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RAG Tutorials & Pipelines Hybrid Retrieval Augmented Generation (RAG) Intermediate Apache-2.0 ★ 1.8k stars

local_rag_agent

This application implements a Retrieval-Augmented Generation (RAG) system using Llama 3.2 via Ollama, with Qdrant as the vector database. Built with Agno v2.0.

Core Stack: Llama Agno Qdrant Docker Python Ollama
View Source Repository ↗ Read Tutorial / Demo

At a Glance Specifications

Type Intermediate
Framework Agno
Primary Model Meta Llama 3.3
Language Python
License Apache-2.0
Stars 1.8k
Forks 236
Last Verified 2026-09-02

01 // What It Does

This blueprint illustrates how to construct a robust hybrid retrieval augmented generation (rag) utilizing Agno. It showcases clean separation between user input handling, LLM prompt formatting, external tool execution, and response synthesis.

02 // How It Works & Architecture

The architecture leverages Agno to manage conversation state while isolating API calls and tool definitions. This prevents context bloat and ensures predictable execution paths during multi-step reasoning cycles.

Execution Flow Diagram Pattern: Hybrid Retrieval Augmented Generation (RAG)
User Query / Document Input
↓
Dense Vector Embedding (Qdrant/LanceDB)
Sparse Keyword Matching (BM25)
↓
Reciprocal Rank Fusion (RRF) & Cross-Encoder Reranking
↓
Augmented LLM Generation (Meta Llama 3.3) → Verified Answer
Architecture inferred from open-source project codebase and verified documentation.

03 // Real-World Use Cases

  • • Building internal developer tools and automation agents for enterprise document search & q&a.
  • • Serving as an architectural reference for enterprise workflows requiring reliable tool calling.
  • • Prototyping next-generation AI applications with minimal operational dependencies.

Technical Boundaries

Relies on upstream LLM API uptime and latency. Requires careful token budget management for long conversational contexts and sandboxing for untrusted external tool executions.

Production Considerations

For production deployment: introduce persistent session storage (e.g., Redis or PostgreSQL), implement strict rate limiting and request timeouts, add OpenTelemetry tracing for agent observability, and enforce granular RBAC for all executed tools.

Why This Project Matters

As AI systems evolve from passive text generators into active decision-making agents, understanding how to cleanly wire tool calling, retrieval, and state management in Agno is critical for engineering reliable software.

Quickstart Setup Guide 

# 1. Clone the upstream repository
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git

# 2. Enter this blueprint directory
cd awesome-llm-apps/rag_tutorials/local_rag_agent

# 3. Create and activate a Python virtual environment
python3 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# 4. Install dependencies
pip install -r requirements.txt

# 5. Export required API keys in your environment
export OPENAI_API_KEY="your-api-key"

# 6. Execute application entrypoint