airweave VS canopy

I'm working on Airweave https://github.com/airweave-ai/airweave , an open-source dev tool that makes any app searchable for AI agents. it connects to a source app, db, or api and converts its contents to accessible knowledge for agents. Airweave automates authentication, ingestion, enrichment, mapping, and syncing to vector stores and graph databases of choice. you can use it via our UI, API, or SDKs https://docs.airweave.ai/

we originally built this for our previous agent startup as an internal solution to ensure agents could find the relevant data on apps they're using. We then pivoted to this after some early positive reactions and decided to open-source it.

here's a short demo: https://tinyurl.com/demo-airweave

we're two engineers/friends based in Amsterdam, NL. We just launched the project, so it's rough around the edges ofc, but we're very eager to get some feedback!

feel free to reach out to me personally if you like this!

At launch we support migrating to Postgres from Pinecone and Qdrant. You can vote for additional providers in the issue tracker and we'll reference that when deciding which vendor to support next.

Thank you. :) I don’t think of it as social media marketing but more of helping our target audience learn useful things.

Probably our learning center is what you’re thinking of. https://www.pinecone.io … The blog is more of a news ticker for product and company news.

Overview: Pinecone offers a fully managed vector database service optimized for fast and scalable similarity searches. It supports various AI and ML applications with its robust infrastructure.

As a recommended option, Bedrock suggests creating a vector store based on Amazon OpenSearch Serverless. In my opinion, OpenSearch is very expensive, so I wanted to choose a cheaper option. First, I tested MongoDB Atlas, which has a free tier. However, the free tier doesn't include the features needed to use it as a vector store in Bedrock. Since Pinecone offers a free tier that can be used as a Bedrock vector store, I used it for this test.

With the rise of LLMs(Large Language Models) and RAG(Retrieval-Augmented Generation), vector databases, like Milvus and Pinecone, are getting a lot of attention. Traditional databases are also catching up in vector search support via third-party extensions, such as pgvector for PostgreSQL and sqlite-vss for SQLite.

Interactive chat applications are becoming increasingly popular, especially those capable of understanding and processing document content. In this tutorial, we'll build a secure PDF chat AI application using Langchain, Next.js, Pinecone DB, and Arcjet.com. This application will allow users to upload PDFs and interact with an AI that can answer questions based on the content of the uploaded documents.

Pinecone — Vector database for machine learning and AI applications.

To create a PineCone account, sign up via this link: https://www.pinecone.io/

So you might have seen in my last blog post that I showed you how to accelerate video frame extraction using GPU's and Babit multimedia framework. In this blog we are going to improve upon our video frame extractor and create a video similarity search(Reverse video search) utlizing different RAG(Retrival Augemented Gerneation) concepts with Pinecone, the vector database that will help us build knowledgeable AI. Pinecone is designed to perform vector searches effectively. You'll see throughout this blog how we extrapulate vectors from videos to make our search work like a charm. With Pinecone, you can quickly find items in a dataset that are most similar to a query vector, making it handy for tasks like recommendation engines, similar item search, or even detecting duplicate content. It's particularly well-suited for machine learning applications where you deal with high-dimensional data and need fast, accurate similarity search capabilities. Reverse video search works like reverse image search but uses a video to find other videos that are alike. Essentially, you use a video to look for matching ones. While handling videos is generally more complex and the accuracy might not be as good as with other models, the use of AI for video tasks is growing. Reverse video search is really good at finding videos that are connected and can make other video applications better. So why would you want to create a video similarity search app?