AI Chat Assistant

What it does

This platform utilizes advanced semantic search algorithms to analyze and retrieve relevant data from this diverse content repository. After processing the input prompt, the system performs a semantic search to fetch pertinent content, which is then used as references for the language model to enhance the accuracy and relevance of its outputs.

The platform is designed to handle inputs across a diverse formats including images, YouTube links, and pdf files. For videos, it fetches metadata through the YouTube API, which is then effectively summarized using a LLM to provide concise and relevant content overviews. Similarly, the system is equipped to summarize content from images, ensuring a comprehensive and versatile user experience that accommodates various types of media.

Additionally, the platform incorporates the capability to generate images using the Stable Diffusion algorithm, further enriching the user interaction experience by providing visual content generation based on textual prompts. The system stores generated images in Pinata (IPFS) and also maintains a comprehensive chat history, enabling seamless continuity in interactions and the ability to reference previous conversations when needed.

How we built it

NextJS Langchain LangGraph Huggingface API Pinecone Pinata Firebase

Accomplishments that we're proud of

Advanced Semantic Search:

Efficiently analyzes and retrieves relevant data from a vector store. Utilizes the LangGraph tool to optimize workflows by invoking the RAG tool only when necessary. Multi-Format Input Support:

Processes images, YouTube links, PDFs, and text files. Retrieves video metadata via the YouTube API and generates concise summaries using LLMs. Summarizes content from images for seamless multi-media analysis. Image Generation:

Integrates Stable Diffusion to generate images based on user-provided text prompts. Stores generated images using Pinata (IPFS) for decentralized access. Comprehensive Chat History:

Allows users to select which chats to store and easily reference previous interactions. Optimized Performance:

LangGraph tool ensures that RAG is only called when necessary, enhancing response relevance and efficiency.