How does Recursive Retriever works in LlamaIndex?

The Recursive Retriever in LlamaIndex is designed to efficiently retrieve relevant context from a hierarchical data structure, especially useful for long documents or collections of documents organized in a tree-like manner.  It's particularly helpful when dealing with summaries or nested information.  Here's how it works:   

Core Idea: The Recursive Retriever leverages the hierarchical structure of your data to perform targeted searches. Instead of naively searching the entire dataset, it starts at a higher level (e.g., a summary document or a top-level node in a tree) and recursively drills down to more specific content only when necessary.   

How it Works (Step-by-Step):

Hierarchical Data Structure: You provide LlamaIndex with data organized hierarchically. This could be:

A document with sections, subsections, and paragraphs.

A collection of documents with summaries and sub-documents.

Any data that can be represented as a tree or nested structure.

Top-Level Retrieval: When you issue a query, the Recursive Retriever first searches at the highest level of the hierarchy.  For example, it might search the summaries of all documents or the top-level sections of a long document.   

Relevance Check: The retriever determines if the top-level content it retrieved is relevant to the query.  This could be done using similarity search (comparing query embeddings to summary embeddings), keyword matching, or other methods.

Recursive Drill-Down: If the top-level content is deemed relevant, the retriever recursively descends to the next level of the hierarchy.  For instance, if a document summary is relevant, it will then search the sub-documents associated with that summary.   

Repeat: Steps 3 and 4 are repeated until the retriever reaches the desired level of granularity or until it finds enough relevant context.  It keeps going down the tree as long as the content at the current level is relevant.

Context Aggregation: Finally, the retriever gathers all the relevant content it has found during the recursive search and returns it as the context for your LLM query.

Example (Conceptual):

Imagine you have a book with chapters, sections, and paragraphs.

You ask a question about a specific topic.

The Recursive Retriever first searches the chapter titles (top level).

It finds a chapter title that seems relevant.

It then searches the section headings within that chapter (next level).

It finds a section heading that's even more relevant.

It finally retrieves the paragraphs within that section (lowest level).

It returns those paragraphs as the context for your question.

Benefits of Recursive Retrieval:

Efficiency: Avoids searching the entire dataset, significantly speeding up retrieval, especially for large hierarchical data.

Relevance: Focuses the search on the most promising parts of the data, leading to more relevant context.   

Scalability: Works well with large datasets because the search is targeted and doesn't involve exhaustive scanning.   

Handles Hierarchical Data: Specifically designed for data with a tree-like structure, which is common in many real-world scenarios.

When to use it:

Long documents: When you have a single document with internal structure (chapters, sections, etc.).

Document collections: When you have multiple documents organized hierarchically (e.g., by topic, category, etc.).

Summarization tasks: When you want to use summaries at different levels of granularity.

Knowledge graphs: For traversing and retrieving information from knowledge graphs.

In LlamaIndex, you would use the RecursiveRetriever class and configure it with your hierarchical data structure.  You can then use it like any other retriever to fetch context for your LLM queries.