The process involves building complex pipelines using LangGraph, a framework designed for orchestrating machine learning models, particularly large language models (LLMs), in a structured and efficient manner. The focus is on overcoming the limitations of previous frameworks that became cumbersome due to frequent changes in their API and documentation, making it difficult for users to scale beyond basic use cases. LangGraph simplifies this by utilizing a Directed Acyclic Graph (DAG) structure, which is commonly used in complex system orchestration, such as KubeFlow for machine learning and AirFlow for data engineering. This structure allows engineers to build scalable systems without becoming overwhelmed by the entire pipeline's complexity.

A main example involves creating a Retrieval Augmented Generation (RAG) pipeline. This type of pipeline retrieves relevant data from a database based on a query and then uses that data as context to generate a response via an LLM. The pipeline pulls data from a repository using a document loader and stores it in a local vector database. The data is then indexed, making it easily retrievable. The state of the pipeline, which includes the user's question, retrieved documents, and generated responses, is captured using Pydantic, a data validation library for Python.

The retrieval node takes the query, uses it to retrieve documents from the database, and stores these documents in the pipeline state. The generation node uses a language model to generate a response based on the retrieved documents and the query. This generated response is then stored in the state.

Finally, the process explains how to assemble these nodes into a complete pipeline using LangGraph’s StateGraph class. The nodes are connected in sequence—first retrieval, then generation—and compiled into a functioning pipeline. The pipeline can then be queried with new questions, and the system will stream the execution events as it retrieves and generates answers. The process emphasizes efficiency in scaling complex pipelines while abstracting away unnecessary complexities, allowing engineers to focus on specific components of the system.

There is also a mention of the importance of rewriting the query for optimized retrieval, suggesting the use of techniques such as the HyDE method for more effective database queries. Overall, LangGraph is presented as a powerful tool for building and scaling sophisticated pipelines while avoiding the pitfalls of other frameworks.

https://newsletter.theaiedge.io/p/how-to-build-ridiculously-complex

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