the AI-native open-source embedding database
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Updated
Aug 22, 2024 - Rust
the AI-native open-source embedding database
Distributed vector search for AI-native applications
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.
Vietnamese long form question answering system with documents retrieval.
Implementation of ECIR 2022 Paper: How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation
Retrieves the top 10 documents from the Wikipedia corpus for a user inputted free-text query
Document Querying with LLMs - Google PaLM API: Semantic Search With LLM Embeddings
Run text embeddings with Instructor-Large on AWS Lambda.
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.
Client SDK for starpoint.ai
Code and dataset for the paper "Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness"
Built prediction and retrieval models for document retrieval, image retrieval, house price prediction, song recommendation, and analyzed sentiments using machine learning algorithms in Python
Compilation of Information Retrieval codes.
Initially implement Document-Retrieval-System with SBERT embeddings and evaluate it in CORD-19 dataset. Afterwards, fine tune BERT model with SQuAD.v2 dataset so as to evaluate it in Question Answering task.
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.
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