RAG function realization and advantage analysis
Implementation steps::
- Create retrieval nodes to read external data (e.g. TXT/PDF)
- Add a generator node to process the search results
- expense or outlay
connect()Establishing a data pipeline
code example::
flow.add_node("retrieve", lambda x: open("data.txt").read())
flow.add_node("generate", lambda x: f"总结：{x[:100]}...")
flow.connect("retrieve", "generate")Core Advantages::
- No need for middleware such as vector databases
- Retrieve-generate process visualization
- Any data preprocessing node can be inserted (e.g. PDF parsing)
- Support for dynamic updating of data sources (just modify the node function)
In the real test, the amount of code to build the basic RAG application can be reduced by 70% compared with the traditional solution.
This answer comes from the articlePocketFlow: A Minimalist Framework for AI Application Development in 100 Lines of CodeThe
May not be reproduced without permission:AI productivity tools " How does PocketFlow's RAG feature work? What are the advantages over traditional implementations?
