Local RAG in 1.3s: LangGraph + Ollama (Free, No API Keys)
Most local RAG pipelines are painfully slow. But with the right routing and a simple prompt trick, you can get sub-2-second answers running entirely on your laptop. Here is the exact LangGraph architecture that makes local AI usable again.
Build a local RAG agent that answers in ~1.3s—free, private, and fast.
We’ll use LangGraph + Ollama with lightweight models, smart agentic routing, a relevance grader, and a single prompt tweak that fixes messy answers.
Notebook & Code: https://github.com/LLM-Implementation/Practical-LLM-Implementation/blob/main/agents_frameworks/LangGraph_rag.ipynb
What you’ll build
Agentic RAG graph (retrieve → grade → rewrite → generate) with conditional edges
Local-first stack: Ollama + LangChain/LangGraph + HuggingFace embeddings
Prompting fix: clearly labeled Retrieved Context in triple quotes for focused answers
Speed & cost: ~1.3s end-to-end on my machine, $0 per run
Models & tools used
ChatOllama (Granite family) for agent, grader, and answer
https://ollama.com/library/granite4
Embedding-Gemma via HuggingFace (dim truncated to 256)
https://huggingface.co/google/embeddinggemma-300m
In-memory vector store + LangChain tool wrapper
https://docs.langchain.com/oss/python/langgraph/agentic-rag#5-rewrite-question
Chapters
00:00 Local RAG: Faster & Free
00:47 Architecture Overview (Agentic Graph)
02:11 Environment Setup (Local-First)
02:47 Step 1: Preprocess Docs
03:16 Step 2: Local Retriever (Embeddings)
03:55 Step 3: Agent Node (Tool Use)
04:28 Step 4: Relevance Grader
05:03 Step 5: Question Rewriter
05:45 Step 6: Answer Generator
06:24 Step 7: Assemble the Graph
07:17 Run & Stream the Agent
07:27 The Prompting Trick (Triple-Quoted Context)
08:22 Results: ~1.3s, $0
08:40 Code & Notebook Links
08:57 Outro
Notes & fairness
The speed comparison references the official LangChain RAG tutorial trace on my local hardware. Your results may vary by machine, models, and retrieval corpus. All trademarks belong to their owners.
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Chapters (15)
Local RAG: Faster & Free
0:47
Architecture Overview (Agentic Graph)
2:11
Environment Setup (Local-First)
2:47
Step 1: Preprocess Docs
3:16
Step 2: Local Retriever (Embeddings)
3:55
Step 3: Agent Node (Tool Use)
4:28
Step 4: Relevance Grader
5:03
Step 5: Question Rewriter
5:45
Step 6: Answer Generator
6:24
Step 7: Assemble the Graph
7:17
Run & Stream the Agent
7:27
The Prompting Trick (Triple-Quoted Context)
8:22
Results: ~1.3s, $0
8:40
Code & Notebook Links
8:57
Outro
🎓
Tutor Explanation
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