Distributed vector search for AI-native applications
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Updated
May 24, 2024 - Go
Distributed vector search for AI-native applications
the AI-native open-source embedding database
Train and Infer Powerful Sentence Embeddings with AnglE | 🔥 SOTA on STS and MTEB Leaderboard
A basic and intuitive Python module for (Vector Space) IR system. (Focuses on simplicity and understandability)
Evaluations among different Retrieval Models with open source platforms/tools.
Vietnamese long form question answering system with documents retrieval.
a minimal local embedding database.
An out-of-the-box, corpus-agnostic query expansion tool for lexical retrieval systems.
Client SDK for starpoint.ai
The universal tool suite for vector database management. Manage Pinecone, Chroma, Qdrant, Weaviate and more vector databases with ease.
Vector search demo with the arXiv paper dataset, RedisVL, HuggingFace, OpenAI, Cohere, FastAPI, React, and Redis.
The Intelligent "ASKDOC" project combines the power of Langchain, Azure, OpenAI models, and Python to deliver an intelligent question-answering system, that scans your PDF documents and answer queries based on its contents. It can be queried using Human Natural Language.
Document Querying with LLMs - Google PaLM API: Semantic Search With LLM Embeddings
Dive into LangChain, a powerful platform that lets you interact with your data like never before. This guide offers insights on its unique capabilities, helping you tap into your data in conversational ways.
We address the task of learning contextualized word, sentence and document representations with a hierarchical language model by stacking Transformer-based encoders on a sentence level and subsequently on a document level and performing masked token prediction.
Run text embeddings with Instructor-Large on AWS Lambda.
An attempt of creating a model and pipeline for retrieving italian legal documents given a prompt from the user.
A two-stage information retrieval model using baseline TF-IDF model and refined BM25.
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