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NiFi LangChain Processors

Introduction

The NiFi LangChain processors enable the construction of LangChain pipelines within NiFi, leveraging the capabilities of Large Language Models (LLMs) for various data processing tasks. These processors are designed to work with NiFi 2.0's Python Extensions, aligning with the LangChain Expression Language (LCEL) paradigm.

As the implementation focuses on data pipeline functionalities, features specific to interactive applications (e.g., chat history) and autonomous systems (e.g., agent capabilities) are not supported.

Given NiFi's flowfile-based architecture, this integration primarily use the LCEL's invoke method to synchronously process individual flowfiles, with potential future support the LCEL's batch method on NiFi records.

LCEL Component Function Processors
Chat Model Text completion using an LLM Model
Output Parser Parsing LLM outputs OutputParser
PromptTemplate Build prompt templates from user inputs PromptTemplate
RunnableParallel Create parallel, composable execution paths in LCEL RunnableParallel, RunnableParallelMerge
Retriever Retrieve relevant context information Retriever

Routing (Runnable Branch) can be achieved through existing NiFi processors and are not replicated here.

Quick Start

To quickly get started with the NiFi LangChain processors, use the Docker image available on GitHub Container Registry. This image comes preinstalled with all the processors and includes example NiFi flows for demonstration.

docker run --name nifi-langchain \
  -p 8443:8443 \
  ghcr.io/lifan0127/nifi-langchain:latest

NiFi web UI: https://localhost:8443 (username: admin, password: nifi+langchain)

Hello LangChain

Demonstrates a minimal LCEL pipeline with prompt template, model, and output parser.

Hello LangChain

Retrieval Augmented Generation (RAG)

Uses Runnable Parallel to combine question and context for a RAG system.

RAG

Dynamic Routing

First classifies a question and then use NiFi native RouteOnAttribute processor to select a prompt for response synthesis. It demonstrates the composibility of LCEL pipelines.

Dynamic Routing

Installation

For installation in your NiFi environment (version 2.0 or higher required), download the NAR and ZIP files from the latest release. Copy the NAR file into NiFi's standard extension directory and extract the ZIP's contents into the python_extension directory.

Configuration

Each processor may have one or a few required parameters. For example, the Model processor necessitates selecting an LLM model. Additionally, you can directly pass parameters into the underlying LangChain LCEL components of the following processors by declare NiFi properties with prefixes.

Processor Prefix Examples
Model langchain.model. langchain.model.model_name (set model name, e.g. 'gpt-4-turbo')
Retriever langchain.retriever. langchain.retriever.

The RunnableParallel and RunnableParallelMerge processors should be used in pairs, with RunnableParallel defining paralle paths. Each custom property prefixed with nifi.runnable_parallel. define a new output relationship to route your flowfile. For example, custom property nifi.runnable_parallel.context defines a new context relationship.

Please check out the example flow definitions.

Contribution

Contributions in all forms are highly appreciated!

References