> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/vectifyai/pageindex/llms.txt
> Use this file to discover all available pages before exploring further.
# Reasoning-Based RAG
> How PageIndex uses LLM reasoning for retrieval instead of vector similarity
PageIndex introduces a fundamentally different approach to Retrieval-Augmented Generation (RAG) by using **reasoning** instead of **semantic similarity** for document retrieval. This paradigm shift addresses a core limitation of traditional vector-based RAG systems.
## The Core Problem: Similarity ≠ Relevance
Traditional vector-based RAG systems rely on semantic similarity to retrieve relevant document chunks. However, **similarity is not the same as relevance**.
### Vector-Based RAG Limitations
Vector similarity is a "black box" - it's unclear why certain chunks were retrieved
Embeddings don't understand document structure or relationships between sections
Similar words don't guarantee relevant content for complex queries
Chunking destroys the document's natural organization and context
**Example**: A query about "risk mitigation strategies" might retrieve chunks containing similar words like "risk" and "strategy", but miss the actual section discussing mitigation approaches because it uses different terminology.
## Reasoning-Based Retrieval
PageIndex uses LLMs to **reason** about which document sections are relevant to a query, simulating how human experts navigate and extract knowledge from complex documents.
### Two-Step Process
1. **Tree Structure Generation**: Create a hierarchical index of the document
2. **Reasoning-Based Tree Search**: Use LLM reasoning to navigate the tree and find relevant sections
```mermaid theme={null}
graph TD
A[Query: "What were the financial vulnerabilities in 2023?"] --> B[Tree Search]
B --> C{Examine Root Nodes}
C --> D["Preface (pages 1-4)"]
C --> E["Financial Stability (pages 21-31)"]
C --> F["Supervision (pages 31-59)"]
E --> G{Examine Child Nodes}
G --> H["Monitoring Financial Vulnerabilities (pages 22-28)"]
G --> I["International Cooperation (pages 28-31)"]
H --> J[Retrieve Relevant Pages]
```
The LLM evaluates each node based on its **title**, **summary**, and **context** to determine relevance - not just keyword matching.
## How It Works: Tree Search
PageIndex performs retrieval through **agentic tree search**, where the LLM acts as an intelligent agent navigating the document structure:
### Step 1: Understanding the Query
The LLM first analyzes the user's query to understand what information is being requested:
```
Query: "What were Disney's Q1 fiscal 2025 Entertainment segment results?"
LLM Reasoning:
- Need: Financial results for a specific segment
- Time period: Q1 fiscal 2025
- Focus area: Entertainment segment
- Looking for: Revenue, operating income, highlights
```
### Step 2: Navigating the Tree
The LLM examines node titles and summaries to decide which branches to explore:
```json theme={null}
{
"title": "Financial Results for the Quarter",
"summary": "Revenue increased 5% to $24.7 billion... Entertainment operating income increased by $0.8 billion to $1.7 billion...",
"reasoning": "This section contains Entertainment segment financial results for Q1 FY2025",
"action": "retrieve"
}
```
### Step 3: Intelligent Pruning
The LLM can reason about which sections to skip:
```
Node: "Guidance and Outlook" (pages 2-2)
Reasoning: "This discusses future projections, not Q1 actual results"
Action: Skip
```
This reasoning-based approach allows PageIndex to handle complex, multi-hop queries that require domain expertise and contextual understanding.
## Comparison: Vector RAG vs Reasoning RAG
| Aspect | Vector-Based RAG | PageIndex Reasoning RAG |
| ------------------------ | ------------------------------- | ------------------------------------- |
| **Retrieval Method** | Embedding similarity | LLM reasoning over structure |
| **Document Processing** | Chunking (arbitrary boundaries) | Tree structure (natural sections) |
| **Context Preservation** | Lost in chunks | Hierarchical relationships maintained |
| **Explainability** | Opaque similarity scores | Traceable reasoning path |
| **Query Complexity** | Best for simple queries | Handles multi-step reasoning |
| **Accuracy** | Depends on embeddings | 98.7% on FinanceBench |
| **Traceability** | Approximate chunk locations | Exact page and section references |
## State-of-the-Art Results
PageIndex-powered systems achieve superior performance on professional document analysis:
**98.7% accuracy** on the FinanceBench benchmark using reasoning-based RAG with PageIndex tree structures.
This significantly outperforms traditional vector-based RAG solutions in financial document analysis.
[Read the full benchmark results →](https://github.com/VectifyAI/Mafin2.5-FinanceBench)
## Key Advantages
### 1. True Relevance
The LLM can determine if a section is **truly relevant** to the query, not just **similar**:
```
Query: "What caused the operating income decline in Domestic Parks?"
Relevant (found by reasoning): "Experiences segment had adverse impact
due to Hurricanes Milton and Helene and pre-opening expenses..."
Similar but irrelevant (might be found by vectors): "Operating income
for International Parks increased by 28%..."
```
### 2. Multi-Step Reasoning
PageIndex can handle queries requiring multiple reasoning steps:
```
Query: "Compare Entertainment and Sports segment growth rates"
Reasoning Steps:
1. Locate Entertainment segment results
2. Locate Sports segment results
3. Extract growth percentages
4. Compare the two
```
### 3. Domain Expertise
The LLM can apply domain knowledge during retrieval:
```
Query: "What were the regulatory changes?"
Reasoning: "Regulatory changes would be in the 'Regulatory Developments'
section under 'Supervision and Regulation', not in 'Supervisory Developments'"
```
This level of understanding is impossible with vector similarity alone, which operates purely on mathematical distance between embeddings.
## Human-Like Document Navigation
PageIndex simulates how experts work with documents:
1. **Scan the table of contents** to identify relevant sections
2. **Use context and structure** to navigate to the right location
3. **Apply domain knowledge** to interpret section relevance
4. **Follow logical paths** through hierarchical information
## When to Use Reasoning-Based RAG
Reasoning-based RAG with PageIndex is especially powerful for:
* **Professional documents** requiring domain expertise (financial, legal, technical)
* **Long documents** exceeding LLM context windows
* **Complex queries** requiring multi-step reasoning
* **Scenarios requiring explainability** and traceability
* **Hierarchically structured documents** (reports, manuals, textbooks)
For simple semantic search over unstructured text, traditional vector RAG may be sufficient and more cost-effective. Use reasoning-based RAG when accuracy and explainability are critical.
## Next Steps
Learn why PageIndex doesn't need vector databases
Hands-on tutorial with working code examples