Build a Smarter Search with LangChain Context Retrieval
Create a smarter search engine with LangChain’s context retrieval system. Learn how to enhance search results with AI-driven context and improve information retrieval accuracy.
At a Glance
Quickly and easily retrieve relevant text segments from large document collections with an information retrieval system built with LangChain. In this guided project, learn to use four types of retrievers: the Vector Store-backed Retriever for semantic similarity, the Multi-Query Retriever for varied queries, the Self-Querying Retriever for automatic query refinement, and the Parent Document Retriever for maintaining context. At the end, you are equipped to implement these retrievers in your own projects, enhancing information retrieval beyond traditional keyword-based methods.
A look at the project ahead
- Use four types of retrievers in LangChain to efficiently extract relevant document segments from text.
- Apply the Vector Store-backed Retriever to solve problems involving semantic similarity and relevance in large text data sets.
- Utilize the Multi-Query Retriever to address situations where multiple query variations are needed to capture comprehensive results.
- Implement the Self-Querying Retriever to automatically generate and refine queries, enhancing the accuracy of information retrieval.
- Employ the Parent Document Retriever to maintain context and relevance by considering the broader context of the parent document.
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