canopy VS RocksDB

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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?

Pinecone: A scalable vector database service that facilitates efficient similarity search in high-dimensional spaces. Ideal for building real-time applications in AI, such as personalized recommendation engines and content-based retrieval systems.

In this post we’ll build a Python script that uses StripeDocs Reader, a loader on LlamaIndex, that creates vector embeddings of Stripe's documentation in Pinecone. This allows a user to ask questions about Stripe Docs to an LLM, in this case OpenAI, and receive a generated response.

Pinecone is a managed vector database service that simplifies the process of building and scaling vector search applications. It offers a simple API for embedding vector search into applications, providing accurate, scalable similarity search with minimal setup and maintenance.

In case of AIMD, I am doing this all in-memory, but you could also do this in a database (e.g. Pinecone). It all depends on how much data you have and how much compute you have available.

A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving to meet the new challenge.

You can find details in official wiki in github https://github.com/facebook/rocksdb/wiki/Basic-Operations

RocksDB: A high-performance embedded database optimized for multi-core CPUs and fast storage like SSDs. Its use of a log-structured merge-tree (LSM tree) makes it suitable for applications requiring high throughput and efficient storage, such as streaming data processing.

[RocksDB](https://rocksdb.org/) isn’t a distributed storage system, fwiw. It’s an embedded KV engine similar to LevelDB, LMDB, or really sqlite (though that’s full SQL, not just KV)

To output the top 3 rocks, our engine has to first store all the rocks in some sorted way. To do this, we of course picked RocksDB, an embedded lexicographically sorted key-value store, which acts as the sorting operation's persistent state. In our RocksDB state, the diffs are keyed by the value of weight, and since RocksDB is sorted, our stored diffs are automatically sorted by their weight.

The in-memory version of Memgraph uses Delta storage to support multi-version concurrency control (MVCC). However, for larger-than-memory storage, we decided to use the Optimistic Concurrency Control Protocol (OCC) since we assumed conflicts would rarely happen, and we could make use of RocksDB’s transactions without dealing with the custom layer of complexity like in the case of Delta storage.

I was looking at https://github.com/facebook/rocksdb/ but it seems to not allow queries by value, as my last requirmenet.

Tuning RocksDB well is a very very hard challenge, and one that I am happy to not do day to day anymore. RocksDB is very powerful but it comes with other very sharp edges. Compaction is one of those, and all answers are likely workload dependent.

If you are worried about write amplification then leveled compactions are sub-optimal. I would try the universal compaction.

- https://github.com/facebook/rocksdb/wiki/Universal-Compactio...

It's fairly challenging to write a KV database, and takes several years of development to get the balance right between performance and reliability and avoiding data loss. Maybe read through the documentation for RocksDB https://github.com/facebook/rocksdb/wiki/RocksDB-Overview and watch the video on why it was developed and that may give you an impression of what is involved.

LMDB is much more sain in the sense that it supports real ACID transactions instead of savepoints for RocksDB. The latter is heavy and consumes a lot more memory for a lot less read throughput. However, RocksDB has a much better parallel and concurrent write story, where you can merge entries with merge functions and therefore write from multiple CPUs.