Recursive Retrieval & Query Trees

TLDR

Recursive Retrieval and Query Trees represent the evolution of Retrieval-Augmented Generation (RAG) from "flat" semantic matching to hierarchical, multi-step reasoning. Traditional vector search often fails when information is fragmented across documents or requires logical "hops" to resolve. Recursive retrieval solves this by iteratively refining queries or traversing document hierarchies—such as moving from a summary node down to specific leaf chunks. By utilizing Trie structures (prefix trees for strings) for deterministic routing and A (comparing prompt variants) to optimize query decomposition, engineers can build systems capable of answering complex, multi-faceted questions. This approach is essential for high-stakes domains like legal discovery, academic synthesis, and complex technical troubleshooting where the "lost in the middle" phenomenon and semantic drift render standard RAG insufficient.

Conceptual Overview

The fundamental limitation of standard RAG is the "semantic gap" between a user's high-level query and the granular, often disconnected chunks stored in a vector database. In a flat retrieval model, the system attempts to find the $k$ most similar chunks in a single pass. However, for a query like "How did the fiscal policy changes in 2022 affect the R&D budget of subsidiary X?", the answer might not exist in one place. It requires finding the policy change, identifying subsidiary X's relationship to the parent company, and then locating the specific budget line items.

Recursive Retrieval transforms this linear process into a directed search. Instead of returning a final set of documents immediately, the system uses the initial retrieval to inform a second, more targeted search. This is often structured as a Query Tree.

The Anatomy of a Query Tree

A Query Tree is a logical structure where:

1. The Root: Represents the original, complex user intent.
2. Branches: Represent decomposed sub-queries or different "perspectives" of the original question.
3. Nodes: Represent intermediate states, such as document summaries or metadata categories.
4. Leaves: Represent the final, high-granularity data chunks used for generation.

This hierarchical approach allows the system to "zoom in" on relevant information. It mirrors the human research process: start with a broad table of contents, identify the relevant chapter, find the specific section, and finally read the pertinent paragraph.


The Logic of Recursion

In a recursive system, the retriever is not a static function but an agentic loop. The output of one retrieval step (e.g., a document summary) contains a "pointer" or a "reference" to another set of data (e.g., the full document or a related sub-section). The system evaluates these references and decides whether to "descend" further into the tree. This prevents the LLM from being overwhelmed by irrelevant context while ensuring that the specific, high-precision data needed for the final answer is eventually surfaced.

Practical Implementations

Implementing recursive retrieval requires moving beyond simple vector_store.search() calls. It involves sophisticated indexing strategies and agentic control loops that manage the state of the search.

1. Small-to-Big Retrieval (Parent-Document)

One of the most common recursive patterns is the "Small-to-Big" approach, which decouples the data used for searching from the data used for synthesis.

• The Index: You split documents into small "child" chunks (e.g., 100 tokens) for high-precision embedding matching. Each child chunk contains a reference ID to a larger "parent" context (e.g., 1000 tokens or the whole document).
• The Recursion: When a child chunk is retrieved via vector similarity, the system does not pass the child to the LLM. Instead, it recursively fetches the parent document.
• Why it works: Small chunks are better for embedding models to capture specific semantic matches without the "noise" of a long document. However, LLMs need the surrounding context of the parent to generate accurate, grounded answers.

2. Trie-Based Routing

In systems with massive, structured datasets or specific naming conventions, a Trie (a prefix tree for strings) can be used as a pre-retrieval filter or a routing mechanism.

• Use Case: If a user asks about a specific product ID (e.g., PROD-9982-X) or a legal case number, a Trie can deterministically route the query to the correct partition of the database or a specific branch of the query tree.
• Implementation: The system parses the query for entities. If an entity matches a prefix in the Trie, the search is immediately constrained to that branch.
• Benefit: This prevents "semantic drift" where a vector search might return a similar-sounding but irrelevant product, ensuring the recursive search starts in the correct branch of the data tree.

3. Recursive Summary Indexing (RAPTOR)

The RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) method is a state-of-the-art approach for handling long-form documents.

1. Clustering: Related text chunks are grouped using algorithms like Gaussian Mixture Models (GMMs).
2. Summarization: An LLM generates a summary for each cluster.
3. Hierarchical Building: These summaries are themselves embedded and clustered to create a higher-level summary. This continues until a single root summary is formed.
4. Retrieval: During a query, the system searches the top-level summaries. If a summary is relevant, it recursively explores the children (the original chunks or lower-level summaries) of that node. This allows the system to answer both high-level thematic questions and granular factual ones.

4. Recursive CTEs in Knowledge Graphs

For data stored in relational or graph databases, Recursive Common Table Expressions (CTEs) allow for "multi-hop" discovery within the retrieval pipeline.

• Scenario: A query requires finding all "descendants" of a specific concept or "citations of citations."
• Execution: A recursive CTE can traverse these relationships in a single, efficient database operation. The resulting structured context is then fed into the RAG pipeline. This is particularly powerful for legal research where "precedent" forms a natural tree structure.

Advanced Techniques

To optimize these trees, developers must apply rigorous engineering to the "Agentic" layer that manages the recursion, ensuring it doesn't fall into infinite loops or consume excessive tokens.

Prompt Optimization via A

In recursive systems, the quality of query decomposition is the single biggest bottleneck. If the root query is split into poor sub-queries, the entire tree fails. Engineers use A (comparing prompt variants) to evaluate which instructions produce the most effective sub-queries.

• Experimental Setup: An engineer might compare a "Chain-of-Thought" prompt (asking the LLM to plan the search) against a "Least-to-Most" prompting strategy (breaking the query into a sequence of increasing complexity).
• Metric: The performance is measured by "Sub-query Recall"—how many of the necessary "hops" were successfully identified by the decomposition step.

Pruning and Scoring

Not all branches of a query tree are worth following. Advanced implementations use "Router" models—often smaller, faster LLMs or cross-encoders—to score the potential relevance of a branch before descending.

• Confidence Thresholds: If a sub-query's initial retrieval returns similarity scores below a certain threshold, the system "prunes" that branch to save latency and compute costs.
• Dynamic Depth: The system can decide to stop the recursion early if the retrieved context at a higher node (e.g., a summary) already provides a high-confidence answer.

Self-Correction and Backtracking

If the recursive step reaches a "dead end" (e.g., the retrieved context contradicts the query or is empty), the system can backtrack to a previous node in the tree. It then uses the "failed" information as a negative constraint to generate a new, alternative sub-query. This "Self-RAG" approach ensures that the system doesn't just give up when the first path fails.

Research and Future Directions

The field is rapidly moving toward Agentic RAG, where the retrieval process is fully dynamic and the tree is built on-the-fly.

• Graph-Vector Hybrids: Research is focused on merging the semantic flexibility of vector embeddings with the hard logic of Knowledge Graphs. In these systems, recursive retrieval follows "edges" in the graph (e.g., "is_authored_by" or "cites") while using vector similarity to find the starting "nodes."
• Long-Context Optimization: As LLM context windows reach millions of tokens (e.g., Gemini 1.5 Pro), the role of recursive retrieval is shifting. Instead of fetching data from an external database, recursion is used to "zoom" into specific sections of a massive internal context, effectively treating the context window itself as a hierarchical tree.
• Automated Taxonomy Generation: Future systems will likely use LLMs to automatically build the Trie structures and hierarchical summaries during the ingestion phase, eliminating the need for manual data engineering and allowing the system to adapt to new data types autonomously.
• Multi-Modal Trees: Extending recursive retrieval to images and video, where a query might "zoom in" from a video summary to a specific frame, and then to an object within that frame using a recursive visual search.

By moving toward recursive architectures, we solve the "needle in the haystack" problem by first finding the right barn, then the right hay bale, and finally the needle itself.

Frequently Asked Questions

Q: When should I use Recursive Retrieval instead of standard RAG?

You should implement recursive retrieval when your queries are "multi-hop" (requiring information from multiple sources) or when your documents are long and complex (where a small chunk loses too much context). If a simple vector search is returning irrelevant results or "I don't know" despite the data being present, recursion is likely the solution. It is also preferred when you have a clear hierarchy in your data, such as legal codes or technical manuals.

Q: Does Recursive Retrieval increase latency?

Yes, because it involves multiple LLM calls or database lookups. However, this can be mitigated by using smaller, specialized models (like SLMs) for the decomposition and routing steps, and by parallelizing the execution of sub-queries within the tree. The trade-off is usually higher accuracy for higher latency.

Q: How does a Trie help in a RAG pipeline?

A Trie (prefix tree for strings) provides a deterministic way to filter data based on known prefixes, such as SKUs, dates, or category paths. This ensures that the recursive search is constrained to a valid subset of the data before the probabilistic vector search begins. For example, if a query contains "2023-Q4", the Trie ensures the retriever only looks at documents in that specific temporal branch.

Q: What is "Query Decomposition" in the context of Query Trees?

Query decomposition is the process of breaking a complex question into smaller, sequential, or parallel sub-questions. For example, "Compare the revenue of Company A and Company B" is decomposed into "What is Company A's revenue?" and "What is Company B's revenue?". The Query Tree manages the execution and synthesis of these sub-questions, often using the answer of one sub-query to formulate the next.

Q: How do I evaluate the performance of a Query Tree?

Evaluation requires A (comparing prompt variants) and specialized metrics like Faithfulness (is the answer derived from the retrieved context?) and Relevancy (does the retrieved context actually address the sub-query?). Tools like RAGAS or Arize Phoenix are often used to trace and score each node in the tree, allowing developers to identify exactly which "branch" of the retrieval failed.