langchain/docs/docs/integrations/retrievers/sec_filings.ipynb

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    "# SEC filing\n",
    "\n",
    "\n",
    ">The SEC filing is a financial statement or other formal document submitted to the U.S. Securities and Exchange Commission (SEC). Public companies, certain insiders, and broker-dealers are required to make regular SEC filings. Investors and financial professionals rely on these filings for information about companies they are evaluating for investment purposes.\n",
    ">\n",
    ">SEC filings data powered by [Kay.ai](https://kay.ai) and [Cybersyn](https://www.cybersyn.com/) via [Snowflake Marketplace](https://app.snowflake.com/marketplace/providers/GZTSZAS2KCS/Cybersyn%2C%20Inc).\n"
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    "## Setup\n",
    "\n",
    "\n",
    "First, you will need to install the `kay` package. You will also need an API key: you can get one for free at [https://kay.ai](https://kay.ai/). Once you have an API key, you must set it as an environment variable `KAY_API_KEY`.\n",
    "\n",
    "In this example, we're going to use the `KayAiRetriever`. Take a look at the [kay notebook](/docs/integrations/retrievers/kay) for more detailed information for the parameters that it accepts.`"
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      " ········\n",
      " ········\n"
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    "# Setup API keys for Kay and OpenAI\n",
    "from getpass import getpass\n",
    "\n",
    "KAY_API_KEY = getpass()\n",
    "OPENAI_API_KEY = getpass()"
   ]
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   "source": [
    "import os\n",
    "\n",
    "os.environ[\"KAY_API_KEY\"] = KAY_API_KEY\n",
    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
   ]
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   "source": [
    "## Example"
   ]
  },
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   "source": [
    "from langchain.chains import ConversationalRetrievalChain\n",
    "from langchain_community.retrievers import KayAiRetriever\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "model = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
    "retriever = KayAiRetriever.create(\n",
    "    dataset_id=\"company\", data_types=[\"10-K\", \"10-Q\"], num_contexts=6\n",
    ")\n",
    "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
   ]
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      "-> **Question**: What are patterns in Nvidia's spend over the past three quarters? \n",
      "\n",
      "**Answer**: Based on the provided information, here are the patterns in NVIDIA's spend over the past three quarters:\n",
      "\n",
      "1. Research and Development Expenses:\n",
      "   - Q3 2022: Increased by 34% compared to Q3 2021.\n",
      "   - Q1 2023: Increased by 40% compared to Q1 2022.\n",
      "   - Q2 2022: Increased by 25% compared to Q2 2021.\n",
      "   \n",
      "   Overall, research and development expenses have been consistently increasing over the past three quarters.\n",
      "\n",
      "2. Sales, General and Administrative Expenses:\n",
      "   - Q3 2022: Increased by 8% compared to Q3 2021.\n",
      "   - Q1 2023: Increased by 14% compared to Q1 2022.\n",
      "   - Q2 2022: Decreased by 16% compared to Q2 2021.\n",
      "   \n",
      "   The pattern for sales, general and administrative expenses is not as consistent, with some quarters showing an increase and others showing a decrease.\n",
      "\n",
      "3. Total Operating Expenses:\n",
      "   - Q3 2022: Increased by 25% compared to Q3 2021.\n",
      "   - Q1 2023: Increased by 113% compared to Q1 2022.\n",
      "   - Q2 2022: Increased by 9% compared to Q2 2021.\n",
      "   \n",
      "   Total operating expenses have generally been increasing over the past three quarters, with a significant increase in Q1 2023.\n",
      "\n",
      "Overall, the pattern indicates a consistent increase in research and development expenses and total operating expenses, while sales, general and administrative expenses show some fluctuations. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "questions = [\n",
    "    \"What are patterns in Nvidia's spend over the past three quarters?\",\n",
    "    # \"What are some recent challenges faced by the renewable energy sector?\",\n",
    "]\n",
    "chat_history = []\n",
    "\n",
    "for question in questions:\n",
    "    result = qa({\"question\": question, \"chat_history\": chat_history})\n",
    "    chat_history.append((question, result[\"answer\"]))\n",
    "    print(f\"-> **Question**: {question} \\n\")\n",
    "    print(f\"**Answer**: {result['answer']} \\n\")"
   ]
  }
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