From 766661ea97eac2d211f698f4e0f42fddc89850d1 Mon Sep 17 00:00:00 2001
From: Chen Ling <102888045+ChenLingD@users.noreply.github.com>
Date: Mon, 6 Apr 2026 14:43:07 -0400
Subject: [PATCH] Add files via upload

---
 .../rag/main.ipynb"                           | 458 ++++++++++++++----
 1 file changed, 360 insertions(+), 98 deletions(-)

diff --git "a/\344\275\277\347\224\250Python\346\236\204\345\273\272RAG\347\263\273\347\273\237/rag/main.ipynb" "b/\344\275\277\347\224\250Python\346\236\204\345\273\272RAG\347\263\273\347\273\237/rag/main.ipynb"
index 7eca110..63aa596 100644
--- "a/\344\275\277\347\224\250Python\346\236\204\345\273\272RAG\347\263\273\347\273\237/rag/main.ipynb"
+++ "b/\344\275\277\347\224\250Python\346\236\204\345\273\272RAG\347\263\273\347\273\237/rag/main.ipynb"
@@ -2,14 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "cf723ed9-d8e0-4f1a-911f-887b927f8569",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
+   "execution_count": null,
+   "id": "01173ef8-faa1-4dfd-9ab0-711b4a9e6008",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Before User Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4b6d1b90-86f7-494f-af54-3b451ab51ca5",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -42,7 +47,8 @@
     "from typing import List\n",
     "\n",
     "def split_into_chunks(doc_file: str) -> List[str]:\n",
-    "    with open(doc_file, 'r') as file:\n",
+    "    # Add encoding='utf-8' right here!\n",
+    "    with open(doc_file, 'r', encoding='utf-8') as file:\n",
     "        content = file.read()\n",
     "\n",
     "    return [chunk for chunk in content.split(\"\\n\\n\")]\n",
@@ -55,22 +61,179 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "cfe9bf60-5d21-4696-99a5-7e7f3b94dd06",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
-    "scrolled": true
-   },
+   "execution_count": 4,
+   "id": "624003bb-0a5d-4dcb-a14d-fefee0b43fdf",
+   "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f97cd11c8a24a7ea7f2df4b21686590",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "modules.json:   0%|          | 0.00/230 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\alienware\\rag1\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:138: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\alienware\\.cache\\huggingface\\hub\\models--shibing624--text2vec-base-chinese. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
+      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
+      "  warnings.warn(message)\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a0fd505a77e4c5b94b9db75a0f5c5c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md: 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "492fe391c2c045a7bf4fc692ac360eba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "sentence_bert_config.json:   0%|          | 0.00/54.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e36af05478841f4bc1ccab4b3731541",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/856 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1c6af2fde5247d99d0856e15ccd2c41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/409M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecbcbb2b45754803882c4f9faf69fd18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: shibing624/text2vec-base-chinese\n",
+      "Key                          | Status     |  | \n",
+      "-----------------------------+------------+--+-\n",
+      "bert.embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0dbb96f373841968bf64de2f4982bde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/319 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfa289b6742c4f05807b7aa354018784",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.txt: 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d07cb77b43454429a3d60a9319ffa93b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f3b6d0f4e4d460f92ac9a004ca75961",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/74.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "768\n",
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      ]
     }
    ],
@@ -91,22 +254,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "87f48192-d9f7-4270-ae08-e5e0300bbb32",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
-    "scrolled": true
-   },
+   "execution_count": 5,
+   "id": "ccbd3677-7fa5-4e66-8a68-2360c2db3b4b",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "10\n",
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0.020007014274597168, -0.019597291946411133, -0.0028213020414114, -0.055465202778577805, -0.040571924299001694]\n"
      ]
     }
    ],
@@ -119,12 +276,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "babfbd91-76fc-4467-9ff7-ccaf5ffbbd54",
+   "execution_count": 7,
+   "id": "3d6d2ed1-163b-448b-9c2d-15aee9cb4aaf",
    "metadata": {},
    "outputs": [],
    "source": [
     "import chromadb\n",
+    "# vector database\n",
     "\n",
     "chromadb_client = chromadb.EphemeralClient()\n",
     "chromadb_collection = chromadb_client.get_or_create_collection(name=\"default\")\n",
@@ -142,14 +300,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "9e47b06d-3f7a-40bd-886a-aca6c7e19f0b",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
+   "execution_count": 8,
+   "id": "6659c98a-9409-4c33-8438-29a0fb7ff49b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# After User Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "11570f9c-8591-4755-8993-989c01aa17bd",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -186,15 +349,144 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "e57ac85d-d634-4c1d-93fa-e627cf09a6f1",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
+   "execution_count": 11,
+   "id": "e39376c3-abc8-4b7c-a5ec-b3ddca21b75b",
+   "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c76e58afa074ea284d09d9dfdf0fa9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/891 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\alienware\\rag1\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:138: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\alienware\\.cache\\huggingface\\hub\\models--cross-encoder--mmarco-mMiniLMv2-L12-H384-v1. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
+      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
+      "  warnings.warn(message)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2924dbcbfe94f53a2dbaeb3f3c5ba98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/471M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "553f5baf5de842e2a6150d0203473330",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/201 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mXLMRobertaForSequenceClassification LOAD REPORT\u001b[0m from: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1\n",
+      "Key                             | Status     |  | \n",
+      "--------------------------------+------------+--+-\n",
+      "roberta.embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf2f28392795416382ce4ffa197f5ad3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/435 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "066c3ab0aa06467c974cf0719549c3c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6732da3753324f419ff12dac9498f618",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/17.1M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61987337abff4d6fb92590d778ccacff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f63f3531334b42eeb2da9e68ffa6694f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md: 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -216,6 +508,7 @@
     "    pairs = [(query, chunk) for chunk in retrieved_chunks]\n",
     "    scores = cross_encoder.predict(pairs)\n",
     "\n",
+    "    #score table\n",
     "    scored_chunks = list(zip(retrieved_chunks, scores))\n",
     "    scored_chunks.sort(key=lambda x: x[1], reverse=True)\n",
     "\n",
@@ -229,66 +522,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "79d844d8-846e-4a88-a19f-c8e282839b99",
+   "execution_count": 12,
+   "id": "d75b9766-39d3-4838-9631-323ad4827637",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "你是一位知识助手，请根据用户的问题和下列片段生成准确的回答。\n",
-      "\n",
-      "用户问题: 哆啦A梦使用的3个秘密道具分别是什么？\n",
-      "\n",
-      "相关片段:\n",
-      "三件秘密道具分别是：可以临时赋予超级战力的“复制斗篷”，能暂停时间五秒的“时间停止手表”，以及可在一分钟中完成一年修行的“精神与时光屋便携版”。大雄被推进精神屋内，在其中接受密集的训练，虽然只有几分钟现实时间，他却经历了整整一年的苦修。刚开始他依旧软弱，想放弃、想逃跑，但当他想起静香、父母，还有哆啦A梦那坚定的眼神时，他终于咬牙坚持了下来。出来之后，他的身体与精神都焕然一新，眼神中多了一份成熟与自信。\n",
-      "\n",
-      "最终战在黑暗赛亚人的空中要塞前爆发，特兰克斯率先出击，释放全力与敌人正面对决。哆啦A梦则用任意门和道具支援，从各个方向制造混乱，尽量压制敌人的时空能力。但黑暗赛亚人太过强大，仅凭特兰克斯一人根本无法压制，更别说击败。就在特兰克斯即将被击倒之际，大雄披上复制斗篷、冲破恐惧从高空跃下。他的拳头燃烧着金色光焰，目标直指敌人心脏。\n",
-      "\n",
-      "战后，未来世界开始恢复，植物重新生长，人类重建家园。特兰克斯告别时紧紧握住大雄的手，说：“你是我见过最特别的战士。”哆啦A梦也为大雄感到骄傲，说他终于真正成长了一次。三人站在山丘上，看着远方重新明亮的地平线，心中感受到从未有过的安宁。随后，哆啦A梦与大雄乘坐时光机返回了属于他们的那个年代，一切仿佛又恢复平静。\n",
-      "\n",
-      "请基于上述内容作答，不要编造信息。\n",
-      "\n",
-      "---\n",
-      "\n",
-      "根据提供的片段，哆啦A梦使用的3个秘密道具分别是：\n",
-      "\n",
-      "1.  **复制斗篷**：可以临时赋予超级战力。\n",
-      "2.  **时间停止手表**：能暂停时间五秒。\n",
-      "3.  **精神与时光屋便携版**：可在一分钟中完成一年修行。\n"
-     ]
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "from dotenv import load_dotenv\n",
     "from google import genai\n",
     "\n",
-    "load_dotenv()\n",
-    "google_client = genai.Client()\n",
-    "\n",
-    "def generate(query: str, chunks: List[str]) -> str:\n",
-    "    prompt = f\"\"\"你是一位知识助手，请根据用户的问题和下列片段生成准确的回答。\n",
-    "\n",
-    "用户问题: {query}\n",
-    "\n",
-    "相关片段:\n",
-    "{\"\\n\\n\".join(chunks)}\n",
-    "\n",
-    "请基于上述内容作答，不要编造信息。\"\"\"\n",
-    "\n",
-    "    print(f\"{prompt}\\n\\n---\\n\")\n",
-    "\n",
-    "    response = google_client.models.generate_content(\n",
-    "        model=\"gemini-2.5-flash\",\n",
-    "        contents=prompt\n",
-    "    )\n",
-    "\n",
-    "    return response.text\n",
-    "\n",
-    "answer = generate(query, reranked_chunks)\n",
-    "print(answer)"
+    "load_dotenv()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81bc5147-a5c5-4b9f-a9ed-f5f0ebc94b09",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -307,7 +569,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.9"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,