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Level: Beginner Time to complete: 15 minutes Goal: After completing this tutorial, you’ll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the FileTypeRouter.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
OpenRecall failing with a torch and torchvision conflict or a PyObjC error? Here are the four workarounds from the open issues that actually install it.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Evaluation measures performance using metrics like precision, recall, and relevancy, providing a clear picture of your pipeline’s strengths and weaknesses using LLMs or ground-truth labels.
⭐️ Highlights Haystack 3.0 is a major release for building production-grade agents with full control and flexibility. It ships a wave of new capabilities: a more capable Agent with hooks and first-class skills, built-in run introspection, first-class async for serving, a leaner core, and safer…
A repeatable workflow for building large, realistic vector test datasets: CC News to Parquet to local embeddings to Pinecone bulk import.
In artificial intelligence, multimodal agents are becoming increasingly popular for their ability to understand and integrate multiple types of input, such as text and images.
In this article, we will show you how to use Haystack to evaluate the performance of a RAG pipeline. Note that the code in this article is meant to be illustrative and may not run as is; if you want to run the code, please refer to the python script.
Getting your LLM application into production is a huge milestone, but that’s only the beginning. It’s critical to monitor how your pipeline is performing in the real world so you can keep improving performance and cost, and proactively address any issues that might arise.
The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings.
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