vector-store.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { load } from \"dotenv\";\n",
    "const env = await load();\n",
    "\n",
    "const process = {\n",
    "    env\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { TextLoader } from \"langchain/document_loaders/fs/text\";\n",
    "import { RecursiveCharacterTextSplitter } from \"langchain/text_splitter\";\n",
    "const loader = new TextLoader(\"data/kong.txt\");\n",
    "const docs = await loader.load();\n",
    "\n",
    "const splitter = new RecursiveCharacterTextSplitter({\n",
    "    chunkSize: 100,\n",
    "    chunkOverlap: 20,\n",
    "  });\n",
    "\n",
    "const splitDocs = await splitter.splitDocuments(docs);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "splitDocs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "console.log(splitDocs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { OpenAIEmbeddings } from \"@langchain/openai\";\n",
    "const embeddings = new OpenAIEmbeddings()\n",
    "\n",
    "const res = await embeddings.embedQuery(splitDocs[0].pageContent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { MemoryVectorStore } from \"langchain/vectorstores/memory\";\n",
    "\n",
    "const vectorstore = new MemoryVectorStore(embeddings);\n",
    "await vectorstore.addDocuments(splitDocs);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "const retriever = vectorstore.asRetriever(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "const res = await retriever.invoke(\"茴香豆是做什么用的\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "const res = await retriever.invoke(\"下酒菜一般是什么？\")\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "const res = await retriever.invoke(\"孔乙己用什么谋生？\")\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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