How I Built Graph based AI-Powered Search Engine, All Local

Crafting the Melody of Localized AI Search, which gives sources !

Detailed Article

Objective: My journey involved building a graph-based AI-powered search engine, entirely locally,
Focusing on:
Independence: No reliance on external services like OpenAI, opting for open-source tools.
Innovation: Utilizing the latest technologies for top-notch speed and accuracy.
Flexibility: Ensuring the engine is portable, scalable, and adaptable to diverse setups and future needs.

🚀 Initial Approach: Kickstarting the project involved:
Module Creation: Using langchain for language processing and lamaindex for efficient data organization.
Exploration: Experimenting with vector DB for data storage and retrieval enhancement.
Technique Refinement: Trying different RAG techniques to improve search result generation.
Integration: Adding distributors and API servers for seamless communication between engine components.

🛠️ Challenges Faced:
As I delved deeper into the project, I encountered several hurdles that prompted me to seek alternative solutions.
Writing everything from scratch proved to be exceedingly time-consuming, hindering progress.
Additionally, accuracy issues arose, impacting the reliability of the search results.
Organizing data and establishing accurate relationships posed further challenges, complicating the engine’s functionality.
Moreover, concerns regarding speed and scalability arose, casting doubt on the engine’s ability to handle increasing demands.

🚀 How I Solutionize it:
In response to these challenges, I started on a new exploration, leading me to discover the Gen AI Stack.
This innovative solution offered a structured approach, streamlining the organization of data.
Docker emerged as a valuable tool for scalability, facilitating seamless integration with various models to enhance the engine’s accuracy.
The inclusion of langchain provided robust language processing capabilities, while the integration of neo4j significantly improved the accuracy of data relationships within the engine.

💡 Next Steps:
Distributor Integration: Balancing workload across various models for enhanced performance.
Rust Utilization: Using Rust programming language for optimized model serving.
Multi-Modal Functionality: Adding support for processing diverse data types.
Cloud Support: Enhancing accessibility and scalability through cloud integration.
Accuracy Evaluation: Exploring tools like RAGAs or LangSmith for continuous accuracy tracking.

🔗 Stay Tuned for Updates and Insights!