Stock Q&A Workflow

• Data Analysts: Those who need to analyze large datasets and extract insights quickly.
• Developers: Individuals looking to integrate AI capabilities into their applications without extensive machine learning knowledge.
• Business Professionals: Users who want to automate Q&A processes related to financial or operational data.
• Researchers: Academics who need to retrieve and analyze information from documents efficiently.

This workflow addresses the challenge of quickly retrieving and processing information from large documents, specifically PDFs, by integrating AI models for question answering. It automates the process of fetching data from Google Drive, splitting documents into manageable chunks, and storing them in a vector store for efficient retrieval. This allows users to obtain accurate answers to queries based on the content of the documents without manual searching.

• Step 1: Fetch PDF from Google Drive - The workflow begins by downloading a specified PDF file from Google Drive.
• Step 2: Document Processing - The PDF is converted into a document format suitable for further processing.
• Step 3: Chunking the Document - The document is split into smaller chunks to facilitate easier retrieval and processing.
• Step 4: Embedding Generation - Each chunk is transformed into embeddings using OpenAI's model, allowing for semantic understanding.
• Step 5: Storage in Vector Store - The embeddings are stored in a Qdrant vector store, enabling efficient querying.
• Step 6: Webhook Trigger - The workflow is triggered via a webhook, allowing users to send queries.
• Step 7: Query Processing - The incoming query is processed to retrieve relevant chunks from the vector store.
• Step 8: Response Generation - The relevant information is compiled, and a response is generated using OpenAI's chat model.
• Step 9: Responding to Webhook - Finally, the response is sent back through the webhook, providing users with the answers they need.