How to Build Real-Time Document Search with Pathway

Most retrieval systems only search a snapshot of your data, which means answers go stale as soon as documents change. For teams building search and RAG applications over live data, that lag is a real problem. Pathway is an open-source framework built specifically to keep your AI indexes in sync with your data in real time.

Here is how to start building real-time document search with Pathway.

Step 1: Understand the data flow. Pathway models your system as a stream of events. Instead of periodically re-indexing everything, it watches your data sources and updates indexes incrementally as changes arrive.

Step 2: Connect your data sources. Use Pathway connectors to pull from files, databases, cloud storage, or APIs. You define each source, and Pathway handles watching and updating it continuously.

Step 3: Build your indexing pipeline. Write a simple Pipeline to transform incoming data: parse the documents, chunk the text, and generate embeddings. Pathway’s expressive API makes this straightforward in Python.

Step 4: Keep vectors fresh. As new or edited documents arrive, Pathway recomputes and updates only the affected embeddings. This keeps your vector index accurate without rebuilding the whole thing.

Step 5: Run queries against the live index. Plug your real-time index into a retrieval application or connect it to an LLM for RAG. Users get answers based on the freshest data, not a stale snapshot.

Step 6: Deploy in production. Pathway scales horizontally and handles large volumes, so your search keeps working as data grows. Monitor your pipeline and tune chunking and retrieval to improve result quality.

Real-time search transforms what is possible with AI assistants and knowledge tools. With Pathway, you can build systems that always reflect the latest information, giving users trustworthy, up-to-date answers.