initial commit

.gitignore

@@ -1,6 +0,0 @@
-chroma_db
-venv
-.env
-.langchain.db
-sqlite.db
-__pycache__

=40.0.0

@@ -1 +0,0 @@
-Requirement already satisfied: setuptools in /opt/anaconda3/envs/choozemo-carbon/lib/python3.9/site-packages (70.1.1)

Modelfile

@@ -0,0 +1 @@
+FROM /Users/sherry/Documents/_Personal/ChoozeMo/notebooks/carbon/llm/ollama/taide-7b-a.2-q4_k_m.gguf

RAG_app.py

@@ -1,198 +0,0 @@
-from dotenv import load_dotenv
-load_dotenv('environment.env')
-
-from fastapi import FastAPI, Request, HTTPException, status, Body
-# from fastapi.templating import Jinja2Templates
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import FileResponse
-from fastapi import Depends
-from contextlib import asynccontextmanager
-from pydantic import BaseModel
-from typing import List, Optional
-import uvicorn
-
-from typing import List, Optional
-import sqlparse
-from sqlalchemy import create_engine
-import pandas as pd
-#from retrying import retry
-import datetime
-import json
-from json import loads
-import pandas as pd
-import time
-from langchain.callbacks import get_openai_callback
-
-from langchain_community.vectorstores import Chroma
-from langchain_openai import OpenAIEmbeddings
-from RAG_strategy import multi_query, naive_rag, naive_rag_for_qapairs
-from Indexing_Split import create_retriever as split_retriever
-from Indexing_Split import gen_doc_from_database, gen_doc_from_history
-
-import os
-from langchain_community.vectorstores import SupabaseVectorStore
-from langchain_openai import OpenAIEmbeddings
-from supabase.client import Client, create_client
-from add_vectordb import GetVectorStore
-from langchain_community.cache import RedisSemanticCache  # 更新导入路径
-from langchain_core.prompts import PromptTemplate
-import openai
-
-openai_api_key = os.getenv("OPENAI_API_KEY")
-openai.api_key = openai_api_key
-URI = os.getenv("SUPABASE_URI")
-
-global_retriever = None
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    global global_retriever
-    global vector_store
-    
-    start = time.time()
-    # global_retriever = split_retriever(path='./Documents', extension="docx")
-    # global_retriever = raptor_retriever(path='../Documents', extension="txt")
-    # global_retriever = unstructured_retriever(path='../Documents')
-
-    supabase_url = os.getenv("SUPABASE_URL")
-    supabase_key = os.getenv("SUPABASE_KEY")
-    document_table = "documents"
-    supabase: Client = create_client(supabase_url, supabase_key)
-
-    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
-    vector_store = GetVectorStore(embeddings, supabase, document_table)
-    global_retriever = vector_store.as_retriever(search_kwargs={"k": 4})
-
-    print(time.time() - start)
-    yield
-
-def get_retriever():
-    return global_retriever
-
-
-def get_vector_store():
-    return vector_store
-
-app = FastAPI(lifespan=lifespan)
-
-# templates = Jinja2Templates(directory="temp")
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-@app.get("/answer2")
-def multi_query_answer(question, retriever=Depends(get_retriever)):
-    start = time.time()
-
-    with get_openai_callback() as cb:
-        # qa_doc = gen_doc_from_database()
-        # qa_history_doc = gen_doc_from_history()
-        # qa_doc.extend(qa_history_doc)
-        # vectorstore = Chroma.from_documents(documents=qa_doc, embedding=OpenAIEmbeddings(), collection_name="qa_pairs")
-        # retriever_qa = vectorstore.as_retriever(search_kwargs={"k": 3})
-        # final_answer, reference_docs = naive_rag_for_qapairs(question, retriever_qa)
-        final_answer = 'False'
-        if final_answer == 'False':
-            final_answer, reference_docs = multi_query(question, retriever, chat_history=[])
-
-    # print(CHAT_HISTORY)
-    
-    # with get_openai_callback() as cb:
-    #     final_answer, reference_docs = multi_query(question, retriever)
-    processing_time = time.time() - start
-    print(processing_time)
-    save_history(question, final_answer, reference_docs, cb, processing_time)
-
-    return {"Answer": final_answer}
-
-class ChatHistoryItem(BaseModel):
-    q: str
-    a: str
-
-@app.post("/answer_with_history")
-def multi_query_answer(question: Optional[str] = '', chat_history: List[ChatHistoryItem] = Body(...), retriever=Depends(get_retriever)):
-    start = time.time()
-    
-    chat_history = [(item.q, item.a) for item in chat_history if item.a != ""]
-    print(chat_history)
-
-    # TODO: similarity search
-    
-    with get_openai_callback() as cb:
-        final_answer, reference_docs = multi_query(question, retriever, chat_history)
-    processing_time = time.time() - start
-    print(processing_time)
-    save_history(question, final_answer, reference_docs, cb, processing_time)
-
-    return {"Answer": final_answer}
-
-
-@app.post("/answer_with_history2")
-def multi_query_answer(question: Optional[str] = '', extension: Optional[str] = 'pdf', chat_history: List[ChatHistoryItem] = Body(...), retriever=Depends(get_retriever)):
-    start = time.time()
-
-    retriever = vector_store.as_retriever(search_kwargs={"k": 4,
-                                                         'filter': {'extension':extension}})
-    
-    chat_history = [(item.q, item.a) for item in chat_history if item.a != ""]
-    print(chat_history)
-
-    # TODO: similarity search
-    
-    with get_openai_callback() as cb:
-        final_answer, reference_docs = multi_query(question, retriever, chat_history)
-    processing_time = time.time() - start
-    print(processing_time)
-    save_history(question, final_answer, reference_docs, cb, processing_time)
-
-    return {"Answer": final_answer}
-
-def save_history(question, answer, reference, cb, processing_time):
-    # reference = [doc.dict() for doc in reference]
-    record = {
-        'Question': [question],
-        'Answer': [answer],
-        'Total_Tokens': [cb.total_tokens],
-        'Total_Cost': [cb.total_cost],
-        'Processing_time': [processing_time],
-        'Contexts': [str(reference)]
-    }
-    df = pd.DataFrame(record)
-    engine = create_engine(URI)
-    df.to_sql(name='systex_records', con=engine, index=False, if_exists='append')
-
-class history_output(BaseModel):
-    Question: str
-    Answer: str
-    Contexts: str
-    Total_Tokens: int
-    Total_Cost: float
-    Processing_time: float
-    Time: datetime.datetime
-    
-@app.get('/history', response_model=List[history_output])
-async def get_history():
-    engine = create_engine(URI, echo=True)
-
-    df = pd.read_sql_table("systex_records", engine.connect())  
-    df.fillna('', inplace=True)
-    result = df.to_json(orient='index', force_ascii=False)
-    result = loads(result)
-    return result.values()
-
-@app.get("/")
-def read_root():
-    return {"message": "Welcome to the SYSTEX API"}
-
-
-if __name__ == "__main__":
-    uvicorn.run("RAG_app:app", host='127.0.0.1', port=8081, reload=True)
-    
-# if __name__ == "__main__":
-#     uvicorn.run("RAG_app:app", host='cmm.ai', port=8081, reload=True, ssl_keyfile="/etc/letsencrypt/live/cmm.ai/privkey.pem", 
-#                 ssl_certfile="/etc/letsencrypt/live/cmm.ai/fullchain.pem")
-

README.md

@@ -1,9 +1,19 @@
 # RAG with TAIDE Model
 
-## Running with Docker
+## Prerequisites
+- Docker and Docker Compose
+- Ollama (for creating the taide-local model)
 
-1. Install Docker and Docker Compose on your system.
-2. Clone this repository.
-3. Create a `.env` file in the project root with your API keys.
-4. Run `docker-compose up --build` in the project root.
-5. The application will be available at `http://localhost:8000`.
+## Setting up taide-local
+
+1. Install Ollama on your VM 
+`curl -fsSL https://ollama.com/install.sh | sh`
+2. Create the taide-local model
+`ollama create taide-local -f Modelfile`
+
+## Running the Applciation
+1. Clone this repository.
+`git clone -b public https://github.com/yourusername/your-repo.git`
+2. Create a `.env` file in the project root with your API keys.
+3. Run `docker-compose up --build` 
+4. The application will be available at `http://localhost:8000`.

docker-compose.yml

@@ -2,7 +2,7 @@ services:
   app:
     build: .
     ports:
-      - "8000:8000"
+      - "8081:8081"
     environment:
       - OPENAI_API_KEY=${OPENAI_API_KEY}
       - SUPABASE_URL=${SUPABASE_URL}

dockerfile

@@ -5,6 +5,11 @@ WORKDIR /app
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-COPY . .
+# Copy application files
+COPY RAG_strategy.py .
+COPY RAG_app_copy.py .
+COPY add_vectordb.py .
+COPY Indexing_Split.py
+COPY Documents/ ./Documents/
 
 CMD ["python", "RAG_app_copy.py"]

environment.env

@@ -1,7 +0,0 @@
-# Choozemo
-SUPABASE_URI = "postgresql://postgres:chuz8310xsystex@db.ptt.cx:5432/postgres"
-SUPABASE_URL = "http://db.ptt.cx:8000/"
-SUPABASE_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q"
-OPENAI_API_KEY = "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"
-HF_API_KEY_7B = "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"
-HF_API_KEY_7B4BIT = "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"

main.log

@@ -1,8 +0,0 @@
-[1719898830] Log start
-[1719898830] Cmd: /usr/local/Cellar/llama.cpp/3274/bin/llama-cli --hf-repo taide/TAIDE-LX-7B-Chat-4bit --hf-file /Users/sherryliu/Documents/_Personal/choozemo/notebooks/carbon/taide-7b-a.2-q4_k_m.gguf -p "I believe the meaning of life is" -n 128
-[1719898830] main: build = 3274 (49122a87)
-[1719898830] main: built with Apple clang version 15.0.0 (clang-1500.3.9.4) for x86_64-apple-darwin23.4.0
-[1719898830] main: seed  = 1719898830
-[1719898830] main: llama backend init
-[1719898830] main: load the model and apply lora adapter, if any
-[1719898830] main: error: unable to load model

ollama_chat.py

@@ -1,74 +0,0 @@
-import subprocess
-import json
-from typing import Any, List, Optional, Dict
-from langchain_core.callbacks import CallbackManagerForLLMRun
-from langchain_core.language_models import BaseChatModel
-from langchain_core.messages import BaseMessage, AIMessage, HumanMessage
-from langchain_core.outputs import ChatResult, ChatGeneration
-from pydantic import Field
-
-class OllamaChatModel(BaseChatModel):
-    model_name: str = Field(default="taide-local")
-
-    def _generate(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> ChatResult:
-        prompt = "\n".join([f"{msg.__class__.__name__}: {msg.content}" for msg in messages])
-        
-        command = ["ollama", "run", self.model_name, prompt]
-        result = subprocess.run(command, capture_output=True, text=True)
-        
-        if result.returncode != 0:
-            raise Exception(f"Ollama command failed: {result.stderr}")
-        
-        content = result.stdout.strip()
-        
-        message = AIMessage(content=content)
-        generation = ChatGeneration(message=message)
-        return ChatResult(generations=[generation])
-
-    @property
-    def _llm_type(self) -> str:
-        return "ollama-chat-model"
-
-def check_model_availability(model_name: str):
-    result = subprocess.run(["ollama", "list"], capture_output=True, text=True)
-    if result.returncode != 0:
-        print(f"Error checking model availability: {result.stderr}")
-        return False
-    
-    models = result.stdout.splitlines()
-    return any(model_name in model for model in models)
-
-# Usage example
-if __name__ == "__main__":
-    model_name = "taide-local"
-
-    print(f"Checking availability of model {model_name}...")
-    if not check_model_availability(model_name):
-        print(f"Model {model_name} is not available. Please check if it's correctly installed in Ollama.")
-        exit(1)
-
-    model = OllamaChatModel(model_name=model_name)
-    
-    print(f"Starting chat with {model_name} model. Type 'exit' to quit.")
-    
-    messages = []
-    while True:
-        user_input = input("You: ")
-        if user_input.lower() == 'exit':
-            break
-        
-        messages.append(HumanMessage(content=user_input))
-        try:
-            response = model.invoke(messages)
-            print("AI:", response.content)
-            messages.append(AIMessage(content=response.content))
-        except Exception as e:
-            print(f"Error communicating with Ollama: {e}")
-
-print("Chat session ended. Goodbye!")

out.gv

@@ -1,97 +0,0 @@
-digraph G {
-concentrate=true;
-splines="ortho";
-rankdir="LR";
-subgraph legend{
-    rank = min;
-    label = "legend";
-    Legend [shape=none, margin=0, label = <
-        <table cellspacing="0" cellpadding="0" border="1"><tr><td>Code2flow Legend</td></tr><tr><td>
-        <table cellspacing="0">
-        <tr><td>Regular function</td><td width="50px" bgcolor='#cccccc'></td></tr>
-        <tr><td>Trunk function (nothing calls this)</td><td bgcolor='#966F33'></td></tr>
-        <tr><td>Leaf function (this calls nothing else)</td><td bgcolor='#6db33f'></td></tr>
-        <tr><td>Function call</td><td><font color='black'>&#8594;</font></td></tr>
-        </table></td></tr></table>
-        >];
-}node_7d4b4651 [label="47: lifespan()" name="RAG_app::lifespan" shape="rect" style="rounded,filled" fillcolor="#966F33" ];
-node_4fbd994f [label="116: multi_query_answer()" name="RAG_app::multi_query_answer" shape="rect" style="rounded,filled" fillcolor="#966F33" ];
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-    label="File: RAG_app";
-    name="RAG_app";
-    style="filled";
-    graph[style=dotted];
-};
-subgraph cluster_d5bd3b3c {
-    node_7c28ff05 node_fdd77cdf node_649e182e;
-    label="File: RAG_strategy";
-    name="RAG_strategy";
-    style="filled";
-    graph[style=dotted];
-};
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-    node_ba1cc301 node_b5a8f76f node_03dc279e node_76ea7487 node_6dc6ba18 node_4413d1fa;
-    label="File: add_vectordb";
-    name="add_vectordb";
-    style="filled";
-    graph[style=dotted];
-    subgraph cluster_622c6110 {
-        node_d7743d95 node_5b1175ed node_65de84e4 node_e96a8b7c;
-        label="Class: GetVectorStore";
-        name="GetVectorStore";
-        style="filled";
-        graph[style=dotted];
-    };
-};
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-    node_40bbb064 node_fa144567 node_53c93e6f;
-    label="File: indexing_Split";
-    name="indexing_Split";
-    style="filled";
-    graph[style=dotted];
-};
-}

requirements_semantic_search.txt

@@ -1,6 +0,0 @@
-python-dotenv
-openai
-langchain-openai
-langchain-community
-langchain-chroma
-chromadb

run.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-
-
-
-# 使脚本文件可执行：
-# chmod +x run.sh
-
-# /answer2?question=解釋碳排放法規
-

semantic_cache.ipynb

@@ -1,1339 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### Python = 3.9\n",
-    "import os\n",
-    "from dotenv import load_dotenv\n",
-    "load_dotenv('environment.env')\n",
-    "\n",
-    "import openai \n",
-    "openai_api_key = os.getenv(\"OPENAI_API_KEY\")\n",
-    "openai.api_key = openai_api_key\n",
-    "\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
-    "embeddings_model = OpenAIEmbeddings()\n",
-    "\n",
-    "from langchain_community.document_loaders.csv_loader import CSVLoader\n",
-    "from langchain_chroma import Chroma\n",
-    "\n",
-    "import pandas as pd\n",
-    "import re\n",
-    "# from supabase import create_client, Client \n",
-    "# supabase_url = os.getenv(\"SUPABASE_URL\")\n",
-    "# supabase_key = os.getenv(\"SUPABASE_KEY\")\n",
-    "# supabase: Client = create_client(supabase_url, supabase_key)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "############## Load data from csv ###############\n",
-    "def extract_question(text):\n",
-    "    match = re.search(r'Question: (.+?)(?:\\n|$)', text)\n",
-    "    if match:\n",
-    "        return match.group(1)\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "loader = CSVLoader(file_path=\"/QA_database.csv\")\n",
-    "data = loader.load()\n",
-    "\n",
-    "questions = [extract_question(doc.page_content) for doc in data]\n",
-    "\n",
-    "# ########### load data from supabase ##########\n",
-    "# embeddings_model = OpenAIEmbeddings()\n",
-    "# response = supabase.table(\"tablename\").select(\"question\").execute()\n",
-    "# data = response.data \n",
-    "# created_at = []\n",
-    "# question = []\n",
-    "# ids = []\n",
-    "# answer = []\n",
-    "# video_url = []\n",
-    "\n",
-    "# for item in data:\n",
-    "#     ids.append(item['id'])\n",
-    "#     created_at.append(item['created_at'])\n",
-    "#     question.append(item['question'])\n",
-    "#     answer.append(item['answer'])\n",
-    "#     video_url.append(item['video_url'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "######### generate embedding ###########\n",
-    "embedding = embeddings_model.embed_documents(questions)\n",
-    "\n",
-    "########## Write embedding to the supabase table  #######\n",
-    "# for id, new_embedding in zip(ids, embedding):\n",
-    "#     supabase.table(\"tablename\").insert({\"embedding\": embedding.tolist()}).eq(\"id\", id).execute()\n",
-    "\n",
-    "########### Vector Store #############\n",
-    "# Put pre-compute embeddings to vector store. ## save to disk\n",
-    "vectorstore = Chroma.from_texts(\n",
-    "    texts=questions,\n",
-    "    embedding=embeddings_model,\n",
-    "    persist_directory=\"./chroma_db_carbon_questions\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "###### load from disk  #######\n",
-    "vectorstore = Chroma(persist_directory=\"./chroma_db_carbon_questions\", embedding_function=embeddings_model)\n",
-    "\n",
-    "####### Query it #########\n",
-    "def evaluation(target, query, SIMILARITY_THRESHOLD=0.83):\n",
-    "    #### 參考QA_database的標準問題，給予幾個換句話說問題，目標是讓兩者能夠被exact match。\n",
-    "    #### 若無法被exact match，則檢查兩者的similarity score是否在指定的標準以上，並回傳最近似問題\n",
-    "    correct = 0\n",
-    "    count = 0   \n",
-    "    print(f\"問題：{target[0]}\")\n",
-    "    for q in query:\n",
-    "        docs_and_scores = vectorstore.similarity_search_with_relevance_scores(q, k=1)\n",
-    "        doc, score = docs_and_scores[0]\n",
-    "        print(f\"Variation{count+1}:{q}\")\n",
-    "        if doc.page_content in target: \n",
-    "            print(f\"找到Target | score:{round(score, 2)}\")\n",
-    "            correct += 1 \n",
-    "        elif score >= SIMILARITY_THRESHOLD: \n",
-    "            print(f\"找到近似問題：{doc.page_content} | score:{round(score, 2)}\")\n",
-    "        else:\n",
-    "            print(f\"沒有找到 ｜ score:{round(score, 2)}\")\n",
-    "        count += 1\n",
-    "    print(f\"Accuracy rate {correct} / {count}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：台灣為什麼要制定氣候變遷因應法？\n",
-      "Variation1:氣候變遷因應法是什麼時候制定的\n",
-      "找到Target：score:0.91\n",
-      "Variation2:氣候變遷因應法有什麼內容\n",
-      "找到Target：score:0.87\n",
-      "Variation3:氣候變遷有哪些法律\n",
-      "找到Target：score:0.85\n",
-      "Variation4:台灣有哪些氣候法規\n",
-      "找到近似問題：台灣氣候變遷法是否已設立各階段管制目標? | score:0.87\n",
-      "Variation5:氣候變遷的影響\n",
-      "沒有找到 ｜ score:0.83\n",
-      "Accuracy rate 3 / 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"台灣為什麼要制定氣候變遷因應法？\"]\n",
-    "query = [\"氣候變遷因應法是什麼時候制定的\", \"氣候變遷因應法有什麼內容\", \"氣候變遷有哪些法律\", \"台灣有哪些氣候法規\", \"氣候變遷的影響\"]\n",
-    "evaluation(target, query)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：台灣為什麼要制定氣候變遷因應法？\n",
-      "Variation1:氣候變遷因應法是什麼時候制定的\n",
-      "找到Target：score:0.91\n",
-      "Variation2:氣候變遷因應法有什麼內容\n",
-      "找到Target：score:0.87\n",
-      "Variation3:氣候變遷有哪些法律\n",
-      "找到Target：score:0.85\n",
-      "Variation4:台灣有哪些氣候法規\n",
-      "找到近似問題：台灣氣候變遷法是否已設立各階段管制目標? | score:0.87\n",
-      "Variation5:氣候變遷的影響\n",
-      "沒有找到 ｜ score:0.83\n",
-      "Accuracy rate 3 / 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"台灣為什麼要制定氣候變遷因應法？\"]\n",
-    "query = [\"氣候變遷因應法是什麼時候制定的\", \"氣候變遷因應法有什麼內容\", \"氣候變遷有哪些法律\", \"台灣有哪些氣候法規\", \"氣候變遷的影響\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：國家溫室氣體長期減量目標\n",
-      "Variation1:溫室氣體減量\n",
-      "找到近似問題：溫室氣體 | score:0.92\n",
-      "Variation2:台灣溫室氣體減量目標\n",
-      "找到Target：score:0.94\n",
-      "Variation3:台灣有溫室氣體減量目標嗎\n",
-      "找到Target：score:0.91\n",
-      "Variation4:我們有溫室氣體減量目標嗎\n",
-      "找到Target：score:0.88\n",
-      "Variation5:溫室氣體要怎麼減量\n",
-      "找到近似問題：溫室氣體 | score:0.88\n",
-      "Accuracy rate 3 / 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"國家溫室氣體長期減量目標\"]\n",
-    "query = [\"溫室氣體減量\", \"台灣溫室氣體減量目標\", \"台灣有溫室氣體減量目標嗎\", \"我們有溫室氣體減量目標嗎\", \"溫室氣體要怎麼減量\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：淨零排放意涵為何\n",
-      "Variation1:零排放是什麼\n",
-      "找到Target：score:0.87\n",
-      "Variation2:解釋淨零\n",
-      "找到Target：score:0.82\n",
-      "Variation3:為什麼要淨零\n",
-      "找到Target：score:0.85\n",
-      "Variation4:台灣要把溫室氣體排放歸零嗎\n",
-      "找到近似問題：組織溫室氣體排放量可以用碳權抵減嗎？ | score:0.85\n",
-      "Variation5:零排放\n",
-      "找到Target：score:0.82\n",
-      "Accuracy rate 4 / 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"淨零排放意涵為何\"]\n",
-    "query = [\"零排放是什麼\", \"解釋淨零\", \"為什麼要淨零\", \"台灣要把溫室氣體排放歸零嗎\", \"零排放\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：是否已設立各階段管制目標\n",
-      "Variation1:碳排放的管制目標有什麼\n",
-      "沒有找到 ｜ score:0.81\n",
-      "Variation2:管制碳排放的階段目標\n",
-      "找到近似問題：國家溫室氣體長期減量目標 | score:0.86\n",
-      "Variation3:簡短介紹階段性管制目標\n",
-      "找到Target：score:0.88\n",
-      "Variation4:設立管制目標\n",
-      "找到Target：score:0.9\n",
-      "Variation5:溫室氣體排放的管制\n",
-      "找到近似問題：溫室氣體 | score:0.9\n",
-      "Accuracy rate 2 / 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"是否已設立各階段管制目標\"]\n",
-    "query = [\"碳排放的管制目標有什麼\", \"管制碳排放的階段目標\", \"簡短介紹階段性管制目標\", \"設立管制目標\", \"溫室氣體排放的管制\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：何類對象需執行溫室氣體盤查\n",
-      "Variation1:誰會被溫室提起盤查\n",
-      "找到Target：score:0.84\n",
-      "Variation2:碳盤查查誰\n",
-      "找到近似問題：什麼是碳盤查？ | score:0.88\n",
-      "Variation3:誰會被強制碳盤查\n",
-      "沒有找到 ｜ score:0.83\n",
-      "Variation4:溫室氣體排放盤查對象\n",
-      "找到Target：score:0.89\n",
-      "Variation5:哪些公司會被盤查溫室氣體\n",
-      "找到Target：score:0.83\n",
-      "Accuracy rate 3 / 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"何類對象需執行溫室氣體盤查\"]\n",
-    "query = [\"誰會被溫室提起盤查\", \"碳盤查查誰\", \"誰會被強制碳盤查\", \"溫室氣體排放盤查對象\", \"哪些公司會被盤查溫室氣體\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：何謂總量管制，我國是否已設定總量管制\n",
-      "Variation1:排放氣體總量管制是什麼\n",
-      "找到Target：score:0.84\n",
-      "Variation2:總量管制的目標\n",
-      "找到Target：score:0.88\n",
-      "Variation3:介紹總量管制\n",
-      "找到Target：score:0.89\n",
-      "Variation4:總量管制是規範什麼\n",
-      "找到Target：score:0.9\n",
-      "Variation5:總量管制的法規查詢\n",
-      "找到Target：score:0.87\n",
-      "Accuracy rate 5 / 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"何謂總量管制，我國是否已設定總量管制\"]\n",
-    "query = [\"排放氣體總量管制是什麼\", \"總量管制的目標\",\"介紹總量管制\", \"總量管制是規範什麼\", \"總量管制的法規查詢\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：徵收碳費依據\n",
-      "Variation1:碳排放的收費依據\n",
-      "找到Target：score:0.91\n",
-      "Variation2:碳費收費標準\n",
-      "找到Target：score:0.88\n",
-      "Variation3:碳排放氣體怎麼收費\n",
-      "找到Target：score:0.87\n",
-      "Variation4:攤費怎麼計算的\n",
-      "沒有找到 ｜ score:0.75\n",
-      "Variation5:碳排放依據什麼收費\n",
-      "找到Target：score:0.89\n",
-      "Accuracy rate 4 / 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"徵收碳費依據\"]\n",
-    "query = [\"碳排放的收費依據\", \"碳費收費標準\",\"碳排放氣體怎麼收費\", \"攤費怎麼計算的\", \"碳排放依據什麼收費\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：誰需要繳交碳費？\n",
-      "Variation1:哪類型公司需要交碳費\n",
-      "找到Target：score:0.86\n",
-      "Variation2:碳費的收費對象\n",
-      "找到近似問題：碳費用途 | score:0.91\n",
-      "Variation3:如何知道我需不需要繳交碳費\n",
-      "找到Target：score:0.86\n",
-      "Variation4:製造業需要繳交碳費嗎\n",
-      "找到Target：score:0.88\n",
-      "Variation5:誰需要繳交溫室氣體排放費用\n",
-      "找到Target：score:0.89\n",
-      "Accuracy rate 4 / 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"誰需要繳交碳費？\"]\n",
-    "query = [\"哪類型公司需要交碳費\", \"碳費的收費對象\",\"如何知道我需不需要繳交碳費\", \"製造業需要繳交碳費嗎\", \"誰需要繳交溫室氣體排放費用\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳費一噸多少錢\n",
-      "Variation1:一頓碳多少錢\n",
-      "找到Target：score:0.91\n",
-      "Variation2:碳排放費率\n",
-      "找到近似問題：碳費用途 | score:0.84\n",
-      "Variation3:一頓碳的排放收費\n",
-      "找到近似問題：徵收碳費依據 | score:0.88\n",
-      "Variation4:碳 一頓 的收費多少\n",
-      "找到Target：score:0.92\n",
-      "Accuracy rate 2 / 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳費一噸多少錢\"]\n",
-    "query = [\"一頓碳多少錢\",\"碳排放費率\", \"一頓碳的排放收費\", \"碳 一頓 的收費多少\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳費用途\n",
-      "Variation1:碳費的目的\n",
-      "找到Target：score:0.94\n",
-      "Variation2:碳費的使用\n",
-      "找到Target：score:0.94\n",
-      "Variation3:碳費會被用在什麼事情上\n",
-      "找到Target：score:0.92\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳費用途\"]\n",
-    "query = [\"碳費的目的\",\"碳費的使用\", \"碳費會被用在什麼事情上\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳權來源\n",
-      "Variation1:碳權是什麼\n",
-      "找到Target：score:0.9\n",
-      "Variation2:解釋 碳權\n",
-      "找到Target：score:0.86\n",
-      "Variation3:碳權是怎麼得到的\n",
-      "找到Target：score:0.9\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳權來源\"]\n",
-    "query = [\"碳權是什麼\",\"解釋 碳權\", \"碳權是怎麼得到的\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：組織溫室氣體排放量可以用碳權抵減嗎？\n",
-      "Variation1:組織溫室氣體可以抵銷碳權嗎\n",
-      "找到Target：score:0.93\n",
-      "Variation2:公司可以用碳權付費溫室氣體排放嗎\n",
-      "找到Target：score:0.91\n",
-      "Variation3:組織碳權抵銷溫室氣體\n",
-      "找到Target：score:0.89\n",
-      "Variation4:公司碳權抵銷溫室氣體的規定有哪些\n",
-      "找到Target：score:0.84\n",
-      "Accuracy rate 4 / 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"組織溫室氣體排放量可以用碳權抵減嗎？\"]\n",
-    "query = [\"組織溫室氣體可以抵銷碳權嗎\",\"公司可以用碳權付費溫室氣體排放嗎\", \"組織碳權抵銷溫室氣體\", \"公司碳權抵銷溫室氣體的規定有哪些\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳費可以用碳權抵減嗎\n",
-      "Variation1:碳排放可以抵銷碳權嗎\n",
-      "找到Target：score:0.89\n",
-      "Variation2:可以用碳權付費碳費嗎\n",
-      "找到Target：score:0.93\n",
-      "Variation3:碳權抵銷碳費的規定\n",
-      "找到Target：score:0.87\n",
-      "Variation4:碳權拿來付費碳費 可以嗎\n",
-      "找到Target：score:0.91\n",
-      "Accuracy rate 4 / 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳費可以用碳權抵減嗎\"]\n",
-    "query = [\"碳排放可以抵銷碳權嗎\",\"可以用碳權付費碳費嗎\", \"碳權抵銷碳費的規定\", \"碳權拿來付費碳費 可以嗎\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：老舊汽車汰舊換新溫室氣體減量效益的用途為何\n",
-      "Variation1:舊汽車汰換 溫室氣體排放抵換 \n",
-      "找到Target：score:0.88\n",
-      "Variation2:舊汽車換新 溫室氣體減量效益\n",
-      "找到Target：score:0.93\n",
-      "Variation3:舊汽車換新 溫室氣體排放減量 補助\n",
-      "找到Target：score:0.88\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"老舊汽車汰舊換新溫室氣體減量效益的用途為何\"]\n",
-    "query = [\"舊汽車汰換 溫室氣體排放抵換 \",\"舊汽車換新 溫室氣體減量效益\", \"舊汽車換新 溫室氣體排放減量 補助\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：組織型碳盤查遵循標準\n",
-      "Variation1:組織碳排查 規定 跟標準\n",
-      "找到Target：score:0.9\n",
-      "Variation2:公司碳盤查 法律規定\n",
-      "找到Target：score:0.86\n",
-      "Variation3:組織的碳盤查有哪些規定\n",
-      "找到Target：score:0.92\n",
-      "Variation4:如何確保我的公司遵循碳盤查的規範\n",
-      "找到Target：score:0.88\n",
-      "Variation5:碳盤查 要遵循哪些審查標準\n",
-      "找到Target：score:0.91\n",
-      "Accuracy rate 5 / 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"組織型碳盤查遵循標準\"]\n",
-    "query = [\"組織碳排查 規定 跟標準\",\"公司碳盤查 法律規定\", \"組織的碳盤查有哪些規定\" , \"如何確保我的公司遵循碳盤查的規範\", \"碳盤查 要遵循哪些審查標準\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：溫室氣體種類\n",
-      "Variation1:溫室氣體有哪些\n",
-      "找到Target：score:0.93\n",
-      "Variation2:溫室氣氣包括哪些\n",
-      "找到Target：score:0.87\n",
-      "Variation3:甲烷是溫室氣體嗎\n",
-      "找到近似問題：溫室氣體 | score:0.87\n",
-      "Variation4:二氧化碳是溫室氣體嗎\n",
-      "找到近似問題：溫室氣體 | score:0.87\n",
-      "Accuracy rate 2 / 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"溫室氣體種類\"]\n",
-    "query = [\"溫室氣體有哪些\", \"溫室氣氣包括哪些\" , \"甲烷是溫室氣體嗎\", \"二氧化碳是溫室氣體嗎\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：盤查範圍應如何設定\n",
-      "Variation1:盤查範圍決定\n",
-      "找到Target：score:0.95\n",
-      "Variation2:盤查範圍\n",
-      "找到Target：score:0.95\n",
-      "Variation3:碳排放的盤查範圍\n",
-      "找到近似問題：碳足跡涵蓋範圍 | score:0.87\n",
-      "Variation4:溫室氣體盤查的範圍\n",
-      "找到近似問題：溫室氣體 | score:0.85\n",
-      "Accuracy rate 2 / 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"盤查範圍應如何設定\"]\n",
-    "query = [\"盤查範圍決定\", \"盤查範圍\" , \"碳排放的盤查範圍\", \"溫室氣體盤查的範圍\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：溫室氣體排放量計算方式\n",
-      "Variation1:溫室氣體計算單位\n",
-      "找到Target：score:0.9\n",
-      "Variation2:溫室氣體的計算方式\n",
-      "找到Target：score:0.95\n",
-      "Variation3:溫室氣體排放的計量方式\n",
-      "找到Target：score:0.97\n",
-      "Variation4:如何計量溫室氣體的排放\n",
-      "找到Target：score:0.94\n",
-      "Accuracy rate 4 / 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"溫室氣體排放量計算方式\"]\n",
-    "query = [\"溫室氣體計算單位\", \"溫室氣體的計算方式\" , \"溫室氣體排放的計量方式\", \"如何計量溫室氣體的排放\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：完成盤查作業後，需產出何種文件\n",
-      "Variation1:盤查後的報告文件產出\n",
-      "找到Target：score:0.89\n",
-      "Variation2:盤查後的文件產出\n",
-      "找到Target：score:0.9\n",
-      "Variation3:盤查產出的報告\n",
-      "找到Target：score:0.83\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"完成盤查作業後，需產出何種文件\"]\n",
-    "query = [\"盤查後的報告文件產出\", \"盤查後的文件產出\" , \"盤查產出的報告\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：組織型盤查是否皆需完成第三者查證作業\n",
-      "Variation1:組織盤查 需要第三者驗證碼\n",
-      "找到Target：score:0.91\n",
-      "Variation2:組織盤查 需要外部認證嗎\n",
-      "找到Target：score:0.88\n",
-      "Variation3:組織盤查 第三者檢查\n",
-      "找到Target：score:0.92\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"組織型盤查是否皆需完成第三者查證作業\"]\n",
-    "query = [\"組織盤查 需要第三者驗證碼\", \"組織盤查 需要外部認證嗎\" , \"組織盤查 第三者檢查\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳足跡是指\n",
-      "Variation1:解釋碳足跡\n",
-      "找到近似問題：碳足跡 | score:0.91\n",
-      "Variation2:碳足跡的產生\n",
-      "找到近似問題：碳足跡 | score:0.92\n",
-      "Variation3:碳足跡是什麼\n",
-      "找到Target：score:0.95\n",
-      "Variation4:哪些活動會產生碳足跡\n",
-      "找到近似問題：碳足跡 | score:0.85\n",
-      "Accuracy rate 1 / 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳足跡是指\"]\n",
-    "query = [\"解釋碳足跡\", \"碳足跡的產生\" , \"碳足跡是什麼\", \"哪些活動會產生碳足跡\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳足跡遵循標準\n",
-      "Variation1:碳足跡 法規\n",
-      "找到近似問題：碳足跡 | score:0.9\n",
-      "Variation2:碳足跡相關規定\n",
-      "找到Target：score:0.91\n",
-      "Variation3:產品碳足跡規定\n",
-      "找到Target：score:0.9\n",
-      "Accuracy rate 2 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳足跡遵循標準\"]\n",
-    "query = [\"碳足跡 法規\", \"碳足跡相關規定\" , \"產品碳足跡規定\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳足跡涵蓋範圍\n",
-      "Variation1:碳足跡 範圍舉例\n",
-      "找到Target：score:0.94\n",
-      "Variation2:碳足跡的範圍有多廣\n",
-      "找到Target：score:0.94\n",
-      "Variation3:組織碳足跡 範圍\n",
-      "找到Target：score:0.93\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳足跡涵蓋範圍\"]\n",
-    "query = [\"碳足跡 範圍舉例\", \"碳足跡的範圍有多廣\" , \"組織碳足跡 範圍\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：CEV系統依循標準為何\n",
-      "Variation1:CEV系統 規定\n",
-      "找到Target：score:0.9\n",
-      "Variation2:CEV要遵守什麼\n",
-      "找到Target：score:0.84\n",
-      "Variation3:組織CEV的標準\n",
-      "找到Target：score:0.87\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"CEV系統依循標準為何\"]\n",
-    "query = [\"CEV系統 規定\", \"CEV要遵守什麼\" , \"組織CEV的標準\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：CEV系統可以支援盤查到什麼程度\n",
-      "Variation1:CEV可以盤查到什麼\n",
-      "找到Target：score:0.92\n",
-      "Variation2:CEV的支援盤查範圍\n",
-      "找到Target：score:0.9\n",
-      "Variation3:CEV可以上傳數據嗎\n",
-      "找到Target：score:0.82\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"CEV系統可以支援盤查到什麼程度\"]\n",
-    "query = [\"CEV可以盤查到什麼\", \"CEV的支援盤查範圍\" , \"CEV可以上傳數據嗎\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：什麼是精誠資訊\n",
-      "Variation1:精誠 介紹\n",
-      "找到Target：score:0.84\n",
-      "Variation2:精誠的業務範圍\n",
-      "找到Target：score:0.8\n",
-      "Variation3:精誠有哪些主要服務\n",
-      "找到Target：score:0.84\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"什麼是精誠資訊\"]\n",
-    "query = [\"精誠 介紹\", \"精誠的業務範圍\" , \"精誠有哪些主要服務\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：溫暖化潛勢是什麼？\n",
-      "Variation1:解釋溫暖化潛勢\n",
-      "找到Target：score:0.92\n",
-      "Variation2:解釋global warming potential\n",
-      "找到Target：score:0.78\n",
-      "Variation3:解釋gwp\n",
-      "找到Target：score:0.66\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"溫暖化潛勢是什麼？\"]\n",
-    "query = [\"解釋溫暖化潛勢\", \"解釋global warming potential\" , \"解釋gwp\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：什麼是碳盤查？\n",
-      "Variation1:碳盤查 介紹\n",
-      "找到Target：score:0.9\n",
-      "Variation2:碳盤查是什麼\n",
-      "找到Target：score:0.95\n",
-      "Variation3:碳盤查的意思\n",
-      "找到近似問題：碳盤查的目的是什麼？ | score:0.93\n",
-      "Accuracy rate 2 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"什麼是碳盤查？\"]\n",
-    "query = [\"碳盤查 介紹\", \"碳盤查是什麼\" , \"碳盤查的意思\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳盤查的目的是什麼？\n",
-      "Variation1:為什麼要做碳盤查\n",
-      "找到近似問題：什麼是碳盤查？ | score:0.92\n",
-      "Variation2:碳盤查有什麼好處\n",
-      "找到Target：score:0.91\n",
-      "Variation3:碳盤查的目標\n",
-      "找到Target：score:0.92\n",
-      "Accuracy rate 2 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳盤查的目的是什麼？\"]\n",
-    "query = [\"為什麼要做碳盤查\", \"碳盤查有什麼好處\" , \"碳盤查的目標\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳盤查的標準有哪些類別?\n",
-      "Variation1:碳盤查的盤查標準\n",
-      "找到Target：score:0.92\n",
-      "Variation2:碳盤查有哪些標準\n",
-      "找到Target：score:0.94\n",
-      "Variation3:組織溫室氣體盤查標準\n",
-      "找到近似問題：溫室氣體 | score:0.86\n",
-      "Accuracy rate 2 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳盤查的標準有哪些類別?\"]\n",
-    "query = [\"碳盤查的盤查標準\", \"碳盤查有哪些標準\" , \"組織溫室氣體盤查標準\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳足跡\n",
-      "Variation1:碳足跡 介紹\n",
-      "找到Target：score:0.93\n",
-      "Variation2:碳足跡 解釋\n",
-      "找到Target：score:0.94\n",
-      "Variation3:什麼是碳足跡\n",
-      "找到Target：score:0.93\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳足跡\"]\n",
-    "query = [\"碳足跡 介紹\", \"碳足跡 解釋\",\"什麼是碳足跡\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：14061-1\n",
-      "Variation1:14064-1是什麼\n",
-      "沒有找到 ｜ score:0.83\n",
-      "Variation2:ISO 14064解釋\n",
-      "沒有找到 ｜ score:0.71\n",
-      "Variation3:iso 14064法規\n",
-      "沒有找到 ｜ score:0.76\n",
-      "Variation4:14061\n",
-      "沒有找到 ｜ score:0.81\n",
-      "Accuracy rate 0 / 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"14061-1\"]\n",
-    "query = [\"14064-1是什麼\", \"ISO 14064解釋\",\"iso 14064法規\", \"14061\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：Describe ESG\n",
-      "Variation1:explain esg\n",
-      "找到Target：score:0.9\n",
-      "Variation2:whats esg\n",
-      "找到Target：score:0.91\n",
-      "Variation3:解釋esg\n",
-      "沒有找到 ｜ score:0.79\n",
-      "Variation4:什麼是esg\n",
-      "找到Target：score:0.85\n",
-      "Accuracy rate 3 / 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"Describe ESG\"]\n",
-    "query = [\"explain esg\", \"whats esg\",\"解釋esg\", \"什麼是esg\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：溫室氣體\n",
-      "Variation1:溫室氣體 介紹\n",
-      "找到Target：score:0.94\n",
-      "Variation2:溫室氣體 影響\n",
-      "找到Target：score:0.91\n",
-      "Variation3:溫室氣體解釋\n",
-      "找到Target：score:0.91\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"溫室氣體\"]\n",
-    "query = [\"溫室氣體 介紹\", \"溫室氣體 影響\",\"溫室氣體解釋\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳盤查涉及哪些產業或領域？\n",
-      "Variation1:會被碳盤查的產業\n",
-      "找到Target：score:0.9\n",
-      "Variation2:碳排放 盤查產業\n",
-      "找到Target：score:0.87\n",
-      "Variation3:哪類型公司需要碳盤查\n",
-      "找到Target：score:0.84\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳盤查涉及哪些產業或領域？\"]\n",
-    "query = [\"會被碳盤查的產業\", \"碳排放 盤查產業\",\"哪類型公司需要碳盤查\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：排放係數從哪裡得到？\n",
-      "Variation1:碳排放係數 查詢\n",
-      "找到Target：score:0.84\n",
-      "Variation2:哪裏可以查碳足跡資料\n",
-      "找到近似問題：碳足跡 | score:0.86\n",
-      "Variation3:查詢碳足跡排放係數\n",
-      "找到近似問題：碳足跡遵循標準 | score:0.83\n",
-      "Accuracy rate 1 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"排放係數從哪裡得到？\"]\n",
-    "query = [\"碳排放係數 查詢\", \"哪裏可以查碳足跡資料\",\"查詢碳足跡排放係數\"]\n",
-    "evaluation(target, query)\n",
-    "# 可加入“碳足跡”到“排放係數”"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：台灣氣候變遷法是否已設立各階段管制目標?\n",
-      "Variation1:氣候變遷法 各階段目標\n",
-      "找到Target：score:0.92\n",
-      "Variation2:氣候變遷管制的階段性目標\n",
-      "找到Target：score:0.91\n",
-      "Variation3:台灣氣候變遷管制\n",
-      "找到Target：score:0.92\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"台灣氣候變遷法是否已設立各階段管制目標?\"]\n",
-    "query = [\"氣候變遷法 各階段目標\", \"氣候變遷管制的階段性目標\",\"台灣氣候變遷管制\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：排放係數法是什麼\n",
-      "Variation1:排放係數計算\n",
-      "找到Target：score:0.92\n",
-      "Variation2:碳足跡計算\n",
-      "找到近似問題：碳足跡 | score:0.91\n",
-      "Variation3:排放係數\n",
-      "找到Target：score:0.93\n",
-      "Accuracy rate 2 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"排放係數法是什麼\"]\n",
-    "query = [\"排放係數計算\", \"碳足跡計算\",\"排放係數\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：碳盤查有哪些範疇?\n",
-      "Variation1:碳盤查的範圍\n",
-      "找到Target：score:0.93\n",
-      "Variation2:溫室氣體盤查涵蓋範圍\n",
-      "找到近似問題：溫室氣體 | score:0.85\n",
-      "Variation3:碳排放盤查的範圍有多廣\n",
-      "找到Target：score:0.85\n",
-      "Accuracy rate 2 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"碳盤查有哪些範疇?\"]\n",
-    "query = [\"碳盤查的範圍\", \"溫室氣體盤查涵蓋範圍\",\"碳排放盤查的範圍有多廣\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：制定溫室氣體排放量盤查登錄及查驗管理辦法的目的是什麼\n",
-      "Variation1:溫室氣體排放管理辦法的目的\n",
-      "找到Target：score:0.92\n",
-      "Variation2:溫室氣體查驗的目的\n",
-      "找到Target：score:0.89\n",
-      "Variation3:溫室氣體管理跟盤查的目的\n",
-      "找到Target：score:0.92\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"制定溫室氣體排放量盤查登錄及查驗管理辦法的目的是什麼\"]\n",
-    "query = [\"溫室氣體排放管理辦法的目的\", \"溫室氣體查驗的目的\",\"溫室氣體管理跟盤查的目的\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：請問精誠資訊有什麼產品呢？\n",
-      "Variation1:精誠資訊服務介紹\n",
-      "找到近似問題：什麼是精誠資訊 | score:0.88\n",
-      "Variation2:精誠資訊產品有哪些\n",
-      "找到Target：score:0.93\n",
-      "Variation3:精誠資訊提供哪些服務\n",
-      "找到近似問題：什麼是精誠資訊 | score:0.88\n",
-      "Accuracy rate 1 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"請問精誠資訊有什麼產品呢？\"]\n",
-    "query = [\"精誠資訊服務介紹\", \"精誠資訊產品有哪些\",\"精誠資訊提供哪些服務\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "問題：供應鏈ESG評鑑該怎麼進行?\n",
-      "Variation1:esg評鑑怎麼做\n",
-      "找到Target：score:0.87\n",
-      "Variation2:供應鏈的esg評鑑\n",
-      "找到Target：score:0.93\n",
-      "Variation3:esg評價要準備什麼\n",
-      "找到Target：score:0.84\n",
-      "Accuracy rate 3 / 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "target = [\"供應鏈ESG評鑑該怎麼進行?\"]\n",
-    "query = [\"esg評鑑怎麼做\", \"供應鏈的esg評鑑\",\"esg評價要準備什麼\"]\n",
-    "evaluation(target, query)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " ### Observations\n",
-    "這裡有一些好像是同義詞的詞，會影響到llm的判讀，例如\n",
-    "- 溫室氣體跟碳排放\n",
-    "- 碳足跡跟排放係數\n",
-    "\n",
-    "--------\n",
-    "- 這裡的數字判斷不出來，例如14061-1。 但不是問題，之前測試過在RAG的Prompt裡面強調就ok了\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "choozemo-carbon",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

taide_rag.py

@@ -1,30 +0,0 @@
-from dotenv import load_dotenv
-from langchain.vectorstores import Chroma
-import os
-load_dotenv('environment.env')
-openai_api_key = os.getenv("OPENAI_API_KEY")
-from RAG_strategy import taide_llm, multi_query, naive_rag
-from langchain.vectorstores import FAISS
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.document_loaders import TextLoader
-from langchain.text_splitter import CharacterTextSplitter
-
-
-
-# Load and prepare a sample document
-loader = TextLoader("test_data.txt")
-documents = loader.load()
-text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-docs = text_splitter.split_documents(documents)
-
-# Create a vector store
-embeddings = OpenAIEmbeddings()
-vectorstore = Chroma.from_documents(docs, embeddings)
-retriever = vectorstore.as_retriever()
-
-# Test multi_query
-print("\nTesting multi_query:")
-question = "什麼是碳排放獎勵辦法？"
-answer, docs = multi_query(question, retriever, [])
-print(f"Question: {question}")
-print(f"Answer: {answer}")