Introduction
Welcome to the 'Build ChatGPT to Answer Questions on Your Financial Data' lesson. We'll explore LangChain's benefits for large language models (LLMs) and its use in interpreting financial data. LangChain simplifies development, streamlines text generation from input prompts, and improves accuracy by ensuring consistency.
One of the key features of LangChain is its customizability, offering pre-built prompts and chains for use cases like question answering and summarization. Plus, LangChain is interoperable, integrating easily with tools like Deep Lake, which makes it ideal for handling complex applications.
We'll demonstrate this using Amazon's quarterly financial reports in this lesson. We'll embed the data using OpenAI's API, store it in Deep Lake, and explore it by asking questions through LangChain. This hands-on approach will illustrate how these tools can revolutionize the interpretation of financial data. Let's get started!
The Workflow
The workflow to explore Amazon's revenue growth using LangChain and Deep Lake involves:
- Install required libraries and set API tokens for OpenAI and Activeloop.
- Download Amazon's financial reports, and split them into smaller chunks using LangChain's Text Splitter utility.
- Create a DeepLake vector store, add the text chunks, and use OpenAIChat as the language model.
- Finally, query the model with questions to obtain relevant insights.
In the next lesson, we’ll build a chatbot that allows users to interact with data by asking questions.