Stuffing

Description

The stuffing chain addresses scenarios where the context length of the language model (LLM) is insufficient to process extensive documents or large amounts of data. By dividing large documents into smaller segments and retrieving relevant ones using semantic search, this chain allows these segments to be “stuffed” into the LLM's context for response generation.

Advantages:

• Consolidation of multiple documents: Aggregates several relevant documents to overcome LLM context length limitations.
• Comprehensive information processing: Leverages multiple documents to generate more comprehensive and relevant answers.

Disadvantages:

• Increased complexity: Requires efficient semantic search and a vector database.
• Potential loss of contextual coherency: Retrieving multiple documents might lead to less cohesive answers.

Use Cases

• Document Retrieval Question Answering: For lengthy legal documents, the stuffing chain retrieves relevant sections to provide precise answers to legal questions.
• Complex Question Answering: For research projects, this chain enables dividing scientific papers into chunks, retrieving necessary ones, and synthesizing precise answers to complex queries.