Build an Agentic RAG system using Langchain, Ollama and Milvus by Stephen Batifol

RAG (Retrieval Augmented Generation) helps reduce LLM hallucinations by grounding responses in actual data and providing more transparency in AI systems

Vector databases are essential for scaling RAG systems, supporting features like:

• Similarity search across multiple data types (text, audio, images)
• Metadata filtering
• Hybrid search capabilities
• GPU acceleration for large-scale deployments

Agentic RAG improves upon basic RAG by adding:

• Query routing
• Multi-turn conversations
• Memory/context awareness
• Self-reflection capabilities
• Tool integration
• Task planning

Key considerations for implementing RAG:

• Choose embedding models carefully based on language and use case
• Properly chunk documents for effective retrieval
• Use the same embedding model for both document processing and queries
• Consider scalability requirements (from millions to billions of vectors)

Technical stack components highlighted:

• Langchain for building LLM applications
• Ollama for running LLMs locally
• Milvus for vector storage and retrieval
• Support for multiple programming languages (Python, Java, Go, Node.js)

Challenges addressed by RAG systems:

• Processing unstructured data
• Working with private knowledge bases
• Handling multilingual content
• Managing multi-question queries
• Document summarization limitations