A chatbot powered by a vector database containing all US supreme court cases
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Updated
Jun 30, 2023 - Python
A chatbot powered by a vector database containing all US supreme court cases
This repository contains an application designed to recommend scientific papers that are most similar to a given input paragraph. The application uses the llama and weaviate libraries to achieve this.
Node Proxima converts your repo into embeddings using OpenAI.
Generative AI - Use Watsonx to respond to natural language questions using RAG (context, few-shot, watson-studio, rag, vector-database, foundation-models, llm, prompt-engineering, retrieval-augmented-generation, milvus).
docker compose setup w/ postgis, pgvector, and govgis_nov2023-slim-spatial
Server over Python Faiss serverless implementation to match interfaces used in langchain
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.
Insight-Chat: Chat with your documents
Retreival Augmented Generation (RAG) chatbot for my blog
Vector Storage is a lightweight, browser-based database that enables semantic similarity searches and local storage of text vectors, offering privacy, low latency, and cost-effectiveness for NLP applications.
The open-sourced all-in-one cookbook for Retrieval Augmented Generation (RAG)
ARS: Article Retrieval System
A library to gather structured statistics on the source code files in a software repository, generate embeddings and store in a vector database.
A vector database for querying meaningfully similar data.
Pawsitive Retrieval RAG Project - Erdos Institute Deep Learning Boot Camp - Spring 2024
Jupyter notebooks for the short course "Building applications with vector databases"
A CLI chatbot that uses RAG architecture for improving and adapting LLM to specific context. It allows users to ask questions and get response directly from open-source LLMs(OpenAI, MistralAI etc.) or from the information on a website which is provided as context using the RAG architecture.
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