langchain_templates_and_output_parsers.ipynb

{
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   "source": [
    "## Output Parsers\n",
    "\n",
    "This notebook presents using LangChain prompt templates and output parsers. \n",
    "Prompt templates help to structurize prompt and better instruct LLM to deal with specific tasks.\n",
    "Output parsers are used to ensure type of return values and formatting is always as expected."
   ],
   "id": "2cd9b0b0ec9f1368"
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     "start_time": "2024-12-08T10:00:55.843988Z"
    }
   },
   "source": "!pip install langchain",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
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     "end_time": "2024-12-08T10:01:01.291714Z",
     "start_time": "2024-12-08T10:01:01.252913Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from langchain.chains import LLMChain\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "\n",
    "load_dotenv(find_dotenv())"
   ],
   "id": "79dfa71929a19529",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-08T10:01:03.737913Z",
     "start_time": "2024-12-08T10:01:02.803265Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "llm = ChatOpenAI(model_name=\"gpt-4o\")\n",
    "template = \"\"\"\n",
    "Specify tags and the main topic of the text.\n",
    "tags: What are best tags describing text. Give maximum 5 tags separated by comma.\n",
    "topic: What is the topic of text. Use maximum couple of words\n",
    "\n",
    "Format response as JSON as below:\n",
    "'tags': ['sometag', 'othertag', 'anothertag', 'tag4', 'tag5']\n",
    "'subject': 'Some subject of text'\n",
    "\n",
    "text: {input}\n",
    "\"\"\"\n",
    "\n",
    "prompt_template = ChatPromptTemplate.from_template(template=template)\n",
    "chain = LLMChain(llm=llm, prompt=prompt_template)\n",
    "chain.predict(input=\"They picked a way among the trees, and their ponies plodded along, carefully avoiding the many writhing and interlacing roots.  There was no undergrowth.  The ground was rising steadily, and as they went forward it seemed that the trees became taller, darker, and thicker. There was no sound, except an occasional drip of moisture falling through the still leaves.  For the moment there was no whispering or movement among the branches; but they all got an uncomfortable feeling that they were being watched with disapproval, deepening to dislike and even enmity.  The feeling steadily grew, until they found themselves looking up quickly, or glancing back over their shoulders, as if they expected a sudden blow.\")"
   ],
   "id": "2d6d486fb07a728",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_65142/978405457.py:18: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 1.0. Use :meth:`~RunnableSequence, e.g., `prompt | llm`` instead.\n",
      "  chain = LLMChain(llm=llm, prompt=prompt_template)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'```json\\n{\\n  \"tags\": [\"forest\", \"atmosphere\", \"tension\", \"nature\", \"mystery\"],\\n  \"subject\": \"Forest journey\"\\n}\\n```'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T18:46:10.376475Z",
     "start_time": "2024-12-07T18:46:10.374117Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "68496e933b5f0097",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## ResponseSchema and OutputParsers ",
   "id": "42b743730bc5aa70"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T18:46:58.579354Z",
     "start_time": "2024-12-07T18:46:57.845937Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from langchain.output_parsers import ResponseSchema\n",
    "from langchain.output_parsers import StructuredOutputParser\n",
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "tags_schema = ResponseSchema(\n",
    "    name=\"tags\",\n",
    "    description=\" What are best tags describing text. Give maximum 5 tags separated by comma.\",\n",
    ")\n",
    "topic_schema = ResponseSchema(\n",
    "    name=\"topic\", \n",
    "    description=\"What is the topic of text. Use maximum couple of words.\"\n",
    ")\n",
    "\n",
    "response_schemas = [tags_schema, topic_schema]\n",
    "parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
    "template = \"\"\"\n",
    "Specify tags and the main topic of the text.\n",
    "tags: What are best tags describing text. Give maximum 5 tags separated by comma.\n",
    "topic: What is the topic of text. Use maximum couple of words\n",
    "\n",
    "Format response as JSON as below:\n",
    "'tags': ['sometag', 'othertag', 'anothertag', 'tag4', 'tag5']\n",
    "'subject': 'Some subject of text'\n",
    "\n",
    "text: {input}\n",
    "\n",
    "{instructions}\n",
    "\"\"\"\n",
    "\n",
    "prompt = ChatPromptTemplate.from_template(template=template)\n",
    "instructions = parser.get_format_instructions()\n",
    "\n",
    "messages = prompt.format_messages(\n",
    "    input=\"They picked a way among the trees, and their ponies plodded along, carefully avoiding the many writhing and interlacing roots.  There was no undergrowth.  The ground was rising steadily, and as they went forward it seemed that the trees became taller, darker, and thicker. There was no sound, except an occasional drip of moisture falling through the still leaves.  For the moment there was no whispering or movement among the branches; but they all got an uncomfortable feeling that they were being watched with disapproval, deepening to dislike and even enmity.  The feeling steadily grew, until they found themselves looking up quickly, or glancing back over their shoulders, as if they expected a sudden blow.\",\n",
    "    instructions=instructions,\n",
    ")\n",
    "\n",
    "chat = ChatOpenAI(temperature=0.0)\n",
    "response = chat(messages)\n",
    "print(response)\n",
    "output_dict = parser.parse(response.content)\n",
    "print(output_dict)"
   ],
   "id": "1c0916b61b798cb6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "content='```json\\n{\\n\\t\"tags\": [\"fantasy, adventure, suspense, nature, mystery\"],\\n\\t\"topic\": \"Journey through the forest\"\\n}\\n```' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 33, 'prompt_tokens': 325, 'total_tokens': 358, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-5fb4094a-4bfc-4fac-98bb-aab14359a904-0' usage_metadata={'input_tokens': 325, 'output_tokens': 33, 'total_tokens': 358, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}\n",
      "{'tags': ['fantasy, adventure, suspense, nature, mystery'], 'topic': 'Journey through the forest'}\n"
     ]
    }
   ],
   "execution_count": 6
  }
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