Build a RAG application with Runware and LangChain
📰 Dev.to · Rishi Raj Jain
Learn to build a RAG application with Runware and LangChain to connect LLM answers to your own documents
Action Steps
- Install Runware and LangChain using pip
- Configure a LangChain LLM agent with your own documents
- Build a RAG pipeline using Runware to retrieve relevant documents
- Test the RAG application with sample queries
- Fine-tune the LLM model for better performance
Who Needs to Know This
Developers and data scientists on a team can benefit from this tutorial to improve their LLM-based applications with custom document retrieval
Key Insight
💡 RAG applications can significantly improve the accuracy of LLMs by connecting them to relevant custom documents
Share This
🤖 Build a RAG app with Runware & LangChain to supercharge your LLMs with custom docs! 📄
Key Takeaways
Learn to build a RAG application with Runware and LangChain to connect LLM answers to your own documents
Full Article
Retrieval-augmented generation (RAG) connects LLM answers to your own documents instead of relying on...
DeepCamp AI