'سال',
-            'بالا', 'پایین', 'چپ', 'راست', 'جلو', 'عقب', 'داخل', 'خارج',
-            'بزرگ', 'کوچک', 'بلند', 'کوتاه', 'پهن', 'باریک', 'ضخیم', 'نازک',
-            
-            
-           
-        }
-        return stop_words
-
-    def load_model(self):
-        """
-        Load the sentence transformer model.
-        """
-        try:
-            logger.info("Loading sentence transformer model...")
-            self.model = SentenceTransformer(self.model_name)
-            logger.info("Model loaded successfully!")
-        except Exception as e:
-            logger.error(f"Error loading model: {e}")
-            raise
-    def split_sentence(self, sentence:str):
-        sentences = []
-        sentence_len = len(self.tokenize_sentence(sentence))
-        if sentence_len < 512:
-            sentences.append(sentence)
-        else:
-            temp_sentences = str(sentence).split('.')
-            for sent in temp_sentences:
-                sent_len = len(self.tokenize_sentence(sent))
-                if sent_len > 512:
-                    temp_sentences_2 = str(sent).split('،')
-                    for snt in temp_sentences_2:
-                        sentences.append(snt)
-                else:
-                    sentences.append(sent)
-                    
-        return sentences
-        
-    def load_json_data(self, file_path: str) -> List[str]:
-        """
-        Load Persian sentences from JSON file.
-
-        Args:
-            file_path: Path to the JSON file
-
-        Returns:
-            List of Persian sentences
-        """
-        try:
-            logger.info(f"Loading data from {file_path}")
-            with open(file_path, 'r', encoding='utf-8') as f:
-                data = json.load(f)
-                # convert dict{dict} to list[dict]
-                if type(data) == dict:
-                    temp_data = []
-                    for item in data.items():
-                        temp_data.append(item[1])
-                    data = temp_data
-
-            sentences = []
-            if isinstance(data, list):
-                for index, item in enumerate(data):
-                    print(f'split sentence {index}')
-                    if isinstance(item, dict):
-                        if item['content'] == '':
-                            continue
-                        sentences.append([item['id'],item['content'].strip()])
-                        # for key in ['content']:
-                        #     if key in item and item[key]:
-                        #         # splited_sentences = self.split_sentence(item[key])
-                        #         # splited_sentences = item[key]
-                        #         sentences.append(item[key])
-                        #         # for sent in splited_sentences:
-                        #         #     sentences.append(sent)
-                        #     else:
-                        #         print('fault '+item['sentence-number'])
-                    elif isinstance(item, str):
-                        # splited_sentences = self.split_sentence(item[key])
-                        sentences.append(item)
-                        # for sent in splited_sentences:
-                        #     sentences.append(sent)
-            elif isinstance(data, dict):
-                # If it's a single object, extract all string values
-                for value in data.values():
-                    if isinstance(value, str):
-                        sentences.append(value)
-                        # splited_sentences = str(value).split('.')
-                        # for sent in splited_sentences:
-                        #     sentences.append(sent)
-
-            sentences = [senten for senten in sentences if senten]
-            logger.info(f"Loaded {len(sentences)} sentences")
-            return sentences
-
-        except Exception as e:
-            logger.error(f"Error loading JSON data: {e}")
-            raise
-
-    def preprocess_text(self, text: str) -> str:
-        """
-        Preprocess Persian text.
-
-        Args:
-            text: Raw Persian text
-
-        Returns:
-            Preprocessed text
-        """
-
-        # Normalize text
-        #text = self.normalizer.normalize(text)
-
-        # Remove extra whitespace
-        text = re.sub(r'\s+', ' ', text)
-        
-        # Remove special characters but keep Persian characters
-        text = re.sub(r'[^\u0600-\u06FF\u0750-\u077F\u08A0-\u08FF\uFB50-\uFDFF\uFE70-\uFEFF\s]', '', text)
-
-        return text.strip()
-
-    def tokenize_sentence(self, sentence:str):
-        
-        try:
-            tokenizer = AutoTokenizer.from_pretrained(self.model_name)
-            # print(self.model_name)
-            tokens = tokenizer.tokenize(sentence)
-            return tokens
-        except:
-            error = "An exception occurred in tokenizer : " + self.model_name
-            #file.write( error + '\n' )
-            return []
-
-    def extract_words(self, sentences: List[str]) -> List[str]:
-        """
-        Extract all words from sentences.
-
-        Args:
-            sentences: List of Persian sentences
-
-        Returns:
-            List of all words
-        """
-        all_words = []
-
-        for sentence in sentences:
-            # Preprocess sentence
-            processed_sentence = self.preprocess_text(sentence)
-
-            # Tokenize
-            words = word_tokenize(processed_sentence)
-            # words = processed_sentence.split()
-            # Filter out empty strings and very short words
-            words = [word for word in words if len(word) > 1]
-
-            all_words.extend(words)
-
-        logger.info(f"Extracted {len(all_words)} words from {len(sentences)} sentences")
-        return all_words
-
-    def remove_stop_words(self, words: List[str]) -> List[str]:
-        """
-        Remove stop words from the word list.
-
-        Args:
-            words: List of words
-
-        Returns:
-            List of words without stop words
-        """
-        filtered_words = [word for word in words if word not in self.stop_words]
-        logger.info(f"Removed {len(words) - len(filtered_words)} stop words")
-        return filtered_words
-
-    def get_unique_words(self, words: List[str]) -> List[str]:
-        """
-        Get unique words from the list.
-
-        Args:
-            words: List of words
-
-        Returns:
-            List of unique words
-        """
-        unique_words = list(set(words))
-        logger.info(f"Found {len(unique_words)} unique words from {len(words)} total words")
-        return unique_words
-
-    def compute_word_vectors(self, sentences: List[str]) -> Dict[str, List[float]]:
-        """
-        Compute vector representations for words.
-
-        Args:
-            sentences: List of unique sentences
-
-        Returns:
-            Dictionary mapping sentences to their vector representations
-        """
-        if self.model is None:
-            self.load_model()
-
-        logger.info(f"Computing vectors for {len(sentences)} sections ...")
-        # print(sentences[0])
-        # create list of just sentences
-        just_sentences = [sent['content'] for sent in sentences]
-        # Compute embeddings
-        embeddings = self.model.encode(just_sentences, show_progress_bar=True)
-
-        # Create dictionary
-        sentences_vectors = {}
-        for i, sent in enumerate(sentences):
-            sentences_vectors[f'sentence-{sentences[i]["id"]}'] = {
-                'id': sentences[i]['id'],
-                'fullpath': sentences[i]['fullpath'],
-                'qanon-title': sentences[i]['qanon-title'],
-                'section-prefix': sentences[i]['section-prefix'],
-                'content': sentences[i]['content'],
-                'embeddings': embeddings[i].tolist()
-            }
-            print(f'section {i} embedded!')
-
-        logger.info("section vectors computed successfully!")
-        return sentences_vectors
-
-    def find_closest_words(self, word_vectors: Dict[str, List[float]],
-                          key_words: List[str], top_k: int = 20) -> Dict[str, List[str]]:
-        """
-        Find the closest words to each key word.
-
-        Args:
-            word_vectors: Dictionary of word vectors
-            key_words: List of key words to find neighbors for
-            top_k: Number of closest words to find
-
-        Returns:
-            Dictionary mapping key words to their closest neighbors
-        """
-        logger.info(f"Finding {top_k} closest words for {len(key_words)} key words...")
-
-        # Convert to numpy arrays for faster computation
-        words = list(word_vectors.keys())
-        vectors = np.array(list(word_vectors.values()))
-
-        closest_words = {}
-
-        for key_word in key_words:
-            if key_word in word_vectors:
-                # Get the key word vector
-                key_vector = np.array(word_vectors[key_word]).reshape(1, -1)
-
-                # Compute cosine similarities
-                similarities = cosine_similarity(key_vector, vectors)[0]
-
-                # Get indices of top k similar words (excluding the key word itself)
-                word_indices = np.argsort(similarities)[::-1]
-
-                # Filter out the key word itself and get top k
-                closest_indices = []
-                for idx in word_indices:
-                    if words[idx] != key_word and len(closest_indices) < top_k:
-                        closest_indices.append(idx)
-
-                # Get the closest words
-                closest_words[key_word] = [words[idx] for idx in closest_indices]
-                logger.info(f"Found {len(closest_words[key_word])} closest words for '{key_word}'")
-            else:
-                logger.warning(f"Key word '{key_word}' not found in word vectors")
-                closest_words[key_word] = []
-
-        return closest_words
-
-    def reduce_to_3d(self, word_vectors: Dict[str, List[float]],
-                    method: str = 'tsne') -> Dict[str, List[float]]:
-        """
-        Reduce word vectors to 3D coordinates.
-
-        Args:
-            word_vectors: Dictionary of word vectors
-            method: Dimensionality reduction method ('pca' or 'tsne')
-
-        Returns:
-            Dictionary mapping words to their 3D coordinates
-        """
-        logger.info(f"Reducing dimensions to 3D using {method.upper()}...")
-
-        words = list(word_vectors.keys())
-        vectors = np.array(list(word_vectors.values()))
-
-        if method.lower() == 'pca':
-            reducer = PCA(n_components=3, random_state=42)
-        elif method.lower() == 'tsne':
-            reducer = TSNE(n_components=3, random_state=42, perplexity=min(30, len(vectors)-1))
-        else:
-            raise ValueError("Method must be 'pca' or 'tsne'")
-
-        # Reduce dimensions
-        reduced_vectors = reducer.fit_transform(vectors)
-
-        # Create dictionary
-        word_vectors_3d = {}
-        for i, word in enumerate(words):
-            word_vectors_3d[word] = reduced_vectors[i].tolist()
-
-        logger.info("Dimensionality reduction completed!")
-        return word_vectors_3d
-
-    def save_json(self, data: dict, file_path: str):
-        """
-        Save data to JSON file.
-
-        Args:
-            data: Data to save
-            file_path: Output file path
-        """
-        try:
-            with open(file_path, 'w', encoding='utf-8') as f:
-                json.dump(data, f, ensure_ascii=False, indent=2)
-            logger.info(f"Data saved to {file_path}")
-        except Exception as e:
-            logger.error(f"Error saving to {file_path}: {e}")
-            raise
-
-    # def create_3d_visualization(self, word_vectors_3d: Dict[str, List[float]],
-    #                            selected_words: Dict[str, List[str]],
-    #                            output_path: str = "persian_words_3d.html"):
-    #     """
-    #     Create 3D visualization of words.
-
-    #     Args:
-    #         word_vectors_3d: Dictionary of 3D word coordinates
-    #         selected_words: Dictionary of selected words for each key word
-    #         output_path: Output file path for the visualization
-    #     """
-    #     logger.info("Creating 3D visualization...")
-
-    #     # Prepare data for plotting
-    #     words = list(word_vectors_3d.keys())
-    #     coords = np.array(list(word_vectors_3d.values()))
-
-    #     # Create color mapping for key words and their neighbors
-    #     colors = []
-    #     sizes = []
-    #     hover_texts = []
-
-    #     for word in words:
-    #         # Check if word is a key word
-    #         is_key_word = word in self.key_words
-
-    #         # Check if word is in selected words
-    #         in_selected = False
-    #         key_word_group = None
-    #         for key_word, selected_list in selected_words.items():
-    #             if word in selected_list:
-    #                 in_selected = True
-    #                 key_word_group = key_word
-    #                 break
-
-    #         if is_key_word:
-    #             colors.append('red')
-    #             sizes.append(15)
-    #             hover_texts.append(f"کلیدواژه: {word}")
-    #         elif in_selected:
-    #             colors.append('blue')
-    #             sizes.append(10)
-    #             hover_texts.append(f"کلمه مرتبط با '{key_word_group}': {word}")
-    #         else:
-    #             colors.append('lightgray')
-    #             sizes.append(5)
-    #             hover_texts.append(f"کلمه: {word}")
-
-    #     # Create 3D scatter plot
-    #     fig = go.Figure()
-
-    #     # Add scatter plot
-    #     fig.add_trace(go.Scatter3d(
-    #         x=coords[:, 0],
-    #         y=coords[:, 1],
-    #         z=coords[:, 2],
-    #         mode='markers+text',
-    #         marker=dict(
-    #             size=sizes,
-    #             color=colors,
-    #             opacity=0.8
-    #         ),
-    #         text=words,
-    #         textposition="middle center",
-    #         hovertext=hover_texts,
-    #         hoverinfo='text'
-    #     ))
-
-    #     # Update layout
-    #     fig.update_layout(
-    #         title={
-    #             'text': 'نمایش سه‌بعدی کلمات فارسی',
-    #             'x': 0.5,
-    #             'xanchor': 'center',
-    #             'font': {'size': 20}
-    #         },
-    #         scene=dict(
-    #             xaxis_title='محور X',
-    #             yaxis_title='محور Y',
-    #             zaxis_title='محور Z',
-    #             camera=dict(
-    #                 eye=dict(x=1.5, y=1.5, z=1.5)
-    #             )
-    #         ),
-    #         width=1000,
-    #         height=800,
-    #         showlegend=False
-    #     )
-
-    #     # Save the plot
-    #     fig.write_html(output_path)
-    #     logger.info(f"3D visualization saved to {output_path}")
-
-    #     return fig
-
-    def process_pipeline(self, input_file: str, output_dir: str = "output"):
-        """
-        Run the complete processing pipeline.
-
-        Args:
-            input_file(str): Path to input JSON file
-            output_dir(str): Output directory for results
-        """
-        # Create output directory
-        Path(output_dir).mkdir(exist_ok=True)
-
-        logger.info("Starting Persian Vector Analysis Pipeline...")
-
-        # Step 1: Load data
-        # sentences = self.load_json_data(input_file)
-        sentences = ALL_SECTIONS
-
-        # for s in sentences:
-        #     s_len = len(self.tokenize_sentence(s))
-        #     if s_len > 512:
-        #         print(f'long: {s}')
-        # Step 2: Extract words
-        # all_words = self.extract_words(sentences)
-
-        # Step 3: Remove stop words
-        # filtered_words = self.remove_stop_words(all_words)
-        # filtered_words = all_words
-        
-        # Step 4: Get unique words
-        # unique_words = self.get_unique_words(filtered_words)
-
-        # Step 5: Compute word vectors
-        sentences_vectors = self.compute_word_vectors(sentences)
-
-        # Step 6: Save word vectors
-        self.save_json(sentences_vectors, f"{output_dir}/sections-vec-285k.json")
-
-        # Step 7: Find closest words to key words
-        # selected_words = self.find_closest_words(word_vectors, self.key_words)
-
-        # Step 8: Save selected words
-        # self.save_json(selected_words, f"{output_dir}/selected_words.json")
-
-        # Step 9: Reduce to 3D
-        # word_vectors_3d = self.reduce_to_3d(word_vectors, method='tsne')
-
-        # Step 10: Save 3D vectors
-        # self.save_json(word_vectors_3d, f"{output_dir}/words_vector_3d.json")
-
-        # Step 11: Create visualization
-        # self.create_3d_visualization(word_vectors_3d, selected_words,
-                                #    f"{output_dir}/persian_words_3d.html")
-
-        logger.info("Pipeline completed successfully!")
-
-        # Print summary
-        print("\n" + "="*50)
-        print("PIPELINE SUMMARY")
-        print("="*50)
-        print(f"Input sentences: {len(sentences)}")
-        # print(f"Total words extracted: {len(all_words)}")
-        # print(f"Unique words after preprocessing: {len(unique_words)}")
-        # print(f"Word vectors computed: {len(word_vectors)}")
-        # print(f"Key words processed: {len(self.key_words)}")
-        print(f"Output files saved to: {output_dir}/")
-        print("="*50)
-
-def full_path_text_maker(full_path):
-    """
-    این متد مسیر یک سکشن را می گیرد و متنی را بر اساس ترتیب بخش های آن از جزء به کل بازسازی می کند و بر می گرداند
-    
-    Args:
-        full_path(list): لیستی از عناصر مشخص کننده مسیر درختی این سکشن
-    Returns:
-        full_path_text(str): متن بازسازی شده از مسیر یک سکشن
-    """
-    full_path_text = ""
-    for i, path_item in enumerate(reversed(full_path)):
-        if i == len(full_path) - 1:
-            full_path_text += ''.join(f'{path_item}')
-            break
-        full_path_text += ''.join(f'{path_item} از ')
-    full_path_text = full_path_text.strip()
-    return full_path_text
-
-def main():
-    """
-    Main function to run the Persian Vector Analysis.
-    """
-    # Initialize analyzer
-    analyzer = PersianVectorAnalyzer()
-
-    # Define input and output paths
-    # input_file = "./output-speechs/nahj_speechs_sentences.json"
-    # output_dir = "output-speechs"
-    # input_file = "./majles/data/sections.json"
-    input_file = ""
-    output_dir = "majles-output"
-    
-    # Run the complete pipeline
-    analyzer.process_pipeline(input_file, output_dir)
-
-
-if __name__ == "__main__":
-    eh_obj = ElasticHelper()
-    path = "/home/gpu/data_11/14040611/mj_qa_section.zip"
-    sections_elastic = eh_obj.iterateJsonFile(path, True)
-    all_count = 0
-    dont_cares = []
-    ALL_SECTIONS = []
-    for index, item in enumerate(sections_elastic):
-        all_count +=1
-        source = item['source']
-        section_path = source['other_info']['full_path']
-        id = item['id']
-        
-        filtered_keys = ['فصل','موخره','امضاء','عنوان']
-        section_path = source['other_info']['full_path']
-        flag = False
-        if '>' in section_path:
-            path_parts = section_path.split('>')
-            for key in filtered_keys:
-                if key in path_parts[-1]:
-                    dont_cares.append(id)
-                    flag = True
-                    break
-            if flag:
-                continue
-        else:
-            for key in filtered_keys:
-                if key in section_path:
-                    dont_cares.append(id)
-                    flag = True
-                    break
-            if flag:
-                continue
-        
-        qanon_title = source['qanon_title']
-        full_path_text = full_path_text_maker(section_path.split('>'))
-        section_prefix = f"محتوای {full_path_text} {cleaning(qanon_title)} عبارت است از: "
-        
-        try:
-            content = cleaning(item['source']['content'])
-            # کنار گذاشتن سکشن های خیلی کوچک که عملا محتوا ندارند
-            if len(content.split()) <= 10:
-                continue
-        except Exception as error:
-            print(error)
-            continue
-        data = {
-            'id': id,
-            'fullpath': section_path,
-            'qanon-title': qanon_title,
-            'section-prefix': section_prefix,
-            'content': content
-        }
-        ALL_SECTIONS.append(data)
-    print(f'all_count: {all_count}')
-    print(f'dont_cares: {len(dont_cares)}')
-    print(f'ALL_SECTIONS without dont-cares: {len(ALL_SECTIONS)}')
-    
-    main()
-    
-    """
-    :: *** نکته مهم *** ::
-    NOTE !!! after this process run convert_qavanin_json_to_faiss.py due to create faiss index which is used in RAG process
-    """

llm-answer/credit.txt

@@ -1 +0,0 @@
-194527.67

llm-answer/error-in-llm.txt

@@ -1,10 +0,0 @@
-
-
-query: برای حمایت از ازدواج جوانان و تشکیل خانواده جهت افزایش جمعیت ، چه مواردی پیش بینی شده است؟
-error:Error code: 400 - {'error': {'message': 'Developer instruction is not enabled for this model. Please use a different model that supports developer instructions. Please contact support at support@avalai.ir and include the request ID 01998c28-4ccb-7bc3-97a7-0403baa6ed35 in your email if you believe this is an error.', 'type': 'invalid_request', 'param': None, 'code': 'invalid_argument', 'request_id': '01998c28-4ccb-7bc3-97a7-0403baa6ed35'}} 
--------------------------------
-
-
-query: چرا خانواده مهم هست
-error:Error code: 429 - {'error': {'message': 'Rate limit reached for requests. Check your quota at https://ava.al/limits or contact support at support@avalai.ir and include the request ID 01998fa2-1f98-7060-be1f-7488dd2fcb18 in your email if you believe this is an error.', 'type': 'rate_limit_exceeded', 'param': None, 'code': 'rate_limit_exceeded', 'solution': 'Pace your requests or upgrade to higher levels. Read the Rate limit guide at https://chat.avalai.ir/platform/limits or contact support at support@avalai.ir and include the request ID 01998fa2-1f98-7060-be1f-7488dd2fcb18 in your email if you believe this is an error.', 'request_id': '01998fa2-1f98-7060-be1f-7488dd2fcb18'}} 
--------------------------------

normalizer.py

@@ -1,76 +0,0 @@
-#import hazm
-from cleantext import clean
-import re
-
-def cleanhtml(raw_html):
-    cleanr = re.compile('<.*?>')
-    cleantext = re.sub(cleanr, '', raw_html)
-    return cleantext
-
-#normalizer = hazm.Normalizer()
-wierd_pattern = re.compile("["
-        u"\U0001F600-\U0001F64F"  # emoticons
-        u"\U0001F300-\U0001F5FF"  # symbols & pictographs
-        u"\U0001F680-\U0001F6FF"  # transport & map symbols
-        u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
-        u"\U00002702-\U000027B0"
-        u"\U000024C2-\U0001F251"
-        u"\U0001f926-\U0001f937"
-        u'\U00010000-\U0010ffff'
-        u"\u200d"
-        u"\u2640-\u2642"
-        u"\u2600-\u2B55"
-        u"\u23cf"
-        u"\u23e9"
-        u"\u231a"
-        u"\u3030"
-        u"\ufe0f"
-        u"\u2069"
-        u"\u2066"
-        # u"\u200c"
-        u"\u2068"
-        u"\u2067"
-        "]+", flags=re.UNICODE)
-
-def cleaning(text):
-    text = text.strip()
-    
-    # regular cleaning
-    # text = clean(text,
-    #     fix_unicode=True,
-    #     to_ascii=False,
-    #     lower=True,
-    #     no_line_breaks=True,
-    #     no_urls=True,
-    #     no_emails=True,
-    #     no_phone_numbers=True,
-    #     no_numbers=False,
-    #     no_digits=False,
-    #     no_currency_symbols=True,
-    #     no_punct=False,
-    #     replace_with_url="",
-    #     replace_with_email="",
-    #     replace_with_phone_number="",
-    #     replace_with_number="",
-    #     replace_with_digit="0",
-    #     replace_with_currency_symbol="",
-    # )
-    text = clean(text,
-                 extra_spaces = True,
-                 lowercase = True
-                 ) 
-
-    # cleaning htmls
-    text = cleanhtml(text)
-    
-    # normalizing
-    #text = normalizer.normalize(text)
-    
-    # removing wierd patterns
-    text = wierd_pattern.sub(r'', text)
-    
-    # removing extra spaces, hashtags
-    text = re.sub("#", "", text)
-    text = re.sub("\s+", " ", text)
-    
-    return text

requirements.txt

@@ -1,15 +0,0 @@
-cleantext==1.1.4
-elasticsearch7==7.17.12
-faiss_cpu==1.9.0
-fastapi==0.117.1
-hazm==0.10.0
-langchain_openai==0.3.33
-numpy==1.21.5
-openai==1.108.1
-pandas==2.3.2
-pydantic==2.11.9
-scikit_learn==1.7.2
-sentence_transformers==2.5.1
-torch==2.4.0
-torch==2.1.2
-transformers==4.55.1

run_docker.bash

@@ -1 +0,0 @@
-sudo docker run --name qachat -p 80:80  -v /home/sabr/rag_qavanin_api/:/src/app/ -v /home/sabr/rag_qavanin_api/qavanin-faiss/:/src/app/qavanin-faiss/ -v /home/sabr/rag_qavanin_api/llm-answer/:/src/app/llm-answer/ -v /home/sabr/MODELS:/src/MODELS -it  --restart unless-stopped qachat:1.0.0