langchain/docs/extras/integrations/chat_loaders/discord.ipynb

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    "# Discord\n",
    "\n",
    "This notebook shows how to create your own chat loader that works on copy-pasted messages (from dms) to a list of LangChain messages.\n",
    "\n",
    "The process has four steps:\n",
    "1. Create the chat .txt file by copying chats from the Discord app and pasting them in a file on your local computer\n",
    "2. Copy the chat loader definition from below to a local file.\n",
    "3. Initialize the `DiscordChatLoader` with the file path pointed to the text file.\n",
    "4. Call `loader.load()` (or `loader.lazy_load()`) to perform the conversion.\n",
    "\n",
    "## 1. Creat message dump\n",
    "\n",
    "Currently (2023/08/23) this loader only supports .txt files in the format generated by copying messages in the app to your clipboard and pasting in a file. Below is an example."
   ]
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   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting discord_chats.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile discord_chats.txt\n",
    "talkingtower — 08/15/2023 11:10 AM\n",
    "Love music! Do you like jazz?\n",
    "reporterbob — 08/15/2023 9:27 PM\n",
    "Yes! Jazz is fantastic. Ever heard this one?\n",
    "Website\n",
    "Listen to classic jazz track...\n",
    "\n",
    "talkingtower — Yesterday at 5:03 AM\n",
    "Indeed! Great choice. 🎷\n",
    "reporterbob — Yesterday at 5:23 AM\n",
    "Thanks! How about some virtual sightseeing?\n",
    "Website\n",
    "Virtual tour of famous landmarks...\n",
    "\n",
    "talkingtower — Today at 2:38 PM\n",
    "Sounds fun! Let's explore.\n",
    "reporterbob — Today at 2:56 PM\n",
    "Enjoy the tour! See you around.\n",
    "talkingtower — Today at 3:00 PM\n",
    "Thank you! Goodbye! 👋\n",
    "reporterbob — Today at 3:02 PM\n",
    "Farewell! Happy exploring."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359565a7-dad3-403c-a73c-6414b1295127",
   "metadata": {},
   "source": [
    "## 2. Define chat loader\n",
    "\n",
    "LangChain currently does not support "
   ]
  },
  {
   "cell_type": "code",
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   "id": "a429e0c4-4d7d-45f8-bbbb-c7fc5229f6af",
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   "source": [
    "import logging\n",
    "import re\n",
    "from typing import Iterator, List\n",
    "\n",
    "from langchain.schema import BaseMessage, HumanMessage\n",
    "from langchain.chat_loaders import base as chat_loaders\n",
    "\n",
    "logger = logging.getLogger()\n",
    "\n",
    "\n",
    "class DiscordChatLoader(chat_loaders.BaseChatLoader):\n",
    "    \n",
    "    def __init__(self, path: str):\n",
    "        \"\"\"\n",
    "        Initialize the Discord chat loader.\n",
    "\n",
    "        Args:\n",
    "            path: Path to the exported Discord chat text file.\n",
    "        \"\"\"\n",
    "        self.path = path\n",
    "        self._message_line_regex = re.compile(\n",
    "            r\"(.+?) — (\\w{3,9} \\d{1,2}(?:st|nd|rd|th)?(?:, \\d{4})? \\d{1,2}:\\d{2} (?:AM|PM)|Today at \\d{1,2}:\\d{2} (?:AM|PM)|Yesterday at \\d{1,2}:\\d{2} (?:AM|PM))\",  # noqa\n",
    "            flags=re.DOTALL,\n",
    "        )\n",
    "\n",
    "    def _load_single_chat_session_from_txt(\n",
    "        self, file_path: str\n",
    "    ) -> chat_loaders.ChatSession:\n",
    "        \"\"\"\n",
    "        Load a single chat session from a text file.\n",
    "\n",
    "        Args:\n",
    "            file_path: Path to the text file containing the chat messages.\n",
    "\n",
    "        Returns:\n",
    "            A `ChatSession` object containing the loaded chat messages.\n",
    "        \"\"\"\n",
    "        with open(file_path, \"r\", encoding=\"utf-8\") as file:\n",
    "            lines = file.readlines()\n",
    "\n",
    "        results: List[BaseMessage] = []\n",
    "        current_sender = None\n",
    "        current_timestamp = None\n",
    "        current_content = []\n",
    "        for line in lines:\n",
    "            if re.match(\n",
    "                r\".+? — (\\d{2}/\\d{2}/\\d{4} \\d{1,2}:\\d{2} (?:AM|PM)|Today at \\d{1,2}:\\d{2} (?:AM|PM)|Yesterday at \\d{1,2}:\\d{2} (?:AM|PM))\",  # noqa\n",
    "                line,\n",
    "            ):\n",
    "                if current_sender and current_content:\n",
    "                    results.append(\n",
    "                        HumanMessage(\n",
    "                            content=\"\".join(current_content).strip(),\n",
    "                            additional_kwargs={\n",
    "                                \"sender\": current_sender,\n",
    "                                \"events\": [{\"message_time\": current_timestamp}],\n",
    "                            },\n",
    "                        )\n",
    "                    )\n",
    "                current_sender, current_timestamp = line.split(\" — \")[:2]\n",
    "                current_content = [\n",
    "                    line[len(current_sender) + len(current_timestamp) + 4 :].strip()\n",
    "                ]\n",
    "            elif re.match(r\"\\[\\d{1,2}:\\d{2} (?:AM|PM)\\]\", line.strip()):\n",
    "                results.append(\n",
    "                    HumanMessage(\n",
    "                        content=\"\".join(current_content).strip(),\n",
    "                        additional_kwargs={\n",
    "                            \"sender\": current_sender,\n",
    "                            \"events\": [{\"message_time\": current_timestamp}],\n",
    "                        },\n",
    "                    )\n",
    "                )\n",
    "                current_timestamp = line.strip()[1:-1]\n",
    "                current_content = []\n",
    "            else:\n",
    "                current_content.append(\"\\n\" + line.strip())\n",
    "\n",
    "        if current_sender and current_content:\n",
    "            results.append(\n",
    "                HumanMessage(\n",
    "                    content=\"\".join(current_content).strip(),\n",
    "                    additional_kwargs={\n",
    "                        \"sender\": current_sender,\n",
    "                        \"events\": [{\"message_time\": current_timestamp}],\n",
    "                    },\n",
    "                )\n",
    "            )\n",
    "\n",
    "        return chat_loaders.ChatSession(messages=results)\n",
    "\n",
    "    def lazy_load(self) -> Iterator[chat_loaders.ChatSession]:\n",
    "        \"\"\"\n",
    "        Lazy load the messages from the chat file and yield them in the required format.\n",
    "\n",
    "        Yields:\n",
    "            A `ChatSession` object containing the loaded chat messages.\n",
    "        \"\"\"\n",
    "        yield self._load_single_chat_session_from_txt(self.path)\n"
   ]
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   "cell_type": "markdown",
   "id": "c8240393-48be-44d2-b0d6-52c215cd8ac2",
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   "source": [
    "## 2. Create loader\n",
    "\n",
    "We will point to the file we just wrote to disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1268de40-b0e5-445d-9cd8-54856cd0293a",
   "metadata": {},
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   "source": [
    "loader = DiscordChatLoader(\n",
    "    path=\"./discord_chats.txt\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4928df4b-ae31-48a7-bd76-be3ecee1f3e0",
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   "source": [
    "## 3. Load Messages\n",
    "\n",
    "Assuming the format is correct, the loader will convert the chats to langchain messages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8a0836d-4a22-4790-bfe9-97f2145bb0d6",
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   "source": [
    "from typing import List\n",
    "from langchain.chat_loaders.base import ChatSession\n",
    "from langchain.chat_loaders.utils import (\n",
    "    map_ai_messages,\n",
    "    merge_chat_runs,\n",
    ")\n",
    "\n",
    "raw_messages = loader.lazy_load()\n",
    "# Merge consecutive messages from the same sender into a single message\n",
    "merged_messages = merge_chat_runs(raw_messages)\n",
    "# Convert messages from \"talkingtower\" to AI messages\n",
    "messages: List[ChatSession] = list(map_ai_messages(merged_messages, sender=\"talkingtower\"))"
   ]
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   "cell_type": "code",
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   "id": "1913963b-c44e-4f7a-aba7-0423c9b8bd59",
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     "data": {
      "text/plain": [
       "[{'messages': [AIMessage(content='Love music! Do you like jazz?', additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': '08/15/2023 11:10 AM\\n'}]}, example=False),\n",
       "   HumanMessage(content='Yes! Jazz is fantastic. Ever heard this one?\\nWebsite\\nListen to classic jazz track...', additional_kwargs={'sender': 'reporterbob', 'events': [{'message_time': '08/15/2023 9:27 PM\\n'}]}, example=False),\n",
       "   AIMessage(content='Indeed! Great choice. 🎷', additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': 'Yesterday at 5:03 AM\\n'}]}, example=False),\n",
       "   HumanMessage(content='Thanks! How about some virtual sightseeing?\\nWebsite\\nVirtual tour of famous landmarks...', additional_kwargs={'sender': 'reporterbob', 'events': [{'message_time': 'Yesterday at 5:23 AM\\n'}]}, example=False),\n",
       "   AIMessage(content=\"Sounds fun! Let's explore.\", additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': 'Today at 2:38 PM\\n'}]}, example=False),\n",
       "   HumanMessage(content='Enjoy the tour! See you around.', additional_kwargs={'sender': 'reporterbob', 'events': [{'message_time': 'Today at 2:56 PM\\n'}]}, example=False),\n",
       "   AIMessage(content='Thank you! Goodbye! 👋', additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': 'Today at 3:00 PM\\n'}]}, example=False),\n",
       "   HumanMessage(content='Farewell! Happy exploring.', additional_kwargs={'sender': 'reporterbob', 'events': [{'message_time': 'Today at 3:02 PM\\n'}]}, example=False)]}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8595a518-5c89-44aa-94a7-ca51e7e2a5fa",
   "metadata": {},
   "source": [
    "### Next Steps\n",
    "\n",
    "You can then use these messages how you see fit, such as finetuning a model, few-shot example selection, or directly make predictions for the next message  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08ff0a1e-fca0-4da3-aacd-d7401f99d946",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thank you! Have a wonderful day! 🌟"
     ]
    }
   ],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "\n",
    "llm = ChatOpenAI()\n",
    "\n",
    "for chunk in llm.stream(messages[0]['messages']):\n",
    "    print(chunk.content, end=\"\", flush=True)"
   ]
  },
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