Large Language Models (LLMs) are powerful, but they suffer from hallucinations and lack knowledge of private or real-time data. Retrieval-Augmented Generation (RAG) solves this by combining external document retrieval with generative AI. In this tutorial, we will build a complete server-side RAG pipeline from scratch.
Understanding RAG Architecture & Data Flow
Before writing code, let's look at how data moves through a RAG system. The workflow is split into two phases: Ingestion (offline) and Retrieval/Generation (online).
- Ingestion: Raw documents are split into manageable chunks, converted into vector embeddings, and stored in a vector database.
- Runtime: When a user asks a question, the query is converted into an embedding and matched against the vector database to find the most relevant document chunks.
- Augmentation: The retrieved text chunks are injected into a prompt template alongside the user's original query.
- Generation: The LLM reads the context and generates an accurate, grounded response.
Flow diagram
graph TD;
A[Documents] --> B[Text Chunks];
B --> C[Embeddings Generator];
C --> D[Vector Database];
E[User Query] --> F[Query Embedding];
F --> G[Vector Search];
G --> H[Context + Query];
H --> I[LLM];
I --> J[Response];plaintext plaintext
[Ingestion Phase]Documents -> Text Chunks -> Embeddings Generator -> Vector Database [Runtime Phase]User Query -> Query Embedding -> Vector Search -> Context + Query -> LLM -> ResponseStep 1: Setting Up the Environment and Dependencies
To get started, create a new Node.js project and install the necessary dependencies for handling embeddings, managing vector storage, and performing LLM (Large Language Model) completions. You can accomplish this by executing the following commands in your terminal:
- Initialize a new Node.js project using
npm initto create a package.json file. - Install the required libraries, which may include packages for embedding management, vector storage solutions, and LLM integration. This can typically be done with commands like
npm installfor each dependency.
Ensure that you have Node.js and npm (Node Package Manager) installed on your machine before proceeding with these steps.
Step 2: Generating Embeddings and Storing Chunks
In this step, we will develop a script that performs several key tasks: it will ingest internal documents, divide them into manageable chunks, generate vector embeddings for each chunk, and then store these embeddings either in memory or in a vector database such as Pinecone or Milvus, which are suitable for production environments.
Breaking down the documents into chunks is essential because it allows for more efficient processing and retrieval of information. Each chunk will be transformed into a vector embedding, which is a numerical representation of the text that captures its semantic meaning. This process enables the system to understand and compare the content more effectively.
By storing the generated embeddings in memory or a dedicated vector database, we ensure that our system can quickly access and retrieve relevant information when needed, enhancing the overall performance of the RAG (Retrieval-Augmented Generation) system.
Step 3: Implementing Semantic Search and Generation
In this step, we focus on the runtime phase of our system. When a user submits a question, we calculate the cosine similarity between the embedding of the user's query and the stored vectors in our database. This process allows us to identify and retrieve the most relevant context for the user's inquiry.
Edge Cases & Troubleshooting
When deploying RAG systems to production, keep these common pitfalls in mind: Chunk Size Too Large: Results in diluted embeddings and lost nuance. Keep chunks between 200-500 tokens. Hallucinations Despite Context: If the LLM ignores context, explicitly instruct it in the system prompt: 'If the answer cannot be found in the context, state "I do not know".' API Rate Limits: Implement exponential backoff when batch-embedding thousands of documents.
- Chunk Size Too Large: Results in diluted embeddings and lost nuance. Keep chunks between 200-500 tokens.
- Hallucinations Despite Context: If the LLM ignores context, explicitly instruct it in the system prompt: 'If the answer cannot be found in the context, state "I do not know".'
- API Rate Limits: Implement exponential backoff when batch-embedding thousands of documents.
Key Takeaways
- RAG bridges the gap between private data and LLM knowledge capabilities.
- The pipeline consists of two primary stages: offline document ingestion and runtime retrieval/generation.
- Using cosine similarity helps rank retrieved chunks accurately before feeding them to the model.
Frequently Asked Questions
Why do I need a vector database instead of regular SQL?+
Regular SQL databases perform exact keyword matches. Vector databases use mathematical embeddings to perform semantic search, meaning they can find relevant text even if the exact keywords do not match.
Can I update documents in a RAG system dynamically?+
Yes. You simply need to delete the old vector chunks from your vector database and run the ingestion script on the updated document chunks.