Скачать или смотреть Retrievers Explained — The Memory Engine Behind LangChain AI | Video 23 | LangChain Series

Welcome back, everyone!
By now, we’ve explored how data flows through LangChain — from prompts and embeddings to vector stores. But what happens when your AI needs to find specific information hidden within all that data? That’s where Retrievers come in.

🧠 What are Retrievers?
Retrievers are like the search engine or librarian of your LangChain system. They don’t generate new content — instead, they fetch the most relevant pieces of information from your stored documents when a user asks a question.

When a query comes in, the retriever scans through all the indexed data — PDFs, web pages, notes — and returns only the most meaningful chunks related to the query.

Under the hood, retrievers work closely with Vector Stores.
Each document is transformed into an embedding, a numerical representation of meaning. When a question arrives, it too becomes an embedding — and the retriever finds the closest matches. This way, it’s not just matching words but truly understanding semantic meaning.

⚙️ In this video, we’ll explore:
1️⃣ How retrievers connect your stored knowledge with your AI models
2️⃣ The difference between keyword-based and embedding-based retrieval
3️⃣ How retrievers ensure your LLM gets only the most relevant context
4️⃣ Why retrievers are critical for scalable, context-aware AI systems

By the end of this video, you’ll understand how retrievers act as the bridge between your static data and your intelligent AI, powering dynamic, knowledge-grounded conversations.

And in this video, we’ll dive deeper into one of the most useful retrievers — the WikipediaRetriever, to see how we can fetch real-time knowledge directly from Wikipedia.

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