langchain/docs/modules/chains/combine_docs_examples/chat_vector_db.ipynb

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  {
   "cell_type": "markdown",
   "id": "134a0785",
   "metadata": {},
   "source": [
    "# Chat Vector DB\n",
    "\n",
    "This notebook goes over how to set up a chain to chat with a vector database. The only difference because this chain and the [VectorDBQAChain](./vector_db_qa.ipynb) is that this allows for passing in of a chat history which can be used to allow for follow up questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70c4e529",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.vectorstores.faiss import FAISS\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.chains import ChatVectorDBChain\n",
    "from langchain.document_loaders import TextLoader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdff94be",
   "metadata": {},
   "source": [
    "Load in documents. You can replace this with a loader for whatever type of data you want"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "01c46e92",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = TextLoader('../../state_of_the_union.txt')\n",
    "documents = loader.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9be4779",
   "metadata": {},
   "source": [
    "If you had multiple loaders that you wanted to combine, you do something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "433363a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loaders = [....]\n",
    "# docs = []\n",
    "# for loader in loaders:\n",
    "#     docs.extend(loader.load())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239475d2",
   "metadata": {},
   "source": [
    "We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a8930cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "documents = text_splitter.split_documents(documents)\n",
    "\n",
    "embeddings = OpenAIEmbeddings()\n",
    "vectorstore = FAISS.from_documents(documents, embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c96b118",
   "metadata": {},
   "source": [
    "We now initialize the ChatVectorDBChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b4110f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0), vectorstore)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3872432d",
   "metadata": {},
   "source": [
    "Here's an example of asking a question with no chat history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7fe3e730",
   "metadata": {},
   "outputs": [],
   "source": [
    "chat_history = []\n",
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "result = qa({\"question\": query, \"chat_history\": chat_history})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bfff9cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[\"answer\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e46edf7",
   "metadata": {},
   "source": [
    "Here's an example of asking a question with some chat history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00b4cf00",
   "metadata": {},
   "outputs": [],
   "source": [
    "chat_history = [(query, result[\"answer\"])]\n",
    "query = \"Did he mention who she suceeded\"\n",
    "result = qa({\"question\": query, \"chat_history\": chat_history})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f01828d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Justice Stephen Breyer'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['answer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0f869c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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