#Pinecone

Canopy is an open-source framework built on the Pinecone vector database that streamlines the development of Retrieval Augmented Generation (RAG) applications. It offers efficient text data handling through chunking, embedding, and optimized query processes while managing chat history effectively. Its configurable server setup ensures seamless integration with existing or custom chat applications. Additionally, the CLI tool allows interactive evaluation of RAG workflows, enhancing users' exploration of context retrieval and generation.

Discover how to deploy a QA bot with OpenAI API, Pinecone database, and Streamlit. This guide provides a step-by-step process to establish a basic knowledge-retrieval system. Learn to prepare text for embedding models and upload them to Pinecone DB. Explore integrating a chat app in Streamlit to query movie data from Pinecone's embeddings, all while managing environment variables and API keys effectively.

This innovative project seamlessly integrates GPT, Pinecone, and LangChain to deliver a versatile chatbot platform. It allows users to create diverse chat topics, manage numerous files with embedded content, and operate multiple chat windows efficiently within a browser. The system supports various file formats, such as .pdf, .docx, and .txt, transforming them into embeddings stored within Pinecone namespaces. Automatic storage and retrieval of chat histories are ensured via local storage. Designed for both development and production environments, it offers extensive customization options to meet unique needs. Originally derived from a GPT-4 and LangChain repository, this iteration introduces substantial updates and enhancements, focusing on streamlining chatbot customization and management.

The autonomous-hr-chatbot is a prototype that utilizes AI to manage HR inquiries efficiently. Through the integration of LangChain's agents, ChatGPT, and Pinecone's vector database, this application processes HR queries with ease. It features a Streamlit front-end for seamless interaction and includes tools such as ChatGPT-generated HR policy documents, a dummy employee data handler, and a calculator for complex computations. This project demonstrates the potential of AI tools in improving organizational workflows and data management.

Explore a collection of sample applications and Jupyter Notebooks to understand Pinecone's vector databases and AI techniques. This repo includes examples for both practical use and educational purposes, maintained by Pinecone experts. Ideal for developers aiming to experiment and create diverse AI applications with detailed guides and documentation. Contributions are welcome to enhance this community resource.

The project employs OpenAI's advanced models for creating a semantic search tool that works across YouTube playlists, facilitating precise retrieval of podcast moments. It preprocesses transcripts, generates embeddings, and utilizes a Pinecone index for efficient searches. Featuring a Next.js frontend on Vercel, the project offers capabilities like generating timestamped thumbnails, advanced sorting options, and supports AI-driven podcast discovery.

Explore the Pinecone TypeScript SDK for Node.js, which integrates vector database functionalities into server-side applications. Access comprehensive documentation, migration guides, and examples focused on semantic search, article recommendation, and image search. Efficiently upgrade to newer versions, configure indexes, and secure your API keys, utilizing features like deletion protection and proxy configurations. Leverage Pinecone's scalable database for enhanced data management.

Utilize the OP Stack for efficient document upload and accurate answer retrieval. This tool facilitates interaction with content in a user-friendly way, improving knowledge extraction using OpenAI embeddings and Pinecone database, ideal for managing extensive libraries.

Discover how to create a custom chatbot using OpenAI, TypeScript, LangChain, and Pinecone, designed to enhance retrieval from Notion databases. This guide presents detailed development steps, including project setup, Notion export integration, and cloud deployment using Vercel. With Pinecone's efficient data embedding and retrieval capabilities, the project facilitates easy access to Notion-stored information, offering a sophisticated, AI-driven solution for managing professional knowledge bases.

Discover the integration of OpenAI Embeddings and Pinecone's vector database to build a semantic search engine via this demo app. With technologies like Next.js, Prisma, and Tailwind CSS, it demonstrates sophisticated search methodologies, accessible through free tiers of Pinecone and OpenAI. Suitable for developers seeking to understand semantic text search methodologies.

Learn how to utilize GPT-4 and LangChain to build advanced chatbots capable of handling multiple large PDF files efficiently. This project uses Pinecone, Typescript, and Next.js to guide the creation of scalable AI applications. It includes comprehensive instructions on repository cloning, package installation, setup, and transforming PDFs into embeddings for effective data retrieval. Additionally, it offers troubleshooting tips to ensure proper integration of key components like Pinecone vectorstore and the OpenAI API. Suitable for developers aiming to leverage AI for large-scale document management, this repository provides a detailed approach to modern chatbot development.

The application enables interaction with documents and websites through training a custom GPT on specific content. It supports uploading or defining web data to create OpenAI embeddings stored in Pinecone for similarity search. Compatible with formats like PDF, DOCX, MD, TXT, PNG, JPG, HTML, and JSON, with future support for CSV and PPTX formats. The interface offers real-time interaction with a Perplexity-style look, utilizing OpenAI's GPT-3 for precise, specific discussions.

Documentation Buddy is a Telegram chatbot leveraging GPT and OpenAI technology to analyze uploaded documents, including PDFs, and respond to questions quickly. This tool is designed for efficient information retrieval in data-heavy scenarios. Users need OpenAI and Pinecone accounts as well as s3-like storage. Deployment can be direct via DigitalOcean or manual with OpenAI's embedding project. Users can customize interactions with editable prompts and environment variables.