## SEC filing

> [SEC filing](https://www.sec.gov/edgar) 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.
> 
> `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).

## Setup[​](#setup "Direct link to Setup")

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`.

In this example, we’re going to use the `KayAiRetriever`. Take a look at the [kay notebook](https://python.langchain.com/docs/integrations/retrievers/kay/) for more detailed information for the parameters that it accepts.\`

```
# Setup API keys for Kay and OpenAIfrom getpass import getpassKAY_API_KEY = getpass()OPENAI_API_KEY = getpass()
```

```
import osos.environ["KAY_API_KEY"] = KAY_API_KEYos.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
```

## Example[​](#example "Direct link to Example")

```
from langchain.chains import ConversationalRetrievalChainfrom langchain_community.retrievers import KayAiRetrieverfrom langchain_openai import ChatOpenAImodel = ChatOpenAI(model="gpt-3.5-turbo")retriever = KayAiRetriever.create(    dataset_id="company", data_types=["10-K", "10-Q"], num_contexts=6)qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)
```

```
questions = [    "What are patterns in Nvidia's spend over the past three quarters?",    # "What are some recent challenges faced by the renewable energy sector?",]chat_history = []for question in questions:    result = qa({"question": question, "chat_history": chat_history})    chat_history.append((question, result["answer"]))    print(f"-> **Question**: {question} \n")    print(f"**Answer**: {result['answer']} \n")
```

```
-> **Question**: What are patterns in Nvidia's spend over the past three quarters? **Answer**: Based on the provided information, here are the patterns in NVIDIA's spend over the past three quarters:1. Research and Development Expenses:   - Q3 2022: Increased by 34% compared to Q3 2021.   - Q1 2023: Increased by 40% compared to Q1 2022.   - Q2 2022: Increased by 25% compared to Q2 2021.      Overall, research and development expenses have been consistently increasing over the past three quarters.2. Sales, General and Administrative Expenses:   - Q3 2022: Increased by 8% compared to Q3 2021.   - Q1 2023: Increased by 14% compared to Q1 2022.   - Q2 2022: Decreased by 16% compared to Q2 2021.      The pattern for sales, general and administrative expenses is not as consistent, with some quarters showing an increase and others showing a decrease.3. Total Operating Expenses:   - Q3 2022: Increased by 25% compared to Q3 2021.   - Q1 2023: Increased by 113% compared to Q1 2022.   - Q2 2022: Increased by 9% compared to Q2 2021.      Total operating expenses have generally been increasing over the past three quarters, with a significant increase in Q1 2023.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. 
```