hma 0.1.0

Retrieval-Augmented Hierarchy - A smarter alternative to RAG

HMA — Retrieval-Augmented Hierarchy

A smarter, structural alternative to traditional RAG.

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📖 What is HMA?

HMA (Retrieval-Augmented Hierarchy) is a next-generation retrieval protocol designed to overcome the critical limitations of standard chunk-based RAG systems.

Instead of blindly shredding your documents and code into disconnected chunks and relying on token-heavy vector embeddings, HMA maps the actual structure of your project. It understands files, imports, configurations, and documentation, then uses intelligent LLM routing to find the exact files needed to answer a query.

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🏛️ Architecture Overview

HMA operates in two distinct phases: an offline indexing phase that maps your project, and an online querying phase that traverses the map to assemble context.

graph TB
    subgraph "Phase 2: Querying (Online)"
        G[User Question] --> H[Query Router]
        H --> F
        F --> I[File Selector]
        I --> J[Targeted Reader]
        J --> K{Connected Files?}
        K -->|Yes| L[Read Direct Connections Only]
        L --> M[Context Assembler]
        K -->|No| M
        M --> N[Return Structured Context]
    end

    subgraph "Phase 1: Indexing (Offline)"
        A[Target Directory] --> B[File Walker]
        B --> C[File Analyzer]
        C --> D[Summary Generator]
        C --> E[Relationship Extractor]
        D --> F[Knowledge Map]
        E --> F
    end

    style F fill:#f9a825,stroke:#f57f17,stroke-width:3px

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⚠️ The Problem with Traditional RAG

Traditional RAG breaks all content into flat chunks and performs brute-force similarity search. This causes:

1. Loss of structure — File hierarchies, class definitions, and cross-references are destroyed.
2. Context fragmentation — Related information split across multiple chunks loses its meaning.
3. Wasted Tokens — Vector similarity often pulls in irrelevant chunks that just happen to share keywords.
4. No relationship awareness — Interconnected files (like a script and its config) are treated as isolated islands.

How HMA Solves This:

Traditional RAG: Question → Scan ALL chunks → Return top-k similar chunks → Generate answer
HMA Protocol:    Question → Consult Map → Route to best file(s) → Check single-hop connections → Generate answer

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🔄 Query Data Flow

When a user asks a question, HMA uses your provided LLM to reason over the Knowledge Map, avoiding brute-force scans. Furthermore, it strictly uses Single-Hop Traversal to pull in related context without falling into infinite loops.

sequenceDiagram
    participant U as User
    participant R as Router
    participant M as Knowledge Map
    participant RD as Reader
    participant T as Traverser
    participant LLM as User's LLM

    U->>R: "How does auth work?"
    R->>M: Get all file summaries + topics
    M-->>R: [{auth.py: "Handles JWT..."}, {middleware.py: "Auth middleware..."}, ...]
    R->>LLM: Which files answer this?
    LLM-->>R: [auth.py (0.95), middleware.py (0.82)]
    R->>RD: Read auth.py, middleware.py
    RD-->>R: File contents
    R->>T: Get DIRECT connections for auth.py
    T->>M: Get relationships for auth.py
    M-->>T: [auth.py → imports → models/user.py]
    T->>RD: Read models/user.py
    RD-->>T: Connected content
    Note over T: STOP — single hop only,<br/>do NOT follow user.py's connections
    T-->>R: Assembled context
    R->>LLM: Generate answer with context
    LLM-->>U: "Authentication uses JWT tokens defined in auth.py..."

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🛠️ Tech Stack & Design Principles

HMA is built to be fast, agnostic, and lightweight:

• Core Language: Pure Python (>= 3.9).
• Zero Vector Dependencies: No heavy vector databases (Chroma, Pinecone) or embedding models (SentenceTransformers) required.
• Protocol First: HMA does not bundle an LLM API. You inject your own LLM callable (OpenAI, Gemini, Ollama, HuggingFace).
• Abstract Syntax Trees (AST): Uses Python's native ast module to accurately map Python code structures locally.
• Regex Engines: Employs optimized Regex to extract imports and exports from 20+ other languages (JS, TS, Rust, Go, Java, etc.).
• Optional Document Parsers: Integrates with PyMuPDF and python-docx for rich document processing.

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📦 Installation

Install directly from the repository using pip:

# Basic installation (Code, Markdown, JSON, YAML, etc.)
pip install .

# Installation with PDF and DOCX parsing support
pip install .[docs]

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💻 Usage & Code Examples

HMA exposes both a Python API for integration into your apps and a CLI for rapid terminal use.

1. Python API

The most important concept in HMA is that you provide the LLM. You just need to pass a function that takes a string and returns a string.

import os
from hma import HMA
import google.generativeai as genai

# 1. Setup your preferred LLM (e.g., Gemini)
genai.configure(api_key=os.environ["GEMINI_API_KEY"])
model = genai.GenerativeModel('gemini-1.5-pro')

def my_gemini_llm(prompt: str) -> str:
    """Wrapper function that HMA will use."""
    response = model.generate_content(prompt)
    return response.text

# 2. Initialize the HMA Protocol
rah = HMA(llm=my_gemini_llm)

# 3. Index your project
# This analyzes all files and creates a `.hma.json` Knowledge Map
print("Building Knowledge Map...")
knowledge_map = rah.index("/path/to/your/codebase")
print(f"Indexed {knowledge_map.total_files} files successfully!")

# 4. Ask a structural question
question = "How is the database connection established and where is the config used?"
answer = rah.ask(question)

# 5. Review the results
print("\n--- Answer ---")
print(answer.text)
print("\n--- Telemetry ---")
print(f"Primary Files Consulted: {answer.sources}")
print(f"Single-Hop Connections Read: {answer.connections}")
print(f"Router Reasoning: {answer.reasoning}")

2. Command-Line Interface (CLI)

HMA includes a CLI to easily index and query projects from your terminal. (Note: The CLI currently uses a dummy LLM for demonstration purposes, but can be easily extended to use ENV variables).

# Index a project directory
$ hma index /var/www/my_project
Indexing /var/www/my_project...
✅ Successfully indexed 142 files.
Map saved to /var/www/my_project/.hma.json

# Ask a question
$ hma ask "Where is the payment gateway logic?" --project /var/www/my_project
Question: Where is the payment gateway logic?
==================================================
The payment gateway logic is primarily handled in src/services/stripe.ts...
==================================================

Sources consulted:
  - src/services/stripe.ts
Connected files read (single-hop):
  - src/config/keys.ts

# View statistics of your Knowledge Map
$ hma map /var/www/my_project --stats
Project: /var/www/my_project
Total Files Indexed: 142
Total Relationships: 310
Last Updated: 2026-06-15T12:00:00.000Z

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Built with ❤️ by the HMA Contributors

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