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Learn advanced RAG techniques in this advanced RAG course. Explore pre- and post-retrieval optimization with LangChain, and build intelligent, scalable applications with hands-on projects.

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MultiQuery

RAG-Fusion

Decomposition

SemanticRouting

RoutingClassifier

CrossEncoderReranking

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HyDE

Master advanced retrieval-augmented Generation (RAG) techniques in this comprehensive course for experienced learners. You’ll gain hands-on experience implementing advanced RAG systems with LangChain, equipping you to create intelligent, efficient AI applications.

The course begins by revisiting the core principles of RAG and then dives into pre-retrieval optimization techniques, focusing on strategies like indexing and query formulation. Next, you’ll explore post-retrieval optimization methods, such as RAG-Fusion and cross encoder reranking, to refine and enhance retrieved data.

Finally, you’ll work on a practical project to build a fully functional RAG application, applying advanced pre- and post-retrieval optimization techniques. After completing this course, you’ll have the expertise to design intelligent, scalable systems that harness the full potential of RAG.

Advanced RAG Techniques: Choosing the Right Approach

It uses exact keyword matching to find relevant documents.

It retrieves smaller text chunks to provide concise answers.

It retrieves full parent documents for comprehensive context.

It eliminates the need for text embedding functions.

What is the primary advantage of parent document retrieval (PDR) in advanced RAG systems?

What is the primary advantage of parent document retrieval (PDR) in advanced RAG systems?

Direct keyword matching without modifications

Which step is *not* involved in the process of self-querying retrieval?

Which step is not involved in the process of self-querying retrieval?

Frequent updates to child document embeddings

Limited storage capacity of vector stores

Sparse nature of child document embeddings

During RAG systems that utilize parent document retrieval (PDR), why is indexing child document embeddings in the vector store considered computationally expensive?

During RAG systems that utilize parent document retrieval (PDR), why is indexing child document embeddings in the vector store considered computationally expensive?

It relies solely on keyword matching between the query and document content.

It leverages pretrained word embeddings to represent both documents and user queries.

It requires manual annotation of documents with specific search labels.

It utilizes a complex rule-based system to analyze user queries.

How does the system understand the user’s intent within a document collection when using self-querying retrieval (SQR)?

How does the system understand the user’s intent within a document collection when using self-querying retrieval (SQR)?

Improved retrieval efficiency by filtering based on relevant criteria

Increased data security through obfuscation

Enhanced redundancy for data backup purposes

Reduced data granularity for faster processing

Advanced RAG leverages metadata during the indexing process. What is a key advantage of incorporating metadata into this stage?

Advanced RAG leverages metadata during the indexing process. What is a key advantage of incorporating metadata into this stage?

Which data cleaning technique involves removing words like "the," "a," and "an" to reduce data size and improve retrieval relevance?

Which data cleaning technique involves removing words like “the,” “a,” and “an” to reduce data size and improve retrieval relevance?

Manually categorize documents with predefined labels for retrieval.

Allow users to submit natural language queries to retrieve relevant information.

Require complex query languages with a specific syntax for retrieval.

Focus solely on retrieving documents based on exact keyword matches.

What is the core idea behind self-querying retrieval (SQR) systems?



What is the core idea behind self-querying retrieval (SQR) systems?

**(Select all that apply.)** Which options are used to configure a `SelfQueryRetriever` instance in LangChain?

(Select all that apply.) Which options are used to configure a <code>SelfQueryRetriever</code> instance in LangChain?

Store only the textual descriptions of the projects.

Create a single inverted index for all keywords and researcher names combined.

Build separate inverted indexes for keywords appearing in textual descriptions and researcher names.

Utilize a single embedding vector to represent each project, capturing both textual and researcher information.

**Scenario:** You’re building a knowledge base system that stores information about various research projects. Each project has a title, a list of participating researchers, and a textual description of the research goals and methodology. The system uses a graph structure, where nodes represent projects and researchers, and edges represent participation in projects.

To improve retrieval performance, you’re considering indexing projects using multiple representations.

Which indexing strategy, based on multi-representation indexing (MRI), would be most beneficial for retrieving projects based on a combination of keywords in the textual descriptions and the expertise of participating researchers?

Scenario: You’re building a knowledge base system that stores information about various research projects. Each project has a title, a list of participating researchers, and a textual description of the research goals and methodology. The system uses a graph structure, where nodes represent projects and researchers, and edges represent participation in projects.
To improve retrieval performance, you’re considering indexing projects using multiple representations.
Which indexing strategy, based on multi-representation indexing (MRI), would be most beneficial for retrieving projects based on a combination of keywords in the textual descriptions and the expertise of participating researchers?

It simplifies the search process by requiring only keywords.

It enhances flexibility by allowing adaptation to different document types.

It improves retrieval accuracy by capturing various aspects of document content.

It allows for retrieval based on semantic similarity, even if the exact keywords are not present.

Which statement is *not* an advantage of multi-representation indexing?

Which statement is not an advantage of multi-representation indexing?

Test your understanding of pre-retrieval techniques for optimizing the indexing of advanced RAG.

Getting Started

Introduction to Retrieval-Augmented Generation (RAG)

Advanced RAG: Pre-Retrieval (Optimizing Indexing)

Advanced RAG: Pre-Retrieval (Optimizing Query)

Build a RAG Using LangChain with Google Gemini

Advanced RAG: Post-Retrieval Process

Talk to Your Web Page: A RAG-Powered Chat Interface

Conclusion

Quiz: Pre-Retrieval Techniques for Optimizing Indexing