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

... See more

Ensuring relevant metadata (e.g., date ranges, file names, ownership) is extracted and searchable is crucial for improving search results.

• Extract relevant metadata from your documents


• Include metadata in your search indexes


• Use query understanding to extract metadata from user queries


• Expand search queries with relevant metadata to improve results

For... 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... See more

1. Synthetic Data for Baseline Metrics¶

Synthetic data can be used to establish baseline precision and recall metrics for your reverse search. The simplest kind of synthetic data is to take existing text chunks, generate synthetic questions, and verify that when we query our synthetic questions, the sourced text chunk is retrieved correctly.

Benefi... See more

Pipeline RobustQA Avg. score Avg. response time (secs) Azure Cognitive Search Retriever + GPT4 + Ada 72.36 >1.0s Canopy (Pinecone) 59.61 >1.0s Langchain + Pinecone + OpenAI 61.42 <0.8s Langchain + Pinecone + Cohere 69.02 <0.6s LlamaIndex + Weaviate Vector Store - Hybrid Search 75.89 <1.0s RAG Google Cloud VertexAI-Search + Bison... 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".

Continuously Monitor and Experiment¶

Continuously monitor your system's performance and run experiments to test improvements.

• Set up monitoring and logging to track system performance over time


• Regularly review the data to identify trends and issues


• Design and run experiments to test potential improvements


• Measure the impact of changes on precision,

... See more

All the recommendation systems you see at Twitter, Facebook, TikTok, YouTube, etc. have a similar high-level architecture.

They have a layered architecture that looks something like the following

1. Retrieval - Narrow down the candidates of what to show a user to thousands of potential items


2. First Stage Ranking - Apply a low-level ranking system to

... 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... See more