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b163dc2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | ## 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.
```python
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, ...]
}}
"""
```
<callout>
In our dashboard and retrieval API, we use a combination of LLM tree search and value function-based Monte Carlo Tree Search ([MCTS](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search)). More details will be released soon.
</callout>
### 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**
```python
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.
## 💬 Help & Community
Contact us if you need any advice on conducting document searches for your use case.
- 🤝 [Join our Discord](https://discord.gg/VuXuf29EUj)
- 📨 [Leave us a message](https://ii2abc2jejf.typeform.com/to/tK3AXl8T)
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