canopy
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| canopy | xBehave.net | |
|---|---|---|
| 13 | - | |
| 873 | 378 | |
| 15.1% | - | |
| 9.8 | 8.1 | |
| 26 days ago | almost 3 years ago | |
| Python | C# | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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
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Tracking mentions began in Dec 2020.
What are some alternatives?
ragna - RAG orchestration framework ⛵️
SpecFlow - #1 .NET BDD Framework. SpecFlow automates your testing & works with your existing code. Find Bugs before they happen. Behavior Driven Development helps developers, testers, and business representatives to get a better understanding of their collaboration
tiger - Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning)
BDDfy - BDDfy is the simplest BDD framework EVER!
simple-pgvector-python - An Abstraction Using a similar API to Pinecone but implemented with pgvector python
LightBDD - BDD framework allowing to create easy to read and maintain tests.
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
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.
mlx-examples - Examples in the MLX framework
Moq - Repo for managing Moq 4.x [Moved to: https://github.com/moq/moq]
tonic_validate - Metrics to evaluate the quality of responses of your Retrieval Augmented Generation (RAG) applications.
Verify - Verify is a snapshot tool that simplifies the assertion of complex data models and documents.