RAG Application for 10-Q Filing Reviews
Built using the following specs:
- Data: AirBnB Securities Commision pdf - Form 10-Q filing for Q1 of 2024
- LLM: Llama3-70B running on Groq
- Embedding model: OpenAI text-embedding-3-small
- Infrastructure / Framework: LangChaing
- Vector Store: Qdrant
- UI: Chainlit
- Deployment: Docker on HuggingFace Spaces
RAG Data Ingestion
- Additional details on the Data Pipeline are in this Jupyter Notebook
RAG Inference
Sample Questions
Question 1
- Question: What is Airbnb's 'Description of Business'?
- Response: Airbnb's 'Description of Business' is operating a global platform for unique stays and experiences, connecting hosts and guests online or through mobile devices to book spaces and experiences around the world.
- LangSmith trace: https://smith.langchain.com/public/ebdf5473-64ac-4f85-81ab-bd3c3d624969/r
Question 2
- Question: What was the total value of 'Cash and cash equivalents' as of December 31, 2023?
- Response: The total value of 'Cash and cash equivalents' as of December 31, 2023, is $2,369.
- LangSmith trace: https://smith.langchain.com/public/b0f93487-c729-4ccf-93f9-0354078282d8/r
Question 3
- Question: What is the 'maximum number of shares to be sold under the 10b5-1 Trading plan' by Brian Chesky?
- Response: The maximum number of shares to be sold under the 10b5-1 Trading plan by Brian Chesky is 1,146,000.
- LangSmith trace: https://smith.langchain.com/public/7fc4b549-2ea5-4b86-abf9-71d5e9a62738/r