Index

Tree Search Examples

This tutorial provides a basic example of how to perform retrieval using the PageIndex tree.

Basic LLM Tree Search Example

A simple strategy is to use an LLM agent to conduct tree search. Here is a basic tree search prompt.


prompt = f"""
You are given a query and the tree structure of a document.
You need to find all nodes that are likely to contain the answer.
 
Query: {query}
 
Document tree structure: {PageIndex_Tree}
 
Reply in the following JSON format:
{{
  "thinking": <your reasoning about which nodes are relevant>,
  "node_list": [node_id1, node_id2, ...]
}}
"""

In our dashboard and retrieval API, we use a combination of LLM tree search and value function-based Monte Carlo Tree Search (MCTS ). More details will be released soon.

Integrating User Preference or Expert Knowledge

Unlike vector-based RAG where integrating expert knowledge or user preference requires fine-tuning the embedding model, in PageIndex, you can incorporate user preferences or expert knowledge by simply adding knowledge to the LLM tree search prompt. Here is an example pipeline.

1. Preference Retrieval

When a query is received, the system selects the most relevant user preference or expert knowledge snippets from a database or a set of domain-specific rules. This can be done using keyword matching, semantic similarity, or LLM-based relevance search.

2. Tree Search with Preference

Integrating preference into the tree search prompt.

Enhanced Tree Search with Expert Preference Example


prompt = f"""
You are given a question and a tree structure of a document.
You need to find all nodes that are likely to contain the answer.
 
Query: {query}
 
Document tree structure:  {PageIndex_Tree}
 
Expert Knowledge of relevant sections: {Preference}
 
Reply in the following JSON format:
{{
  "thinking": <reasoning about which nodes are relevant>,
  "node_list": [node_id1, node_id2, ...]
}}
"""

Example Expert Preference

If the query mentions EBITDA adjustments, prioritize Item 7 (MD&A) and footnotes in Item 8 (Financial Statements) in 10-K reports.

By integrating user or expert preferences, node search becomes more targeted and effective, leveraging both the document structure and domain-specific insights.

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