The Challenge: Navigating Large Knowledge Bases

Imagine a tool that could search wikis, extend seamlessly, and retrieve the information you need in seconds. No more losing time, no more forgetting why you opened Git in the first place. With this vision in mind, we embarked on a journey to revolutionize knowledge management.

Our Solution: Retrieval-Augmented Generation (RAG)

After evaluating several approaches, including using pre-trained large language models (LLMs), we opted for a cutting-edge solution: Retrieval-Augmented Generation (RAG). Unlike static LLMs that require retraining for new data, RAG retrieves information directly from up-to-date sources, making it both adaptable and cost-efficient.

How It Works: Key Components

The project is composed of three major elements:

1. Wiki ETL – A process that extracts, transforms, and loads data from our wikis into a vector database.
2. Smart Server – A server that processes user queries, retrieving relevant information from the vector database, and provide LLM model with the necessary context.
3. Smart Wiki Extension – A user-friendly interface that integrates into your IDE, enabling seamless knowledge retrieval.

With these components working together, users can ask the system anything, from specific tasks to complex questions, and receive an instant, accurate response.

Confidentiality and Data Privacy

we understand the critical importance of confidentiality and data privacy in today’s digital world. Our Smart Wiki project is built with strong privacy safeguards to ensure that sensitive information remains protected at all times.

The Future: Expanding Capabilities

While the project is still in development, the future holds exciting possibilities, from improving the system’s accuracy to integrating it with more platforms. Our goal is simple: to make knowledge retrieval as efficient and effortless as possible.