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RAG
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Module 4
Indexing and Retrieval: Vector Stores
1
Vector Stores for Efficient Embedding Retrieval
Explain the role of a vector store in persisting embeddings for efficient retrieval.
2
Populating Vector Stores with Document Chunks and Embeddings
Populate a vector store (e.g., ChromaDB) with document chunks and their embeddings.
3
Vector Search for Relevant Chunks
Perform similarity search on a vector store to retrieve relevant chunks for a given query.
4
Filtering Search Results with Metadata
Apply metadata filters during retrieval to narrow down search results.
5
Balancing Speed and Accuracy: The Purpose of ANN Algorithms
Describe the purpose of Approximate Nearest Neighbor (ANN) search algorithms (e.g., HNSW) in balancing search speed and accuracy.
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Preparing the Knowledge Base: Document Processing
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Core RAG: Assembling the Pipeline