RAG 2

[gauravpandeyDL]

1. Choose Data:

• Example: We decide to build a knowledge base for customer support in a fintech company.
• Data Sources:
  • PDF Documents: Collect internal PDF files containing FAQs, product guides, and compliance information.

2. Prepare Data:

• Extract Text from PDFs: We use a Python library like PyPDF2 to extract text from PDF files.
• Clean & Normalize Text: Use SpaCy to remove punctuation, stop words, and perform stemming or lemmatization (simplifying words) for better analysis.

3. Create Embeddings:

• Use txtai: Load txtai (https://neuml.github.io/txtai/), a powerful library for semantic search.
• Embed Text: Use txtai's embedding models to convert your cleaned text into numerical representations (embeddings) that capture the meaning of the words.

4. Store Data (Knowledge Base):

• Database: Choose a free database like PostgreSQL (open-source) or AWS DynamoDB (cloud).
• Structure: Create tables to store:
  • Documents: Each PDF file with its extracted text and embedding.
  • Entities: Important terms (e.g., "investment", "account", "deposit") and their embeddings.
  • Relationships: Connections between entities (e.g., "investment" is a type of "financial product").

5. Build a Simple Search:

• Web Framework: Use Flask (lightweight and easy).
• API: Create an API that allows users to send search queries.
• Search Function: In code, use txtai's search capabilities to find documents and entities that match the user's query based on semantic similarity.

6. Display Results:

• Web Interface: HTML and JavaScript to create a simple webpage where users can input queries and see the results.
• Visualization: Display the results in a clear format (tables, lists) to make it easy to understand.