The RAG Architecture Blueprint: How to Build an Expert LLM and Give it a Perfect Memory
You ask ChatGPT about that obscure research paper you read last month. It gives you a confident answer that sounds right but is completely made up.
The problem isn’t that these models are dumb. They’re incredibly smart. The problem is they have amnesia about YOUR world.
This is where RAG comes in. Retrieval Augmented Generation. yeah fancy name for a simple idea: give your AI a perfect memory of everything that matters to you, and teach it to remember exactly the right thing at exactly the right time.
Let me show you how this actually works by building something real: a personal research assistant that actually knows what you know.
The Problem With Smart People Who Remember Nothing
Imagine hiring a brilliant research assistant. PhD level intelligence. Can synthesize complex ideas, write beautifully, connect dots across disciplines. There’s just one catch: every morning, they forget everything from the day before.
You’d spend your entire life re-explaining things. “Remember that paper about sleep and memory consolidation?” Blank stare. “The one I showed you yesterday?” Still nothing. “With the graphs about REM cycles?” Nope.
This is what using a base LLM feels like. TinyLlama, GPT, Claude, whatever. They’re trained on the entire internet but know nothing about YOUR specific research, YOUR notes, YOUR carefully curated collection of papers and articles.
Fine-tuning them on your data? Expensive, slow, and every time you learn something new, you need to retrain. It’s like performing brain surgery every time you want to share a new article. :)
RAG is different. RAG is like giving your AI assistant a perfect filing system and teaching them exactly how to use it.
How RAG Actually Works
Let me walk you through the architecture of a RAG system by showing you what happens behind the scenes. We’re building this with open-source tools, so you could actually build this yourself if you wanted.
Flow 1: When You Ask a Question
Picture this. You’re three months into your research on neuroplasticity. You’ve read 50 papers, highlighted sections, saved articles, and scribbled notes. Now you want to ask: “What’s the relationship between exercise and neurogenesis in adults?”
Here’s what happens in the next few seconds:
Step 1: Your question hits the Query Router
This is just an API endpoint, nothing fancy. It receives your question and immediately sends it to a message queue service (RabbitMQ, SQS etc). Think of the queue like a really organized to-do list. It holds your question safely while the system gets ready to process it.
Why a queue? Because sometimes you’re impatient and ask three questions rapid-fire. The queue makes sure each one gets proper attention without anything getting lost or overloaded.
Step 2: The Context Retriever wakes up
A worker service picks up your question from the queue. First thing it does? Transforms your question into a vector using a transformer called all-MiniLM-L6-v2 (we are using this).
A vector is just a list of numbers that represents the meaning of your question. Think of it like a fingerprint, but instead of identifying a person, it identifies an idea.
“What’s the relationship between exercise and neurogenesis?” becomes something like [0.23, -0.15, 0.89, … 384 numbers total]. Every semantically similar question would have a similar fingerprint. “How does physical activity affect brain cell growth?” would have nearby numbers.
Step 3: Searching through your memory
Now here’s where it gets interesting. Remember all those papers and notes you’ve been feeding the system? They’re all sitting in Qdrant, this is not your grandfather’s SQL, this is a vector database, a speedy one, which saves these numerical fingerprints.
The Context Retriever takes your question’s vector and asks Qdrant: “Find me everything in this person’s knowledge base that has a similar fingerprint to this.”
Qdrant is insanely fast at this. It doesn’t read every document word by word like a human would. It’s doing mathematical distance calculations. “Which vectors are closest to this query vector?” In milliseconds, it returns the most relevant chunks from your saved research.
Maybe it finds that section from the Nature paper about hippocampal neurogenesis. That paragraph from your notes on the correlation between cardio and BDNF levels. That article you saved about mice running on wheels and growing new neurons.
Step 4: Building the perfect prompt
Now the Context Retriever does something clever. It doesn’t just send your question to the AI. It builds a rich, contextual prompt that looks something like:
“You are a research assistant. The user has saved the following relevant information: [Chunk from Nature paper about exercise increasing neurogenesis by 200%] [User’s notes on BDNF as the key mechanism] [Article excerpt about optimal exercise duration]
Based on this information from the user’s research, answer the following question: What’s the relationship between exercise and neurogenesis in adults?”
See what happened there? We just transformed a general question into a specific one grounded in YOUR knowledge.
Step 5: TinyLlama does its thing
The prompt goes to TinyLlama, our open-source LLM. TinyLlama is small (only 1.1 billion parameters) but fast and good enough for most tasks. More importantly, it’s free and you can run it on decent hardware.
TinyLlama reads the context, understands your question, and generates an answer based on the specific research you’ve saved. Not based on its general training. Not making stuff up. Based on YOUR sources.
Step 6: The answer flows back
The response travels back through the system. Maybe through the queue again, maybe through a database where it’s stored for your chat history, and finally back to your frontend where you see it.
The entire journey takes a couple of seconds. To you, it feels like magic. To the system, it’s a carefully orchestrated dance of components doing exactly one thing well.
Flow 2: When You Feed It Knowledge (Building the Brain)
The query flow is sexy. This flow is where the real work happens.
You’ve just finished reading a fascinating paper on memory consolidation during sleep. You want your research assistant to remember this. Here’s what happens:
Step 1: Knowledge hits the Ingestion API
You upload the PDF or paste the text into your app. It hits a different API endpoint than queries. This one is specifically designed for ingesting knowledge, not answering questions. Separation of concerns is good architecture.
The Ingestion API immediately puts your document into a message queue. Same idea as before. Multiple uploads don’t crash the system. Everything gets processed in order.
Step 2: The Knowledge Processor takes over
Another worker service, this one specialized for processing documents. It picks up your paper from the queue and starts doing the heavy lifting.
First, it chunks the document. A 30-page paper can’t be stored as one giant blob. It gets broken into meaningful sections. Maybe by paragraph, maybe by section, maybe using smarter semantic chunking that keeps related ideas together. Each chunk is maybe 200–500 words.
Why chunk? Because when you ask a question later, you don’t want the entire 30-page paper shoved into the context. You want the specific three paragraphs that matter.
Step 3: Vectorization happens
Each chunk gets run through that same all-MiniLM-L6-v2 transformer. Each chunk becomes a vector. Each vector gets stored in Qdrant along with the original text.
Now Qdrant has both the fingerprint (for fast searching) and the actual text (for building prompts). The paper about sleep and memory consolidation is now searchable by semantic meaning, not just keywords.
Step 4: The Memory Sync Scheduler (The Secret Weapon)
Here’s where it gets really powerful. You don’t just manually upload documents. You connect integrations.
You link your Notion workspace. Your Google Drive. Your Pocket saved articles. Maybe your Kindle highlights. Your Slack channels where interesting links get shared.
A cron job runs periodically. Once a day, maybe. It polls all these services through their APIs, looks for new content, and automatically feeds it through the Knowledge Processor.
You highlight a passage in a book on your Kindle? Tomorrow morning, your research assistant knows about it. You save an article to Pocket at 2am? It’s in the system by breakfast. You write notes in Notion? Automatically ingested and vectorized.
Your AI assistant’s memory grows with you, automatically, without you doing anything.
This is what I mean by perfect memory. Not because it remembers everything word-for-word like a robot. But because it has instant semantic access to everything you’ve ever thought was important enough to save.
The Part Where I Tell You This Isn’t Perfect :(
So RAG isn’t magic. It’s a power tool that requires you to understand what you’re building.
Chunking is hard. Break your documents wrong and you lose context. That brilliant insight that spans two pages? If your chunks are too small, they’ll be stored separately and might not both get retrieved together.
Retrieval isn’t always perfect. Sometimes the semantically similar chunks aren’t the actually relevant chunks. You ask about exercise and neurogenesis, and it retrieves the section about exercise and cardiovascular health because those vectors happened to be close. The LLM does its best but garbage in, garbage out.
Vector search has limits. It finds semantic similarity, not logical reasoning. If you need to connect three different ideas from three different papers that don’t share similar language, RAG might miss those connections. A human researcher would see it. The vector search won’t.
Quality of sources matters enormously. If you’re feeding it garbage research, contradictory articles, or random internet posts, your assistant becomes confident and wrong. It’ll cite bad sources with the same authority as good ones.
But look these aren’t dealbreakers. They’re just realities. RAG is powerful but it’s not artificial general intelligence. It’s a really good filing system attached to a really good language model.
Building Your Own?
If you want to build this yourself, here’s your shopping list:
Frontend: Whatever you’re comfortable with. React, Vue, vanilla JavaScript, doesn’t matter. You just need to call APIs and display responses.
Query Router & Ingestion API: FastAPI in Python is perfect. Express in Node works too. Keep them simple. They’re just HTTP endpoints that push to queues.
Message Queue: RabbitMQ or Redis Pub/Sub. Both work great. RabbitMQ is more features, Redis is simpler if you’re already using Redis.
https://hub.docker.com/_/rabbitmq
Context Retriever & Knowledge Processor: Python services. Use the sentence-transformers library for the all-MiniLM-L6-v2 model. Use the qdrant-client to talk to your vector database. https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
Qdrant: Runs in Docker beautifully. Their cloud offering is also very reasonable if you don’t want to self-host.
https://qdrant.tech/
TinyLlama: Run it locally using Ollama or text-generation-inference. Or swap in any other open-source model. Mistral, Llama 3, whatever fits your needs and hardware.
https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0
Memory Sync Scheduler: A Python script with schedule library or a proper cron job. Calls the APIs of services you integrate, fetches new content, pushes to your ingestion queue.
The entire stack can run on a decent server or even a beefy laptop for personal use. Scale up as needed.
signing off, see ya all!
