部分修改5

content/LangChain Chat with Your Data/5.检索 retrieval.ipynb

@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "51b15e5c-9b92-4d40-a149-b56335d330d9",
    "metadata": {
     "tags": []
@@ -70,7 +70,9 @@
     "from dotenv import load_dotenv, find_dotenv\n",
     "_ = load_dotenv(find_dotenv()) # read local .env file\n",
     "\n",
-    "openai.api_key  = os.environ['OPENAI_API_KEY']"
+    "openai.api_key  = os.environ['OPENAI_API_KEY']\n",
+    "os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:7890'\n",
+    "os.environ[\"HTTP_PROXY\"] = 'http://127.0.0.1:7890'"
    ]
   },
   {
@@ -84,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "id": "fe368042",
    "metadata": {
     "tags": []
@@ -108,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "a0189dc5",
    "metadata": {
     "tags": []
@@ -120,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "id": "2be10170",
    "metadata": {},
    "outputs": [],
@@ -133,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "id": "3659e0f7",
    "metadata": {
     "tags": []
@@ -143,7 +145,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "209\n"
+      "0\n"
      ]
     }
    ],
@@ -1647,7 +1649,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,

content/LangChain Chat with Your Data/8. 总结.ipynb

@@ -1 +0,0 @@
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content/LangChain Chat with Your Data/8.总结 summary.md

@@ -0,0 +1,18 @@
+# 第八章、总结
+
+这次课程的内容包括：
+1. 使用 LangChain 的 80 多种文档装载器从各种文档源中加载数据。
+2. 将这些文档分割成块，并讨论了其中的一些微妙之处。
+3. 为这些块创建了 Embedding，并将它们放入向量存储器中，并轻松实现语义搜索。
+4. 讨论了语义搜索的一些缺点，以及在某些边缘情况中可能会发生的搜索失败。
+5. 介绍了许多新的高级且有趣的检索算法，用于克服那些边缘情况。
+6. 与 LLMs 相结合，将检索到的文档，和用户问题传递给 LLM，生成对原始问题的答案。
+7. 对对话内容进行了补全，创建了一个完全功能的、端到端的聊天机器人。
+
+**💪🏻 出发 去探索新世界吧**
+
+希望您们在学习过程中受益匪浅。
+
+也感谢开源社区为这个课程做出贡献，如果您在 LangChain 上发现了新的功能和技巧，希望您分享到 Twitter 或者 github 上。
+
+这是一个快速发展的领域，非常令人激动。期待看到您们将所学应用到实际中的样子。