canopy
xUnit
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canopy | xUnit | |
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13 | 36 | |
873 | 4,020 | |
15.1% | 1.6% | |
9.8 | 9.2 | |
25 days ago | 1 day ago | |
Python | C# | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
canopy
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How to choose the right type of database
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.
- Show HN: R2R – Open-source framework for production-grade RAG
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Using Stripe Docs in your RAG pipeline with LlamaIndex
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.
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7 Vector Databases Every Developer Should Know!
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.
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Using Vector Embeddings to Overengineer 404 pages
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.
- Pinecone: Build Knowledgeable AI
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How Modern SQL Databases Are Changing Web Development - #4 Into the AI Era
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.
- FLaNK Stack Weekly 11 Dec 2023
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Embracing Modern Python for Web Development
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).
- Canopy is an open-source Retrieval Augmented Generation (RAG) framework
xUnit
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Optimizing C# code analysis for quicker .NET compilation
Several well-known NuGet packages such as xUnit.net, FluentAssertions, StyleCop, Entity Framework Core, and others include by default a significant number of Roslyn analyzers. They help you adhere to the conventions and best practices of these libraries.
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Integration testing in Umbraco 10+: Validating document types
Most of my rules apply to document types, so let's build some tests for document types. We start by creating a new test class and a new test function and getting a list of all document types. This test is created using xUnit and FluentAssertions:
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Comprehensive Unit Testing: A Line-by-Line Approach
xUnit -> https://xunit.net/
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CI/CD Pipeline Using GitHub Actions: Automate Software Delivery
.NET / xUnit / NUnit / MSTest
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Fluent Assertions: Fluently Assert the Result of .NET Tests
This library extends the traditional assertions provided by frameworks like MSTest, NUnit, or XUnit by offering a more extensive set of extension methods. Fluent Assertions supports a wide range of types like collections, strings, and objects and even allows for more advanced assertions like throwing exceptions.
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FluentValidation in .NET
You can verify the functionality of this validator by writing the following tests (using xUnit):
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Running a XUnit test with C#?
The git repo has other runners. AssemblyRunner appears to be the best fit for an already compiled tests project, but there is a runner that can be wrapped into an MSBuild task for example.
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Setting up a simple testing project with C#
At this point you're going to see a familiar screen asking you to select a project. Here we're looking for a test project. By default, Visual Studio gives you access to 3 different testing frameworks based on your choice of project. These are MSTest, XUnit and NUnit. Ultimately, all 3 of these testing accomplish the same thing, and I've worked with all of them at various points in my career. The difference is mainly in exact syntax and documentation. Although, it's generally considered that MSTest is a little "older" than NUnit or XUnit, so I tend to see it less now. For the purposes of this demo, I'm going to go with NUnit:
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Integration tests for AWS serverless solution
xUnit unit tests tool
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Test-Driven Development
Use a testing framework: Utilize a testing framework like NUnit, xUnit, or MSTest to create, organize, and run your tests. These frameworks provide a consistent way to write tests, generate test reports, and integrate with continuous integration tools.
What are some alternatives?
ragna - RAG orchestration framework ⛵️
Shouldly - Should testing for .NET—the way assertions should be!
tiger - Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning)
NUnit - NUnit Framework
simple-pgvector-python - An Abstraction Using a similar API to Pinecone but implemented with pgvector python
Fluent Assertions - A very extensive set of extension methods that allow you to more naturally specify the expected outcome of a TDD or BDD-style unit tests. Targets .NET Framework 4.7, as well as .NET Core 2.1, .NET Core 3.0, .NET 6, .NET Standard 2.0 and 2.1. Supports the unit test frameworks MSTest2, NUnit3, XUnit2, MSpec, and NSpec3.
deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
Moq - Repo for managing Moq 4.x [Moved to: https://github.com/moq/moq]
mlx-examples - Examples in the MLX framework
NSubstitute - A friendly substitute for .NET mocking libraries.
tonic_validate - Metrics to evaluate the quality of responses of your Retrieval Augmented Generation (RAG) applications.
MSTest - MSTest framework and adapter