Retrievers | 🦜️🔗 LangChain

📄️ Activeloop Deep Memory

Activeloop Deep Memory is a suite of tools that enables you to optimize your Vector Store for your use-case and achieve higher accuracy in your LLM apps.

Amazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.

📄️ Arcee

Arcee helps with the development of the SLMs—small, specialized, secure, and scalable language models.

📄️ Arxiv

arXiv is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.

📄️ AskNews

AskNews infuses any LLM with the latest global news (or historical news), using a single natural language query. Specifically, AskNews is enriching over 300k articles per day by translating, summarizing, extracting entities, and indexing them into hot and cold vector databases. AskNews puts these vector databases on a low-latency endpoint for you. When you query AskNews, you get back a prompt-optimized string that contains all the most pertinent enrichments (e.g. entities, classifications, translation, summarization). This means that you do not need to manage your own news RAG, and you do not need to worry about how to properly convey news information in a condensed way to your LLM.

📄️ Azure AI Search

Azure AI Search (formerly known as Azure Cognitive Search) is a Microsoft cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale.

📄️ Bedrock (Knowledge Bases)

Knowledge bases for Amazon Bedrock is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.

📄️ BM25

BM25 (Wikipedia) also known as the Okapi BM25, is a ranking function used in information retrieval systems to estimate the relevance of documents to a given search query.

📄️ BREEBS (Open Knowledge)

BREEBS is an open collaborative knowledge platform.

📄️ Chaindesk

Chaindesk platform brings data from anywhere (Datsources: Text, PDF, Word, PowerPpoint, Excel, Notion, Airtable, Google Sheets, etc..) into Datastores (container of multiple Datasources).

📄️ ChatGPT plugin

OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions.

📄️ Cohere reranker

Cohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.

📄️ Cohere RAG

Cohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.

📄️ DocArray

DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!

📄️ Dria

Dria is a hub of public RAG models for developers to both contribute and utilize a shared embedding lake. This notebook demonstrates how to use the Dria API for data retrieval tasks.

📄️ ElasticSearch BM25

Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

📄️ Elasticsearch

Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. It supports keyword search, vector search, hybrid search and complex filtering.

📄️ Embedchain

Embedchain is a RAG framework to create data pipelines. It loads, indexes, retrieves and syncs all the data.

📄️ FlashRank reranker

FlashRank is the Ultra-lite & Super-fast Python library to add re-ranking to your existing search & retrieval pipelines. It is based on SoTA cross-encoders, with gratitude to all the model owners.

📄️ Fleet AI Context

Fleet AI Context is a dataset of high-quality embeddings of the top 1200 most popular & permissive Python Libraries & their documentation.

📄️ Google Drive

This notebook covers how to retrieve documents from Google Drive.

📄️ Google Vertex AI Search

Google Vertex AI Search (formerly known as Enterprise Search on Generative AI App Builder) is a part of the Vertex AI machine learning platform offered by Google Cloud.

📄️ JaguarDB Vector Database

[JaguarDB Vector Database](http://www.jaguardb.com/windex.html

📄️ Kay.ai

Kai Data API built for RAG 🕵️ We are curating the world's largest datasets as high-quality embeddings so your AI agents can retrieve context on the fly. Latest models, fast retrieval, and zero infra.

📄️ Kinetica Vectorstore based Retriever

Kinetica is a database with integrated support for vector similarity search

📄️ kNN

In statistics, the k-nearest neighbours algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression.

📄️ LLMLingua Document Compressor

LLMLingua utilizes a compact, well-trained language model (e.g., GPT2-small, LLaMA-7B) to identify and remove non-essential tokens in prompts. This approach enables efficient inference with large language models (LLMs), achieving up to 20x compression with minimal performance loss.

📄️ LOTR (Merger Retriever)

Lord of the Retrievers (LOTR), also known as MergerRetriever, takes a list of retrievers as input and merges the results of their getrelevantdocuments() methods into a single list. The merged results will be a list of documents that are relevant to the query and that have been ranked by the different retrievers.

📄️ Metal

Metal is a managed service for ML Embeddings.

📄️ Milvus Hybrid Search

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.

📄️ NanoPQ (Product Quantization)

Product Quantization algorithm (k-NN) in brief is a quantization algorithm that helps in compression of database vectors which helps in semantic search when large datasets are involved. In a nutshell, the embedding is split into M subspaces which further goes through clustering. Upon clustering the vectors the centroid vector gets mapped to the vectors present in the each of the clusters of the subspace.

📄️ Outline

Outline is an open-source collaborative knowledge base platform designed for team information sharing.

📄️ Pinecone Hybrid Search

Pinecone is a vector database with broad functionality.

📄️ PubMed

PubMed® by The National Center for Biotechnology Information, National Library of Medicine comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.

📄️ Qdrant Sparse Vector

Qdrant is an open-source, high-performance vector search engine/database.

📄️ RAGatouille

RAGatouille makes it as simple as can be to use ColBERT!

📄️ RePhraseQuery

RePhraseQuery is a simple retriever that applies an LLM between the user input and the query passed by the retriever.

📄️ Rememberizer

Rememberizer is a knowledge enhancement service for AI applications created by SkyDeck AI Inc.

📄️ SEC filing

SEC filing is a financial statement or other formal document submitted to the U.S. Securities and Exchange Commission (SEC). Public companies, certain insiders, and broker-dealers are required to make regular SEC filings. Investors and financial professionals rely on these filings for information about companies they are evaluating for investment purposes.

🗃️ Self-querying retrievers

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📄️ SingleStoreDB

SingleStoreDB is a high-performance distributed SQL database that supports deployment both in the cloud and on-premises. It provides vector storage, and vector functions including dotproduct and euclideandistance, thereby supporting AI applications that require text similarity matching.

📄️ SVM

Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.

📄️ Tavily Search API

Tavily's Search API is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.

📄️ TF-IDF

TF-IDF means term-frequency times inverse document-frequency.

📄️ NeuralDB

NeuralDB is a CPU-friendly and fine-tunable retrieval engine developed by ThirdAI.

📄️ Vespa

Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query.

📄️ Weaviate Hybrid Search

Weaviate is an open-source vector database.

📄️ Wikipedia

Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.