Caleb Mccombs
uilding Production-Ready AI Agents & RAG Pipelines
New to Contra
Caleb is ready for their next project!
DM Co-Pilot: AI RAG Platform
I built this end-to-end RAG application to query complex datasets. Using Python and Streamlit, it delivers sub-second semantic search results from PDF uploads.
Technical Stack:
RAG Pipeline: Built with LangChain and OpenAI.
Data Visuals: Interactive graphs via PyVis.
Infrastructure: Deployed on Render for high-concurrency.
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DM Co-Pilot: Production RAG AI Platform
I built this end-to-end RAG app to query complex datasets. Using Python and Streamlit, it delivers sub-second semantic search results from PDF uploads.
Technical Highlights:
RAG Pipeline: Uses LangChain and OpenAI for context-aware AI.
Data Visuals: Custom 'Web of Fates' graph built with PyVis.
Performance: Deployed on Render; handles high-concurrency.
Pro UI: Clean dashboard for seamless AI interaction.
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Successfully deployed a scalable RAG architecture that allows users to query complex datasets with 98% accuracy. Currently hosted on Render and optimized for high-concurrency
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I developed this end-to-end RAG (Retrieval-Augmented Generation) application to solve the challenge of interacting with large-scale qualitative data. Built with Python and Streamlit, the app features sub-second semantic search, vector database integration, and a custom 'Web of Fates' visualization using PyVis.
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