swirl-search VS deepeval

Understanding an Open Source Search Platform: Swirl

Give ⭐ to Swirl on GitHub

Star Swirl on GitHub and become part of this exciting AI search evolution! 🌟

Swirl is an open-source Python project. Contributing to Swirl can help you gain production-level knowledge of Python and improve your skills.

Note: This is not how you write a search engine. There's a lot more stuff that goes into it. If you want to know more, check this GitHub Repository:github.com/swirlai/swirl-search

Give Swirl a Star 🌟 on GitHub. To receive updates from discussions and releases. Click on the image

Swirl is an open-source search engine which is built using Python and Django. Things which makes Swirl more special is that individual developers and organizations can use Swirl without paying single penny and even customize the search results by connecting to Database (E.g. SQL, NoSQL), Public Data Services (E.g. Google) and Enterprise Sources (E.g. Jira). GitHub Link: https://github.com/swirlai/swirl-search

Hello Devs, The team at Swirl has created this amazing guide which contains all the relevant information for anyone who wants to extend Swirl by adding SearchProviders, Connectors, and Processors.

Swirl is an open-source search platform software that simultaneously searches multiple content sources and returns AI-ranked results. You can also use Generative AI Models to get answers based on your data. It’s written in Python.

For the last year I have been working with different LLMs (OpenAI, Claude, Palm, Gemini, etc) and I have been impressed with their performance. With the rapid advancements in AI and the increasing complexity of LLMs, it has become crucial to have a reliable testing framework that can help us maintain the quality of our prompts and ensure the best possible outcomes for our users. Recently, I discovered DeepEval (https://github.com/confident-ai/deepeval), an LLM testing framework that has revolutionized the way we approach prompt quality assurance.

Checkout this instead: https://github.com/confident-ai/deepeval

Also has native ragas implementation but supports all models.

Fair question.

Evaluate refers to the phase after training to check if the training is good.

Usually the flow goes training -> evaluation -> deployment (what you called inference). This project is aimed for evaluation. Evaluation can be slow (might even be slower than training if you're finetuning on a small domain specific subset)!

So there are [quite](https://github.com/microsoft/promptbench) [a](https://github.com/confident-ai/deepeval) [few](https://github.com/openai/evals) [frameworks](https://github.com/EleutherAI/lm-evaluation-harness) working on evaluation, however, all of them are quite slow, because LLM are slow if you don't have infinite money. [This](https://github.com/open-compass/opencompass) one tries to speed up by parallelizing on multiple computers, but none of them takes advantage of the fact that many evaluation queries might be similar and all try to evaluate on all given queries. And that's where this project might come in handy.

A link to a reddit post (with no discussion) which links to this repo

https://github.com/confident-ai/deepeval

Star DeepEval on GitHub and contribute to the advancement of LLM evaluation frameworks! 🌟

Pinecone, the leading closed-source vector database provider, is known for being fast, scalable, and easy to use. Its ability to allow users to perform blazing-fast vector search makes it a popular choice for large-scale RAG applications. Our initial infrastructure for Confident AI, the world’s first open-source evaluation infrastructure for LLMs, utilized Pinecone to cluster LLM observability log data in production. However, after weeks of experimentation, we made the decision to replace it entirely with pgvector. Pinecone’s simplistic design is deceptive due to several hidden complexities, particularly in integrating with existing data storage solutions. For example, it forces a complicated architecture and its restrictive metadata storage capacity made it troublesome for managing data-intensive workloads.