Building a Simple RAG System with Python, ChromaDB and Gemini
Large Language Models are impressively fluent, but they have a blind spot: they can only answer from what they learned during training. Ask one a question about a specific PDF, a company's internal handbook, or a niche textbook, and it may guess, generalize, or simply admit it doesn't know. Retrieval-Augmented Generation (RAG) closes this gap by giving an LLM a way to look things up before it…
Retrieval-Augmented Generation (RAG) is a technique that enables large language models (LLMs) to answer questions using information from external documents, rather than relying solely on their pre-existing knowledge. In a recent project while interning at Valentius Kryptix, a lightweight RAG system was built to demonstrate the benefits of this approach.
The system was designed to bridge the gap between vector databases, which excel at finding relevant information, and language models, which are skilled at generating coherent explanations.
A key aspect of RAG is ensuring that the retrieval process is semantically relevant, meaning that it matches the underlying meaning of the query rather than just matching exact words. To achieve this, the system uses embeddings to convert both the document and the user's question into vectors that represent their semantic meaning. In this case, the all-MiniLM-L6-v2 model from Sentence Transformers was employed to generate these embeddings.
The ChromaDB vector database was then used to store these embeddings, allowing the system to quickly locate conceptually related content, even when the exact wording differs. To illustrate the effectiveness of this approach, the project compared the model's responses with and without retrieval. When asked "What are the operations performed on a queue?", without RAG, Gemini provided a general answer based on its training data.
However, with RAG, the model generated a more accurate and detailed response, mentioning specific queue operations such as Enqueue, Dequeue, Peek/Front, Rear, isFull, and isEmpty.
The comparison between the RAG-enhanced and non-RAG responses highlighted the value of retrieval in providing contextually relevant information. Ultimately, the project emphasized that a RAG system is an essential combination of a vector database and a language model. While each component has its strengths and weaknesses individually, together they form a powerful tool for document-based question-answering. The full build process, along with code and implementation details, can be found in the original article.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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