canopy VS NSubstitute

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.

In the dynamic world of web development, Python has emerged as a dominant force, especially in backend development – the primary focus of this blog post. Although it's worth mentioning that there are ongoing efforts to use Python for the frontend as well, like Reflex (previously known as Pynecone, they presumably had to change their name because of Pinecone vector database), which even garnered support from Y Combinator. Samuel Colvin (creator of Pydantic) is also working on FastUI (he literally just released the first version in December 2023).

a. you might do so purely out of argo cult, i.e. because someone told you this was the right thing to do™, and that's a silly exercise. b. you could also be doing this for a good reason: to use the interface with a mocking tool like NSubstitute

When comparing it to one of its most well-known alternatives, NSubstitute, which has "only" reached 85.6 million downloads, it is fair to say that Moq is the most widely used mocking library in the .NET ecosystem.

Maintainer rejected the PR. They have temporarily disabled the integration in https://github.com/moq/moq/pull/1375 but kept the SponsorLink project reference in the source code. It seems like their intent is to re-enable the integration at a later point :(

NSubstitute [0] might be an alternative. Or to fork Moq pre-4.20.

[0] https://nsubstitute.github.io/

In terms of mocking there are several frameworks you can use, but I've mainly relied on Moq and NSubstitute. Within this demo, I'm going to use NSubstitute as I've found it a little easier to use.

it will change the URL from https://github.com/nsubstitute/NSubstitute to https://github.dev/nsubstitute/NSubstitute (or you can just nav there yourself).

NSubstitute

🔗 NSubstitute documentation | NSubstitute

If you use NSubstitute (the best mocking .NET framework), then you have to install this small yet useful plugin right now. It kindly generates mocks and Arg.Is / Arg.Any.

Another nice mocking framework is NSubstitute https://nsubstitute.github.io/