Langflow Integration

GridGain provides Langflow components that can be used to supercharge your AI workflow with full power of GridGain. This version is based on Langflow 1.1.3 and can be used to start working with GridGain vector store components before they are available in the official Langflow release.

GridGain Langflow module is currently in early access. To use GridGain in Langflow, download the early access version and follow the steps below.

Prerequisites

The following is required to install early access Langflow integration:

uv 0.4 or newer. Installation instructions are available in the ud documentation;
Node.js 22.12;
npm 10.9.

Using Early Access Version

Currently, GridGain provides early access version of Langflow components in a prebuilt Langflow fork that includes GridGain components. To use Langflow:

Download the latest version from GridGain website.
Unpack the downloaded archive.

After the archive is downloaded, you need to configure local version of Langflow

Linux or MacOS Configuration

You can quickly configure a local langflow installation by using the provided makefile.

make init

Detailed instructions are available in the Langflow Development Guide.

Windows

Instead of running Langflow natively on Windows, we recommend running a Linux environment in WSL. If it is configured, you can start Langflow by performing the make init command. This command performs the following operations:

Installs backend dependencies
Installs frontend dependencies
Builds frontend static files
Initializes the project
Starts the application

Langflow Components

GridGain Vector Store

Vector databases in Langflow store vector data, which backs AI workloads like chatbots and Retrieval Augmented Generation. Vector database components establish connections to existing vector databases or create in-memory vector stores for storing and retrieving vector data. GridGain vector store component implements a vector store by using GridGain.

Inputs

Name Type Description

Name	Type	Description
`cache_name`	`String`	Required. The name of the cache within GridGain where vectors will be stored.
`host`	`String`	Required. GridGain server host address.
`port`	`Integer`	Required. GridGain server port number.
`score_threshold`	`Float`	Required. Minimum similarity score threshold for search results. Default value: 0.6.
`ingest_data`	`Ingest Data`	Data to be ingested into the vector store.
`embedding`	`Embeddings`	Embedding model to use for vector creation.
`search_query`	`String`	Query string for similarity search.
`number_of_results`	`Integer`	Number of results to return in similarity search Default value: 4.)

cache_name

String

Required. The name of the cache within GridGain where vectors will be stored.

host

String

Required. GridGain server host address.

port

Integer

Required. GridGain server port number.

score_threshold

Float

Required. Minimum similarity score threshold for search results. Default value: 0.6.

ingest_data

Ingest Data

Data to be ingested into the vector store.

embedding

Embeddings

Embedding model to use for vector creation.

search_query

String

Query string for similarity search.

number_of_results

Integer

Number of results to return in similarity search Default value: 4.)

Outputs

Name Type Description

Name	Type	Description
`vector_store`	GridGainVectorStore	Built GridGain vector store instance.
`search_results`	List[Data]	Results of the similarity search as a list of Data objects.

vector_store

GridGainVectorStore

Built GridGain vector store instance.

search_results

List[Data]

Results of the similarity search as a list of Data objects.

GridGainChatMemory Component

Memory components in Langflow store and retrieve chat messages by session_id. They are distinct from vector store components, because they are built specifically for storing and retrieving chat messages from external databases. Memory components provide access to their respective external databases as memory. This allows Large Language Models (LLMs) or agents to access external memory for persistence and context retention. This component creates a chat message history using GridGain, enabling storage and retrieval of chat messages using GridGain’s distributed caching capabilities.

Inputs

Name Type Description

Name	Type	Description
`host`	String	Required. GridGain server host address. Default value: "localhost".
`port`	String	Required. GridGain server port number. Default value: "10800".
`cache_name`	String	Required. Name of the cache for storing messages. Default value: "langchain_message_store".
`session_id`	MessageText	Chat session ID. Uses current session ID if not provided.
`client_type`	String	Required. Type of client to use. Must be pyignite or pygridgain.

host

String

Required. GridGain server host address. Default value: "localhost".

port

String

Required. GridGain server port number. Default value: "10800".

cache_name

String

Required. Name of the cache for storing messages. Default value: "langchain_message_store".

session_id

MessageText

Chat session ID. Uses current session ID if not provided.

client_type

String

Required. Type of client to use. Must be pyignite or pygridgain.

Outputs

Name Type Description

Name	Type	Description
`message_history`	BaseChatMessageHistory	An instance of GridGainChatMessageHistory for the session.

message_history

BaseChatMessageHistory

An instance of GridGainChatMessageHistory for the session.

Langflow Tutorial

GridGain provides a Langflow tutorial that shows how to use it to solve real world problems. Full tutorial is available in the Low-Code RAG Application Building with GridGain and Langflow section.

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Last updated on Feb 28, 2025