HttpRequest Automate

HttpRequest Automate streamlines the retrieval and processing of files from Supabase, enabling efficient extraction and analysis of text and PDF documents. By integrating with LangChain, it automates the vectorization of content, allowing for quick, AI-driven queries and contextual information retrieval. This workflow eliminates manual file handling, reduces processing time, and enhances data accessibility, making it easier to interact with large document repositories.

7/8/2025
33 nodes
Complex
manualcomplexlangchainextractfromfilesupabasesplitinbatchesaggregatesticky noteadvancedapiintegrationfilesstoragelogicconditionalrouting
Categories:
Complex WorkflowManual Triggered
Integrations:
LangChainExtractFromFileSupabaseSplitInBatchesAggregateSticky Note

Target Audience

Who should use this workflow


- Data Analysts: Need to automate file retrieval and processing to save time and reduce manual effort.
- Developers: Looking for a way to integrate AI capabilities with document management systems like Supabase.
- Businesses: Want to enhance their document handling processes by using AI for content extraction and querying.
- Researchers: Require efficient methods to analyze large sets of documents quickly.

Problem Solved

What problem does this workflow solve


This workflow automates the retrieval, processing, and analysis of files stored in Supabase, addressing issues such as:
- Inefficiency: Manually retrieving and analyzing documents is time-consuming.
- Duplication: Prevents duplicate processing of files by comparing newly fetched files with existing records.
- File Type Handling: Supports multiple file types (e.g., PDF, text) and processes them accordingly.
- Contextual Retrieval: Enables AI-powered querying of documents, making it easier to extract relevant information based on user requests.

Workflow Steps

Detailed explanation of the workflow process


1. Manual Trigger: The workflow starts when the user manually triggers it.
2. Fetch File List: Retrieves a list of files from Supabase storage.
3. Aggregate Data: Combines and prepares the retrieved file data for processing.
4. Loop Over Items: Iterates through each file to handle them individually.
5. Condition Check: Evaluates if a file has already been processed to avoid duplication.
6. Download Files: Downloads files that need processing based on their type.
7. File Processing: Uses different nodes to handle various file types:
- PDF Extraction: Extracts text from PDF files.
- Text Handling: Directly processes text files.
8. Text Splitting: Splits large text into manageable chunks for better processing.
9. Generate Embeddings: Creates vector embeddings for the processed content using OpenAI.
10. Store in Supabase: Inserts the vectorized data into a Supabase vector store for later retrieval.
11. AI Chatbot Integration: Allows users to interact with the system by querying the documents through an AI agent.
12. Result Return: Outputs the relevant information based on user queries.

Customization Guide

How users can customize and adapt this workflow


- File Storage Configuration: Update the storage URL and credentials to match your Supabase setup.
- File Type Handling: Modify the Switch node to add support for additional file types if needed.
- Chunk Size and Overlap: Adjust the parameters in the Recursive Character Text Splitter to optimize processing based on your document sizes.
- Embedding Model: Change the OpenAI embedding model used based on your requirements for accuracy and performance.
- AI Agent Responses: Customize the AI agent's behavior by modifying the parameters in the OpenAI Chat Model nodes to tailor responses to specific user needs.
- Data Storage: Alter the Supabase table names and structure to fit your existing database schema.