📄️ Knowledge Bases for Amazon Bedrock
Knowledge Bases for Amazon Bedrock is a fully managed support for end-to-end RAG workflow provided by Amazon Web Services (AWS). It provides an entire ingestion workflow of converting your documents into embeddings (vector) and storing the embeddings in a specialized vector database. Knowledge Bases for Amazon Bedrock supports popular databases for vector storage, including vector engine for Amazon OpenSearch Serverless, Pinecone, Redis Enterprise Cloud, Amazon Aurora (coming soon), and MongoDB (coming soon).
📄️ Chaindesk Retriever
This example shows how to use the Chaindesk Retriever in a retrieval chain to retrieve documents from a Chaindesk.ai datastore.
📄️ ChatGPT Plugin Retriever
This example shows how to use the ChatGPT Retriever Plugin within LangChain.
📄️ Dria Retriever
The Dria retriever allows an agent to perform a text-based search across a comprehensive knowledge hub.
📄️ Exa Search
The Exa Search API provides a new search experience designed for LLMs.
📄️ HyDE Retriever
This example shows how to use the HyDE Retriever, which implements Hypothetical Document Embeddings (HyDE) as described in this paper.
📄️ Amazon Kendra Retriever
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.
📄️ Metal Retriever
This example shows how to use the Metal Retriever in a retrieval chain to retrieve documents from a Metal index.
📄️ Supabase Hybrid Search
Langchain supports hybrid search with a Supabase Postgres database. The hybrid search combines the postgres pgvector extension (similarity search) and Full-Text Search (keyword search) to retrieve documents. You can add documents via SupabaseVectorStore addDocuments function. SupabaseHybridKeyWordSearch accepts embedding, supabase client, number of results for similarity search, and number of results for keyword search as parameters. The getRelevantDocuments function produces a list of documents that has duplicates removed and is sorted by relevance score.
📄️ 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.
📄️ Time-Weighted Retriever
A Time-Weighted Retriever is a retriever that takes into account recency in addition to similarity. The scoring algorithm is:
📄️ Vector Store
Once you've created a Vector Store, the way to use it as a Retriever is very simple:
📄️ Vespa Retriever
This shows how to use Vespa.ai as a LangChain retriever.
📄️ Zep Retriever
This example shows how to use the Zep Retriever in a retrieval chain to retrieve documents from Zep memory store.