A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere) using AWS CDK on AWS

An open-source, high-performance SQL vector database built on ClickHouse.

JS/TS library to make to easy to build with LLMs. Full support for various LLMs and VectorDBs, Agents, Function Calling, Chain-of-Thought, RAG, Semantic Router and more. Base on the popular Stanford DSP paper. Create and compose efficient prompts using prompt signatures. 🌵 🦙 🔥 ❤️ 🖖🏼

LangStream. Event-Driven Developer Platform for Building and Running LLM AI Apps. Powered by Kubernetes and Kafka.

A collection of apps powered by the LangChain LLM framework.

AI Native database for embedding vectors

Awesome List of Vector DB resources

Zephyr 7B beta RAG Demo inside a Gradio app powered by BGE Embeddings, ChromaDB, and Zephyr 7B Beta LLM.

pinecone.io Library in Elixir

Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 ,Agents.

ABAP Vector Search Library

AWS SageMaker, SeldonCore, KServe, Kubeflow & MLflow, VectorDB

NotionGPT, a practical tool built on top of ChatGPT large language model, make it your note-taking assistant!

Python SDK for FirstBatch: Real-time personalization using vectorDBs

A universal Qdrant table frontend based on transformers.js

Deployment of chromadb into AWS resources through terraform

This project demonstrates how to parse emails, process them using OpenAI's GPT-3.5, and load the data into a Weaviate vector database for enhanced search capabilities. Utilizing few-shot prompts and parallel processing, it showcases the power of combining NLP techniques with vector search.

AI Toolkit for Node.js (LLM, Image Generation, Embeddings, Vector Search)

ChatPDF is a Streamlit app allowing users to query PDF & DOCX content via natural language. It indexes documents for conversational interactions using OpenAI's API & FAISS for rapid matching. Upload, ask, and get insightful responses instantly.

Question Answering application with Large Language Models (LLMs) and Amazon Aurora Postgresql using pgvector