diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..7a73a41 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,2 @@ +{ +} \ No newline at end of file diff --git a/content/docs/chatflow/index.ja.mdx b/content/docs/chatflow/index.ja.mdx new file mode 100644 index 0000000..40b8739 --- /dev/null +++ b/content/docs/chatflow/index.ja.mdx @@ -0,0 +1,38 @@ +--- +title: Dify チャットフロー +description: Dify チャットフロー開発について学ぶ +enableComments: true +author: "Chase" +github_username: "ChasePassion" +--- + +## 🤔 チャットアシスタント vs チャットフロー:何が違うの? + +Dify プラットフォームでは、さまざまなタイプの AI アプリケーションを構築できます。対話型 AI の開発では、一般的に **チャットアシスタント**(主に LLM に基づいた基本的な質問応答やチャットボット)と **チャットフロー**(対話の流れを編成できるもの)の 2 種類のモードに出会います。では、その違いは何でしょうか? + +例えば、病院の案内チャットシステムでは、チャットフローを使うことで、ユーザーに選択肢を提示し、シナリオを切り替え、特定のナレッジベースを使って検索させることができます。これにより、複雑なシーンでも回答精度が大幅に向上し、システム負荷も軽減されます。 + +🌰 主な違いの比較表: + +| 特徴 | チャットアシスタント | チャットフロー | +| ---------- | --------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | +| 主な目的 | 質問応答、テキスト生成などを素早く実装 | 複雑なロジックや多段階のやり取り、ユーザーガイド付きの対話アプリを構築 | +| 対話モード | 単発または簡易なマルチターン | 多段階、文脈対応、制御可能な対話フローに重点 | +| 編成能力 | 限定的または可視化なし。主にプロンプト + RAG | ノード接続による強力な可視化フロー、条件分岐・変数・ツール呼び出しなどをサポート | +| 状態管理 | LLM の短期記憶や簡単な文脈伝達に依存 | 内蔵されたメモリでノード間の会話履歴を管理・伝達可能 | +| 想定シーン | 基本的な Q&A ボット、コンテンツドラフト、プロトタイピング | スマートカスタマーサポート、タスク指向ボット、高度な意味理解、パーソナライズ、外部サービスとの統合など | +| 特殊ノード | 特定ノードへの依存は少なめ | `Answer`(ストリーム出力)、`sys.query`や`sys.conversation_id`などの入力変数 | +| 拡張機能 | 基本機能が中心 | オープニングメッセージ、質問提案、TTS、高度なファイル処理、コンテンツモデレーションなどをサポート | + +## はじめよう + +### 簡単なフロー構築:最初のスマートチャットフローを作ってみよう + + + +

+ Dify + のビジュアルインターフェースを使ってシンプルなチャットボットを作成する方法を学びましょう。 +

+
+
diff --git a/content/docs/chatflow/index.mdx b/content/docs/chatflow/index.mdx new file mode 100644 index 0000000..358bd6c --- /dev/null +++ b/content/docs/chatflow/index.mdx @@ -0,0 +1,37 @@ +--- +title: Dify Chatflow +description: Learn about developing with Dify Chatflow +enableComments: true +author: "Chase" +github_username: "ChasePassion" +--- + +## 🤔 Chat Assistant vs Chatflow: What's the Difference? + +On the Dify platform, you can build various types of AI applications. When we talk about conversational AI, we often encounter two modes: **Chat Assistants** (typically basic Q&A or LLM-based bots) and **Chatflows** (orchestrated dialog flows). So, what's the difference? + +A practical example is a hospital guide system. In a Chatflow, you can guide users to select one of several options, switch scenes, and retrieve knowledge from specific databases. This can significantly improve answer accuracy and reduce system load in complex scenarios like hospitals. + +🌰 Key Differences at a Glance: + +| Feature | Chat Assistant | Chatflow | +| ------------- | ------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | +| Main Purpose | Quickly implement Q&A, text generation, etc. | Build dialogue applications with complex logic, multi-turn interaction, and user guidance | +| Interaction | Single or simple multi-turn conversations | Emphasizes multi-turn, context-aware, and controllable dialog flows | +| Orchestration | Limited or no visual orchestration, mostly Prompt + RAG | Powerful visual orchestration using node linking, with branching, variables, tool calls | +| State | Relies on LLM’s short-term memory or simple context | Built-in memory allows passing and managing dialog history across nodes | +| Use Cases | Basic Q&A bots, content drafting, rapid prototyping | Smart customer service, task-oriented bots, semantic understanding, personalized suggestions, service integration | +| Unique Nodes | Few special orchestration nodes | `Answer` (stream output), special input variables like `sys.query`, `sys.conversation_id` | +| Extensions | Basic features | Supports greetings, follow-up suggestions, TTS, advanced file handling, content moderation, etc. | + +## Get Started + +### Easy Orchestration: Build Your First Smart Chatflow + + + +

+ Learn how to use Dify's visual interface to create a simple dialog bot. +

+
+
diff --git a/content/docs/chatflow/index.zh.mdx b/content/docs/chatflow/index.zh.mdx new file mode 100644 index 0000000..8d820cb --- /dev/null +++ b/content/docs/chatflow/index.zh.mdx @@ -0,0 +1,35 @@ +--- +title: Dify 聊天流 +description: 了解 Dify 聊天流的开发 +enableComments: true +author: "Chase" +github_username: "ChasePassion" +--- + +## 🤔 聊天助手 & 聊天流:有什么区别? + +在 Dify 平台中,您可以构建多种类型的 AI 应用。当我们谈论对话式 AI 时,通常会遇到"聊天助手"(通常指基于 LLM 的基础问答或聊天机器人)和"Chatflow"(可编排的对话流)这两种模式。那么这两种模式有什么区别呢? + +一个直观的例子是医院的导医便民系统,在 Chatflow 里面你可以要求用户选择指定的选项之一,进入一个新的场景,使用特定的知识库检索,这在医院这种复杂场景下可以大大提高答案准确度、降低系统负荷。 + +🌰 核心区别一览: + +| 特性 | 聊天助手 | 聊天流 | +| -------- | -------------------------------------------- | ---------------------------------------------------------------------------------- | +| 核心目标 | 快速实现问答、文本生成等功能 | 构建具有复杂逻辑、多轮交互、可引导用户的对话式应用 | +| 交互模式 | 单轮或简单的多轮,直接反馈 | 强调多轮、上下文感知、可控的对话流程 | +| 编排能力 | 有限或无可视化编排,通常是 Prompt 工程 + RAG | 通过节点连接实现强大的可视化编排,支持条件分支、变量、工具调用等 | +| 状态管理 | 依赖 LLM 的短期记忆或简单上下文传递 | 内置聊天记忆 (Memory),可在节点间传递和管理多轮对话历史 | +| 适用场景 | 基础问答机器人、内容初稿生成、快速原型验证 | 智能客服、任务导向型机器人、复杂语义理解与应答、个性化推荐、集成外部服务的对话应用 | +| 特有节点 | 较少依赖特定编排节点 | `Answer` (流式输出)、特定输入变量 (`sys.query`, `sys.conversation_id`) 等 | +| 扩展功能 | 基础功能为主 | 支持对话开场白、后续问题建议、TTS、更复杂的文件处理逻辑、内容审核等 | + +## 开始使用 + +### 轻松编排:构建您的第一个智能聊天流 + + + +

学习使用 Dify 的可视化界面编排一个简单的对话机器人。

+
+
diff --git a/content/docs/chatflow/meta.ja.json b/content/docs/chatflow/meta.ja.json new file mode 100644 index 0000000..585dd5d --- /dev/null +++ b/content/docs/chatflow/meta.ja.json @@ -0,0 +1,10 @@ +{ + "title": "チャットフロー", + "icon": "flow-chart", + "defaultOpen": true, + "pages": [ + "node", + "playground", + "xueyeyoutong" + ] +} \ No newline at end of file diff --git a/content/docs/chatflow/meta.json b/content/docs/chatflow/meta.json new file mode 100644 index 0000000..7bdb217 --- /dev/null +++ b/content/docs/chatflow/meta.json @@ -0,0 +1,10 @@ +{ + "title": "chatflow", + "icon": "flow-chart", + "defaultOpen": true, + "pages": [ + "node", + "playground", + "xueyeyoutong" + ] +} \ No newline at end of file diff --git a/content/docs/chatflow/meta.zh.json b/content/docs/chatflow/meta.zh.json new file mode 100644 index 0000000..a29be6b --- /dev/null +++ b/content/docs/chatflow/meta.zh.json @@ -0,0 +1,10 @@ +{ + "title": "聊天流", + "icon": "flow-chart", + "defaultOpen": true, + "pages": [ + "node", + "playground", + "xueyeyoutong" + ] +} \ No newline at end of file diff --git a/content/docs/chatflow/xueyeyoutong.ja.mdx b/content/docs/chatflow/xueyeyoutong.ja.mdx new file mode 100644 index 0000000..f9e9fbf --- /dev/null +++ b/content/docs/chatflow/xueyeyoutong.ja.mdx @@ -0,0 +1,200 @@ +--- +title: "学業優通" +description: "単位、第二教養単位、GPA、総合評価、優秀学生選抜、奨学金、大学院推薦など、すべての情報を質問できます。" +enableComments: true +author: "Chase" +github_username: "ChasePassion" +--- + +⬇️ 読む前に、試してみてください + +学院の文書の機密性を考慮して、ここでは表示しません…… + + + (ドメインを持っていないなんて、言えないけどね……) + + +--- + +# チャットボットの構築 + +このチュートリアルは、主に 3 つの部分に分かれています。大規模モデル API の取得、知識ベースの構築、そしてチャットフローの構築です。 + +## 大規模モデル API + +大規模モデル API は AI アプリケーションの基盤です。API を使用することで、その API に対応するモデルを使用できます。では、モデルの API をどのように取得するのでしょうか?私たちは SiliconFlow(https://cloud.siliconflow.cn/models)を使用できます。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/1.77dstzf7np.webp) + +広場には SiliconFlow がサポートする API が表示されています。次に、左側の API キーをクリックします。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/2.64e3j3k5ul.webp) + +「新規 API キーを作成」をクリックすると API が取得できます(SiliconFlow のこの API はすべてのモデルで共通であり、具体的な課金は使用するモデルによって異なります)。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/3.7zqobpz90v.webp) + +クリックして備考を入力した後、「キーを作成」をクリックし、取得した API キーをコピーして Dify に戻ります。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/4.175mp99or7.webp) + +設定をクリックします。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/5.23244pjx31.webp) + +左側の「モデルプロバイダー」をクリックし、「モデルプロバイダーをインストール」インターフェースまでスクロールダウンし、必要なモデルプロバイダーをインストールします。ここでは SiliconFlow をインストールします。すでにインストール済みなので、ここではデモンストレーションは行いません。インストール後、**未設定**リストで「設定」をクリックし、API キーを入力すると、モデルの設定が成功します。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/6.7w72e0830a.webp) + +## 知識ベース + +これは最も重要なステップです。Q&A チャットボットにとって、その知識ベースと検索の正確性が最も重要です。 + +### 最初に「知識ベースを作成」をクリック + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/7.26lq2fe5z4.webp) + +### 次にファイルをインポート + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/8.1sfabk6act.webp) + +このステップでは、インポートするファイルは.md または.txt 形式が最適です。ここでは.md 形式を推奨します。md ドキュメントは編集しやすく、モデルが理解して変換しやすいためです。.md 形式の場合、問題は、ファイルが通常これら 2 つの形式ではない可能性があることです。PPT または PDF の場合があります。では、PPT と PDF を.md に変換するにはどうすればよいでしょうか?幸いなことに、markdown の構文を学習して自分で.md ドキュメントを作成する必要はありません。以下に公開します。 + +### 3 番目にファイルを md 形式に変換 + +まず、PDF や PPT など、さまざまな形式のファイルが必要です。次に、aistudio にアクセスします(グローバル VPN が必要です)。 + +[https://aistudio.google.com/prompts/new_chat](https://aistudio.google.com/prompts/new_chat) + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/9.3gon8qx7ku.webp) + +次に、ファイルを直接ドラッグして、プロンプトを入力します。「このファイルのコンテンツをすべて抽出し、markdown 形式に変換してください。md ドキュメントに記述する必要があります。」 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/10.23244pmum5.webp) + +次に、ファイルを直接ドラッグして、プロンプトを入力します。「このファイルのコンテンツをすべて抽出し、markdown 形式に変換してください。md ドキュメントに記述する必要があります。」 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/11.51ee87vsk3.webp) + +次に、フォルダをクリックし、md ドキュメントを作成し、モデルの出力コンテンツをコピーして貼り付けます。ここでは、markdown ドキュメントのコンテンツをある程度整理する必要がありますが、これについては後で詳しく説明します。 + +### 4 番目に知識ベース処理設定 + +直接的な md ドキュメントは、実際には検索には適していません。ドキュメントのコンテンツを分割する必要があります。ファイルをドラッグすると、次のインターフェースが表示されます。 + +#### 分割設定 + +これはテキストの分割方法です。以下では、一般的な分割と親子分割について説明します。 + +- **一般的な分割:**記事の内容を段落に分割します。各段落には Dify によって自動生成されたキーワードがいくつか含まれています。ユーザーがテキストを入力すると、モデルはキーワードを抽出し、これらのキーワードを使用して知識ベースを検索し、同じキーワードを持つブロックをモデルの応答の参考資料として返します。これが呼び出されるブロックです。 +- **親子分割:**テキストのコンテンツを親セグメントと子セグメントに分割します。親セグメントは子セグメントを含みます。検索時には子セグメントの内容が検索されますが、応答時には親セグメント全体が返されるため、モデルは完全なコンテキストを取得できます。ここでは親子分割を推奨します。 + +分割最大長と分割重複長は必要に応じて設定できます。 + +**上記の内容が理解できませんか?大丈夫です。重要なのは、使用し、実験することです。たくさん実験すれば理解できるでしょう!** + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/12.1ovodufkv7.webp) + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/13.3k896gt0r0.webp) + +**プレビューブロック:**クリックすると、この分割設定で分割されたセグメントがどのように見えるかを確認できます。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/14.70akyk30to.webp) + +#### インデックス作成方法 + +これは、モデルがファイルを検索する方法です。ここでは高品質を推奨します。キーワードの直接一致は精度が非常に低いためです。以下の Embedding モデルはテキスト処理用のモデルです。このモデルの機能はテキストを処理することであり、API で提供されているモデルを使用できます。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/15.6bhbejfzlb.webp) + +#### 検索設定 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/16.wisw418z5.webp) + +ここではハイブリッド検索を推奨します。知識ベースのタイプに応じて重み設定と Rerank モデルを設定し、Top K とスコアしきい値を設定します。ここでは Rerank モデル、Top K を 10、スコアを無効に設定しています。Rerank により、モデルが最初に表示する情報が最も関連性の高い情報であることが保証され、以下の 2 つの設定により、モデルが取得する情報が包括的で完全であることが保証されます。 + +**ここから続きです。markdown ドキュメントの処理を続行する必要があります。** + +### 5 番目に markdown ドキュメントの処理(より正確な分割のため) + +**なぜこのステップが必要なのですか?** + +ここに例があります。右側は md ドキュメント、左側は分割効果です。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/17.2a5c05cpng.webp) + +**この例から、2 つの段落を 1 つの親セグメントに含めたい場合、それらの間に改行を 1 つだけ設け、つまり隣接させ、間に空行を入れないのが最善であることがわかります。H1、H2 は影響せず、どちらも 1 つの親セグメントです。したがって、最も簡単な形式は次のとおりです。** + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/18.8ojxvqv0lc.webp) + +このようにして、モデルは赤いタイトル全体から次の赤いタイトルまでを親セグメントとし、その中の各段落を子セグメントとすることができます。 + +**ドキュメントの処理が完了すると、クリーンで完全なデータベースが完成します!** + +## チャットフロー + +**次に、チャットボットを構築します。一般的な考え方として、まずモデル選択を実装し、次に知識ベースにアクセスします。** + +まず、ホームページの「空白のアプリケーションを作成」をクリックします。名前の入力を完了すると、初期インターフェースが表示されます。ここでのチャットフローは、Dify の新しいバージョンの初期設定です! + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/19.9kgfb75v2b.webp) + +「開始」で「入力フィールドを追加」をクリックします(画像にはすでに GPGPU が追加されていますが、以下で具体的な操作方法をデモンストレーションします)。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/20.8vn5r6ioif.webp) + +クリックするとウィンドウがポップアップします。次にドロップダウンオプションを選択し、以下に示すようにパネルを設定します。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/21.32i7hvvk9o.webp) + +次にマウスを「開始」に移動し、「ノードを追加」をクリックします! + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/22.3gon8r482f.webp) + +「条件分岐」を選択します! + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/23.esr7j38vm.webp) + +条件分岐の右側のパネルを以下に示すように設定します。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/24.2a5c05g467.webp) + +次に、空白のインターフェースを右クリックし、新しい LLM ノードを作成し、以下に示すように分岐を設定します(LLM2 の後のノードは初期設定と同じです)。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/25.7pjc3hub6.webp) + +次に、「開始」と「条件分岐」の間に知識検索ノードを追加し、以下に示すようにパネルを設定します(ここではクエリ変数として sys.query、つまりユーザー入力を選択します)。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/26.1sfabkfh2g.webp) + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/27.13m0rjsc6g.webp) + +次に、モデルのコンテキストで変数値を知識検索の結果に設定します。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/28.4cl4o7g7vh.webp) + +次に、プロンプトにコンテキストを挿入します。 + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/29.5j4fwt5h4c.webp) + +最終的な効果は以下の通りです! + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/30.4ub6csie96.webp) + +これでチャットボットの設定は完了です。ぜひ試してみてください! + +--- + + + } + href="https://discord.gg/PwZDHH4mv3" + title="ワークフローを入手する" + > +

DSLファイルを入手するには、FirstLabに参加してください

+ Twitterレシート +
+
diff --git a/content/docs/chatflow/xueyeyoutong.mdx b/content/docs/chatflow/xueyeyoutong.mdx new file mode 100644 index 0000000..aedf529 --- /dev/null +++ b/content/docs/chatflow/xueyeyoutong.mdx @@ -0,0 +1,185 @@ +--- +title: "Academic Assistant" +description: "Ask anything about credits, extracurricular points, GPA, comprehensive evaluation, awards, scholarships, and postgraduate recommendations." +enableComments: true +author: "Chase" +github_username: "ChasePassion" +--- + +⬇️ Try it out before reading + +Considering the confidentiality of the college documents, they won't be +displayed here... + + + (I won't admit that I don't have a domain name...) + + +--- + +# ChatBot Setup + +This tutorial is mainly divided into three parts: obtaining a large model API, building a knowledge base, and building a chat flow. + +## Large Model API + +Large model API is the foundation of AI applications. Using API, we can use the model corresponding to this API. So how to obtain the model's API? We can use SiliconFlow (https://cloud.siliconflow.cn/models) + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/1.77dstzf7np.webp) + +The square shows the APIs supported by SiliconFlow. Below, we click on the API Key on the left. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/2.64e3j3k5ul.webp) + +Click on "Create New API Key" to get the API (this API in SiliconFlow is the same for all models, and the specific billing will depend on which model you use). + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/3.7zqobpz90v.webp) + +After clicking and entering remarks, click "Create Key", copy the obtained API key, and return to Dify. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/4.175mp99or7.webp) + +Click Settings. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/5.23244pjx31.webp) + +Click on "Model Providers" on the left, slide down to the "Install Model Provider" interface, and install the model provider you need. Here we install SiliconFlow. Since I have already installed it, I will not demonstrate it here. After installation, in the **To be configured** list, click "Settings", enter the API Key, and the model will be configured successfully. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/6.7w72e0830a.webp) + +## Knowledge Base + +This is the most important step. For a Q&A chatbot, its knowledge base and retrieval accuracy are the most crucial. + +### First Click Create Knowledge Base + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/7.26lq2fe5z4.webp) + +### Second Import File + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/8.1sfabk6act.webp) + +Regarding this step, the files we import are best in `.md` or `.txt` format. Here, `.md` format is recommended because md documents are easy to edit and easy for models to understand and convert. For `.md` format, the question is, files are usually not in these two formats, they may be PPT or PDF. So how to convert PPT and PDF to `.md`? Fortunately, you don't need to learn markdown syntax to write an `.md` document yourself. Let's reveal it below. + +### Third Convert File to md Format + +First, we need a file in various formats such as PDF or PPT. Then, visit aistudio (requires global VPN): + +[https://aistudio.google.com/prompts/new_chat](https://aistudio.google.com/prompts/new_chat) + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/9.3gon8qx7ku.webp) + +Then directly drag the file in and enter the prompt: "Extract all content from this file and convert it into markdown format. I need to write it in an md document." + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/10.23244pmum5.webp) + +Then wait for a while, and you will get a beautifully formatted and complete markdown file! + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/11.51ee87vsk3.webp) + +Next, we click on the folder, create an md document, and copy and paste the model's output content. Here we also need to organize the content in the markdown document to a certain extent, which we will not elaborate on for now. + +### Fourth Knowledge Base Processing Settings + +Direct md documents are actually not suitable for retrieval. We need to segment the document content. After dragging the file, the following interface appears: + +#### Segmentation Settings + +This is the text segmentation method. Below we will introduce general segmentation and parent-child segmentation. + +- **General Segmentation:** Divides the article content into paragraphs. Each paragraph has some keywords automatically generated by Dify. When a user inputs a piece of text, the model will extract keywords and use these keywords to retrieve the knowledge base, returning blocks with the same keywords as reference materials for the model's reply, which are the recalled blocks. +- **Parent-child Segmentation:** Divides the text content into parent and child segments. The parent segment contains child segments. When retrieving, the content of the child segment will be retrieved, but the entire parent segment will be returned, so that the model can obtain the complete context. Here I recommend parent-child segmentation. + +The maximum segment length and segment overlap length can be set as needed. + +**Can't understand the above content? It doesn't matter, the key is to use, experiment, and you will understand after more experiments!** + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/12.1ovodufkv7.webp) + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/13.3k896gt0r0.webp) + +**Preview Block:** After clicking, you can see what the segments divided by this segmentation setting look like. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/14.70akyk30to.webp) + +#### Indexing Method + +That is, the way the model retrieves files. Here, high quality is recommended, because direct keyword matching has very low accuracy. The Embedding model below is a model for text processing. The function of this model is to process text. We can use the model provided in the API. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/15.6bhbejfzlb.webp) + +#### Retrieval Settings + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/16.wisw418z5.webp) + +Here, hybrid retrieval is recommended. Set the weight settings and Rerank model according to the type of your knowledge base, and set Top K and Score threshold. Here, my setting is Rerank model, Top K is set to 10, and Score is not enabled. Rerank can ensure that the first information the model sees is the most relevant, and the following two settings can ensure that the information obtained by the model is comprehensive and complete. + +**Here we continue from the above, we need to continue processing the markdown document.** + +### Fifth Processing Markdown Document (for more accurate segmentation) + +**Why is this step needed?** + +Here is an example. The right side is an md document, and the left side is the segmentation effect. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/17.2a5c05cpng.webp) + +**From this example, we can know that if we want two paragraphs to be in one parent segment, then we'd better have only one line break between them, that is, they should be adjacent, without empty lines in between, and H1, H2 have no effect, both are one parent segment. So the simplest format is:** + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/18.8ojxvqv0lc.webp) + +In this way, the model can take the entire red title up to the next red title as the parent segment, and each paragraph within it as a child segment. + +**After processing the document, you will get a clean and complete database!** + +## Chat Flow + +**Next, we will build a chatbot. The general idea is to first implement model selection, and then access the knowledge base.** + +First, click on "Create Blank Application" on the homepage. After completing the naming input, you will come to an initial interface. The chat flow here is the initial configuration of the new version of Dify! + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/19.9kgfb75v2b.webp) + +Come to "Start" here, click "Add an input field" (already added in the picture, will demonstrate how to operate specifically below). + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/20.8vn5r6ioif.webp) + +After clicking, a window will pop up. Then we select the drop-down option and configure the panel as shown below. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/21.32i7hvvk9o.webp) + +Next, we move the mouse to "Start" and click "Add Node"! + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/22.3gon8r482f.webp) + +Select "Conditional Branch"! + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/23.esr7j38vm.webp) + +Configure the right panel of the conditional branch as shown below. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/24.2a5c05g467.webp) + +Then right-click on the blank interface, create a new LLM node, and configure the branch as follows (the node after LLM2 is the same as the initial configuration). + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/25.7pjc3hub6.webp) + +Then we add a knowledge retrieval node between "Start" and "Conditional Branch", and configure the panel as shown below (here we select sys.query for the query variable, which is user input). + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/26.1sfabkfh2g.webp) + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/27.13m0rjsc6g.webp) + +Then we set the variable value in the model's context to the result of knowledge retrieval. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/28.4cl4o7g7vh.webp) + +Next, we insert the context into the Prompt. + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/29.5j4fwt5h4c.webp) + +Final effect as follows! + +![Dify tutorial screenshot](https://ChasePassion.github.io/picx-images-hosting/Dify教程/30.4ub6csie96.webp) + +At this point, the chatbot setup is complete. Go and try it out! diff --git a/content/docs/chatflow/xueyeyoutong.zh.mdx b/content/docs/chatflow/xueyeyoutong.zh.mdx new file mode 100644 index 0000000..a9e65a8 --- /dev/null +++ b/content/docs/chatflow/xueyeyoutong.zh.mdx @@ -0,0 +1,176 @@ +--- +title: "学业优通" +description: "询问关于学分、二课分、绩点、综测、评优评先、奖学金、保研的一切信息。" +enableComments: true +author: "Chase" +github_username: "ChasePassion" +--- + +# ChatBot 搭建 + +这个教程主要分为三个部分,一个是获取大模型 API,一个是搭建知识库,一个是搭建聊天流 + +## 大模型 API + +大模型 API 是 AI 应用的基础,使用 API,我们就可以使用这个 API 对应的模型,那么如何获取模型的 API 呢?我们可以使用硅基流动(https://cloud.siliconflow.cn/models) + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/1.77dstzf7np.webp) + +广场显示了硅基流动支持的 API,下面我们点击左侧的 API 密钥 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/2.64e3j3k5ul.webp) + +点击新建 API 密钥即可得到 API(硅基流动中的这个 API 对于所有模型来说都是一样的,具体计费的时候会看你使用的是什么模型) + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/3.7zqobpz90v.webp) + +点击并输入备注之后,点击新建密钥,复制得到的 API 密钥,回到 Dify + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/4.175mp99or7.webp) + +点击设置 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/5.23244pjx31.webp) + +点击左侧的模型供应商,向下滑动到安装模型供应商界面,安装你需要的模型供应商,这里我们安装硅基流动,由于我已经安装过,所以这里不做演示,安装之后,在**待配置**列表里面,点击设置,输入 API 密钥,模型就配置成功了。 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/6.7w72e0830a.webp) + +## 知识库 + +这最重要的一步,对于一个问答式的聊天机器人来说,其知识库和检索的准确性是最重要的。 + +### First 点击创建知识库 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/7.26lq2fe5z4.webp) + +### Second 导入文件 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/8.1sfabk6act.webp) + +关于这一步,我们导入的文件最好是.md 或者.txt 格式,这里推荐.md 格式,因为 md 文档易于编辑并且易于模型理解转化。对于.md 格式,那么问题是,文件通常并不是这两个格式的,可能是 PPT 或者 PDF,那么如何把 PPT 和 PDF 转化为.md 呢?值得庆幸的是,并不需要学习 markdown 语法自己去写一个.md 文档,下面揭晓。 + +### Third 将文件转化为 md 格式 + +首先我们需要有一个 PDF 或者 PPT 等各种格式的文件,接着,访问 aistudio(需要翻墙开全局) + +[https://aistudio.google.com/prompts/new_chat](https://aistudio.google.com/prompts/new_chat) + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/9.3gon8qx7ku.webp) + +接着直接把文件拖入,输入 prompt:把这个文件的内容全部提取出来,转化为 markdown 格式,我需要写在 md 文档里面 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/10.23244pmum5.webp) + +接下来等待一会,就可以得到格式精美,信息完整的 markdown 格式的文件啦! + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/11.51ee87vsk3.webp) + +接下来我们点击文件夹,创建 md 文档,将模型的输出内容复制粘贴即可。这里我们还需要对 markdown 文档里的内容进行一定的整理,这里我们先按下不表。 + +### Fouth 知识库处理设置 + +直接的 md 文档事实上并不适合检索,我们需要对文档的内容分段,将文件拖入后,于是有下面的界面: + +#### 分段设置 + +这是文本分段方式,下面介绍一下通用分段和父子分段 + +- **通用分段:**将文章内容按照段落划分,每个段落有一些由 Dify 自动生成的关键词,当用户输入一段文本的时候,模型会提取关键词,用这些关键词去检索知识库,将关键词相同的分块返回,作为模型回复时的资料参考,也就是召回的块 +- **父子分段:**将文本的内容按照父段和字段划分,父段包含子段,当检索的时候会检索字段的内容,但是在返回的时候会返回整个父段的内容,这样模型就可以获得完整的上下文,这里我推荐父子分段。 + +分段最大长度和分段重叠长度依据需要设置即可 + +**上面的内容听不懂?没关系,关键在于使用,试验,试验多了就懂了!** + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/12.1ovodufkv7.webp) + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/13.3k896gt0r0.webp) + +**预览块:**点击之后可以看到这种分段设置分出来的段是怎么样的 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/14.70akyk30to.webp) + +#### 索引方式 + +也就是模型检索文件的方式,这里推荐高质量,因为直接拿关键词匹配准确度非常低,下面的 Embedding 模型就是对文本处理的模型,这个模型的作用是处理文本,我们使用 API 里面提供的模型即可。 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/15.6bhbejfzlb.webp) + +#### 检索设置 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/16.wisw418z5.webp) + +这里推荐混合检索,根据你的知识库的类型选择权重设置和 Rerank 模型,设置 Top K 和 Score 阈值,这里我的设置是 Rerank 模型,Top K 设置为 10,Score 不开启。Rerank 可以保证模型看到的第一个信息是最相关的,下面的两个设置可以保证模型得到的信息全面不会遗漏。 + +**这里我们接续上文,我们需要继续处理 markdown 文档** + +### Fifth 处理 markdown 文档(使分段更准确) + +**为什么需要这一步呢?** + +这里是一个示例,右边是 md 文档,左边是分段效果 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/17.2a5c05cpng.webp) + +**从这个示例我们可以知道,如果我们想要让两个段落在一个父分段里面,那么我们最好让它们之间只有一个换行符,也就是紧挨着,中间不要有空行,而 H1,H2 没有影响,都是一个父分段。于是最简单的格式就是,** + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/18.8ojxvqv0lc.webp) + +这样模型就可以把整个红色标题一直到下一个红色标题作为父段,其中的每一个段落作为子段。 + +**对文档处理完成之后,就可以得到一份干净完整的数据库啦!** + +## 聊天流 + +**接下来我们构建一个聊天机器人,大体思路是先实现模型选择,接着接入知识库** + +首先点击首页的创建空白应用,完成命名输入之后来到一个初始界面,这里的聊天流是新版 Dify 的初始化配置! + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/19.9kgfb75v2b.webp) + +来到开始这里,点击添加一个输入字段(图片中已经添加完成,下面会演示具体如何操作) + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/20.8vn5r6ioif.webp) + +点击之后会弹出一个窗口,接着我们选择下拉选项,将面板配置如下 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/21.32i7hvvk9o.webp) + +接着我们将鼠标移到开始,点击添加节点! + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/22.3gon8r482f.webp) + +选择条件分支! + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/23.esr7j38vm.webp) + +将条件分支的右侧面板配置为如下图所示 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/24.2a5c05g467.webp) + +接着右键空白界面,新建一个 LLM 节点,将分支配置如下(LLM2 后面的节点与初始配置相同) + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/25.7pjc3hub6.webp) + +接着我们在开始和条件分支之间添加一个知识检索的节点,将面板配置如下(这里我们的查询变量选择 sys.query,也就是用户输入) + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/26.1sfabkfh2g.webp) + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/27.13m0rjsc6g.webp) + +然后我们在模型的上下文中设置变量值为知识检索的 result + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/28.4cl4o7g7vh.webp) + +接着我们在 Prompt 中插入上下文 + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/29.5j4fwt5h4c.webp) + +最终的效果如下! + +![Dify 教程截图](https://ChasePassion.github.io/picx-images-hosting/Dify教程/30.4ub6csie96.webp) + +至此,聊天机器人的搭建就完成了,快试试效果吧! + +--- diff --git a/content/docs/meta.ja.json b/content/docs/meta.ja.json index 7657607..22953e3 100644 --- a/content/docs/meta.ja.json +++ b/content/docs/meta.ja.json @@ -5,6 +5,7 @@ "quickstart", "contributing", "workflow", + "chatflow", "plugin", "knowledge-base" ], diff --git a/content/docs/meta.json b/content/docs/meta.json index a3ef745..0e01741 100644 --- a/content/docs/meta.json +++ b/content/docs/meta.json @@ -5,6 +5,7 @@ "quickstart", "contributing", "workflow", + "chatflow", "plugin", "knowledge-base" ], diff --git a/content/docs/meta.zh.json b/content/docs/meta.zh.json index 7657607..22953e3 100644 --- a/content/docs/meta.zh.json +++ b/content/docs/meta.zh.json @@ -5,6 +5,7 @@ "quickstart", "contributing", "workflow", + "chatflow", "plugin", "knowledge-base" ], diff --git a/next.config.mjs b/next.config.mjs index bb2807b..d6e04d2 100644 --- a/next.config.mjs +++ b/next.config.mjs @@ -1,4 +1,4 @@ -import { createMDX } from 'fumadocs-mdx/next'; +import { createMDX } from "fumadocs-mdx/next"; const withMDX = createMDX(); @@ -7,32 +7,44 @@ const config = { reactStrictMode: true, turbopack: false, images: { - domains: ['s2.loli.net', 'hellodify.com', 'avatars.githubusercontent.com', 'twimg.com', 'pbs.twimg.com'], + domains: [ + "s2.loli.net", + "hellodify.com", + "avatars.githubusercontent.com", + "twimg.com", + "pbs.twimg.com", + ], remotePatterns: [ { - protocol: 'https', - hostname: '**.hellodify.com', - pathname: '**', + protocol: "https", + hostname: "**.hellodify.com", + pathname: "**", + }, + { + protocol: "http", + hostname: "**.hellodify.com", + pathname: "**", }, { - protocol: 'http', - hostname: '**.hellodify.com', - pathname: '**', - } + protocol: "https", + hostname: "**.github.io", + pathname: "**", + }, ], - } + }, }; config.headers = async () => [ { - source: '/(.*)', + source: "/(.*)", headers: [ { - key: 'Content-Security-Policy', - value: "frame-ancestors 'self' *.hellodify.com; frame-src 'self' *.hellodify.com http://*.hellodify.com https://*.hellodify.com https://giscus.app https://www.youtube.com https://*.youtube.com https://youtube.com https://youtu.be https://*.bilibili.com https://www.bilibili.com; child-src 'self' *.hellodify.com http://*.hellodify.com https://*.hellodify.com https://giscus.app https://www.youtube.com https://*.youtube.com https://youtube.com https://youtu.be https://*.bilibili.com https://www.bilibili.com; img-src 'self' data: *.hellodify.com http://*.hellodify.com https://*.hellodify.com s2.loli.net https://avatars.githubusercontent.com https://twimg.com https://pbs.twimg.com https://i.ytimg.com https://*.bilibili.com https://*.hdslb.com; script-src 'self' 'unsafe-inline' 'unsafe-eval' https://giscus.app https://www.googletagmanager.com https://www.google-analytics.com https://www.youtube.com https://*.youtube.com https://player.bilibili.com https://s1.hdslb.com; connect-src 'self' https://api.github.com https://www.google-analytics.com https://www.googletagmanager.com https://analytics.google.com https://*.youtube.com https://www.youtube.com https://*.bilibili.com https://www.bilibili.com;" - } - ] - } + key: "Content-Security-Policy", + value: + "frame-ancestors 'self' *.hellodify.com; frame-src 'self' *.hellodify.com http://*.hellodify.com https://*.hellodify.com https://giscus.app https://www.youtube.com https://*.youtube.com https://youtube.com https://youtu.be https://*.bilibili.com https://www.bilibili.com; child-src 'self' *.hellodify.com http://*.hellodify.com https://*.hellodify.com https://giscus.app https://www.youtube.com https://*.youtube.com https://youtube.com https://youtu.be https://*.bilibili.com https://www.bilibili.com; img-src 'self' data: *.hellodify.com http://*.hellodify.com https://*.hellodify.com s2.loli.net https://avatars.githubusercontent.com https://twimg.com https://pbs.twimg.com https://i.ytimg.com https://*.bilibili.com https://*.hdslb.com; script-src 'self' 'unsafe-inline' 'unsafe-eval' https://giscus.app https://www.googletagmanager.com https://www.google-analytics.com https://www.youtube.com https://*.youtube.com https://player.bilibili.com https://s1.hdslb.com; connect-src 'self' https://api.github.com https://www.google-analytics.com https://www.googletagmanager.com https://analytics.google.com https://*.youtube.com https://www.youtube.com https://*.bilibili.com https://www.bilibili.com;", + }, + ], + }, ]; export default withMDX(config); diff --git a/package-lock.json b/package-lock.json index 2143e51..5474442 100644 --- a/package-lock.json +++ b/package-lock.json @@ -11,11 +11,12 @@ "dependencies": { "@giscus/react": "^3.1.0", "@orama/tokenizers": "^3.1.6", - "anime": "^0.1.2", + "@tailwindcss/oxide-win32-x64-msvc": "^4.1.7", "animejs": "^4.0.2", "fumadocs-core": "15.3.0", "fumadocs-mdx": "11.6.3", "fumadocs-ui": "15.3.0", + "lightningcss-win32-x64-msvc": "^1.30.1", "lucide-react": "^0.511.0", "mermaid": "^11.6.0", "next": "15.3.1", @@ -130,6 +131,80 @@ "integrity": "sha512-YslZMgtJUyuMbZ+aKvfF3x1f5liK4mWNxghFRv7jqRR9C3R3fAOGTTKvxXDa2Y1s9zSbcpuO0cAxDYsc9SrXoQ==", "license": "Apache-2.0" }, + 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