Zep: Long-Term Memory for AI Assistants.
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Updated
Jun 24, 2024 - Go
Zep: Long-Term Memory for AI Assistants.
The Fast Vector Similarity Library is designed to provide efficient computation of various similarity measures between vectors.
Platform for building personalized AI applications
Question-Answering App Over Your Own Data with LLamaindex and ElasticSearch !
Memory Management Service, a Long Term Memory Solution for AI
Question Answering Generative AI application with Large Language Models (LLMs) and Amazon OpenSearch Service
Simple and pure Julia-based implementation of ChatGPT retrieval plugin logic
High-level ElasticSearch client for Julia
How to use configure haystack to use weaviate
DocuMentor is a sophisticated chatbot application designed to assist users in extracting valuable information from uploaded PDF documents. Users can upload PDF files, chat with the AI chatbot to ask questions or seek information related to the document, and receive well-informed responses.
Hybrid Search demo on Movies Dataset using Couchbase with Native Python SDK & LangChain Vector Store integration & Streamlit
🔎 A vector based image search engine using Visual Transformer model type.
⚡️ Build quick LLM pipelines for AI applications
MediCopilot uses AI to assist healthcare professionals
This Python Flask application is designed to process and rank resumes based on job descriptions. It uses Azure's Document Analysis Client for document processing, and a MongoDB database for storing job descriptions and resumes. The application also generates embeddings for the processed documents using AzureOpenAI.
A generative AI based smart information retrieval system featuring Hybrid Contextual Search,
Q&A Chatbot Demo using Couchbase, LangChain, OpenAI and Streamlit
The Project "Vector Search RAG" utilises advanced frameworks and language models (LangChain and OpenAI APIs) to enhance query responses by retrieving relevant documents and generating contextually accurate answers. This repo contains End-to-End implementation of RAG for training LLMs in custom data.
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