wechaty with langchain

jekyll/_contributors/bestk.md

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+---
+name: bestk
+site: https://github.com/bestk
+avatar: /assets/contributors/bestk/avatar.webp
+bio: java/node/python/golang
+twitter: 
+---
+
+普适程序员
+
+## Contact me
+
+- Github: <https://github.com/bestk>

jekyll/_posts/2023-07-07-wechaty-chat-with-langchain.md

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+---
+title: "使用 wechaty langchain 部署私有 chatgpt"
+author: bestk
+categories: article
+tags:
+  - chatgpt
+  - langchain
+image: /assets/2023/07-wechaty-chat-with-langchain/logo.webp
+---
+
+
+WeChaty 是一个基于 Node.js 的开源微信机器人框架，而 LangChain 是一个用于部署私有化 GPT 模型的工具。通过结合 WeChaty 和 LangChain，你可以创建一个私有化的 GPT 机器人，使其在微信平台上运行。
+
+Setup：
+
+我们使用 `wechaty-puppet-wechat4u`
+
+```Text
+package.json:
+"wechaty": "^1.20.2",
+"wechaty-puppet-wechat4u": "1.14.1"
+"langchain": "^0.0.102",
+"@pinecone-database/pinecone": "^0.1.6",
+"pdf-parse": "^1.1.1", // 篇幅原因这里只演示 pdf
+```
+
+```javascript
+import { WechatyBuilder} from 'wechaty'
+
+const wechaty = WechatyBuilder.build({
+  name: 'wechaty-chatgpt',
+  puppet: 'wechaty-puppet-wechat4u',
+  puppetOptions: {
+    uos: true,
+  },
+});
+
+```
+
+设置 pinecone ,openai
+
+```bash
+PROMPTLAYER_API_KEY=pl_... # PROMPTLAYER 是一个用于记录 api 调用时 prompt 与 response 的工具
+PINECONE_API_KEY=89e...
+PINECONE_ENVIRONMENT=us-west4-gcp-free
+PINECONE_INDEX=...
+```
+
+以下代码为当接收到支持的文件对文件进行向量化成功后返回提示
+
+  ```javascript
+   wechaty.on('message', async message => {
+    const contact = message.talker();
+    currentAdminUser = contact.payload.alias === process.env.ADMIN
+    const receiver = message.listener();
+    let content = message.text().trim();
+    const room = message.room();
+    const target = room || contact;
+    const isText = message.type() === wechaty.Message.Type.Text;
+    const isAudio = message.type() === wechaty.Message.Type.Audio;
+    const isFile = message.type() === wechaty.Message.Type.Attachment;
+
+    if (isFile) {
+      const filebox = await message.toFileBox()
+      if (supportFileType(filebox.mediaType)) {
+        await saveFile(filebox)
+        await loadDocuments()
+        await send(room || contact, `${filebox.name} Embeddings 成功`)
+        return
+      }
+    }
+   })  
+  ```
+
+![image1.webp](/assets/2023/07-wechaty-chat-with-langchain/image1.webp)
+
+langchain 相关代码
+
+  ```javascript
+  import { PineconeClient } from "@pinecone-database/pinecone";
+  import dotenv from 'dotenv';
+  import { VectorDBQAChain } from "langchain/chains";
+  import { DirectoryLoader } from "langchain/document_loaders";
+  import { DocxLoader } from "langchain/document_loaders/fs/docx";
+  import { PDFLoader } from "langchain/document_loaders/fs/pdf";
+  import { TextLoader } from "langchain/document_loaders/fs/text";
+  import { OpenAIEmbeddings } from "langchain/embeddings/openai";
+  import { PromptLayerOpenAI } from "langchain/llms/openai";
+  import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
+  import { PineconeStore } from "langchain/vectorstores";
+
+  dotenv.config();
+  const client = new PineconeClient();
+
+  await client.init({
+      apiKey: process.env.PINECONE_API_KEY,
+      environment: process.env.PINECONE_ENVIRONMENT,
+  });
+
+  const pineconeIndex = client.Index(process.env.PINECONE_INDEX);
+
+
+  async function loadDocuments(directory = 'resource') {
+      console.log('loadDocuments...')
+      const loader = new DirectoryLoader(directory,
+          {
+              ".pdf": (path) => new PDFLoader(path),
+              ".txt": (path) => new TextLoader(path),
+              ".doc": (path) => new DocxLoader(path),
+              ".docx": (path) => new DocxLoader(path),
+          });
+      // 将数据转成 document 对象，每个文件会作为一个 document
+      const rawDocuments = await loader.load();
+      console.log(`documents: ${rawDocuments.length}`);
+
+      // 初始化加载器
+      const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 500 });
+      // 切割加载的 document
+      const splitDocs = await textSplitter.splitDocuments(rawDocuments);
+
+      // 持久化数据
+      // const docsearch = await Chroma.fromDocuments(splitDocs, embeddings, { collectionName: "private_doc" });
+      // docsearch.persist();
+
+
+      await PineconeStore.fromDocuments(splitDocs, new OpenAIEmbeddings(), {
+          pineconeIndex,
+      });
+      console.log(`send to PineconeStore`);
+
+  }
+
+
+  async function askDocument(question) {
+      const llm = new PromptLayerOpenAI({ plTags: ["langchain-requests", "chatbot"] })
+      // 初始化 openai 的 embeddings 对象
+
+      // 加载数据
+      const vectorStore = await PineconeStore.fromExistingIndex(
+          new OpenAIEmbeddings(),
+          { pineconeIndex }
+      );
+
+      /* Search the vector DB independently with meta filters */
+      const chain = VectorDBQAChain.fromLLM(llm, vectorStore, {
+          k: 1,
+          returnSourceDocuments: true,
+      });
+      const response = await chain.call({ query: question });
+      console.log(response);
+
+      // const response = await vectorStore.similaritySearch(question, 1);
+      // console.log(response);
+
+      return response.text
+  }
+
+  function supportFileType(mediaType) {
+      const types = ['doc', 'docx', , 'pdf', 'text']
+      return types.filter(e => mediaType.includes(e)).length > 0
+  }
+
+
+  export { askDocument, loadDocuments, supportFileType };
+  ```

package.json

@@ -1,6 +1,6 @@
 {
   "name": "wechaty-jekyll",
-  "version": "0.11.47",
+  "version": "0.11.49",
   "description": "Wechaty Official Website for News, Blogs, Contributor Profiles",
   "private": true,
   "type": "module",