This article will provide a detailed tutorial on implementing a local, reliable agent using LangGraph, combining concepts from Adaptive RAG, Corrective RAG, and Self-RAG papers, and integrating Langchain, Elasticsearch Vector Store, Tavily AI for web search, and LLaMA3 via Ollama.

Background information

What is an LLM Agent?

Agent System Overview

Planning

Memory

Tool Use

Types of LLM Agents and Use Cases

Approach: ReAct/Langchain Agent vs LangGraph ?

Prerequisites

Python Code

1. Indexing

2. Retrieval Grader

3. Generator

4. Hallucination Grader and Answer Grader

Hallucination Grader

Answer Grader

5. Router

6. Web Search

7. Control Flow

State Definition

Node Functions

Conditional Edges

Workflow Definition

8. Build Graph

All Done !!

What do you see in the output of both these tests?

For `Test 1`

For `Test 2`

Key Points of the Workflow for Test 1 vs Test 2

Conclusion

Llama3, LangGraph and Elasticsearch: Build a local agent for vector search

A tutorial on building local agent using LangGraph, LLaMA3 and Elasticsearch vector store from scratch

Author

Pratik Rana

Articles

A tutorial on building local agent using LangGraph, LLaMA3 and Elasticsearch vector store from scratch