Let's design a prototype for a system that answers questions based on financial documents. To make this concrete, imagine a user needs to quickly find information within a company's annual report (a PDF containing financial statements, management discussion, etc.). The user might ask questions like:

1. "What was the company's revenue for the last fiscal year?"
2. "What were the primary risks identified by management?"
3. "How did operating expenses change compared to the previous year?"

Describe your approach to building a prototype that can accurately and efficiently answer these types of questions. Consider aspects such as:

• Document ingestion and processing: How would you load and pre-process the financial documents (e.g., PDF parsing, text extraction)?
• Information retrieval: What techniques would you use to identify the relevant sections of the document that contain the answer?
• Question answering: How would you extract the answer from the relevant text (e.g., using rule-based methods, machine learning models)?
• Data storage: How would you store the information from the documents to allow for efficient querying?
• User interface: How would the user interact with the system to ask questions and receive answers?
• Evaluation: How would you evaluate the performance of your prototype?

Prototype for Question Answering on Financial Documents

This outlines a prototype for a system that answers questions based on financial documents, focusing on annual reports as a concrete example. The system aims to efficiently and accurately extract information to answer user queries.

1. Requirements

• Use Cases:
  • Users can upload financial documents (e.g., annual reports in PDF format).
  • Users can ask questions in natural language about the document.
  • The system returns accurate and relevant answers, ideally with source citations.
• User Stories:
  • "As an investor, I want to quickly find the company's revenue for the last fiscal year."
  • "As an analyst, I want to know the primary risks identified by management."
  • "As a financial professional, I want to understand how operating expenses changed compared to the previous year."

2. High-Level Design

The system will consist of the following components:

1. Document Ingestion and Processing: Responsible for loading and pre-processing financial documents.
2. Information Retrieval: Identifies the relevant sections of the document that potentially contain the answer.
3. Question Answering: Extracts the answer from the relevant text.
4. Data Storage: Stores processed information to allow for efficient querying.
5. User Interface: Provides an interface for users to ask questions and receive answers.

[High-Level Design Diagram]

(Imagine a diagram here showing the components and their interactions: User Interface -> Document Ingestion & Processing -> Information Retrieval -> Question Answering -> Data Storage and back to User Interface)

3. Data Model

We'll use a relational database (e.g., PostgreSQL) to store processed document information.

Table: Documents

Field	Type	Description
document_id	SERIAL	Unique identifier for the document
filename	VARCHAR	Name of the uploaded file
upload_date	TIMESTAMP	Date and time the document was uploaded

Table: Chunks

Field	Type	Description
chunk_id	SERIAL	Unique identifier for the chunk
document_id	INTEGER	Foreign key referencing the Documents table
page_number	INTEGER	Page number in the document where the chunk originates
content	TEXT	Text content of the chunk
embedding	VECTOR	Vector embedding of the chunk content (for semantic similarity search)

4. Endpoints

• /upload (POST):

  • Request:

  {
    "file": "file_content (e.g., base64 encoded PDF)",
    "filename": "annual_report_2023.pdf"
  }
  

  • Response:

  {
    "document_id": 123,
    "message": "Document uploaded and processed successfully"
  }
  

• /query (POST):

  • Request:

  {
    "document_id": 123,
    "question": "What was the company's revenue for the last fiscal year?"
  }
  

  • Response:

  {
    "answer": "The company's revenue for the last fiscal year was $1.5 billion, as stated on page 15 of the annual report.",
    "source": {
      "document_id": 123,
      "page_number": 15,
      "chunk_id": 456
    }
  }
  

5. Tradeoffs

Component	Approach	Pros	Cons
Document Ingestion	PDF parsing with PyPDF2/pdfminer	Simple, readily available libraries	Can be unreliable for complex PDFs, may lose formatting
Information Retrieval	Vector embeddings with cosine similarity	Captures semantic meaning, robust to variations in wording	Requires pre-trained models or fine-tuning, can be computationally expensive
Question Answering	Fine-tuned BERT model	High accuracy, understands context	Requires significant training data and resources, can be difficult to debug
Data Storage	Relational database (PostgreSQL)	Structured data storage, efficient querying, ACID properties	Can be less flexible for unstructured data, requires schema definition
User Interface	Simple web interface with Flask/Streamlit	Easy to develop and deploy, provides a user-friendly experience	Limited customization options compared to a full-fledged front-end framework

6. Other Approaches

• Document Ingestion: Instead of PyPDF2/pdfminer, consider using cloud-based OCR services like AWS Textract or Google Cloud Document AI for more robust PDF parsing, especially for scanned documents. This would handle complex formatting and image-based text more effectively.
• Information Retrieval: Use keyword-based search (e.g., TF-IDF) as a simpler alternative to vector embeddings for faster but potentially less accurate retrieval. Combine keyword search with vector search for a hybrid approach.
• Question Answering: Use rule-based methods or regular expressions for specific, well-defined question types (e.g., extracting numerical values from tables). This can be more efficient and easier to implement than training a full machine learning model for every type of question.
• Data Storage: Consider using a NoSQL database (e.g., MongoDB) if the data is highly unstructured or if you anticipate frequent schema changes. This would provide more flexibility but may sacrifice some querying efficiency.

7. Edge Cases

• Scanned Documents: The system should handle scanned documents and images containing text. This requires OCR (Optical Character Recognition) to extract the text before further processing. AWS Textract or Google Cloud Document AI can be leveraged here.
• Complex Tables: Financial reports often contain complex tables. The system needs to be able to correctly identify and parse these tables to extract relevant data. Consider using libraries like tabula-py or Camelot for table extraction.
• Ambiguous Questions: The system should handle ambiguous or poorly worded questions. This might involve providing clarifying questions to the user or returning multiple potential answers with different levels of confidence.
• Unsupported File Formats: The system should handle cases where the uploaded file is not a supported format (e.g., not a PDF). It should provide a clear error message to the user.
• Large Documents: Processing very large documents can be time-consuming and resource-intensive. Implement techniques like document chunking and parallel processing to improve performance. Consider using asynchronous processing with Celery.

8. Future Considerations

• Scalability: As the number of users and documents increases, the system needs to be scalable. This can be achieved through techniques like horizontal scaling, caching, and database sharding. Consider using a cloud-based infrastructure (e.g., AWS, Azure, GCP) to easily scale resources.
• Multi-Document Support: Extend the system to handle questions that require information from multiple documents. This would involve implementing a more sophisticated information retrieval mechanism that can identify relevant documents across a corpus.
• User Authentication and Authorization: Implement user authentication and authorization to control access to documents and data. Use industry-standard protocols like OAuth 2.0.
• Integration with External Data Sources: Integrate the system with external data sources (e.g., financial news APIs, stock market data) to provide more comprehensive answers.
• Continuous Learning: Implement a mechanism for continuous learning to improve the accuracy of the question answering system over time. This could involve using user feedback to fine-tune the machine learning models.
• Improved UI/UX: Enhance the user interface to provide a better user experience. This could involve features like auto-completion, question suggestions, and visual representations of the answers.

Document Ingestion and Processing:

• PDF Parsing: Use a library like PyPDF2 or pdfminer.six in Python to extract text from PDF documents. Consider cloud-based OCR services like AWS Textract or Google Cloud Document AI for scanned documents.
• Text Cleaning: Clean the extracted text by removing irrelevant characters, HTML tags, and excessive whitespace. Normalize the text by converting it to lowercase and removing punctuation.
• Chunking: Divide the text into smaller chunks (e.g., paragraphs or sentences) to facilitate information retrieval. Experiment with different chunking strategies to optimize performance. Consider using semantic chunking to group related sentences together.

Information Retrieval:

• Embedding Generation: Use a pre-trained language model (e.g., BERT, RoBERTa, or Sentence Transformers) to generate vector embeddings for each chunk of text. These embeddings capture the semantic meaning of the text.
• Similarity Search: When a user asks a question, generate a vector embedding for the question. Then, use a similarity search algorithm (e.g., cosine similarity) to find the chunks of text that are most similar to the question embedding. Use a vector database like Faiss, Annoy, or Milvus for efficient similarity search.

Question Answering:

• Extractive QA: Use a pre-trained extractive question answering model (e.g., BERT, RoBERTa, or ELECTRA fine-tuned on SQuAD) to extract the answer from the relevant text chunks. Provide the question and the relevant text as input to the model, and the model will output the span of text that answers the question.
• Abstractive QA (Optional): For more complex questions that require synthesis of information from multiple sources, consider using an abstractive question answering model (e.g., T5 or BART). However, abstractive QA models are generally more complex to train and deploy than extractive QA models.

Data Storage:

• Database: Store the extracted text chunks, embeddings, and metadata (e.g., document ID, page number) in a database. Consider using a relational database (e.g., PostgreSQL) or a NoSQL database (e.g., MongoDB) depending on the data structure and query requirements.
• Vector Database: Use a vector database (e.g., Faiss, Annoy, or Milvus) to store and efficiently search the vector embeddings.

User Interface:

• Web Application: Create a simple web application using a framework like Flask or Streamlit. The user interface should allow users to upload documents, ask questions, and view the answers. Display the source document and page number for each answer.

Evaluation:

• Metrics: Use metrics like precision, recall, F1-score, and exact match to evaluate the performance of the prototype. Manually review the answers to assess their accuracy and relevance.
• Dataset: Create a dataset of financial documents and corresponding questions and answers to use for evaluation. Use publicly available annual reports and create questions based on their content.