Vectorless RAG - Local Financial RAG Without Vector Database | Tree-Based Indexing with Ollama

2026-02-11 · Source: Venelin Valkov · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, long

Summary

This content introduces "Vectorless RAG," a Retrieval Augmented Generation (RAG) system that operates without a vector database, leveraging document structure for retrieval. The approach involves creating a tree-based index from structured documents, such as financial 10Q/10K filings, and using a Large Language Model (LLM) like Llama 3 (4B parameter version) to reason over this structure. The system processes a markdown document, builds a hierarchical tree, summarizes each leaf node, and then uses the LLM to select relevant chunks based on a user query. This method is demonstrated using an Nvidia financial statement, successfully answering queries about revenue, earnings per share, AI infrastructure partnerships, and Q4 outlook, citing specific figures like Q3 2026 revenue of $57,006 and a diluted EPS of $13.

Key takeaway

For AI Engineers building RAG systems with highly structured documents and aiming to reduce infrastructure dependencies, consider implementing a vectorless RAG approach. This method, particularly effective for smaller document sets, allows your LLM to reason directly about document structure, potentially offering more transparent retrieval logic and easier debugging compared to embedding-based systems. Be mindful of increased LLM inference costs and the need for a sufficiently capable LLM for summarization.

Key insights

Vectorless RAG uses document structure and LLMs for retrieval, eliminating vector databases for structured data.

Principles

• Structured documents enable tree-based indexing.
• LLMs can reason over document hierarchies.
• Retrieval cost shifts from embeddings to LLM calls.

Method

Build a tree index from markdown, summarize leaf nodes bottom-up using an LLM, then use the LLM to select relevant nodes for query answering based on the tree structure.

In practice

• Apply to financial (10Q/10K) or legal documents.
• Use markdown header splitters for tree creation.
• Tune prompts for domain-specific accuracy.

Topics

• Vectorless RAG
• Tree-Based Indexing
• Retrieval-Augmented Generation
• Large Language Models
• Financial Document Analysis

Best for: Machine Learning Engineer, AI Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Venelin Valkov.