Most developers who works with AI eventually hit the same wall - context. You can pipe tools together, chain AI prompts, or write clever workflows, but at some point, you realize your agent isn’t really thinking. It’s reacting. You need something different.
I hope you used n8n, LangChain, or a similar tool, and you probably created pipelines where each AI step feeds the next. That works for formatting data or guiding workflows. And it is fine for simple agents, but what if your agent needs to remember? What if it needs to learn across conversations? How about adapt to changes? ... or retrieve knowledge like a human?
Before diving into the code, let’s break down AI memory into three simple categories:
Stateless (No Memory):
The agent processes each prompt independently. It’s a great for reformat data, transform or have quick answer. Let's call it "Simple transformation"
Short-Term Memory:
Think of a chatbot that remembers the last couple of interactions. Usually 10-20 last messages. Each chat is isolated. Context is limited to a session window.
Long-Term Memory:
This is more about intelligence. This agent builds an evolving knowledge base across all chats. It try to remember remembers previous user interactions, and connects concepts. This is made possible by vector databases and semantic embeddings.
In this article we will explore how RAG works and what it is, how it differs from traditional AI pipelines, and how you can build your own local memorable agent (using Python and FAISS—fully intelligent memory for offline use or deploying to your own infrastructure).
RAG stands for Retrieval-Augmented Generation.
AI Pipelines vs. RAG
Let’s start with a misconception:
“I already have an AI agent that processes inputs through multiple steps. Isn’t that the same?”
Not quite.
So what’s the Difference?
What is a Traditional AI Pipeline:
- steps chained together (for example, summarize → extract → classify)
- each step operates on the output of the previous one or wait for multiple
- no persistent memory or knowledge base (data come - data leave)
- repetition of work if context is lost
That's what people usually see after 10-20 messages. Their context has been lost and they need to remind AI agent about it. It is sad... But that's the cost of simple approach.
RAG Architecture on other hand:
- uses a vector database as external memory
- retrieves semantically relevant information (instead of guessing from the prompt)
- uses both the current prompt and retrieved knowledge to generate a smarter response
- memory is structured, persistent, and scalable (...and costly, hahah)
So you might think "How Memory Works in RAG"?
RAG agents don’t store raw text — they store meanings using embeddings. Think of it like associative memory: when you say “I want to automate tasks”, the system doesn’t look for exact matches, it looks for concepts that are semantically close. Each AI is just an LLM on steroids. Where one of the steroid is RAG. So the RAG should consume data, let's call it "memory entry".
Each memory entry includes:
- original text
- vector (embedding)
- metadata (who, when, source, etc.)
This allows fast, flexible search across tens of thousands of interactions without leaking sensitive data to the public. This is very important to users. Moreover "public" here means users that will use the shared between them RAG. So "public" in this context is a group of people, that can be a real public... this article is not about philosophy anyway...
Let’s rather build a basic RAG memory system using:
- FAISS — Facebook’s local vector search engine
- OpenAI’s embedding API (or you replace it with any public/local embedding model later)
# deps to install
pip install faiss-cpu openai
Now when we have dependencies installed, let's store the memory on a local machine:
import faiss
import openai
import numpy as np
openai.api_key = "YOUR_API_KEY"
# Sample data
texts = [
"Client wants to automate invoice generation.",
"Client asks about CRM integration options.",
"He discussed API for syncing customer data.",
]
# Convert to embeddings
def get_embedding(text):
response = openai.Embedding.create(
input=text,
model="text-embedding-ada-002"
)
return np.array(response['data'][0]['embedding'], dtype='float32')
embeddings = np.array([get_embedding(t) for t in texts])
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings)
Here is a link to OpenAI embedding intro and OpenAI embedding models.
So when data is stored, we should be able to retrieve it
query = "How do I automate customer data sync?"
query_vec = get_embedding(query)
D, I = index.search(np.array([query_vec]), k=2)
for idx in I[0]:
print("Relevant memory:", texts[idx])
This request will return us only relevant memory from DB. Output will be:
Relevant memory: He discussed API for syncing customer data.
Relevant memory: Client wants to automate invoice generation.
This is a CORE of your system. Now you are able to pick data by asking human-language questions.
While we just jump on this, let's see why local RAG rocks:
You can easily scale this to 100K+ entries, integrate it with a local LLM like LLama - find one yourself on huggingface. ...or deploy it to your own infrastructure. No cloud dependencies required 💪
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