RAG

rerankers

A lightweight unified API for various reranking models. Developed by @bclavie as a member of answer.ai

Welcome to rerankers ! Our goal is to provide users with a simple API to use any reranking models.

Updates

• v0.3.1: T5 bugfix and native default support for new Portuguese T5 rerankers.


• v0.3.0: 🆕 Many changes! Experimental support for Rank

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Welcome to RAGatouille

Easily use and train state of the art retrieval methods in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.

The main motivation of RAGatouille is simple: bridging the gap between state-of-the-art research and alchemical RAG pipeline practices. RAG is complex, and there are many moving parts. To g... See more

Unlike some other popular algorithms, DiskANN is designed to keep memory usage to a minimum. This makes it a great match for use cases where Turso already excels at.

#Multitenancy

Turso allows for an easy implementation of a database-per-tenant pattern, where databases can be cheaply created on-demand. Keeping memory consumption at bay is critical f... See more

FuzzTypes

FuzzTypes is a set of "autocorrecting" annotation types that expands upon Pydantic's included data conversions. Designed for simplicity, it provides powerful normalization capabilities (e.g. named entity linking) to ensure structured data is composed of "smart things" not "dumb strings".

Introducing Wren Engine

The advent of Trend AI agents has revolutionized the landscape of business intelligence and data management. In the near future, multiple AI agents will be deployed to harness and interpret vast amounts of internal knowledge stored within databases and data warehouses. To facilitate this, a semantic engine is crucial. This e... See more

You’ve got a vector database that has all the right database fundamentals you require, has the right incremental indexing strategy for your use case, has a good story around your metadata filtering needs, and will keep its index up-to-date with latencies you can tolerate. Awesome.

Your ML team (or maybe OpenAI) comes out with a new version of their... See more

Due to the nature of the most modern ANN vector search algorithms, incrementally updating a vector index is a massive challenge. This is a well known “hard problem”. The issue here is that these indexes are carefully organized for fast lookups and any attempt to incrementally update them with new vectors will rapidly deteriorate the fast lookup pro... See more

Fast full-text search engine library written in Rust

If you are looking for an alternative to Elasticsearch or Apache Solr, check out Quickwit, our distributed search engine built on top of Tantivy.

Tantivy is closer to Apache Lucene than to Elasticsearch or Apache Solr in the sense it is not an off-the-shelf search engine server, but rather a crat... See more

Elasticsearch at Twitter

Elasticsearch is a search engine based on the Lucene library. It is a popular open source tool widely used in industry and is known for its distributed nature, speed, scalability, and simple REST APIs.

The Search Infrastructure team builds infrastructure to host search as a service. Since we are such a central infrastructur... See more

Balance Latency and Performance¶

Finally, make informed decisions about trade-offs between system latency and search performance based on your specific use case and user requirements.

• Understand the latency and performance requirements for your application


• Measure the impact of different configurations on latency and performance


• Make trade-offs based

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