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Integrate LangChain Token Splitter in your LLM apps and 422+ apps and services

Use Token Splitter to easily build AI-powered applications with LangChain and integrate them with 422+ apps and services. n8n lets you seamlessly import data from files, websites, or databases into your LLM-powered application and create automated scenarios.

Popular ways to use Token Splitter integration

Default Data Loader node
Summarize node
Supabase Vector Store node
Embeddings OpenAI node
Notion Trigger node
+4

Store Notion's Pages as Vector Documents into Supabase with OpenAI

Workflow updated on 17/06/2024:** Added 'Summarize' node to avoid creating a row for each Notion content block in the Supabase table.* Store Notion's Pages as Vector Documents into Supabase This workflow assumes you have a Supabase project with a table that has a vector column. If you don't have it, follow the instructions here: Supabase Vector Columns Guide Workflow Description This workflow automates the process of storing Notion pages as vector documents in a Supabase database with a vector column. The steps are as follows: Notion Page Added Trigger: Monitors a specified Notion database for newly added pages. You can create a specific Notion database where you copy the pages you want to store in Supabase. Node: Page Added in Notion Database Retrieve Page Content: Fetches all block content from the newly added Notion page. Node: Get Blocks Content Filter Non-Text Content: Excludes blocks of type "image" and "video" to focus on textual content. Node: Filter - Exclude Media Content Summarize Content: Concatenates the Notion blocks content to create a single text for embedding. Node: Summarize - Concatenate Notion's blocks content Store in Supabase: Stores the processed documents and their embeddings into a Supabase table with a vector column. Node: Store Documents in Supabase Generate Embeddings: Utilizes OpenAI's API to generate embeddings for the textual content. Node: Generate Text Embeddings Create Metadata and Load Content: Loads the block content and creates associated metadata, such as page ID and block ID. Node: Load Block Content & Create Metadata Split Content into Chunks: Divides the text into smaller chunks for easier processing and embedding generation. Node: Token Splitter
dataki
Dataki
OpenAI Model node
Binary Input Loader node
Google Drive node

Load and summarize Google Drive files with AI

This workflow includes advanced features like text summarization and tokenization, it's ideal for automating document processing tasks that require parsing and summarizing text data from Google Drive. To use this template, you need to be on n8n version 1.19.4 or later.
n8n-team
n8n Team

About Token Splitter

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