Congratulations!
1. Congratulations!
Congratulations on completing the course! You've come a long way!2. Chapter 1
In chapter 1, you learned about the fundamentals of building RAG systems: from loading and splitting documents to embedding and storage.3. Chapter 1
LangChain helped us along the way to streamline this process and connect prompt templates, models, vector databases, and output parsers.4. Chapter 2
In chapter 2, you discovered techniques to enhance your RAG system. We learned how to load and split code files, split on tokens to ensure we don't exceed our model's context window, and even learned how to split semantically with the use of embedding models.5. Chapter 2
You also demystified the concept of evaluating RAG systems beyond comparing the final outputs using the ragas framework.6. Chapter 3
Finally, in Chapter 3, you were introduced to Graph RAG, which allowed you to overcome some of the limitations of vector RAG. You converted unstructured text documents into graphs using LLMs, and stored these documents in a Neo4j graph database.7. Chapter 3
You then used LangChain to build a Graph RAG architecture that used LLMs to translate user inputs into Cypher queries to query the graph database. The returned graph documents were than converted back into natural language for the end user.8. Let's practice!
We hope you enjoyed the course and learned a lot along the way. Time for you to get out there and start building your own AI-powered RAG applications with LangChain!Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.