Introduction

The beacoder/llm project brings together LLM application experiments such as RAG, GraphRAG, Agentic RAG, and tool calling. Based on Ollama local deployment and open-source models (Mistral, Qwen2.5), it demonstrates the complete technical evolution path from basic retrieval augmentation to intelligent agents, providing practical references for LLM application development from entry to advanced levels.

Project Background and Value

With the rapid evolution of LLM technology, transforming general capabilities into practical tools is a core concern for developers. This project implements a runnable prototype on a personal workstation (NVIDIA RTX4070 Laptop GPU), providing a 'from entry to advanced' learning path, which has important reference value for in-depth understanding of LLM application development. Meanwhile, the local deployment strategy (Ollama + open-source models) adapts to scenarios with sensitive data privacy, high API costs, or network restrictions, which is an important trend in current AI development.

Technology Selection and Experimental Methods

Technology Selection

• Ollama: Local LLM runtime, no cloud API required
• Open-source models: Mistral (7B), Qwen2.5 (7B), adapted for consumer GPUs
• Streamlit: Quickly build interactive web interfaces
• Python virtual environment: Strict dependency management to ensure reproducibility

Experimental Methods

Each experiment follows a structured process:

1. Environment preparation: Create virtual environment and install dependencies
2. Data/configuration: Clarify sources and requirements
3. Run commands: Copyable execution commands
4. Known issues: Record failure cases and limitations
5. Result display: Intuitive presentation of effects via screenshots

Experiment architectures:

• Basic RAG: nomic-embed-text embedding model + local vector storage + Ollama calling model
• GraphRAG: Build graph with graphrag_index, support local/global queries (global query not working yet)
• Agentic RAG: Multi-step reasoning, dynamic decision-making, tool interaction
• Tool calling: local_tools (local execution), docker_tools (Docker isolation)

Experimental Results and Key Findings

• GraphRAG limitations: Global query not working due to code issues; the author honestly records failure cases
• Agentic RAG performance: Better than GraphRAG in most cases, can handle Chinese queries (e.g., character relationship questions about Jin Ping Mei)
• Tool calling cases: Generate complete Minesweeper game code (HTML/CSS/JS), support Docker isolation to ensure multi-user safety
• Environment adaptation: 7B models run smoothly on consumer GPUs with 8GB VRAM

Project Insights and Technical Trends

• LLM applications are evolving from 'prompt engineering' to 'architecture engineering', requiring mastery of combined technologies such as retrieval, memory, planning, and tool usage
• Open-source ecosystem power is significant: Tools like Ollama, LangChain, GraphRAG allow individual developers to build complex AI systems
• Local deployment of open-source models has become an important option, adapting to various scenario needs

Learning and Practice Recommendations

1. Follow the project's experimental path (Basic RAG → GraphRAG → Agentic RAG → Tool Calling) to gradually establish a systematic understanding of LLM application architecture
2. Emphasize experimental methodology: Record environment configurations and known issues to ensure reproducibility
3. Maintain an experimental spirit: Understand technical principles through hands-on practice, and pay attention to the educational value of failure cases
4. Focus on the open-source tool ecosystem, use combined advantages to lower development thresholds