Document retrievers
Last updated
Last updated
In the inference workflow of a Retrieval-Augmented Generation (RAG) application, document retrievers play a crucial role in accessing stored vectorized data. By efficiently retrieving relevant information, they enhance the system's ability to provide accurate and contextually relevant responses.
Dynamiq offers a variety of document retrievers, each with unique features and configurations. Let's explore these options:
Configuration:
Name: Assign a unique identifier for the retriever.
Connection: Establish a connection to Weaviate, a vector database optimized for retrieval.
Index Name: Specify the index to search within.
Max Documents: Set the maximum number of documents to retrieve.
Filters: Apply filters to refine search results.
Options:
Enable Caching: Speeds up retrieval by caching frequently accessed data.
Configuration:
Name: Define a name for easy identification.
Connection: Connect to Pinecone, a scalable vector database service.
Index Name: Enter the index name for data retrieval.
Namespace: Use namespaces to segment data.
Max Documents: Limit the number of documents retrieved.
Filters: Use filters to narrow down results.
Options:
Enable Caching: Improves performance by storing frequently accessed data.
Configuration:
Name: Provide a name for the retriever.
Connection: Connect to Chroma for managing vector data.
Index Name: Specify the index for retrieval.
Max Documents: Define the maximum documents to fetch.
Filters: Apply filters for targeted retrieval.
Options:
Enable Caching: Enhances retrieval speed by caching data.
Configuration:
Name: Set a name for easy reference.
Connection: Establish a connection to Qdrant, a high-performance vector database.
Index Name: Enter the index name for retrieval.
Max Documents: Specify the maximum number of documents to retrieve.
Filters: Use filters to refine search results.
Options:
Enable Caching: Speeds up data retrieval by caching commonly accessed vectors.
Input:
Provide the query vector to initiate the retrieval process.
Configuration:
Select the appropriate retriever based on your retrieval needs.
Configure necessary parameters such as connection, index name, and filters.
Output:
The retriever fetches relevant documents, making them available for further processing in the RAG application.
Efficient Retrieval: Quickly accesses relevant data for accurate responses.
Scalability: Handles large datasets, supporting extensive knowledge bases.
Flexibility: Offers various configurations to suit different retrieval needs.
By effectively utilizing document retrievers, your RAG application can deliver precise and contextually relevant information efficiently.
