ContextIQ

Final Devlog: Hi everyone , I have successfully completed the project and now I am going to submit this project.

devlog #6: Hi everyone, I have a good news and bad news.While doing the logging i found out that the app had too many bugs so fixing those bugs costed me alot of time. The good news is that I have decided to implement some exciting new features like FOLLOW UP QUESTIONS and FLASH CARDS in ContextIQ this will take this project to the next level. I am currently half way through in implementing these features once I implement those features I will ship this project right away.

Hello Everyone, I am back with an update. So today I have completed my frontend. Now I just have to debug and test the whole app to find any bugs. Once I am finished with the testing I will try to implement some new features to make it more attractive.

ContextIQ : RAG Agent
Update:
Hi everyone! I am almost done with the backend now i am moving towards the frontend.
The UI is one of the most important and the very first thing a user notices. I am trying to keep the UI minimal and to the point.
will give you guys another update once I finish the UI as well.

One of the most important milestones of ContextIQ is now complete—the entire document ingestion pipeline is up and running! 🎉This pipeline is responsible for transforming raw enterprise documents into an AI-ready knowledge base. Every document is loaded, cleaned, intelligently chunked, converted into vector embeddings, and stored inside Qdrant, making it ready for fast and accurate semantic retrieval.With the foundation in place, ContextIQ can now efficiently understand and search through large collections of documents. The next step is building the retrieval and reasoning pipeline that will enable the AI to generate accurate, context-aware answers with source citations.The brain is officially being built. 🧠⚡

Every great AI assistant starts with one challenge: it needs knowledge before it can answer questions.Today I built the document loading pipeline for ContextIQ. Instead of manually copying text into the AI, the system can now automatically read enterprise documents and prepare them for processing. This is the first step in transforming static PDFs into an intelligent knowledge base.Although it might seem simple, this stage is incredibly important. A reliable document loader ensures that the AI receives clean, structured information, laying the foundation for accurate retrieval and trustworthy responses later in the pipeline.Next up, I’ll be implementing smart chunking and embeddings so ContextIQ can understand and retrieve the most relevant information from large document collections.

Meet ContextIQ: My first attempt at building an Enterprise-Grade RAG application. 🤖 From Portkey LLM Gateways to Logfire observability, I’m implementing the same tools used by professional AI engineers to ensure this system is robust, scalable, and secure. Stay tuned for the next update! 🛠️ #BuildInPublic #ContextIQ #HackathonLife