Retrievers & Contextual Compression
Turn a vector store into a tunable retriever, control how many documents come back, and use contextual compression to strip irrelevant text before it reaches the LLM.
From Vector Store to Retriever
A vector store knows how to search, but agents talk to a retriever — a thin interface with one job: given a query, return relevant documents.
Any vector store exposes as_retriever() to produce one.
retriever = vectorstore.as_retriever()
docs = retriever.invoke('How do I reset my password?')Controlling k
The k parameter sets how many documents to return. Too few misses context; too many wastes tokens and adds noise.
retriever = vectorstore.as_retriever(
search_kwargs={'k': 4}
)All lessons in this course
- Document Loaders Explained
- Text Splitters & Embeddings
- Vector Stores for Retrieval
- Retrievers & Contextual Compression