Best AI tools for< Rerank Documents >

LangSearch is an AI tool that offers a free Web Search API and Rerank API, serving as the World Engine for AGI. It allows users to connect their LLM applications to access clean, accurate, high-quality context from billions of web documents, including news, images, videos, and more. The tool supports natural language search and provides enhanced search details for various content types.

VecRank is an AI-powered Vector Search and Reranking API service that leverages cutting-edge GenAI technologies to enhance natural language understanding and contextual relevance. It offers a scalable, AI-driven search solution for software developers and business owners. With VecRank, users can revolutionize their search capabilities with the power of AI, enabling seamless integration and powerful tools that scale with their business needs. The service allows for bulk data upload, incremental data updates, and easy integration into various programming languages and platforms, all without the hassle of setting up infrastructure for embeddings and vector search databases.

Cohere is the leading AI platform for enterprise, offering products optimized for generative AI, search and discovery, and advanced retrieval. Their models are designed to enhance the global workforce, enabling businesses to thrive in the AI era. Cohere provides Command R+, Cohere Command, Cohere Embed, and Cohere Rerank for building efficient AI-powered applications. The platform also offers deployment options for enterprise-grade AI on any cloud or on-premises, along with developer resources like Playground, LLM University, and Developer Docs.

Cohere is the leading AI platform for enterprise, offering generative AI, search and discovery, and advanced retrieval solutions. Their models are designed to enhance the global workforce, empowering businesses to thrive in the AI era. With features like Cohere Command, Cohere Embed, and Cohere Rerank, the platform enables the development of scalable and efficient AI-powered applications. Cohere focuses on optimizing enterprise data through language-based models, supporting over 100 languages for enhanced accuracy and efficiency.

Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. It is developed under the MIT License and powers inference behind Gradient.ai. The API allows users to deploy models from SentenceTransformers, offers fast inference backends utilizing various accelerators, dynamic batching for efficient processing, correct and tested implementation, and easy-to-use API built on FastAPI with Swagger documentation. Users can embed text, rerank documents, and perform text classification tasks using the tool. Infinity supports various models from Huggingface and provides flexibility in deployment via CLI, Docker, Python API, and cloud services like dstack. The tool is suitable for tasks like embedding, reranking, and text classification.

llm-rankers is a repository that provides implementations for Pointwise, Listwise, Pairwise, and Setwise Document Ranking using Large Language Models. It includes various methods for reranking documents retrieved by a first-stage retriever, such as BM25. The repository offers examples and code snippets for using LLMs to improve document ranking performance in information retrieval tasks. Additionally, it introduces a new setwise reranker called Rank-R1 with reasoning ability.

The official Node.js client for Pinecone, written in TypeScript. This client library provides a high-level interface for interacting with the Pinecone vector database service. With this client, you can create and manage indexes, upsert and query vector data, and perform other operations related to vector search and retrieval. The client is designed to be easy to use and provides a consistent and idiomatic experience for Node.js developers. It supports all the features and functionality of the Pinecone API, making it a comprehensive solution for building vector-powered applications in Node.js.

RankLLM is a suite of prompt-decoders compatible with open source LLMs like Vicuna and Zephyr. It allows users to create custom ranking models for various NLP tasks, such as document reranking, question answering, and summarization. The tool offers a variety of features, including the ability to fine-tune models on custom datasets, use different retrieval methods, and control the context size and variable passages. RankLLM is easy to use and can be integrated into existing NLP pipelines.

MemFree is an open-source hybrid AI search engine that allows users to simultaneously search their personal knowledge base (bookmarks, notes, documents, etc.) and the Internet. It features a self-hosted super fast serverless vector database, local embedding and rerank service, one-click Chrome bookmarks index, and full code open source. Users can contribute by opening issues for bugs or making pull requests for new features or improvements.

pyLLMSearch is an advanced RAG system that offers a convenient question-answering system with a simple YAML-based configuration. It enables interaction with multiple collections of local documents, with improvements in document parsing, hybrid search, chat history, deep linking, re-ranking, customizable embeddings, and more. The package is designed to work with custom Large Language Models (LLMs) from OpenAI or installed locally. It supports various document formats, incremental embedding updates, dense and sparse embeddings, multiple embedding models, 'Retrieve and Re-rank' strategy, HyDE (Hypothetical Document Embeddings), multi-querying, chat history, and interaction with embedded documents using different models. It also offers simple CLI and web interfaces, deep linking, offline response saving, and an experimental API.

RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite. It offers configurable options for choosing LLM providers, database types, and rerankers. The toolkit is fast and permissive, utilizing lightweight dependencies and hardware acceleration. RAGLite provides features like PDF to Markdown conversion, multi-vector chunk embedding, optimal semantic chunking, hybrid search capabilities, adaptive retrieval, and improved output quality. It is extensible with a built-in Model Context Protocol server, customizable ChatGPT-like frontend, document conversion to Markdown, and evaluation tools. Users can configure RAGLite for various tasks like configuring, inserting documents, running RAG pipelines, computing query adapters, evaluating performance, running MCP servers, and serving frontends.

HippoRAG is a novel retrieval augmented generation (RAG) framework inspired by the neurobiology of human long-term memory that enables Large Language Models (LLMs) to continuously integrate knowledge across external documents. It provides RAG systems with capabilities that usually require a costly and high-latency iterative LLM pipeline for only a fraction of the computational cost. The tool facilitates setting up retrieval corpus, indexing, and retrieval processes for LLMs, offering flexibility in choosing different online LLM APIs or offline LLM deployments through LangChain integration. Users can run retrieval on pre-defined queries or integrate directly with the HippoRAG API. The tool also supports reproducibility of experiments and provides data, baselines, and hyperparameter tuning scripts for research purposes.

PapersGPT For Zotero is an AI plugin that enhances papers reading and research efficiency by integrating cutting-edge LLMs and offering seamless Zotero integration. Users can ask questions, extract insights, and converse with PDFs directly, making it a powerful research assistant for scholars, researchers, and anyone dealing with large amounts of text in PDF format. The plugin ensures privacy and data safety by using locally stored models and modules, with the ability to switch between different models easily. It provides a user-friendly interface for managing and chatting documents within Zotero, making research tasks more streamlined and productive.

Rankify is a Python toolkit designed for unified retrieval, re-ranking, and retrieval-augmented generation (RAG) research. It integrates 40 pre-retrieved benchmark datasets and supports 7 retrieval techniques, 24 state-of-the-art re-ranking models, and multiple RAG methods. Rankify provides a modular and extensible framework, enabling seamless experimentation and benchmarking across retrieval pipelines. It offers comprehensive documentation, open-source implementation, and pre-built evaluation tools, making it a powerful resource for researchers and practitioners in the field.

RAG Web UI is an intelligent dialogue system based on RAG (Retrieval-Augmented Generation) technology. It helps enterprises and individuals build intelligent Q&A systems based on their own knowledge bases. By combining document retrieval and large language models, it delivers accurate and reliable knowledge-based question-answering services. The system is designed with features like intelligent document management, advanced dialogue engine, and a robust architecture. It supports multiple document formats, async document processing, multi-turn contextual dialogue, and reference citations in conversations. The architecture includes a backend stack with Python FastAPI, MySQL + ChromaDB, MinIO, Langchain, JWT + OAuth2 for authentication, and a frontend stack with Next.js, TypeScript, Tailwind CSS, Shadcn/UI, and Vercel AI SDK for AI integration. Performance optimization includes incremental document processing, streaming responses, vector database performance tuning, and distributed task processing. The project is licensed under the Apache-2.0 License and is intended for learning and sharing RAG knowledge only, not for commercial purposes.

Ask Astro is an open-source reference implementation of Andreessen Horowitz's LLM Application Architecture built by Astronomer. It provides an end-to-end example of a Q&A LLM application used to answer questions about Apache Airflow® and Astronomer. Ask Astro includes Airflow DAGs for data ingestion, an API for business logic, a Slack bot, a public UI, and DAGs for processing user feedback. The tool is divided into data retrieval & embedding, prompt orchestration, and feedback loops.

Aichat is an AI-powered CLI chat and copilot tool that seamlessly integrates with over 10 leading AI platforms, providing a powerful combination of chat-based interaction, context-aware conversations, and AI-assisted shell capabilities, all within a customizable and user-friendly environment.

CodeFuse-muAgent is a Multi-Agent framework designed to streamline Standard Operating Procedure (SOP) orchestration for agents. It integrates toolkits, code libraries, knowledge bases, and sandbox environments for rapid construction of complex Multi-Agent interactive applications. The framework enables efficient execution and handling of multi-layered and multi-dimensional tasks.

Sage is a tool that allows users to chat with any codebase, providing a chat interface for code understanding and integration. It simplifies the process of learning how a codebase works by offering heavily documented answers sourced directly from the code. Users can set up Sage locally or on the cloud with minimal effort. The tool is designed to be easily customizable, allowing users to swap components of the pipeline and improve the algorithms powering code understanding and generation.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

This repository provides a set of ROS 2 packages to integrate llama.cpp into ROS 2. By using the llama_ros packages, you can easily incorporate the powerful optimization capabilities of llama.cpp into your ROS 2 projects by running GGUF-based LLMs and VLMs.

Vectara Answer is a sample app for Vectara-powered Summarized Semantic Search (or question-answering) with advanced configuration options. For examples of what you can build with Vectara Answer, check out Ask News, LegalAid, or any of the other demo applications.