LCoders

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  Air Pollution Document Summary

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  5 Day Workout Document Summary

Inspiration

time consumed to go through all unrelevant documentations and endless lectures to get most nearest to relevant required solution.

What it does

solves your problem to get most important summaries of books or pdf documents u ever needed.

How we built it

buid on top of vector db(chroma) for document embeddings, used google Gemini to leverage AI capabilities of taking summary points from document which are most important. All things are stuffed together with langchain and used python to work in sync.

Challenges we ran into

integrate diff libraries with each other and to get relevant output everytime for same document, to increase accuracy.

Accomplishments that we're proud of

What we learned

different technoloies/tools like langGraph/langchain, rag agents, Google Gemini, vector db and its working ,python.

What's next for LCoders

Optimizing our solution and response time, support img, handwritten notes summarization.

Built With

• chroma
• gemini
• langchain
• python
• streamlit
• vectordb