🧠 Reasoning-Based RAG with PageIndex

We provide a simple example of using PageIndex to build reasoning-based retrieval systems for documents.

Preprocessing Workflow

1. Process documents using PageIndex to generate tree structures
2. Store the tree structures and their corresponding document IDs in a database table
3. Store the contents of each node in a separate table, indexed by node ID and tree ID

Reasoning-Based RAG Pipeline

1. Query Preprocessing

   • Analyze the query to identify the required knowledge
   • Break down complex queries into sub-queries

2. Document Search (Learn more →)

   • Search the relevant documents and their IDs
   • Fetch corresponding tree structures from the database

3. Node Search (Learn more →)

   • Search through tree structures to identify relevant nodes
   • Prioritize nodes based on relevance and context

4. LLM Generation

   • Fetch the corresponding contents of the selected nodes
   • Format and extract the relevant information
   • Send the assembled context with the original query to the LLM
   • Generate contextually informed responses

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