An LLM based Chatbot using Langchain
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Updated
Apr 19, 2024 - Jupyter Notebook
An LLM based Chatbot using Langchain
🤖 DataSciencePilot 🚀 is an innovative chat-based interface designed to interact with custom PDF files. It leverages the power of Pinecone for efficient vector database management and LLaMA-2 for advanced query response capabilities.
console based game based on a llm
RAG-based Streamlit app that uses Langchain, OpenAI Embeddings, GPT, and Pinecone Vector Database to answer questions about a user-provided document
Retrieval Augmented Generation Example with SemaDB
🔎📚 This document processing system is designed to efficiently analyze user documents and provide accurate responses to user queries related to the content. Powered by advanced algorithms, it offers a seamless experience for users seeking insights or information within their documents.
Spring AI RAG vector store sentiment search on custom data loaded by tiko with a REST API.
AI semantic search for discord channels
A chatbot powered by a vector database containing all US supreme court cases
Utilizing Redis Vector Similarity Search, this demo project streamlines expense categorization from bank transactions. The approach harnesses pre-trained models, sidestepping the need for finetuning. This ensures efficient, accurate expense categorization without complex model adjustments
An example of Named-entity Recognition and relation mapping using an LLM and Vector Database. Also includes a Branching Hybrid-Search Chatbot to utilize extracted relations.
A GPT powered CLI chatbot to talk with your speeches 🤷🏾♂️
Python 3.10-slim with VectorDB (vectordb2==0.1.9) and certain models initialized, split by image tag for efficiency.
The open-sourced all-in-one cookbook for Retrieval Augmented Generation (RAG)
This project is based on the Cocktail Recommendation System, which utilizes the Retrieval-Augmented Generation (RAG) approach to provide users with personalized cocktail recommendations based on their queries.
Simple conversational agent with LLM and RAG
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