How I Built a Vector Database from Scratch in Python
Quantara, a lightweight local vector database built for speed and quick experimentation
Modern AI applications rely heavily on vector databases for semantic search, RAG pipelines, recommendation systems, and embedding-based retrieval. Rather than treating vector databases as black boxes, I decided to build one from scratch in Python to understand how they work internally.
Quantara is a lightweight, local vector database that aims to be fast and to support rapid prototyping. As the heading suggests, this is my personal project that I have now opened up for anyone to use. Quantara is a local-first vector database, built as a learning project and experimentation platform. It is actually very useful if you don't want to deal with all the API setups and multiple account creations for endless vector services. Plus, it works offline if you have the right tools preinstalled with you.
But how did I approach this project? Let's understand some concepts first...
What is a Vector Database?
A vector database is a specialized, production-grade database system designed to store, manage, index, and query high-dimensional vector data (known as vector embeddings).
A vector database is useful, particularly in a RAG system, where the user demands the contexts from the database on the basis of the query passed. The fundamental idea is to convert the text to an array of numbers and perform some form of mathematical operations on it to get back relevant contexts from the database.
What are vectors?
Vectors are a list of numbers that represent an entity in a multi-dimensional space. The vectors are often trained on a huge corpus of data to understand the content of words and the degree of similarity between them. More often, vectors are used for similarity matching, thus often used for representing contextual textual information in a usable format.
High-Level Architecture of Quantara in a Nutshell
Quantara has three layers of abstraction
Collections, which are similar to tables in a database
Indexes, which are the indexes of the database for querying
Persistence, storing the actual content of the data on the disk
Each record stored in the database is of the type Record that stores the parameters ID, name, vector, and metadata. ID is a random UUID generated at the time of insertion and is not something the user worries about. The user only worries about the name (content to be embedded), vector (the actual embedding), and metadata (possible data about the content).
That's it for the high-level design!
The package API is clean and very user-friendly. While inserting data, if a user wants to add data to a particular collection, the user first has to create a collection. Then, using the required method, the user may include the collection name in the optional parameters and continue with the task. If no collection name is provided, all records by default get stored in the "default" collection.
Coming to indexing, the current package supports a brute-force search algorithm, which takes in the embedding and linearly compares it with all the vectors in the database for a particular collection. The package API also allows a user to import the base class for index creation and create their own indexing algorithm. Once done, the user can register their index class with the API and start using it in their processes.
Persistence is always defaulted to True, unless specified otherwise by the user. The entire process runs in-memory, including indexing. The user can save the indexes of the collection data locally and upload them when running the module again.
Overview of the Entire Design
I started Quantara with a simple goal: build a vector database that I completely understood from the ground up.
Most modern AI applications rely on vector databases for semantic search, retrieval-augmented generation (RAG), recommendation systems, and similarity search. While existing solutions such as Pinecone, Chroma, and Weaviate provide powerful functionality, I wanted to understand the underlying architecture rather than treat them as black boxes.
1. Defining the Core Data Model
The first question I asked was, "What is the smallest unit of information a vector database needs to store?"
After some experimentation, I settled on a simple record structure consisting of
A unique identifier
A human-readable document name
An embedding vector
Arbitrary metadata
This design allowed me to associate embeddings with real-world information while also enabling future features such as filtering and categorization.
2. Separating Storage from Search
One of the earliest architectural decisions was to separate data storage from retrieval logic.
A very common mistake in small projects is tightly coupling search functionality to the storage layer. I wanted Quantara to remain extensible, so I treated storage and indexing as independent concerns.
The database became responsible for:
Managing records
Managing collections
Persisting data
Indexes became responsible for:
Searching vectors
Ranking results
Implementing similarity algorithms
This separation later made it possible to introduce custom indexes without modifying the database implementation itself.
3. Introducing Collections
The initial prototype stored every document in a single container.
While functional, it quickly became clear that real-world applications require logical separation of data.
For example:
Research papers
Financial documents
Medical reports
Books
should not necessarily exist in the same search space.
To solve this, I introduced collections, allowing each group of documents to maintain its own records and indexes while still being managed by the same database instance. Think of it like dedicated tables for distinctive tasks in a relational database.
4. Implementing Similarity Search
At the heart of every vector database lies similarity search.
The first implementation used brute-force search:
Compare the query vector against every stored vector.
Compute a similarity score.
Sort the results.
Return the top matches.
Although computationally expensive for large datasets, brute-force search provided a simple and correct baseline implementation.
For the project, I implemented support for:
Cosine Similarity
Dot Product
Euclidean Distance
This allowed users to choose the metric most appropriate for their embeddings and use case.
5. Designing a Pluggable Indexing System
As the project evolved, I realized that brute-force search should be just one possible search strategy.
Instead of hardcoding search logic into the database, I created an abstract indexing interface and a registry system.
This enables users to register custom indexes dynamically.
The resulting architecture follows a plugin-based design:
Database → Collection → Index
This approach allows future integration of advanced indexing algorithms such as HNSW, IVF, Product Quantization, or entirely user-defined search strategies.
6. Persistence and Recovery
A database is not useful if its contents disappear after the program exits.
I experimented with multiple persistence mechanisms before settling on a lightweight approach suitable for local-first applications.
The persistence layer became responsible for:
Saving collections
Saving indexes
Loading existing data
Reconstructing database state
This transformed Quantara from an in-memory prototype into a usable database system.
7. Building for Extensibility
One of the most important lessons I learned was that software architecture matters more than implementation details.
Rather than optimizing prematurely, I focused on designing clear abstractions:
Records represent stored data.
Collections organize records.
Indexes perform retrieval.
Registries manage index discovery.
Persistence manages durability.
By keeping these responsibilities separate, the codebase remained easy to extend while avoiding unnecessary complexity.
The Result
The final result was Quantara: a local-first vector database engine supporting collections, semantic search, metadata filtering, persistence, custom indexing, and multiple similarity metrics.
More importantly, building Quantara gave me a deeper understanding of how vector databases work internally and exposed me to all the pain points to build a system that mimics a real-world scenario.
What started as a learning project ultimately became a reusable Python package and a foundation for future work in approximate nearest neighbor search, vector quantization, and AI infrastructure.
A Rough Example
The Quantara API looks like the following:
from quantara import Database
db = Database("my_db")
db.insert_doc( name="Artificial Intelligence", vector=embedding )
results = db.search_doc( embedding, top_k=3 )
Assume embedding is the vector that the user has pre-calculated for the texts.
Lessons learned
While building this project, I have learned a lot of things. First of all, building a very basic database is not a piece of cake. One has to deal with a multitude of concepts and topics just to scratch the surface. I believe this is what I have done right now... just scratched the surface, but I have learned so many concepts along the way.
The second thing is that indexes need to be separate from storage, at least in my implementation. Then comes the plug-in system to make future integrations easy for anyone using it. And lastly, persistence of data is often harder than CRUD, at least in my opinion. CRUD operations are always easy; it's always the ACID that makes or breaks the deal. I think the current version of Quantara is just a baby in terms of its development. Hope I keep building it to grow into a beast.
Packaging Quantara
The packaging of Quantara was not a very difficult process. The most important things to have when packaging are the code directory, the README.md file, pyproject.toml, LICENSE, account for PyPI, and some hope that your code does not break :)
You can always use Quantara for testing and building your own projects by just running the following command:
pip install quantara