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# Build RAG from Scratch

Phil Nash proves you can build a vector search engine from scratch using just Node.js. Ditch traditional keyword matching and master the math behind Retrieval-Augmented Generation.

- **Speakers:** [philnash](https://www.wearedevelopers.com/@philnash)
- **Event:** World Congress 2024
- **Published:** August 20, 2024
- **Duration:** 28:04
- **URL:** https://www.wearedevelopers.com/videos/1115-build-rag-from-scratch

## Summary

A fundamental limitation of Large Language Models (LLMs) is that they only know their localized training data and lack access to private or recent information. To solve this, Retrieval-Augmented Generation (RAG) acts as an architectural bridge, fetching relevant private data to dynamically augment a prompt before generation. In this deep dive, Phil Nash breaks down the underlying mechanics of RAG by building a similarity search engine from scratch using JavaScript and Node.js. He shifts the developer paradigm from traditional keyword filtering to "searching by vibes"—using vector embeddings to capture the literal meaning and context of natural language queries.

To demystify the black box of embeddings, the demonstration starts with creating a custom "bag of words" sparse vector. By mapping a corpus of text to a 6,000-dimension array based strictly on word frequency, developers visualize how text translates into numbers. Because vectors represent direction and magnitude in multi-dimensional space, comparing them requires mathematics rather than simple string matching. Nash implements custom JavaScript functions to calculate cosine similarity—combining the dot product and vector magnitudes—proving that vectors pointing in the exact same direction represent closely related semantic meaning with a score approaching 1.0.

While building a sparse vector search illuminates the underlying math, it quickly reveals scaling and accuracy limits: it is highly vocabulary-dependent, computationally expensive via JavaScript, and inherently blind to contextual nuances like word order. For production environments, the leap to semantic reliability requires dense embedding models (like OpenAI's highly-dimensional text-embedding models) paired with dedicated vector databases. Nash transitions the application to DataStax Astra DB to showcase how native vectorization and indexing radically improve query accuracy. Ultimately, mastering RAG goes beyond simple similarity search into advanced methodologies, utilizing token-level ColBERT storage and content-centric knowledge graphs to guarantee highly accurate data retrieval for enterprise generative AI applications.

**Keywords:** retrieval-augmented generation, vector embeddings, similarity search, cosine similarity calculation, sparse vs dense vectors, LLM context injection, vector database indexing, bag of words model, javascript RAG implementation, datastax astra DB, openai embedding models, google gemini integration, colbert retrieval methodology, AI knowledge graphs, prompt augmentation

## Chapters

1. **Overcoming large language model knowledge gaps** (01:30) — Large language models lack access to private organizational data and suffer from strict knowledge cutoff dates.
1. **Understanding the architecture of retrieval augmented generation** (03:06) — Augmenting user prompts with natively retrieved data context gives models accurate information to generate informed responses.
1. **Searching by semantic meaning with vector embeddings** (04:26) — Converting text into structured lists of numbers allows systems to mathematically represent and search for semantic meaning.
1. **Creating a sparse bag-of-words vector from plain text** (05:28) — Extracting a unique glossary enables generating a sparse array representation of text frequency using basic scripts.
1. **Measuring dimensional vector angle with cosine similarity** (11:42) — Calculating multidimensional vector direction through dot product and magnitude equations determines the structural similarity of records.
1. **Returning relevant matches with local similarity sorting** (17:39) — Mapping cosine similarity scores against incoming search vectors provides a functional foundation for local context retrieval.
1. **Understanding the performance and semantic limits of bag-of-words** (19:38) — A raw vocabulary approach suffers from exact-word dependency, calculation overhead, and ignores inherent linguistic context.
1. **Switching from sparse representations to dense embedding models** (21:16) — Purpose-built LLM embedding protocols automatically compress complex phrasing and negative inference into densely packed numerical dimensions.
1. **Scaling vector similarity search using dedicated databases** (22:53) — Vector databases handle high-volume embedding storage and native nearest-neighbor indexing far more efficiently than iterated local arrays.
1. **Advancing semantic boundaries with colbert and knowledge graphs** (26:04) — Incorporating token-level evaluation logic, knowledge graphs, and dedicated related-content engines significantly deepens modern system retrieval capabilities.

## Related Moments

- [Understanding basic retrieval-augmented generation architectures in chatbots](https://www.wearedevelopers.com/videos/1130-chatbots-are-going-to-destroy-infrastructures-and-your-cloud-bills) (from "Chatbots are going to destroy infrastructures and your cloud bills")
- [Building local RAG architectures using the Anything LLM tool](https://www.wearedevelopers.com/videos/1597-self-hosted-llms-from-zero-to-inference) (from "Self-Hosted LLMs: From Zero to Inference")
- [Understanding overarching retrieval and generation steps in RAG architectures](https://www.wearedevelopers.com/videos/1982-stop-guessing-start-measuring-evaluating-rag-systems-with-synthetic-test-data) (from "Stop Guessing, Start Measuring: Evaluating RAG Systems with Synthetic Test Data")
- [Expanding AI capabilities using retrieval-augmented generation](https://www.wearedevelopers.com/videos/950-supercharge-your-cloud-native-applications-with-generative-ai) (from "Supercharge your cloud-native applications with Generative AI")
- [Reviewing the basic retrieval-augmented generation pipeline](https://www.wearedevelopers.com/videos/1235-carl-lapierre-exploring-advanced-patterns-in-retrieval-augmented-generation) (from "Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation")
- [Enhancing language models with retrieval-augmented generation](https://www.wearedevelopers.com/videos/844-enter-the-brave-new-world-of-genai-with-vector-search) (from "Enter the Brave New World of GenAI with Vector Search")

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