The official TypeScript/Node client for the Pinecone vector database

Java version of LangChain

A NodeJS RAG framework to easily work with LLMs and embeddings

Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences

.NET clients for Pinecone Vector database

A lightweight library that leverages Language Models (LLMs) to enable natural language interactions, allowing you to source and converse with data.

transform your documents into lively conversations

Chat-with-pdf app uses RAG (Retrieval Augmented Generation) to retrieve relevant context, then answers user question based on the provided context and chat history.

Use the universal VDF format for vector datasets to easily export and import data from all vector databases

Learn Cloud Applied Generative AI Engineering (GenEng) using OpenAI, Gemini, Streamlit, Containers, Serverless, Postgres, LangChain, Pinecone, and Next.js

Pinecone is a fully-fledged C# library for the Pinecone vector database. It aims to provide identical functionality to the official Python and Rust libraries. This is fork of Pinecone.NET.

OpenAI chatGPT hybrid search and retrieval augmented generation

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Seed project demonstrating how to use Pinecone Scala client

Scala client for Pinecone vector database

🪿 LinGoose is a Go framework for building awesome AI/LLM applications.

This project is creating a fullstack chatbot app that takes a document and query as input and returns what you want from that document.

This Python application creates a simple document assistant using Streamlit, pinecone (vector store) and a language model (openai) for generating responses to user queries.

This is AI Research Assistant repository using Gen AI and RAG model. This AI Research Assistant app is powered by Google Gemini. It helps in question answering about the provided research paper by uploading them through Streamlit, we save it in the Data folder and clean the document, index it by using Llamma-Index using Gemini Embedding Model.