Chapter 2 -- The 50-Day Plan For Building A Personal Assistant Agentic System (PAAS)

PHASE 1: FOUNDATIONS (Days 1-10)

Day 9-10: Vector Databases & Embeddings

These two days are dedicated to mastering vector search technologies that will form the backbone of your information retrieval system. You'll explore how semantic similarity can be leveraged to find related content across different information sources. You'll learn how embedding models convert text into vector representations that capture semantic meaning rather than just keywords. You'll develop an understanding of different vector database options and their tradeoffs for your specific use case. You'll also build practical retrieval systems that can find the most relevant content based on semantic similarity rather than exact matching.

• Morning (3h): Study vector embeddings and semantic search

  • Embedding models (sentence transformers): Understand how modern embedding models transform text into high-dimensional vector representations that capture semantic meaning. Compare different embedding models like OpenAI's text-embedding-ada-002, BERT variants, and sentence-transformers to determine which offers the best balance of quality versus performance for your intelligence gathering needs.
  • Vector stores (Pinecone, Weaviate, ChromaDB): Explore specialized vector databases designed for efficient similarity search at scale, learning their APIs, indexing mechanisms, and query capabilities. Compare their features, pricing, and performance characteristics to select the best option for your project, considering factors like hosted versus self-hosted and integration complexity.
  • Similarity search techniques: Study advanced similarity search concepts including approximate nearest neighbors, hybrid search combining keywords and vectors, and filtering techniques to refine results. Learn how to optimize vector search for different types of content (short social media posts versus lengthy research papers) and how to handle multilingual content effectively.

• Afternoon (3h): Build a simple retrieval system

  • Generate embeddings from sample documents: Create a pipeline that processes a sample dataset (e.g., research papers or news articles), generates embeddings for both full documents and meaningful chunks, and stores them with metadata. Experiment with different chunking strategies and embedding models to find the optimal approach for your content types.
  • Implement vector search: Build a search system that can find semantically similar content given a query, implementing both pure vector search and hybrid approaches that combine keyword and semantic matching. Create Python functions that handle the full search process from query embedding to result ranking.
  • Test semantic similarity functions: Develop evaluation approaches to measure the quality of your semantic search, creating test cases that validate whether the system retrieves semantically relevant content even when keywords don't match exactly. Build utilities to visualize vector spaces and cluster similar content to better understand your data.