Available Components
AWS Bedrock Knowledge Base Retriever
Connects to AWS Bedrock Knowledge Base for retrieving information from large knowledge bases
Cohere Rerank Retriever
Utilizes Cohere’s reranking capabilities to improve document relevance
Custom Retriever
Allows customization of retrieval output format, including both document content and metadata
Embeddings Filter Retriever
Uses embeddings to filter out unrelated documents, improving retrieval relevance
Extract Metadata Retriever
Extracts metadata from documents for better retrieval
HyDE Retriever
Leverages Hypothetical Document Embeddings to improve retrieval performance
LLM Filter Retriever
Employs a language model to extract only relevant content from initially retrieved documents
Multi Query Retriever
Generates multiple queries from different perspectives to overcome limitations
Prompt Retriever
Stores and manages prompt templates for dynamic selection in conversational AI systems
Reciprocal Rank Fusion Retriever
Uses the RRF algorithm to re-rank search results from multiple query generations
Similarity Score Threshold Retriever
Filters and retrieves documents based on a minimum similarity percentage
Vector Store Retriever
A general-purpose retriever for storing and querying vector representations of data
Voyage AI Rerank Retriever
Enhances retrieval using Voyage AI’s reranking models for better semantic relevance
- Enhancing search relevance in document retrieval systems
- Improving question-answering capabilities by providing more accurate and diverse context
- Refining information retrieval for research and analysis tasks
- Customizing retrieval processes for specific domain requirements
- Integrating with large-scale knowledge bases and AI services