langchain/docs/extras/modules/agents/agent_types/openai_functions_agent.ipynb

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    "# OpenAI Functions Agent\n",
    "\n",
    "This notebook showcases using an agent that uses the OpenAI functions ability to respond to the prompts of the user using a Large Language Model\n",
    "\n",
    "Install openai,google-search-results packages which are required as the langchain packages call them internally\n",
    "\n",
    ">pip install openai google-search-results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c0a83623",
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   "source": [
    "from langchain import (\n",
    "    LLMMathChain,\n",
    "    OpenAI,\n",
    "    SerpAPIWrapper,\n",
    "    SQLDatabase,\n",
    "    SQLDatabaseChain,\n",
    ")\n",
    "from langchain.agents import initialize_agent, Tool\n",
    "from langchain.agents import AgentType\n",
    "from langchain.chat_models import ChatOpenAI"
   ]
  },
  {
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    "The agent is given ability to perform 3 search functionalities with the respective tools\n",
    "\n",
    "SerpAPIWrapper:\n",
    ">This initializes the SerpAPIWrapper for search functionality (search).\n",
    "\n",
    "LLMMathChain Initialization:\n",
    ">This component provides math-related functionality.\n",
    "\n",
    "SQL Database Initialization:\n",
    ">This component provides the agent to query in Custom Data Base.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6fefaba2",
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   "source": [
    "# Initialize the OpenAI language model\n",
    "#Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual OpenAI key.\n",
    "llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\",openai_api_key=\"<your_api_key>\")\n",
    "\n",
    "# Initialize the SerpAPIWrapper for search functionality\n",
    "#Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual SerpAPI key.\n",
    "search = SerpAPIWrapper(serpapi_api_key=\"<your_api_key>\")\n",
    "\n",
    "# Initialize the LLMMathChain\n",
    "llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)\n",
    "\n",
    "# Initialize the SQL database using the Chinook database file\n",
    "# Replace the file location to the custom Data Base\n",
    "db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")\n",
    "\n",
    "# Initialize the SQLDatabaseChain with the OpenAI language model and SQL database\n",
    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)\n",
    "\n",
    "# Define a list of tools offered by the agent\n",
    "tools = [\n",
    "    Tool(\n",
    "        name=\"Search\",\n",
    "        func=search.run,\n",

    "        description=\"Useful when you need to answer questions about current events. You should ask targeted questions.\"\n",
    "    ),\n",
    "    Tool(\n",
    "        name=\"Calculator\",\n",
    "        func=llm_math_chain.run,\n",
    "        description=\"Useful when you need to answer questions about math.\"\n",
    "    ),\n",
    "    Tool(\n",
    "        name=\"FooBar-DB\",\n",
    "        func=db_chain.run,\n",
    "        description=\"Useful when you need to answer questions about FooBar. Input should be in the form of a question containing full context.\"\n",
    "    )\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ff6cee9",
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   "source": [
    "mrkl = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba8e4cbe",
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     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new  chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `Search` with `{'query': 'Leo DiCaprio girlfriend'}`\n",
      "\n",
      "\n",
      "\u001b[0m\u001b[36;1m\u001b[1;3mAmidst his casual romance with Gigi, Leo allegedly entered a relationship with 19-year old model, Eden Polani, in February 2023.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `Calculator` with `{'expression': '19^0.43'}`\n",
      "\n",
      "\n",
      "\u001b[0m\n",
      "\n",
      "\u001b[1m> Entering new  chain...\u001b[0m\n",
      "19^0.43\u001b[32;1m\u001b[1;3m```text\n",
      "19**0.43\n",
      "```\n",
      "...numexpr.evaluate(\"19**0.43\")...\n",
      "\u001b[0m\n",
      "Answer: \u001b[33;1m\u001b[1;3m3.547023357958959\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\u001b[33;1m\u001b[1;3mAnswer: 3.547023357958959\u001b[0m\u001b[32;1m\u001b[1;3mLeo DiCaprio's girlfriend is reportedly Eden Polani. Her current age raised to the power of 0.43 is approximately 3.55.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"Leo DiCaprio's girlfriend is reportedly Eden Polani. Her current age raised to the power of 0.43 is approximately 3.55.\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrkl.run(\n",
    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f5f6743",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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