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   "source": [
    "# TextGen\n",
    "\n",
    "[GitHub:oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.\n",
    "\n",
    "This example goes over how to use LangChain to interact with LLM models via the `text-generation-webui` API integration.\n",
    "\n",
    "Please ensure that you have `text-generation-webui` configured and an LLM installed.  Recommended installation via the [one-click installer appropriate](https://github.com/oobabooga/text-generation-webui#one-click-installers) for your OS.\n",
    "\n",
    "Once `text-generation-webui` is installed and confirmed working via the web interface, please enable the `api` option either through the web model configuration tab, or by adding the run-time arg `--api` to your start command."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set model_url and run the example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_url = \"http://localhost:5000\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import langchain\n",
    "from langchain import PromptTemplate, LLMChain\n",
    "from langchain.llms import TextGen\n",
    "\n",
    "langchain.debug = True\n",
    "\n",
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
    "llm = TextGen(model_url=model_url)\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
    "\n",
    "llm_chain.run(question)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streaming Version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should install websocket-client to use this feature.\n",
    "`pip install websocket-client`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_url = \"ws://localhost:5005\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import langchain\n",
    "from langchain import PromptTemplate, LLMChain\n",
    "from langchain.llms import TextGen\n",
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "\n",
    "langchain.debug = True\n",
    "\n",
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
    "llm = TextGen(model_url=model_url, streaming=True, callbacks=[StreamingStdOutCallbackHandler()])\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
    "\n",
    "llm_chain.run(question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = TextGen(\n",
    "    model_url = model_url,\n",
    "    streaming=True\n",
    ")\n",
    "for chunk in llm.stream(\"Ask 'Hi, how are you?' like a pirate:'\",\n",
    "        stop=[\"'\",\"\\n\"]):\n",
    "    print(chunk, end='', flush=True)"
   ]
  }
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