📄️ Knowledge Bases for Amazon Bedrock
Overview
📄️ 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 module has been deprecated and is no longer supported. The documentation below will not work in versions 0.2.0 or later.
📄️ Dria Retriever
The Dria retriever allows an agent to perform a text-based search across a comprehensive knowledge hub.
📄️ Exa
Overview
📄️ 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
Overview
📄️ Metal Retriever
This example shows how to use the Metal Retriever in a retrieval chain to retrieve documents from a Metal index.
🗃️ Self-querying retrievers
8 items
📄️ 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
📄️ 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 Cloud Retriever
Zep is a long-term memory service for AI Assistant apps.
📄️ Zep Open Source Retriever
Zep is a long-term memory service for AI Assistant apps.