Hi everyone,
I’m building an AI Search system where a user types a query, and the system performs a similarity check against a document corpus. While working on the initialization, I realized that the query and documents could benefit from preprocessing, optimization, and careful handling before performing similarity computations.
Instead of figuring out all the details myself, I’m wondering if there’s a blueprint, best-practice guide, or reference implementation for building an end-to-end AI Search pipeline — from query/document preprocessing to embedding, indexing, and retrieval.
Any guidance, references, or examples would be greatly appreciated.
Thank you very much for your precise blueprint.
If I may offer some feedback: the documentation as a whole is somewhat confusing. It contains repeated information at different levels of detail, which makes it difficult to follow. Even the numbering is inconsistent. While the content is very helpful, the way it is presented makes it nearly impossible to use effectively. Even when I ask an LLM for help, it remains confusing, which makes it essentially unusable. Sorry. This is a resource collection prioritizing redundancy while essentially ignoring readability. It’s intended to be used as part of the clues fed to an LLM (RAG). If prioritizing human readability, would it look something like this? https://huggingface.co/datasets/John6666/forum3/blob/main/ai_search_blueprint_1r.md
If you dislike AI-generated documents, just ignore it… Thank you for updating it. This is much better now!
aaraya 6 Hi @EroStefano, a while back we wrote a blog post about how to tackle this problem of contextual searches using a document corpus, embeddings and other techniques; you can find it here
Adrian Araya
Machine Learning Engineer at [RidgeRun.ai](http://RidgeRun.ai)
Contact us: [support@ridgerun.ai](mailto:support@ridgerun.ai)
Thank you, it looks great!
Implementing AI Search? Start with cleaning and preprocessing your data, then use vector embeddings for similarity checks. For retrieval, vector databases help a lot with indexing speed and accuracy.