RAG

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

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

... 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

#################################

# Definition of used LLM

#################################

##########################################################################

def graphPrompt(input: str, metadata={}, model="mixtral:latest"):

if model == None:

model = "mixtral:latest"

chunk_id = metadata.get('chunk_id', None)

# model_info = client.show(model_name=m... See more

Analyze user queries and feedback to identify topic clusters, capabilities, and areas of user dissatisfaction. This will help you prioritize improvements.

Why should we do this? Let me give you an example. I once worked with a company that provided a technical documentation search system. By clustering user queries, we identified two main issues:

1. Top

... 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... See more

Utilize both full-text search and vector search (embeddings) for retrieving relevant documents. Ideally, you should use a single database system to avoid synchronization issues.

• Implement both full-text search and vector search


• Test the performance of each method on your specific use case


• Consider using a single database system to store both types of

... See more

LLMs struggle when handling tasks which require extensive knowledge. This limitation highlights the need to supplement LLMs with non-parametric knowledge. This paper Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts analyze the effects of different types of non-parametric knowledge, such as textu... 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

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