Blockchain

Development

Internship

**First thing first: the team and the brainy stuff**

Before starting the internship, I wasn’t really feeling nervous as I felt I had a good understanding of the basics of both Solidity and blockchain. Also, my colleague shared some insider information with me regarding the atmosphere, so I wasn’t surprised by the openness of the team and their willingness to help me and the other interns.
Firstly, we slowly studied the stack and the task itself. Then we got familiar and learned to work with the new blockchain technology that enables easier management of smart contracts. We were excited to start developing our own solutions!

Since blockchain is still a quite new technology, you can learn something new about it every day, as long as you know what to search for. Us interns were involved in the whole development cycle starting from the very beginning. First, we went back and forth researching the technologies and deciding which ones would be the best for this project and the internship, before deciding on Scaffold-ETH.

**You’ve heard it several times already: tHErE aRe No STuPid qUEsTioNs!**

![mulan-mushu.gif](https://strapi.suprabit.eu/uploads/mulan_mushu_eea0c9c292.gif)

 

Well, I’m going to say it again, there really are no stupid questions in Suprabit. Around here, everybody is eager to help each other, be it solving a bug or replacing the coffee pod. Don’t waste your time asking yourself “What will they think of me asking many questions or the same question many times?” because nobody will turn their back or roll their eyes at you. They all know the common goal is gaining understanding and moving forward.
 
The atmosphere is chill and most enjoyable. Once you've had enough of the keyboard tapping, you can make your way to our local table tennis station situated inside the office. Ask away, someone will surely join you! (Beware of the boss, you have to let him win, otherwise...).
 
 
**Future opportunities**

The internship program was well planned and adapted to students with or without previous knowledge of blockchain technology. The people were great, and I enjoyed the entire experience. I'm grateful for the things that I’ve learned during the internship and I'm happy that I was able to stay at Suprabit as an employee after the internship had finished. I know that Suprabit will continue to support my growth and make me a better blockchain developer (and a better table tennis player)!
 
![DSC00401-min.jpg](https://strapi.suprabit.eu/uploads/DSC_00401_min_d756afe30c.jpg)



large_WhatsApp Image 2022-10-26 at 13.44.46.jpeg

medium_WhatsApp Image 2022-10-26 at 13.44.46.jpeg

small_WhatsApp Image 2022-10-26 at 13.44.46.jpeg

thumbnail_WhatsApp Image 2022-10-26 at 13.44.46.jpeg

WhatsApp Image 2022-10-26 at 13.44.46.jpeg

This summer, I decided to spend my summer-break gaining as much knowledge on blockchain as possible. After a couple of hours of research, a few interviews and one solved solidity smart contract task, I landed my internship at Suprabit.

My Blockchain Internship at Suprabit!

Vector databases are essential for AI-driven applications, enabling efficient storage, retrieval, and querying of high-dimensional embeddings. These databases power similarity search and are widely used in natural language processing (NLP), recommendation engines, image recognition, and retrieval-augmented generation (RAG) tasks. OpenAI offers vector embedding capabilities through its Embeddings API, but how do they compare to dedicated vector databases? Let’s explore their architecture, indexing strategies, and retrieval performance.

## Understanding Vector Databases and Search Mechanisms

A vector database stores multi-dimensional feature representations (embeddings) and enables similarity searches using approximate nearest neighbor (ANN) algorithms. Unlike traditional relational databases that rely on structured querying (SQL), vector databases leverage distance metrics such as:

- **Cosine similarity**: Measures the angle between two vectors, commonly used in NLP tasks where semantic similarity matters.
- **Euclidean distance**: Computes the straight-line distance between two points in vector space, widely applied in image and video retrieval.
- **Manhattan distance**: Computes the sum of absolute differences between vector dimensions, useful in some specialized clustering tasks.

## Indexing Methods for Efficient Vector Search

Vector search performance relies on optimized indexing methods that structure and organize vectors to enable efficient retrieval. These methods significantly impact search speed, memory efficiency, and accuracy, making their selection crucial for real-world applications. Some of the most commonly used indexing techniques include:

### HNSW (Hierarchical Navigable Small World Graphs)

HNSW builds a multi-layer graph structure where each node represents a vector and edges connect nearest neighbors. This structure allows for logarithmic search complexity, making it significantly faster than brute-force approaches. Due to its efficiency, HNSW is widely used in high-performance vector databases, such as FAISS and Milvus, to support low-latency, high-accuracy searches in large-scale applications.

### IVF (Inverted File Index)

IVF organizes vectors into clusters, known as Voronoi cells, which helps narrow down the search space. During retrieval, only the closest clusters are searched rather than the entire dataset, improving query efficiency. However, its accuracy depends on the clustering granularity—choosing too few clusters leads to coarse approximations, while too many increase computational overhead. This method is commonly paired with Product Quantization (PQ) for better memory efficiency.

### LSH (Locality-Sensitive Hashing)

LSH uses hash functions to assign similar vectors to the same hash buckets, allowing for rapid approximate nearest neighbor search. Unlike tree-based or graph-based methods, LSH achieves sub-linear search complexity but at the cost of reduced accuracy. It is particularly useful in real-time applications, such as recommendation systems and anomaly detection, where approximate matches are sufficient and low-latency queries are prioritized over perfect accuracy.

### PQ (Product Quantization)

PQ compresses high-dimensional vectors by splitting them into smaller subspaces and quantizing each subspace separately. This reduces storage requirements while maintaining retrieval quality. The technique is often combined with IVF (IVF-PQ) to enable fast searches with lower memory consumption, making it particularly useful for applications dealing with billions of vectors. Although it trades off some precision for efficiency, PQ remains one of the best techniques for memory-constrained environments like mobile and edge AI applications.

## OpenAI’s Approach to Vector Storage and Retrieval

OpenAI provides a managed Vector Store integrated into its platform. OpenAI’s Vector Store allows users to upload files, which are automatically parsed, chunked, and embedded using OpenAI’s embeddings. This enables semantic search within the assistant framework, allowing for context-aware retrieval of relevant information.

The Vector Store is designed for applications where users need efficient document retrieval without managing the complexities of an external vector database. However, since OpenAI does not expose its indexing structure or allow direct control over vector storage, it functions more as a black-box solution compared to fully customizable vector databases.

For users who require fine-tuned indexing strategies or self-hosted vector search, OpenAI’s Vector Store may not be sufficient, and integrating with external databases would be a better choice. However, for those looking for a fully managed solution that seamlessly integrates with OpenAI’s models, the Vector Store can simplify implementation and retrieval workflows.

## Using OpenAI’s Embeddings API

To generate vector embeddings using OpenAI’s Embeddings API and store them in a vector database, you can follow this Python example:

```python
import openai

# Replace 'your-api-key' with your actual OpenAI API key
openai.api_key = 'your-api-key'

# Generate embeddings for a text input
response = openai.Embedding.create(
    input="What is the meaning of life?",
    model="text-embedding-ada-002"
)

# Extract the embedding vector
embedding_vector = response['data'][0]['embedding']
print(embedding_vector)
```

This generates a high-dimensional vector representation of the given text input, which can be stored in a vector database like FAISS, Pinecone, or Weaviate.

OpenAI does not provide a standalone vector database but instead offers an Embeddings API that converts text into dense numerical representations. These embeddings can be stored in external vector databases such as:

### Pinecone

A fully managed, cloud-native vector database optimized for real-time similarity search. It is particularly useful for enterprise-scale applications where low-latency and high availability are critical. Pinecone abstracts the complexities of vector indexing and retrieval, allowing developers to focus on integrating AI-powered search without managing infrastructure.

### Weaviate

An open-source, highly scalable vector database supporting hybrid search (text + vector-based retrieval). Weaviate enables semantic search by combining vector embeddings with traditional keyword-based search, making it a powerful tool for AI-driven applications. It also provides automated schema generation and supports GraphQL queries, allowing for flexible and structured retrieval of stored data.

### FAISS (Facebook AI Similarity Search)

A fast, self-hosted similarity search library designed for large-scale vector retrieval. Developed by Facebook AI, FAISS is optimized for high-throughput similarity searches on GPUs and CPUs, making it ideal for applications dealing with billions of embeddings. Its support for multiple indexing techniques, such as HNSW and IVF-PQ, allows users to balance accuracy and performance based on their specific needs.

### Milvus

An open-source vector database optimized for distributed storage and retrieval. Milvus is designed for high-scale AI workloads and supports various indexing strategies, including IVF, HNSW, and ANNOY. It seamlessly integrates with machine learning pipelines, making it an excellent choice for AI-driven recommendation systems, anomaly detection, and multimedia search.

### Qdrant

A high-performance vector search engine optimized for deep learning applications. Qdrant is designed to handle large-scale neural network embeddings efficiently, providing robust indexing and querying capabilities. It supports filtering, metadata storage, and custom scoring mechanisms, making it a great fit for personalized AI-driven search experiences.

## Strengths of OpenAI’s Embeddings API

- **High-Dimensional Feature Representation**: OpenAI’s models generate highly contextualized embeddings, capturing deep semantic relationships for NLP, search, and retrieval tasks.
- **Transformer-Based Context Awareness**: OpenAI embeddings leverage deep learning techniques, such as self-attention mechanisms, ensuring embeddings retain meaning beyond simple word similarity.
- **Scalability via External Databases**: OpenAI’s API is designed to integrate with cloud-based vector databases, allowing for easy deployment and scaling.
- **Optimized for RAG (Retrieval-Augmented Generation)**: OpenAI embeddings power knowledge-augmented AI applications, enabling intelligent search, AI chatbots, and summarization.

## Limitations and Considerations

- **No Native Indexing or Search**: OpenAI does not store or index embeddings, requiring integration with external vector databases.
- **Latency Overhead**: API-based embedding retrieval can introduce network latency, which may be slower than self-hosted solutions.
- **Cost Considerations**: OpenAI’s embeddings API is priced per token, making it more expensive than self-hosted vector databases for large-scale workloads.
- **Lack of Customizability**: Unlike self-hosted solutions (FAISS, Milvus, Weaviate), OpenAI’s API does not allow fine-tuned indexing optimizations.

## Comparing OpenAI’s Embeddings API with Dedicated Vector Databases

<table style="border-collapse: collapse; width: 100%;">
  <thead>
    <tr>
      <th style="border: 1px solid #ccc; padding: 8px;">Feature</th>
      <th style="border: 1px solid #ccc; padding: 8px;">OpenAI Embeddings API</th>
      <th style="border: 1px solid #ccc; padding: 8px;">FAISS</th>
      <th style="border: 1px solid #ccc; padding: 8px;">Pinecone</th>
      <th style="border: 1px solid #ccc; padding: 8px;">Milvus</th>
      <th style="border: 1px solid #ccc; padding: 8px;">Weaviate</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style="border: 1px solid #ccc; padding: 8px;">Indexing Type</td>
      <td style="border: 1px solid #ccc; padding: 8px;">No built-in storage</td>
      <td style="border: 1px solid #ccc; padding: 8px;">IVF, HNSW, PQ</td>
      <td style="border: 1px solid #ccc; padding: 8px;">HNSW, LSH</td>
      <td style="border: 1px solid #ccc; padding: 8px;">IVF, HNSW</td>
      <td style="border: 1px solid #ccc; padding: 8px;">HNSW, LSH</td>
    </tr>
    <tr>
      <td style="border: 1px solid #ccc; padding: 8px;">Scalability</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Cloud API</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Self-hosted</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Fully managed</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Distributed</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Hybrid cloud</td>
    </tr>
    <tr>
      <td style="border: 1px solid #ccc; padding: 8px;">Latency</td>
      <td style="border: 1px solid #ccc; padding: 8px;">API call overhead</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Low</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Optimized</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Moderate</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Optimized</td>
    </tr>
    <tr>
      <td style="border: 1px solid #ccc; padding: 8px;">Customization</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Limited</td>
      <td style="border: 1px solid #ccc; padding: 8px;">High</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Medium</td>
      <td style="border: 1px solid #ccc; padding: 8px;">High</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Medium</td>
    </tr>
    <tr>
      <td style="border: 1px solid #ccc; padding: 8px;">Cost Efficiency</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Token-based pricing</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Free (self-hosted)</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Paid (SaaS)</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Open-source</td>
      <td style="border: 1px solid #ccc; padding: 8px;">Paid & Open-source</td>
    </tr>
  </tbody>
</table>


## When to Use OpenAI’s Embeddings API?

Use OpenAI’s embeddings if you need:

- Seamless integration with LLMs: OpenAI embeddings are fine-tuned for natural language applications that require semantic understanding.
- A managed, cloud-based solution: Ideal for users who don’t want to manage vector databases manually.
- High-quality feature extraction: OpenAI embeddings outperform traditional word embeddings for text similarity, ranking, and clustering tasks.

## When to Choose an Alternative Vector Database?

Consider alternative vector databases if you need:

- Self-hosted indexing with full control: FAISS or Milvus offer customizable indexing strategies and on-premises deployments.
- Real-time, low-latency vector search: Pinecone and Weaviate provide high-speed retrieval with minimal infrastructure management.
- Lower costs for large-scale storage and retrieval: Open-source solutions eliminate API-based pricing, making them more cost-efficient.

## Conclusion

OpenAI’s Embeddings API provides high-quality vector representations that excel in NLP and AI-driven applications. However, for production-grade vector search and large-scale retrieval, dedicated vector databases like FAISS, Pinecone, or Weaviate offer better performance and cost efficiency. The choice depends on your specific use case—whether prioritizing seamless LLM integration, scalability, or cost-effective high-performance search.

large_blogpost.png

medium_blogpost.png

small_blogpost.png

thumbnail_blogpost.png

blogpost.png

Discover whether OpenAI’s Embeddings API is the right fit for your vector search needs. Compare it with top vector databases like FAISS, Pinecone, Milvus, and Weaviate.

 Are OpenAI’s Vector Databases Good Enough for Your Needs?

Most employees [spend 1.8 hours every day to 9.3 hours per week ](https://www.proprofskb.com/blog/workforce-spend-much-time-searching-information/) searching and gathering information they need to get their job done. This lost time doesn’t just affect individual performance, it can slow down projects, create frustration, and ultimately hinder overall business growth.

This is where a well-structured knowledge management system can make a difference. By making information easy to find and share, you can empower your employees to focus on meaningful work rather than spending time digging through countless documents and emails. A good knowledge management approach can enhance collaboration, streamline decision-making, and help your organization work more effectively as a whole.

Read on to know more about the concept of knowledge management and its significance in organizational setting

## What is Knowledge Management?

Knowledge management is the process of creating, organizing, and sharing information within your company to ensure that it is easily accessible to everyone when needed. Essentially, it's about building a reliable knowledge base for your organization's benefit. 

In simple terms, Knowledge management promotes a culture of collaboration and knowledge sharing, encouraging growth and continuous learning among team members.

## Managing Different Forms of Knowledge

Before we get deeper into what Knowledge management is, let's look at the different forms of knowledge: 

- **Explicit Knowledge**: This is information that can be easily documented, like policies, training manuals, and guidelines. They are usually stored in documents or databases and shared across the team.

- **Implicit Knowledge**: This refers to the practical know-how people acquire through experience. While it may not be explicitly documented, it plays a crucial role in decision-making processes. 

- **Tacit Knowledge**: Tacit knowledge is highly personal and based on experience. It’s challenging to document sometimes since its about skills and insights developed over time. Examples include troubleshooting issues or managing client relationships. Though difficult to capture, tacit knowledge holds immense value in real-world applications.

## Challenges in Managing Knowledge

Without a well planned out knowledge management strategy, organizations usually face the following challenges: 

- **Information Silos**: Teams that don't share information create barriers that hinder collaboration and productivity. For instance, one department might have valuable data that could enhance another team's strategy, but without sharing, those insights remain isolated.

- **Duplicate Efforts**: It’s incredibly frustrating to spend hours working on a task only to discover that another team has already completed it. Without a centralized knowledge system, duplicated efforts are inevitable, leading to wasted time and energy.

- **Inconsistencies**: When people each follow their own methods without alignment on processes and policies, inconsistencies arise. This can lead to confusion and prevent employees from reaching their full potential.

- **Missed Collaboration Opportunities**: When information isn’t easily accessible, individuals may miss out on opportunities to build on each other's work, which can hinder creativity and overall team effectiveness.

## Benefits of Knowledge Management

Here are some key ways that good knowledge management can help:

- **Team Consistency**: A central knowledge hub ensures that everyone has access to up-to-date information quickly. This minimizes errors and speeds up tasks, so no one has to waste time digging through old emails or outdated files, everyone knows exactly where to find the answers.

- **Identifying Skill Gaps**: Consolidating all knowledge makes it easier to identify areas where skills and knowledge are lacking. This allows the organization to provide targeted training or recruit new talent as needed.

- **Knowledge Retention**: People come and go, but the knowledge they hold should remain with the organization. A well-managed knowledge base ensures that critical information is not lost, facilitating smooth transitions for new employees and enabling continuity for ongoing projects.

- **Enhanced Data Security**: Centralizing information also makes it easier to safeguard data by restricting access to only authorized personnel, ensuring compliance with data protection regulations.

## Tips for Establishing an Effective Knowledge Management System

Creating an good knowledge management system involves careful planning. Here are some key tips for establishing a solid knowledge management system:

1. **Identify Your Knowledge Needs** : Start by identifying the types of knowledge that your organization needs to manage. This can include documents like policies, procedures, FAQs, training materials, and insights from past projects. Assessing what knowledge is crucial to your team’s work will help you prioritize and organize the information effectively.

2. **Select the Right Tools** :  The tools you choose will be the backbone of your knowledge management system. Depending on your needs, these tools could range from, **Knowledge Bases** or even  **Knowledge Management Portals**. These tools provide a central repository where all important company documents are stored.
   
3. **Use Conversational AI bot** :  Traditional knowledge management systems often require employees to search through documents manually, which can be time-consuming and frustrating.  Instead of searching, employees can ask an AI-powered tool questions directly to get quick, accurate answers. For example with **SupraChat** an AI conversational assistant, employees can quickly access information through natural, conversational queries. This eliminates the need to manually search through extensive documents.

4. **Keep Knowledge Up-to-Date** : A knowledge management system is only as good as the information it contains. Regularly update content to ensure it remains accurate and relevant. Assign responsibility to specific individuals or teams to maintain certain areas of the knowledge base. Tools like AI assistants can also help identify outdated or redundant information that needs revision.

5. **Train Your Team** : Once you have a knowledge management system in place, train your team on how to use it. Hold workshops or training sessions to demonstrate how to search for information, how to contribute new content, and best practices for using the system efficiently. By investing in training, you can help maximize the potential of the system and ensure everyone is making the most out of it.

## Conclusion: Building a Unified Knowledge Foundation

Having a knowledge management system can unify your team and significantly impact your company’s success by promoting collaboration and fostering innovation through shared information in a cohesive work environment. By putting the right strategies in place, you can transform knowledge into a powerful tool that boosts productivity, supports growth, and helps your team excel in their roles.


large_cover.png

medium_cover.png

small_cover.png

thumbnail_cover.png

cover.png

Explore smarter ways to manage knowledge that drive efficiency, innovation, and seamless team collaboration

My Blockchain Internship at Suprabit!

If you like this article, we're sure you'll love these!

Are OpenAI’s Vector Databases Good Enough for Your Needs?

Knowledge Management: Applications for Modern Enterprises